Algal Cultures, Analogues of Blooms And Applications (2 volume set)

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Algal Cultures Analogues of Blooms and Applications Volume 1

Dedication In friendship to Dr. George F. Humphrey, Australia and Dr. James E. Stewart, Canada, and to the memory of my parents Sastri and Seshamma Durvasula. SUBBA RAO Editor

Algal Cultures, Analogues of Blooms and Applications Volume 1

Editor

D.V. Subba Rao Bedford Institute of Oceanography Dartmouth, NS Canada

Science Publishers Enfield (NH), USA

Plymouth, UK

Photos of microalgal species Front cover: Emiliania huxleyi a coccolithophore (Credit: Dr. S.W. Jeffrey, CSIRO, Australia) Color plate as frontispiece 1. Dinophysis norvegica 2. Biddulphia sp. 3. Thalassiosira sp. 4. Pseudo-nitzschia pungens f. multiseries 5. Anabaena circinalis 6. Small volume cultures 7. Skeletonema costatum 8. Dunaliella tertiolecta, 9. Rhodomonas salina 10. Chaetoceros sp. (Credits: #1. Dr. Rajashree Gouda # 2, 4. and 10. Subba Rao and # 3,5,6,7,8,9 Dr. S.W. Jeffrey) Black and white plate on the reverse of frontispiece 1. Bacteriastrum sp. 2. Paralia sulcata 3. Chaetoceros lascinosum 4. Gymnodinium catenatum 5. Picoplankters 6. Naviculoid diatom 7. Ornithocercus magnificus 8. Cryptophyte 9. Dinophysis fortii 10. Planktoniella sol. (Credits: #1, 2, 3, 4, 6, 7, 8, 9,10 Dr. S.W. Jeffrey and #5 Subba Rao)

SCIENCE PUBLISHERS An imprint of Edenbridge Ltd., British Channel Islands. Post Office Box 699 Enfield, New Hampshire 03748 United States of America Internet site: http://www.scipub.net sales @ scipub.net (marketing department) editor@ scipub.net (editorial department) [email protected] (for all other enquiries) Library of Congress Cataloging-in-Publication Data Algal cultures, analogues of blooms and applications / editors, D.V. Subba Rao. p. cm. Includes bibliographical references (p.). ISBN 1-57808-393-1 1. Algae—Cultures and culture media. 2. Algal blooms. I. Subba Rao, D.V. QK565.2.A438 2005 579.8--dc22

2005051701

ISBN (Set) 1-57808-393-1 ISBN (Vol. 1) 1-57808-392-3 ISBN (Vol. 2) 1-57808-394-X © 2006, Copyright Reserved All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior written permission. This book is sold subject to the condition that it shall not, by way of trade or otherwise, be lent, re-sold, hierd out, otherwise circulated without the publisher’s prior consent in any form of binding or cover other than that in which it is published and without a similar condition including this condition being imposed on the subsequent purchaser. Published by Science Publishers, Enfield, NH, USA An imprint of Edenbridge Ltd. Printed in India.



Preface

Marine phytoplankters, the free-floating photosynthetic life, play a crucial role in the production of oxygen and in food web dynamics in the seas. Their diversity in taxonomy, morphology, size, and nutritional requirements continue to fascinate biological oceanographers. The spatial and temporal variations of algae are enormous. To obtain a steady supply of algae for biochemical and physiological experimentation, it soon became necessary to culture the algae under defined laboratory conditions. Although studies on natural assemblages of marine phytoplankton and laboratory cultured algae were initiated about the same time (1893), unlike the former, culture studies did not progress till the 1960s as rapidly as one would have wished. Due to the development of tracer carbon-14 technique in 1952, interest in studying marine micro algal cultures has been growing rapidly and many unexpected and exciting discoveries have already emerged. Four examples that involve marine micro algae may be cited: Discovery of photosynthetic picoplankton, UV light and climate, Geoengineering and climate and genetic engineering. New techniques have been developed in recent years and rapid advances have been made relating measurements of primary organic production of marine micro algae to their photosynthetic pigments, evident from the plethora of papers and reviews published. In general, principles of terrestrial plant physiology and biochemistry have been extended to study the physiological ecology of marine micro algae. Algal cultures are being used as excellent experimental materials to model growth, nutrient kinetics, physiological ecology, pollution research, phycotoxin research, remote sensing and climatic studies. However, most of the cultures are isolated from temperate seas and very few from other regions. It is to be noted that utility of algal cultures in tropics is mostly limited mariculture and to species isolated elsewhere but not native to their seas.

LEEE Algal Cultures, Analogues of Blooms and Applications Despite some differences, several similarities exist between the data obtained on blooms and cultures. As a result of the ease with which some of the algae can be cultured, considerable interest is currently evinced to see if cultures could be used to gain insights to understand some of the ecological principles such as species succession, periodicities, physiological adaptations. Additionally, research is focused on the application of algae in mariculture operations, marine biotechnology, in space research for waste recycling systems, and as source of natural compounds such as antiviral and antifungal compounds and pharmaceuticals. Of the 5000 confirmed taxa of marine micro algae, about 300 species contribute to blooms, both benign and toxigenic. More and more of novel nuisance phytoplankton blooms are recorded and the connection between their global expansion and human activities is actively sought. About 500 species, mostly from the temperate seas, are brought into culture and about 30 species are studied in considerable detail. A few important toxigenic dinoflagellate taxa still baffle any attempts to culture. Needless to add that research on algae will be actively pursued in the decades to come. When Science Publishers, New Hampshire approached me to collate and edit a volume on marine micro algae, I thought it would be useful to broaden the range of topics and bring out a thematic volume with recent developments in micro algal research. This book contributed by colleagues from 14 nations encompasses numerous scientific disciplines. I tried to involve serious researchers who have made excellent contributions on marine micro algae. They were requested to incorporate the latest findings specifically to address how best algal cultures can be utilized as analogues of natural blooms, their utility in understanding the ecological principles and their applications in biotechnology. Each chapter is contributed by an expert or group of experts, reviewed internally by colleagues and by outside referees as well. I am grateful to each of the contributors for their high level of professional and scholarly efforts, cordial, and prompt cooperation extended to me. I have gained from their efforts but the omissions and commissions are mine. The scientific opinions expressed in this book are those of the authors and not that of any institution. This book is not intended to be a compendium of everything worth knowing about marine micro algae given the fact that the knowledge base is constantly expanding. It is hoped that this volume will be useful to our colleagues in biological oceanography as well as other scientists, advanced undergraduate and graduate students as a summary of current thoughts in physiological ecology.

Contents

EN

Acknowledgements

Special thanks are due to Dr. Shirley Jeffrey for the generous assistance with most pictures of algae, and to Dr. Rajashree Gouda. My sincere thanks are extended to Mr. Arthur Cosgrove, Technographics, Bedford Institute of Oceanography, for his artistic skills in the design of the cover and the plates of algae. For her infinite patience, excellent help with the formatting, correspondence and unstinting support I am most grateful to my wife Bala T. Durvasula.


Contents

Preface Acknowledgements List of Contributors

vii ix xv

Volume 1 Chapter

1

Why Study Algae in Culture? D.V. Subba Rao

Chapter

2

Photosynthetic Pigments in Marine Microalgae: Insights from Cultures and the Sea S.W. Jeffrey and S.W. Wright

Chapter

131

5

Algal Blooms and Bacterial Interactions Bopaiah Biddanda, Paulo Abreu and Clarisse Odebrecht

Chapter

91

4

Allelopathic Interactions Among Marine Microalgae Geneviève Arzul and Patrick Gentien

Chapter

33

3

Phases, Stages and Shifts in the Life Cycles of Marine Phytoplankton Marina Montresor and Jane Lewis

Chapter

1

163

6

Viral Infection in Marine Eucaryotic Microalgae Keizo Nagasaki

189

NEE Algal Cultures, Analogues of Blooms and Applications

Chapter

7

Autecology of Bloom-Forming Microalgae: Extrapolation of Laboratory Results to Field Populations and the Redfield-Braarud Debate Revisited Theodore J. Smayda

Chapter

8

The Trace Metal Composition of Marine Microalgae in Cultures and Natural Assemblages Tung-Yuan Ho

Chapter

343

11

Role of the Cell Cycle in the Metabolism of Marine Microalgae Jacco C. Kromkamp and Pascal Claquin

Chapter

301

10

Osmotrophy in Marine Microalgae Alan J. Lewitus

Chapter

271

9

Algal Cultures as a Tool to Study the Cycling of Dissolved Organic Nitrogen Deborah A. Bronk and Kevin J. Flynn

Chapter

215

385

12

Nutritional Value of Microalgae and Applications John K. Volkman and Malcolm R. Brown

407

Volume 2 Chapter

13

Effects of Small-Scale Turbulence on the Physiological Functioning of Marine Microalgae Elisa Berdalet and Marta Estrada

Chapter

14

Mechanistic Models of Algal Physiology Kevin J. Flynn

Chapter

459

501

15

Competition of Aquatic Microalgae in Variable Environments: The Disturbance Effect Sabine Flöder and Ulrich Sommer

533

Contents

Chapter

16

Effects of Temperature and Irradiance on Marine Microalgal Growth and Physiology Peter Thompson

Chapter

801

23

From Microscope to Magnet: Probing Phytoplankton Population Structure and Physiology Using Mammalian Antibodies Louis Peperzak and Sonya T. Dyhrman

Chapter

769

22

Biotechnology of Immobilized Micro Algae: A Culture Technique for the Future? Thierry Lebeau and Jean-Michel Robert

Chapter

715

21

Molecular Biology and Genetic Engineering in Microalgae Oliver Kilian and Peter G. Kroth

Chapter

685

20

Effects of Ultraviolet Radiation on Microalgal Growth, Survival and Production Andrew T. Davidson

Chapter

671

19

Absorption, Fluorescence Excitation and Photoacclimation Egil Sakshaug and Geir Johnsen

Chapter

639

18

Photosynthetic Response and Acclimation of Microalgae to Light Fluctuations Johan U. Grobbelaar

Chapter

571

17

Photosynthesis—Irradiance Relationships in Marine Microalgae Pedro Duarte

Chapter

NEEE

839

24

Prospects for Paratransgenic Applications to Commercial Mariculture using Genetically Engineered Algae Ravi V. Durvasula, Ranjini K. Sundaram, Scott K. Matthews, Pazhani Sundaram and D.V. Subba Rao

865

NEL Algal Cultures, Analogues of Blooms and Applications

Chapter

25

Development of Statistical Models for Prediction of the Neurotoxin Domoic Acid Levels in the Pennate Diatom Pseudo-nitzschia pungens f. multiseries Utilizing Data from Cultures and Natural Blooms Ilya Blum, D.V. Subba Rao, Youlian Pan, S. Swaminathan and N.G. Adams

891

Appendix 1 Algal cultures D.V. Subba Rao

917

Appendix 2 Algal culture centers D.V. Subba Rao

931

About the Contributors

933

Acknowledgements to Reviewers

947

Index

949

Contents

List of Contributors

Paulo Abreu Departmento de Oceanografia Fundação Universidade Federal do Rio Grande (FURG) Caixa Postal 474, 96201-900 Rio Grande, RS, Brazil. Nicholas G. Adams National Marine Fisheries Service, NFSC 2725 Montlake Blvd., East Seattle, Washington 98112, USA. Geneviève Arzul Ifremer, DEL-PC, BP 70 F-29280 Plouzané, France. Elisa Berdalet Institut de Ciències del Mar Centre Mediterrani d’Investigacions Marines i Ambients (CSIC) Pg. Marítim, 37-49 E-08003 Barcelona, Catalunya, Spain. Bopaiah Biddanda Annis Water Resources Institute and Lake Michigan Center Grand Valley State University 740 W Shoreline Drive, Muskegon, MI 49441, USA. Email: [email protected] Ilya Blum Department of Mathematics Mount Saint Vincent University Halifax, N.S. Canada, B3M2J6

NL

NLE Algal Cultures, Analogues of Blooms and Applications Deborah A. Bronk Department of Physical Sciences The College of William and Mary Virginia Institute of Marine Science Route 1208; Greate Rd. Gloucester Point, VA 23062, USA. Email: [email protected] Malcolm R. Brown CSIRO Marine Research and Aquafin CRC GPO Box 1538 Hobart, Tasmania 7001 Australia. Pascal Claquin Laboratoire de Biologie et de Biotechnologies Marines Université de Caen Basse-Normandie Esplanade de la paix, 14032 Caen Cedex, France. Andrew T. Davidson Australian Antarctic Division Channel Highway, Kingston Tasmania 7050, Australia. Pedro Duarte CEMAS – University Fernando Pessoa Praça 9 de Abril, 349, 4249-004 Porto Portugal. Ravi V. Durvasula Department of Epidemiology and Public Health Yale University School of Medicine, New Haven, CT, USA. Email: [email protected] Sonya T. Dyhrman Biology Department MS #32, Woods Hole Oceanographic Institution Woods Hole, Massachusetts 02543 USA.

List of Contributors Contents

Marta Estrada Institut de Ciències del Mar Centre Mediterrani d’Investigacions Marines i Ambients (CSIC) Pg. Marítim, 37-49 E-08003 Barcelona, Catalunya, Spain. Sabine Flöder Institut für Botanik Universität zu Köln Gyrhofstr. 15 50931 Köln Germany. Email [email protected] Kevin J. Flynn Institute of Environmental Sustainability University of Wales Swansea, Singleton Park SA2 8PP, UK. Patrick Gentien Ifremer, CREMA, F-17137 L’Houmeau, France. Geir Johnsen Biological institute Norwegian University of Science and Technology N-7491 Trondheim, Norway. Johan U. Grobbelaar Department of Plant Sciences Botany, University of the Free State Bloemfontein 9300, South Africa. Tung-Yuan Ho Department of Earth and Environmental Sciences National Chung Cheng University, Ming-Hsiung, 621, Chia-Yi, Taiwan. Email: [email protected]

NLEE

NLEEE Algal Cultures, Analogues of Blooms and Applications S.W. Jeffrey CSIRO Marine Research GPO Box 1538, Hobart Tasmania, 7001, Australia. Oliver Kilian Fachbereich Biologie Universität Konstanz 78457 Konstanz, Germany. Jacco C Kromkamp Centre for Estuarine and Marine Ecology Netherlands Institute of Ecology PO box 140 4400 AC Yerseke, the Netherlands. Email: [email protected] Peter G. Kroth Fachbereich Biologie Universität Konstanz 78457 Konstanz, Germany. Thierry Lebeau, Laboratoire de Biologie Marine (UPRES EA 2663), Institut des Substances et des Organismes de la Mer (ISOmer) Université de Nantes, 2, rue de la Houssinière, BP 92208, 44322 Nantes cedex 3, France. Jane Lewis Research Centre Director School of Biosciences University of Westminster 115, New Cavendish Street London W1W 6UW UK. Alan J. Lewitus Belle W. Baruch Institute for Marine and Coastal Sciences University of South Carolina, and Marine Resources Research Institute


NEN

South Carolina Department of Natural Resources P.O. Box 12559, Charleston, South Carolina, USA. USA 29422-2559. Scott K. Matthews Department of Epidemiology and Public Health Yale University School of Medicine, New Haven, CT, USA. Marina Montresor Stazione Zoologica ‘A. Dohrn’ Villa Comunale 80121 - Naples, Italy. Keizo Nagasaki Harmful Algae Control Section, Harmful Algal Bloom Division, National Research Institute of Fisheries and Environment of Inland Sea 2-17-5 Maruishi, Ohno, Saeki, Hiroshima 739-0452, Japan. Email: [email protected] Clarisse Odebrecht Departmento de Oceanografia Fundação Universidade Federal do Rio Grande (FURG) Caixa Postal 474, 96201-900 Rio Grande, RS, Brazil. Youlian Pan Institute for Information Technology National Research Council of Canada 1200 Montreal Road, Building M50 Ottawa Canada, KIA OR6 Louis Peperzak National Institute for Coastal and Marine Management/RIKZ P.O. Box 8039 NL-4330 EA Middelburg The Netherlands. Jean-Michel Robert Laboratoire de Biologie Marine (UPRES EA 2663), Institut des Substances et des Organismes de la Mer (ISOmer) Université de Nantes, 2, rue de la Houssinière, BP 92208, 44322 Nantes cedex 3, France.

Why Study Algae in Culture?

Why Study Algae in Culture? D.V. Subba Rao1 1

ERD, Bedford Institute of Oceanography, P.O. Box 1006, Dartmouth, N.S. Canada, B2Y 4A2.

Abstract Marine micro algae play a central role in the biogeochemical cycles and sustain all forms of life in the oceans. Studies on marine phytoplankton and algal cultures were contemporaneous and aimed at increasing the harvestable food from the sea. Due to the ease with which samples can be collected, spatial and temporal studies of phytoplankton assemblages progressed rapidly. Geographical variations in the qualitative and quantitative abundances were related to the prevailing physical and chemical, particularly the nutrient conditions. Due to the non-availability of dependable methodology and defined culture media, process-oriented studies such as nutrient assimilation, photosynthesis and respiration rates did not progress for nearly 50 years as one would have wished. Subsequent availability of sophisticated analytical techniques for nutrients and chlorophylls, carbon-14 tracer method to measure photosynthesis facilitated measurements of nutrient kinetics, division rates, carbon assimilation rates, adaptations to environmental variables, and biochemical variables both in natural and culture populations. Several principles of plant physiology were extended to cultures grown under monotonic conditions of temperature and light and the data demonstrated existence of similarities between natural populations, particularly the blooms, and culture populations. The various chapters in this book present studies that facilitate our understanding of the structure and physiological functioning of the marine microalgae. Recent culture studies have shed light on life cycles of algae, biochemical and physiological changes associated with cell cycles, multinutrient kinetics, allelopathic interactions, fluorescence dynamics, and physiological adaptations of algae. As a result because of these developments it is now possible to understand some aspects of the ecological principles governing phytoplankton dynamics, and the structure and functioning of the marine microalgae and their production characteristics.

Algal Cultures, Analogues of Blooms and Applications Algal cultures are increasingly applied in mariculture studies, and in those aimed at mechanisms of production of biotoxins, measurements of biomass and production using remote sensing techniques, remediation of marine pollution, as sources of natural biochemical and bioactive compounds, pharmaceuticals, in understanding the role of algae in climatic variations and their applications in genetic engineering. It is possible that studies based on cultures grown as single species and in mixed species, in dialysis bags, or diffusion chambers or in microcosms or mesocosms under simulated natural conditions in respect of temperature, spectral characteristics of light and at nutrient levels comparable to those in nature would be instructive and facilitate our understanding of these primary producers.

INTRODUCTION Phytoplankton, a term coined in 1897, describes the free-floating pulsating photosynthetic microalgae in the aquatic environment. Marine phytoplankton cell volume in the oceans ranges between 6.10–20 and 4.10–9 m3 (Raven, 2001) and account for nearly 50% of global primary production by plants on earth. Thus marine microalgae play a crucial role as a biological pump by consuming carbon dioxide at the surface and sequestering carbon as particulate and dissolved organic to deep oceans (Buesseler et al. 2004). Nearly 75% of the surface primary production is recycled in the euphotic zone through consumption by heterotrophs and about 20% of the surface production is transferred to the mesopelagic zone. Only 5% of the surface primary production makes it to the bottom (Coale, 2001). The main purpose of phytoplankton studies is to understand the production processes so that primary production, particularly in the nitrogen deficient oceanic waters, can be primed and food production from the oceans could be augmented to feed the anticipated nine billion human population. At present the annual global harvest from the oceans is approximately 103 ¥ 106 tonnes which is about 6% of the human protein requirement (Schmitt, 1970) equivalent to 200 ¥ 106 cattle. As microalgae sustain either directly or indirectly on all marine life including the commercially important fisheries, studies on microalgae are crucial to our understanding of the structure and functioning of the aquatic ecosystems. Additionally, marine microalgae produce an array of bioactive compounds. To understand their diversity, size, metabolism and their role in the biogeochemical cycles, cultures of microalgae readily provide the necessary reference material for experimentation. Algal cultures are utilized to understand their life cycles and their role in microbial loops, energy


!

transformation, and species succession. Although not enough is known, algal cultures are finding increasing applications in biotechnology, genetic engineering and space research. In the marine environment there are about 17,000 photosynthetic species of which about 7000 are planktonic (Falkowski et al. 2003) of which about 5000 are confirmed (H. John Heinz III Center for Science, 2002; Sournia 1995, Sournia et al. 1991). These microalgae are diversified in morphology, size, metabolism and biochemistry. The simplest and the most abundant in the oceans are the cyanobacterial cells (108 l–1) such as the coccoid picoplankter Synecchocystis (Furhman and Capone, 2001) and the most ornate are the dinoflagellates (see Frontispiece and inside cover page) such as Ornithocercus magnificus “with extarvagant development of bizzare excrescences on the thecae-spines flattened into wing-like expansions and so forth – that have usually been interpreted as organs of floatation” (Hart, 1963). From an evolutionary point of view the cyanobacteria have existed for some 3500 million years (The Archaean Period) and the first algae began about 1200 million years ago in The Proterozoic Era (Bengston, 1994). The explosion of sea life began in the Paleozoic period (540-250 million years) and the first diatom occurred about 120 million years in the early Cretaceous (Gersonde and Harwood 1990). The planktonic ways of life, particularly of diatoms, evolved over geological time are the best adapted to their environmental conditions to out-compete the larger forms (Fogg, 1991).

CLASSIFICATION OF PHYTOPLANKTON The phytoplankters range from the smallest unicellular picoplankton (< 3 mm diameter) to 8 cm long chains of 10,000,000 cells of the diatom Navicula gravelliana (Hendy, 1964). Based on their cell size, three major categories of phytoplankton are recognized i.e. the picoplankton (< 3 mm), the nanoplankton (< 20 mm) and the net or microplankton (> 20 mm). Of significant interest is the recent discovery of plankton life the SAR 11 cells (< 0.25 to 0.7 mm cells from Sargasso Sea) in the oceans (Morris, 2002). Their estimated microbial biomass of 2.4 ¥ 1028 cells in the oceans with 50% in the euphotic zone will have a major impact on regional geochemistry and on the surrounding plankton. Although their exact physiological activities and roles in oceanic carbon cycling are not known, their abundance and presence in the northwestern Sargasso Sea waters to depths of 3000 m and in Oregon coastal surface waters suggests they are efficient competitors for resources.

" Algal Cultures, Analogues of Blooms and Applications Phytoplankton are represented by both Prokaryotes and Eukaryotes which differ in their characteristics as shown below (Table 1.1). While both groups provide interesting research material, the Eukaryotes are valuable tools in biotechnological industries and genetic engineering studies (see chapters: 21. Kilian and Kroth, 22. Lebeau and Robert, 24. Durvasula et al.). Table 1.1 Characteristics of Prokaryotes and Eukaryotes Characteristics

Prokaryotes (Example-Blue greens)

Eukaryotes (Example-Other algae)

Size Genetic make up Genetic system Nuclei Chromosomes Division Introns (non-coding regions in coding regions of the gene Organelles like Mitochondria and chloroplasts Histones associated with DNA Cell contents

Small Haploid Simple Not membrane enclosed Absent Binary fission Absent

Usually 10 ¥ larger Both haploid and diploid Complex Membrane enclosed Occur usually as homologous pairs Cell division by mitosis Present

Metabolic products

Fe: P Siderophores Metabolism – carbon source

Absent

Membrane enclosed; mitochondria extract energy, chloroplasts capture solar energy Absent Histones present Peptidoglycan present Absent Single RNA polymerase Multiple RNA polymerase Long-chain poly Saturated and monosaturated fatty acids, unsaturated fatty acids triglycerides extracellular polysacharides, D-galactose and L-galactose High Low Present Absent Oxygenic photosynthesis Oxygenic photosynthesis Aerobic respiration and Aerobic respiration Anaerobic respiration Anaerobic fermentation Anoxic photosynthesis Chemoautotrophy

Algal Phylogeny and Pigments There are 12 major taxonomic phyta or divisions representing marine phytoplankton. Common to all phytoplankters is the photosynthetic pigment chlorophyll a, which has become the touchstone for measuring algal biomass. The presence of additional characteristic auxiliary pigments imparts the characteristic color to specific algae (see chapter 2 Jeffrey and Wright). A strong correlation exists between the pigment types and


#

phylogenetic trees of 37 species of algae (Zapata et al. 2004). Based on the utility of accessory pigments in their photosynthetic physiology, these algae can be separated into those with chlorophyll b – the green plastid group, that contrasted with the chlorophyllide c plastid-containing group. The latter group have been ecologically successful over the past 250,000,000 years (Grebyk et al. 2002). The only exception to this is the prokaryote Acaryochloris marina which has chlorophyll d as its major photosynthetic pigment (Miyashita et al. 1996, Kurano and Miyachi, 2004). Despite a few exceptions to pigment distributions, four lines of algae are recognized (Table 1.2).

Earlier studies The Kiel school in Germany initiated phytoplankton studies in 1897. Until 1920 these studies were concerned with primary systematics, spatial and temporal variations with estimates of relative abundance, plankton dynamics, geographical patterns and seasonal changes (Hart 1963, Mills, 1989). Subsequently, between 1920 and 1935 physical and chemical events were implicated to explain formation of seasonal plankton blooms based on the nutrient cycles particularly of phosphate and nitrogen, related changes in phytoplankton to light and effective length of day (Mills 1989). Emphasis continued mostly on spatial and temporal distribution patterns of phytoplankton (Talling, 1984) in relation to environmental variables such as temperature, salinity, light and nutrient levels that could be routinely determined with ease. Culturing diatoms with a view to harvest more food from the sea however, had been initiated at Plymouth (Allen, 1910). Utilizing the “Dark and Light bottle technique” of Gaarder and Gran (1927), Marshall and Orr (1928) and Jenkin (1937) initiated studies on oxygen exchange by diatom cultures suspended at various depths in the sea. In the seas, the only thing constant is the constant change of the qualitative and quantitative abundance of the populations. This variability is rather an inherent property of planktonic systems (Colijn et al. 1998). The qualitative and quantitative abundance, growth and production of phytoplankton progress over various scales of space and time (Harris, 1986) are governed by a suite of physical, chemical and biological variables. In the temporal scale these events range from < 1 min (molecular processes) to > 1 year (growth cycle of zooplankters) and in the spatial scale from 1 mm (< 0.25 to 0.7 mm SAR 11 cells) to 10 km (zooplankton patches).

Nutrition Their diverse modes of nutrition make the microalgae fascinating and explains the great plasticity in their end products (Fogg, 1989). Most

Chlorophyta Euglenophyta Rhodophyta

Chl a,b

a and phycocyanin, phycoerythrin, carotene, allophycocyanin a and phycocyanin, phycobilin, phycoerythrin

Green

Red

*Includes fresh water and marine (See Graham and Wilcox 2000)

Cyanophyta Prochlorophyta

Bacillariophyta Dinophytal, Chrysophyta Haptophyta Xanthophyta Cryptophyta Eumastigophyta

Chl a,c

Golden-brown

Blue-green

Phyta or Division

Characteristic pigments

Major lines and divisions of algae

Line of algae

Table 1.2

Blue greens Prochlorophytes

Diatoms Dinoflagellates Golden algae Prymnesiophytes Yellow greens Cryptomonads Yellow-green algae Green flagellates Euglenoids Red algae

Representatives

2,000

12,000 4,000 1,200 300 600 200 600 17,000 900 6,000

Total species*

Synecchocystis Prochloron

Coscinodiscus Dinophysis Chrysosphaera Emiliana Tribonema Cryptomonas Dinobryon Chalmydomonas Euglena Porphyra

Example

$ Algal Cultures, Analogues of Blooms and Applications


%

microalgae are autotrophic and photosynthetic but contain exponents of all methods of nutrition. A few are capable of osmotrophy to assimilate dissolved organic substances, particularly the extracellular amino acid oxidases and proteolytic enzymes (see chapters: 9 Bronk and Flynn; 10, Lewitus). The ‘new’ dissolved organic nitrogen may even stimulate growth of many algae including those forming harmful algal blooms. Some of the unicellular, nonheterocystis, symbiotic nitrogen-fixing cyanobacteria exist as symbionts within the host cells of the coral (Lesser et al. 2004) or in the colonial diatom Hemiaulus hauckii (Carpenter et al. 1999). The heterocystous, N2-fixing cyanobacterial Richelia intracellularis exists as an endosymbiont in the diatom and the symbiotic association adds an average of 45 mg Nm2d–1 to the water column (Carpenter et al. 1999). While a few dinoflagellates are parasitic, some are holozoic as in Gymnodinium fungiform. Hundreds of this dinoflagellate attach to the surface of prey organism by a peduncle and ingest the cytoplasm or body fluids of its prey (Moree and Spero, 1981). The most bizzare mode of nutrition is in the toxic estuarine dinoflagellate Pfiesteria piscicida and P. shumwayae that ambush the prey. These are attracted to fish, produce toxins that cause stress, disease and death to estuarine fish (Burkholder et al. 2001).

Algal Blooms and Species Succession As a review of the extensive literature on the qualitative and quantitative abundance of marine microalgae in the marine environment and the governing factors is beyond the scope of this chapter, some salient aspects where culture experiments contributed or would contribute further to our understanding of the physiological ecology of marine microalgae are briefly presented. In the temperate waters when the local conditions are favorable for algal growth several species grow rapidly. However, only a few species dominate leading to the formation of a bloom (> 20 mg chl a l–1), usually a major one during spring and again a minor one during fall (Cushing, 1975). The spring bloom is attributed to seasonal mixing, spring thaw, availability of nutrients, more solar radiation essential for photosynthesis, and general warming of the environment. For a bloom to occur it is imperative that the water column be seeded with resting stages of diatoms with an ability to exploit favorable conditions; the vegetative form of the initial population results in a bloom (Ishikawa and Furuya, 2004). It may be noted that life histories of algae will have to be worked out with algal cultures (see chapter: 3 Montresor and Lewis). At low N: P ratios recruitment of resting stages induced blooms of Microcystis (Stahil-Delbango et al. 2003). Even in the

& Algal Cultures, Analogues of Blooms and Applications oceanic waters niche segregation exists between Prorochlorococcus and Synchococcus in the central Atlantic depending on light (Augusti, 2004). Seasonal blooms are ephemeral and may be caused by the complex life cycles associated with the physiological stages of the alga. Bloom formation may be regulated by phases in the life cycle of the alga which may be timed by an endogenous clock (Anderson and Kieafer, 1987) or by the lunar phase (Wyatt and Jenkinson, 1997). Usually the bloom lasts up to 21 days and during this time a few of the initial species are replaced by others, ecologically known as species succession. Species succession could be interpreted in terms of bacterial action (see chapter: 5 Biddanda et al.) or due to nutrient ratios and enrichment as shown with algal assemblages in a microcosm enrichment experiment (Estrada et al. 2003; see chapter 15: Flöder and Sommer). It is also possible that microalgae at the species level produce biologically active molecules the “allelochemicals” which cause a temporary dominance of the producer (the “donor”) over the “receptor” species. This “chemical warfare” (Smetacek, 2001) influences species succession, biodiversity and food web structure (see chapter: 4 Arzul and Gentien). Smayda in this book reopened the Redfield-Braarud debate and advocated a functional group approach to understand the ecophysiology of species and their succession. He further argued about the need to combine autecological and synecological concepts, field and culture approaches to discern properties diagnostic of physiological status (see chapter: 7 Smayda). In addition to local conditions, events on a greater scale also influence phytoplankton successions. Due to riverine fluxes, and increased usage of fertilizers the atomic ratios of nitrogen: phosphorus: silica in the coastal waters have changed globally from 16:1:16 to 26:1:16 causing species succession (see chapter: 8 Tung-Yuan Ho). Ratios below 1:1 Si: N, seem to replace diatoms by non-diatoms and P and Si limitation seems to lead to noxious blooms (Turner et al. 2003). During spring diatoms and dinoflagellates generally decreased in the Baltic proper but decreased in the Kattegat. In the Kattegat and Baltic cyanobacteria decreased (Wasmund and Uhlig, 2003). The North Atlantic Oscillation (NAO) seems to cause climatic variability in the northeast United States, as well as in the North Sea, Celtic Sea, Skagerrak and\Central North Atlantic. NAO resulted in a long-term increase in temperature ca 2.3oC to 3.0oC during the winter quarter from 1964 to 1996 (Smayda et al. 2004). This in turn decreased the occurrences of the benign winter-spring blooming diatom Detonula confervacea, Skeletonema costatum. Climatic changes due to NAO seem to effect the distribution of three


'

toxic species of Dinophysis in the Gullmarg Fjord, Sweden (Belgrano et al. 1999). The number of species contributing to a bloom varies and increases towards the tropics. In the Arctic approximately 15 species, mostly diatoms, contribute to the bloom (Subba Rao, et al. 1988) whereas in the temperate coastal seas about 30 species, mostly centric diatoms go through a succession pattern and contribute to the bloom (Platt and Subba Rao, 1970, Subba Rao and Smith, 1978). In the waters off Spain a few microflagellates and 10 diatoms contributed to the spring bloom (Casa et al. 1999) and in the Black Sea although 179 species were recorded, 7 diatoms and 22 ( dinoflagellates constituted the bloom (Túrko g lu and Koray, 2002). For example, in Narragansett Bay, Rhode Island during a 38 year study 9 diatoms contributed to the bloom during 1959–1978 and during 1990–1996; during the intervening years 1979–1989 diatoms decreased, flagellates increased and six harmful algal bloom species occurred (Borkman and Smayda, Subba Rao et al. 2003). In the arid zone waters off Kuwait 28 species, mostly diatoms, constituted the bloom (Subba Rao, et al. 1999). In the coastal tropical waters of the Bay of Bengal, although about 100 species were abundant during spring, 36 species dominated the bloom and followed a succession pattern (Subba Rao, 1971, 1973). In the Arabian Sea on the west coast of India 74 species were abundant but 39 of them contributed to blooms (Subrahmanyan and Sarma, 1961). In experimental studies, small-scale turbulence of only a few millimeters could influence the extracellular micronutrient environment, leading to nitrogen assimilation, dominance of diatoms or dinoflagellates and appearance or disappearance of blooms (see chapter: 13 Berdalet and Estrada). There are about 300 bloom forming species (H. John Heinz III Center for Science 2002) of which about 78 species are toxin producers including 60 dinoflagellates (Subba Rao, 2002). Atypical algal blooms are non-seasonal, usually monospecific and cause red-tide phenomenon with biomass levels as high as ~900 mg chl a l–1 (Subba Rao et al. 2003). Some of the red-tide species can be toxigenic leading to Paralytic Shell fish poisoning (PSP), Diarrhetic Shellfish poisoning (DSP), Neurotoxin Shellfish poisoning (NSP) and Ciguetera episodes with far reaching harmful effects on the human health and on commercially important fisheries. Some of the causative species of the monospecific blooms are not amenable for culturing thereby necessitating concentration of cells utilizing the right sieves for physiological studies as in Gonyaulax digitale (Amadi et al. 1992) or the toxigenic Dinophysis norvegica (Subba Rao and Pan, 1993). Such species when isolated

Algal Cultures, Analogues of Blooms and Applications and eventually cultured serve as research material for determining their growth, physiology (see chapter: 23 Peperzak and Dyhrman) and biochemistry to gain insights into their functioning. Formation of blooms in offshore waters is quite rare and is mostly detected by satellite imagery. The most widespread species is the coccolithophore Emiliania huxleyi that contributes dimethylsulphide to the atmosphere. Blooms of this algal species have been observed in the southeast Bering Sea (Olson and Strom, 2002, Sukhanova et al. 2004), North Sea (Robinson 2002), and the Central North Atlantic (Balch et al. 1996). They were characterized by chlorophyll levels that ranged from 0.4 to 4.50 mg Chl a l–1 (Olson and Strom, 2002), low photosynthesis: respiration of 0.9 (Robinson et al. 2002), and the waters yielded dimethylsulphide (DMS) ranging 1.06 to 93.8 nmol dm–3 (Malin et al. 1993). Elsewhere blooms can be advected as in the toxic dinoflagellate Gymnodinium breve (= Ptychodiscus brevis); these originate in the Gulf of Mexico and are advected into the South Atlantic Bight by the Florida current and the Gulf Stream (Tester et al. 1993). Off the Western tropical Atlantic, Coles et al. (2004) located blooms of the diazotrophic cyanophyte Trichodesmium during summer.

Viruses It is possible that the abundant viruses (upto 107 ml–1) play a significant role in the species succession, in the mortality of phytoplankton and may terminate algal blooms through lysogeny (Bratbak et al. 1993, McDaniel et al. 2002; see chapter: 6 Nagasaki). Specific cyanophages seem to influence Prorochlorococcus or Synechococcus hosts differentially (Sullivan et al. 2003). Virus-like particles (VLPs) isolated from natural populations in the coastal bays of New Jersey and in culture populations of the brown tide bloomforming Aureococcus anophagefferens were similar and potentially caused the termination of the bloom (Gastrich et al. 2004). Virus-mediated lysates are critical to the recycling of organically complexed iron in the coastal upwelling high-nutrient low-chlorophyll (HNLC) waters, that support up to 90% of primary production. Viruses seem to play an important role in the regeneration of bioavailable iron (Wilhelm et al. 2001).

Energy Transformation Sun is the primary source of energy and ~1200 kilocalories reach a square meter surface area in a 12 day and ranges with the latitude. In the low latitudes i.e. 0–30° N and 0–30° S it is ~3000 kcal m2 d–1 and at the mid


latitudes 30–60° N and 30–60° S it is ~ 1500 kcal m2 d–1 and much less in the higher latitudes. The total solar energy received by earth is roughly equivalent to 10,000 times the total energy consumed by humanity. In the oceans as light travels through the water column it is attenuated exponentially with increasing depth following the Beer-Lambert law expressed as: A = ebc Where A is absorbance (no units, since A = log10 P0/P) e is the molar absorbtivity with units of L mol–1 cm–1 b is the path length expressed in centimeters. c is the concentration of the compound in solution, expressed in mol L–1. Studies of the electromagnetic spectrum of visible light (350–750 nm) showed that water absorbs light differentially i.e. the longer the wavelength, the lower the energy and faster it gets absorbed as follows: Table 1.3 Light transmission characteristics in the sea Color Red Orange Yellow Green Blue Violet

Wavelength nm

Depth (m) where most is absorbed

600–750 600–575 500–575 525–475 475–500 450–400

5–10 10–15 15–25 30–50 60–100 10–30

Most (~50%) of the infra-red, is absorbed in the first one meter of the sea. Ultraviolet light (< 400 nm), the shortest wavelength with the highest energy is an exception; it is scattered by particles. Of the visible spectrum the red component gets absorbed first and the blue component penetrates deeper. In the oceanic waters 84% of the incoming radiation is absorbed in the top one meter and about 99% in the top 10 m. In the coastal waters which have more suspended particles than in the oceanic province, 99.5% of the light is absorbed in the top 10 m through absorption by algae and also through scattering by water molecules and suspended particles. Chlorophyll, because of its maximal light absorption coefficient between the wavelengths 430–670 nm, is the most important molecule on the earth and photosynthesis is the most important physiological process in the transformation of solar energy into chemical energy (see chapters: 19 Shakshug and Jensen; 18 Grobellar). The upper 200 m of the ocean is the illuminated zone also known

Algal Cultures, Analogues of Blooms and Applications

as the “euphotic zone” characterized by active photosynthesis. The 200– 1000 m is the “twilight zone” or the mesopelagic zone. The deeper waters with no light constitute the “aphotic zone”. Members of each algal division have their characteristic photosynthetic pigments and harvest light energy in the visible spectrum (350–750 nm) and thus provide a spectral signature. Based on the spectral signatures using remote sensing it is possible to map detailed distributions of photosynthetic biomass in surface layers (see chapter: 2 Jeffrey and Wright).

Photosynthetic Production and Biogeochemical Cycles The introduction of tracer carbon-14 technique (Steemann-Nielsen, 1952) opened up a new chapter in phytoplankton physiology. Phytoplankton being autotrophic convert inorganic carbon in the seawater to organic matter via photosynthesis and supply oxygen to the oceans. They are referred to as primary producers or organic producers, analogous to terrestrial pastures. Phytoplankters could be unicellular or multicellular. In distribution they occur under extremes of climatic conditions between the tropics and the polar waters. Usually limited to the illuminated or euphotic waters, these biota act as floating oxygen farms, carry on photosynthesis during which inorganic carbon dioxide is converted into organic matter. 6CO2 + 6H2O + light 8 photons fi C6H12O6 + 6O2 If we include the macronutrient (N, P, S) and the trace element (Fe, Zn, Mn,) uptake: 106CO2 + 16NO3 + PO4 + SO4 + 10–2 Fe + 4 ¥ 10–3 Zn + 4 ¥ 10–4 Mn fi (C106 H263 O110 N16 PS) + 138O2 In this transformation two products are most important: a) the organic matter produced constitutes the foundation of food web in the marine environment, and b) the waste product oxygen sustains all metabolic activity which involves the transfer of electrons between oxidized and reduced substances in reactions called Redox reactions. Annually about 1.25 ¥ 1024 cal of solar energy strikes the earth of which 10% is available to photosynthetic pigments for photosynthesis (Fig. 1.1). Assuming photosynthesis is 2% efficient, the annual potential primary productivity is 2.5 ¥ 1021 cal or 2 ¥ 1017 g C or 200 ¥ 109 t C y–1 (Isaacs, 1969). The oceans receive nutrients and 750 ¥ 1015 g C y–1 via the dust storms and other atmospheric activities (Adhiya and Chisholm, 2001). From the land and soil, anthropogenic activities contribute 2.05 ¥ 1018 g C y–1. In the oceans

CMYK !

CMYK

CMYK


Fig. 1.1 Biogeochemical relationships in the ocean CMYK

" Algal Cultures, Analogues of Blooms and Applications there are essentially five interacting components a) the primary producers b) the herbivores – competing for the autotrophs, secondary producers, the carnivores and the supracarnivores feeding on the herbivores c) the decomposers d) the detrivores and e) the bacteria and viruses. The microorganisms are the foundation of the food web. The picoplankton, nanoplankton and the net plankton constitute the primary producers; they utilize solar energy, carry on photosynthesis (P) and incorporate 45 ¥ 1015 g C y–1 (Falkowski et al. 2004). Like the rest of biota they also respire and consume oxygen. Carbon bound in the surface waters and oceans amounts to 18 ¥ 1018 g C (Adhiya and Chisholm 2001). The consumers consisting of the mesozooplankton, herbivores, and the carnivores consume oxygen and release metabolic wastes into the environment. Upon their death and decay the primary producers and the consumers sink and contribute to the dissolved organic matter (DOM) pool; this DOM pool receives 1 ¥ 1015 g C y–1 and 0.15 ¥ 1015 g N y–1 and acts as a storehouse of nutrients (Moron and Zepp, 1997). The DOM pool also acts as a source of food for the detrivores – the benthic organisms; the unused DOM gets buried in the sediments and contributes to intermediate and the deep ocean reserve of 38 ¥ 1018 g C and 78 ¥ 1018 g C to the sediment and fossil fuel reserves respectively (Adhiya and Chisholm, 2001). Bacteria and viruses act on the DOM and release the nutrients most needed in the euphotic waters (see chapter: 5 Biddanda et al.). The microbial loop comprising of the heterotrophic bacteria, SAR 11 bacteria and microciliates play a very significant role in the energy transformation. These heterotrophs utilize the waste products from the primary producers and funnel the nutrient energy back to the primary producers. Assuming an ellipsoid bacterial cell of 0.4 mm ¥ 0.4 mm and 0.4 pg C per/mm3 of bacterial biomass, the SAR 11 bacteria (Morris et al. 2002) account for ~322 ¥ 1011 g C. It is of interest to note that some of the microalgae release dimethylsulfide (DMSU) to the atmosphere. Although there are no precise estimates of the total DMSU released, Watson and Liss (1998) suggest it may be a powerful influence on the climate by changing the number of cloud condensate nuclei.

Microalgal cultures Culturing of marine algae, specifically diatoms dates back to 1893 (Miquel, 1893), almost simultaneously with the coining the term phytoplankton. Initially Thalassiosira gravida, a diatom was cultured in artificial sea water to raise and support production of animal life in the sea (Allen and Nelson, 1910), but subsequently it was evident that addition of 1% of natural sea water or a minute trace of an organic substance such as aerobic soil bacteria


#

upon peat acted as a powerful stimulant to algal growth (Allen 1914). Since then physiologists and biochemists around the world evinced a keen interest, a sort of academic curiosity, in utilizing algal cultures for various purposes (Appendix 1, Table 1.1). Although several 100 strains have been brought into culture only approximately 30 species have been studied in considerable detail as feed organisms for invertebrates and commercially important organisms (Appendix 1, Table 1.2). A variety of methods are available to culture marine micro algae (Fogg, 1975). The medium could be a semisolid such seawater agar slants or liquid medium; it is either precisely defined by enrichment of artificial sea-water (Appendix 1, Table 1.3) or natural seawater enriched with known quantities of nutrients and soil extract of an unknown composition (Appendix 1, Table 1.4). Algal cultures contaminated with bacteria can be purified by a variety of methods including most commonly, treatment with antibiotics. Usually they are monospecific but very few investigators have succeesfully grown two or more species simultaneously. While growing athecate dinoflagellates and a few armoured dinoflagellates such as Alexandrium, Prorocentrum, and a few Ceratium is possible, culturing Dinophysis is still a problem. At best Dinophysis could be maintained through a few generations for few months (Subba Rao, 1995). Only in recent years has it been possible to culture the Cyanophyte Trichodesmium (Appendix 1, Table 1.5). Culture volumes for the different algae range from a few milliliters to > 10 m3 mesocosms. Batch cultures and continuous cultures are raised and utilized for various applications (Appendix 1, Table 1.6). Although the importance of mixed species cultures in species succession was realized by Allen and Nelson (1910), obtaining reproducible Table 1.4

Broad comparison of seasonal blooms, atypical blooms and batch cultures

Feature Growth medium Bacteria Number of species Duration Cells 106 l–1 Chlorophyll a mg l–1 Divisions day–1 Carbon assimilation ratios mg C h–1 mg chl a Photosynthesis: Respiration

Seasonal bloom

Atypical bloom

Batch culture

Moderately Nutrient rich sea water Present ~25 21 days 30–,>-carotene; no acyloxy fucoxanthins detected Kryptoperidinium foliaceum (Stein) Lindemann (previously known as Peridinium foliaceum), Peridinium balticum (Lev.) Lemm Scripps Institution of Oceanography Dinoflagellate Collection; UTEX; CSIRO Algal Culture Collection Gold TLC (sucrose; polyethylene); RP-HPLC (Zapata et al. 2000) Tertiary endosymbiosis of a diatom within a dinoflagellate host; supported by ultrastructural studies (Tomas and Cox 1973, Jeffrey and Vesk 1976) and molecular phylogeny (Chesnick et al. 1997). However, see comments below. Jeffrey et al. (1975), Withers et al. (1977), Chesnick et al. (1997), Jeffrey and Zapata (unpub) Recent evidence Yoon et al. (2002), Morden and Sherwood (2002) suggests that dinoflagellate plastids containing Chls c1, c2 and fucoxanthin are ancestral to dinoflagellates with peridinin.

Table 2.18 Pigment characteristics of Type 4 dinoflagellates (Dinophyta) with phycobilins and/or alloxanthin Chlorophylls: Carotenoids: Phycobilins Cultures examined: Dinoflagellates from coastal blooms: Species color Pigment methods used:

Likely endosymbiosis: References:

Comments:

Chlorophyll a; c2 present in Dinophysis norvegica Ehrenberg Alloxanthin (D. norvegica) Cryptomonad-type phycoerythrin (red), Cryptomonad-type phycocyanin (blue-green) Gymnodinium acidotum Nygaard; Amphidinium wigrense. No cultures available of Dinophysis spp. Dinophysis spp. – Dinophysis norvegica, D. caudata Saville-Kent, D. fortii Pavillard, D. acuminata Clapar de et Lachmann Red-brown to colorless (Dinophysis spp.); blue-green to colorless (G. acidotum; A. wigrense) Absorbance and fluorescence emission spectra of individual cells of Dinophysis spp. frozen before analysis (Hewes et al. 1998); Gold beads coated with phycoerythrin antibodies applied to sections of thylakoids of Dinophysis spp. (Vesk et al.1996); HPLC (Kraay et al. 1992) Tertiary endosymbiosis of a cryptomonad in various stages of establishment within a dinoflagellate host Wilcox and Wedemeyer (1984), Hallegraeff and Lucas (1988), Schnepf and Elbr chter (1988, 1992), Lucas and Vesk (1990), Fields and Rhodes (1991), Vesk et al. (1996), Hewes et al.(1998), Meyer-Harms and Pollehne (1998), Hackett et al. (2003) Inspiration for culturing Dinophysis spp. required (see Hackett et al. (2003) for clues that Dinophysis may be feeding on red algae)

66 Algal Cultures, Analogues of Blooms and Applications Table 2.19 Pigment characteristics of Type 5 dinoflagellates (Dinophyta) with chlorophyll b Chlorophylls a, b; no Chl c detected b,b-carotene, neoxanthin, violaxanthin, and zeaxanthin. Prasinoxanthin was found in Gymnodinium chlorophorum (W.W.C. Gieskes, personal communication to Elbr chter and Schnepf 1996). However, prasinoxanthin was not detected in a G. chlorophorum field sample which had an unknown major carotenoid (P. Henriksen, personal communication to present authors). No peridinin or fucoxanthin detected Lepidodinium viride Watanabe et al., Gymnodinium chlorophorum Cultures examined: Elbr chter and Schnepf (1996) Culture Collections used: Oceanography Laboratory, NIES, Tsukuba, Japan; Sammlung von Algal Kulturen, G ttingen, Germany Culture color: Green Pigment methods used: Paper chromatography (Watanabe et al. 1987); HPLC (Dr. W.W.C. Gieskes, personal communication to Elbr chter and Schnepf 1996; details not published); P. Henriksen (personal communication to authors) Likely endosymbiosis: Secondary endosymbiosis of prasinophyte (green algae) within a dinoflagellate host (supported by ultrastructural and genetic analysis). References: Watanabe et al. (1987, 1990), Watanabe and Sasa (1991), Elbr chter and Schnepf (1996) Comments: Needs re-examination with latest HPLC methods, if only to determine the full pigment complement of the prasinophyte endosymbiont Chlorophylls: Carotenoids:

our latest overview of the subject: the distribution of major and taxonomically significant pigments across microalgal Divisions/Classes. Only those pigments securely identified have been included; pigments ‘expected’ but not reported, such as b,e-carotene in Type 4 dinoflagellates, are obviously not included. These pigment patterns are based on examination of significantly more species than were available in the 1997 compilation, but will surely change as more species are cultured and examined by the best methods, particularly in the little-known pico-eukaryote group (Potter et al. 1997).

PIGMENT INSIGHTS FROM NATURAL PHYTOPLANKTON POPULATIONS Early Understandings The first extensive records of phytoplankton biomass in the sea were from research cruises in the 1950s and 1960s. Data were based on a simple spectrophotometric method for Chls a, b and c (Richards and Thompson 1952), which was applied to summer and winter sections/depth profiles across large areas of the Indian and Pacific Oceans (Humphrey 1966, 1970).

Chl c series MgDVP Chl c 1 Chl c2 Chl c3 Chl c2 P. gyrans type Chl c2 MGDG [18/14] Chl c2 MGDG [14/14] np Chl c1 - like MV-Chl c3

Pigment types Chlorophylls Chl a DV-Chl a Chl b DV-Chl b Chl d

Pigments

Cyanophyta 2

3

Prochlorophyta ®

®

4

® ®

®

® ® ®

1

Chlorophyta 1

1

2

3

Euglenophyta 1

Rhodophyta 1

Cryptophyta 1

Bacillariophyta 1

Bolidophyta 1

1

Chrysophyta 2

3

1

1

1

2

3

4

5

6

7

8

1

2

3

4

5

® ® ® ®

® ® ® ® ®

t ® ® ® ¯ ¯ ® ® ® ® ¯ ® ® ® ¯ ® ® ¯ ® ¯

¯ ¯

t t t t t t t t ® ® ® ® ® ® ® t ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ¯ ® ® ®

®

® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ®

5

Prasinophyta

Red Lineage

Raphidophyta

Green lineage Eustigmatophyta

Cyanobacterial radiation

Haptophyta

Algal Division/Class

Dinophyta

Table 2.20 Distribution of major and taxonomically significant pigments across microalgal Divisions/Classes. ® = present; ¯ = significant, but not present in all species tested; t = trace. For pigment code, see Abbreviations.

Photosynthetic Pigments in Marine Microalgae

67

Contd.

Xanthophylls Alloxanthin Antheraxanthin Astaxanthin But-fucoxanthin Canthaxanthin Cryptoxanthin Crocoxanthin Diadinoxanthin Diatoxanthin

Carotenes Former IUPAC terminology a b, e b b,b g b ,y e e ,e Lycopene y,y

Biliproteins Allophycocyanin Phycocyanobilin Phycoerythrobilin

Unknown Chl c-like Chl c cs-170

Table 2.20

2

3

t

4

t t

®

5

1

1

2 ¯

3

1

1

® ® ® ® ®

1

1

¯

®

¯

¯

¯

® ® ® ®

¯

® ®

®

®

® ®

t

1

® ® ® ®

¯

® ® ® ¯ ¯ ® ® ® ® ® ® ® ® ® ® ® ®

® ® ® ® ® ®

1

2

3

1

1

1

2

3

4

5

6

7

8

1

2

3

® ¯

® ® ® ¯ ®

¯ ®

¯

¯

® t

®

®

® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ®

t

® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ® ®

1

®

® ®

4

®

5

68 Algal Cultures, Analogues of Blooms and Applications

Contd.

Dinoxanthin Echinenone Fucoxanthin 4-keto-fucoxanthin Gyroxanthin diester Hex-fucoxanthin 4-keto-Hex-fucox Loroxanthin Lutein Micromonal Monadoxanthin 9'-cis neoxanthin P-457 + P-468 Peridinin Peridininol Prasinoxanthin Pyrrhoxanthin Siphonaxanthin esters 6-Hydroxy siphonein Uriolide Vaucheriax. esters Violaxanthin Zeaxanthin

Table 2.20

2 t

3

4

5

1

2

t ®

3

1

® ® ®

®

® ® ® ® ®

® ®

¯

1

® ® ® ® ® ® ® ® ® ® ® ® ®

¯

1

®

1

®

1

1

1

2

3

1

1

® ® ® ® ® ® ® ® ® ®

¯

® ® ® ® ® ®

1

2

3

4

5

6

7

8

® ® ® ® ® ®

® ® ® ® ® ® ® ® ®

1

®

® ® ®

®

1

3

® ®

® ®

2

4

® ®

¯

®

¯

5


69

70 Algal Cultures, Analogues of Blooms and Applications Although the methods over-estimated both Chls a and c because early extinction coefficients were too low (discussed by Jeffrey and Welschmeyer 1997, Humphrey and Jeffrey 1997), the program resulted in a comprehensive atlas of data from the Indian Ocean giving both chlorophyll and productivity profiles (Krey and Babenerd 1976). TLC and early HPLC methods were used for field samples in the 1970s and 1980s and several new pigment insights from the sea were gained. These included: • Chlorophyte pigments (Chl b) in the oligotrophic central North Pacific Ocean (Jeffrey 1976b), now in retrospect thought probably due to DV-Chl b from the then unknown Prochlorococcus populations; • Alloxanthin in HPLC samples from the North Sea which probably originated from microscopically unrecognizable cryptomonads, first highlighted by Gieskes and Kraay (1983a); • Unknown Chl a derivatives found in North Sea and tropical Atlantic phytoplankton HPLC samples by Gieskes and Kraay (1983b), probably the first separation of DV-Chl a from then unknown Prochlorococcus marinus; • A new Chl c-like pigment (probably Chl c3 ) separated by Gieskes and Kraay (1986) by HPLC from a North Sea bloom of the haptophyte Corymbellus aureus. All these observations foreshadowed later advances. The subsequent sophistication of pigment separation techniques that came in the 1980s and 1990s, allowed far more accurate estimates of Chl a phytoplankton ‘biomass’, both from satellites, in situ fluorescence detection, and HPLC analysis.

Wider perceptions Remote sensing of ocean colour by sensors on aircraft or satellites (e.g. NASA’s SeaWiFS) has become a powerful tool for estimating surface algal chlorophyll concentrations in the world oceans from space. Spectral variations in light leaving the sea surface (differential absorption and backscatter of irradiance), corrected for atmospheric distortion, are detected by satellite-mounted spectroradiometers at wavelengths appropriate to the detection of phytoplankton pigments in vivo (Sathyendranath 1986). Remote sensing of Chl a now provides detailed images of surface phytoplankton distributions from all oceans on a daily basis. As an example, Fig. 2.4 shows exquisite patterns of surface Chl a concentrations around south-eastern

CMYK Photosynthetic Pigments in Marine Microalgae 140°E

145°E

71

150°E

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Chlorophyll a Chlorophyll a Concentration Concentration mg/m3 mg/m3

Fig. 2.4 Single scene, SeaWiFS false color image on 6 January 2003 of the South-East corner of Australia with Tasmania the large island in the center of the image (between 40°- 45° S and 145° - 150° E). The surface chlorophyll concentration is clearly shown and reveals mesoscale oceanographic features including upwelling along the NSW coast south from Sydney, and near Cape Jaffa (37 ° S, 140 ° E), plus many eddy features in the low-nutrient Subtropical Convergence region east and west of Tasmania, interacting with the higher nutrient Subantarctic Zone further south. A chlorophyll color scale is shown. This image has been provided by the SeaWiFS project, NASA/Goddard Space Flight Center and ORBIMAGE. We are indebted to Mr. Brian Griffiths for providing this figure. CMYK

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72 Algal Cultures, Analogues of Blooms and Applications Australia in the summer of 2003, revealing the response of phytoplankton to mesoscale oceanographic features such as eddies, fronts and coastal upwellings. New higher resolution satellite sensors (e.g. MODIS with 36 wavelength bands) are now able to improve detection of spectral characteristics of waterleaving radiances from algal populations. Near-surface populations of diatoms and the cyanobacterium Trichodesmium can now be discriminated from mixed phytoplankton by ocean colour data, allowing these populations to be mapped even in optically complex waters (Subramaniam et al. 2002, Sathyendranath et al. 2004). Reliable estimates of Chl a concentrations from ocean colour data require algorithms that are appropriate to local algal populations (Morel 1997), highlighting the continued need for groundtruthing pigment samples of the study region. While the satellite sensors can detect only near-surface Chl a, groundtruthing of chlorophyll in the water column from ships by fluorescence detection, can find and measure sub-surface chlorophyll maxima. HPLC pigment analysis can also locate the distribution of key accessory pigments or pigment suites that signal discrete populations of algal types in the water column.

Deeper visions Use of individual pigments as chemotaxonomic markers is a powerful aid to studying field populations of phytoplankton, but only if they are unambiguous markers for a single algal source (such as peridinin or DV-Chl a, for Type 1 dinoflagellates or Type 4 prochlorophytes, respectively), or where a pigment is derived largely from a dominant taxon in a given phytoplankton population. This is often the case for zeaxanthin from cyanobacteria, or fucoxanthin from diatoms, when haptophytes and other non-diatom sources are insignificant, as determined by microscopy. The variability of pigment composition in response to environmental conditions means that reliable quantitative taxonomic analyses of field populations can not simply be derived by applying the results of cultures grown under standard conditions. Environmental factors that strongly influence pigment composition include light intensity, quality and daylength (Wood 1985, Bidigare et al. 1989, Partensky et al. 1993, Johnsen et al. 1994, Moore et al. 1995), nutrient status, notably iron concentration (Goericke and Montoya 1998, van Leeuwe et al. 1998, Schlüter et al. 2000, Henriksen et al. 2002), growth phase, and strain differences (Stolte et al. 2000, Zapata, et al. 2004a). These factors need to be more rigorously tested experimentally with algal cultures under environmental conditions specific


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to particular aquatic habitats in order to obtain more reliable baseline pigment ratios for application to field studies. Due to the uncertainty of pigment composition in the field, marker pigment: Chl a ratios must be computed from the data by use of simultaneous equations (Letelier et al. 1993) or matrix factorization (CHEMTAX software, Mackey et al. 1996). These techniques are now frequently used for determining pigment ratios in field samples (reviewed by Jeffrey et al. 1999). For example Fig. 2.5 shows the application of CHEMTAX software to pigment data from a transect crossing the retreating Antarctic ice edge along longitude 150 ºE (Wright and van den Enden 2000b). This transect encompassed a region of complex oceanography that included the Antarctic Slope Front, a large cold-core eddy, the Antarctic Divergence (where relatively warm (+1.8 ºC) upwelling water brings nutrients into the surface layers), and retreating sea ice that produced a shallow layer of meltwater overlying the mixed layer. Fig. 2.5b shows the total Chl a concentrations along the transect. Subsequent panels show calculated contributions to Chl a by algal groups, apportioned by CHEMTAX on the basis of the concentrations of various marker pigments. CHEMTAX was able to separate the components of the phytoplankton populations and clearly showed that they were differently placed in relation to oceanographic features (Fig. 2.5cj). The algal group names in Fig. 2.5 refer to pigment categories representing algal Divisions/Classes rather than the actual taxa themselves.

Clonal isolates from blooms There has always been a concern that cultures from clonal isolates may not be truly representative of ‘natural’ species in field populations. We discuss here several examples from the literature in which pigment characteristics from cultures were found to match closely those of the blooms from which they were derived. • Carreto et al. (2001) reported a massive outbreak of phytoplankton (99% Gymnodinium sp.) in southern Chile, which caused high mortality to fish and shellfish, and had a concentration of 8 – 9 x 106 cells L–1 in high density patches. Cells from the bloom were cultured, and using the advanced HPLC methods of Garrido and Zapata (1997) and Zapata et al. (2000), full suites of pigments, typical of Type 2 dinoflagellates, were found. Major pigments included 19’hexanoyloxyfucoxanthin, fucoxanthin, 19’-butanoyloxyfucoxanthin, Chls a, c2, c3, Chl c2 – MGDG (14:0/14:0) and gyroxanthin diester. The match between cultures from this bloom and other icthyotoxic

CMYK

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Fig. 2.5 Oceanographic section (20-23 March, 1996) and calculated phytoplankton stocks for the upper 200 m of the water column near the retreating Antarctic ice edge along longitude Contd. CMYK


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Gymnodinium species was excellent, thanks to the enhanced selectivity of the HPLC methods used. • Virtually monoalgal populations of Prochlorococcus strains (up to 97% of the autotrophic biomass) were found at depth (80 to 140 m) in lowlight, suboxic environments of the Arabian Sea and the eastern tropical North Pacific (Goericke et al. 2000). The carotenoid suites of these field populations were similar to those of other cultured Prochlorococcus strains, except for high concentrations of a dihydro-derivative of zeaxanthin, suggesting acclimation to low irradiances. It is suggested that Prochlorococcus is uniquely adapted to exploit this low-light, lowoxygen niche. • Prochlorococcus is found at a range of depths over which light intensities can vary by up to four orders of magnitude. Moore et al. (1998) hypothesized that coexistence of genetically different populations adapted for growth at high- and low-light intensities might explain this wide adaptability. Multiple Prochlorococcus populations from two locations in the North Atlantic Ocean, sorted by flow cytometry into high-and low- fluorescence cells, were brought into culture, and compared with similar cultures from the Gulf Stream and the Sargasso Sea. Following phylogenetic diversity studies and pigment responses to light climate in these Prochlorococcus strains, it was concluded that multiple Prochlorococcus ecotypes could occur in a particular environment (possibly a general microbial characteristic), allowing greater survival to environmental stress than could be achieved by physiologically and genetically homogeneous populations (Moore et al. 1998). • Blooms of diatoms in the East Australian Current off Sydney in 1980 allowed the isolation of over 50 diatom species in culture (Dr J. Stauber). These showed almost universal simple diatom pigment complements, with Chl a, c1, c2, fucoxanthin, diadinoxanthin and b,bcarotene (Stauber and Jeffrey 1988; and Table 2.8). These pigment Fig. 2.5 Contd. 150 °E. Plots show A: Temperature (°C), B: Chl a (mg.L– 1). Panels C to J show contributions to the total Chl a by various algal taxa, calculated by CHEMTAX software as described in the text. C: Diatoms, D: Dinoflagellates; E: Haptophytes-3; F: Haptophytes-4; G: Prasinophytes; H: Chlorophytes; I: Cryptophytes; J: Cyanobacteria. Haptophyte types (3 & 4) were as defined by Jeffrey and Wright (1994), now Type 6 (cocco lithophorids) and Type 8 (Phaeocystis spp.), respectively, in Table 2.14. Sea ice is shown as a small rectangle at top left of each panel. Other oceanographic features include the Antarctic Slope Front (ASF), a cold-core eddy (CCE), Antarctic Divergence (AD), two pycnoclines (pyc), and the temperature minimum layer (Tmin). Dots indicate sample locations (from Wright and van den Enden 2000b).

76 Algal Cultures, Analogues of Blooms and Applications suites matched those found in spring diatom bloom field samples in the same location (Hallegraeff 1981). These examples comparing the pigment composition of cultures and the blooms from which they were isolated, give confidence that the physiology of cells from both environments are not dissimilar. A closer match will be gained when cultures can be grown under identical environmental conditions to those experienced by field populations – a challenge for the future!

CONCLUSIONS Developments in genetic technology and formulation of the endosymbiotic theory have given a framework within which to understand the diverse array of pigments found in phytoplankton. These developments highlight the complexity of distributions, particularly in the dinoflagellates. As more cultures are isolated and analysed, new pigments are being identified, giving increased power to discriminate taxa in mixed phytoplankton populations. Convenient sources of such pigments are required. Twelve algal cultures were recommended by SCOR Working Group 78 as sources of pure pigments (Table 2.1). Several additions should now be made to that list to include new pigments of taxonomic value found by the advanced HPLC methods. Possible candidates include: Prochlorococcus marinus (DV-Chl a, DV-Chl b), Karenia brevis (gyroxanthin diester), Pyramimonas amylifera (siphonaxanthin esters, 6-hydroxysiphonein), Nannochloropsis oculata (vaucheriaxanthin esters), Chrysochromulina polylepis (Chl c2 MGDG [14:0/ 14:0]), and Ochrosphaera neopolitana (4-keto-fucoxanthin). Clonal designations and ease of culture of appropriate strains should be identified. While pigment data from cultured algal species has already given great insights into phytoplankton populations in the sea, much more information is required to facilitate quantitative interpretation of oceanographic pigment data. In particular: • The pigment composition of some classes remain unclear or inconsistent (e.g. Chrysophyta), a problem that needs isolation and genetic characterization of further strains. • Most analyses of pigment composition in cultures have used standard conditions of light, nutrients etc. so that genetic differences between strains can be distinguished (Zapata et al. 2004a). These measurements must be extended to real underwater conditions, to determine how the accessory pigment : Chl a ratios respond to varying environments.


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• More cultures need to be isolated and characterized, particularly the eukaryotic pico-plankton (López-García et al. 2001, Moon-van der Staay et al. 2001). For instance, Diez et al. (2001) found 255 clones of eukaryotic pico-plankton by DNA analysis, of which 53 were abundant but unknown and uncultured stramenopiles. Other wellknown species, e.g Dinophysis spp. and members of the Parmales, have not yet been successfully cultured. • More model marine chromophyte algae are needed for photosynthetic and light-harvesting research – “organisms that could be the ‘spinach’ of the algal light-harvesting world” (Macpherson and Hiller 2003). The present volume offers the terrestrial plant scientist further opportunities to select model organisms from our rich resource of cultured microalgae. Clearly, cultures represent the key to understanding phytoplankton in the sea. There is a need to “re-connect” them with their phytoplankton cousins under conditions closer to those they would normally experience in their natural habitat.

ACKNOWLEDGEMENTS We thank Mr. Brian Griffiths, CSIRO Division of Marine Research for helpful comments on the text and for provision of Fig 2.4, Dr. Peter Henriksen for unpublished pigment data, Ms. Louise Bell for graphics assistance, Ms. Denise Schilling for excellent and cheerful word-processing talent, and Dr. Subba Rao V. Durvasula for kind patience and encouragement. S.W.J. especially thanks Dr. Tony Haymet, Chief of the CSIRO Division of Marine Research for continued support.

Dedication This chapter is dedicated to our friend and colleague, Professor Dr Fauzi Mantoura, Director of the International Atomic Energy Agency Marine Environment Laboratory, Monaco, with whom we had our first great adventure in defining, testing and writing the 1997 UNESCO monograph “Phytoplankton Pigments in Oceanography: Guidelines to Modern Methods”.

Abbreviations But-fucoxanthin CCAP

19’-butanoyloxyfucoxanthin The Culture Collection of Algae and Protozoa, UK

78 Algal Cultures, Analogues of Blooms and Applications CCMP Chl Chl c2-MGDG [18:4/14:0]

Chl c2-P. gyrans type Chl cCS-170 DV-Chl DV-Pchlide Hex-fucoxanthin HPLC HPTLC MgDVP MODIS MV-Chl c3 NIES np Chl c1-like RP SCOR SSU rDNA TLC UTEX Vaucheriax. esters

Provasoli-Guillard Centre for Culture of Marine Phytoplankton Chlorophyll Chlorophyll c 2-monogalactosyl diacylglyceride ester. [18:4/14:0] denotes the chain lengths (18, 14) and number of double bonds (4, 0) of the two esterified fatty acids, respectively Chlorophyll c2 Pavlova gyrans type Chlorophyll c from Micromonas pusilla, CS-170 Divinyl chlorophyll Divinyl protochlorophyllide (synonym for MgDVP) 19’-hexanoyloxyfucoxanthin High performance liquid chromatography High performance thin-layer chromatography Magnesium divinyl pheoporphyrin a5 mono-methyl ester Moderate Resolution Imaging Spectrometer Monovinyl Chlorophyll c3 National Institute of Environmental Studies, Tsubuka, Japan Non polar Chlorophyll c1-like Reverse phase Scientific Committee on Oceanographic Research Small subunit ribosomal DNA Thin-layer chromatography University of Texas Algal Culture Collection Vaucheriaxanthin esters

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Phases, Stages and Shifts in the Life Cycles of Marine Phytoplankton

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! Phases, Stages and Shifts in the Life Cycles of Marine Phytoplankton Marina Montresor1 and Jane Lewis2 1

Stazione Zoologica ‘A. Dohrn’ Villa Comunale 80121 - Naples, Italy School of Biosciences University of Westminster 115, New Cavendish Street London W1W 6UW United Kingdom 2

Abstract Marine phytoplankton includes a wide variety of species, however, information on their life histories is still extremely scanty, and is limited to a few classes. The vast majority of phytoplanktonic species are characterized by complex life cycles that comprise morphologically and physiologically distinct stages. The development of culture media and culturing techniques permits the establishment of clonal strains, and the manipulation of culture conditions allows the different life stages to be produced and the possibility of furthering our understanding of the factors that induce and modulate shifts among them. Phylogenetic lineages have distinct life cycle patterns; nevertheless, they share the occurrence of a sexual cycle and the capability of forming resting stages. Diatoms are characterized by a peculiar division modality that causes the progressive reduction of population cell size; in several species, this sets the dimensional window within which sexual reproduction occurs. Dinoflagellates are an extremely heterogeneous lineage including phototrophic, mixotrophic and heterotrophic taxa. Their wide diversity is mirrored by a great diversity in their life cycles, however we are just beginning to unravel this complexity. Different kinds of resting stages can be produced by phytoplankton species, some capable of withstanding short-term disturbances, others to remain dormant and/or quiescent for years. Laboratory investigations with cultured strains coupled with in situ studies provide information on the factors inducing shifts among the different phases and elucidate their role in species ecology.

INTRODUCTION Phytoplankton (mostly unicellular species) increase their number and consequently their biomass by vegetative division. However, the vast majority

92 Algal Cultures, Analogues of Blooms and Applications of phytoplankton organisms have complex, heteromorphic life histories, including stages that differ in their morphology, size, ultrastructure, ploidy, and physiology. Distinctive stages, which do not reproduce vegetatively but undergo resting periods of variable lengths, are known among groups spreading over the whole phylogenetic diversity, e.g. dinoflagellates, diatoms, haptophytes, raphidophyceans and prasinophyceans. Moreover, single cells, multicellular colonies and/or cells with markedly different size ranges can all be included in the life cycle of the same species. A sexual cycle including syngamy and meiosis is reported for an increasingly large number of species. The sexual phase can be linked to the production of distinctive life stages, such as resting cysts in dinoflagellates, auxospores in diatoms, and stages with heteromorphic coccoliths in haptophytes. Different species have different life history patterns, reflecting adaptations to distinct environmental conditions gained through their evolutionary history, and we are just beginning to unravel the intricate patterns of this diversity. Shifts among life stages, induced by endogenous rhythms or by the interaction with the surrounding chemical-physical and biological environment, can have profound implications for population dynamics, such as bloom development and decay, and species succession (Garcés et al. 2002). Life cycle observations of microalgae have been made since the very beginning of phytoplankton studies (Pouchet 1883). Such observations were, however, sporadic; early studies relied on the use of field samples and were dependent on frequent sampling in the field, or survival of mixed cultures in the laboratory to follow through life histories. The existence of resting stages and their potential role in explaining different timing of diatom appearance during the annual cycle was postulated by Hensen (1887), who observed the presence of heavily silicified spores inside diatom frustules in phytoplankton samples collected in the Kiel Bight. Some of the first evidence that diatoms have complex life histories, including a sexual cycle, dates back to Klebahn (1896) who described auxospore formation in the freshwater species Rhopalodia gibba (Ehrenberg) O. Müller. However, the finding that fossil remnants of dinoflagellates were resting stages produced within non-fossilizable vegetative cells is relatively recent (Evitt 1961). It was during the second half of the past century that germination experiments and studies carried out on cultured species started providing evidence for the link between dinoflagellate motile cells and their non-motile benthic stages, several of which show morphological similarity with fossil stages (e.g. Wall et al. 1967, Fensome et al. 1993).


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Isolation and maintenance of unialgal cultures allows direct and repeated observation of life cycle changes to be made until all aspects of the behavior of the organism under study are known and a coherent picture is established. Latterly the advent of ultra-clean, readily available tissue culture technology has greatly improved this endeavor. However, not all microalgae are amenable to culture and the detailed analysis of life cycles requires an unusual degree of dedication from the observer, so after more than a century of effort, relatively few species have been examined in a thorough fashion. Furthermore, our understanding of the role that phytoplankton life histories have in species ecology is dramatically hampered by the limited possibilities of our current technology to carry out extensive observations in situ. We can all see when a tree starts producing leaves, flowers, fruits and seeds, and how they are dispersed by animals or wind, how insects transport pollen from one flower to the other. However, the vast majority of the corresponding events in phytoplankton is hidden from our perception and is only captured almost by chance by the standard sampling techniques used in biological oceanography. Manipulation of culture conditions and experimental settings is often needed to obtain shifts among stages or to induce the onset of specific processes. All that can be tricky: experimental conditions lend themselves to testing of a single or a few parameters at a time, oversimplifying the complex interactive networking of factors acting at sea. However, culture studies do allow us to describe the backbone of the multifaceted life histories of phytoplankters vital to our improved understanding of their ecology and evolution. In this chapter we aim to illustrate our assertion that heterogeneous life cycles are common in a variety of microalgae and are driven by broadly similar mechanisms over a variety of groups. We aim to establish that cultures provide an excellent means of exploring these life cycles and the drivers of life cycle phases, improving our interpretational skills and providing hypotheses and methodologies for testing in field situations. We do not aim to provide an extensive review of all the achievements obtained in life history studies for phytoplankton organisms. Several review papers dealing with different aspects of marine phytoplankton life histories have been published in the last decades and the reader should refer to them to gain a more complete overview (Fryxell 1983, Steidinger and Walker 1984, Pfiester and Anderson 1987, Pfiester 1989, Round et al. 1990, Billard 1994, McQuoid and Hobson 1996, Edlund and Stoermer 1997, Garcés et al. 2002). Rather we seek to illustrate selected aspects of the life cycle of marine phytoplankton organisms, mainly focusing on diatoms and dinoflagellates,

94 Algal Cultures, Analogues of Blooms and Applications with the aim of providing a picture of the diversity and complexity of their life cycles, on the achievements obtained by culture studies and on a selection of the aspects that, in our opinion, would be worthwhile pursuing in the future.

CULTURING TO ELUCIDATE ELEMENTS OF LIFE HISTORIES When naming a phytoplankton species, we visualize an image that synthesizes the main morphological features of the vegetative cell. Rarely do we think of a composite image constituted of morphologically or dimensionally different stages, although this would be a more accurate picture for many species. Being able to play different games i.e. having a heteromorphic life cycle, can be an advantage and allows a species to spread its genotype into distinct forms, sizes and stages, and thus expand the range of environmental conditions under which it can survive. Only by following a single species (often a single cell) in culture are we able to reconcile the distinct stages of the life cycle into a single picture. This does not only provide basic information on the biology of the different taxa, but also contributes to a better circumscription of species identity, and to achieve further elements for tracing their evolutionary histories. In fact, the phylogenetic framework of phytoplanktonic taxa is still largely based on the morphological features of only one stage in the life cycle, very seldom considering its whole diversity.

Diatoms Small, Medium, Large, X-large Diatoms are encased in a rigid box-shaped silica frustule constituted by one larger (the lid) and one smaller (the box) valve. Upon division, the two daughter cells keep the parental valves as the outer larger ones and synthesize the smaller ones. As vegetative reproduction proceeds, this peculiar mechanism of cell division causes a progressive cell size reduction of the population. For most diatom species size enlargement is only possible following sexual reproduction and the formation of the auxospore, within which the maximum-sized cell is reconstituted. However, the notable size range a diatom can experience during the life cycle merits some consideration per se. Cell volume can span over one order of magnitude: from 2900 to 100 mm3 for Chaetoceros curvisetus Cleve, from the larger-sized initial cell to the minimum dimension at which cell death occurs (Furnas


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1978). A considerable variation in cell volume also occurs in the pennate diatom Pseudo-nitzschia delicatissima (Cleve) Heiden, where larger and intermediate cells are capable of forming chains, whereas the smallest size fraction is present only as single cells (M.M. unpubl). Thus, the same species can experience different morphologies within its vegetative life cycle, presumably with implications for survival success. For example, different predators can graze upon specific size classes, and long colonies formed only in distinct size ranges would deter some of them. Interestingly, also parasites seem to have preferences for distinct size classes, as demonstrated in a number of freshwater species (Holfeld 2000). There are few species for which the whole size range has been determined in culture conditions and we must be cautious in applying this information to the field, as it seems that the smallest sizes recorded in culture may not be found in the natural environment (Paasche 1973). A puzzling exception is represented by Pseudo-nitzschia galaxiae Lundholm and Moestrup, a species that shows considerable morphological variability along its wide size range (from 82 to 10 mm). Cells of the smallest size (£ 20 mm) regularly bloom during the late winter in the Gulf of Naples (Mediterranean Sea) (Cerino et al. 2005). This species also represents a good example of how culture studies can help in linking different morphotypes. Cells in the smallest size range, in fact, would never have been identified as members of the genus Pseudo-nitzschia when observed by light microscopy: they are single-celled and can be easily mistaken for the fusiform morphotype of Phaeodactylum tricornutum Bohlin. However, observations of size reduction in culture, ultrastructural features and genetic analyses proved them to belong to Pseudo-nitzschia galaxiae. Based on a general allometric rule, larger cells have lower division rates due to the large energy requirements for duplicating cell biomass. An inverse relationship between cell size and division rates was indeed reported for several diatom species in which growth was estimated over different size classes (Findlay 1972, Paasche 1973, Durbin 1977). However, higher growth rates were reported for populations with the larger cell volume in Thalassiosira weissflogii (Grunow) Fryxell and Hasle (= T. fluviatilis Hustedt), where size distribution within the population was also observed to influence size and timing of division peaks (Chisholm and Costello 1980, Costello and Chisholm 1981).

Sexual Cycle Diatoms have a diplontic life cycle, where gamete fusion rapidly follows meiosis and the dominant stage is diploid. Centric and pennate diatoms

96 Algal Cultures, Analogues of Blooms and Applications have different reproductive modalities: the first group is characterized by oogamy i.e. motile uniflagellate sperm cells fuse with non motile egg cells (Fig. 3.1a), while the second group produces non-motile, morphologically undifferentiated but functionally distinct, gametes (Round et al. 1990). The diploid zygote produced after syngamy is not surrounded by the silica frustule and enlarges forming the auxospore, within which a large-sized vegetative cell is formed (Figs. 3.1a, 3.2). This life stage should not to be confused with the spore that is a resting stage, generally unrelated to the sexual phase. The sexual cycle in diatoms thus links the meiotic process and consequent genetic recombination to the re-establishment of the maximum size of the population. Studies on the life cycle of diatoms have been numerous in the first half of the past century, when the intricate conjugation modalities of pennate, mainly freshwater species, were carefully depicted using natural samples and culture material (reviewed in Drebes 1977, Round et al. 1990, Edlund and Stoermer 1997). However, it was only relatively recently that definitive evidence for oogamus reproduction in centric diatoms was provided by culture studies (von Stosch 1950). Until recently, diatoms were considered homothallic (Drebes 1977). This still holds true for centric species, where auxospore formation occurs in cultures established from the isolation of a single cell, but there is increasing evidence that pennate diatoms are basically heterothallic, i.e. conjugation only occurs when compatible strains are mixed. This evidence mainly stems from a number of culture studies carried out on freshwater species (e.g. Mann 1989, Mann et al. 1999, Chepurnov et al. 2002) but a heretothallic life cycle has been also described for species of the marine genus Pseudo-nitzschia (Davidovich and Bates 1998). Extremely complex breeding systems exist among diatoms. Monoecious (capable of self-reproduction within a strain), unisexual (of different mating type, they have to meet a cell of complementary mating type to produce the auxospore) and bisexual (they can act as different mating types, both + and -, when crossed with other strains) strains exist, for example, in Achnanthes longipes C.A. Agardh. The results of crosses between different reproductive types and the effects of inbreeding have been unraveled in a series of elegant laboratory studies (Chepurnov and Mann 1997, Chepurnov and Mann 1999, Chepurnov 2000).

Vegetative Enlargement There are exceptions to the general rule that diatoms have to go through sexual reproduction to regain size, but very little is known about the occurrence and relevance of this process. Cells can extrude the cytoplasm


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Fig. 3.1 Schematic drawing representing the life cycle of Chaetoceros diadema (modified from French III and Hargraves 1985). A) Vegetative cells diminish their diameter (a, b); motile sperm cells are produced within male gametangia (c-f) and fertilize the female gametangium containing the egg cell (g); the initial vegetative cell forms within the auxospore envelope (h, i). B) Spores can be formed within cells of any size (a-c); spore valves separate and setae are formed following germination (d), spore valves cast off (e).


Fig. 3.2 Light micrographs of different life stages of Pseudo-nitzschia delicatissima. Chainforming vegetative cells (a), paired gametangia (b), two early-stage auxospores (c), two mature auxospores, note the almost complete large initial cell inside the left auxospore (d). Scale bars = 20 mm (Courtesy of Alberto Amato, Naples). from the silica frustule and build an auxospore-like structure within which a larger vegetative cell is built. This process, called ‘vegetative cell enlargement’ was first described by von Stosch (1965a) as a manipulation tool for maintaining cultures without inducing sexual reproduction. A number of centric diatoms have been shown to undergo this process in culture (Skeletonema costatum (Greville) Cleve, Gallagher 1983; Leptocylindrus danicus Cleve, French III and Hargraves 1986; Coscinodiscus wailesii Gran and Angst, Nagai et al. 1995) but also the pennate diatom Achnanthes longipes has been shown to regain size apparently without undergoing a sexual phase (Chepurnov and Mann 1999). Species capable of regaining size bypassing the sexual process could have a selective advantage in avoiding the cost (in terms of energy requirements for producing gametes) and risks (in terms of the possible failure of finding a mate, or failure of the mating process) of sexual reproduction.


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Resting Stages Diatoms, as many other phytoplankters produce resting stages, either spores or resting cells, which have been reported for about 120 marine species (McQuoid and Hobson 1996). Spores are identified by their heavily silicified frustules, whose morphology can be relatively similar or drastically different from that of the corresponding vegetative cells (Hargraves 1976) (Figs 3.1b, 3.3). Unlike the formation of dinoflagellate resting cysts, spore formation in diatoms is not linked to the sexual phase but it is the result of two subsequent division processes in which the modified valves are synthesized (Fig. 3.1b). Resting cells are apparently undifferentiated from the normal vegetative cells and only their ultrastructure reveals reduced cytoplasmic organelles and a high amount of reserve material that characterize all stages deputed to a resting phase (Anderson 1975).

Fig. 3.3 Chaetoceros diadema, light micrographs of vegetative cells (a) and resting spores (b). Scale bars = 200 mm (a) and 20 mm (b) (Courtesy of Ugo Sacchi, Naples). Evidence for the capability to produce resting stages came from laboratory observations showing that cells were able to stop growing when stored in the dark and resume growth when re-exposed to the light (Antia and Cheng 1970). Not only spore-forming diatoms are able to survive prolonged dark conditions, demonstrating that morphologically undifferentiated resting cells could represent a widespread survival strategy among diatoms (Peters 1996, Peters and Thomas 1996, Lewis et al. 1999). Both spores and resting cells are able to retain intact their photosynthetic apparatus, maintain low respiration rates, and are able to withstand prolonged darkness. Laboratory investigations showed higher C:N and C:Chl a ratios in resting stages, providing evidence for an enhanced storage of reserve material, low photosynthetic and respiratory activities (Anderson 1976, Hargraves and French 1983, Kuwata et al. 1993). Dark survival seems to be markedly longer

100 Algal Cultures, Analogues of Blooms and Applications for species living at high latitudes, which are able to regain successful growth after four to nine months of storage, as compared to temperate species, whose survival is shorter (Peters 1996, Peters and Thomas 1996). This might represent a species-specific adaptation to the long-lasting adverse conditions for vegetative growth (prolonged darkness) that are found at higher latitudes. A recharging mechanism by which spores would be able to perform short-term photosynthesis, so as to take advantage of occasional re-suspension in the water column and prolong their survival capability, was also suggested (French and Hargraves 1980). Spores are easily identified in phytoplankton or sediment trap samples and their role in population dynamics has been addressed in different environments, such as coastal seas and upwelling areas. Spores have higher sinking rates and can accumulate on the bottom sediments or at the pycnocline in the water column, from where they are resuspended following upwelling events or the seasonal mixing of the water column (Garrison 1981, Pitcher 1986, Eilertsen and Wyatt 2000). Spores do not have an obligate dormancy, but are quiescent stages that can rapidly resume vegetative growth, thus inoculating a new population in the water column. By contrast, the role of resting cells in population dynamics is largely unknown, with the exception of a few reports from freshwater systems, where resting cells have been recorded in sediments and were able to rejuvenate when exposed to the light under normal oxygen concentrations (Sicko-Goad et al. 1989). The life cycle of the diatom Leptocylindrus danicus appears to be unique since the resting spore is formed within the auxospore, the product of the sexual cycle (French III and Hargraves 1985). The maximum size cell is produced following the germination of the spore and this led to investigations aimed at following the occurrence of sexual events at sea by monitoring size distribution of the natural population (French III and Hargraves 1986). However, only for one out of 70 strains brought into culture, was it possible to observe sexual reproduction and spore formation, whereas in the other strains cell enlargement occurred through vegetative enlargement. Upon EM examination, these asexual strains were shown to lack a sub-central pore in the valve and were designated as L. danicus Cleve var. apora French III and Hargraves. Laboratory investigations thus showed that apparently identical strains of the same species turned out to have minor morphological differences but substantial differences in their life histories, with considerable implications for population dynamics.


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Dinoflagellates Heteromorphic vegetative stages The formation of heteromorphic stages during the vegetative cycle is also known for dinoflagellates. One of the distinctive features of some species (e.g. several species of the genus Alexandrium, Gymnodinium catenatum Graham, Pyrodinium bahamense Plate) is the capability of forming chains, where cells are joined in a head-to-tail fashion. Depending on different temperature and salinity conditions, cultures of Gymnodinium catenatum are dominated by either single cells or colonies of different lengths, suggesting a notable level of plasticity (Blackburn et al. 1989). Chain formation has been suggested as an adaptation to favor bloom formation by increasing cell motility, thus allowing chain-formers to exploit effectively stratified water columns (Margalef 1998). Sub-populations with different size and growth rates have been recorded in Karenia mikimotoi (Miyake and Kominami ex Oda) G. Hansen and Moestrup (= Gymnodinium cf. nagasakiense Takayama and Adachi) (Partensky and Vaulot 1989). Small-sized cells are often interpreted as gametes in the dinoflagellate life cycles, but in K. mikimotoi small cells were able to maintain vegetative division at high rates, thus probably gaining a competitive advantage of building up biomass during bloom development. Small cells, previously considered as distinct species, have been recorded also for a number of species of the genus Dinophysis both in natural samples and in laboratory incubations (Reguera and GonzálezGil 2001). These small cells are engulfed by normal-sized cells originating a planozygote with two trailing flagella; it is thus likely that they are a stage in the sexual cycle. Dinoflagellates can also switch between motile and non-motile stages during their vegetative phase. These non-motile stages, known as temporary or pellicle cysts, have been often reported in cultures and have been interpreted as an adaptation to withstand short-term adverse environmental conditions (Garcés 2002). Cells shed flagella and thecal plates (ecdysis) and appear as featureless globular bodies. Alexandrium taylorii Balech produces temporary cysts in culture and in the natural environment, where it shifts from motile stage to temporary cyst over a circadian rhythm. Temporary cysts produced during the night phase revert to the flagellate stage during the light cycle, thus alternatively colonizing water column and surface sediments, possibly enhancing their survival capabilities (Garcés et al. 1998). Temporary cysts are also known for Lingulodinium polyedrum (Stein) Dodge (Fig. 3.4) where this process has been reported in early observations


Fig. 3.4 Schematic drawing representing the life cycle of Lingulodinium polyedrum (reproduced, with permission, from Lewis and Hallett 1977). 1. Hypnozygote (cyst), in the sediment (diploid condition). When suitable conditions prevail excystment may take place to form a naked cell (planomeiocyte), which subsequently divides to form vegetative cells (haploid condition). Excystment takes around 15 min and thecal formation thereafter takes several h. 2. Vegetative reproduction occurs by binary fission – either by ecdysis or by thecal sharing. 3. Ecdysis may take place in response to environmental cues or as part of cell division. 4. Gamete formation. 5. Gamete fusion, after cells come together, fusion takes some 45 min. 6. Planozygote formation. The planozygote has distinctive ‘ski track’ flagella and is enlarged with respect to vegetative cells, often with broad intercalary bands. 7. Cyst (hypnozygote) formation takes place within the theca and an outer expanding membrane. Cyst formation is rapid (within 20 min) and is complete with the rupture of the outer membrane. The cyst subsequently falls to the sediment and outer thecal material is lost or decays. 8. Cyst undergoes a mandatory dormancy period when excystment cannot take place, thereafter the cyst remains quiescent until suitable conditions for excystment prevail. of bloom material (Torrey 1902). In this species ecdysis can be induced by a variety of unfavorable conditions such as cooling, reduced photoperiod,


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oxygen depletion, pH decrease, and mechanical stress (Lewis and Hallett 1997). In some species temporary cysts can also act as division cysts, giving rise to a variable number of motile cells upon germination (Montresor 1995, Garcés et al. 1998, Parrow and Burkholder 2003a, b). Indeed, mechanisms of vegetative division are notably diverse among dinoflagellates, with species undergoing binary fission in their planktonic stages, keeping a half or completely discharging the parental cell wall and building new thecae, or forming division cysts from which a variable number of flagellate stages emerge (Elbrächter 2003). Careful studies with culture material are required to sort out the different division strategies. These studies, besides providing additional characters for building phylogenetic relationships among species, will also provide a better characterization of the species lifestyle.

Sexual Cycle Dinoflagellates have a haplontic life cycle, where meiosis rapidly follows syngamy, and the dominant stage is haploid. The general scheme of the sexual cycle (Fig. 3.4) involves the conjugation of isomorphic or anisomorphic (i.e. of similar or different morphology, respectively) gametes. Contrary to reports for diatoms, gametes are generally indistinguishable from vegetative cells, and gametogenesis may be affected by an internal switch within vegetative cells that does not affect their external morphology (Xiaoping et al. 1989, Probert et al. 2002). In some species gamete formation may be preceded by a reduction division that provides cells with smaller size and reduced pigmentation (von Stosch 1973). This differentiation is remarkable in species of the genus Ceratium, where smaller and morphologically different male gametes are engulfed by normal-shaped female gametes (von Stosch 1964, von Stosch 1965b), or in Pyrophacus where a small cell with a distinct shape and plate pattern conjugate with a larger cell (Pholpunthin et al. 1999). Once formed, gametes must locate each other. There has been little experimental evidence of possible mechanisms in dinoflagellates and it has been speculated that toxins might play a role acting as pheromones (Wyatt and Jenkinson 1997). Destombe and Cembella (1990) investigated mating type determination and gametic recognition in Alexandrium and concluded that efficiency of mating depended on compatibility between clones. However, gametic recognition did not guarantee mating success and it has been observed that gamete fusion is not necessarily followed by formation of viable zygotes (Probert et al. 2002). The product of gamete fusion is a planozygote, a diploid large motile cell, characterized by two longitudinal flagella. In some species, this stage

104 Algal Cultures, Analogues of Blooms and Applications undergoes dramatic morphological and physiological transformations and turns into a resting cyst. Cysts have notably thick and resistant walls (Kokinos et al. 1998) and internally have a high proportion of storage material. The meiotic division that reestablishes the haploid vegetative stage occurs in the planozygote and is evidenced by the pairing of chromosomes within the nucleus, a process called nuclear cyclosis (Pfiester 1989). When the planozygote encysts, meiosis can occur either within the cyst or in the motile stage that emerges after cyst dormancy is over. As a result of culturing experiments, species have been defined as homothallic (self-compatible) or heterothallic (two mating types, usually denoted + and -). Experiments are carried out following incubation of pairs of strains and cyst production is evidence for sexual reproduction and strain compatibility. However, while positive results offer evidence for sexual compatibility, negative ones can be biased by non-optimal experimental conditions for encystment or a long-term culture history, which often impairs encystment success. In such mating experiments Destombe and Cembella (1990) showed that Alexandrium tamarense (Lebour) Balech (= Alexandrium excavatum (Braarud) Balech and Tangen) was not simply heterothallic and noted that incompatibility between clones relaxed over time in culture. Also Gymnodinium catenatum has a multiple group mating system with varying levels of compatibility, planozygote viability, and encystment success between groups (Blackburn et al. 2001). This calls for thorough investigations in order to properly evaluate the strength of isolation barriers among dinoflagellates. Scrippsiella trochoidea (Stein) Loeblich III constitutes an example of homothallic life cycle: here the production of peculiar cysts surrounded by calcareous spines occurs within a culture established by the isolation of a single cell, without the need for crossing strains with distinct mating types (Montresor et al. 2003). Multiple strains should be always tested when assessing dinoflagellate mating behavior. In fact, in the isolation of a single cell from which a culture is derived it would be possible to isolate a planozygote without being aware that this was the case giving rise to a notionally clonal culture (i.e. a culture established by the isolation of a single vegetative cell) that is actually mixed (i.e. a culture containing two mating types). The same is true of cultures derived from single cysts. The vast majority of information on dinoflagellate life cycles stems from phototrophic species, due to the greater feasibility of maintaining them in culture. However, a large number of dinoflagellates are heterotropic (Jacobson 1999) or mixotrophic (Stoecker 1999), which implies that an optimal food source has to be identified in order to grow them in the laboratory. Pfiesteria piscicida Steidinger and Burkholder and P. shumwayae


105

Glasgow and Burkholder are heterotrophic dinoflagellates that can cause massive fish kills and induce neurological problems in humans. Early work on P. piscicida described a multi-phase, complex life cycle including different kinds of cysts and amoeboid stages (Burkholder and Glasgow jr. 1997). Recently, there has been a debate in the scientific community concerning the veracity of this complexity in the life cycle of these species. Detailed laboratory investigations focused on asexual and sexual reproduction in algal-fed clonal cultures provided evidence that asexual division occurs within division cysts and sexual reproduction is characterized by fusion of gametes, formation of planozygotes, zygotic cyst and finally release of flagellate cells (Litaker et al. 2002, Parrow et al. 2002, Parrow and Burkholder 2003b, 2004). There is some evidence that sexual stages (fusing gametes and planozygotes) were more abundant in mixed clones of Pfiesteria shumwayae but further investigations are required to assess the mating behavior of heterotrophic dinoflagellates (Parrow and Burkholder 2003b). The discrepancies between the more classical, haplobiontic life cycle and the previous reports including the high number of diversified life stages may reside in the observation that toxic Pfiesteria strains, where toxicity is induced by the presence of fish prey, have a more complex life cycle (Burkholder et al. 2001, Burkholder and Glasgow 2002).

Haptophytes Haptophytes include flagellate species surrounded by organic scales as well as coccolithophorids, in which the cell is covered by calcium carbonate platelets of biogenic origin: the coccoliths. Haptophytes have a haplodiplobiontic life cycle, where meiosis and syngamy are separated in space and time, implying that both diploid and haploid stages can perform vegetative division. Alternation of morphologically different life stages, at times characterized by different ploidy levels, is widely known for haptophytes, although detailed information on the modalities and occurrence of the sexual phase are unknown for most species (Billard 1994). Recent investigations confirm the ability of haploid and diploid phases to reproduce indefinitely vegetatively and provide evidence of sexual fusion and meiosis (Houdan et al. 2004). Coccoliths can be distinguished on the basis of their crystallographic ultrastructure: heterococcoliths are a mixture of crystals of variable shape whereas holococcoliths are made by one single type of calcium carbonate crystal. The finding that coccolithophorids can have a life cycle including morphologically distinct forms, including both coccolith and non-coccolith

106 Algal Cultures, Analogues of Blooms and Applications bearing stages or coccolith with different morphological and crystallographic ultrastructure, dates back to the 1960s (von Stosch 1955, Parke and Adams 1960). Parke and Adams (1960) succeeded in culturing the motile holococcolith-bearing Crystallolithus hyalinus Gaarder and Markali. A nonmotile stage developed in old cultures and, within a few days, large-sized coccoliths started to be produced underneath the hyaline layer containing the small holococcoliths. That coccoid stage fit the description of the heterococcolith-bearing Coccolithus pelagicus (Wallich) Schiller, thus providing evidence that the two species were in fact distinct morphotypes within the life cycle of the same species. The life cycle of Emiliania huxleyi (Lohmann) Hay and Mohler, one of the key-species of the modern oceans, also encompasses different morphotypes: a flagellate stage (S-cell) surrounded by organic scales, a coccoid stage surrounded by heterococcoliths (C-cell), and a naked coccoid stage (N-cells) (Klaveness 1972). All three stages are capable of reproducing vegetatively by binary fission but occasionally N-cells appear in cultures of C-cells, and S-cells can appear in cultures of N-cells; S-cells can switch to C-cells and vice versa. Paasche and Klaveness (1970) first showed that C- and N-cells have the same amount of DNA, while S-cells have a smaller chromosome number. Flow cytometric analysis definitively confirmed that S-cells have half the DNA of C-cells, thus representing the haploid and diploid stage, respectively, most probably linked by a sexual phase (Green et al. 1996). In the last few years, intensive sampling efforts have allowed the observation of several holococcolith-heterococcolith combinations, i.e. cells surrounded by two layers of coccoliths normally considered to belong to different taxa (Cros et al. 2000, Geisen et al. 2002, Young and Henriksen 2003). Analogous to that known for Crystallolithus hyalinus/Coccolithus pelagicus and E. huxleyi, the holococcolith-heterococcolith combinations have been interpreted as the product of syngamy between two haploid holococcolith stages that give rise to a transition stage in which the heterococcoliths of the diploid stage are produced on top of. This fits into a haplodiplobiontic life cycle reported for other haptophyceans (Chrysochromulina Edvardsen and Vaulot 1996, Paasche et al. 1990; Prymnesium Larsen and Edvardsen 1998). Phase shifts have been studied in culture only for a few species (Billard, 1994, Geisen et al. 2002) and, recently, phase transitions have been obtained through the manipulation of experimental condition for Calyptrosphaera sphaeroidea Schiller (Noël et al. 2004). Improved culturing techniques, coupled with ploidy analysis and mating tests will undoubtedly elucidate the characteristics of these complex life cycles and the factors that induce shifts among the stages. Besides considerable implications for


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coccolithophorid taxonomy, the existence of distinct life stages could imply different niche partitioning in the natural environment. The genus Phaeocystis includes species whose life cycle includes motile and non-motile stages and, in some cases (P. antarctica Karsten, P. globosa Scherffel, P. pouchetii (Hariot) Lagerheim), the formation of large colonies. However, species within the genus differ in the number and ultrastructural features of the different stages and there are species for which colony formation is not known, or is constituted by an aggregation of a few nonmotile stages within a mucus matrix (P. jahnii Zingone, Zingone et al. 1999). Cells differ in size and relative DNA content and are linked into a complex life pattern where, however, syngamy has not been located yet (Rousseau et al. 1994, Peperzak et al. 2000).

DRIVERS FOR SHIFTS AMONG LIFE CYCLE STAGES We have illustrated how cultures are the primary means of establishing the variety and nature of life cycle stage in marine microalgae. But cultures are also a unique tool to investigate timing and triggers inducing shifts among stages, which is essential information to understand the role of heteromorphic life cycles in species ecology.

When does Sex Occur? Locating the timing and occurrence of sexual phases such as gametic fusion (syngamy) and meiotic events is challenging both in the field and when following a single species in culture. These events can be ephemeral processes in e.g. haptophytes, where stages of different ploidy suddenly appear in culture but the cytological process originating them has not yet been observed (Larsen and Edvardsen 1998). Culturing has been used to investigate drivers for the sexual phase in dinoflagellates. Attention has been almost exclusively focused on cyst-forming species in which the planozygote can transform into a resting stage (see below). However, the formation of cyst cannot be considered an unequivocal indicator of the occurrence of sexual reproduction: apparently not all planozygotes perform this transformation and a better estimate of the onset and relevance of sexual reproduction would be provided by gamete formation (Anderson 1998). Unfortunately, gametes are often indistinguishable from vegetative cells, fusing pairs can be mistaken for dividing cells and even unequivocal identification of planozygotes can be difficult in some species. All this makes our appreciation of the occurrence and importance of the sexual phase in dinoflagellates in the field and also in culture extremely limited.

108 Algal Cultures, Analogues of Blooms and Applications Diatoms are somehow less prudish and provide morphological evidence for gamete formation and conjugation process. The attainment of a speciesspecific size window (usually 30-40% of the maximum cell size, Drebes 1977) seems to be a prerequisite for the induction of gametogenesis and thus sexual reproduction in diatoms (Fig. 3.5). However, within this permissive window, an environmental stimulus is required to operate the switch from asexual to sexual phase. Light (Furnas 1985, Vaulot and Chisholm 1987, Armbrust et al. 1990), nutrients (French III and Hargraves 1985), temperature shifts (Drebes 1966), salinity (Schmid 1995), but most often a combination of several of these are effective in inducing gametogenesis and auxospore formation or modulating their occurrence. At times an increase and at times a decrease of the environmental factors are effective. Gametogenesis in Thalassiosira weissflogii can be induced by shifting the culture from saturating light to darkness, and again to continuous light, but only the fraction of cells in the G1 phase of the cell cycle respond to the trigger, showing a possible link between gametogenesis and cell cycle (Vaulot and Chisholm 1987). In the pennate diatom Pseudo-nitzschia multiseries (Hasle) Hasle sexual reproduction occurs only when exponentially growing strains are mixed without any apparent need for special manipulations (Davidovich and Bates 1998). Daylength has seldom been tested as a factor inducing gametogenesis and both positive, negative and lack of effect of different light:dark cycles on the sexual phase have been detected (Holmes 1966, Hiltz et al. 2000). This diversity of effective triggers for sexual reproduction is not surprising considering the notable phylogenetic and ecological diversity of the species that have been investigated. In recent years, the general assumption that sexual reproduction occurs in the smallest size range has been challenged by observations on both centric and pennate diatoms. In Thalassiosira weissflogii spermatogenesis took place regardless of the size range of the population (Armbrust et al. 1990); an overlap in size ranges of initial cells and gametangia has been reported for Coscinodiscus granii Gough (Schmid 1995). Also in the pennate Pseudo-nitzschia multiseries the size range for sexual induction seems to be rather wide, ranging from 23 to 70% of the maximal cell size (Davidovich and Bates 1998); in P. delicatissima, cells can undergo sexualization over almost their entire size range (Amato et al. 2005) (Fig. 3.5). The information is still too scanty to draw general conclusions, however, it is likely that species living in different habitats (planktonic vs. benthic), or with different mating behaviors, have distinct constraints on the occurrence of sexual events. A restricted narrow size range could be effective in preventing too frequent sexual events in benthic diatoms, where distances among cells are generally


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Chaetoceros diadema cells sexual phase spores 44

20

6

Stephanopyxis palmeriana

160

90

60

20

Leptocylindrus danicus

15

8

3

Pseudo-nitzschia multiseries

140

102

30

Pseudo-nitzschia delicatissima

80 94

14 10

Fig. 3.5 Size range of vegetative cells (gray), size window for the sexual phase (black) and for spore formation (dashed) in centric (C. diadema, S. palmeriana and L. danicus) and pennate (P. multiseries and P. delicatissima) diatoms (modified from Drebes 1966, French III and Hargraves 1985, Hiltz et al. 2000 and Amato et al. 2005). smaller and gamete encounters are facilitated by sliding movements. A too narrow size range could instead represent a serious limit for planktonic species, in which cell encounter rate is less frequent. This might be especially true for pennate heterothallic species, which have the further challenge of mating with a cell of opposite polarity and, in contrast to centric diatoms, lack multiple sperm cells that enhance fertilization success. Unfortunately, there are almost no records for the occurrence of sexual events at sea, possibly due to the difficulty of identifying fragile sexual stages in preserved samples, or to the fact that they represent a very small proportion of the total cell number. A notable exception is represented by the record of a bloom of Corethron pennatum (Grunow) Ostenfeld (= C. criophilum Castracane) undergoing a massive sexual phase in the Southern Ocean (Crawford 1995). The examination of cell size distribution over the years

110 Algal Cultures, Analogues of Blooms and Applications allows insights into the timing of sexuality. In fact, the sexual event is manifested by the appearance of large cells, the initial cells produced in the auxospore, in the population. This meticulous work has been carried out only for a few freshwater species (Cocconeis scutellum Ehrenberg var. ornata Grunow, Asterionella formosa Hassall, Nitzschia sigmoidea (Nitzsch) W. Smith, Aulacoseira subarctica (O. Müller) Hawroth) and results show that the life cycle in the different species takes variable time periods to complete, spanning from 1 to 40 years (Mizuno and Okuda 1985, Mann 1988, Jewson 1992). Centric diatoms are homothallic, i.e. sexual reproduction occurs within a monoclonal culture. However, mechanisms could have evolved to avoid or limit inbreeding in the natural environment, by analogy with what is known for plants. In Skeletonema costatum, high irradiance values induce the formation of female gametangia, whereas at low irradiance male gametangia are produced (Migita 1967). In Coscinodiscus concinnus Wm. Smith, male gamete production seems to occur over a wide range of temperature and photoperiod conditions, with a preference for short daylengths (Holmes 1966). Female gametangia were not distinguishable, but auxospore formation showed a restricted range of occurrence, with preference for short daylengths and higher temperatures. These results suggest that gametangia of the two sexes might form at different environmental conditions. Different timing in gamete formation might represent another possible mechanism to limit self-fertilization. In Chaetoceros curvisetus a daily timing in male gametogenesis has been shown, which started after 12 to 16 h from the onset of the light cycle, regardless of daylength (Furnas 1985); unfortunately, no corresponding information is available for the female gametangia.

From Growing to Resting and Back Diatoms Experiments with batch cultures showed that spore formation takes place as a response to either nitrogen or phosphorous depletion (Durbin 1978, Davis et al. 1980, Hargraves and French 1983, Oku and Kamatani 1995). The presence of silica for building up the heavy silicified wall is essential for spore formation. Kuwata and Takahashi (1990) and Kuwata et al. (1993) showed that Chaetoceros pseudocurvisetus Mangin forms resting spores when nitrogen is depleted but silica is available, while resting cells were produced under nitrogen limitation but in the absence of silica. This species thus shows two complementary strategies to withstand different levels of nutrient depletion. Spore formation in Chaetoceros pseudocurvisetus takes


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place also in growth media where nutrients were not depleted (Oku and Kamatani 1995) and this parallels numerous findings in the natural environment where, indeed, spore formation is recorded towards the end of the bloom but not necessarily under nutrient-limiting conditions (Garrison 1981, Pitcher 1986). The two cold-water species Thalassiosira nordenskioeldii Cleve and Detonula confervacea (Cleve) Gran produced spores under nitrogen limitation only at the lower range (0-10 °C) of the temperature window for vegetative growth (up to 15 °C) and the survival time after dark storage was higher at lower temperatures (Durbin 1978). Hence temperature sets a latitudinal barrier for the distribution of this species, and resting spore formation acts as a survival strategy for cold and dark conditions recorded during the winter at high latitudes. There is a puzzling and poorly explored link between spores and diatom size range. At least in some species, spore production does not take place over the whole size spectrum but is restricted or more common in certain size windows. In Leptocylindrus danicus and Stephanopyxis palmeriana (Greville) Grunow spore formation only occurs in cells whose cell diameter is toward the lower size range, while Chaetoceros diadema (Ehrenberg) Gran produces spores within cells of any size (Drebes 1966, French III and Hargraves 1985) and Stephanopyxis turris (Arnott in Greville) Ralfs in Pritchard only in cells within the wider size range (Fig. 3.5). Smaller strains of Ditylum brightwellii (West) Grunow showed lower commitment to spore formation, whereas larger strains produced more resting spores (Hargraves 1982). Thus, for this species a strain-specific capability for spore formation has been recorded: not all strains could be induced to produce resting stages, and strains not committed to spore formation when in the smaller size range did not form spores even after vegetative cell enlargement.

Dinoflagellates The most frequently cited factors capable of inducing encystment are nutrient (nitrogen or phosphorous) deficiency (Pfiester and Anderson 1987) (Table 3.1). However, there is conflicting evidence from laboratory and field studies where sexuality, as judged by cyst formation, appears to have occurred in times of nutrient sufficiency (Anderson and Morel 1979, Anderson et al. 1983, Montresor et al. 1998). This probably reflects methodological problems centered on the scale of nutrient measurements, the difficulty of determining the nutrient status of cells, and the identification of gametes. Nutrient concentration measurements in the field and in culture are made on water samples that integrate seawater volumes in the order of tens of milliliters, while cells in situ experience a wide variety of nutrient

112 Algal Cultures, Analogues of Blooms and Applications Table 3.1 Factors inducing cyst formation in culture Species

Factors

Alexandrium catenella Alexandrium lusitanicum Alexandrium pseudogonyaulax Alexandrium tamarense

Bacterial promotion Adachi et al. 1999 P, N or Fe depletion, turbulence Blanco 1995 N or P depletion Montresor and Marino 1996 N or P depletion, light, Turpin et al. 1978, Anderson and temperature, medium purity, Lindquist 1985, Anderson et al. Fe stress 1984, Doucette et al. 1989 Prey availability, spontaneous Parrow and Burkholder 2003a P or N depletion Blanco 1995 Nutrient depletion Blackburn et al. 1989 N or P depletion, temperature Ellegaard et al. 1998 Prey reduction Spero and Moree 1981 Short daylength von Stosch 1973 Cell contact Uchida 2001 N or P depletion, temperature Anderson et al. 1985 P or N depletion, turbulence, Blanco 1995 conditioning by other species Short daylength, temperature, Sgrosso et al. 2001 nutrient depletion Prey availability, darkness Anderson et al. 2003 Prey availability, spontaneous Parrow and Burkholder 2003b spontaneous, prey starvation Morey-Gaines and Ruse 1980, Nagai et al. 2002 Nutrient depletion Olli and Anderson 2002 Daylength, temperature, nutrient Sgrosso et al. 2001 depletion Day length, temperature, nutrient Sgrosso et al. 2001 depletion N or P depletion, temperature, Wall et al. 1970, Watanabe et al. salinity, cell contact, vitamin 1982, Binder and Anderson 1987, addition Uchida 1991, Uchida 1991, Uchida 2001 Daylength, temperature, nutrient Sgrosso et al. 2001 depletion

Cryptoperidiniopsoids Ensiculifera sp. Gymnodinium catenatum Gymnodinium nolleri Gymnodinium fungiforme Gymnodinium pseudopalustre Gyrodinium instriatum Gyrodinium uncatenum Lingulodinium polyedrum

Pentapharsodinium tyrrhenicum Pfiesteria piscicida Pfiesteria shumwayae Polykrikos kofoidii Scrippsiella lachrymose Scrippsiella operosa (=Calciodinellum operosum) Scrippsiella rotunda Scrippsiella trochoidea

S. trochoidea var. aciculifera

References

concentrations in the scale of microliters. The switch to gamete formation in an individual cell must be driven by the intracellular status of that particular cell, which could be markedly different within the population. Laboratory investigations have suggested that gamete formation takes place when intracellular nutrient concentrations fall to a threshold value (Anderson et al. 1983, Anderson and Lindquist 1985, Anderson et al. 1985). However, is one nutrient pathway critical over another? Interpretation of field and


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laboratory data in order to answer this question is complicated by the fact that the different pathways governing the internal physiology of the cell are interdependent. For example, P-stressed cells may be forced to shut down an N-uptake pathway because of a lack of ATP, hence they also become N-stressed (Syrett 1981, Davies and Sleep 1989, Flynn et al. 1996). Detailed experiments following intracellular amino acid pools in Alexandrium minutum Halim suggest that it is the N-pathway that is critical, and that when cells reach a critical intracellular N-status, which may be driven in the first instance by P-limitation, cells act as gametes (Probert 1999). It has been noted in this and other laboratory studies that gamete formation was reversible if intracellular status changed. The environmental conditions (e.g. temperature and salinity) under which encystment occurs may be more restricted than the conditions suitable for vegetative growth. Factors that have been documented to influence the process are: temperature (Ellegaard et al. 2002), day length (Sgrosso et al. 2001), density of cells (Uchida 2001), and iron depletion (Doucette et al. 1989, Blanco 1995). One can hypothesize that nutrient stress triggers gamete formation and, once formed, gamete survival, mating success, and the subsequent steps are modulated by other environmental factors. Bioassays carried out with bacterial isolates from natural populations were shown to promote encystment in Alexandrium catenella (Whedon and Kofoid) Balech and strains of Proteobacteria were also detected, which showed an inhibitory effect on the encystment process (Adachi et al. 1999, Adachi et al. 2001). However, care has to be taken in such experiments to separate the effects of bacterial presence per se and any effects they may be having on the physical environment of the culture (for example nutrient limitation). A few records of encystment exist for heterotrophic species: Polykikos kofoidii Chatton is reported to produce cysts either spontaneously (Morey-Gaines and Ruse 1980) or following starvation (Nagai et al. 2002), while Gymnodinium fungiforme Anissimova encysted in response to prey deprivation (Spero and Moree 1981). Other records link sexuality in heterotrophic species with dense cultures and swarming behavior around senescent prey (Parrow et al. 2002). Non-toxic strains of Pfiesteria piscicida are capable of producing long-term resting cysts in foodlimited cultures, apparently without undergoing the sexual phase, while short-lasting hypnozygotes were produced following gametic fusion (Litaker et al. 2002). The formation of temporary cysts is not linked to the sexual phase and these cysts are considered as short-term resting stages produced as a response to environmental disturbance. Evidence has been presented

114 Algal Cultures, Analogues of Blooms and Applications showing that Alexandrium ostenfeldii (Paulsen) Balech et Tangen can shift from the motile stage to temporary cyst to avoid infection by the parasite Parvilucifera infectans Norén et Moestrup (Toth et al. 2004). Motile cells are very sensitive to the parasite infection, while temporary cysts are more resistant possibly because sealed by a continuous cyst wall. A. ostenfeldii shifts to the encysted stage also when exposed to the culture medium where the parasite was growing thus suggesting the presence of a water-soluble chemical signal ‘perceived’ by the dinoflagellate. After encystment, the cyst undergoes dormancy or a mandatory resting phase, during which no germination can occur because of an active endogenous inhibition of growth. This is followed by a quiescent period when excystment may occur under favorable conditions and it is thus modulated by an exogenous control (Anderson 1998). Of the possible factors that might affect the dormancy period, temperature has been the most frequently cited. In the studies where this has been investigated in the laboratory, dormancy period was reduced at higher storage temperatures, as might be predicted from physiological considerations (Anderson 1980, Montresor and Marino 1996). Intriguingly, it can be seen from pooling dormancy results that the length of dormancy of one species varies with the geographic origin of the population under consideration (Hallegraeff et al. 1998). When stored at around 5°C, dormancy varies from four months (Alexandrium tamarense strains from the Gulf of Maine, USA) to 12 months (A. tamarense strains from St. Lawrence estuary, Canada) (Anderson 1980, Castell Perez et al. 1998). It is tempting to speculate on a latitudinal trend here but this is perhaps unwise on the basis of so few studies. Sporadic records are available concerning cyst survival (reviewed in Lewis et al. 1999). From these data, heterotrophic species (e.g. Protoperidinium) appear to have shorter survival times, up to two years, than autotrophic species (e.g. Scrippsiella, Lingulodinium) of up to around 10 years. These survival times have to be regarded as minima, as they often reflect the time period of the experiment. Most data are from laboratory-held material and it is not clear that these survival times will be the same in the environment. Indeed, survival times recorded for Ceratium hirundinella (O.F. Müller) Schrank from varved field samples (6.5 and 12.5 years) are among the highest known (Huber and Nipkow 1923). Indirect estimates from sedimentological data support field survival times of at least these values (Keafer et al. 1992) and field data from Sweden implies even greater survival times of up to 37 years for both autotrophic (Gymnodinum nolleri Ellegaard and Moestrup, Protoceratium reticulatum (Claparède and Lachmann) Bütschli) and


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heterotrophic (Oblea rotunda (Lebour) Balech ex Sournia and Diplosalis sp.) species (McQuoid et al. 2002). Germination conditions have been investigated for a limited range of species. Attempts have been made to assess cyst germination in situ (Ishikawa et al. 1995) but the majority of data have been gained from laboratory studies. Where there has been detailed investigation, it seems that oxygen is required for cyst germination (Anderson et al. 1987, Rengefors and Anderson 1998, Kremp and Anderson 2000). It has also been shown that light may be obligate for germination for some although not all species (Binder and Anderson 1986, Park and Hayashi 1993, Nuzzo and Montresor 1999) and that it may also influence the rate of germination (Anderson et al. 1987). Further than this, temperature plays a key role setting a speciesspecific window in which germination can take place and within that window increasing temperatures tend to accelerate germination (Binder and Anderson 1987, Lewis and Hallett 1997). Presence of predators (Rengefors et al. 1998), endogenous rhythms (Anderson and Keafer 1987), and growth factors (Costas et al. 1993) can also affect excystment. We can illustrate this interplay of different factors with an example borrowed from a freshwater system that provides a simplified scenario with marked seasonal forcing, as compared to coastal temperate areas (Rengefors and Anderson 1998, Rengefors et al. 1998). Ceratium hirundinella is a summer species, whereas Peridinium aciculiferum Lemmermann is recorded in the water column under the ice in winter. In the laboratory, C. hirundinella was found to germinate between 6 and 21°C after a maturation period of 4.5 months, whereas P. aciculiferum germinated in a narrow temperature window between 3 and 7.5 °C, after a dormancy period of 2.5 months. Maximum germination in laboratory was achieved at the time of year the species would normally occur in the field, suggestive (although not unequivocal proof) of control by an endogenous clock. Experiments with exudates from zooplankton cultures showed that germination was, however, reduced in the presence of exudates at all temperatures in the case of P. aciculiferum and at intermediate temperatures in the case of C. hirundinella. As a large robust species, C. hirundinella suffers little grazing pressure and germination at the most suitable temperatures is not affected by grazing cues. In contrast, the winter species (P. aciculiferum), more vulnerable to grazers, germinates at low temperatures when grazing pressure would be low in the lake; furthermore germination is reduced when cues for grazers are present. The proportion of cysts that germinate in laboratory studies is variable but, when the mandatory dormancy period is over and optimal conditions

116 Algal Cultures, Analogues of Blooms and Applications are met, high final excystment rates (80-100%) are usually reported. However, in the field these rates will be tempered by the age of the cysts, and hence their viability, and their environmental opportunity (e.g. if they are buried in anoxic layers of the sediment they will not germinate). Very few studies have considered the conditions affecting the survival or success of the planomeiocyte, the motile stage produced from cyst germination. Kremp (2001) showed that for Scrippsiella hangoei (Schiller) Larsen and Peridiniella catenata (Levander) Balech, light was important in survival and, counter intuitively, turbulence improved survival of P. catenata. This is an important but neglected area. In our experience in the laboratory, germination is no guarantee of success; cysts will excyst under conditions where subsequently the planomeiocyte will perish.

LIFE CYCLES AND BLOOM DYNAMICS The challenging endeavor is to integrate the knowledge of life histories gained from culturing species in the laboratory to the sea in order to understand crucial aspects of species ecology, such as bloom dynamics, species succession and recurrence. Models are starting to be developed in which population dynamics are examined not only in the light of physical and chemical constraints, but also considering different aspects of speciesspecific life history traits (Eilertsen and Wyatt 2000, Gentien 2002, Wyatt 2002). However, there is a desperate need for data to feed these models. Blooms are the result of a balance between accumulation and losses, represented by cell death caused by grazing, parasitic or viral arracks. Biomass accumulation is enhanced by favorable physical and chemical conditions that increase growth rates and limit dispersal. However, defense capabilities should not be disregarded as factors improving fitness and success of blooming species (Smetacek 2001). Phaeocystis globosa forms large, multicellular balloon-like colonies surrounded by a tough wall that confers resistance to viral attacks and grazing. This might explain the notable ecological success of this species and its capability of producing extensive blooms (Hamm et al. 1999). Species-specific allelochemicals could also play a significant role in reducing predator pressure, and thus bloom development and maintenance (Matsuoka et al. 2000, Tillmann and John 2002). Life cycle strategies have important implications for the onset, maintenance, and termination of blooms, as demonstrated by studies coupling laboratory experiments with field observations (Imai et al. 1998, Smayda 1998, Anderson 1998, Rengefors and Anderson 1998, Kremp and Heiskanen 1999, Kremp and Anderson 2000). Production of resting stages can be responsible for bloom termination (Kremp and Heiskanen 1999) and


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laboratory experiments support field observations demonstrating that massive encystment can occur in some species (Sgrosso et al. 2001, Olli and Anderson 2002). In situ studies are presently hampered by our limited observational capabilities for locating and sampling thin layers where cells might accumulate, assessing germination rates of resting stages in the sediments, identifying different life stages in the water column. Laboratory studies might fail in considering key-triggering factors, or interactions among them, and caution must be taken in extrapolating the results obtained with cultures to species dynamics in the natural environment (Caceres and Schwalbach 2001). However, combining the two approaches and taking advantage of new molecular and technological tools, represent the best possible strategy and will act as cross-validation of working hypotheses. One of the most puzzling features of phytoplankton blooms is their temporal occurrence: some species show surprisingly regular seasonal patterns over the years (Zingone et al. 2002), while others bloom with apparently longer periodicity (Gjøsæter et al. 2000). Research efforts have been allocated in defining environmental windows for an optimal growth of vegetative cells, but those investigations failed in providing a convincing framework to explain species occurrence and recurrence. Specific timing in the transition among life cycle phases might provide significant insights. An endogenous clock has been advocated to explain regular germination patterns of Alexandrium tamarense cysts (Anderson and Keafer 1987), and a possible control by lunar phases on the germination process has been also hypothesized (Wyatt and Jenkinson 1997). Regulation of diatom spore germination and growth by photoperiod has been proposed to support the synchronized timing of Chaetoceros blooms over a wide latitudinal range (Eilertsen et al. 1995). However, recurrent blooms are produced by many species for which benthic resting stages are not known and alternative mechanisms need to be researched to explain their punctual appearances (Noji et al. 1986).

CONCLUSIONS Considerable diversity has been recorded in life cycle patterns, morphology and physiology among the different species. However, even a basic pool of information is far from being complete. We have to consider that important achievements concerning different aspects of phytoplankton life cycles have been attained only in recent years. As an example, the peculiar microreticulate cyst of the harmful dinoflagellate Gymnodinium catenatum was described less than 20 years ago (Bravo 1986). By now, we know that

118 Algal Cultures, Analogues of Blooms and Applications there are at least two other species that produce microreticulate cysts (Bolch et al. 1999). We also know the main features of the life cycle of G. catenatum and its complex mating behavior, but we still know very little about the closely related species. The genus Alexandrium includes more than 20 species, several of which are harmful, but solid information concerning life strategies is available only for a few. Similar statements can be made for diatoms, where the life cycle of the ASP-producing Pseudo-nitzschia was elucidated only a few years ago (Davidovich and Bates 1998). And what pitiful knowledge of the life cycle of other diatoms or coccolithophorids, key-players in different biogeochemical cycles, do we have? As a result of our deliberations we would like to highlight a few areas that we believe merit further consideration.

One Strain is not Enough Phytoplankton species are characterized by variable levels of intraspecific diversity in their morphological features, physiological performances (Brand 1989), and life cycle traits. Strain-specific variability in resting cell formation has been detected for the raphidophycean Heterosigma akashiwo (Hada) Hada (Han et al. 2002), diversity in encystment success was reported for the dinoflagellate Scrippsiella trochoidea (Montresor et al. 2003), and a strain-specific commitment in spore formation has been reported for the diatom Ditylum brightwellii (Hargraves 1982). Extrapolating any information we gain from the study of a single strain to the whole species or population can be misleading and multiple, recently isolated, strains should be considered to get a proper picture of life cycle traits.

Culture Maintenance A wide variety of culture media have been developed in recent years for phototrophic phytoplankton, however, there are species whose growth in culture is still problematic (Nishitani et al. 2003). The setting of specific diets is crucial for maintaining in culture mixotrophic and heterotrophic species where prey quality and availability might influence phase transitions (Anderson et al. 2003, Nagai et al. 2002, Parrow and Burkholder 2003a).

The Role of Interactions Among Species Establishing a clonal culture implies, by definition, the isolation of a single cell, from which a clonal population is obtained. Shifts among life cycle stages can be induced by changes in physical or nutritional conditions, but


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the interaction with other species mediated by direct cell contact or by the action of chemical compounds should be also considered as potential triggers for changes. The dinoflagellate Heretocapsa circularisquama has been found to kill Karenia mikimotoi by cell contact but it transforms into a temporary cysts when cell concentration of K. mikimotoi exceeds a threshold concentration (Uchida et al. 1999, Uchida 2001). A density-dependent mechanism mediated by cell contact was also postulated to induce the transformation of planozygote into cyst (Uchida 2001). Infochemicals produced by grazers seem to inhibit cyst germination in dinoflagellates (Rengefors et al. 1998) and increase colony size of Phaeocystis globosa (Jakobsen and Tang 2002).

Experimental Settings Phytoplankton cultures are generally kept under constant environmental (temperature, irradiance, photoperiod, turbulence) conditions, while in the field cells experience continuous variability, both at micro- and at a seasonal scale. Small culture vessels impair cell migrations, and extremely high cell concentrations attained in batch cultures might cause unnatural pH values or nutrient concentrations. All these aspects need more consideration when designing experimental settings for testing different aspects of species life cycles and behaviors. Considerable attention has been devoted in the last decades to investigate the role of abiotic factors in inducing and modulating phytoplankton life cycle strategies. This avenue has provided important and interesting results and it is worthy of pursuit. However, it is now time to explore also the potential of biotic signals and interactions in activating shifts among life stages. By analogy to what is known for the terrestrial environment, phytoplankton organisms evolved in a fluctuating physical-chemical environment but also in a biological context, represented by interactions, with other species, with grazers and pathogens. Life histories are the product of this intricate evolutionary history and it is our challenge to unravel this complexity improving our observational capabilities in the environment and our experimental skills in the laboratory.

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Wall, D., R.R.L. Guillard and B. Dale. 1967. Marine dinoflagellate cultures from resting spores. Phycologia 6: 83-86. Wall, D., R.R.L. Guillard, B. Dale, E. Swift and N. Watabe. 1970. Calcitic resting cyst in Peridinium trochoideum (Stein) Lemmermann, an autotrophic marine dinoflagellate. Phycologia 9: 151-156. Watanabe, M.M., M. Watanabe and Y. Fukuyo. 1982. Encystment and excystment of red tide flagellates. I. Induction of encystment of Scrippsiella trochoidea. Res. Rep. Nat. Inst. Environ. Stud. 30: 27-42. Wyatt, T. 2002. How can we combine the population dynamics of life history stages? pp. 112-115. In E. Garcés, A. Zingone, M. Montresor, B. Reguera and B. Dale [eds]. LIFEHAB: Life History of Microalgal Species Causing Harmful Blooms. European Commission, Brussels, Belgium. http://www.icm.csic.es/bio/projects/lifehab/. Wyatt, T. and I.R. Jenkinson. 1997. Notes on Alexandrium population dynamics. J. Plankton Res. 19: 551-575. Xiaoping, G., J.D. Dodge and J. Lewis. 1989. Gamete mating and fusion in the marine dinoflagellate Scrippsiella sp. Phycologia 28: 342-351. Young, J.R. and K. 2003. Henriksen. Biomineralization within vesicles: the calcite of coccoliths. In P. M. Dove, J.J. De Yoreo, S. Weiner [eds]. Biomineralisation. Reviews in Mineralogy and Geochemistry 54: 189-215. Zingone, A., M.-J. Chrétiennot-Dinet, M. Lange and L. Medlin. 1999. Morphological and genetic characterization of Phaeocystis cordata and Phaeocystis jahnii (Prymnesiophyceae), two new species from the Mediterranean Sea. J. Phycol. 35: 1322-1337. Zingone, A., P. Licandro, M. Nardella and D. Sarno. 2002. Seasonality and interannual variation in the occurrence of species of the genus Pseudo-nitzschia in the Gulf of Naples (Mediterranean Sea). p. 315. In Abstract Book of the 10th International Conference on Harmful Algae, October 21-25 2002. St. Pete Beach, Florida, USA.

" Allelopathic Interactions Among Marine Microalgae Genevi ve Arzul1 and Patrick Gentien2 1 2

Ifremer, IFREMER, BE, BP 70, F-29280 Plouzan , France Ifremer, CREMA, F- 17137 L’Houmeau, France

Abstract Allelopathy is defined as the interactions mediated by allelochemicals produced by some plant species. Allelochemicals are secondary metabolites actively or passively excreted by living plants, or released by cells on decay, and these substances can alter the metabolism of other organisms. The process is probably involved in the quasi-monospecificity of harmful algal blooms. By limiting the sources of variation (spatial diffusion, temporal decay) phytoplankton cultures constitute a suitable tool for the study of allelopathy. Various bioactive substances from phytoplanktonic origin have been isolated and chemically described. Their biological effects in controlled experiments have been compared in a few cases to observations in the field. Allelopathy expression is an intrinsic property of the species and plays a role at the species, community and biocenose levels. Here, we provide a short review of the allelopathic agents, of the experimental evidence and of the difficulties encountered in extrapolating the results to the field.

INTRODUCTION In a recent paper, Smetacek (2001) summarized the concept of the ‘watery arms race’ occurring in the ocean: one of the major processes structuring the planktonic community is thought to be chemical warfare. Allelopathy is the denomination of the chemical interactions between the microalgal species. Allelopathic interactions between marine microalgae result in growth inhibition, due to substances produced by one or several species (Molisch 1937, Putnam and Tang 1986). The substances are biologically active molecules, and called “allelochemicals” ; they are produced by one

132 Algal Cultures, Analogues of Blooms and Applications organism called the “donor” and the impacted organisms are the “receptors”. Some biologically active molecules can be extremely beneficial, but allelochemicals are inhibitors (etymology, allelo: reciprocal, and pathos: disease). Allelopathy plays an important role in the structure, balance and succession of phytoplankton populations (Keating 1977, Vardi et al. 2002, Mulderij et al. 2003, Fistarol et al. 2004a). Allelopathy is often associated with ichthyotoxicity due to the haemolytic activity of seawater induced by allelopathic species (Gentien 1998, Legrand et al 2003, Granéli and Johansson 2003). Although allelopathy is potentially important in the regulation of phytoplankton communities, its effects have not been extensively studied for two major reasons. Historically, phytoplankton has been generally studied as a bulk property estimated by the chlorophyll proxy, and stress has been placed on biomass production in trophic web models of an ‘average species’. It goes without saying that this ‘average species’ of phytoplankton does not exist. However, the diversity of species expressed in one year and their interplay is essential to the stability and resilience of ecosystems. The second reason results from technical problems. Allelopathy expression from a given species is the intrinsic property of this species. Therefore, the results are species-specific and do not allow extrapolation to other species. A study of one single species is long and delicate since it requires the production of a proper blank, testing against different strains and species, isolation and identification of the frequently labile chemicals and the understanding of the physiological conditions leading to the production of active substances. Studies have long been hampered by the lack of a proper experimental blank. As a consequence, there is no general understanding of the role of allelopathy structuring planktonic communities. Allelopathy success may lead to the temporary dominance of one organism in possibly lowering the growth rate of other species present in the community and in directing selectivity in grazers, thus affecting the whole biocenose. Allelopathy was first observed and described in terrestrial superior plants in 1832 by Augustin de Candolle. Since then, it has become an essential topic in agronomy. Many reviews relating to terrestrial and aquatic environments have been compiled (Molish 1937, Berland et al. 1974, Maestrini and Bonin 1981, Rice 1984, Putnam and Tang 1986, Keating 1999, Legrand et al. 2003). Allelopathy was properly demonstrated in a marine phytoplankton species, by the removal of active substances and the production of proper blanks (Gentien et al. 1991). Recently, more attention has been focussed on some phytoplankton species (harmful ones)

Allelopathic Interactions Among Marine Microalgae

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and new developments in methods, thus focussing on processes (not only nutritional) leading to the development of quasi-monospecific blooms. Therefore, allelopathy has become a topic of interest when trying to understand phytoplankton bloom formation especially in red tide outbreaks, or the discrimination of grazers against algae. Allelopathy provides a competitive advantage to the donor, which displays specific adaptation to protect itself against the allelochemicals, and may use the nutrient resource (Granéli and Johansson 2003). Chemical cues are mediated by a large variety of compounds, differing in their chemical structure and their mode of action. These can be pheromones acting at the species level; they can also mediate information between individuals belonging to different species, and even different groups and different realms. This chapter considers allelopathy, as defined above: a repressive action by secondary metabolites produced by plants, micro and macro algae, against other species present in the biocenose. Allelopathy requires at least two prerequisites: 1. extracellular release of biologically active chemicals, the activity of which depends on the balance between the production and the decay rates 2. mediation by secondary metabolites, substances which do not play any obvious role in the energetic activity of the organism (Turner 1971). In the field, the study of such a process is confronted by spatial (at small scale), temporal and biological variations. Monospecific controlled cultures are the ideal tool for limiting the sources of variation. Thus algal cultures are composed of experimental materials in order to study and understand allelopathy through the identification of the bioactive compounds. Conversely, additional field observations can be explained by the experimental results, and support the in vitro models. We present successively: • Methods for allelopathic studies • A short summary of allelochemicals involved in allelopathy • Allelopathy in natural assemblages and in algal blooms • Role of allelopathy from species to ecosystem level • Conclusions

METHODS FOR ALLELOPATHIC STUDIES Laboratory experiments have been based on experimental cultures that intends to reproduce and interpret the field observations. The effect of

134 Algal Cultures, Analogues of Blooms and Applications allelopathy on growth is difficult to demonstrate, due to the interference of other factors such as: the differences in the ecophysiological requirements of the species (optimal temperature and light, nutrient affinity), the tolerance to biocides of anthropic origin that may be present in the medium (pesticides, antifouling, metals) or the selection by predators (grazing). Some authors have used the complexity of physiological responses in the natural environment to sustain the allelopathy controversy (Moebus 1972a, b, Forsberg et al. 1990), and this fact underlines the importance of experimental studies. These allow the different parts to be broken down: donor action/ receptor response and environmental pressure, clarifying the real action of allelochemicals before understanding the whole process. The most common method to demonstrate any interaction is cross culturing or plurispecific cultures in batches, or in continuous flow cultures, with additional enrichment inputs to exclude interactions with nutrient competition (Kayser 1979). It should, however, be noted that results on plurispecific cultures are often difficult to interpret. Allelopathic interaction can be estimated by changes in growth lag time, growth rate, motility, or cell lysis and death of the receptor, or grazer deterrence. The study should include several steps, 1. to show that the effect varies with the cell concentration of the donor, 2. to isolate the allelochemical(s) involved, 3. and to verify that the allelochemical(s) produce(s) an effect similar to that observed in the field . One approach involves using extracts of culture or of cells or culture filtrate, for testing on growth of other species: Pratt (1966) used mixed cultures, and separated media to explain the competition between Skeletonema costatum and Olisthodiscus luteus. The results seemed to point to the production of ectocrine by O. luteus, acting as S. costatum growth stimulator at low concentration, and as inhibitor when concentrated. Gentien and Arzul, (1990a) developed a specific technique for the production of blanks. These authors adsorbed the active allelochemical principle onto a Waters‰ Florisil® cartridge which allowed them to produce proper blanks (Fig. 4.1). They demonstrated the effect of the Karenia mikimotoi filtered culture medium on the growth of diatom, compared to the same medium purified by adsorption (Fig. 4.2). This effect was proportional to the K. mikimotoi cell density (Gentien and Arzul 1990a, b). The same method was applied to a bloom of the same species on the Ushant front where the experimental strain had previously been isolated (Arzul et al. 1993). Above a threshold of 10,000 cell L–1, a positive relation between K. mikimotoi density and the growth rate


135

Fig. 4.1 Flow chart of the protocol of experiment by Gentien and Arzul (1990a) reduction of a diatom was evidenced, demonstrating that, at the onset of a bloom, allelopathy may be an essential process in the temporary establishment of a dominant species. By using mixed batch cultures, Subba Rao et al. (1995) demonstrated the reciprocal allelopathic activity of Pseudonitzschia pungens and Rhizosolenia alata cultures. Schmitt and Hansen (2001) studied the allelopathic activity of


ela e luoresce ce u

s

1

08

co rol

es

06

04

02

0 0

50

100

150

200 hours

Fig. 4.2 Response growth curve of Chaetoceros gracilis grown with crude filtered Karenia mikimotoi culture (4.4 ¥ 106 cell L– 1) (test), and with detoxified water (control) following the protocol of Gentien and Arzul (1990a) Chrysochromulina polylepis towards seven species of dinoflagellates, testing their motility in relation to the C. polylepis density and the pH of the medium. At the exponential phase of growth, the minimal C. polylepis cell density producing 40% non-motile Heterocapsa triquetra was about 3 ¥ 104cell mL–1, and this activity was enhanced when pH varied up to 9.6. The allelopathic activity tested against algae belonging to different taxa revealed that the dinoflagellate Prorocentrum minimum was unaffected by the presence of C. polylepis. The allelopathic property was shown to be specific-dependent by Arzul et al. (1999), in bioassays involving three Alexandrium species, A. minutum, A. catenella and A. tamarense. The most potent inhibitor for the growth of the diatom Chaetoceros gracilis was A. tamarense, and the allelopathic activity varied in parallel to the haemolytic activity in the culture filtrate, which has been found to be related to saxitoxin production. The dinoflagellates Karenia mikimotoi (ex Gymnodinium mikimotoi) and Scrippsiella trochoidea were less affected or totally unaffected by A. catenella and A. tamarense, A. minutum being the least potent. The total repression of the diatom growth observed in the stationary growth phase of Alexandrium suggest that the allelopathic activity of Alexandrium is due to different active substances produced by the senescent cultures. Pushparaj et al. (1999) studied the allelopathic activity of acetone extract of the cyanobacteria Nodularia harveyana towards eubacteria,


137

two chlorophyceae, several cyanobacteria, fungi, rotifers and crustaceans. A high inhibition activity was observed in the cases of Nannochloris sp., Anabaena genus and Spirulina platensis, likewise for Streptococcus, Fusarium, and the animals. Another approach uses solid medium (agar): it has been successfully applied for investigating allelopathy among phytoplankton. Plating technique on Petri dish and paper disk method, as used for bacterial bioassays, allows rapid screening tests (Berland et al. 1973, Smith 1994, Tillman and John, 2002). However, some difficulties arise when extrapolating results to their ecological consequences, especially due to possible interferences with organic substrate constituents (Chan et al. 1980). In order to clearly relate the effect to one compound, it is necessary to identify the chemical composition of this allelochemical and then determine its mode of action. Some examples of allelochemicals chemically identified, produced by marine algae and acting against other marine organisms are given below.

A SHORT SUMMARY OF CHEMICALS INVOLVED IN ALLELOPATHY For general consideration, it may be assumed that any chemical involved in most cases of allelopathy should have a short half-life in water, either because it is very labile or volatile, in order not to saturate the medium. The in situ effect will depend on the production, decay rates and potency. A good example is the case of the exotoxin produced by K. mikimotoi (an octadecapentaenoic acid) which has a 20 min half-life in sea water and in the dark (Gentien, unpubl data). This is contrary to what has been observed in the terrestrial environment where allelochemicals can saturate the ground (e.g. the juglone produced by the walnut tree). In aquatic ecological studies, it is therefore necessary to ensure that the active substance is not transformed during isolation. The characterization of compounds involved in allelopathy has been carried out in some laboratory studies. Various classifications of allelochemicals are presented, according to chemical composition (Whittacker and Feeny 1971, Rice 1984) or mode of action (Legrand et al. 2003). An exhaustive review is outside the scope of this chapter. However, we have grouped allelochemicals and compounds with potential allelopathic properties into three major sets according to their chemical components: • polyunsaturated fatty acids

138 Algal Cultures, Analogues of Blooms and Applications • aminoacids • hydrocarbon

1-Allelochemicals Containing Polyunsaturated Fatty Acids 1-1 APONIN APparent Oceanic Naturally occurrINg cytolins, (APONINs) are produced by some microorganisms (Table 4.1): e.g. the cyanobacteria Gomphosphaeria aponina and the Chlorophyta belonging to Nannochloris genus (N. oculata, N. eucaryotum) (Krienitz et al. 1996, Yamamoto 2001) Deleterious effects of APONINs were observed towards phytoplankton (except for thecate dinoflagellates and Prymnesium parvum) and fungi (Moon and Martin 1981). Until now, no effect has been reported on bacteria, crustaceans, molluscs or fish but that remains to be proved, especially for larval stages. Conversely, very low concentrations of APONIN stimulate phytoplankton growth (Perez et al. 2001), and Nannochloris oculata is used in the artificial nutrition chain, for example, to raise oyster spat from larvae (Ben-Amotz 1984, Yongmanitchai and Ward 1991). Table 4.1

Example of APONIN producers involved in allelopathy

Donor

Receptor

References

Gomphosphaeria aponina

Gymnodinium breve*

Gomphosphaeria aponina Gomphosphaeria aponina Nannochloris sp.

Chattonella subsala Ptychodiscus brevis* Fungi : Dendryphiella salina, Curvularia sp. Ptychodiscus brevis*

Martin and Martin 1976 McCoy et al. 1979 Halvorson and Martin 1980 Moon and Martin 1981 Halvorson et al. 1984

Nannochloris sp. Nannochloris oculata, N. eucaryotum

Gymnodinium breve*

Martin and Martin 1987 Perez et al. 1997 Perez 1999

*Ptychodiscus brevis = Gymnodinium breve = Karenia brevis

APONIN has been studied by several authors and first described as yellow colored and chloroform extractable from cell-free cultures (Martin and Martin 1976, Martin and Martin 1987, de Majid and Martin 1983). APONIN is made up of more than 30 different fractions including polyunsaturated fatty acids (PUFAs) and their derivatives. APONIN-3 from Nannochloris oculata is a mixture of ester palmitate and methyl stearate. These fractions have various biological properties, and act differently: some


139

molecules induce sessile formation activity in Ptychodiscus brevis, others are cytolytic (Perez et al. 2001, de Majid and Martin 1983). Crude APONIN showed no alteration in cytolytic activity towards K. brevis, when stored in seawater for 10 d, or submitted to temperatures between 30 and 110 °C, or maintained in acidic condition for 48 h, but APONIN was base labile. The action mechanisms of the different components have not yet been demonstrated. Cytolytic fractions act as a surfactant towards the cell membrane and the lytic action of APONIN-4 could be explained by the formation of the sterol-APONIN complex (Barltrop and Martin 1984). The active production of APONIN by Gomphosphaeria aponina and Nannochloris sp. was observed during the exponential growth phase and a linear correlation was obtained for the rates of APONIN and DNA production (Martin and Gonzalez 1978, Derby et al. 2003).

1-2 Allelochemicals with glycolipid derivatives The major classes of glycolipids found in algae consist of one or two galactose units linked to a glycerol residue containing acyl moieties esterified at the sn-2 and/or sn-3 position of the glyceryl structure. The major classes are monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerol (DGDG), and their respective lyso equivalents monogalactosyl monoacylglycerol (MGMG) and digalactosylmonoacylglycerol (DGMG). Glycolipids display an allelopathic effect when the fatty acids are polyunsaturated (PUFAs). PUFAs are constituents of all vegetal tissues, and play an important role in membrane flexibility and permeability, cell floatability, and the energetic metabolism. PUFAs are also essential for molluscs and fish larval development, genitor fecundity and gonadic maturation (Delauney et al. 1993, St. John et al. 2001). Intracellular PUFA composition in phytoplankton determines its effect as a stimulator or noxious agent for the environment. Some phytoplankton species are considered as excellent forage due to their lipid composition (20:4n6, 20:5n6, 22:5n3) while others are associated with deleterious effects on the accompanying aquatic plants or animals. Lipid content in allelopathic species is characterized by large amounts of unsaturated fatty acids, especially octadecapentaenoic acid (18:5n3), free or associated with sugar (MGDG and DGDG). Glycolipids are sufficiently hydrosoluble to render seawater toxic for surrounding organisms (Yasumoto et al. 1990, Bodennec et al. 1995). Considering the 20 min half-life time and a standard molecular diffusion coefficient, the action radius of the fatty acid octadecapentaenoic

140 Algal Cultures, Analogues of Blooms and Applications acid (18:5n3) is only a few centimeters around the cell. Populations concentrated in thin layers in the pycnocline are commonly observed; therefore, the algae can modify the environment and render it unfit for the growth of phytoplankters sensitive to allelochemicals. Some examples of allelopathic effects between species attributed to PUFAs under glycolipid forms are presented Table 4.2. Table 4.2 Examples of algal species involved in allelopathy attributed to glycolipids Donor species

Receptor species, targets

References

Gyrodinium cf. aureolum Chrysochromulina polylepis Phaeodactylum tricornutum Brown alga: Cladosiphon okamuranus Synechococcus sp.

Chaetoceros gracilis 12 phytoplankton taxa Cylindrotheca fusiformis Heterosigma akashiwo

Gentien and Arzul 1990b, 1991 Schmidt and Hansen 2001 Chan et al. 1980 Kakisawa et al. 1988

Fish

Mitsui et al. 1989

PUFAs are extracted by chloroform-methanol from the algal cells, or freecell cultures then purified by HPLC (Shilo 1967, Yasumoto et al. 1990). After partitioning according to polarity, the active fractions were detected in algal bioassays or haemolytic tests (Gentien and Arzul 1990a). Chemical and structural analyses of the compounds by the Chromarod-Iatroscan system, GC-MS and GC-FID revealed that glycolipids were the main hydrosoluble form released by algae in the medium. Glycolipids containing PUFAs are considered to be the bioactive chemicals responsible for allelopathic and ichthyotoxic effects on marine organisms in the case of ichthyotoxic algal species (Yasumoto et al. 1990, Parrish et al. 1994a, b, Bodennec et al. 2000). The antibiotic activity of PUFAs was also demonstrated towards Vibrio fischeri and the most potent allelopathic action on the growth of the diatom Chaetoceros gracilis was observed with 18:5n3 (Arzul et al. 1995, 2000) (Fig. 4.3).

Fig. 4.3 Example of glycolopid involved in allelopathy: general structure of Karenia mikimotoi hemolysin; the carbohydrates can differ, but never more than 2 C (according to Yasumoto et al., 1990)


141

In chains containing the same number of Carbon, the allelopathic potency varies in parallel with the double-bond numbers and depends on their position along the carbon chain (Parrish et al. 1994a, b, Kakisawa et al. 1988, Arzul et al. 1995, 2000). PUFAs are amphiphilic molecules and can act as a surfactant, producing the saponification of membrane lipids. In addition the reaction of PUFA insaturations in the presence of oxidative substances produces oxygen radicals, known to be highly reactive, and responsible in the destabilization of the cell wall lipid-bilayer (Pacifici et al. 1994). Oxygen appeared to be a cofactor in PUFA haemolytic activity and toxicity toward fish (Arzul et al. 1998). The stability of PUFAs is controversial, and their high reactivity towards oxidative substances involves the production of malonaldehyde, which is not toxic (Ikawa et al. 1997). A pH incease (8.1 to 9.6) in Chrysochromulina polylepis culture filtrate had a dramatic effect on Heterocapsa triquetra motility, and previous experiments showed that palmin and acid toxicity were highest in alkalin pH (Schmidt and Hansen 2001). PUFA production depends on the physiological status and environmental conditions of phytoplankton populations, and may explain the differences in allelopathic activity observed in situ and in cultures (Gentien and Arzul 1990a). This also explained the difficulties in establishing dose-effect relationships in allelopathic activity through different events. Phosphorus, nitrogen or silicon limitation increased PUFA production in many phytoplankton species (Edvardsen et al. 1990, Roessler 1990). The loss of C. polylepis allelopathic activity grown in f/2 medium at pH 8 to 9, was observed at the stationary phase. A lower availability of inorganic carbon could explain the toxicity decrease in strains cultivated in the laboratory for long periods of time (Schmidt and Hansen 2001). Due to the lability of the 18:5n3, it was impossible to isolate sufficient amounts in order to understand the mode of action. This fatty acid was, therefore, synthesized from 22:6n3 through a g-iodo lactonisation. The mode of action was then tested on fish gills and tegument (Sola et al. 1999, Fossat et al. 1999). The direct non-specific action of 18:5n3 on membrane ATPases explains not only the allelopathy on other phytoplankton species but also fish kills through action on the chloride cells of the gills.

2-Allelochemicals with Aminoacids Aminoacids with allelopathic properties which have been isolated mainly in cyanobacteria, include a large number of substances with biological properties (mainly toxic), that offer important perspectives for medical

142 Algal Cultures, Analogues of Blooms and Applications applications. These secondary metabolites are peptidic products with antibiotic and cytotoxic activities, and induce deterrent effects on predators either directly or indirectly, when the excreted substance is transformed by the consumer and becomes either toxic or unpalatable (Table 4.3) (Pennings et al. 1996, 1997). These substances were extracted from cells and cell-free medium in organic solvent or solid support, then purified, separated and identified according to various techniques including HPLC, TLC, HR-FAB mass spectrometry, 2D-NMR spectroscopy and chiral-GC-MS analyses (Burja et al. 2002, Milligan et al. 2000). The isolated metabolites present different chemical structures, thus allowing their characterization. Table 4.3 Examples of cyanobacteria involved in allelopathy attributed to peptidic allelochemicals Donor species


References

Fischerella muscicola

Cyanobacteria, purple bacterium, green alga, higher plants fish, urchin and crab

Gross et al 1991., Hagmann and J ttner 1996, Srivastava et al., 1998. Pennings et al. 1997

Fish, shrimp, mollusc, bacteria

Burja et al. 2002, Thacker et al. 1997, Orjala et al. 1995, Milligan et al. 2000).

Hormothamnion enteromorphoides Lyngbya majuscula

2-1 Cyclic Peptides Fischerellin A is the most active allelochemical produced by the cyanobacteria Fischerella muscicola (Gross et al 1991, Hagmann and Jüttner 1996). Composed of two heterocycles including amines and a C15 enedyine moiety, pure fischerellin A is a colourless powder resulting from the methanolic extraction of the cells. It inhibits the photosystem II activity in chlorophyllian organisms, and thus presents anti-cyanobacterial, anti-algal and herbicidal activity (Srivastava et al. 1998). Laxaphycin A is the major component of cyclic peptides mixture produced by a tropical benthic marine cyanobacterium Hormothamnion enteromorphoides and was effective in deterring feeding in experiments with parrotfish (Pennings et al. 1997). The marine filamentous cyanobacterium Lyngbya majuscula produces a great variety of endocellular and extracellular secondary metabolites made of cyclic or linear lipopeptides, malyngamide A, B and malyngolide, three secondary metabolites different from laxaphycins. These substances have antibiotic, toxic and feeding deterrence properties (Burja et al. 2002, Thacker et al. 1997). Curacin A and lyngbyabellin


143

B(1), cyclic polypeptides (depsipeptides) produced by L. majuscula, are potent brine shrimp toxins (Orjala et al. 1995, Milligan et al. 2000). Nodularin and microcystin produced by Nodularia spumigena have a repulsive effect on zooplankton (Stolte et al. 2002) (Fig. 4.4).

Fig. 4.4 Example of cyclic peptide involved in allelopathy: microcystin, produced by Nodularia spumigena The production of bioactive substances is directly correlated with cyanobacterial growth, and takes place either during the entire culture development, or at the beginning and end of the exponential growth phase (Rossi et al. 1997, Burja et al. 2002,). A possible method for inducing secondary metabolite production by cyanobacteria is stress induction, such as the addition of an antagonist organism (Burja et al. 2002).

2-2 Indirect allelopathic effects associated to aminoacids Among the various amino acid derivatives produced by phytoplankton, glycoproteins are sometimes released as an accompanying B12 vitamin binder, and render the B12 unavailable for the other phytoplankton competitors (Pintner and Altmeyer 1973, Davies and Leftley 1985). Domoic acid (Amnesic Shellfish Poison: ASP) produced by Pseudonitzschia sp. presents a chemical structure that resembles ironcomplexing agents (siderophores) which suggests a possible role in trace metal chelator (Rue and Bruland 2001) (Fig. 4.5). Fig. 4.5 Example of amino acid The glycocalyx of Heterosigma akashiwo associated with allelopathy: domoic (ichthyotoxic) is a complex polysaccharide acid (Amnesic Shellfish Poisoning) polypeptide, and presents an inhibiting produced by Pseudonitzschia sp.

144 Algal Cultures, Analogues of Blooms and Applications effect on Skeletonema costatum (Honjo 1993). Ichthyotoxicity associated with H. akashiwo (alias H. carterae) can be mitigated by the use of antioxidant agents (Yang et al. 1995).

3-Allelochemicals Associated with Hydrocarbon 3-1 Volatile derivates Extractible volatile hydrocarbon substances include biogenic alkanes (15 and 17 carbon number), alkenes (17, 19, 21, 27 and 29 carbon number), and terpenoid cyclic polyunsaturated molecules are produced, exuded, or excreted by phytoplankton and several other organisms (seaweeds, zooplankton (Mesodiunium rubrum) and bacteria) (Saliot 1975). Some of these substances have a biological effect on the surrounding organisms (Table 4.4). Table 4.4 Examples of volatile allelochemical producers involved in allelopathy Donor species


References

Skeletonema costatum Phaeocystis globosa Phaeocystis pouchetii Mesodinium rubrum Protogoniaulax spp. Gymnodinium nagasakiense

Bacteria Bacteria Bacteria E. marina Bacteria Dinoflagellates, raphydophyceae

Bianchi and Varney 1998 Noordkamp et al. 1998 Turner et al. 1996 Bianchi and Varney 1998 Kodama and Ogata 1983 Kajiwara et al. 1992

A butanedione derivative with antibiotic activities and metal trace chelator is produced by Prorocentrum minimum in culture. It is believed that these substances could be degradation products of carotenoids (Andersen et al. 1980). Cubenol and homologous cadinol sesquiterpene alcohols were identified as characteristic volatile exudate components of Gymnodinium nagasakiense (i.e. Karenia mikimotoi) cultures (Kajiwara et al. 1992). Cubenol was reported to cause cell destruction of swimming red tide species such as Heterosigma akashiwo, Chatonella antiqua, C. marina, K. mikimotoi and Prorocentrum minimum, in a similar way to that of polyunsaturated eicosaenoic fatty acids. DMS is the most abundant volatile sulphur in seawater. With its co-product acrylate, it results in enzymatic conversion by DMSP-lyase of dimethylsulfoniopropionate (DMSP), and ensures the defence of the producer when ingested by predators (Wolfe and Steinke 1996) (Fig. 4.6). High acrylate concentration in various phytoplankton has been demonstrated to be grazer deterrent (zooplankton, ciliate or other heterotroph phytoplankton) (Wolfe 2000). Moreover the absence of bacteria in Phaeocystis


145

CH3 CH3

Fig. 4.6 DMSP

Production of DMS, volatile sulfur, and acrylate from enzymatic conversion of

cultures in spite of the abundant mucus that embedded the cells, suggested the antibacterial property of their acrylate excretions (Sieburth 1960). Acrylic acid (2-propionic acid C3H4O2) antibiotically active against bacteria, was found in scallops contaminated by Protogonyaulax spp. (Slezak et al. 1994, Kodama and Ogata 1983). The production of acrylic acid by Phaeocystis and its antibacterial action on different bacteria have been studied by several authors (Verity et al. 1988, Noordkamp et al. 2000). In culture at pH 8, acrylic acid at 10 mM reduced bacterial leucine and thymidine incorporation by more than 50%, whereas the reduction was unapparent in uncontrolled pH conditions (Slezak et al. 1994).

3-2 Halogenated hydrocarbon Halogenated hydrocarbons are also synthesized by several marine organisms, and have a deleterious effect on phytoplankton. The red marine algae Corallina pilulifera and Lithophyllum yessoense produce bromoform, dibromomethane and chlorodibromomethane (Fig. 4.7). Due to their low solubility in seawater, the substances remain at the surface of the algae before transfer to the atmosphere, thus avoiding epiphyte diatom deposition (Ohsawa et al. 2001). The cleaning potentiality of bromoform was confirmed and measured with special equipment giving precise measurements of the elimination rate, necessary to prevent epiphyte microalga fixation on C. pilulifera (range 10–2 ng min–1 cm–2) (Ohsawa et al. 2001).

Fig. 4.7 Example of halogenated hydrocarbon presenting allelopathic effect on phytoplankton: bromoform (I), dibromomethan (II) and chlorodibromomethan (III), produced by red marine algae

146 Algal Cultures, Analogues of Blooms and Applications The production of volatile organohalogens by phytoplankton was followed by isotopic labeling of the inorganic carbon source (Na H 13CO3). Labile methyl halides were obtained, chloroform production was checked after extraction and gas chromatography coupled with a mass spectrometry analysis (GC-MS) (Murphy et al. 2000).

3-3 Polyethers Polycyclic ether toxins or closely related compounds from the dinoflagellate Gambierdiscus toxicus reduce the growth rate of Nitzschia longissima (Bomber et al. 1989). There was a coincidence between the various effects of Prymnesium parvum excretions (prymnesin producer) into the growth medium: ichtyotoxicity, haemolysis, cytotoxicity and allelopathy were observed towards various targets, but were not clearly attributed to the same compound (Shilo 1981, Igarashi et al. 1995, Granéli and Johansson 2003, Fistarol et al. 2003, Skovgaard and Hansen 2003). The medium which contained Prorocentrum lima (Diarrehic Shellfish Poison producer) inhibited the growth of its potential competitors, the benthic dinoflagellates Gambierdiscus toxicus, Coolia monotis and Ostreopsis lenticularis but Amphidinium klebsii was unaffected. However, the growth-inhibition observed in P. lima medium was not entirely attributed to the phosphatase inhibition produced by pure okadaic acid (Sugg and VanDolah 1999) (Fig. 4.8).

Fig. 4.8 Example of polycyclic ether toxin: Okadaic Acid, from Prorocentrum lima, producing phosphatase inhibition on potentially inhibitors

3-4 Siderophores Algae, fungi and bacteria may produce binders for metal sequestration in view of their necessity to cope with low marine iron concentrations. These substances called siderophores, include a complex association of hydroxamates and cyclic molecules such as cathecols, which are iron ligands and favor iron uptake. They are produced mainly by procaryotes (cyanobacteria and bacteria) and the production of hydroxamate siderophore by Prorocentrum minimum seems to be an exception (Andersen et al. 1983, Macrellis et al. 2001). Competition for iron complexation by phytoplankton


147

involves a different iron binding system constituted of the porphyrin complex (tetrapyrrolic pigments). Each strategy works differently: ironsiderophores are available to all organisms but preferred by cyanobacteria (specific receptors), while eucaryotes preferentially uptake iron chelated by porphyrins (Kuma et al. 2000; Hutchins et al. 1999).

ALLELOPATHY IN NATURAL ASSEMBLAGES AND IN ALGAL BLOOMS Experimental results should be validated by observations in the field. However, few descriptions of the biota composition before and after blooms are published, and more often papers demonstrate the effects of allelochemicals during in vitro experiments, and explain a posteriori some partial observations in the field. This can be attributed to the fact that biological observations and physico-chemical measurements are not available to describe the situation before the bloom, and often competition for nutrients or light, and concentration by physical entrainment are considered. The following examples of bloom developments suggest that allelopathic interaction between species does play a role in the field.

1-Dinoflagellate Blooms In 1987, a summer Karenia mikimotoi outbreak (alias Gyrodinium cf. aureolum) occurred in the Bay of Brest (Atlantic coast, France) and extended rapidly, producing massive kills of marine fauna (Gentien and Arzul 1990a). Before the bloom, field observations showed a predominance of diatom Chaetoceros sp.. Within two days, K. mikimotoi dominated the algal population by 95% with more than 3 ¥ 105 cell L–1. The experimental inoculation of Chaetoceros gracilis in bloom seawater filtrate revealed the presence of repressive allelochemicals for diatom growth. A large-scale bloom of Gymnodinium sp. developed around Chiloe Island in Chile during March-April 1999 (Austral autumn), associated with extensive kills in marine fauna (Clément et al. 2001). Initially, brown patches, advected from the ocean entered the coastal areas and inland sea during neap tides during a period of unusual sunny weather. Then the coloured water extended, and displaced following the currents. The algal population was 99.9% Gymnodinium sp., with a maximal density of 4 to 8 ¥ 106 cell L–1 in the patches. The bloom collapsed during the spring tides period when it mixed with low salinity water mass containing high diatom concentrations. During the bloom event, the biological activity of bloom

148 Algal Cultures, Analogues of Blooms and Applications seawater was studied in the laboratory. The samples presented allelopathic activities towards the diatom Leptocylindrus minimus, but no inhibiting effect on the toxic dinoflagellate Alexandrium catenella (Tillman and John, 2002). Moreover, strong red blood cell haemolysis was obtained with freshly preserved bloom samples.

2-Diatom Bloom During the autumn-winter period 1987, a Pseudonitzschia pungens bloom developed in Cardigan Bay, Eastern Prince Edward Island (Canada) (Subba Rao et al. 1995). To explain bloom monospecificity, the corresponding seawater filtrate was added to Rhizosolenia alata, a species blooming at that time in a nearby area: Hillsborough River estuary. R. alata growth was totally inhibited in the P. pungens sea water bloom and the allelopathic effect observed against R. alata could well explain this monospecificity.

3-Raphydophyte Blooms In spring 2001, a massive bloom of Chattonella marina and Heterosigma akashiwo covered the northeast part of Skagerrak, following the annual diatom spring bloom mainly dominated by Chaetoceros spp. (Naustvoll et al. 2002). The arrival of water masses with lower salinity resulted in stratification, with a salinity of 22 to 28 psu and temperatures between 1 to 3°C in the 5-10 m upper layer. Maximal cellular concentrations of the raphidophytes reached 9.5 ¥ 106 cell L–1 at the end of March, and resulted in fish mortalities. Nitrate and phosphate were low, but silicate was relatively high except near the pycnocline. Inside the area affected by the bloom, few representatives of Apedinella, Pseudopedinella and Chrysochromulina were present, while diatoms stayed in the deeper water mass. This event suggests that allelopathy was probably involved in the mechanism of raphidophyte local dominance.

4-Prymnesiophyte blooms During a Phaeocystis bloom that occurred during spring 1997 in the Marsdiep tidal inlet, between the North Sea and Dutch Waadden Sea (the Netherlands), acrylate production and antibacterial activity followed (Noordkamp et al. 2000). Phaeocystis abundance was around 20 to 45 ¥ 106 cell L–1 during the bloom. Acrylate was highly concentrated in the colonies’ mucus, and reached 6.5 mM, more than 1000-fold the concentrations measured in unfractionated samples (in culture and in the field) (Noorkamp et al 1998). Phaeocystis is known as the most potent acrylate producer. The


149

effect of acrylate on the bacterial populations seems complex: although the antibacterial activity of this compound was observed in culture and in situ (Sieburth 1960, Slezak et al. 1994), bacterial counting during the Marsdiep bloom revealed a five-fold increase in their number. As mentioned previously, the pH value seems to be a determining factor in the bactericidal activity of acrylate, and this could explain the different effects obtained by the authors. The 1988 Chrysochromulina polylepis bloom that developed from the Kattegat and extended to the Skagerrak and along the Norwegian coast produced noxious phenomena of economic importance (Maestrini and Granéli 1991). Diatom spring blooms occurred until April, producing a decrease in silicate concentration. These nutritional conditions associated with the absence of turbulence and stratification, were favourable to nonsiliceous species and C. polylepis became the dominant primary producer. A possible decrease in grazing pressure due to the repellent effect of the algal toxin content also contributed to the prominence of C. polylepis (107 to 108 cell L–1). The description of the event suggests that the deleterious effects on all components of the food chain most likely contributed to the C. polylepis bloom formation, rather than the sole allelochemical repression towards C. polylepis competitors.

5-Cyanobacteria bloom Toxic Nodularia spumigena blooms were recorded in Australian estuaries and coastal areas during 1992-1993 period (Blackburn and Jones 1995). The algal population was monospecific in 98% of the total biomass (1081010 cell L–1) and lasted during the summer time until February 1993, when the bloom decayed. Diatom concentrations varied inversely compared to the cyanobacteria, and the maximal concentrations in Nitzschia closterium, Chaetoceros socialis and Pleurosigma spp. were attained when the nodularin content was lower than 10 mg g–1 dry weight in bloom samples.

6-Iron Binders Production The limitation in primary production due to iron binder allelochemicals was demonstrated in the Southern Ocean (Boye et al. 2001, Hutchins et al. 1999). The chemical speciation of iron (approx. total concentration 0.25 nM) showed that organic complexes are dominant, and organic ligands are in excess of dissolved iron by approx. 0.5 nM. Organic ligands are made up of siderophores excreted mainly by procaryotic cells, and porphyrins produced by phytoplankton. However maximal ligand concentrations do

150 Algal Cultures, Analogues of Blooms and Applications not coincide with bacterial activity and chlorophyll concentration, and the increase in biological activity may result from iron uptake. The uptake of siderophore iron ligands is easier for cyanobacteria than for phytoplankton, whereas porphyrin iron ligands are used preferentially by eucaryotic cells. Siderophore production by bacteria and cyanobacteria could build up a strong complex and render iron less available for eucaryotic phytoplankton, especially in iron-limited environments such as land remote oceanic regions.

7-Volatile Compounds Production Evidence of phytoplanktonic sources of the volatile compounds alkane and alkenes was obtained in April 1988, during a spring bloom of Skeletonema costatum in the Southampton water estuary located on the coastline of Southern England (Bianchi and Varney 1998). In late May and June, a bloom of Mesodinium rubrum (autotrophic ciliate) followed the collapse of Exuviaella marina (Dinoflagellate). The chemical composition of M. rubrum extracts included: n-pentadecane and n-heptadecane. Volatile organosulphide methanethiol (i.e. methyl mercaptan), dimethylsulphide (DMS) and dimethyldisulphide (DMDS) concentrations increased 10-fold from spring S. costatum blooms to summer blooms with Scrippsiella cf. trochoidea and M. rubrum (Turner et al. 1996, Bianchi and Varney 1998). However in this study, other bioactive molecules could be involved in algal species succession, in addition to volatile compounds.

ROLE OF ALLELOPATHY FROM SPECIES TO THE BIOCENOSE LEVEL The in situ validation of in vitro experiments is essential to developing of an understanding of the underlying mechanisms and processes leading not only to monospecific blooms but also to the establishment of typical assemblages. This type of process which directly influences the small scale environment must be integrated at the community level.

1-Species Level Any monospecific phytoplankton culture exhibits a growth which can be modelled by the logistic equation:

FH

dN N = rN 1 dt K

IK

with N, cell density; r, growth rate and K, the “carrying capacity”.


151

The integration of this equation leads to a typical sigmoid curve with an asymptote which represents the maximal attainable yield in the culture. This equation is the mathematical formulation of the Malthus’ population theory. The Malthus’ underlying assumptions are that food production can only increase linearly when the population exhibits an exponential growth. It may be the reason why the carrying capacity is in most cases interpreted as a resource limitation. However, the concept is rather fuzzy since any addition of a potentially limiting nutrient and micro-nutrient does not generally lead to any increase in cell density. One could imagine that each cell requires a void volume in its vicinity. The reasons are still unknown, but one factor may well be auto-inhibition. The production and accumulation of an autoinhibitor by Skeletonema costatum have been demonstrated from the exponential growth phase up to the stationary phase (Pratt 1940). The substance (extracted by ethyl acetate) was growth repressive for Chatonella antiqua and Chatonella marina, but without effect on the dinoflagellates Prorocentrum minimum and Karenia mikimotoi (Imada et al. 1991).

2-Community Level The allelopathic potential may be exemplified at the community level in response to environmental conditions: nutritional, physical, biological (Havens et al. 2001). This interpretation is sustained by the fact that allelopathy increases in nutrient-deprived media, suggesting that allelochemical excretion is stimulated (Havens et al. 2001, Granéli and Johansson 2001). The excretion rate may differ depending on the species considered; it is higher in prymnesiophytes than in prasinophytes and chlorophytes (Reitan et al. 1994). Among the various physiological responses to nutritional shortage, lipid storage is a common process in several phytoplankton species (Reitan et al. 1994, Pernet et al. 2003). Differences in responses could determine competitive advantage, and allelopathic interaction of APONIN has been applied in modelling interactions for Gymnodinium breve red tide management (Perez et al. 2000). The role of allelochemicals in the physiology of the producers is not very well understood. However, the production of fatty acids by Gymnodinium cf. nagasakiense (now Karenia mikimotoi) cultivated under low light conditions and at 18°C corresponded to an increase in floatability and facilitated displacement (Bodennec et al. 1995). To prevent light attenuation due to bacterial and phytoplankton fouling, seaweeds maintain their photosynthetic

152 Algal Cultures, Analogues of Blooms and Applications activity thanks to a clean leaf surface. Diterpene alcohols produced by the brown alga Dictyota menstrualis or bromoform produced by the red marine alga Corallina pilulifera constitute efficient fouling preventive substances (Schmitt et al. 1995, Ohsawa et al. 2001). Photosynthesis in the epiphytic diatom Nitzschia palea Kützing and natural microalgal communities was diversely inhibited by two sulfuric compounds extracted from the charophytes Chara (fresh water macrophyte), dithiolane and trithiane (Mulderij et al. 2003). This was observed in some charophyte culture conditions, depending on the Chara strains and their development stage. Allelopathy has a differential effect on the accompanying species, in the way it eliminates sensitive organisms, while selecting others. In response to Alexandrium tamarense, Karenia mikimotoi and Chrysochromulina polylepis allelochemical exposure, a temporary cyst formation in the dinoflagellate Scrippsiella trochoidea was produced experimentally by Fistarol et al. (2004b). This resistance strategy constitutes an interesting example of coevolution, completing the Lewis’ interpretation of allelochemical interactions in microalgae (1986). Sometimes, resistant species can be encountered in species producing similar allelochemical production: Karenia mikimotoi is unaffected by Chrysochromulina polylepis glycolipids (Schmidt and Hansen 2001) and points to a similar protective mechanism. Prorocentrum micans presents particular resistance to several allelochemicals: PUFAs, APONINs, okadaic acid and DTX1 (Grzebyk et al. 1997). The allelopathic effects of plankton excretion can be dose-dependent and complex. Small proportions of Rhizosolenia alata culture filtrate stimulated Pseudonitzschia pungens divisions, while higher levels were inhibiting (Subba Rao et al. 1995).

3 Biocenose Level The production of chemicals is used by phytoplankton for protection against predator, competitor and pathogens stress. The consequences for the producer is more safety and less competition. APONIN from Nannochloris sp. is fungistatic (Halvorson et al.1984) which could explain fungistasis in seawater. Allelochemicals play an important role in communities, acting on the structure, balance and succession of populations (Keating 1977, Smith and Doan 1999, Vardi et al. 2002, Fistarol et al. 2003, Mulderij et al. 2003, Fistarol et al. 2004a). The allelopathic effect on grazer predators induces an uptake of non-toxic species and thus the predominance of the allelochemical producer in the medium (Naeem and Li 1998).


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Several metabolites produced by microalgae control grazer predation: DMS could be an indicator for seabirds to detect zooplankton and it may constitute an indirect defence compound for phytoplankton (Steinke et al. 2002). Lipophylic substances produced by Olisthodiscus luteus and Dunaliella tertiolecta are unpalatable for Mytilus edulis, likewise Karenia mikimotoi ectocrine for the copepods (Gentien 1998). Evidence from K. mikimotoi that the same toxins not only affect phytoplankton growth but also kill fish demonstrates that exotoxins may act at different levels of the ecosystem. These effects may even be deferred by several months since fish and shellfish larvae appear to be sensitive to ichtyotoxins.

CONCLUSIONS As described above, a proper understanding of the role of allelopathy is rather difficult to obtain but, in any case, in vitro cultures are essential to the documentation of the processes involved which should then, be validated in the field. Allelopathy encompasses interactions from different aspects of the environment, and may strongly modify foodweb structures, community composition by reducing biodiversity, and the pathway of biogeochemical cycles (Keating 1977, Vardi et al. 2002, Mulderij et al. 2003, Gross 2003). Allelochemical production should then be considered as a response to environmental signals, and the biological integration of the aggression by the receptors should equally induce their adaptation. The role of receptor sensitivity remains unknown and could correspond to a general ecosystem regulation (Lewis 1986). Even if seasonal species succession is grossly simulated by models involving nutritional limitations, population biodiversity is relatively dependent on interspecies interactions and allelopathic processes. Moreover, interactions affect several trophic levels and allelochemistry plays a major, but by no means a solitary role in the structuring of the ecosystem’s communities (Keating 1999).

ACKNOWLEDGEMENT We gratefully acknowledge the assistance of Guy Bodennec in offering valuable suggestions in lipid chemistry, Pierre Bodénès for the illustrations and the referee for helpful comments.


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# Algal Blooms and Bacterial Interactions Bopaiah Biddanda1, Paulo Abreu 2 and Clarisse Odebrecht 2 1

Annis Water Resources Institute and Lake Michigan Center, Grand Valley State University, 740 W Shoreline Drive, Muskegon, MI 49441, USA. 2 Departmento de Oceanografia, Funda o Universidade Federal do Rio Grande (FURG), Caixa Postal 474, 96201-900 Rio Grande, RS, Brazil.

Abstract Interaction between phytoplankton and bacteria is the most critical ecological relationship prevalent in pelagic ecosystems. How algae and bacteria interact has major implications for the flow of carbon and nutrients, and the structure and function of the aquatic food web. Frequently, bacterioplankton abundance and activity track those of phytoplankton – even during spring blooms of phytoplankton. Occasionally, however, disconnects (or decoupling) occur between phytoplankton and bacteria – such as those observed during some surf zone diatom blooms and coastal blooms of cyanobacteria. The cause of these disconnects may be bacterial inhibition by low temperatures or bactericidal exudates of the phytoplankton, significant rates bacterivory/viral induced cell lysis, or bacterial dependence primarily on substrates other than those of phytoplankton origin (e.g., terrigenous materials). However, the relationship between bacteria and phytoplankton is not unidirectional, i.e. bacteria too may stimulate or inhibit microalgal growth by the production of growth factors or even algicides. Furthermore, the action of specific bacteria may influence phytoplankton succession, leading to the emergence and control of harmful algal blooms. External factors, like inorganic nutrient availability or UV radiation, can interfere in the balance between phytoplankton and bacteria, with direct influence on the local aquatic food web. Conversely, the phytoplankton-bacteria dynamics during and after algal blooms can affect large-scale phenomena such as dimethyl sulfide (DMS) production and sequestration of carbon dioxide from the atmosphere. By combining field observations and insights gained by using algal cultures as analogs of natural blooms of algae, this chapter attempts to describe the bidirectional interactions between algae and bacteria occurring in freshwater and marine pelagic environments.


INTRODUCTION The interactions of primary and secondary producers are a central consideration in ecology (Pomeroy 1991). In the vast pelagic environments of both freshwater and marine ecosystems, the principal autotrophs are the phytoplankton, whereas the principal heterotrophs are bacterioplankton (Cotner and Biddanda 2002). Once phytoplankton fix carbon into organic matter in surface waters, heterotrophic processes within the food web dominated by bacterioplankton process carbon and associated elements until it becomes stored in sediments or is exchanged with the atmosphere (Sherr and Sherr 1996, Azam 1998, Falkowski et al. 1998, Cotner and Biddanda 2002). Therefore, understanding the nature of algal-bacterial interactions is central to the study of aquatic ecosystems. Most of the organic matter in pelagic ecosystems directly or indirectly originates from phytoplankton. Phytoplankton influence the composition of natural waters by the uptake of inorganic carbon and nutrients for synthesis of cellular organic materials (Eppley and Peterson 1979). Furthermore, the occurrence of phytoplankton blooms has been observed to cause physicochemical changes in the water milieu through redistribution of inorganic nutrients (McAllister et al. 1961) and synthesis and release of organic compounds (Mague et al. 1980, Jenkinson and Biddanda 1995, Biddanda and Benner 1997a). As the major sink for dissolved organic matter, heterotrophic bacterioplankton depend on phytoplankton for sustenance (Cole et al. 1988, Van den Meerche et al. 2004). Measurements routinely show that up to half of pelagic primary production is channeled through bacterioplankton (Cole et al. 1988, Ducklow 2000, Biddanda and Cotner 2002). Consequently, heterotrophic bacteria are now recognized as major consumers of organic matter in natural waters (Williams, 1981, Sherr and Sherr, 1996, Azam 1998, Cole 1999, Karl 1999, Cotner and Biddanda 2002). However, due to their high levels of cellular N and P, bacteria have a higher N and P requirement, and sometimes compete with phytoplankton for dissolved inorganic nutrients (Kirchman 2000, Cotner and Biddanda 2002). Nonetheless, a host of cross-ecosystem observations have demonstrated that microheterotrophic activity is closely associated with that of primary producers (Cole et al. 1988, Cotner and Biddanda 2002). Indeed, numerous studies have also shown that bacterial abundance and activity are enhanced in the vicinity of phytoplankton blooms (Sieburth 1968, Coveny and Wetzel 1995, Simon et al. 1998). But others have demonstrated the opposite influence (viz., decoupling between

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phytoplankton and bacteria) depending upon phytoplankton community composition (Abreu et al. 2003, Mayali and Azam 2004). For example, Sieburth (1968) has extensively considered the food web consequences of algal antibiosis. In oligotrophic systems, bacterial production + respiration can approach that of primary production, decreasing in relative significance in eutrophic systems (Williams, 1984, del Giorgio et al. 1997, Cole 1999, Biddanda et al. 2001). Consequently, the relative biomass as well as the magnitude of carbon flux through bacteria is larger in oligotrophic than in eutrophic water bodies (Biddanda et al. 1994, del Giorgio et al. 1997, Biddanda et al. 2001). In oceanographic as well as limnetic literature, there is emerging evidence that the contribution of heterotrophic bacteria to total planktonic biomass and metabolic activity is high in oligotrophic systems and decreases along an increasing productivity gradient (Biddanda et al.1994, 2001, del Giorgio et al. 1997, Cotner and Biddanda 2002). Understanding such trends in the distribution of biomass and activity of autotrophs and heterotrophs across productivity gradients is likely to contribute to our knowledge of phytoplankton-bacterioplankton interactions and biogeochemical cycling occurring within phytoplankton bloom events in nature (Fig. 5.1). Blooms are, by nature, seasonal. There is emerging evidence in the literature for seasonal cycles of planktonic as well as bacterial metabolic activity (Pomeroy et al. 1991, Pomeroy and Wiebe, 1993, Griffith and Pomeroy1995; Biddanda and Cotner, 2002). Seasonal cycles in planktonic activity have major impacts on the food web structure and carbon balance of natural waters (Pomeroy and Wiebe 1993, Sherr and Sherr 1996). For example, several studies have recorded large buildup of dissolved organic carbon (DOC) in surface waters during the winter-spring period followed by its drawdown during fall-winter (Carlson et al. 1994, Williams 2000, Biddanda and Cotner 2002). Strong positive correlations have been observed between the production of phytoplankton and bacterioplankton over the seasons in intensely monitored Lake Lawrence, Michigan (Coveney and Wetzel 1995), and Lake Constance, Germany (Simon et al. 1998). However, seasonal studies in Newfoundland waters, on the southeastern US continental shelf, and in Lake Michigan have all recorded shifts between net autotrophy during winter-spring and net heterotrophy during summer-fall (Pomeroy et al. 1991, Griffith and Pomeroy, 1995, Biddanda and Cotner, 2002). The formation and fate of blooms have implicit large-scale consequences through its effects on food web dynamics and carbon sequestration in aquatic ecosystems. At present, we are striving to understand these interactions.

166 Algal Cultures, Analogues of Blooms and Applications 100

o

o al

cropla o ro uc o a esp ra o

copla o ro uc o a esp ra o

0 creas

h a

u r e s or ro uc

Fig. 5.1 Broadly generalized conceptual diagram of how plankton composition and metabolic rate processes respond to Light, Nutrient and Productivity gradients affecting carbon balance in natural waters. There is a systematic shift in the relative importance of small versus large organisms to planktonic biomass and metabolism across this gradient. High abundance and activity of small picoplankton (< 1.0 mm) prevail under low nutrient-light conditions and low rates of primary production. Autotrophic and heterotrophic processes are in near balance in low-productivity environments (resembling non-bloom conditions) that are dominated by dissolved nutrients and osmotrophic microorganisms. This situation is reversed when the euphotic zone is influenced by high nutrient-light inputs under which conditions, metazoan phagotrophs, and larger microplankton such as large diatoms and colonial cyanobacteria (> 1.0 mm) play a relatively greater role. Autotrophic and heterotrophic processes are not quite in balance in high primary productivity environments (resembling bloom conditions) that are dominated by particulate matter and larger phagotrophic plankton (Figure modified from Biddanda et al. 2001). A combined approach, one utilizing field observations as well as phytoplankton cultures, can aid in the understanding of the complex interactions taking place between algae and bacteria under bloom conditions. In order to present this case, we have relied on the published literature, and utilized extensively results obtained in our respective laboratories in the USA and Brazil.

METHODS Standard limnological and oceanographic methods of analysis have been used in the studies that are discussed here. For example, phytoplankton abundance was determined by inverted microscopy (Utermöhl 1958), heterotrophic bacterial abundance by epifluorescence microscopy (Hobbie et al. 1977), Chlorophyll a by fluorometry (Parsons et al. 1984), and batch cultures of phytoplankton were grown using standard seawater-based growth media (Guillard and Ryther 1962). Seawater dilution growth cultures were set up by adding 1 part < 1.0 mm filtered water (containing bacteria) to 9 parts < 0.2 mm filtered water (containing few bacteria and viruses, but all of the dissolved nutrients) according to the method of


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Ammerman et al. 1984. More recently, molecular biology techniques like fluorescent in situ hybridization – FISH, Dot-Blot hybridization, denaturating gel gradient electrophoresis – DGGE, and sequencing of 16sRNA have been applied for the determination of bacterial species composition present in nature and in many diatom and flagellates cultures (Groben et al. 2000, Riemann et al. 2000, Mayali and Doucette, 2002, Shäfer et al. 2002, Green et al. 2004, Hare et al. 2004, Mayali and Azam 2004). Additional methodological details can be found in Biddanda 1988, Biddanda and Pomeroy 1988, Abreu et al. 1992, Odebrecht et al. 1995, Biddanda and Benner 1997a, b, Biddanda et al. 2001, Biddanda and Cotner 2003, and Abreu et al. 2003.

ALGAL BLOOMS IN LAKES AND THE OCEAN One of the classic seasonal cycles of life observed in temperate and polar seas and lakes is the seasonal increase of phytoplankton in the spring, known as the spring bloom. Although the causal mechanisms of bloom formation in temperate lakes and oceans are not fully understood, they are thought to be a response to increased light in the presence of excess nutrients and increasing water column stability (Sverdrup 1953). Blooms here are usually typified by a temporal disconnect between the autotrophs and heterotrophs, enabling substantial autotrophic biomass to buildup during the initial phases of the bloom. In the subtropics and tropics, blooms are sporadic, forced by physical events (such as upwellings and lateral intrusions of water masses) that inject nutrients that are limiting for plankton growth into the euphotic zone. Blooms here occur under a variety of circumstances, and do not always involve a temporary imbalance between autotrophic phytoplankton and heterotrophic bacteria and zooplankton (Pomeroy 1991). Several studies have monitored the succession of phytoplankton species within bloom events (Barlow et al. 1993, Sieracki et al. 1993), and the immediate (Pomeroy and Deibel 1986) as well as eventual fate of blooms (Smetacek 1985). The formation and fate of blooms in lakes (e.g., Microcystis, Anabena, Cylindrospermopsis) and the ocean (e.g., Alexandrium, Trichodesmium, Phaeocystis, Emiliania) are of considerable current interest because of the implications for human health and the global carbon cycle.

BACTERIAL INTERACTIONS WITHIN ALGAL BLOOMS As most of the dissolved organic matter in the pelagic ecosystem originates directly or indirectly from phytoplankton production (Biddanda and Pomeroy 1988), bacterioplankton are dependent on phytoplankton for

168 Algal Cultures, Analogues of Blooms and Applications growth substrates – and typically consume a large fraction of pelagic primary production (Cole et al. 1988, Cotner and Biddanda 2002). In fact, several studies of spring phytoplankton blooms in various locations have demonstrated tight coupling between algal and bacterial biomass and production (Sieburth 1968, Lancelot and Billen 1985, Coveney and Wetzel 1995, Simon et al. 1998, Weisse et al. 2000). However, other studies have noted a characteristic time delay between phytoplankton blooms and increase in bacterial activity (Ducklow 2000; Van den Meersche 2004), as well as a continual offset between bloom-forming algae and heterotrophic bacteria (Abreu et al. 2003). There is also some evidence in the literature that the increase in abundance of algicidal bacteria may coincide with the decline of algal blooms (Mayali and Azam 2004). Indeed, there may be situations where little or none of the primary production is consumed contemporaneously by bacteria. During the early phases of the spring bloom of phytoplankton in Newfoundland waters, measurements indicated that there was undetectable levels of bacterial activity (presumably inhibited by the low temperatures), whereas phytoplankton continued to grow (Pomeroy and Deibel 1986, Pomeroy et al. 1991) – arguably sustaining the high productivity of spring time fisheries in these waters. However, low temperature inhibition of bacterioplankton can be apparently overcome in the presence of excess dissolved organic substrates (Pomeroy and Wiebe 1993, 2001).

Trichodesmium Bloom – Bacteria Interactions: A Positive Relationship Trichodesmium is a globally significant colony-forming cyanobacterium that frequently forms blooms in the warm surface waters of tropical and subtropical oceans during the summer period – contributing significantly to oceanic biomass, production and N2 fixation (Capone et al. 1997). In fact, Trichodesmium has been implicated in shifting the North Pacific Gyre from a traditionally N-limited system into a P-limited system during El Niño years (Karl et al. 1995) by accounting for up to a third of the new production in this environment through the release of nitrogenous compounds (Letelier and Karl 1996). It is thought that Trichodesmium blooms under warm (wellstratified) and nutrient depleted conditions in the N-limited surface ocean, where it has the advantage of being able to fix N2. Eventually, however, the bloom collapses – probably due to P limitation – and because there are no known consumers of this episodically blooming colonial cyanobacteria, for


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the most part it sinks or enters the microbial food web where it undergoes decomposition. We observed massive blooms of Trichodesmium during a cruise transecting the Gulf of Mexico during August 1995 (Biddanda 1995, Biddanda and Benner 1997b). Our measurements of Trichodesmium bundles and heterotrophic bacterial abundance from surface bucket samples collected during this period suggest there was a significant positive correlation between the colonial cyanobacteria and heterotrophic bacteria (Fig. 6.2). Trichodesmium colonies are known to support significant biomass and productivity of heterotrophic bacteria (Nausch 1996), and this may account for the positive relationship we observed between the primary producer and consumer groups in the pelagic ocean during this period (Biddanda 1995). Indeed, high primary production rates sustained by Trichodesmium may have fueled the relatively high water column respiration rates that were measured in the Gulf of Mexico during the summer of 1995 (Biddanda and Benner 1997b).

p

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Fig. 5.2 Relationship between the abundance of Trichodesmium sp. colonies (1-3 mm long colonies, each composed of 5– 10 filaments or trichomes) and heterotrophic bacteria in the surface waters of Gulf of Mexico (transect from Port Aransas, Texas to Key West, Florida) during August, 1995. Data from Biddanda 1995 and Biddanda and Benner 1997b.

Surf-zone Diatom Bloom – Bacteria Interaction: A Negative Relationship The surf-zone diatom Asterionellopsis glacialis (Castracane) Round is the main primary producer in the intermediate/dissipative Cassino Beach in southern Brazil near the entrance to one of the largest coastal lagoons in the world (Abreu et al. 1992, 2003). High diatom abundance (108 – 109 cell l–1) in

170 Algal Cultures, Analogues of Blooms and Applications the surf-zone sustains a huge biomass – up to a maximum of 1647 mg L–1 chlorophyll a (Odebrecht et al. 1995). This surf-zone diatom biomass is mainly consumed by benthic filter-feeding organisms like the crustacean Emerita brasiliensis and the bivalves Mesodesma mactroides and Donax hanleyanus. These benthic organisms, in turn, serve as food for rich communities of fish and migratory birds (Garcia and Gianuca 1997). High abundance of A. glacialis is not just the result of a bloom process, but it is mainly a consequence of cell accumulation generated by wave and winds that resuspend the diatoms from the sediment and concentrate them in the surf-zone. Due to wind action, most of the cells are deposited on the beach where they are consumed by the benthic macroinvertebrates, but some are transported back to the sediment behind the surf-zone by rip currents (Odebrecht et al. 1995; Rörig and Garcia 2003). Besides the high biomass, A. glacialis produce large quantities of dissolved organic carbon, reaching a maximum of 68% of total (particulate +dissolved) diatom carbon production, which may reach 3.44 mg C L–1 h–1 (Reynaldi 2000). Additionally, riverine input of dissolved organic matter (DOM) from the nearby Patos Lagoon Estuary supplies high amounts of DOM to Cassino Beach waters. During the occurrence of A. glacialis patches, DOM can be as high as 7 mg C L–1. Such enhanced concentrations of dissolved organic carbon should sustain high amount of bacterial biomass. However, this is not what is observed at Cassino Beach, where typical bacterial abundance levels were about an order of magnitude lower than those reported for environments with comparable DOM and Chlorophyll a values (Cole et al. 1988). A year round study measuring Chlorophyll a and bacterial abundance in the Cassino Beach (August 1992-July 1993, Abreu et al. 2003) showed an inverse relationship between bacteria and A. glacialis (Fig. 5.3a). Furthermore, daily observation of events at the end of a bloom from 1–22 July 1997 also confirmed the decoupling between A. glacialis and heterotrophic bacteria in this surf-zone environment (Fig. 5.3b). Bacterial abundance did not show any significant increase during the first three days after the decrease in A. glacialis. However, following this initial period, bacterial abundance increased steadily thereafter (Abreu et al. 2003). Previous studies carried out in the dissipative beaches of South Africa, where the surf-zone diatom Anaulus australis Drebes et Schulz dominate, concluded that large amounts of dissolved primary production would be consumed by bacteria (McLachlan and Bates 1985; Heymans and McLachlan 1996). However, the results from the Cassino Beach study pointed out a clear and consistent trend of decoupling between bacteria and


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Fig. 5.3 Heterotrophic bacteria abundance variation in Cassino Beach, Brazil during August 1992 to July 1993 (Fig. 5.3a), and immediately following the collapse of the surf-zone diatom Asterionellopsis glacialis bloom during July 1-22, 1997 (Fig. 5.3b). Chlorophyll a peaks in Fig. 5.3a are caused by the A. glacialis. Figures modified from Abreu et al. 2003 the surf-zone diatom A. glacialis (Abreu et al. 2003). Laboratory experiments measuring the bacterial growth in A. glacialis cultures and in the water from the beach sampled during the occurrence of the surf-zone diatom patches showed a large lag phase in the bacterial growth curve, indicating suppression of bacterial activity (see below). Abreu et al. 2003 considered five possible reasons for this diatom-bacteria decoupling: 1) viral infection, 2) bacterial grazing, 3) DOC quality, 4) nutrient competition and 5) antibiotic production. Bacterial grazing and

172 Algal Cultures, Analogues of Blooms and Applications nutrient competition were discarded, since measurements of protozoan grazing activity conducted in the Cassino Beach did not justify the low bacterial abundance observed (Hickenbick 2002). Moreover, the dissolved inorganic nutrients measured in this beach were low, but do not characterize an oligotrophic condition, especially after high rainfall, when large amounts of freshwater enter into the coastal region. The possible effect of viral infection (Bratbak et al 1990, Fuhrman 2000) remains to be tested and, though not measured, the antibiotic production by this diatom was considered as the most plausible hypothesis. Indeed, the antibiotic produced by A. glacialis was first described by Aubert et al. (1970) as a nucleoside made up of pyrimidinic base and a pentose sugar, probably arabinose. Its structure is analogous to thymidine and it has an antimitotic action. Despite all evidences that the antibiotic produced by A. glacialis could cause the decoupling between this diatom and bacteria, it is very likely that the antibiotic action could be the result of the overall quality of dissolved organic carbon exuded by the surf-zone diatom. For instance, Sander and Purdie (1998) demonstrated that the response of bacteria to blooms of the coccolithophorid Emiliana huxleyi and the diatom Skeletonema costatum were quite different mainly due to DOC quality produced by both organisms. In the case of E. huxleyi, peaks of the coccolithophorid and bacteria were almost synchronous, while bacteria took about one week to start growing after the S. costatum attained maximum abundance. These differences were related to the release of simpler monomeric organic compounds by E. huxleyi in contrast to the more complex organic matter produced by S. costatum. The hypothesis whether DOC quality released by A. glacialis could influence the bacterial growth in the Cassino beach was further tested by us in laboratory studies (see below).

Coastal Harmful Algal Blooms – Bacterial Interactions in River-Dominated Systems The emergence of harmful algal bloom (HAB) species in coastal waters has been an area of increasing interest and study (Anderson 1997, Smayda 1997). Due to toxin production and possible ecological and human health problems, the implications of algae-bacteria relationship in such environments are of concern. Several field studies have pointed out the importance of bacteria in the initiation, development and termination of harmful algal blooms (Doucette et al. 1996). Such possible participation of bacteria in the toxin production or biotransformation has been inferred by the fact that some putative toxigenic bacteria were present in high number


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when toxic dinoflagellates were blooming (Tobe et al. 2004). Furthermore, field observations have shown that bacteria can participate in the cyst formation of the toxic dinoflagellate Alexandrium tamarense (Adachi et al. 1999). On the other hand, this dinoflagellate had no effect on the bacterial community (Fistarol et al. 2004). It is possible that bacteria are also involved in the degradation of toxins in the water that are produced by toxic cyanobacteria (Maruyama et al. 2003). On the other hand, by regulating algal community structure, algicidal bacteria may influence the nature of algal bloom dynamics (Mayali and Azam 2004). Recent advances in molecular biology techniques could lead to better understanding of the interactions between bacteria and HAB species interaction. In river dominated coastal water ecosystems, it is thought that nutrient discharge determines the balance between a predominantly microbial loop driven planktonic community at low nutrient levels, and a highly productive grazer dominated food web composed of larger plankton at high nutrient levels (Legendre and Michaud 1998, Dagg et al. 2004). Bacterial productivity is commonly found to be enhanced at the boundary region where river water mixes with coastal water, wherein bacteria appear to utilize both riverine organic matter as well as organic matter produced by the local phytoplankton (Chin-Leo and Benner 1992, Pakulski et al. 2000). Furthermore, studies suggest that terrestrial organic matter undergoes rapid transformations during its transit to coastal waters (Hedges et al. 1997), and that bacteria and sunlight interactions likely play a critical role in this process (Miller and Moran 1997, Biddanda and Cotner 2003). We observed a weak positive relationship between the distribution of Chlorophyll a and heterotrophic bacteria along the Maumee River – Lake Erie transect in August 2003 (Biddanda and Tester, unpublished data, Fig. 5.4). Maumee River is a major tributary to Western Lake Erie (Wilhelm et al. 2003), where large blooms of the cyanobacteria Microcystis were occurring during the study period. Extremely high phytoplankton biomass values (> 100 mg Chlorophyll a l–1) and heterotrophic bacterial production rates (> 300 mg C l–1 d–1) were measured in parts of western Lake Erie. However, the apparent lack of a strong relationship between algal biomass and bacterial abundance that prevailed here (Fig. 5.4) may reflect the possibility that bacteria are sustained in part by terrigenous inputs via the tributary, and that they could also be negatively influenced by toxins that are known to be produced by Microcystis (Christoffersen 1996). Any production of algicides by bacteria would likely further weaken the prevailing relationship between bacteria and algae (Mayali and Azam 2004).


e ero roph c ac er a x10 8

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Fig. 5.4 Relationship between Chlorophyll a and heterotrophic bacterial abundance in Western Lake Erie in the vicinity of the Maumee River during August, 2003. Unpublished data of Biddanda and Tester.

CULTURES AS ANALOGS OF BLOOMS – INSIGHTS INTO COMMUNITY AND CARBON DYNAMICS Cultures of algae can serve as useful analogs of blooms in nature by providing a controllable environment where algal growth and decomposition may be studied under well-defined conditions. Studies relating to succession of organisms as well as nutrient dynamics can be performed under controlled conditions using algal cultures in defined growth media (Biddanda 1988, Biddanda and Pomeroy 1988) or dilution cultures of bacteria in natural filtered water (Reynaldi 2000, Abreu et al. 2003).

Succession of Microorganisms and Fate of Phytoplankton Bloom in Cultures Several studies have examined the succession of microorganisms and nutrient dynamics during the growth and death of phytoplankton using batch cultures of select algae (Fukami et al. 1985). In their studies, Biddanda and Pomeroy (1988) and Biddanda (1988) examined the microbial succession and carbon dynamics associated with detritus derived from three phytoplankton belonging to diverse taxanomic groups (Synechococcus, Dunaliella and Cylindrotheca) in natural seawater. The authors found a remarkably similar pattern of microbial succession involving heterotrophic bacteria and bacterivorous protozoa occurring in all three cases with resultant aggregation and disaggregation of the algal detritus – the same


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kind of scenario that is frequently observed following the death of algal blooms in natural waters. Tracing the fate of algal derived carbon within the same experiment, Biddanda (1988) found that nearly half of the carbon in both the dissolved and particulate fractions was consumed by the bacteria with a higher proportion of the carbon being assimilated in the earlier stages of algal degradation than at the later stages. This suggests that dying blooms in nature initially fuel heterotrophic production, but will eventually fuel heterotrophic respiration. Additionally, the early aggregation phases of the algae may serve as food for metazoan consumers or as vehicles for vertical transport of surface derived carbon, whereas the later disaggregated phases may serve as substrates for microbial mineralization of limiting nutrients within the water-column.

Carbon, Nitrogen and Carbohydrate Fluxes During the Growth of Phytoplankton Although the principal source of carbon in pelagic waters is phytoplankton, experimental data on carbon and nitrogen mass balance during their growth cycle is seriously lacking. The first study to address the carbon mass balance of phytoplankton during their growth cycle was that of Eppley and Sloan (1965). Biddanda and Benner (1997a) grew batch cultures of phytoplankton from widely different taxanomic groups (Synechococcus, Phaeocystis, Emiliania, and Skeletonema) in defined synthetic seawater media, and monitored changes in particulate and dissolved carbon, nitrogen and carbohydrates during the entire growth cycle. Over 14 days of the study, there was a close molar balance between dissolved inorganic carbon (DIC) uptake and total organic carbon (TOC = dissolved + particulate organic carbon) production in all the phytoplankton except Emiliania, which synthesizes carbonate-containing coccoliths. Dissolved organic carbon production by the phytoplankton was dominated by the carbohydrate fraction (primarily polysaccharides), constituted a substantial 10-30% of the TOC production, and was maximal during the senescent phases of the cultures. Furthermore, the phytoplankton produced dissolved organic matter consisted of ~35% high molecular weight compounds having high C:N ratios (~21) and ~65% low molecular weight compounds having low C:N ratios (~6). Excess carbohydrate production towards the later phases of the bloom, and the variable nitrogenous composition of the high and low molecular weight DOM fractions could have important consequences for the microbial food web and the eventual fate of carbon produced by

176 Algal Cultures, Analogues of Blooms and Applications algal blooms (Kepkay et al. 1993, Amon and Benner 1996, Biddanda and Benner 1997a).

Use of Cultures to Demonstrate Decoupling Between Surf-Zone Diatoms and Bacterioplankton Most of the insights about the interaction between the surf-zone diatom Asterionellopsis glacialis and bacteria come from laboratory experiments. Regarding the influence of bacteria on the diatom growth, it was observed that a strain named Pseudomonas sp. 022 added to A. glacialis culture was able to stimulate the growth of this diatom, while the opposite occurred with the addition of bacteria classified as Vibrio sp. 05 (Riquelme et al. 1987). It was found that the Pseudomonas sp.022 produced a glycoprotein that serves as a growth factor to the algae. On the other hand, A. glacialis exudate inhibited the growth of Staphylococus aureus (Aubert et al. 1970) and Vibrio spp., while it stimulated the growth of a bacteria belonging to the Pseudomonas genera (Riquelme et al. 1989). Such results demonstrate that bacteria can potentially influence the development and decline of algal blooms (Fukami et al. 1997, Mayali and Azam 2004). Furthermore, the different responses of bacteria species to A. glacialis could indicate that the antibiotic produced by this diatom, as described by Aubert et al. (1970), is very specific (i.e., not of a broad spectrum), or that different bacteria species could use the exudates produced by the algae in distinctly different ways. To test the hypothesis whether the decoupling between A. glacialis and bacteria in the Cassino Beach was caused by the DOC quality, Reynaldi (2000) conducted a series of seawater dilution culture experiments where bacterial growth was measured in filtered water collected inside and outside the diatom patches. The results were compared to those growing in dilution cultures with filtered water samples from the diatom patch that were enriched with Glucose (~100 mM). Bacteria in the glucose enriched water showed a faster growth with maximum abundance occurring about 48 h following bacteria inoculation. Subsequently, the next highest abundance of bacteria occurred in the water collected outside the diatom patch, followed by the water sampled directly in the region of maximum diatom abundance (inside the patch). Like the observations made by Sanders and Purdie (1998) with blooms of phytoplankton in nutrient-enriched microcosms, the exudate produced by A. glacialis is also of complex composition – such that it would restrict its immediate utilization by the bacteria, leading to temporary accumulation of dissolved organic carbon in the water.


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Abreu et al. 2003 reexamined the issue of the inhibition of bacterial growth by the surf-zone diatom in two separate culture studies. In the first study, they utilized seawater dilution cultures that were established using filtered water from Cassino Beach. In dilution cultures set up during the period of maximum A. glacialis abundance (Day 0, in Fig. 5.3b), it was shown that the bacterial lag- phase extended up to 4 days. On the other hand, in dilution cultures set up during the period of minimum A. glacialis abundance following the collapse of the bloom at Cassino Beach (Day 14, in Fig. 5.3b), the initial bacterial lag-phase was reduced to just one day. In the second study, Abreu et al. 2003 isolated A. glacialis from Cassino Beach and grew it in batch culture using F2 medium. Dilution cultures were set up using filtrate from the cultures (9 parts < 0.2 mm culture water) and an inoculum of bacteria from Cassino Beach Water (1 part < 1 mm water from Cassino Beach). In dilution cultures set up during the growth phase of A. glacialis cultures (Day 7), the bacterial lag-phase extended for a minimum of one full day. On the other hand, in dilution cultures set up during the senescent phase of A. glacialis cultures (Day 14), there was no discernible bacterial lag-phase at all. The above two experimental studies clearly demonstrate the inhibitory effect of A. glacialis on heterotrophic bacterial growth – the effect being significant during the growth phase of the surf-zone diatom when it is most abundant, and insignificant during the senescent phases of the surf-zone diatom life cycle. The authors hypothesized that such decoupling between the surf-zone diatom and bacteria may be a mechanism developed by the diatom to surpass bacteria in the direct competition for dissolved inorganic nutrients. If true, this is probably of vital importance to A. glacialis, considering the small window of time (few days) available for its growth between resuspension from the sediment and concentration of the algae in the surf-zone. One implication of these field observations and laboratory studies is that, due to bacterial growth suppression, large amount of diatom production is directly transferred to metazoan consumers, making the Cassino Beach and adjacent near shore habitat one of the most productive systems along the 8,500 km Brazilian coast (Garcia and Gianuca 1997, Seeliger et al. 1997).

Study of Bacteria – Harmful Algal Blooms (HAB) Interaction in Algal Cultures The interaction between phytoplankton and bacteria can be positive as well as negative: algae and bacteria can influence the growth, activity, and even succession of each other, characterizing a very complex relationship, mostly

178 Algal Cultures, Analogues of Blooms and Applications developed by chemical signals of elements exuded into the water (Fukami et al. 1997, Gross 2003). It is estimated that the impacts of harmful algal blooms (HAB) have increased worldwide in the last years, especially due to anthropogenic inputs of nutrients in most coastal regions (Hallegraeff 1993, Anderson 1997, Smayda 1997). According to Green et al. (2004), bacteria play an important role in the formation and development of HAB not only by participating in the process of toxin production and biotransformation, but also influencing the algae population dynamics due to positive and negative allelopathic interactions. Although the possible influence of bacteria on toxin production is still a matter of controversy, some laboratory studies have analyzed the importance of these microorganisms in, for example, the Paralytic Shellfish Toxin – PST production after germination of Gymnodinium catenatum cysts (Green et al. 2004), Domoic Acid (DA) production in Pseudo-nitzschia multiseries cultures (Bates et al. 2004), although in the latter case, no effect of bacteria on toxin production could be detected. More information about laboratory studies of bacterial phycotoxins production can be found in Doucette et al. (1996). Other studies have examined the bacterial production of algicidal compounds that affect several HAB species (Doucette et al. 1999, Mayali and Doucette, 2002, Hare et al. 2004). According to Hare et al. (2004), algicidal bacteria could be used as a ‘short-term’ solution for HAB problems in coastal waters. Algicidal bacteria may show different degrees of specificity to HAB species, varying from a broad range of effects to specific action against one or more closely related species (Doucette et al. 1999). There are also differences regarding the way bacteria can act - some need direct contact with algal cells, while others excrete active compounds into the water (Doucette et al. 1999, Bates et al. 2004). Most studies on bacterial algicidal effect begin with the isolation of bacteria present in the environment or in the water where microalgae is growing. Afterwards, a large amount of cultivated bacteria are added to the algae. Positive (stimulatory) or negative (inhibitory) action of bacteria is determined by the variation of microalgae abundance over time. It is important to stress that such high abundance of a specific group of bacteria rarely occurs in nature. Mayali and Doucette (2002) observed that algicidal action of the bacteria 41-DBG2 on the dinoflagellate Karenia brevis was only effective when specific bacterial abundance reached 106 cells ml–1. The authors considered the possibility of a bacterial ‘threshold concentration’. In this scenario, bacteria must reach high threshold abundance in order to trigger the algicidal response. In their survey of the literature, Mayali and


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Azam (2004) conclude that evidence for bacterial algicidy causing the decline of algal blooms does exist, but it is circumstantial. Another important aspect that has emerged in the recent years from laboratory studies is the phylogenetic identification of bacteria in microalgae cultures, especially HAB cells, using molecular biology techniques. Shäfer et al. (2002) studied the diversity of accompanying bacterial communities, the so-called ‘satellite’ bacteria, in six algal cultures. In their study most bacterial communities had representative species of the μ-Proteobacteria and Cytophaga-Flavobacterium-Bacteroides (CFB) groups, i.e., typical marine phylotype representatives. However, the bacterial community composition was unique for each phytoplankton culture and the bacterial diversity was always less in culture than in nature. The loss of bacterial species diversity in cultures probably reflects the lack of a complex suite of algal exudates within the cultures as opposed to that produced by a relatively diverse community of producers in natural waters. According to Hare et al. 2004, all marine algicidal bacteria belong to Proteobacteria and CFB phylogenetic groups and similarities observed for bacterial flora among dinoflagellates cultures could be explained by selective mechanisms running in laboratories. The other possibility is that specific groups of bacteria may be favored, since they could be important to the growth and physiology of dinoflagellates cells (Green et al. 2004).

CONCEPTUAL MODEL OF ALGAE-BACTERIAL INTERACTIONS WITHIN BLOOMS According to conceptual food web models developed by Legendre and LeFevre (1995), Legendre and Rassoulzadegan (1995), Biddanda et al. (2001), Cotner and Biddanda (2002), the non-bloom low productivity environments are likely to be characterized by microbial food webs composed of small cyanobacteria and phytoplankton, heterotrophic bacteria and protozoans – a system sustained by regenerated production. On the other hand, high productivity bloom environments are likely to be characterized by herbivorous food webs composed of larger phytoplankton and zooplankton – a system sustained by new production. Microbial food web dominated low productivity environments may produce little or no export of carbon to the lake/ocean floor, whereas herbivorous food web dominated bloom environments may result in significant vertical export flux of carbon (Peinert et al. 1989, Biddanda et al. 2001, Fig. 5.1). From the many examples cited above, it is clear that the phytoplanktonbacteria interaction is bidirectional, and understanding the balance

180 Algal Cultures, Analogues of Blooms and Applications between both groups of microorganisms is essential if we are to comprehend the functioning of microbial food web and biogeochemical cycles in aquatic ecosystems. For example, as early as 1968 (Sieburth1968), investigators have considered the food web consequences of algal antibiosis. More recently, there has been considerable progress regarding the ecological and evolutionary role of algicidy by bacteria (Mayali and Azam 2004). In the revised conceptual model presented here, we emphasize the bidirectional characteristic of phytoplankton-bacteria interaction, and point to some external factors that can influence both microorganisms (Fig. 5.5). Conversely, due to their high biomass and ubiquitous presence in all aquatic systems, the results of phytoplankton-bacteria interaction have worldwide implications, influencing large-scale phenomena like global climate. ar o ro uc o a e ues ra o

ssol e r a c a or a c ure s h opla

e pera ure ra ressure rus

o –

–

– ro uc o

ec o ac er a

sher

s le h a l ra ole a a o

Fig. 5.5 Conceptual diagram emphasizing the bidirectional interactions taking place between phytoplankton and bacteria in natural waters. Phytoplankton and bacteria are both being influenced by biotic (competition for resources, mutualism/symbiosis, grazing pressure, virus infection, bactericidy, algicidy) and abiotic (temperature, dissolved organic and inorganic nutrients, light and ultraviolet radiation) factors. The outcome of these interactions, influence large scale events like global climate due to carbon production and sequestration, as well as, planetary cooling due to cloud formation nucleated by dimethyl sulfide (DMS), originating from dimethyl sulfoniopropionate (DMSP) produced by haptophytes and dinoflagellates. Similarly, the balance between phytoplankton and bacteria may have strong influence on the world fisheries. Positive and negative signals beside the arrows indicate the resulting positive and negative influences. Although bacteria depend on phytoplankton as the primary source of organic matter for their nutrition, bacteria can significantly influence phytoplankton growth, succession, and decomposition. The way both organisms respond to each other, and the main consequences of this interaction, is highly influenced by external biotic and abiotic factors. For


181

instance, bacteria and phytoplankton can respond in different ways to water temperature (Pomeroy and Wiebe 1993), dissolved inorganic and organic nutrients supply (Kirchman 2000, Cotner and Biddanda 2002), and the amount of visible light and ultraviolet radiation (Carrillo et al. 2002). Similarly, consumer grazing pressure (Strom 2000) and viral infection (Bratbak et al. 1990, Fuhrman 2000) can impact phytoplankton and bacteria differently. Different outcomes of the phytoplankton-bacteria interaction may have considerable influence on large-scale events, such as global warming, due to carbon dioxide production and uptake (Hoppe et al. 2002, Biddanda and Cotner 2002), or due to the albedo generated by clouds formed by dimethyl sulfide (DMS). Major DMS source comes from dimethyl sulfoniopropionate (DMSP) found in high concentration in some haptophytes and dinoflagellates. The conversion of DMSP to DMS depends of the action of the enzyme DMSPlyase present in high amount in bacteria and in some microalgae (Burkill et al. 2002). Similarly, depending on the kind of interaction between microalgae and bacteria, microbial food webs may enhance or reduce, fisheries production (Pomeroy and Deibel 1986, Azam 1998, Azam and Worden 2004, Osidele and Beck 2004). The challenge for the future is to elucidate the complex ecological interactions amongst microbial plankton because they constitute the very bases of biogeochemical cycling of elements in natural waters.

CONCLUSIONS Autotrophic phytoplankton and heterotrophic bacteria constitute two of the most fundamental and complementary functional units in pelagic ecosystems. Consequently, how algae and bacteria interact has important consequences to carbon and nutrient flow and food webs in aquatic ecosystems. In the pelagic environment, bacteria primarily depend on phytoplankton for organic matter. Bacterial mineralization of organic matter supplies limiting nutrients to the algae. Consequently both bacterial abundance and activity are usually closely linked to phytoplankton abundance and production. Exceptions to this rule do occur when algal exudates inhibit bacterial growth, resulting in reduced bacterial growth and accumulation of dissolved organic matter. On the other hand, bacteria may compete with phytoplankton for limiting nutrients, and the presence and activity of specific bacteria may influence species succession in the phytoplankton communities – potentially affecting the outcome of bloom composition including the emergence and control of harmful algal blooms.

182 Algal Cultures, Analogues of Blooms and Applications Combining field and laboratory culture studies can be expected to provide increased understanding of the multi-faceted and vitally important interactions between algae and bacteria in nature. Recent advances in new molecular biology techniques has helped to refine the study of microalgaebacteria interaction, enhancing our capacity to understand possible relationships at the species levels of both algae and bacteria. Nevertheless, laboratory studies allow us to conduct well-controlled experiments in order to the test hypotheses generated by observations made in nature.

ACKNOWLEDGEMENTS We are thankful to an anonymous reviewer (Editorial Board), Pat Tester (NOAA), Ying Hong (University of Michigan), Scott Kendall and Sarah Barnhard (Grand Valley State University), for comments that helped improve the first draft of the manuscript. Authors are thankful to Gary Fahnenstiel (NOAA) for ship time on board the R/V Laurentian, and Pat Tester for the use of unpublished Chlorophyll a data for western Lake Erie. BB’s work was supported through grants from NASA and NOAA. PA and CO were supported by the CNPq (Brazilian Science Foundation).

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Viral Infection in Marine Eucaryotic Microalgae

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$ Viral Infection in Marine Eucaryotic Microalgae Keizo Nagasaki Harmful Algae Control Section, Harmful Algal Bloom Division, National Research Institute of Fisheries and Environment of Inland Sea, 2-17-5 Maruishi, Ohno, Saeki, Hiroshima 739-0452, JAPAN

Abstract Viruses are the most abundant biological agents in aquatic environments, and their ecological significance has been highlighted recently. A wide variety of phytoplankton including Chlorophyceae, Chrysophyceae, Prasinophyceae, Prymnesiophyceae, Dinophyceae, Raphidophyceae, and Bacillariophyceae have been shown to be exposed to viral attacks. So far, more than 14 viruses infecting marine eucaryotic microalgae have been isolated and successfully cultured in the laboratory. They are all polyhedral in shape, but they are highly diverse both in size (25–220 nm in diameter), genome type (dsDNA, dsRNA, or ssRNA), and genome size (4.4 kb – 560 kbp). Some of these viruses have been examined from the viewpoint of their ecological roles. Evidence has accumulated showing that viral infection may affect not only the quantity (biomass) of algal blooms to cause interspeciessuccession, but also the quality (clonal composition) to cause intraspeciessuccession. In other words, even within a bloom where a single species is dominant, it is composed of clones that are diverse in terms of virus sensitivity, and the virus population attacking the bloom is highly diverse in terms of intraspecies host specificity; thus, the ecological interaction between algal hosts and viruses in natural waters is considered to be highly complex. In this chapter, characteristics of the marine microalgal viruses isolated up until now, phylogenetic studies of microalgal viruses, methods to make microalgal viruses into culture, and the possibilities of their practical use are summarized.


INTRODUCTION With the recent realization that viruses and virus-like particles (VLPs) are highly abundant in various aquatic environments, interests in aquatic viruses have been increasing. They should include viruses infectious to bacteria, phytoplankton, zooplankton, seaweeds, crustacean, fishes, and any other larger organisms; thus, several studies have highlighted their ecological significance (Bergh et al. 1989, Proctor and Fuhrman 1990, Thingstad et al. 1993, Suttle 2000, Wommack and Colwell 2000, Brussaard 2004a). Among them, VLPs or viruses have been found in more than 50 species in 12 of the 14 recognized classes of eucaryotic algae (Van Etten et al. 1991, Zingone 1995, Castberg 2001, Brussaard 2004a). The successful isolation of these microalgal viruses has accelerated the progress of ‘algal virology’. Viruses in culture that are infectious to marine eucaryotic algae are listed in Table 6.1; they are diverse in size (25–220 nm in diameter), genome type (dsDNA, dsRNA or ssRNA), genome structure (single molecule or fragmentary), and genome size (4.4 kb – 560 kbp). Although there have been no reports of algal viruses harboring a single-stranded DNA genome, considering the high diversity of these viral isolates, their future findings will not be surprising at all. In the sections below, studies on microalgal viruses isolated so far are summarized.

Viruses Infecting Marine Raphidophytes Heterosigma akashiwo (Hada) Hada (Raphidophyceae) is a typical HAB (harmful algal bloom) -causing microalga. It occurs in coastal waters of subarctic and temperate areas of both northern and southern hemispheres, and often causes mortality of cultured fish such as salmon, yellowtail, red sea bream, and greater amberjack (Honjo 1993, Smayda 1998). As H. akashiwo has a wide distribution, causes annual dense blooms, and is easy to culture, a great number of studies have been made with regard to this species. Three distinct types of virus infecting H. akashiwo have ever been reported; H. akashiwo virus (HaV), H. akashiwo nuclear inclusion virus (HaNIV), and H. akashiwo RNA virus (HaRNAV).

HaV HaV is icosahedral, large (202 nm in diameter), and replicates in the cytoplasm of H. akashiwo (Fig. 6.1; Nagasaki and Yamaguchi 1997). By means of one-step growth experiments, its latent period and burst size of HaV were estimated at 30–33 h and ca. 770 infectious units cell–1, respectively (Nagasaki et al. 1999). Pulse-field gel electrophoresis revealed

Aureococcus anophagefferens Chrysochromulina brevifilum Chrysochromulina ericina Emiliania huxleyi Heterosigma akashiwo Heterosigma akashiwo Heterosigma akashiwo Heterocapsa circularisquama Heterocapsa circularisquama Micromonas pusilla Micromonas pusilla Pyramimonas orientalis Phaeocystis pouchetii Phaeocystis globosa Rhizosolenia setigera

Algal host 140 nm 145– 170 nm 160 nm 170– 200 nm 202 nm 30 nm 25 nm 30 nm 197 nm 115 nm 65– 80 nm 180– 220 nm 130– 160 nm 100– 170 nm 32 nm

Size – 14– 19 h 12– 14 h 30– 33 h > 42 h < 12 d 33– 48 h 40– 56 h 7– 14 h 36 h 14– 19 h 12– 18 h 10– 16 48 h

–

b

Genome

500 dsDNA > 320 dsDNA 1800– 4100 dsDNA, 510 kbp 400– 1000 dsDNA, 410– 415 kbp 770 dsDNA, 294 kbpc 5 ca. 10 – – ssRNA, 9.1 kb 7200– 43000 ssRNA, 4.4 kb 1300– 2440 dsDNA, 356 kbpc 72 dsDNA, 200 kbp 460– 520 dsRNA, 25.5 kbpd 800– 1000 dsDNA, 560 kbp 350– 600 dsDNA, 485 kbp – dsDNA 1010– 3100 ssRNA, 11.2 kbe

Latent period Burst size

Gastrich et al. (1998) Suttle and Chan (1995) Sandaa et al. (2001) Wilson et al. (2002), Castberg et al. (2002) Nagasaki and Yamaguchi (1997) Lawrence et al. (2001) Tai et al. (2003) Tomaru et al. (2004a) Tarutani et al. (2001), Nagasaki et al. (2003) Cottrell and Suttle (1991) Brussaard et al. (2004b) Sandaa et al. (2001) Jacobsen et al. (1996) Brussaard et al. (2004a) Nagasaki et al. (2004b)

References

Two types of viruses infecting Aureococcus anophagefferens have been ever reported, AaV (Milligan and Cosper 1994) and BtV (Gastrich et al. 1998). However, the presence of AaV is now under discussion (Suttle 2000), therefore we have referred to only BtV in this table, b No data, c Nagasaki et al. (2005a), d total length of eleven dsRNA segments, e smaller RNA molecules (0.6– 1.5 kb) are occasionally observed.

a

BtV CbV CeV EhV HaV HaNIV HaRNAV HcRNAV HcV MpV MpRNAV PoV PpV PgV RsRNAV

a

Virus

Table 6.1 Viruses isolated so far which are infections to maribe eucaryotic algae


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Fig. 6.1 Heterosigma akashiwo and its virus, HaV. (A) An optical micrograph of vegetative cells of H. akashiwo (ca. 10– 15mm in length), (B) a transmission electron micrograph of an intact H. akashiwo cell, (C) a negatively stained HaV particle (ca. 0.2 mm in diameter), and (D) a HaVinfected H. akashiwo cell. (B and D: reproduced from Nagasaki et al. 1994a with publisher’s permission). that HaV has a dsDNA genome about 294 kbp in length (Nagasaki et al. 2005a). Fragmental data of the putative ATPase found in HaV-DNA has demonstrated a close relationship between HaV and a typical algal virus PBCV-1 (Paramecium bursaria Chlorella virus 1) that infects a Chlorella-like alga hosting P. bursaria (Nagasaki et al. 2001). Recently, the DNA polymerase gene of HaV was identified and its high similarity to PBCV-1 and the other dsDNA viruses (see below) infecting marine microalgae was found (Nagasaki et al. 2005a). These data suggest that HaV very likely belongs to the new family Phycodnaviridae (Van Etten 2000). Since several distinct viruses, including HaV, that are infectious to H. akashiwo have recently been isolated, its nomenclature should be reconsidered (e.g. HaDNAV = Heterosigma akashiwo DNA virus). HaV was first found through a transmission electron microscopic (TEM) study of field material. Nagasaki et al. (1994a) found that ca. 5 % of natural cells in a H. akashiwo population were visibly infected by HaV-like large particles. Furthermore, a specific increase of virus-harboring cells within


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natural H. akashiwo population at the final stage of its bloom was soon deduced by Nagasaki et al. (1994b). Based on these observations, the possible interrelationship between viral infection and the H. akashiwo bloom termination was predicted. Following studies revealed that the relationship between viruses and H. akashiwo was much more complex than had been expected. Although HaV is specifically infectious to H. akashiwo, all HaV strains do not always infect all H. akashiwo strains, i.e., the viral infectivity is clearly strain-specific (Nagasaki and Yamaguchi 1998, Nagasaki et al. 2000). Tarutani et al. (2000) and Tomaru et al. (2004a) revealed the coexistence of multiple types of host and HaV (at least three types of HaV and six types of H. akashiwo) in natural H. akashiwo blooms. To date, however, the mechanism determining the intra-species host specificity has not yet been clarified. Ecological relationship between H. akashiwo and HaV can be summarized as follows: i) HaV infection influences both the total abundance and the clonal composition of H. akashiwo blooms, ii) H. akashiwo is diverse among clones in terms of sensitivity to HaV infection, and distinct types of H. akashiwo clone coexist in natural environments (even within a bloom), iii) HaV is diverse among clones in terms of infectivity to H. akashiwo, and distinct types of HaV clone coexist in natural environments (even within a bloom), iv) HaV infection is one of the significant factors causing the H. akashiwo bloom termination. It is not as simple as the relationship between predator and prey because of the diversity in the viruses’ infection spectra and the hosts’ sensitivity spectra. Based on these observations, Tarutani et al. (2000) predicted that viral infection contributes to the maintenance of the genetic and physiological diversity within H. akashiwo populations, and it may allow populations to thrive over a broad range of environmental conditions; hence, viral infection might play a role in enhancing the ecological fitness of the host species.

HaNIV HaNIV (H. akashiwo nuclear inclusion virus) is ca. 30 nm in diameter, and it replicates in the nucleus of H. akashiwo, often forming paracrystalline arrays (Lawrence et al. 2001). Its infectivity is also strain-specific. Although the genome type of HaNIV has not been clarified yet, its morphology and replication site demonstrate that HaNIV is clearly different from HaV and the other viruses in the family Phycodnaviridae (or probably belong to the family Phycodnaviridae). Its latent period was 24 h, and the number of HaNIV particles within an infected cell was estimated at 3 ¥ 105 based on TEM

194 Algal Cultures, Analogues of Blooms and Applications images. In HaNIV-infected cells, an apoptotic feature (margination of heterochromatin within the nucleoplasm) was detected, but its association with the viral-induced apoptosis or programmed cell death has not been elucidated. This host-virus system is expected to be interesting material to study the mechanism of algal cell lysis.

HaRNAV HaRNAV, the first RNA virus which infects microalgae that was made into culture, assembles within the cytoplasm in infected H. akashiwo cells (Tai et al. 2003). Its infection is also strain-specific. In HaRNAV-infected cells, apoptotic features such as swelling of the endoplasmic reticulum, vacuolation of the cytoplasm, and appearance of numerous clusters of fibrils are observed. This host-virus system is also expected to be interesting material to study the mechanism of algal cell lysis. This small virus (ca. 25 nm in diameter) has a ssRNA (positive strand) genome that is 8587 nucleotides (nts) long, plus poly(A) tail (length unknown). The genome contains one open reading frame (ORF) 7743-nts in length encoding a polyprotein that presumably codes for RNA-dependent RNA polymerase, helicase, and conserved picorna-like protein(s). The result of genome analysis did not support that HaRNAV belongs within any currently defined virus family. In the viral lysate, both noninfectious and infectious HaRNAV particles are included, and they can be separated by a sucrose gradient centrifugation. By comparing the structural proteins of the noninfectious and infectious HaRNAV particles, Lang et al. (2004) predicted the maturation process (i.e., protein processing events to achieve infectivity) of the virus particles. Based on the specific features of HaRNAV, a new virus family Marnaviridae was proposed by Lang et al. (2004) in which HaRNAV should be the first member. Finding of HaRNAV not only emphasized the diversity of H. akashiwo pathogens but the algal virus pathogens, and also indicated the complexity of virus-host interactions in natural waters. By using RT-PCR methods specific to picorna-like viruses targeting RNA-dependent RNA polymerase fragments, amplicons highly similar to the HaRNAV sequence were repeatedly obtained from the same geographic location (Straight of Georgia, British Columbia) over a four-year period, which suggest persistent HaRNAV infection in the area (Culley et al. 2003). The RT-PCR technique is expected to be a sensitive tool to monitor the dynamics of RNA viruses in marine environments in future studies.


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Viruses Infecting Marine Dinophytes (Dinoflagellates) Heterocapsa circularisquama Horiguchi sp. nov. is a small thecate dinoflagellate (Fig. 6.2 A.B: 20–29 mm in length, 14–20 mm in width), which was recently discovered from Ago Bay, central Japan (Horiguchi 1995). Since this dinoflagellate was recorded for the first time in Uranouchi Bay located in the western part of Japan in 1988, the distribution area has ex-

2m

2m 200

50

Fig. 6.2 Heterocapsa circularisquama and its viruses, HcV and HcRNAV. (A) An optical micrograph of vegetative cells of H. circularisquama (ca. 20 mm in length), (B) a transmission electron micrograph of an intact H. circularisquama cell, (C) a HcV-infected H. circularisquama cell, (D) a thin section of HcV (ca. 0.2 mm in diameter), (E) a crystalline array of HcRNAV within a H. circularisquama cytoplasm, and (F) negatively stained HcRNAV particles. (B: reproduced from Tarutani et al. (2001) with permission of the publisher, E and F: reproduced from Tomaru et al. (2004a) with publisher’s permission).

196 Algal Cultures, Analogues of Blooms and Applications panded rapidly into embayments throughout central and western Japan (Matsuyama 1999). With this distribution expansion, this species has often formed large-scale red tides and caused mass mortality of bivalves such as pearl oysters Pinctada fucata, oysters Crassostrea gigas, and shortnecked clams Tapes philippinarum (Matsuyama et al. 1996, Nagai et al. 1996, Matsuyama 1999). The most notable case occurred in 1998 when a large bloom resulted in severe financial losses of approximately 4 billion yen to the oyster culture industry in Hiroshima Bay (Matsuyama 1999). There are two viruses infecting H. circularisquama that have been reported so far; H. circularisquama virus (HcV) and H. circularisquama RNA virus (HcRNAV). Both viruses can affect natural H. circularisquama blooms (Tomaru and Nagasaki 2004).

HcV HcV was first isolated from the western coast of Japan. It is icosahedral, lacking tail, ca. 197 nm in diameter, and contains an electron dense core of dsDNA (Fig. 6.2 C, D) (Tarutani et al. 2001). Nagasaki et al. (2003) reported that HcV was widely infectious to H. circularisquama strains isolated in the western part of Japan, i.e., algal lysis occurred in 94.7 % of host-virus combination between H. circularisquama clones and HcV clones (n = 530). Considering the morphological features, replication site, genome size (Nagasaki et al. 2005a), and host range, HcV is also a member of the family Phycodnaviridae. However, determination of any phylogenetic relationships of HcV requires further genomic analysis of its DNA. As HcV isolation was followed by HcRNAV infecting the same host alga (see below), its nomenclature should be reconsidered (e.g. HcDNAV = H. circularisquama DNA virus).

HcRNAV HcRNAV was isolated from coastal waters of Japan (Tomaru et al. 2004a). Its infection is highly strain-specific; i.e., HcRNAV strains can be divided into two types having complementary host ranges. Typical strains of each type, HcRNAV34 and HcRNAV109, were icosahedral, ca. 30 nm in diameter, (Fig. 6.2 E)and harbored a single molecule of ssRNA approximately 4.4 kb in size. Thus, in morphology, nucleic acid type, and genome size, HcRNAV is apparently distinct from HcV. Virus particles appeared in the cytoplasm of the host cells within 24 h post infection, and crystalline arrays (Fig. 6.2 F) or unordered aggregations of virus particles were observed. The burst size and latent period were estimated at 3.4 ¥ 103 to 2.1 ¥ 104 infectious particles cell–1and


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24–48 h, respectively. Based on the analysis on nucleotide sequence of genomic RNA, HcRNAV34 and HcRNAV109 were highly similar to each other (similarity = ca. 97 %), and in addition, had some similarities to plant viruses belonging to genera Sobemovirus and Luteovirus. The genome sequence predicts two open reading frames (ORFs): ORF-1 encoding a polyprotein that contains putative RNA-dependent RNA polymerase region, and ORF-2 coding for a single major structural protein of HcRNAV ca. 38 kDa in molecular weight. Comparative studies of HcRNAV sequence predict that a highly distinct region in amino acid sequence within ORF-2 may be responsible for determining their complementary strain-specificities (Nagasaki et al. unpublished data). This host-virus system would be promising material to study the mechanism of specificity in virus infection. Nagasaki et al. (2004a) examined the possible relationship between H. circularisquama and HcRNAV through a field survey in Ago Bay, Japan, in 2001, when a H. circularisquama bloom occurred in July. The bloom peaked in mid July and disintegrated during a few days at the end of July; then, the abundance of viruses infectious to H. circularisquama was high from the peak of the bloom and throughout in the post-bloom period, but became negligible by the end of August. At the peak of the bloom, 88% of the H. circularisquama cells in the population harbored small virus-like particles (VLPs). Based on transmission electron microscopic observations, a morphological resemblance between these VLPs and HcRNAV isolated from the bloom was noticeable. The fluctuation patterns of the viruses indicated that there coexisted at least two distinct types of virus having different host specificity spectra, which agreed with the results of laboratory experiments on HcRNAV types mentioned above (Tomaru et al. 2004a). Specific increase in viral abundance in the sediments was also observed in the middle of the bloom, and they were likely able to maintain their infectivity for more than three months. These results support the idea that viruses have a significant impact on the biomass and clonal composition of algal populations in the natural environment given by Lawrence et al. (2002).

Viruses Infecting Marine Prymnesiophytes Emiliania huxleyi virus (EhV) Emiliania huxleyi (Lohmann) Hay and Mohler (Prymnesiophyceae) is a bloom-forming coccolithophore, which is well known as a key phytoplankton species for the current studies on global biogeochemical cycles (Westbroek et al. 1994). Bratbak et al. (1993) first found VLPs in the cytoplasm of E. huxleyi, and pointed out the possibility that viral infection is an important factor in

198 Algal Cultures, Analogues of Blooms and Applications eliminating E. huxleyi blooms because morphologically distinct large VLPs specifically increased following the peak of E. huxleyi blooms. Later, Brussaard et al. (1996) also found the possible infection of two different types of virus in E. huxleyi cells based on electron microscopic observations of natural field samples. Recently, EhV was isolated and characterized by European research groups (Castberg et al. 2002, Wilson et al. 2002). Castberg et al. (2002) reported that EhV is a dsDNA virus with a genome size of ca. 415 kbp. Its latent period and burst size are 12–14 h and 400–1000 viral particles per cell. They also demonstrated EhV should be assigned to the Phycodnaviridae virus family based on its DNA polymerase amino acid sequences. Schroeder et al. (2002) also succeeded in isolating EhV, and demonstrated the diversity among EhV clones by host range analysis and sequence analysis of a gene fragment encoding part of their putative major capsid protein. By using a well-designed denaturing gradient gel electrophoresis (DGGE) of the putative major capsid protein gene fragments amplified by PCR from water samples, Schroeder et al. (2003) demonstrated the succession of EhV clones during an E. huxleyi bloom in a mesocosm experiment, and they found that only a few genotypes of EhV might be responsible for the decay of the E. huxleyi bloom. Combination of virus-specific PCR and DGGE is a promising tool to monitor the change in clonal composition of algal viruses in marine systems.

Viruses infecting the genus Phaeocystis Phaeocystis Pouchetii (Hariot) Lagerheim (Prymnesiophyceae) is a colonyforming microalga with virtually a world-wide distribution often forming massive mucilaginous blooms (Lancelot et al. 1994). P. globosa is also one of the typical causative agents of dense bloom occurrences in temperate coastal waters of the North Sea. They have different life stages of which the single flagellated and colonial nonflagellated cells are the most prominent. There are two viruses infecting the genus Phaeocystis that have been reported so far; P. pouchetii virus (PpV) and P. globosa virus (PgV). PpV, a dsDNA virus infecting P. pouchetii was isolated from Norwegian coastal waters (Jacobsen et al. 1996). It is icosahedral, 130–160 nm in diameter, and its latent period and burst size were estimated at 12–18 h and 350–600 viral particles per lysed cells, respectively (Jacobsen et al. 1996). It has a large dsDNA genome ca. 485 kbp in length, and its partial sequence suggested similarity to Chlorella virus PBCV–1 (Castberg 2001). Intensive studies on PpV’s replication were conducted: Bratback et al. (1998)


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examined the relationship between viral replication and physiological condition of host cultures, and demonstrated that (i) P. pouchetii was susceptible to viral infection in all stages of growth, (ii) condition of the host cells growth did not affect the infectivity of the progeny viruses and the length of the lytic cycle, and (iii) it had a significant impact on the burst size, i.e., high and low burst sizes were found in exponentially growing cultures and stationary phase cultures, respectively. Later, Thyrhaug et al. (2002) revealed that the production and the latent period of PpV were independent of the host’s cell cycle. On the other hand, a novel virus infecting P. globosa (PgV) propagating within its cytoplasm was isolated from Dutch coastal waters and characterized (Brussaard et al. 2004a). Phylogenetic analysis of DNA polymerase gene fragments of PgV clones revealed that they formed a closely related monophyletic group within the family Phycodnaviridae, which grouped most closely with those of viruses infecting Chrysochromulina brevifilum and C. strobilus. Based on these data, Brussaard et al. (2004a) suggested that Chrysochromulina and Phaeocystis viruses share a common ancestor.

Viruses infecting the genus Chrysochromulina Chrysochromulina is a cosmopolitan phytoplankter which can comprise >50% of the photosynthetic nanoplanktonic cells in the ocean, and some species of Chrysochromulina can cause toxic blooms (Estep and MacIntyre 1989). There are two viruses infecting the genus Chrysochromulina that have been reported so far; C. brevifilum virus (CbV) and C. ericina virus (CeV). Suttle and Chan (1995) isolated a virus infecting C. brevifilum. It is large (145–170 nm in diameter), polyhedral, and proliferates itself within the host’s cytoplasm. Considering that this virus is also lytic to C. strobilus, this may be an interesting system to study the mechanism determining its host specificity. In order to examine genetic relatedness among the viruses infecting eucaryotic algae, Chen and Suttle (1995, 1996) compared DNA polymerase gene fragments that were amplified by specific primers, and revealed the close relatedness among Chlorella viruses, C. brevifilum virus, and Micromonas pusilla virus. On the other hand, Sandaa et al. (2001) isolated a virus (CeV) that is infectious to C. ericina, which has a large dsDNA genome (ca. 510 kbp). By means of the one-step growth experiments, they estimated the latent period and burst size of CeV at 14–19 h and 1800–4100 infectious units cell–1, respectively.


Viruses infecting marine prasinophytes Micromonas pusilla (Butcher) Manton et Parke is a small (1.5–3.0 mm) photosynthetic flagellate belonging to Prasinophyceae. It occurs widely in both coastal and oceanic waters ordinarily in low abundances. There are two viruses infecting the cosmopolitan phytoplankter M. pusilla that have been reported so far; M. pusilla virus (MpV) and M. pusilla RNA virus (MpRNAV). Other than these, a virus infecting Pyramimonas orientalis was also isolated and studied (Sandaa et al. 2001).

MpV MpV, a dsDNA virus infecting M. pusilla was the first marine microalgal virus isolated (Mayer and Taylor 1979). It is icosahedral, ca. 115 nm in diameter, lacking tails, and the genome size is ca. 200 kbp (Cottrell and Suttle 1991, Suttle pers. comm.). Considerable variation among MpV clones was demonstrated by restriction fragment analysis, SDS polyacrylamide gel electrophoresis (Cottrell and Suttle 1991), DNA hybridization (Cottrell and Suttle 1995), phylogenetic analysis of DNA polymerase gene fragments (Chen and Suttle 1995), and cross-reactivity tests (Sahlsten 1998). The ecological implication of the diversity among virus clones is of great interest. Waters and Chan (1982) reported that M. pusilla cells can mutate to virus resistance at the cell surface and that MpV is also changeable exhibiting variable infectivity in different strains of M. pusilla. Although its mechanism has not been experimentally clarified yet, mutations can be one of the causes increasing the diversity of MpV and its host. This host virus system is expected as promising material to study the host range mutation events.

MpRNAV A novel dsRNA virus MpRNAV infecting M. pusilla was isolated by Brussaard et al. (2004b), which proliferates itself in the cytoplasm. It is medium sized (65–80 nm in diameter) among algal viruses isolated so far. The dsRNA genome was composed of 11 segments ranging between 0.8 and 5.8 kb, with a total size of approximately 25.5 kb, suggesting some similarity to dsRNA genera Rotavirus and Aquareovirus. MpRNAV is the only algal virus harboring dsRNA genome that has been isolated so far, and the finding of microalgal viruses harboring dsRNA also emphasizes the diversity of the algal-virus pathogens.


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PoV P. orientalis Butcher is a potential bloom-causer whose cell is covered with numerous scales and widely distributed in the world. Sandaa et al. (2001) isolated a virus (PoV) that is infectious to P. orientalis, which has the largest dsDNA genome among algal viruses (ca. 560 kbp). By means of the one-step growth experiments, they estimated the latent period and burst size of PoV at 14–19 h and 800–1000 infectious units cell–1, respectively.

Viruses Infecting Marine Bacillariophytes (Diatoms) A bloom-forming diatom Rhizosolenia setigera belongs to the order Centrales and occurs widely throughout the world: in the North Atlantic Ocean, North Sea, Baltic Sea, English Channel, Mediterranean Sea and Pacific Ocean. In 2002, a ssRNA virus specifically infecting the bloomforming diatom R. setigera (R. setigera RNA virus: RsRNAV) was isolated from the Ariake Sea, Japan (Nagasaki et al. 2004b). Viral replication occurred within the cytoplasm, and the virus particle was icosahedral, lacking a tail, 32 nm in diameter on the average. The major nucleic acid extracted from the RsRNAV particles was a single-stranded RNA (ssRNA) molecule 11.2 kb in length, although smaller RNA molecules (ranging in size of 0.6, 1.2, and 1.5 kb) were occasionally observed. The latent period of RsRNAV was 2 d, and the burst size was 3,100 and 1,010 viruses per host cell when viruses had been inoculated to the host culture at the exponentially growing phase and stationary phase, respectively. More recently, Nagasaki et al. (2005b) isolated the second diatom-infecting virus, CsNIV (Chaetoceros salsugineum nuclear inclusion virus). CsNIV genome consists of a single molecule of covalently closed circular single-stranded DNA (ssDNA, 6005 nt) as well as a segment of linear ssDNA (997 nt). As for the detailed feature of this virus, refer to Nagasaki et al. (2005b).

Viruses Infecting Marine Chrysophyte Aureococcus anophagefferens gen et sp. nov. is a picoplankter ca. 2.5 mm in diameter which causes brown tide (Cosper et al. 1987). BtV is a large (140– 160 nm in diameter) dsDNA virus infecting A. anophagefferens. It proliferates itself within the host’s cytoplasm, and host cells are lysed within 24 h. Considering these features, BtV most likely belongs to the family Phycodnaviridae (Milligan and Cosper 1994, Gastrich et al. 1998). Gastrich et al. (2004) reported that as high as 37.5% of VLP-infected A. anophagefferens

202 Algal Cultures, Analogues of Blooms and Applications cells occurred at the termination of the brown tide bloom in New Jersey in 2002, indicating that viruses may be a major source of mortality for brown tide blooms.

PHYLOGENY OF MICROALGAL VIRUSES In the last decade of the 20th century, more than 10 marine viruses infecting microalgae were isolated. They were all large (> 100 nm in diameter) and harbored dsDNA genome. Before the 1990’s, algal viruses infecting symbiont Chlorella-like alga had been isolated and intensively studied (Van Etten et al. 1991). Van Etten and Ghabrial (1991) established the new virus family Phycodnaviridae and positioned the Chlorella viruses in it. Studies on the other large algal viruses demonstrated that several had properties in common with the Chlorella viruses in terms of the morphology, host range, genome size, and some other characteristics. Chen and Suttle (1995, 1996) examined the genetic relatedness among microalgal viruses by comparing sequences of the DNA polymerase gene fragments amplified by a refined nested-PCR technique. They designed degenerate primers targeting three conserved regions found within the DNA polymerase gene of some microalgal viruses. This technique was applicable to Chlorella viruses, MpV, and CbV, and also able to amplify possible DNA polymerase gene fragments of unknown viruses from natural seawater samples (Chen and Suttle 1995); therefore, the technique has been frequently used as a powerful tool for fingerprinting natural virus communities combined with denaturing gradient gel electrophoresis (Short and Suttle 2000, 2002, 2003). Schroeder et al. (2002) also succeeded in amplifying the DNA polymerase gene fragment of EhV by a similar technique, and examined its phylogenetic relationship with other algal viruses. Now, many algal virologists regard DNA polymerase gene as a key region to discuss the phylogeny of algal viruses, and now four genera (Chlorovirus, Prasinovirus, Prymnesiovirus, and Phaeovirus) are categorized within the family Phycodnaviridae (Van Etten 2000, Van Etten et al. 2002). However, in some algal viruses, amplification of DNA polymerase gene fragments by the degenerate primers was unsuccessful (Chen and Suttle 1995, Castberg 2001, Nagasaki et al. 2005a). In the case of Ectocarpus siliculosus virus (a virus infecting multicellular alga) and HaV, a shotgun cloning technique was used to determine the nucleotide sequence of its genomic DNA, and phylogenetic comparison of DNA polymerase gene was conducted (Delaroque et al. 2001, Nagasaki et al. 2005a). Phylogenic analysis for RNA viruses infecting microalgae will be a future problem.


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FUNDAMENTAL TECHNIQUES TO STUDY MICROALGAL VIRUSES Thanks to algal virologists’ efforts to date, evidence showing that viruses play a key role affecting the dynamics of microalgae in marine systems has been accumulated. To investigate microalgal viruses, it is necessary to make target viruses into laboratory cultures. Thus, isolation, cultivation, and maintenance of microalgal viruses in a suitable condition are important procedures that sustain the progress of algal virology. The flow of necessary procedures is simple: screening, cloning, and maintenance, and the techniques used in each procedure are described in this section. Also refer to some papers describing methods for microalgal virus isolation (Suttle et al. 1990, Suttle 1993, Nagasaki 2001).

Preparations of Algal Host Cultures Needless to say, preparation of host microalgal cultures is essential to isolate microalgal viruses. For isolation experiments, it is preferable to use unialgal and axenic cultures as hosts for making estimation of viral effect easy and clear. To establish axenic microalgal cultures, a micropipetting method or the other washing methods are commonly employed, often using some antibiotics (Connell and Cattolico 1996). Also refer methods given by Lee (1993). A simple technique proposed by Imai and Yamaguchi (1994) is recommended for washing phytoflagellates. It is important to use multiple clones of host alga for virus isolation, because virus sensitivity spectra can differ among host clones (Nagasaki et al. 2000, Tomaru et al. 2004a). It is also preferable to keep host algae under optimal conditions for their growth. Generally, exponentially growing cultures tend to be more sensitive to viral attacks than stationary phase cultures (Nagasaki et al. 2003). It is presumably because viruses utilize the biosynthetic function of hosts such as DNA synthesis and protein synthesis, and host cells in the vigorously growing phase have a higher biosynthesis activity.

Screening of Microalgal Viruses Microalgal viruses are included not only in seawaters but also in marine sediments. Recent studies suggest sediments are also a reservoir of viruses (Lawrence et al. 2002, Nagasaki et al. 2004a). By shaking sediments with a suitable medium (e.g. SWM3, Chen et al. 1969), viruses can be easily

204 Algal Cultures, Analogues of Blooms and Applications suspended, and low-speed centrifugation separates the virus suspension from the sediment debris. Prior to inoculation of the virus suspension to host cultures, aggregation of bacteria and larger organisms (zooplankton, phytoplankton, nanoflagellates, fungi, etc.) should be removed by filtration. Considering the size of microalgal viruses (25–220 nm in diameter), 0.2 mm, 0.22 mm or 0.45 mm nominal pore-size polycarbonate membrane filter (Nuclepore) is recommended. Then, the resultant filtrate (viral fraction) is added to vigorously growing potential host cultures. Growth of hosts is traced and compared to that of control cultures without virus inoculation. To isolate microalgal viruses from natural seawater, two interesting methods have been proposed: the concentration method and ultraviolet (UV)-treatment. In the former method, the virus-size fraction is concentrated using a specialized ultrafiltration column (Suttle et al. 1990). Some viruses have been successfully isolated by this ultrafiltration technique: viruses infecting M. pusilla (Cottrell and Suttle 1991), A. anophagefferens (Milligan and Cosper 1994), C. brevifilum (Suttle and Chan 1995), H. akashiwo (Nagasaki and Yamaguchi 1998), HaNIV (Lawrence et al. 2001), and HaRNAV (Tai et al. 2003). A detailed protocol of the ultrafiltration technique is given by Suttle (1993). The UV-treatment technique is intended to induce possibly latent viruses in natural algal cells, but its actual mechanism has not been verified yet. Briefly, natural seawater containing the target phytoplankton species is collected and centrifuged to obtain a cell concentrate which is then poured into a petri dish, and exposed to UV-irradiation for an appropriate time (Jacobsen et al. 1996). Next, the algal cell suspension is incubated under appropriate condition (Jacobsen et al. 1996), and the resultant cell debris is removed by centrifugation before inoculation into a fresh host culture. By using this method, EhV (Bratbak et al. 1996) and PpV (Jacobsen et al. 1996) were successfully isolated. On the other hand, Brussaard et al. (2004a) reported that incubation of natural seawater for a week at in situ temperatures promoted the isolation of viruses infecting P. globosa. This procedure may enhance the maturation of viruses in the natural seawater samples.

Cloning and Maintenance of Microalgal Viruses Following the procedures given above, when a decay of the tested host algal cultures is detected, cloning of the algicidal factor should be tried as soon as possible. In many cases, the extinction dilution method is used for cloning microalgal viruses (Suttle 1993). In the author’s group, two cycles of the


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extinction dilution procedure are often used for cloning viral pathogens; briefly, the culture lysate is diluted with modified SWM3 medium in a series of 10-fold dilution steps. Aliquots (100 ml) of each dilution are added to 8 wells in cell-culture plates with 96 round bottom wells, mixed with 150 ml of exponentially growing host culture, and incubated under the conditions suitable for the host’s growth. Lysed cultures are removed from the most diluted wells in which lysis occurred and the above entire procedure is repeated. The lysate in the most diluted wells of the second assay is sterilized by filtration through 0.1 mm or 0.2 mm pore size polycarbonate membrane filters and transferred into an exponentially growing host culture. After removing cell debris from the resulting lysate by low-speed centrifugation, the supernatant is used as the clonal pathogen suspension. In many cases, microalgal viruses are maintained by repetitious transfer of the viral lysate to fresh host cultures. However, it should be noted that it might cause a loss of infectivity, which is presumably caused by the increase of defective interfering particles (Bratbak et al. 1996). Microalgal viruses are diverse in terms of stability. For example, the titer of PpV, HcV, and HaV gradually decreases even during storage at 4°C in the dark. Thus, the establishment of a suitable method to keep the infectivity of each virus is necessary. For example, conditions for cryopreserving HaV are the addition of 10–20% dimethyl sulfoxide and storage at –196°C (Nagasaki and Yamaguchi 1999), and those for PpV are the addition of 10–20% sucrose and storage at –70°C (Nagasaki 2001).

Enumeration of Microalgal Viruses To examine the basic characteristics of microalgal viruses in terms of their replication ability, techniques for counting viruses are essential. Currently, several methods are used for enumerating viruses: plaque assays, most probable number (MPN) assays, transmission electron microscopy (TEM), epifluorescence microscopy (FM), and flow cytometry (FCM). According to the experimental objectives, the most suitable method should be selected and employed, because the results of each method can be interpreted differently. The abundance of viruses that are infectious and lytic to the algal strain used in the measurement can be quantified by means of plaque assays and MPN assays. Plaque assays are available only when the potential host alga grows on solid phase medium to form a lawn (e.g. M. pusilla, E. huxleyi). However, given the counting results of the plaque assay method for MpV, it appears less efficient than the other methods employing liquid media (Cottrell and Suttle 1995). Moreover, cultivation of marine algal flagellates on solid phase

206 Algal Cultures, Analogues of Blooms and Applications medium is generally difficult (Nagasaki and Imai 1994). In contrast, TEM gives the abundance of ‘particles that are virus-like in appearance’ and FM gives that of ‘particles that harbor nucleic acids stainable with the dye (DAPI, SYBR GREEN I, etc.)’ used in the measurement. Refer to the detailed protocols given by Suttle (1993) and Wilhelm and Poorvin (2001). The recent rise of accessibility to FCM has eased the labor requirement for counting fluorescence-labeled large DNA virus particles, and has significantly improved the speed of analysis and accuracy of counting (Marie et al. 1999, Brussaard et al. 2000). Refer to the conditions reported by Brussaard (2004b) who conducted a detailed optimization of procedures for counting viruses by FCM. FCM is also applicable to study phytoplankton viability following viral infection by specifically staining using a membrane impermeant nucleic acid dye such as SYTOX-Green (Brussaard et al. 2001). Estimation of the latent period and the burst size by means of the one-step growth experiment is an essential process in the characterization of microalgal viruses. Then, the choice of enumeration techniques for viruses is an important point determining the implication of resultant data. When a plaque assay or an MPN assay is used for viral enumeration, the resultant burst size indicates the number of infective centers (an infectious virus particle and its aggregation) that are lytic to the host strain per host cell. In contrast, when TEM, FM, or FCM are used for viral enumeration, the resultant burst size indicates the total number of virus particles harboring its genomic nucleic acids; thus, it may also include defective particles (virus particles that lack infectivity). Currently, both methods are employed to enumerate burst size of microalgal viruses; hence, the results are shown as either “infectious units per infected cell” or “particles per infected cell”. In the case of ssRNA microalgal virus HcRNAV, as it was difficult to detect and count by FM even if they are stained with an appropriate dye (SYBR GREEN II or SYBR GOLD), the MPN assay was used for its enumeration (Tomaru et al. 2004a).

FUTURE STUDIES FOR MICROALGAL VIRUSES So far, more than 14 microalgal viruses have been isolated and characterized, and at present, researchers’ likely interest centers on their ecological roles, molecular aspects, genomes, etc. Indeed this field of study is still young and lots of insights into the marine ecosystem have been revealed year by year, it is necessary to consider in which industrial field and how we can apply and utilize the data in future. In the last section, a few examples of studies on possible use of microalgal viruses are introduced.


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Possible Use of Viruses to Control HABs As mentioned above, recent studies revealed that viral infection is one of the most significant factors controlling the dynamics of algal blooms and especially causing their termination. These observations led to an idea for the possible use of viruses to control HABs. It is a concept of a biotic pesticide to control a harmful organism using its natural enemy based on the relationship between the host and the infecting microorganism. In the field of agriculture, some biological agents have already been used practically, and utilization of natural organisms to control pests has been widely studied. By promoting the studies on microalgal viruses, the possibility of using a natural organism to control a pest in the marine field can be assessed. The possibility of their use in preventing outbreaks of HABs and problems involved in the challenge are summarized as follows; 1) Basically, microalgal viruses are regarded as the most specifically infectious agents to the HAB-causing microalgae. Their infection to organisms other than their hosts has not been reported yet (except for the case of CbV that is infectious to both C. brevifilum and C. strobilus), 2) In many cases, they originated from the same place as their host algae, i.e., host-virus systems existing in nature. They were derived from natural seawater and have not been subjected to any genetic manipulation, 3) They have considerably high replication abilities, and are able to replicate as long as their hosts exist in the environment, 4) Production of microalgal viruses does not require any special equipment or expensive reagents. Since microalgal viruses do exist in natural waters and are involved in the natural ecosystems, it appears reasonable to utilize these ‘natural antiHAB factors’ to reduce damage to fisheries. In the field of studies of viruses infecting cyanophages, several attempts have over the years been made to control cyanobacterial blooms, but with rather limited success (Martin and Benson 1988). The predictable problem is that, in many cases, viral infectivity is normally ‘strain-specific’ rather than ‘species-specific’; in other words, a single clone of microalgal virus cannot eliminate the whole host algal population which is composed of different types of clones in terms of virus sensitivity spectra. However, in the case of studies on HaV, it seems most types of virus clones can be collected by using multiple types of host clones (Tomaru et al. 2004b). As we all know, this applied biological science is in its infant stage, and it is necessary to accumulate fundamental information on various host-virus systems to assess the possibility of this new technique. It is also essential to give enough explanation on viruses’ safety for obtaining the public acceptance.


Source of Novel Enzymes Algal viruses are also expected as a source of various novel enzymes (Yamada et al. 1999). Recently, the 331 kbp dsDNA genome of Chlorella virus (PBCV-1) and the 336 kb dsDNA genome of E. siliculosus virus (EsV-1) were sequenced, and 231 and 375 possible protein-encoding genes were found, respectively (Van Etten et al. 2002). They contain a variety of enzymes involved in the metabolism of polysaccharides, amino acids, lipids, nucleotides and nucleosides. Some of the viral enzymes have already been successfully utilized in biotechnological fields; e.g., Chlorella virus endonucleases and methylases having specific restriction sites. Yamada et al. (1999) suggested the possible applications of viral chitinase and chitosanase in the recycling of natural resources. Further studies on the genome of microalgal viruses would lead to more interest in their novel enzymes.

ACKNOWLEDGMENTS Works on HaV, HcV, HcRNAV, CsNIV, and RsRNAV were supported by funding from the Fisheries Agency of Japan, the Ministry of Agriculture, Forestry and Fisheries of Japan, the Japan Science and Technology Corporation, the Industrial Technology Research Grant Program in 2000– 2004 from the New Energy and Industrial Technology Development Organization (NEDO) of Japan, and the Society for Techno-Innovation of Agriculture, Forestry and Fisheries (STAFF). I am grateful to Dr. K. Tarutani for his critical reading of the manuscript and useful suggestions.

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Tomaru, Y. and K. Nagasaki. 2004c. Widespread occurrence of viruses lytic to the bivalve-killing dinoflagellate Heterocapsa circularisquama in the western coast of Japan. Plankton Biol. Ecol. 51, 1-6. Van Etten, J.L. and S.A. Ghabrial. 1991. Phycodnaviridae. pp. 137-139. In R.I.B. Francki, C.M. Fauguet, D.L. Knudson, and F. Brown [eds.] Classification and Nomenclature of Viruses. Arch. Virol. Supple. 2, Springer-Verlag, Vienna, Austria. Van Etten, J.L., L.C. Lane and R.H. Meints. 1991. Viruses and viruslike particles of eukaryotic algae. Microb. Rev. 55: 586-620. Van Etten, J.L. 2000. Phycodnaviridae. pp. 183-193. In M.H.V. Van Regenmortel, C.M. Fauquet, D.H.L. Bishop, E.B. Carsten, M.K. Estes, S.M. Lemon, J. Maniloff, M.A. Mayo, D.J. McGeoch, C.R. Pringle, and R.B. Wickner [eds.] Virus Taxonomy, Classification and Nomenclature of Viruses, Seventh Report. Academic Press, San Diego, USA. Van Etten, J.L., M.V. Graves, D.G. Müller, W. Boland and N. Delaroque. 2002. Phycodnaviridae - large DNA algal viruses. Arch. Virol. 147: 1479-1516. Waters, R.E. and A.T. Chan. 1982. Micromonas pusilla virus: the virus growth cycle and associated physiological events within the host cells; host range mutation. J. Gen. Virol. 63: 199-206. Westbroek, P., J.E. van Hinte, G.J. Brummer, M. Veldhuis, C. Brownlee, J.C. Green, R. Harris and B.R. Heimdal. 1994. Emiliania huxleyi as a key to biosphere-geosphere interactions. pp. 321-334. In J.C. Green and B.S.C. Leaderbeater [eds.] The Haptophyte Algae, vol 51. Clarendon Press, Oxford, USA. Wilhelm, S.W. and L. Poorvin. 2001. Quantification of algal viruses in marine samples. Met. Microbiol. 30: 53-65. Wilson, W.H., G.A. Tarran, D. Schroeder, M. Cox, J. Oke and G. Malin. 2002. Isolation of viruses responsible for the demise of an Emiliania huxleyi bloom in the English Channel. J. Mar. Biol. Assoc. UK. 82: 369-377 Wommack, K.E. and R.R. Colwell. 2000. Virioplankton: Viruses in aquatic ecosystems. Microbiol. Mol. Biol. Rev. 64: 69-114. Yamada, T., N. Chuchird, T. Kawasaki, K. Nishida and S. Hiramatsu. 1999. Chlorella viruses as a source of novel enzymes. J. Biosci. Bioeng. 88: 353-361. Zingone, A. 1995. The role of viruses in the dynamics of phytoplankton blooms. Giorn. Bot. It. 129: 415-423.

% Autecology of Bloom-Forming Microalgae: Extrapolation of Laboratory Results to Field Populations and the Redfield-Braarud Debate Revisited Theodore J. Smayda1 1

Graduate School of Oceanography, University of Rhode Island, Kingston, RI 02881, USA

Abstract The relevance and limits of autecological experiments extrapolated to in situ phytoplankton behavior are evaluated from the perspective of the unresolved Redfield-Braarud debate. Redfield discouraged autecological experiments, being skeptical of their ecological relevance. He argued that field-descriptions of natural populations are incomplete and compromise experimental design. Braarud dismissed this view, arguing that autecological experiments were not premature, but essential to quantify in situ phytoplankton behavior. In revisiting their debate, frequent reference is made to harmful algal bloom dynamics, a phenomenon that is highly species-specific, and the current focus of much experimentation. Some major issues dealt with include: the influence of early experimentation and new discovery in molding the opposing views of Redfield and Braarud, and the advances made in these areas since their debate; the conceptual differences between autecological and synecological approaches to phytoplankton ecology, and their respective experimental merits and limitations to quantify natural behavior; the three modes of phytoplankton growth (cellular, population, community growth); the two types of nutrient limitation (yield and physiological limitation); the various nutrient limitation models, and experiments examining the nutrient affinity, resource and ratio theories of phytoplankton species selection and competition. The nutrient issues are discussed as part of a general evaluation whether in situ nutrient limitation occurs and, if so, whether it is single nutrient limitation, as posited by Liebig’s Law of the Minimum and Droop’s model. Experiments with clones, on the

216 Algal Cultures, Analogues of Blooms and Applications suite of responses to nutrients, and on growth rates are used as tests of fidelity of in situ extrapolation of autecological experimental results. It is suggested that the classical definition of the phytoplankton needs to be modified for conceptual and experimental design purposes. Significant differences in ecophysiology distinguish flagellates (swim strategists) from diatoms (sink strategists). The ecological behavior of diatoms is in greater accord with the classical definition of the phytoplankton, but is an inadequate template when applied to flagellate ecology and experimental design. Conflation of the definitions of primary production and phytoplankton ecology also presents significant conceptual and methodological problems affecting experimentation. Methodological and ecophysiological impediments to ecologically relevant experiments include: the occurrence of physiological strains (clones); difficulties in simulating in situ nutrient and grazing conditions; experimental enclosure dampening dispersion and vertical microstructure; substitution of static, batch culture conditions for the more dynamic in situ nutrient recycling mode captured in continuous culture experiments. Incubation artifacts always compromise ecological extrapolation of experimental results. Laboratory-based experiments intrinsically are inadequate to explain in situ behavior: some major ecological behavior and processes cannot be addressed adequately, either in ‘bottle experiments’ or in large enclosures such as mesocosms. In situ experimentation also presents formidable practical and analytical difficulties, has problems of ecological fidelity, and its efficacy is unconvincing. Considerable evidence, including mass balance calculations and extrapolation, supports Braarud’s view that autecological experiments on species ecophysiology provide an ecological ‘orientation’ that is not available from field measurements, but are essential to quantify in situ phytoplankton behavior and oceanic biogeochemical issues. Multiple evidence supports Redfield’s countering argument that laboratory-based and in situ experimentation are inadequate to explain in situ ecological behavior. Despite this apparent contradiction, it is argued that Redfield and Braarud’s opposing views are complementary rather than exclusive, and reflect their focus on different aspects and experimental needs of phytoplankton ecology. This does not negate that ecologically relevant experiments for some major features of in situ behavior may be beyond reach, whether based on laboratory experiments or in situ manipulations, and whether examined as ‘top-down’ or ‘bottom-up’ effects. Phytoplankton experimentation can avoid some of Redfield’s concerns over the adequacy of ecological experiments and take fuller value from Braarud’s advocated approach by applying a functional group, or phylogenetic, experimental strategy to in situ behavior. Functional group and phylogenetic experimentation should combine autecological and synecological concepts, field and laboratory approaches, and use advanced technology for measurements of in situ molecular and biochemical properties diagnostic of physiological status, and for description of habitat growth conditions and micro-structure. Other guidelines in following this new experimental approach to phytoplankton ecology are also given.

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INTRODUCTION The Redfield-Braarud Debate Four decades ago, Alfred Redfield, of Redfield Ratio fame (Redfield 1934, 1958), and Trygve Braarud, a phytoplankton ecologist recognized (Mills 1989) “as one of the pioneers of quantitative biological oceanography”, debated the relevance of experimentation to biological oceanography (Redfield 1960, Braarud 1961). Redfield’s perspective (p. 22 in Redfield 1960) was that marine biology (his term) is “primarily ecological [with the] objective to develop effective models, and ultimately a single, consistent model, of life in the sea”. He stated that these models should be based on a combination of field observations, experiments and inference. The first task in this effort, Redfield advised, was to acquire representative descriptions of the in situ behavior of natural populations because building ‘effective models’ requires substantial knowledge of its prototype. Knowledge of in situ behavior is needed also to design ecologically relevant experiments. And since new discovery of in situ behavior and habitat features can greatly alter general concepts, this also influences experimental design. Redfield concluded that current (i.e. then) knowledge was very incomplete – “a tremendous range of phenomena still awaits simple observation” – presenting a major stumbling block to the design of ecologically relevant experiments and model building. This diagnosis led to Redfield’s (1960) opinion highlighted in the title of his essay “the inadequacy of experiment in marine biology”. Redfield was not averse to experimentation; he believed “marine biology ... can profit from the experimental approach in defining its problems and in elaborating the details of its solutions”. But, he was skeptical that ecologically relevant experimentation was within reach. He cautioned that experiments usually are unnatural because they often are designed to give yes - no answers, or seek to quantify behavior in response to only one or two of the variables relevant to the process being examined. The other variables supposedly are then kept under experimental control or assigned neutral effects. Redfield (p. 18 in 1960) expressed a skeptical view of experiments: they are “essentially observations made under conditions rigged so as to answer particular questions”. This intrinsic bias, Redfield added, is aggravated by the incomplete field-based descriptions of in situ behavior which obstruct the proper design of ecologically relevant experiments. Redfield then advanced his startling conclusion: ecological extrapolations of laboratory results to field populations were inadequate, could even mislead, and should be discontinued. He suggested (p. 21 in 1960) “the greater need at the moment is more knowledge of the phenomenon to be explained”.

218 Algal Cultures, Analogues of Blooms and Applications Braarud (1961), in response to Redfield, countered that autecological (= species-specific) experiments were neither premature nor invalid, but essential to quantify in situ phytoplankton dynamics. Braarud (1962) asserted that the inadequate understanding of phytoplankton distribution patterns was “mainly due to the deplorably small advances which have been made in [their] autecology”. Braarud based his opposing view on his extensive field experience and single-species laboratory experiments that he carried out to assess environmental influences on in situ behavior. He even discussed problems in phytoplankton ecology that required experimentation for their resolution (Braarud 1961). Notwithstanding his advocacy of experimentation, Braarud acknowledged the ecological limitations of single factor experiments, particularly the problems of extrapolating laboratory results with monospecific cultures to field behavior. He puzzled over the discrepancies found between the in situ behavior of species in response to temperature and that expected from their laboratory growth (Karentz and Smayda 1984), and he emphasized the need for factor-interaction experiments. Although Braarud shared some of Redfield’s concerns over the inadequacy of experiments, he touted their value in providing ecological perspectives (= ‘orientation’) not revealed by field investigation. Braarud concluded (p. 294 in 1961), at least with regard to the phytoplankton, that “Dr. Redfield’s warning against too one-sided an experimental approach does not apply at all”. Redfield did not focus on the phytoplankton in challenging the adequacy of ecological experiments to explain in situ behavior; his approach was more general. But the thrust of his arguments and the experimental methods and results then available suggest that he would not have been dissuaded by Braarud’s opposing view, to which he apparently did not respond. Since Redfield and Braarud were experimentalists well versed in oceanographic principles and plankton ecology, their divergent views warrant serious consideration. Revisiting their discourse is further desirable for several other reasons. Their conflicting views were not reconciled then, nor have been rigorously assessed since; there has been a huge increase in laboratory and field-based experimentation based on more sophisticated methods and ecological insights; major discoveries of overlooked trophic communities and processes have been made; and possibly an antiquated trophic definition of the phytoplankton currently is being applied which compromises experimental approaches to in situ phytoplankton bloom and other behavior. This chapter evaluates the issue of the reliability and the limits of laboratory-based experiments, where extrapolated to in situ phytoplankton behavior, from the perspective of the Redfield-Braarud discourse. There will


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be frequent reference to harmful algal bloom dynamics, a phenomenon that is highly species-specific and currently the focus of considerable laboratorybased autecological experimentation.

SOME ELEMENTS OF THE REDFIELD-BRAARUD DISCOURSE New Discovery Redfield’s anticipated discovery of unknown biotic groups and trophic behavior that would lead to more representative ecological experiments was very prescient. Major discoveries made since include: the abundant and ubiquitous picophytoplankton (prochlorophyte/synechococcoid) community (Johnson and Sieburth 1979, Waterbury et al. 1979); discovery of the microbial loop (Azam et al. 1983, Laybourn-Parry and Parry 2000) and thin-layer communities (Rines et al. 2002); and recognition of Fe-limited phytoplankton growth in regions previously thought to be N-limited or free of nutrient limitation (Martin et al. 1991, Kolber et al. 1994, Behrenfeld et al. 1996, Boyd et al. 2000, Tsuda et al. 2003). These revelations, neither predicted, nor anticipated from models and experimental data, have bolstered Redfield’s argument that incomplete qualitative descriptions of in situ population behavior compromise experimental design, data extrapolation, and concept development. Redfield’s call to anticipate unrecognized phenomena has been reiterated (Parsons 1985, Longhurst 1998). Parsons gives special importance to this, contending (p. 110 in Parsons 1985) that “it is the anomaly that has been the true driving force for new science and not hypothesis testing and rigorous statistics”. The unexpected discovery that Fe may be limiting in erstwhile N-limited regions (Martin et al. 1991) has stimulated intense field experimentation (Kolber et al. 1994, Behrenfeld et al. 1996, Boyd et al. 2000, Tsuda et al. 2003), consistent with Parsons’ view. A current phenomenon falling into the category of ‘new discovery’ is the global spreading of harmful algal blooms (HABs), which include blooms of cryptic, novel and newly discovered species (Smayda 1990). This phenomenon no longer can be considered an outbreak of rogue blooms having minor trophic significance. Their increased frequency, global behavior, and adverse trophic consequences are an emergent biotic event driven by unknown stimuli. I share Redfield’s belief that a prerequisite to incorporating new in situ discovery into experimental approaches is first to establish, through comparative ecological investigation, the spatial and temporal distribution and abundance of the biotic component(s) newly

220 Algal Cultures, Analogues of Blooms and Applications revealed. Since field descriptions of HAB behavior are still in this ‘information-gathering’ phase, continuing, comparative field study of this phenomenon is needed to design appropriate ecological experiments. The important feature of unexpected biotic discoveries relevant to the RedfieldBraarud discourse is not taxonomic discovery, but awareness of the presence, the functional group significance, and trophic role of the new species being discovered.

Experimental Techniques Culture media Redfield and Braarud obviously could not consider experimental results obtained subsequently using better phytoplankton culturing media and improved methodology, including chemostats and mesocosms. Inadequate culturing media, techniques, and experimental dependence on ‘weed’ species were a major, early experimental stumbling block that precluded more exacting experiments on ecologically relevant species and processes. Early investigators distinguished between ‘good’ vs. ‘bad’ sea water after establishing that sea water varies seasonally and regionally in its capacity to support phytoplankton growth, even when emended with the essential macro-nutrients nitrogen and phosphorus (Provasoli et al. 1957). Given that natural sea water is an unreliable basal medium, many investigators relied on chemically undefined, soil extract (Erdschreiber) enrichments of sea water to rear cultures (Schreiber 1927, Sweeney 1951), but continued the long-term quest begun already in the early 1900s (Allen and Nelson 1910, Allen 1914) to develop artificial media for more reliable cultivation of representative species. Redfield, himself, contributed to this development (Ketchum and Redfield 1938). Just prior to the Redfield-Braarud debate, Provasoli et al. (1957) made a major advance with their artifical media formulations which contained essential vitamins and chelating agents to control micro-nutrient availability, such as Fe and Mn. This breakthrough, Guillard’s equally famous ‘medium f/2’ (Guillard and Ryther 1962) and other media (Morel et al. 1979, Berges et al. 2001) have greatly stimulated ecological experimentation on representative phytoplankton species and cellular processes. Nonetheless, and despite numerous attempts, some species still resist cultivation, such as autotrophic species in the dinoflagellate genus Dinophysis which cause diarrhetic shellfish poisoning (Maestrini 1998). A Dinophysis species was last brought successfully into sustained culture (though briefly) 70 years ago (Barker 1935). And, only relatively recently the fastidious N-fixing cyanobacterial bloom species


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Trichodesmium erythraeum and T. thiebautii have been established in culture (Ohki et al. 1986).

Static vs. Pulsed Experiments as in situ Analogues Despite the advances in culture media formulations, unresolved experimental impediments and issues relevant to the Redfield-Braarud debate remain. Laboratory experiments have the methodological problem of simulating the natural in situ pulsing of cellular and population growth factors that occurs. The half-life of cellular exposure to a fixed level of irradiance, nutrients and grazer abundance, for example, is very brief. Exposure and access to a given nutrient or irradiance level can last only seconds-to-minutes, while grazing intensity varies hourly or less. In contrast, the short-term fluctuations in growth factors such as temperature and salinity experienced by cells are relatively insignificant. The in situ flux of nutrients from physical and biological (remineralization) processes, essential to phytoplankton growth, is particularly difficult to reproduce experimentally. The standard practice of using ‘batch’ culture techniques in which a fixed amount of nutrient is added, and then decreases with growth, precludes this dynamic. Another common, but unnatural experimental procedure is to expose cultures to a constant irradiance level and photoperiod. This deprives the cells of a natural (= sinusoidal) diel irradiance cycle and, within this, exposure to in situ variations in irradiance induced by water column mixing (Denman and Gargett 1983). Brzezinski and Nelson (1988b) have shown that different rates of nutrient pulsing in combination with photoperiod variations affect the outcome in diatom competition experiments. Batch culture experiments at the time of Redfield and Braarud, and since, have been the primary source of ecological data available for in situ extrapolation. A major shortcoming of the batch culture approach is that it presents a static, progressively deteriorating experimental environment for growth. It can be difficult to determine whether the observed, averaged behavior is primarily a response to stress or is ecologically valid. Chemostats, i.e. continuous culture, in contrast, allow growth to be assessed in response to nutrients fluxed at fixed supply rates that can be varied from experiment-to-experiment to obtain responses to a suite of flux rates. Chemostats have provided very useful data on nutrient uptake kinetics, experimentally the best quantified of the processes that regulate in situ phytoplankton growth (Droop 1974, Conway and Harrison 1977, Harrison et al. 1976, Pan et al. 1996b, among many others). Semi-continuous culturing techniques, a hybrid of the ‘batch’ and ‘chemostat’ approaches, to mimic in situ dynamics (Paasche 1975, Tilman and Kilham 1976, Takahashi

222 Algal Cultures, Analogues of Blooms and Applications and Fukazawa 1982, among others), and in situ incubations of phytoplankton enclosed within permeable dialysis sacs (Sakshaug and Jensen 1978) have been used less frequently. A problem that experimentalists confront is whether a ‘batch’ vs. ‘chemostat’ type experiment is more suitable to examine the behavior of interest. Nutrient uptake rates, the effects of nutrient ratios on species selection, and species competition experiments are better approached, i.e. have greater ecological value, using continuous culture than batch culture methodology. Batch culture technique is probably more suitable for cellular-level studies of a species’ life history; to establish its essential nutrient requirements and ecological minima, maxima and optima for irradiance, temperature and salinity, i.e. the ecological limits of its tolerance to those parameters; and its suitability as prey. However, since in situ population dynamics are driven by multiple, interactive growth factors within a macro- and micro-habitat structure operating in pulsed rather than static mode, the extrapolation of batch culture behavior to natural populations is compromised, particularly if the species’ autecological profile is based primarily on monospecific experimentation. Some investigators have employed both experimental approaches to confirm a result, or to examine the process from different perspectives (Pan et al. 1996a, b). Standardized techniques for ecological experimentation on phytoplankton do not exist, nor should be expected. The complexities and regulation of the phytoplankton life mode, and the diverse adaptations of species in response to pelagic survival in basically a nutritionally dilute medium and regionally variable ocean (Longhurst 1998) preclude standardized approaches. The remarkable buoyancy-regulated, nutrient gathering migrations of large diatoms in oligotrophic seas is one expression of the complex ecology and adaptive capacity of the phytoplankton (Villareal et al. 1996). Other nutrient-inspired behavior includes the nutrient-gathering migrations of dinoflagellates (Smayda 1997a) and diatom sinking behavior to overcome nutrient limitation (Brzezinski and Nelson 1988a). Standardization of methods is less a problem than the need for investigators to design and carry out ecologically relevant experiments, a reliance often unfulfilled. Two notable features of contemporary phytoplankton ecology and experimentation are the traditional and imbalanced experimental focus on whole community (synecological) issues over organismal behavior, and the limited efforts at hypothesis testing and experimental proof of concept. Available experimental results largely validate Braarud’s (1961) view that the primary value of experiments still is in providing ‘ecological orientations’, rather than meeting Redfield’s (Redfield 1960) more exacting criterion of facilitating representative model building.


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EARLY EXPERIMENTATION The Redfield-Braarud discourse did not take place in an era when experiments and quantitative approaches were ignored. It was predated by a long tradition of experimental research on phytoplankton cultures and natural populations to quantify in situ behavior. The Plymouth School and other groups in the UK, particularly, pioneered monospecific culture experimentation to determine bloom species’ growth rates; depth of the euphotic zone; photosynthesis-irradiance relationships; nutrient requirements; suitability as prey for copepods, and extrapolated the results to natural populations (Harvey et al. 1935, Harvey 1933, 1942, Jenkin 1937, Marshall and Orr 1928, and Mills 1989). Decades earlier, from 1916 to 1922, the Norwegian phytoplankton ecologist H.H. Gran enriched spring bloom and autumn samples with NH4, NO3 and PO4 to determine species-level, nutrientgrowth relationships (Gaarder and Gran 1927, Gran 1927). Gran also pioneered measurement of primary production, having developed the light and dark bottle O2 method, and carrying out such measurements already in 1916. This effort ultimately led to Steemann Nielsen’s introduction in 1952 of the now ubiquitously used 14C method (Barber and Hilting 2002). Redfield undoubtedly was aware of the major results of plankton experiments in recommending their abeyance until more representative descriptions of in situ behavior were obtained. His recommendation is probably not attributable to the instructions that attendees at the 1956 Symposium on Perspectives in Marine Biology received: to focus on problems “of marine life ready for experimental attack [and not to] survey past accomplishments” (Buzzati-Traverso 1960). Note the operative instruction: problems “ready for experimental attack”. Redfield and Braarud both were aware that poor methods impeded early investigators from establishing key species into culture, and that their experiments were compromised by limited insights into key ecological processes such as nutrient-regulated behavior, bloom dynamics and grazing. Although early experimental results often were of limited ecological value, and their in situ extrapolations were tenuous, important concepts were being developed from this crude data bank. Gran (p. 5 in 1929), for example, stated “we may consider it proved that the limiting factor in the production of plankton, both in marine and fresh waters, in the majority of cases is the available amount of nutritive salts in solution”. Conversely, Gran characterized nutrient enrichment experiments to evaluate in situ species responses as ‘a statistical investigation’ making it necessary to “renounce any possible critical account of the limitations of the species” (Gaarder and Gran 1927). Redfield

224 Algal Cultures, Analogues of Blooms and Applications expressed a similar view regarding the role of correlation in efforts to understand the distribution and abundance of life in the sea. He stated (p. 22 in Redfield 1960) the approach to be followed “must be primarily statistical [based on] significant relationships between large quantities of observations on biological and physical events... in widely scattered places”. Phytoplankton field ecologists traditionally have followed this statistically-based, descriptive approach by measurement of pre-selected environmental and biotic variables, and using the correlations obtained as evidence for, or against the relationship being assessed. The insights gained from field investigations have greatly influenced experimental design and paradigm formulation. Phytoplankton experimentation, historically, has evolved primarily from field-based observations, and rarely from organismal perspectives. There is a great difference between these two approaches in their expectations and use of experimental data. Gran’s contradiction in accepting experimental data as explaining in situ behavior is a case in point (Gran 1929, Gaarder and Gran 1927). In evaluating his nutrient enrichment experiments to explain species behavior, Gran seemingly agreed with Braarud (1961) that the primary value of autecological experiments is in providing an ecological ‘orientation’. Yet, he accepted the equally limited (semi-quantitative) experimental and fieldbased data then available as proof for the nutrient limitation hypothesis (Gaarder and Gran 1927). Ambivalent acceptance and application of experimental data persists in contemporary phytoplankton ecology. This is particularly evident in the use of experimental data and field evidence to evaluate in situ nutrient limitation, and examined in a later section.

CONCEPTS, PARADIGMS AND EXPERIMENTS Current plankton concepts derive largely from those in formulation at the time of the Redfield-Braarud discourse. This raises the question of whether these concepts and derivative paradigms have a firm experimental basis. Neither Redfield nor Braarud considered the role of experimentation in hypothesis testing and concept development, a surprising neglect given Redfield’s skepticism of ecological experiments. Redfield stressed that field observations had yet to provide representative descriptions and statistically based correlations of ocean-wide in situ behavior, insights needed to design experiments to explain this behavior. An intense, subsequent regional oceanographic effort has reduced this deficiency (Longhurst 1998). Field efforts continue to focus on mapping and measuring the distributions, abundance and productivity of plankton, and underlying habitat conditions.


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To a large extent, this is a techno-driven effort. The emergence of satellite oceanography has energized this effort. In contrast, there is remarkably little effort at ecological hypothesis testing, either during field investigations or derivative from such efforts. A notable exception is the experimentation directed to the Fe-limitation hypothesis (Kolber et al. 1994, Behrenfeld et al. 1996, Boyd et al. 2000, Tsuda et al. 2003). The major thrust of experimentation is, seemingly, directed towards establishing the rates of processes viewed as isolated events rather than in interaction with the combined physical, chemical and biological milieu that underlies and regulates plankton behavior. Recognition of flawed and biased experimentation, whether old or more recent, is relatively easy. It is more difficult to detect unwarranted, broad macroecological extrapolations of experimental data because the ecological connections being made often appear reasonable, but mask actual relationships. Concepts usually result from reductionist searches for common behavior and mechanisms and, when formulated (= presumed), such erstwhile uniformity is applied collectively to functional groups (diatoms, dinoflagellates, etc.), to processes (blooms, primary production, etc.), and mechanistically (critical depth, grazer control, etc.). This reductionism tends to focus on whole community (synecology) behavior over autecological investigation of individual species, from which synecological extrapolations are made. Both approaches carry serious risk of unwarranted extrapolation of data and concepts. For example, while there is some support (Pratt 1966) for the long-standing ‘ectocrine theory’ (Lucas 1947, 1955), which seeks to explain species succession, grazing and other species-level behavior, the frequent general application of such allelochemical and allelopathic regulation lacks experimental confirmation, and is unwarranted (Smayda 1980). A global expansion in harmful algal blooms, including blooms of unusual species and novel events, is in progress (Smayda 1990). This perplexing phenomenon is often attributed to ballast water seedings of starter populations, but supporting field and experimental evidence, as for the ‘ectocrine theory’, is very limited (Smayda 2002b). Phytoplankton ecologists tend to use unverified and attractive unifying concepts such as the ‘ectocrine’ and ‘ballast water’ theories (both experimentally difficult to validate) to account for events not explained applying existing dogma. Such efforts are not without value. The weakly silicified, intertidal benthic diatom Phaeodactylum tricornutum (= Nitzschia closterium f. minutissima), a rock pool species established into culture nearly a century ago (Allen 1910), was a major, early experimental ‘work horse’ to help resolve in situ phytoplankton behavior. Early experimental results

226 Algal Cultures, Analogues of Blooms and Applications obtained with this ‘weed’ species (and later with other non-fastidious species) were extrapolated to more representative species applying the premise of shared ecological requirements and physiological behavior. Subsequent experiments validated this extrapolation from such ‘unexacting species’ (Harvey 1955). The widely applied premise of a ‘common autecology’ is consistent with Redfield’s view that inference is important in model building; the inferred behavior (response) is then subject to experimental validation. The notion that biochemical and ecological uniformity characterizes the phytoplankton is discussed in a later section. There is a more basic conceptual and experimental issue relevant to Redfield’s (1960) view that the ultimate objective of biological oceanography is to build a single, consistent model of life in the sea. The traditional definition of the phytoplankton is based on their taxonomy and ecological role as primary producers. I suggest that this should be modified, both conceptually and for experimental design. Redefinition particularly is needed to deal with harmful algal species’ blooms, which primarily are blooms of phylogenetically diverse flagellates. Two basic strategies characterize the phytoplanktonic life mode: the sink strategy of diatoms and the swim strategy of flagellate species (Smayda 1997a). Diatoms and flagellates differ also in nitrate metabolism (Lomas and Glibert 2000), and many (if not most) flagellate species, unlike diatoms, are not obligate autotrophs but capable of both photosynthesis and mixotrophy (Granéli and Carlson 1998). That is, many are both primary producers and grazers (often bactivorous). Moreover, many non-toxic dinoflagellates assumed to be photosynthetic are heterotrophic (Hansen 1991, Larsen and Sournia 1991). Yet, laboratory experiments with flagellate species rarely accommodate both nutritional modes, and experimental procedures usually repress ecologically significant behavior associated with motility, i.e. diel and nutrient gathering migrations, thin-layer accumulations, etc. This frequent experimental mismatch with flagellate life-form requirements is further aggravated by application of the ‘diatom template’ bias – the assumption that the experimental approaches commonly applied to diatom ecological issues are valid for the phytoplankton generally. Diatoms, the most recently evolved, major phytoplankton group, unquestionably are important in trophic and bloom processes which justifies their intense experimentation. However, while diatoms most closely adhere to the classical definition of the phytoplankton, flagellates diverge from this conformity in many important ecophysiological features and in situ behavioral patterns (Smayda 1997a, Smayda and Reynolds 2003). Dinoflagellates appear to have incorporated into their ecology and adaptive strategies some of the swarming behavioral


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features of insects (Smayda 2002a). In dealing with the capacity of autecological experiments to help quantify harmful algal bloom dynamics, functional group distinctions must be recognized.

SYNECOLOGY, AUTECOLOGY AND PHYTOPLANKTON GROWTH MODES Redfield and Braarud’s opposing views reflected their different approaches to plankton processes. Redfield emphasized ‘whole community’ processes, i.e. a synecological perspective. Braarud pursued an organismal (autecological) approach to community dynamics. The focus, inferences and experimental methodology of these two approaches differ. Current knowledge and paradigms of in situ phytoplankton processes are based primarily on field measurements that have applied mass balance, reductionist techniques and synecological concepts. While this effort has been partially guided by experimental results on organismal ecophysiology, the autecological (organismal) approach has developed virtually as a separate and secondary line of enquiry within biological oceanography. This divergence has become increasingly evident since the Redfield-Braarud debate, but it is not a direct consequence. The separation of community and organismal based ecology in field and experimental studies contrasts with the historical inclusion of species assemblages, their blooms and successions in early field investigations and primary productivity assessments (Mills 1989). Gaarder and Gran (1927) partitioned among individual species the primary production measured for the total community. And, it is generally overlooked that Hensen (1887) in inspiring the reductionist, mass balance approach reached his conclusions based on detailed analyses of the planktonic flora and fauna in applying his perspectives as a physiologist interested in fishery yields. There is reason to believe (Barber and Hilting 2002) that the introduction, in 1952, of Steemann Nielsen’s 14C method to measure primary production hastened the natural tendency of the autecological and synecological approaches to diverge. The ability of the powerful 14C method to measure primary productivity with precision, accuracy and ease, combined with more quantitative phytoplankton and nutrient analytical techniques (Strickland, 1960, Strickland and Parsons 1972), elevated synoptic field investigations to a quantitative level not yet matched by routine floristic assessments. Emphasis on whole community dynamics over individual species dynamics has been further propelled by interests in oceanic biogeochemical processes; the need to quantify food web dynamics and trophic transfer (Cushing 1975) and, more recently, concern over potentially

228 Algal Cultures, Analogues of Blooms and Applications adverse impacts of global warming, hypernutrification of coastal waters, and over-fishing. These concerns are best approached from a community, rather than a species-specific perspective. This skewed emphasis, however, has begun to change. The global expansion of harmful algal blooms (Anderson 1989, Smayda 1990, Hallegraeff 1993), a highly species-specific phenomenon, has refocused attention on organismal biology because mass balance, synecological approaches are inadequate to quantify individual species’ bloom selection, behavior and toxicity. An interesting dichotomy has resulted: harmful algal bloom (HAB) studies have become intensely autecological, whereas biological oceanography continues to develop through, and to emphasize synecological approaches. The species-based knowledge to be gained from field and experimental study is essential to development of predictive models, mitigation techniques, and early warning criteria for aquacultural and human health protection. The concerns expressed in the Redfield Braarud discourse are relevant to these needs, and to the issues of the reliability and limits of experimentation applied to harmful bloom dynamics. To evaluate this, the methodological and conceptual divergence between autecological and synecological approaches must be recognized, since in reality both ecologies combine to structure and regulate marine community dynamics.

Synecological Field and Experimental Approaches The synecological approach – the ‘whole community’ approach – is based on a ‘Notion of Equivalence’. Conceptually, the abundance and processes of the phytoplankton community are viewed in aggregate. Operationally, total community abundance is measured in bulk, usually as chlorophyll (= biomass), and primary production is the usual surrogate measurement for the collective physiological processes. A key assumption made is that the assembled species have similar kinetics and food web value, and respond synchronously. Species-specific processes are ignored, and the untested assumption applied that the measured whole community responses reflect the behavior of the dominant species. The contributions of minor species, usually so categorized because of their low cell abundance, are considered to be insignificant. This assumption, where applied to harmful blooms, can be risky (Smayda 1997b). Dinophysis species at abundance levels of < 600 cells L–1 can have toxic effects (Maestrini 1998). Another synecological bias is the traditional focus on the spring diatom bloom as the major annual bloom event driving pelagic processes. Seasonal and aperiodic blooms of


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other taxa, and blooms that are ephemeral, unpredictable or of low magnitude are considered trophodynamically insignificant irrespective of their frequency or ecological impact. Field and experimental synecological approaches basically have been developed using a diatom-based template of dynamics and environmental regulation. In essence, synecology is a mass balance and biogeochemical approach in which biomass and primary production are the major expressions of phytoplankton dynamics, and nutrient regulation is assumed to be the dominant control mechanism of these two properties. This conceptual and practical approach to phytoplankton ecology has been in force for nearly 125 years, ever since Hensen’s (1887) brilliant efforts to quantify phytoplankton production, and reinforced by the classical Redfield Ratio which codifies the mass balance approach (Redfield 1934, 1958). Conceptually, the mass balance approach applies the photosynthesis - respiration reaction (Eqs. 1, 2) in combination with the stoichiometric relationships (Redfield Ratio) that occur between nutrients (N, P) and the photosynthesis and respiration of organic matter. In this relationship, nitrogen (N) and phosphorus (P) are bound and released during the synthesis and respiration of organic carbon (C) which, in turn, results either in the release or utilization of oxygen (O). The quantities of the four elements processed in this biochemistry can be expressed in terms of atoms, or by weight: O:C:N:P = 212:106:16:1 (by atoms)

(Eq. 1)

O:C:N:P = 109:41:17.2:1 (by weight)

(Eq. 2)

Thus, during photosynthesis (primary production), for every atom of P assimilated 16 atoms of N will be assimilated and 106 atoms of C fixed into organic matter (from CO2), liberating 212 atoms of O. This assimilation of N and P leads to phytoplankton growth (i.e. biomass = carbon, chlorophyll, etc.) and oxygenates the watermass. The biogeochemical thrust of synecology has been fundamental to the development of biological oceanographic principles. An unfortunate consequence has been conflation of the definitions of primary production and phytoplankton ecology. Primary production and phytoplankton ecology are regularly used synonymously, which influences concepts and methodology, including experimental approaches. This extravagance is not surprising, accepting that determination of primary production “has been a major, if not the major goal of biological oceanography [since] the mid nineteenth century” (p. 16 in Barber and Hilting 2002). This synonymic confusion, I believe, has promoted the divergence of autecological and synecological approaches, rather than their needed fusion.

230 Algal Cultures, Analogues of Blooms and Applications The ecology of a phytoplankton species and its assembly into communities is a triad of its cellular requirements and population (both autecological) and community-based (synecological) behavior and regulation. Primary productivity is only one aspect of phytoplankton ecology, albeit an important one, but it is only the outcome and not the defining basis of this ecology. It is the behavior of the species comprising the marine phytoplankton in toto- their taxonomy, life cycles, distribution, seasonalities, physiology, photosynthesis, trophic impacts, etc. – that constitutes their ecology, and not a particular subset of behavior such as primary production or bloom patterns. Fundamentally, phytoplankton ecology is the composite of species-level processes that occur as genetically programmed cellular behavior in response to habitat conditions and disturbances. This requires fusion of the autecological and synecological approaches, both in field studies and experimentally, to quantify phytoplankton processes such as primary production and blooms. Neither Redfield nor Braarud in their discourse commented on this dual approach; Redfield seemingly advocated a whole community approach and Braarud an organismal approach.

The Autecological Approach While mass balance, synecological experiments are primarily field oriented, autecology is primarily laboratory-based. Autecology is the study of an individual organism or species and its interactions with its abiotic and biotic environment. The autecology of phytoplankton species is a composite of their phylogenetic, genetic and biophysical traits. Phylogeny influences their behavioral and physiological adaptations, including pigment complexes, motility, and nutritional idiosyncrasies. Genetics influences their growth capacity, nutrient uptake rates and clonal (strain) variability, etc. Biophysical constraints are imposed on tolerance to turbulence, motility and migratory behavior; metabolic rates are influenced by cell size and shape. The ‘Notion of Biodiversity’ is fundamental in the autecological approach, i.e. recognition that phytoplankton communities are polymixtures of species of overlapping, yet differing cell size, life-form and ecological tolerances. The taxonomic diversity of the assembled species leads to ecophysiological diversity and species-specific differences, rather than to uniform biochemical and ecological behavior, unlike the assumption of ecological equivalence that underlies the synecological approach. The autecological approach assumes that the species making up the community are in different growth stages, i.e. that succession is taking place; that different factors can regulate the assembled species, and that a given species


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may be under multifactorial rather than single factor regulation. The autecological approach focuses on the in situ interrelationships among species, which vary in their abundance and seasonality, at four different degrees of complexity: organism-environment; organism-organism; cellular vs. population dynamics; and population - population interactions. This differs from the synecological, mass balance focus on group dynamics (i.e. whole community) in response to a given habitat parameter. The autecological approach seeks to integrate the dynamics of individual species, including their successions, blooms, growth requirements and food web value, with whole community behavior. In this, while it is assumed that the overall properties of the communities assembled during succession are a composite of the species then selected for growth and assembly, the dynamics of a given species cannot be deduced from knowledge of overall community behavior, or from the behavior of a competing species. Major assumptions underly autecological experimentation and in situ extrapolations: the behavior of a species in laboratory culture is assumed to be manifested in situ with regard to essential nutrient requirements, uptake kinetics and cellular quotas; the optima and range of tolerance for temperature and salinity; its growth rates, and photosynthesis-irradiance relationships. Thus, if a particular vitamin must be provided for growth in culture, it is assumed to be required in situ. And, a species highly efficient in culture at taking up NH4 at low concentrations, i.e. it has a low Ks coefficient, is expected to have a similar capacity in situ. A species exhibiting a very high growth rate in culture, for example m = 2.5 d–1, is assumed capable of similar in situ growth at equivalent environmental conditions. It is also assumed that species do not mutate while in culture, and that the responses of the cultured strain are representative of that species generally. These assumptions are examined in a later section. Autecological in situ behavior poses great experimental difficulties, as Braarud (1961) acknowledged. Its species-specific focus requires that the natural community with its populations of the individual species assembled be isolated for in situ experimental manipulation. Since incubation of the entire community without loss of key, regulatory habitat features is virtually impossible, this limitation has favored the reductionist (synecological) approach. The usual alternative strategy is to isolate into culture the species of experimental interest. However, this greatly limits the types of ecologically relevant experiments that can then be carried out. Monospecific laboratory experiments, whether in batch or in continuous culture mode, and factor interaction studies, usually do not provide data useful to synecological assessments. A compromise is to enclose natural

232 Algal Cultures, Analogues of Blooms and Applications communities into mesocosms for experimental manipulation to observe the induced behavior of the process or species of interest (Grice and Reeve 1982, van der Wal et al. 1994, Verity et al. 1988). Discussion of mesocosm experiments falls outside the scope of this chapter. This omission does not alter the conclusions to be reached. Before evaluating the evidence for whether meaningful autecological and synecological experiments can be achieved, the two different types of phytoplankton nutrient-limitation and the three modes of phytoplankton growth are considered.

Modes of Phytoplankton Growth and Limitation Growth rate and biomass accumulation are the traditional measures of the in situ success of phytoplankton species and communities, with primary production rate also used for the latter. When growth rates and biomass levels are low, the concept of ‘limited growth’ is applied, and efforts are then made to identify the specific limiting factor. A problem with this approach, and primarily a synecological shortcoming, is that investigators generally do not distinguish between the two types of growth limitation that occur: biomass (= population) limitation and physiological (= cellular) limitation (Falkowski et al. 1992). In situ population growth follows the well known growth minus loss relationship: Nt = Noe(K + Ki -Ka-Km-Ks-kg)t

(Eq. 3)

where the population growth rate (Nt) is the difference between the sum of the cellular growth (K) and immigrant cell recruitment (Ki) rates, and the combined population losses due to washout or flushing (Ka), mortality (Km), sinking (Ks), and grazing (Kg). Ignoring the loss terms in Equation 3, cellular growth rate is influenced by the combined effects of the physical (turbulence, temperature, irradiance), chemical (nutrients) and biological (allelochemical effects) factors making up the environment. The presence and success of a species can be controlled either by qualitative and quantitative deficiences in these growth factors, or if they exceed the ecological limits of tolerance for that species. In the case of nutrients, the rate of supply determines the amount of biomass (cells) produced in a dose-yield relationship, i.e. the standing stock (biomass) increases in direct, linear response to the amount of nutrient supplied. Thus, nutrients regulate both a species’ growth rate (an autecological effect) and determine the environmental carrying capacity for its biomass (a synecological effect), i.e. nutrients influence both cellular and population behavior.


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Phytoplankton growth in regions and at times of low nutrient supply is often considered to be nutrient-limited, if not stressed. Numerous exceptions to this generalization occur, as Goldman et al. (1979) showed and who stated (p. 4 in 1979) “at low, steady state nutrient levels it is possible to have simultaneously low or undectable residual nutrient levels and high growth rates regardless of the biomass concentrations”. That is, the in situ standing stock of a population (or a species in culture) can be limited by the amount and rate of supply of required nutrients, but the population (species) is not then necessarily impaired physiologically. Physiological limitation sets in when cellular nutrient requirements, i.e. cell quota (Droop 1974), exceed nutrient supply rates. Physiologically limited cells not only decline in abundance, they are outcompeted and replaced by other species in the local succession. Physiological limitation is a cellular response, while biomass (bloom) accumulation is the residual population response reflective of overall community dynamics, i.e. it is a synecologically influenced response. The convergence of these two ecologies compromises in situ experiments seeking to relate bloom and successional behavior to specific environmental conditions. Classically, statistical analyses have been used to relate changes in phytoplankton abundance and bloom events to nutrient levels. Experimental mass balance approaches (Eqs. 1, 2) have provided a firmer foundation for nutrient regulated growth (Dugdale 1967), but the problems of distinguishing between biomass and physiological limitation, or identification of the specific, limiting nutrient within the macro- and micronutrient pool remain. Tracers, i.e. an isotope of the nutrient element of interest, used to measure the pathways of uptake, transfer and release have helped to establish nutrient limitation effects (Dugdale and Wilkerson 1992, Nelson and Brzezinski 1990). Falkowski et al. (1992) have suggested that empirical identification of the symptoms of physiologically limited growth in natural populations and the specific limiting factor (i.e. nutrients, temperature, irradiance, etc.) should be possible using cellular biochemical markers symptomatic of the specific deficiency and impaired process. An advantage of this assay would be the analytical detection of physiological limitation without the need for experimental incubation. The in situ dynamics of phytoplankton species and communities are shaped by combinations of fixed and flexible autecological parameters, modified by variable synecological processes. Eventually, growth of all species organizing the community becomes limited, both in biomass and physiologically. Given the occurrence of succession, it seems unlikely that communities per se become physiologically limited, although nutrient

234 Algal Cultures, Analogues of Blooms and Applications availability might restrict the gross biomass level attainable, i.e. prior to the loss terms in Equation 3. The consequences of the two different impacts of nutrient limitation vary with the mode of phytoplankton growth and the stage within these modes. Three different, concurrent phytoplankton growth modes occur and must be distinguished experimentally: cellular, population and community growth (Smayda 1997a).

Celluler, Population and Community Growth Cellular growth is the active, basic growth unit; the outcome of coupled physiological processes under genetic and multifactorial control. The specific growth requirements, adaptive responses and the rates of response to growth factors such as nutrients, irradiance, and tolerance to factors that influence this physiology, such as temperature, are also under genetic control. Cellular growth is represented by the term, K, in the population growth equation (Eq. 3). Population growth (Nt) is the environmentally modified outcome of cellular growth; it is the recruitment term and therefore the bloom unit. While it is dependent on cellular growth (K), the factors regulating cellular and population growth rates are not identical. Zooplankton grazing and advective washout, for example Kg and Ka, respectively in Equation 3, influence population growth rate, but not cellular growth. A given factor may influence cellular and population growth rates in different ways. Temperature for example, affects cellular growth rate via photosynthesis and nutrient uptake, but influences population growth through an affect on grazing rates. Nutrients also influence both cellular and population responses. At the cellular (autecological) level, nutrients regulate life cycle changes (Garcés et al. 2002) and growth rate, while the amount and supply rate of nutrients determine the population abundance (= carrying capacity). Community growth rate can be expressed in various ways: from changes in pro-rated carbon assimilation rate (i.e. the amount of carbon fixed per unit chlorophyll and time [mgC mgChl–1 h–1]); community biomass, or as the succession rates of the species that organize the community (Smayda 1980). Cellular and population growth rates are the usual experimental focus. Phytoplankton communities are transient assemblages of multiple species, each in different bloom cycle stages and regulated (potentially) by different combinations of growth factors. Community organization and growth obviously are the outcome of the cellular and population growth rates of the species organizing the community, with interspecific competition also an important structuring element.


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While community dynamics are a direct outcome of the three types of growth rates recognized, there are unpredictable stochastic contributions. Smayda and Reynolds (2001), based on a detailed analysis, concluded that the highly unpredictable blooms of harmful species often appear to be the result of the species “being in the right place at the right time”. Stochastic bloom events can result from variable excystment of resting stages deposited onto seed banks, or during unpredictable innoculations of cells from pelagic seed banks during watermass intrusions, Ki in Equation 3 (Smayda 2002a). Unusual local meteorological and ephemeral habitat disturbances can selectively stimulate or repress species, and unpredictably alter community dynamics. Smayda (2002b), based on niche considerations, has hypothesized that the selection of bloom species is heirarchical, which poses experimental problems. He has proposed that harmful bloom-species selection follows a taxonomic heirarchical pathway progressing from phylogenetic to generic to species selection, in that sequence and with considerable unpredictability as to which phylogenetic group(s) will bloom; the bloom genus (genera) selected from within that group, and the selection of the bloom-species from within the genus.

Autecological Extrapolations: Fidelity to in situ Behavior Phytoplankton ecologists have had to simplify the complexities of in situ phytoplankton growth for methodological and other practical reasons en route to elucidation of first-order processes. Field observations have focused on specific events (e.g. the spring bloom), processes (e.g. primary production; grazing), and selected habitat conditions perceived to be important regulators of those events and processes (e.g. nutrient regulation). The conceptual and methodological advances applying this approach, and the large body of ecophysiological data acquired, no longer support the traditional ecological approach to treat the biota, their events and processes in isolation. Species’ populations are heterogeneous in life cycles, physiological states and genetic makeup, and they vary in space and time as a result of interactions with other, equally heterogeneous and dynamic populations and physical conditions. We also know that the great reliance on mass balance, synecological approaches has overlooked relevant biotic contributions. This complexity poses great problems to the design of ecologically relevant experiments to explain in situ behavior, and raises questions as to the reliability, not only of field observations – Redfield’s concern – but also extrapolation of laboratory-based autecological data to in situ behavior, which Braarud encouraged.

236 Algal Cultures, Analogues of Blooms and Applications The following sections examine the fidelity of experimental results extrapolated to in situ behavior for three major ecological issues: are the results obtained with a clone (strain) applicable to the species at large? Are laboratory responses to nutrients, including type, amount and ratios, and also experimental growth rates representative of in situ behavior? Other autecological assumptions could also be tested, but evaluation of these three basic and major issues provides a reliable insight into the overall efficacy of autecological experiments.

Clones and Autecological Extrapolation Braarud (1961) addressed a fundamental, dual question: are experimental results obtained with single clones (strains) of microalgae representative of the species at large, and of the population from which they were isolated? Embedded within this is the question: do strains remain constant in their responses to environmental factors? Braarud’s experiments on the salinity and temperature responses of dinoflagellate strains isolated into culture from different geographic regions and from within the same locality led him to conclude (p. 278 in Braarud 1961): “even with the use of a clone from only one locality, the results may be applied for populations of a large area”. Braarud dismissed as experimental artifact the different temperature optima for growth that he found for geographically isolated strains. Since his cultures were not bacteria-free, he thought this might explain the variability found. The corollary of Braarud’s conclusion – that different physiological strains of phytoplankton do not seem to occur – had a major impact. This notion that the responses of strains are generally representative of the species energized autecological experimentation and encouraged ecologists to extrapolate the results for a given strain to that species generally. This autecological reductionism was, and continues to be applied despite subsequent contradictory evidence. Jensen et al. (1974) for example, found that strains of the diatom Skeletonema costatum varied in their Zn tolerance based on the proximity of the source population to waste discharge from a smeltering factory. Zinc tolerance was greatest in strains isolated from Znenriched waters. This finding challenged the notion that strains are conservative, and raised basic questions concerning the mechanisms and rates of adaptation among phytoplankton species. Gallagher’s (1980, 1982) classical study on strains of S. costatum isolated from Narragansett Bay demonstrated that significant interclonal differences in physiological behavior indeed occur, at least among diatoms. She concluded (p. 473 in Gallagher 1980) “no single clone is ever representative


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of the entire species Skeletonema costatum, or even, necessarily, of the majority of the population from which it was isolated”. The responses of strains can be influenced by culture media, but Gallagher 1982) found that genetic differences, not experimental manipulation, explained her results. The physiological variability among the S. costatum strains potentially was of great ecological significance: strain growth rates ranged 50-fold; cellular chlorophyll levels varied 8-fold, and their hourly carbon uptake rate per unit cellular chlorophyll varied 7-fold (Gallagher 1982). Significant genetic, ecophysiological and behavioral differences have since been found among populations and strains of all phytoplankton species. The diatom genus Pseudo-nitzschia, which includes species that produce the toxin domoic acid (DA), exhibits high genetic variation and forms complex genetic groups (Parsons et al. 1999, Stehr et al. 2002). Multiple strains of toxic and non-toxic Pseudo-nitzschia pseudodelicatissima coexist in Louisiana coastal waters (Parsons et al. 1999). A bloom of this species in Danish waters was composed of at least six different isozyme subgroups, and another bloom in that region was also polyclonal (Skov et al. 1997). Wood and Leatham (1992) list 13 physiological and biochemical traits that vary among phytoplankton strains, including temperature-dependent growth rate, nitrogen and silicon metabolism, vitamin requirements, and toxicity. Clearly, available data do not support Braarud’s still generally applied view that phytoplankton strain differences are relatively unimportant ecologically. The more valid conclusion has been expressed by Wood and Leatham (p. 723 in 1992): “for nearly every physiological character examined, significant interclonal variability is found essentially every time that strains from the same putative taxon are compared”. This trait presents major impediments to confident autecological extrapolation of experimental results to species-specific in situ behavior, particularly environmental regulation of bloom patterns and successions. There is also an experimental design problem. The magnitude of within-species variation must be determined when seeking to identify the significant physiological differences among competing species in efforts to explain their bloom selection and regulation. As Wood and Leatham (1992) pointed out, without such ‘in-group’ estimates it is impossible to determine whether single-strain differences found among competing species are any greater than might be observed in random sampling of the individual populations of the competing species. Modelers are also affected given their reliance on experimentally derived rates of cellular-based growth, photosynthesis, nutrient uptake, etc.

238 Algal Cultures, Analogues of Blooms and Applications It is generally assumed that species brought into culture do not lose or modify their ecophysiological traits. This may not apply to the synthesis of secondary substances that have allelochemical effects. Gentien and Arzul (1990) report that the dinoflagellate Karenia mikimotoi lost most of its diatom inhibiting capacity after six to nine months in culture. Another strain of this species demonstrated a loss in toxicity against juvenile and adult scallop during nine months in culture (Erard-Le Denn et al. 1990). It may be that where a trait is sensitive to environmental conditions, it can vary in its expression depending on the suitability of the culturing conditions to elicit and support that trait.

Clones: Environmental Regulation vs. Stochastic Occurrences Do strains occur stochastically, or are they environmentally regulated in a manner analogous to the succession of taxonomic species? Definitive evidence requires determination of the temporal (seasonal) and spatial scales at which differences in strain behavior occur. This requires clonal isolations along time-space gradients. Available evidence suggests that strains are environmentally regulated, rather than occur as stochastic ‘pop-ups’. Seasonal patterns in the genetic composition of a S. costatum population correlated with changes in environmental conditions (Gallagher 1982). The geographic origin of dinoflagellate strains influences their genetic features (Scholin 1998), and considerable genetic diversity can occur among regional sub-populations of a species. Maranda et al. (1985) reported a north-to-south latitudinal decline in the level of paralytic shellfish poison (PSP) toxicity in Alexandrium populations. Clones from northern populations contained carbamate, the most potent saxitoxin derivative, and southern populations contained the less potent (sulfamate) saxitoxins (Anderson 1997). The UK populations of Alexandrium tamarense, common in European waters, exhibit a north-south gradient in PSP toxicity (Higman et al. 2001), with two distinct lineages represented and based on molecular evidence (Higman et al. 2001, Medlin et al. 1998). Strains isolated from northern (Scottish) populations were toxic in culture and assigned to the toxic ‘North American lineage’ because of shared toxicity and molecular features. Southern populations (England, Ireland) were not toxic, but molecularly related to the non-toxic ‘Western European Lineage’. Such distributional patterns and gradients in physiological strains provide further evidence that their emergence and occurrence are not primarily stochastic events, but represent adaptive behavior.


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Clones: Physiological Variants or Taxonomic Species? Ecologists and taxonomists face a basic problem: are strains physiological variants of a given species or are they different species? The genetic variations that accompany diversification of a putative species into strains (Gallagher 1982, Medlin et al. 1991, Scholin 1998, Wood and Leatham 1992) suggest that they are evolved features rather than transient (opportunistic) adaptive behavior. Coexistence of genetically distinct sub-populations is ecologically beneficial. It increases the chances that species will grow and survive (Eq. 3) in temporally and spatially variable environments. On the other hand, the putative strains may, in fact, represent different species difficult to distinguish applying standard taxonomic techniques. The problem this poses to in situ extrapolations of experimental results is obvious. The assignment of toxic dinoflagellates to various ‘species complexes’ because of morphologic and genetic overlap, rather than to a distinct species, is an example of this unresolved issue (Bravo et al. 1995). Some strains of Alexandrium tamarense, A. catenella and A. fundyense isolated from North American coastal waters are genetically very similar despite morphological differences (Scholin 1998). Conversely, some strains of A. tamarense isolated from western Europe and North America are morphologically indistiguishable, but genetically distinct. Uncertainty over the taxonomic status of genetic strains vs. “morphospecies” can compromise autecological extrapolation of experimental results. There is need to know whether a putative strain is, in fact, an infraspecific unit or a different species, and this should be based on robust taxonomic and ecophysiological criteria (Gallagher 1998). Coefficients used in numerical models are usually taken at random from laboratory experiments. The choice of the experimental clone used, and whether it is a bonafide strain of that species, a cryptic member of some other species, or is a member of a ‘species complex’ could significantly affect the modeled outcome and extrapolations. Differences in geographical bloom behavior of a species despite a common presence may reflect the absence of the strain (sub-population) adapted to bloom under those conditions. Physiological differences occur among geographically segregated populations (meta-populations) of ichthyotoxic Heterosigma akashiwo (Smayda 1998a). The taxonomic confusion generated by variable morphological and genetic overlap among strains and species of harmful dinoflagellates (Scholin 1998) is not unique to flagellates. Three clones of the nano-diatom Thalassiosira pseudonana (Ø = ca. 1.5 - 14 mm; Hasle 1983), considered initially to be taxonomically identical because of minor differences in morphology,

240 Algal Cultures, Analogues of Blooms and Applications differed significantly in their temperature, salinity and vitamin requirements, and nutrient uptake kinetics (Guillard and Ryther 1962, Murphy and Guillard 1976). The physiological results obtained using these clones were commonly extrapolated to in situ behavior, and underpin many current concepts. The clones were isolated originally from distinct locations along an onshore-offshore nutrient gradient extending from southern New England to the Sargasso Sea: from a nutrient enriched bay (clone 3H), continental slope water (clone 7-15) and oligotrophic Sargasso Sea (clone 13-1). The physiological divergence among these clones, despite their morphological similarity, initially was interpreted as the adaptive capacity of phytoplankton species to establish physiological races in response to different habitat conditions. In electrophoretic tests, Clone 7-15 exhibited morphs intermediate between the other two clones. This was interpreted as evidence for genetic exchange between the neritic and oceanic clonal populations (Murphy and Guillard 1976), and that T. pseudonana consisted of neritic and oceanic “races” (Brand et al. 1981). Electron microscope analyses of cellular morphology later revealed that the clonal differences in ecophysiology presumed to characterize a single species, T. pseudonana, are, in fact, the behavior of three distinct species, T. pseudonana (3H), T. guillardi (7-15), and T. oceanica (13-1) (Hasle 1983). Notwithstanding the potential confusion of taxonomic species with infraspecific cloning, ecophysiological clones within a given species are an important feature of the phytoplanktonic life mode (Wood and Leatham 1992). No single clone of a taxonomic species is truly representative of that species.

Autecological Experimentation: Nutrients Nutrient limitation models An enormous number and variety of field studies and experiments have examined a central issue in phytoplankton ecology: nutrient regulation of the distribution, abundance and growth of phytoplankton. Two basic notions anchor this effort: that nutrient limitation occurs in the sea and this is usually by a single nutrient. The assumption of single nutrient limitation is based on Liebig’s Law of the Minimum formulated for agricultural systems (de Baar 1994) and applied to the sea a century ago (Brandt 1899). Since nutrients are consumable, they vary in their concentrations and ratios (Eqs. 1, 2), with their specific instantaneous, seasonal and regional variations dependent on uptake and resupply rates. Nutrient regulation has a family of effects – on the life cycle, distribution, growth, and biomass – and functions in multiple ways: by element, concentration, and ratios. All this,


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together with the axiom that nutrient limitation cannot occur in the absence of competition for nutrients. Nutrient uptake, limitation and competition, and growth rate are linked processes; the competition among species for nutrients within this linkage determines community structure and dynamics. Tilman (1977) has termed the interspecific competition for nutrients “resource competition”. It has been taken for granted that nutrient limitation occurs in the sea, but definitive proof of this hypothesis is lacking. Models of nutrient limitation are based on laboratory experiments using batch or continuous culture techniques and, as discussed earlier, differ in the type of nutrient impairment induced (Cullen et al. 1992). Growth in batch culture experiments is unbalanced and leads to nutrient starvation, while growth in continuous culture under photocycle control is balanced and the nutrient supply rate sets the degree of nutrient limitation. Four types of nutrient limitation models have been developed (Droop 1983; Sommer 1989). The ‘dose-yield’ model, referred to earlier, is a log-log regression of the amount of biomass produced in reponse to the concentration of limiting nutrient. This model applies the mass balance approach of Eqs. 1 and 2. The three other limitation models focus on the linked processes of nutrient uptake and growth. They are saturation-based, i.e. nutrient uptake and growth rate exhibit a rectangular hyperbolic response to increasing nutrient concentrations up to an upper, asymptotic maximal response level. The Dugdale model (Dugdale 1967) describes the dependence of the uptake rate to the external concentration of the limiting nutrient: v = Vmax S/(S + Ks)

(Eq. 4)

where v is the specific uptake rate per cell or unit of biomass; Vmax is the maximum velocity of uptake; S is the nutrient concentration, and Ks is the half-saturation coefficient at which the velocity of uptake is one half Vmax, i.e. Vmax/2. The Dugdale model derives from the Monod model, which describes the dependence of the growth rate (m) on the external concentration of the limiting nutrient (S): m = mmax S/(S + Ks) (Eq. 5) where mmax is the maximum growth rate. Note that both models emphasize external nutrients as the source, and treat nutrient uptake and growth rate as kinetically similar. Continuous culture (chemostat) experiments have demonstrated that the uptake of ammonia, nitrate, phosphorus, iron and vitamin B12 is a hyperbolic function of their concentration (Droop 1974, 1983; Davies 1988). The growth rate (m) of a monospecific phytoplankton population is also a

242 Algal Cultures, Analogues of Blooms and Applications function of the internal concentration (= cell quota) of the rate limiting nutrient. These general findings led to Droop’s (1974) internal stores, or cell quota model, the third type of saturation model: m = mmax (1 - qo/q) (Eq. 6) where growth rate (m) is dependent on the intracellular concentration of the limiting nutrient (“cell quota”, q), and qo is the minimal (or subsistence) cell quota, below which the cell is physiologically impaired and mortality exceeds growth. The relationship 1 - qo/q can be defined as the intensity of nutrient limitation, which is inversely proportional to the cell quota (Sommer 1989). Droop stressed that the relation between cell quota (q) and growth rate (μ) has a much firmer experimental basis (from continuous culture studies) than that between external nutrient concentrations and growth rate. Determination of which nutrient is rate limiting should be based on their relative concentrations within internal cellular pools, and not their external (extracellular) concentrations. This is a major conceptual and methological revision of traditional field approaches and a divergence from the Dugdale and Monod models which use external nutrient concentrations as the measure of their potential limitation. Droop also pointed out that in seeking to identify the effects of nutrient limitation, a cell’s nutrient uptake history ideally should be known since this prehistory influences instantaneous uptake behavior. As discussed earlier, in situ nutrient supply rates occur as a series of transient episodes of varying inputs, to which phytoplankton are physiologically adapted through their evolved capacity for surge uptake (= above steady state rates) during pulsed nutrient delivery (McCarthy and Goldman 1979, French and Smayda 1995). While nutrient uptake can be continuous and the velocity of uptake varies with concentration (Eq. 4), the biochemical products of this metabolism will accumulate until reaching the biophysical threshold triggering cell division (m). Droop’s cell quota model is a reasonable representation of nutrientlimited growth in continuous culture experiments, and is widely used (Hecky and Kilham 1988). However, while cell quotas are easily measured in laboratory experiments, the utility of Droop’s model to in situ behavior (Jones et al. 1978) is impeded. Not only is there the problem of measuring the cell quota (q) for individual species in natural populations, these measurements are contaminated by the presence of detritus, microheterotrophs, and other seston components. Measurements of the oscillating nutrient supply at appropriate ecological scales are also usually beyond analytical detection or sampling procedures. Contamination of in situ population cell quota measurements and failure to measure nutrient supply


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rates and particulate (i.e. biomass) nutrient can compromise ecological conclusions regarding nutrient limitation. The interdependence of specific growth rate and internal nutrient concentration led Droop (p. 852 in 1974) to state: the “most important implication of this is that the potential of a body of water for supporting further growth may depend as much on the nutrient already inside the cells, as that yet to be taken up”. Cellular chemical composition is frequently used (Eqs. 1, 2) to assess phytoplankton nutritional status, and for biomass and nutrient element conversions. Brzezinski (1985), for example, evaluated the Si:C:N ratio for 27 diatoms reared at various experimental conditions to assess the effects of nutrient limitation on diatom elemental composition. His objective was to convert field estimates of biogenic Si into C and N units and to estimate silicate production from primary production (14C). Sakshaug and HolmHansen (1977) evaluated the influence of nitrate, phosphate and iron limitation on the chemical composition of experimental cultures for use as indicators of nutrient deficiency. Goldman (Goldman et al. 1979, Goldman 1980) was among the first to apply deviations from the Redfield Ratio for N:P of 16:1, by atoms, as an indicator of the degree of nutrient limitation and stress on oceanic phytoplankton growth rates. Sakshaug et al. (1983) used a similar stoichiometric approach to assess nutrient limitation in Norwegian coastal waters. Such efforts to establish physiological status and degree of nutrient stress based on cellular stoichiometric ratios are relatively insensitive and subject to considerable variability. A more sensitive and preferable alternative approach would be to use cellular biochemical and physiological diagnostic techniques to establish the nutrient status of natural populations (Falkowski et al. 1992, Cullen et al. 1992, La Roche et al. 1995).

Nutrient Limitation—Field and Experimental Evidence Two major, venerable hypotheses of the in situ regulation of the growth and temporal and spatial distribution of phytoplankton have been advanced: the irradiance-nutrient hypothesis, and the grazer hypothesis (Yentsch 1980). In reality, these different control mechanisms interact and blur detection of the active factor or combinations of factors that control the type of nutrient-biomass relationship characterizing a given watermass or bloom event. Of these, nutrient limitation has been the principal focus and hypothesis, prompting the question of does nutrient limitation occur in situ; has this been confirmed experimentally and, if so, does it follow Liebig’s Law of the Minimum? The origins of the nutrient-limitation hypothesis lie in field measurements of the temporal and spatial variations in nutrient concentrations and accompanying phytoplankton abundance. Three basic

244 Algal Cultures, Analogues of Blooms and Applications nutrient-biomass conditions and regions have been used to infer nutrientlimited behavior: high nutrient levels and low phytoplankton biomass (HNLB); low nutrients and high biomass (LNHB), and low nutrients and low biomass (LNLB). [Chlorophyll is usually the biomass estimate.] The progressive decline in nutrients during spring blooms, for example, that accompanies increasing chlorophyll biomass, and whose continued buildup eventually depletes nutrient concentrations and the bloom collapses, results in a progression from HNLB to LNHB. The low phytoplankton abundance typical of oligotrophic seas (LNLB) has also been considered symptomatic of nutrient- limitation, either through dose-yield control or physiologically (Goldman et al. 1979). Where a condition of HNLB persists, some other factor is assumed to be limiting, such as irradiance, in delaying inception of the spring bloom or, a more recent revelation, low iron concentrations that prevent complete assimilation of available macronutrients (de Baar 1994). Grazing can confound the nutrient-biomass relationship via cropping of the biomass and lead either to a condition of HNLB or LNLB. Ever since Brandt’s (1899) application of Liebig’s Law of the Minimum a century ago, this notion has had, and continues to have a major conceptual influence on nutrient-limitation research and to encourage quasi-statistical, descriptive field approaches in search of validation. This simplistic view is based primarily on relating phytoplankton growth to the inorganic macronutrients, N, P and Si. Liebig’s Law and the nutrient limitation models (Eqs. 4, 5, 6) apply an important assumption: when (if) nutrient limitation occurs, it is because of a single nutrient (Davies 1988). Continuous culture experiments with nutrient pairs – vitamin B12 + PO4 (Droop 1974) and NO3 + PO4 (Rhee 1978) – led to the conclusion that only one of the nutrient pairs was limiting, and did not interact with the other. This interpretation is in accord with Liebig’s Law of the Minimum, i.e. the specific growth rate is related to the cell quota (qo) of a single nutrient rather than is an additive or multiplicative function of two or more nutrient cell quotas (Droop 1974, Davies 1988). Droop (p. 825 in 1974) expressed this, as follows: “non-limiting nutrients exert no control at all over the patterns of growth — control follows a threshold rather than multiplicative pattern — the limiting nutrient is the one that shows the smallest cell quota: subsistence ratio”. This conclusion may hold for monospecific laboratory experiments, but extrapolation of the single, uniform nutrient limitation hypothesis to in situ behavior is suspect for several reasons. Natural communities are assemblies of species representing different phylogenies and, hence, have differing nutrient requirements.


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When nitrogen-fixing cyanobacteria and diatoms occur simultaneously different nutrients can become limiting contemporaneously. For the cyanobacteria, phosphorus may become the limiting nutrient, while silicon may become limiting to the diatoms. When diatoms co-occur with dinoflagellates, the latter may become either N- or P-limited, and the diatoms N-, P- or Si-limited. When oceanic populations of coccolithophores and diatoms co-occur, the latter may be Fe-limited and the coccolithophores Plimited. Thus, communities composed of phylogenetically diverse species, having different nutritional requirements potentially can become limited simultaneously by two or more different nutrients. Based on ambient field measurements, intracellular nutrient concentrations and Ks considerations (Eq. 4), (Levasseur et al. 1990) concluded that nitrogen and silicate deficiency simultaneously limited the phytoplankton community in a coastal jet-front. In Antarctic ecosystems, experiments suggest that Fe is one of a suite of limiting factors, rather than the sole factor controlling productivity (de Baar 1994). Nutrient enrichment experiments provide evidence that nutrient colimitation occurs (Davies 1988). The joint addition of phosphate with nitrate or ammonia had a synergistic, stimulatory effect on photosynthesis rates and chlorophyll synthesis, an interaction consistent with the stoichiometric relationship found between the essential nutrients N and P (Eqs. 1, 2). Ironlimited cells of certain Pseudo-nitzschia species lose their ability to assimilate nitrate (Maldonado et al. 2002). Nitrogen-fixing cyanobacteria require large amounts of iron which may stimulate oceanic blooms of Trichodesmium which then become vulnerable to P-limitation (Walsh and Steidinger 2001). Nutrient uptake can be co-limited by non-nutritional factors, such as irradiance. Cells exposed to low irradiance require much more iron for growth (Raven 1990). The bloom dinoflagellate Karenia mikimotoi cannot take up nitrate in the dark when N-sufficient, unlike the bloom species Prorocentrum minimum (Paasche et al. 1984). Experiments on nutrient colimitation in natural communities and on celluar processes of individual species are very limited, as are factor interaction experiments, even though both inter- and intraspecific co-limitation are expected to occur in natural populations. This expectation compromises application of Liebig’s Law of the Minimum, and use of the nutrient limitation models (Eqs. 4-6) and monospecific experimentation for in situ extrapolation. Droop (p. 894 in 1974) concluded that “mathematical models have great relevance in the laboratory, but their predictive value in the field is severely limited... being concerned with single species or even single clones”. He added “their value would be greatly advanced if... they could be applied to mixed populations

246 Algal Cultures, Analogues of Blooms and Applications and even to adapting species and species successions”. Three decades have passed since Droop’s statement without development of autecological and synecological experimental techniques to overcome these persistent limitations. This compromises ecologically relevant experiments and satisfaction of Redfield’s (1960) criterion that the objective of biological oceanography “is to develop effective models, and ultimately a single, consistent model, of life in the sea”. Since nutrient limitation is a major paradigm, what is the evidence that it occurs in situ, and is it amenable to experimental verification? The extensive laboratory evidence leaves no doubt that cellular nutrient limitation can be induced experimentally, but does it occur in natural communities?

Does Nutrient Limitation Occur in situ? The specific nutrient hypothesized to be limiting in situ largely has been a technique-driven conclusion. The presumptive limiting nutrient has changed with analytical improvements and improved field descriptions of in situ nutrient distributions and behavior. Experiments largely have sought to confirm field-based conclusions, rather than evaluate other potentially limiting nutrients, or other growth-limiting factors. At one time or another, ammonia, nitrate, phosphate, silicate, iron and vitamin B12, each, was projected to be, either directly or potentially, the nutrient most limiting to phytoplankton growth and biomass. Ryther and Dunstan’s (1971) nutrient enrichment bioassays in a polluted coastal embayment have led to the general acceptance that nitrogen is the primary limiting nutrient in marine waters rather than phosphorus, and unlike in freshwater systems. (Hecky and Kilham 1988). This prompted Howarth and Cole’s (1985) unsuccessful experimental effort to demonstrate that Mo insufficiency underlay this presumptive nitrogen-limitation. Hecky and Kilham (1988) have vigorously challenged the field and experimental evidence that marine waters are nitrogen-limited. Numerous nutrient enrichment experiments of different spatial and temporal coverage, mostly single species bioassays, consistently show that when enriched with nitrogen, abundance increases relative to phosphorus enrichment and the unenriched control (Johnston 1963, Smayda 1971, 1974, Maestrini et al. 1997, Bonin et al. 1986). However, Hecky and Kilham contend that difficulties in extrapolating these results to natural systems and the lack of experimental rigour compromise the nitrogenlimitation hypothesis. They argue that the experimental evidence for phosphorus limitation of freshwater systems is much stronger, and concluded (p. 796 in Hecky and Kilham 1988) “the extent and severity of nitrogen limitation in the marine environment remain an open question”.


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Similar reservations have been expressed regarding the Fe-limitation hypothesis, which Cullen et al. (1992) rejected. The a priori invocation of uniform nitrogen limitation in marine waters continues to be challenged. Peeters and Peperzak (2002) in their review of the literature cite evidence for nitrogen, phosphorus and silicon limitation, and within-region variations in the type of limitation. For example, western Mediterranean waters are believed to be phosphate-limited, but eastern (oligotrophic) Mediterranean waters nitrogen-limited. The Phaeocystis spring bloom in Belgian coastal waters appears to be nitrogen-limited, but phosphate-limited off the Dutch coast. In defense of nutrient enrichment bioassays, Peeters and Peperzak (2002) argue that it is the only method that allows determination of which nutrient potentially is the first to become limiting, and also allows ranking of the other nutrients in order of likelihood to become limiting. However, the results and interpretation of nutrient enrichment experiments are influenced by the choice of bioassay species, sampling locations and depths, experimental protocol, and unrecognized changes and responses that occur in the chemical milieu of modified sea water media (Smayda 1970). In nutrient-limited systems, the better competitors survive and the most limited species become rare, if not en route to successional replacement (Cullen et al. 1992). In ‘species-blind’ experiments with nutrient-enriched natural populations, i.e. biomass or primary production is the measured response. The enhanced growth obtained may reflect the disproportionate responses of physiologically stressed, rarer cells to the enrichment, and not the dominant species less likely to show nutrient-limited behavior. The many permutations of this prospect, further influenced by the bloom-species and successional stages, complicate the design and interpretation of enrichment experiments with natural populations. This is very evident in the in situ Fe-enrichment experiment carried out by Kolber et al. (1994). Increases in chlorophyll fluorescence of all phytoplankton size classes in response to iron enrichment relative to unenriched control waters led them to conclude that control waters led them to conclude that the community was Fe-limited. Despite this cellular response to added iron, Fe-enrichment did not lead to ‘large’ increases in biomass or a corresponding reduction in macronutrient concentrations. This led to Kolber et al.’s dubious conclusion that perhaps “grazing maintained a low standing stock of phytoplankton”. This, in turn, led to their broader ecological extrapolation, unwarranted either by the experimental results or design (p. 148 in Kolber et al. 1994): “Hence, the growth of phytoplankton in the equatorial Pacific is physiologically limited by iron rather than by extrinsic factors such as grazing”.

248 Algal Cultures, Analogues of Blooms and Applications Autecological laboratory experimentation also suffers the potential of enthusiastic ecologists to over-extrapolate experimental results. To be sure, this is encouraged by the evidence that, in general, microalgae from the diverse phylogenies making up natural communities have remarkably similar physiological and cellular chemical compositional responses to nutrient limitation (Hecky and Kilham 1988). This notion of physiological and biochemical uniformity can lull experimentalists and ecologists into over-extrapolation of experimental results to in situ responses, a danger recognized by Redfield (1960). The inorganic nitrogen uptake by several bloom-forming dinoflagellates exhibited significant interspecific differences that prompted Paasche et al. (1984), who sought to extrapolate their results to in situ behavior, to point out the difficulty (and danger) of associating mass occurrences of harmful and benign dinoflagellates in nature with any particular nutritional mode. There is also need for caution because of discrepancies that can occur between laboratory vs. field behavior, such as the demonstration that natural populations of Skeletonema costatum took up urea 30-times faster than a laboratory culture (Wheeler et al. 1974). This may be related both to clonal issues and differing growth conditions. These diverse observations lead me to concur with Hecky and Kilham (1988) that nutrient enrichment experiments and the limitation models (Eqs. 4-6) intrinsically are unsuited to establish ecologically relevant nutrient limitation of in situ phytoplankton communities. The premise of nutrient experimentation is that limitation occurs. However, there is growing evidence that this may not be so or, at least, this has not been confirmed experimentally. It might be expected that oligotrophic regions would provide a very strong signal of nutrient limitation, but this is not the case. Massive blooms of the ichthyotoxic dinoflagellate Karenia brevis occur regularly in the oligotrophic Gulf of Mexico (Steidinger et al. 1998). Goldman (1980) showed that near maximum growth rates of phytoplankton can be supported when ambient nutrient concentrations are below detectable concentrations and biomass levels are exceedingly low, i.e. a condition of LNLB. Goldman concluded (p. 190 in 1980) that “descriptive water quality data on gross biomass and nutrients provide absolutely no insight as to the magnitude of [phytoplankton] growth rates”. That is, residual nutrient levels cannot be used as evidence for nutrient limitation. Cullen et al. (p. 69 in 1992) reviewed the literature on nutrient regulation of phytoplankton photosynthesis and concluded: “ When it comes to nutrient limitation of marine photosynthesis, a good paradigm is hard to find”. The photosynthesis assimilation number (PB) can remain high despite nutrient limitation. Just as phytoplankton have an


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enormous capacity to exploit nutrient micropatches (Goldman 1980), they also adapt to limiting nutrient levels by regulating their cellular chlorophyll levels (Cullen et al. 1992). These capacities help to overcome potential physiological impairment because of reduced nutrient concentrations. Sommer, who approached this issue from the absence of steady state conditions in situ, concluded, based partly on experimental evidence, that “nutrient limitation [may occur] more as a bottleneck for only a few generations rather than as a constant steady state ” (p. 66 in Sommer 1989). Harris (p. 156 in 1986) concluded “the response to nutrient depletion in surface waters... is a community response and the only time when severe nutrient depletion and a reduction in growth should be observed is when a single species bloom occurs”. In this instance, the subsistence cell quota (Eq. 6) exceeds the nutrient supply from the nutrient stocks depleted by the bloom. The population loss rate then exceeds the growth rate (Eq. 3). Monospecific blooms are characteristic harmful algal species behavior (Smayda 1997a). The cumulative evidence suggests that the nutrient limitation hypothesis remains to be validated experimentally, and that extant field and experimental results and approaches are inadequate to this task. The alternative hypothesis that grazing, rather than nutrients, is the primary regulator of the phytoplankton dynamics (Yentsch 1980) presents the same experimental autecological and synecological impediments as those for nutrient regulation. [Discussion of experimental grazing studies lies beyond the scope of this chapter.] Some additional complications of applying experimental approaches to resolve in situ phytoplankton behavior will be briefly considered prior to assessment of the current merits and relevance of the Redfied-Braarud debate.

Nutrient Competition among Species Competition among species for nutrient resources influences their distributional and abundance patterns. When several species compete for the same resource, the species with the lowest requirement for that nutrient should become dominant at equilibrium (Tilman et al. 1981). Its growth rate becomes superior to that of competing species and leads to its dominance. Two experimental and theoretical approaches to interspecific competition have been applied: the efficiency of nutrient uptake based on Ks coefficients (Eqs. 4, 5; Eppley et al. 1969) and resource-ratio kinetics (Tillman 1977, Tilman et al. 1981, Grover 1989, Smith 1993). Dinoflagellate occurrences and abundance are generally associated with low nutrient concentrations and

250 Algal Cultures, Analogues of Blooms and Applications highly stratified watermasses which dampen upward pulsing of nutrients from deeper layers (Smayda 2002a). Yet, blooms often develop and persist in oligotrophic waters such as the enormous blooms of ichthyotoxic Karenia brevis in the Gulf of Mexico (Steidinger et al. 1998). This behavior has led to the assumption that dinoflagellates have efficient nutrient uptake mechanisms that allow their survival and growth in nutrient-poor watermasses. In contrast, the spring and upwelling blooms of diatoms are associated with high nutrient concentrations. This has led to the view that diatoms are inefficient nutrient-gatherers and require high nutrient concentrations to bloom. Eppley et al. (1969) proposed that the efficiency of a species’ nutrient uptake capacity could be gauged from its half-saturation coefficient for uptake, Ks, as formalized in Equation 4. Ks is considered an index of a species’ affinity for nutrient uptake and its potential competitive ability at low nutrient concentrations. Species with high nutrient affinity have low Ks coefficients. Their selection is hypothesized to be favored in seasons and regions of chronically low nutrient supply rates, when they are expected to out-compete species less able to take up nutrients at low nutrient concentrations, i.e. they have a high Ks coefficient (Smayda 1997a). Thus, extrapolating Monod type kinetics (Eqs. 4, 5) to field distributions and bloom behavior suggests that dinoflagellates should have low Ks coefficients and diatoms high coefficients. Available experimental data (Smayda 1997a) reveal the opposite: collectively, harmful bloom flagellates lack the expected high affinity for nutrient uptake, while diatoms have a considerably greater affinity for nutrients. This is seemingly a needless adaptation since diatoms thrive in enriched seas. The ecological basis of this paradox is obscure, but in the case of flagellates their motility-based behavior and facultative mixotrophy may be adaptive (compensating) corrections to such physiological limitation. The point being made is the dual difficulty encountered: making accurate physiological extrapolations based on field observations (this also influences experimental design), and accurate ecological extrapolations to field behavior based on experiments.

Nutrient Ratios Cultural eutrophication of coastal waters is one of four primary causation theories advanced to explain the global expansion in harmful algal blooms (Smayda 2002b). Two aspects of nutrient enrichment have been focused on: the increase in nutrient concentration, and changes in ratios of the macronutrients N, P and Si. A review of long-term, regional relationships between blooms and anthropogenic enrichment of N and P led Smayda (1990) to hypothesize that the accompanying decline in ratios of Si:N and


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Si:P promoted flagellate blooms. This hypothesis has stimulated considerable interest and research (Anderson et al. 2002, Hodgkiss and Lu 2004). The proposed selection for flagellate blooms extended (Officer and Ryther’s 1980) suggestion that coastal eutrophication could lead to seasonal Si depletion and eliminate diatoms from the communities. In Smayda’s view, the remaining N and P would then become available to promote blooms of undesirable flagellate species no longer in competition with diatoms. A considerable experimental literature has documented the sensitivity of diatoms to Si availability, depletion of which or very low N:Si ratios (< 1:1) select against diatoms (Riegman et al. 1992). Smayda did not propose that all harmful blooms are nutrient-regulated events, only that the potential for such Si:N and Si:P nutrient ratio effects is greatest in waters anthropogenically enriched. This hypothesis is a variant of Tilman’s (1977) ‘resource-ratio theory’. Confirmation of the theory and its application require experimentation.

Resource-ratio Theory Tilman’s theory posits that the changes in resource-supply ratios, e.g. N:P, N:Si, P:Si, should change the principal limiting nutrient and, in so doing, regulate phytoplankton community structure (Smith 1993). This mechanistic theory of competition is based on the Monod model (Eq. 5). Tilman’s (1977) steady state experiments showed that diatom species have an optimal Si:P ratio, above which the species becomes limited by one of the paired nutrients, and below which by the other nutrient. His mixed-diatom cultures experiments confirmed that two species will either co-exist, or one or the other will become competitively dominant dependent on the varying proportions of the Si:P ratios supplied. The general features of these competitive relationships were reliably modeled by the Monod (Eq. 5) and Droop (Eq. 6) models. Thus, laboratory experiments tend to validate the nutrient-ratio theory or, in the words of Sommer (1989) “make it plausible”. Competition experiments apply steady state dynamics to mixtures of competing species free from grazers and advective losses, e.g. some of the terms in Eq. 3. None of these experimental conditions is a realistic simulation of in situ conditions. Steady state does not occur in situ; nutrient concentrations vary greatly, and the frequency and magnitude of their pulsed supply influences uptake and growth kinetics; nutrient gathering migrations of flagellates (Smayda 1997a) and certain diatoms occur (Villareal et al. 1996); and there is a plethora of grazer types ranging from copepods to nanoplanktonic, heterotrophic flagellates. Moreover, species replacement rates during successions within mixed-species blooms are

252 Algal Cultures, Analogues of Blooms and Applications much more rapid than when competing in steady state chemostats, suggestive of more important regulatory factors (Smayda and Krawiec in prep.). Grover (1989), however, found neither nutrient pulsing, nor grazing on daily to weekly scales altered the taxonomic responses of natural fresh water communities exposed to variations in Si:P supply ratios in semi-continuous culture. He concluded that the observed responses were generally consistent with expectations based on resource competition theory. Although the potential for resource-ratio regulation of in situ species selection is present, it probably is rarely realized in marine communities. Numerous confounding factors influence the outcome of species competition, experimental simulation of which is difficult. Sommer (1989) is more optimistic. His mesocosm experiments to assess the effects of altered nutrient ratios led him to suggest that nutrient ratios can successfully predict the gross taxonomic composition of marine phytoplankton communities “even during periods of elevated grazing” (Sommer et al. 2004). The lack of grazer influences on the outcome of resource ratio perturbations on competing species selections that Sommer et al. (2004) and Grover (1989) claim to have observed is puzzling.

Experimental Growth Rates The most important rate measurement in efforts to model phytoplankton behavior is their rate of growth (Eq. 3). The scientific literature abounds with growth rate measurements based on cell abundance, biomass (chlorophyll; cell volume), paired cell frequency, assimilation numbers from photosynthesis experiments, etc. These rates are often by-products of experiments designed to assess other physiological feature; they have been made for all stages of the population growth curve; are usually averaged over various periods of growth, and less often are based on experiments specifically designed to establish the growth rate of species in response to a given factor, or the full range of ecological factors influencing growth. Growth rates have been reviewed by Furnas (1990); related to temperature (Eppley 1972, Goldman and Carpenter 1974) and compared allometrically (Tang 1995). The results consistently show that diatom growth rates are higher than those for flagellates, which suggests a significant phylogenetic difference. Tang (1996) has addressed the issue of why dinoflagellate growth rates are so low. The impressive feature of the dinoflagellate growth literature is the surprisingly high number of estimates suggestive of growth rates equivalent to a cell division every three days. In contrast, many diatoms


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can divide two and even three times daily. The apparently sluggish growth rate of dinoflagellates poses a problem in dealing with harmful blooms. The question is whether their often precipitous and ephemeral blooms result from the physical accumulation of slow growing, poorly grazed species, or whether they reflect aperiodically high growth rates that lead to blooms and discolored water. Alternatively, are they largely an experimental artifact associated with finicky growth requirements difficult to provide in laboratory culture? In an effort to resolve this, Smayda (1996) determined the daily growth rates of 14 taxa of bloom dinoflagellates incubated in monospecific culture outdoors in flow-through incubators at five different incident irradiance levels over the population lag, exponential and stationary stages in media heavily enriched to avoid nutrient limitation. Maximal growth of seven of the species exceeded m = 2.0 d–1, and only one species failed to grow at m = ≥ 1.0 d–1. While this does not resolve the issue of slow vs. rapid growth rate as a factor in harmful bloom formation, the results of this outdoor incubation suggest that the majority of growth rates on dinoflagellates obtained in culture may be primarily stress-related responses because of inadequate growth conditions, particularly in irradiance quality and intensity, and failure to provide experimental conditions suitable to motilitybased behavior. Caution should be exercised in their extrapolation to in situ harmful bloom behavior. This conclusion certainly needs to be confirmed. The point is that it is ecologically risky to use cellular growth rate data for a species selected at random from autecological experiments, particularly when estimates of μmax are needed for modeling purposes (Eqs. 3, 5).

THE REDFIELD-BRAARUD DEBATE REVISITED: CONCLUSIONS Ecological Limitations of Autecological Experiments The major advances in phytoplankton ecology made since Redfield and Braarud debated the relevance of autecological experiments to in situ behavior prompt the question: is their discourse now primarily of historical interest or do their opposing views need to be reconciled? Evaluation of this builds upon the material presented in earlier sections of this chapter i.e. the distinctions between autecological and synecological approaches to phytoplankton ecology; the merits and limitations of their experimental capacity to quantify natural behavior; the three types of in situ growth; the two types of nutrient limitation; the sink (diatom) vs. swim (flagellate) strategies; experimental results with clones; on the suite of responses to

254 Algal Cultures, Analogues of Blooms and Applications nutrients, and growth rates as indicators of the overall efficacy of autecological experimentation extrapolated to in situ behavior. The autecological objective to generate experimental data to explain in situ phytoplankton behavior has several rigid requirements. Failure to meet these criteria compromises the ecological value and extrapolation of experimental results. The in situ behavior of interest should have quasipredictable occurrence patterns, be a general feature, and amenable to laboratory and field experimentation. The spring diatom bloom largely meets these criteria, unlike harmful algal blooms. Harmful flagellate blooms, often monospecific, are highly unpredicable as to when, where and which species will bloom (Smayda and Reynolds 2001, 2003, Smayda 2002b). At least 11 distinct types of variability characterize the bloom and occurrence patterns of individual species (Smayda 1998b). The irregular and unpredictable bloom behavior of HAB species poses significant experimental problems in efforts to quantify their ecology. Insights from field-based behavior are essential to experimental design (Redfield 1960), yet how does one design experiments to explain bloom behavior when the behavior itself is unpredictable, erratic and even stochastic? Experiments on HAB ecology are additionally compromised by the still incomplete descriptions and regional comparisons of bloom behavior of the various types of toxic species found, i.e. those responsible for paralytic, diarrhetic and amnesic shellfish poisoning, and ichthyotoxicity. New species, toxins, toxicity types and bloom events are continuously being reported. The autecological approach, itself, is an impediment, and there are ecophysiological complications. As discussed earlier, experimental problems include the occurrence of clones; difficulties in simulating in situ nutrient and grazing conditions; experimental enclosure which dampens dispersion and vertical microstructure; and substitution of static, batch culture conditions for the more dynamic in situ nutrient recycling mode. Incubation artifacts always compromise ecological extrapolation of experimental results; laboratory-based experiments intrinsically are inadequate to explain in situ behavior. Major ecological behavior and processes cannot be addressed adequately by ‘bottle experiments’, or even in large enclosures such as mesocosms. These include blooms, species competition, succession and in situ grazing control. In essence, ecological problems that are beyond experimental approach are insolvable. To increase the ‘naturalness’ of experimental systems decreases the experimenter’s control (Hecky and Kilham 1988). Experimental control requires simplification of experimental design, but this compromises ecological relevance and extrapolation. There is also vulnerability to Redfield’s (p. 18 in 1960) concern that experiments


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can become “essentially observations made under conditions rigged so as to answer particular questions”. The great difficulty in carrying out relevant and reliable ecological experiments on population and community dynamics, historically, has led to an emphasis on cellular ecophysiology based on “bottle experiments”. This may have led to an unrecognized, derivative problem: the bias of applying cellular experimental approaches and techniques to in situ population and community dynamics where new conceptual and different experimental approaches are required. In situ experimentation – the alternative to ‘bottle experiments’ – also presents problems of ecological fidelity. Efforts to quantify bloom regulation, bloom-species selection and species succession, for example, require knowledge of the population losses from grazing, mortality and advective washout (Eq. 3). Evaluation of in situ nutrient-regulation requires experimental provision of the diverse, key biological and physical sources and pulsed nutrient resupply. This, in turn, requires provision of the full suite of relevant physical, chemical and biotic factors in synecological combination, and at suitable spatial and temporal scales. This general protocol for the design of in situ experiments with natural populations (e.g. Eq. 3) is partly evident in Martin’s (1992) recognition that tests of the Fe-limitation hypothesis are not amenable to ‘bottle experiments’, but require large scale in situ enrichment experiments (he recommended a 100 km2 patch study). The use of tracers, such as sulfur hexafluoride (SF6), to track patch dispersion and mixing is essential in experimental deployments in situ (Ledwell et al. 1993, Rees et al. 2001), and have been incorporated into Fe-enrichment experiments (Kolber et al. 1994, Tsuda et al. 2003). The practical and analytical difficulties encountered in in situ experimentation are formidable. There are problems associated with suitable controls, replication, experimental patch dispersion, mixing of the experimental habitat with surrounding waters, variable population structure, with the results obtained further compromised by differing experimental responses of the entrained species in competition with each other and exposure to selective grazing (Kolber et al. 1994, Tsuda et al. 2003). The efficacy of in situ experimentation remains unconvincing. This impacts a large class of ecological problems whose resolution requires in situ manipulation of natural populations; the required methodology is unavailable, or the process itself is not amenable to such experimentation. How does one conduct a relevant in situ grazing experiment, or one on regulation of species succession? There is also indirect evidence that the autecological approach is inadequate. The progress in phytoplankton ecology since the Redfield-

256 Algal Cultures, Analogues of Blooms and Applications Braarud debate suggests that experiments have not greatly altered insights into the mechanisms influencing the temporal and spatial regulation of phytoplankton distribution, their abundance, blooms and production. The persistent failure of ecophysiological knowledge and models to predict field-based discovery of major, new biotic groups and trophic behavior is indicative of this marginal, experimental contribution. Field-based new discovery has been the primary driver of advances in phytoplankton ecology, and not autecological experimentation. This continues the trend noted by Redfield (1960) and Parsons (1985). These advances largely have been facilitated by an increasingly sophisticated technology. The growing reliance on technology has focused on descriptive measurements of in situ biotic behavior and habitat features; in contrast, experiments designed to test hypotheses and to develop theory have been relatively neglected. In many respects, phytoplankton ecology is an immature science (particularly HAB ecology) still in the information gathering stage, rather than hypothesis driven. Longhurst (1998) has discussed the role of remote sensing, a particularly important new technology, in expanding our descriptive knowledge base, stating (p. 17 in 1998): “it is difficult now to remember how ignorant we were of the extent, variability, and seasonal algal blooms prior to their global visualization by the CZCS (= Coastal Zone Color Scanner)”.

Evidence for, and Against Braarud and Redfield’s Views The diverse body of evidence, collectively, favors Redfield’s view that autecological experiments have limited in situ application, and counter to Braarud’s view. Notwithstanding, important aspects of their seeminly contradictory views remain relevant. Braarud (1961, 1962) emphasized that autecological experiments on cellular behavior provide an ecological ‘orientation’ not available from field measurements that is needed to understand in situ species behavior. This perspective is well founded. Subsequent experimental advances in understanding cellular processes and life histories, and application of this knowledge have strengthened Braarud’s view. The cumulative value of species-specific autecological profiles, already great, will increase as the data bank expands and facilitate insights into harmful algae, whose blooms and harmful affects are highly species-specific. It is beginning to allow grouping of phytoplankton species into life-form types (Smayda and Reynolds 2001, Smayda 2002b) that better reflect in situ ecophysiological behavior than that based on the autecology of a very limited number of key species. This facilitates identification of


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functional groups, experimental design, concept development, and improves modelling. It also provides insights into neglected issues, such as the rules of assembly of phytoplankton communities (Smayda 2002b, Smayda and Reynolds 2003). The value of these physiological data is also evident in their generalized use as rate functions (photosynthesis, growth, etc.) in global ocean, mass balance and other oceanic and planetary biogeochemical issues [see also below]. Redfield’s argument that laboratory-based approaches (and here I include in situ experiments) are inadequate to explain individual species in situ ecological behavior is supported by the evidence. Laboratory-based results on cellular physiology and life history behavior only marginally have advanced knowledge of in situ behavior, such as the selection of bloomspecies, their bloom dynamics, nutrient regulated behavior, successions, distributions, assembly into communities, and partitioning among grazers exhibiting prey-preferences. The difficulties of in situ experimentation exposed by the Fe-enrichment experiments have been pointed out. At best, some very general ecological extrapolations can be obtained from such experiments, but site-specific events, whole community behavior and anomalous behavior are not tractable for the most part. The difficulties in such experimentation are intrinsic: they persist independent of potential new discovery, increased knowledge of cellular-based kinetics, physiology, life histories, and in situ phytoplankton behavior. Redfield’s disparagement of autecological experimentation does not diminish Braarud’s advocacy that experiments provide valuable ecological “orientation”, nor negate the evidence since Braarud that physiological rates generated in autecological experiments are important to mass balance and modelling efforts. Rather than being contradictory, their differences, which reflect their focus on different aspects of phytoplankton ecology, are complementary rather than exclusive. Hence, I agree both with the views of Braarud and Redfield. With Braarud (1961), that autecological experiments provide valuable ecological orientations and information on speciesbased cellular behavior such as nutrient requirements and uptake rates; photosynthesis-irradiance relationships; growth rates and diel division patterns; ecological tolerance profiles to minimum, maximum and optimal temperature, salinity, trace metal concentrations; clonal variations; life cycle; prey value to a given grazer, etc. With Redfield (1960), that experimental autecological information alone is inadequate to explain the in situ population and community behavior of the phytoplankton. Braarud’s focus was on species behavior; Redfield’s focused on population and community behavior, with the ultimate objective of building representative

258 Algal Cultures, Analogues of Blooms and Applications models. Redfield, in advocating discontinuance of experimentation until representative descriptions of natural population behavior was achieved, reached his conclusion as a corollary rather than from analysis of the experimental literature. His position was not that experiments per se were invalid, but pre-mature. He (p. 26 in Redfield 1960) believed that biological oceanography (phytoplankton ecology) “can develop fully only if it employs all means of discovery at its disposal, observation, comparison, correlation and experiments”. He reasoned that proper experimental design requires representative knowledge of natural population behavior, but since this knowledge is incomplete, ecological experiments are therefore inadequate and should be discontinued. One might argue, following this reasoning, that given the inadequacy of field measurements they, too, should be discontinued because of the faulty impressions gained. However, no one would seriously recommend cessation of field study until all the needed methodology became available. It is important to distinguish between the need to understand organisms in the ocean and the ocean per se. With regard to the ocean per se, Redfield’s view is too rigid in calling for a halt to experimentation until descriptions of in situ behavior are complete. Since new discovery depends on field-based study, how does one know when the essential details of in situ behavior have been acquired? And, there is the need to start somewhere with experiments. The mass balance approaches and use of extant experimental data to quantify oceanic processes relevant to planetary ecology are both proper and tractable using current techniques and insights. An added impetus is that anthropogenic modifications, realized and potential, associated with climate change, hypernutrification, aquaculture and over-fishing have increased the need to apply experimentally-based solutions to minimize ecosystem disruption. This ‘whole system’ approach, to be sure, reflects organismal biology and ecosystem function. However, the smoothing of species-based physiological behavior to generalized rates is expected to have a lower error factor than application of autecological data to explain in situ blooms, bloom-species selection, species succession and grazing control of biomass. In agreeing with Redfield on the ecological inadequacies of autecological experiments, I suggest that ecologically relevant experiments leading to definitive explanation of some major in situ behavior may be beyond reach, including species’ bloom dynamics, co-limitation effects, species succession, and in situ grazer regulation, whether based on laboratory experiments or in situ manipulations, and whether examined as ‘top-down’ or ‘bottom-up’ effects. This incapacity is not because knowledge gaps in


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natural population behavior compromise experimental design, or that the experimental approach is invalid. Rather, because this class of first-order behavior has a particularly complex ecology regulated by multiple oceanographic features, neither the behavior, nor the habitat drivers are amenable to experimental manipulation. In these cases, combining autecological and synecological field and experimental approaches with better in situ molecular and biochemical diagnostic, and habitat descriptor technology, at best, can help to establish candidate cause and effect relationships, but not definitive proof. Even then, the complex regional oceanography (Longhurst 1998) limits general ecological extrapolation.

Functional Group Experiments: Coalescing Redfield and Braarud’s Views Critics of this endorsement of Redfield’s view might point to the highly predictable spring diatom bloom, and its well-established initial regulation by critical depth conditions (Sverdrup 1953), followed by a decline in nutrients and increases in grazing pressure which contribute to bloom termination. The mass balance approach applying Sverdrup’s critical depth concept and the nutrient-grazer role has been of exceptional value – but its value is not in accounting for individual species’ blooms. The predictive power is of another type – a functional group prediction, i.e. that the spring bloom will be a diatom bloom. While certain species typically make up the bloom community, the predictive power is not of their occurrence, but that they occur because they are diatoms. It just so happens that certain diatom species (Skeletonema costatum, for example) usually bloom during spring, an appearance that we then come to expect. There is greater certainty that the spring diatom bloom will be a diatom bloom, and not a dinoflagellate or coccolithophore bloom, than which species will bloom. Phytoplankton experimentation can minimize some of Redfield’s concerns and take fuller value from Braarud’s recommended approach by applying a functional group, or phylogenetic experimental approach to in situ behavior. This is more tractable and ecologically relevant than current experimental manipulations of species for in situ extrapolation. The nitrogenfixing cyanobacteria, the calcium (coccolithophorids) and silicon (diatoms) mineralizers, the phylogenetically diverse flagellates, and the non-motile vs. motile strategies are key examples of the types of functional group and phylogenetic behavior that have major ecophysiological, biochemical and biophysical differences amenable both to field experimentation and laboratory study. There is already considerable baseline data on in situ

260 Algal Cultures, Analogues of Blooms and Applications distributions and dynamics of these ubiquitous groups to minimize problems associated with new discovery. Conceptually and methodologically, experimental designs and results on functional (phylogenetic) groups are more tractable ecologically, and have greater first-order relevance and validation prospects than species-based experiments or those based on cell size fractionation. The functional group approach allows experimental focus on the sites, bloom periods, dominance and dynamics of diatom vs. dinoflagellate vs. coccolithophore, etc. without regard to which species will bloom, which carries significant ‘biological noise’ and stochastic behavior. Among the experimental approaches that could be followed are resourceratio and competive exclusion type experiments refined to address phylogenetically behavior driven by nutritional differences (Tilman et al. 1986, Grover 1989, Sommer 1989, Sommer et al. 2004). The influence of nutrient ratios on the competitive outcome between two or more vying phylogenies (e.g. diatoms vs. flagellates vs. N-fixing cyanobacteria) may be more tractable and ecologically relevant, unlike experiments on the competitive outcome between two, or more species from within the same genus, or between genera. The experimental matrix could be modified to examine co-limitation effects such as between nutrients, or nutrients + grazing, and other growth-factor combinations. The heirarchical selection of bloom species proposed for harmful blooms could serve as a template for functional group experiments (Smayda 2002b, Smayda and Reynolds 2003). The very successful combination of field and experimental studies on haptophyte nuisance species of Phaeocystis is an encouraging example of the value of the functional group approach (Lancelot et al. 1991, Peperzak 2002). Phaeocystis and its blooms have become persistent in the Dutch Wadden Sea since the mid-1970s, out-competing diatoms as the dominant spring bloom floral element as it established itself within the community. Phaeocystis globosa is probably the best understood and modelled species based on its behavior in those waters. This has been the result of an effective, combined use of field observations, time series behavior, experimental data from “bottle experiments” and mesocosms, and ecophysiological contrasts with diatoms and their local behavior. In advocating a functional group approach, it is not recommended that laboratory-based autecological experimentation on species be discontinued. Such experimentation is very important for the reasons already discussed, and very relevant to the proposed functional group and phylogenetic approach. Whatever the ecological problem of interest, it is essential that the experimental approach followed be designed in harmony with the oceanographic processes that control in situ phytoplankton behavior, also


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bearing in mind Harvey’s guideline (p. 80 in Harvey 1955): in “considering how each factor may affect a single plant, it is helpful to consider how the tangle of variables affects the whole population of marine plants in nature, a population which waxes and wanes, changing its constitution through the seasons and differs both in constitution and in quantity from one sea area to another”.

ACKNOWLEDGEMENTS This research was funded by the EPA’s Science to Achieve Results (STAR) Program, supported by EPA Grant No. R82-9368-010 awarded to Dr. Smayda. STAR is managed by the EPA’s Office of Research and Development (ORD), National Center for Environmental Research and Quality Assurance (NCERQA). STAR research supports the Agency’s mission to safequard human health and the environment. I thank Dr. Subba Rao for his kind invitation to prepare this chapter.

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The Trace Metal Composition of Marine Microalgae

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& The Trace Metal Composition of Marine Microalgae in Cultures and Natural Assemblages Tung-Yuan Ho1 1

Department of Earth and Environmental Sciences, National Chung Cheng University, Ming-Hsiung, 621, Chia-Yi, Taiwan

Abstract Marine microalgae deplete some essential trace metals in surface oceans and thus influence their distribution and cycling in the ocean; relatively, due to the scarce concentrations in the surface waters, the trace metals can be important in controlling algal production and regulating their community structure. However, there is limited information available for the average trace metal composition either from culture or field studies when compared to our understanding on macronutrient composition in marine microalgae. This chapter reviews the reliable field and culture studies to assess the trace metal composition (Fe, Mn, Zn, Cu, Ni, Co, and Cd) and its variability in marine microalgae. Remarkable similarities of the average trace metal composition among different culture and field studies are observed. The average composition estimated from the selected field studies can be expressed as P1000, Fe5.1±1.6, Mn0.68±0.54, Zn2.1±0.88, Cu0.41±0.16, Ni0.70±0.54, Co0.15±0.06, Cd0.42±0.20. The similar metal/P composition ratios between deep ocean water and phytoplankton for Zn and Cd indicate that the two metals are regulated by microalgae like macronutrients. The metal/P ratios for Fe and Mn are about one order of magnitude smaller in deep oceanic water than in microalgae, suggesting the relative importance of scavenging processes in controlling the cycling of Fe and Mn in the ocean. Overall, these evidences support that the constant stoichiometric concept for algal elemental composition may be usefully broadened from the major macronutrients to the trace metals. The stoichiometric ratios then can provide a quantitative basis to understand how marine microalgae regulate or influence the cycling and distribution of the metals in the ocean.


INTRODUCTION Elemental Composition of Marine Microalgae Marine microalgae (phytoplankton), with their large biomass and high growth turnover rates in the ocean, are responsible for almost half of global primary production (Field et al. 1998). Considering their critical role on regulating elemental cycling in the ocean, knowledge of elemental composition of marine microalgae is essential to quantitatively understand how marine microalgae influence elemental cycling in the ocean. As early as 1930, based on some field measurements on macronutrient composition of the bulk plankton assemblages and the concentrations of nitrate and phosphate in the seawater samples, Redfield et al. (1963) concluded that the macronutrient composition of microalgae assemblages follow the constant ratios –C:N:P=106:16:1– and the N/P ratio are close to the corresponding nutrient ratios in seawater (Redfield 1934; Redfield et al. 1963; Flemming 1940). Over the past 70 years, this constant stoichiometry concept has been widely applied in numerous oceanography and marine biogeochemistry studies as reviewed in the studies (Broecker and Peng 1982; Hecky et al. 1993; Falkowski 2000; Geider and La Roche 2002; Li and Peng 2002). The ratios are especially useful for modeling plankton processes, nutrient cycling, and estimating primary productivity from the supply of a known limiting nutrient. Likewise, the information of micronutrient (essential trace metals) composition of marine phytoplankton may also provide a basis for examining how microalgae influence the relative distribution and the vertical transport of the trace metals in the ocean. It would thus be very useful if the traditional Redfield-type elemental composition ratios can be extended from the major nutrients to the trace metals–Fe, Zn, Mn, Cu, Ni, Co, and Cd.

Roles of the Trace Metals in Regulating Oceanic Productivity It is now well known that the availability of some certain trace metals in seawater are tightly linked with the growth and community structure of marine phytoplankton. Trace metals, including Fe, Zn, Mn, Cu, Ni, Co, Mo, are cofactors of many essential metalloenzymes and proteins that carry out various metabolic processes such as photosynthesis, respiration and major nutrient assimilation in phytoplankton. However, the dissolved concentrations of many of the trace metals are extremely low in oceanic surface waters, ranging from low nM to sub-nM levels in general (Bruland 1980; Martin and Gordon 1988). Moreover, most of the metals are chelated by


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organic ligands with strong chelating ability (Bruland 1989; Rue and Bruland 1995; Moffett 1995; Saito and Moffett 2001), which are not bioavailable to most of the marine microalgae. The bioavailable concentrations of some of the essential metals are so low in the surface waters that the growth of marine algae may be limited or colimited by the metals under certain circumstance (Martin and Gordon 1988, Morel et al. 1994; Saito et al. 2002). For example, iron, the most well studied trace element in the ocean, is known to be a major limiting nutrient for algal growth in the high nutrient low chlorophyll (HNLC) regions, which at least include the Southern Ocean, the Northeast Pacific Ocean, and the equatorial Pacific Ocean (Martin and Fitzwater 1988; Martin and Gordon 1988; Martin et al. 1994; Coale et al. 1996; Boyd et al. 2000). In spite of the increasing understanding for the importance of trace metals on influencing algal productivity and regulating community structure in the ocean, compared to the abundant studies on major nutrient composition in marine phytoplankton, there is relatively little information on marine cellular metal composition from field studies.

Previous Culture and Field Studies While discovering the important roles of the trace metals in the ocean, a variety of culture studies have been carried out to study marine phytoplankton-trace metal interactions during the past two decades (Brand et al. 1983; Harrison and Morel 1986; Price and Morel 1990, Lee and Morel 1995, Sunda and Huntsman 1983, 1995a, b, c; 1996; 1998 a, b, c; 2000). These algal culture experiments were mainly focused on the following three studies: the interaction between medium concentrations and growth rates, interaction between growth conditions and algal metal requirement, and metal-metal interaction. These culture studies were generally limited to a few model species and to one or two essential trace metals. Among the studies, Sunda and Huntsman have published systematic detailed trace metalalgae culture studies using the model algal species, particularly the coccolithophore Emiliania huxleyi and the diatoms Thalassiosira pseudonana (neritic) and Thalassiosira oceanica (oceanic) in addition to Thalassiosira weissflogii (Sunda and Huntsman 1995a, b, c; 1996; 1997; 1998a, b; 2000). These studies have greatly enhanced our understanding on microalgaetrace metal interaction and also reported some trace metal composition for the model species under certain growth conditions. However, it appears to be inadequate to use the trace metal composition obtained from the model species in the culture studies mentioned above to represent the composition of the whole phytoplankton assemblages in the ocean. The culture studies to determine the average metal composition

274 Algal Cultures, Analogues of Blooms and Applications simultaneously in various marine microalgae were scarce, which is mainly due to the difficulty in preparing an ideal algal culture medium to simulate the metal concentrations in natural oceanic surface waters, the difficulty in precisely determining the various extremely low trace metal composition in the cells, and the difficulty in choosing representative algal species to represent natural algal assemblage. The ideal approach is to directly determine the metal composition in natural phytoplankton assemblage. Nevertheless, given the sampling and contamination problems on collecting marine microalgae assemblages in oceanic surface water, there have been only a few reliable field studies reported so far. Bruland et al. (1991) compiled the results of three field studies (Martin and Knauer 1973; Martin et al. 1976; Collier and Edmond 1984) from 15 plankton samples with low Al contents and gave a rough approximate for the metal composition, representing as P1000Fe5Zn2(Cu, Mn, Ni, Cd)0.4. Kuss and Kremling (1999), using large volume pumping sampling technique, also proposed a Redfield-type metal composition from 9 biogenic samples collected in the North Atlantic Ocean, expressed as P1000Fe5(Zn, Mn)2Ni1Cd0.5Cu0.4Co0.2. It should be noted that the trace metal composition obtained from field studies are not only easily biased by the interferences of lithogenic and other biogenic particles but also influenced by different sampling methods. Intercomparison of the trace metal data among the field studies using different sampling methods needs to consider the influence of their sampling methods on the data. Alternatively, comparison of the trace metal data between culture and field studies may be an appropriate approach to establish a representative trace metal composition in marine phytoplankton. A recent algal-trace metal culture study (Ho et al. 2003; 2004) was designed to test the feasibility of this idea. The study examined trace metal composition in a variety of marine microalgae species –15 marine eukaryotic phytoplankton representing five major marine phyla – and grew the species under an identical culture medium designed to mimic natural seawater condition. The major findings of the study would be briefed in the following section. In 1985, Morel and Hudson has already initiated the concept of extending the Redfield ratio to the trace metals, expressed as P1000 (Fe, Zn, Mn)10 (Cu, Cd, Ni)1, based on scant culture studies on a couple of algal species. After two decades, limited field data sets and the possibility of the field data biased by the contamination of abiogenic particles still hinder our confidence to conclude whether it is appropriate and useful to establish an average trace metal composition for marine microalgae. By comparing the trace metal composition and its variability obtained from reliable culture


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and field studies, and also comparing the average algal composition with deep water metal/P ratios, whether the Redfield ratio may also be usefully extended to the trace metals is evaluated.

METHODS Preparation of Algae-trace Metal Culture Medium Due to the extraordinarily low bioavailable concentrations in natural oceanic surface waters, the trace metal concentrations rarely act as toxicants in the ocean, though many of them can be at elevated levels (Sunda and Guillard 1976; Brand et al. 1986). The studies at the high and toxic metal concentration levels would not be the interest of this chapter. To mimic natural surface seawater conditions, bioavailable trace metal concentrations in trace metal culture medium, compared to traditional algal culture media like f/2 (Guillard 1975), have to be maintained at relatively low and constant concentration levels. Trace metal ion buffer system has been proved to be an effective and easy approach to sustain low and constant supply for the bioavailable metal ions in the culture medium (Sunda and Guillard 1976). The bioavailable (inorganic) concentrations in culture medium, denoted as M¢, can be buffered by organic chelators like EDTA (ethylenediaminetetraacetic acid), which have strong chelation ability on the transition metals to keep relatively low bioavailable metal concentrations in batch culture medium. By using organic chelators to regulate M¢ in algal culture medium, it was revealed that both algal growth rates and metal uptake rates of the model algal species used are directly related to M¢ but are independent of the total metal concentrations (Sunda and Guillard 1976; Anderson et al. 1978). Given the concentrations of the total trace metals and organic chelators, the bioavailable metal concentrations and aquated free metal ions concentrations can be calculated with the complexation constants. The bioavailable and aquated free concentrations for all of the trace metals used may be precisely calculated by using chemical equilibrium computer models like MINEQL (Westall et al. 1986). Aquil, an EDTA-trace metal ion buffered medium designed for algae-trace metal studies, has been widely used in marine microalgae-trace metal studies (Morel et al. 1979; Price et al. 1988/ 1989; Price and Morel 1990; Yee and Morel 1996; Morel et al. 1994). The procedures for preparing the Aquil medium are briefly described here. Details for the preparation of the medium and trace metal ion buffer system may be found in Price et al. (1989/1990) and Sunda et al. (2004). The seawater used for trace metal-algae growth studies should contain relatively low trace metal impurities, which can be obtained either directly

276 Algal Cultures, Analogues of Blooms and Applications from natural oceanic surface waters with low metal concentrations or from trace metal free synthetic ocean water (SOW). To obtain the natural seawater, trace metal clean techniques are required during the collection and storage procedures to avoid contamination. To prepare the trace metal free SOW, the trace metal impurities in raw SOW can be removed by passing SOW through trace metal chelating resins like Chelex-100 (Price et al. 1989/1990). The low trace metal seawater is then first enriched with sterile and metal free major nutrients and vitamins. In Aquil medium, the final concentrations of the major nutrients and vitamins are 150 mM NaNO3, 10 mM Na2HPO4, and 40 mM Na2SiO3, plus 0.1 mM vitamin B12, 0.1 mM biotin, 20 mM thiamin. After adding EDTA stock solution to reach a final 100 mM final concentration, sterile trace metal stock standards are then added into the culture medium. The medium should stay for at least 3h to reach complete complexation equilibrium before inoculating algae (Price et al. 1988/1989). In Aquil medium, total trace metal concentrations are prepared to the following concentrations: FeT = 8600 nM, MnT = 120 nM, ZnT = 80 nM, CuT = 20 nM, and CoT = 50 nM, calculated in the presence of 100 mM EDTA to yield unchelated constant concentrations of Fe’ = 20 nM, Mn’ = 10 nM, Zn’ = 20 pM, Cu’ = 0.2 pM, and Co’ = 20 pM (Westall et al. 1986, Price et al. 1988/ 1989). Due to its broad application in marine biogeochemistry, cadmium is usually added with CdT = 15 nM to yield unchelated concentrations of Cd’ = 20 pM (Westall et al. 1986). It should be noted there are uncertainties regarding the exact unchelated concentrations in the medium because the complexation constants for metal-EDTA in seawater are not all precisely known. Comparison among different data sets thus requires careful matching of total metal and ligand concentrations (Ho et al. 2003).

Choice of Fe’ in the Culture Medium With the exception of Fe, the choices of M¢ in Aquil medium are fairly low and generally near what are thought to be the bioavailable concentrations in natural surface seawater: in the range of 1-20 pM for Cd’ and Zn’ (Bruland 1989, 1992), 0.4-5 pM for Cu’ (Moffett 1995), and 0.1-100 fM for Co’ (Saito and Moffett 2001). Previous studies have shown that the metal concentration levels in Aquil medium are high enough to allow maximum growth rates for the model microalgae (Brand et al. 1983, 1986, Brand 1991, Sunda and Huntsman 1992; 1997). However, to avoid limiting the growth of coastal species, the Mn’ is regulated at 10 nM in Aquil medium, a value that is typical of coastal waters but about ten times higher than in the open ocean (Landing and Bruland 1980). Likewise, the Fe concentration maintained in


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Aquil culture medium is set to be extremely high to avoid limiting the growth of some coastal algal species (Morel et al. 1979; Price et al. 1989/1990). The bioavailable Fe concentration (Fe’) is set at 20 nM in Aquil medium, which is much higher than the bioavailable concentration in the open ocean – ca 0.05-1 pM (Rue and Bruland 1995). Precipitation of Fe oxides on algal cell surfaces is unavoidable under the high concentration, and the extracellular wash for the precipitate is thus required (Hudson and Morel 1989). To obtain meaningful trace metal concentration of microalgae to mimic natural algal assemblage, it is critical to choose an appropriate Fe concentration in algal culture medium. On one hand, low unchelated Fe medium concentrations usually lead to low growth rates for coastal algal species (Brand et al. 1983); on the other hand, high Fe medium concentrations unavoidably result in precipitation of hydrous ferric oxide onto cell surfaces (Hudson and Morel 1989). Since ferric oxide may adsorb other trace metals, without removing the surface Fe oxide, the precipitation can lead to a significant overestimation for the adsorbed trace metals in addition to Fe (Morel and Hering 1993). To obtain high growth rates and minimize the problem of extracellular Fe precipitation, Ho et al. (2003) examined the change of intracellular and extracellular Fe concentrations and growth rates for the model diatom Thalassiosira weissflogii by varying Fe¢ in culture medium. The results showed that more than 98% of Fe measured under the Aquil recipe (a total Fe concentration of 8200 nM with 100 mM EDTA) was extracellular Fe and the percentage decreased sharply with the decreasing Fe’. At the Fe concentration of a total Fe concentration of 82 nM with 100 mM EDTA, corresponding to an unchelated Fe concentration, Fe’= 0.2 nM at 250 mE light intensity, Thalassiosira weissflogii grew at 85% of its maximum growth rate and the extracellular Fe were less than 30% of the total Fe measured. This choice of Fe’ (0.2 nM) thus allows one to obtain reasonably accurate value for algal cellular Fe concentrations without the tedious procedures to wash the cells for removing Fe oxides precipitate (Ho et al. 2003).

Culturing and Sampling To avoid contamination during culturing and pretreatment, all apparatus used for the culture medium preparation, algal culturing and sampling, and elemental analysis are prepared according to rigorous acid cleaning procedures (Cullen and Sherrell 1999; Ho et al. 2003). The algae are usually grown in polycarbonate bottles at 20∞C using a 12h:12h light dark cycle with a Cool White light source between 125 and 250 mmol quanta m–2s–1 light

278 Algal Cultures, Analogues of Blooms and Applications intensity. Replicate culture samples (bottles) are usually maintained in exponential growth through a minimum of 6 generations before harvesting. Cell density and size are determined daily, usually with a Coulter particle counter to calculate growth rates and cell volumes. Cells are usually harvested with a plastic filtration apparatus consisting of polypropylene filter holder and filters, which are also pre-washed with 10% trace metal grade HCl to avoid trace metal contamination. Right after harvesting the cells, the filters with the cells should be thoroughly rinsed with trace metal free seawater or NaCl solution to remove the residues of the culture medium. The filters with the cells are then digested with trace metal grade concentrated HNO3 in pre-cleaned Teflon vials for metal analysis (Ho et al. 2003).

Trace Metal Analysis The trace metal composition in the digested algal culture samples may be determined by any analytical instruments with high sensitivity for determination of trace metals. Sector field high resolution inductively coupled plasma mass spectrometry (HR-ICPMS) and graphite furnace atomic absorption spectrometry (GFAAS) are the most common analytical methods for the purpose. Especially, HR-ICPMS, providing an efficient means for simultaneous multi-elemental analysis including some major elements like P and S, is an ideal analytical tool for the study.

Sampling Methods for Phytoplankton Assemblages in the Ocean The trace metal composition in marine microalgae may be obtained directly by determining the composition in phytoplankton assemblages collected in the field. However, due to the extremely low trace metal contents in marine microalgae and extremely high trace metal concentrations in lithogenic particles in natural seawater, it is unavoidable encountering the interference from abiogenic particles in plankton assemblage samples (Fig. 8.1). The problems were clearly noted by Martin and Knauer in 1973 in their classic study for elemental composition of plankton, “the reasons for the scarcity of information concerning the elemental (metal) composition of the primary producers and primary consumers of the sea are not difficult to ascertain…”, which include the contamination from terrestrial particles, difficulty to separate phytoplankton from zooplankton, and contamination from vessels and sampling apparatus. After three decades, the sampling problems proposed by Martin and Knauer for the shortage of the field data in


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Fig. 8.1 Comparison of the Al, Mn, Fe, and Ti concentration normalized to dry weight of the suspended particles collected in the surface waters of the South China Sea by size fractionated plankton nets with 10, 60, and 150 mm net apertures (Ho et al. in prep.). The samples were collected from 5 stations across the coastal region, through continental shelf, to deep water regions. The symbols of open circle, diamond, and cross circle represent the data obtained from 10, 60, and 150 mm nets, respectively. Square symbols stand for the data of the samples collected by 0.4 mm polycarbonate filters. phytoplankton do not lessen much. Although trace metal clean methodology may effectively decrease the contamination from vessels and sampling procedures, it is still challenging to remove the interferences of abiogenic materials and larger particles from the collected suspended particles. While applying the metal data of suspended particles obtained in the field to represent the composition of plankton assemblages, it is essential to exclude the influence of abiogenic composition from the samples.

280 Algal Cultures, Analogues of Blooms and Applications Moreover, while comparing trace metal composition of suspended particles among different data sets, it should be noticed that different sampling methods represent different size particles collected. Algal assemblages and suspended particles in the oceans are usually collected by planktonic tows (Martin and Knauer 1973; Martin et al. 1976; Collier and Edmond 1984), pumping and centrifuge (Kuss and Kremling 1999), or pumping with membrane filtration (Sherrell and Boyle 1992; Cullen and Sherrell 1999; Cullen et al. 2003). Given the relatively large net aperture (several tenths of mm in usual), plankton nets are prone to retain larger particles like zooplankton; instead, membrane filtration methods (with 0.45 mm pore size in general) would collect almost all particulate particles, and centrifuge method may collect even smaller particles. Compared to the true phytoplankton elemental composition, the composition in the samples collected by plankton nets are biased with larger particles like zooplankton; samples collected by centrifuge with membrane filtration methods tend to be biased by smaller abiogenic particles as well as larger zooplankton. As witnessed by Al concentration, Figure 8.1 clearly shows that the influence of abiogenic particles on the essential trace metal composition in the suspended particles is severe and the influence is strongly correlated to the particle size, the smaller the particles; the higher percentage of the abiogenic particles in the samples (Fig. 8.1). The contamination problem from abiogenic particles in natural algal assemblages is commonly observed both in coastal and open oceans studies (Martin and Knauer 1973; Martin et al. 1976; Sherrell and Boyle 1992; Kuss and Kremling 1999). By 76 mm plankton nets, Martin and Knauer (1973), collected the phytoplankton samples in Monterey Bay, California, on 28 different dates. With relative constant concentrations on major cations (e.g., Na, K, Mg), there were more than 80% of the ‘phytoplankton’ samples in the study containing extremely high amount of Al, Fe, and/or Ti. As Al or Ti are indicators for lithogenic particle concentration, the samples with high Al or Ti are highly likely to be severely biased by the composition of lithogenic particles due to the high concentration of the essential trace metals in lithogenic particles. It was found in the data set of Martin and Knauer (1973) that the essential trace metals of plankton, including Fe, Mn, Cu, Ni, Zn, were all elevated in the samples containing high Al and/or Ti, indicating that the trace metal data set from the planktonic samples with high Al and/ or Ti concentration are contaminated by lithogenic particles. By using Al or Ti as an indicator for lithogenic particles, it is clearly shown that most of the particulate materials collected in the field studies, both in neritic and open ocean regions, contained considerable amount of lithogenic particles


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(Martin and Knauer 1973; Sherrell and Boyle 1992; Kuss and Kremling 1999). Since natural planktonic assemblage samples are likely to be hampered by the contamination of lithogenic particles, reliable data for natural algal metal composition must be cautiously assessed from the studies that had sufficient information of the indicative elements like P, Al, and Fe. Given the known and precise metal composition of the crust dust in sampling regions, the influence of lithogenic particles on samples might be corrected. In this chapter, the samples that had a low fraction of lithogenic material and high algal biomass as witnessed by low Al concentrations and high P concentrations are chosen. Following the constraint of Bruland et al. (1991), a cutoff of Al < 100 μg/g dry weight, which corresponds to ca. 1 μmol Fe/g dry weight in crustal rock, equal to the lowest algal cellular Fe concentration (Ho et al. 2003), would be used to exclude the samples that are likely biased by lithogenic particles.

TRACE METAL COMPOSITION AND ITS VARIABILITY IN MARINE MICROALGAL CULTURE Control of the Trace Metal Composition: Internal and External Factors Before examining the metal composition and its variability in marine microalgal culture, the major factors affecting the composition and the variability are briefly discussed here. Trace metal composition in marine microalgae reflects an interactive balance between external environmental conditions and internal cellular biochemical responses. The variability may come from the change of the external factors such as the nutrient concentrations, light intensity, and temperature; or due to the internal factors like the difference of algal physiological demand on the micronutrients, cellular adaptation to the habitat, or evolutionary heritage.

Bioavailable metal concentrations (M¢¢) Bioavailable metal concentration, M¢, is one of the major external factors controlling the cellular metal quotas. The essential trace metals are transported into cells by membrane proteins, thus algal metal uptake mechanism generally follows the Michaelis-Menten kinetics (Hudson and Morel 1993). Under steady state exponential growth condition, algal metal uptake rates are equal to specific growth rate (m) multiplied by cellular metal quotas (Q). As the bioavailable metal concentration (M¢) in surface seawater are far

282 Algal Cultures, Analogues of Blooms and Applications smaller than the Michaelis-Menten half saturation constant, the MichaelisMenten equation can thus be simplified and metal uptake rate would be proportional to M¢. Laboratory culture studies also found that the metal uptake rates and cellular metal quotas were both proportionally related to external labile inorganic concentrations (M¢) when the cellular growth rates are limited by the metals (Sunda and Huntsman 1998c). Under metal limitation condition, as specific growth rates are also regulated by M¢ and are proportional to M¢, the metal quota increasing extent is lessened due to the simultaneous increase of cellular growth rates.

Metal-Metal Interaction Due to the non-specific property of the metal transport sites on algal cellular membrane, trace metals with similar size/charge ratios or chemical nature would compete for the same transport sites as well as intracellular metal binding sites (Hudson and Morel 1993; Bruland et al 1991). The relative concentrations of different metals in culture medium would thus affect cellular uptake for the ‘similar’ metals. Depending on the relative concentrations of the metals, the interactive relationship among trace metals can be either synergistic or antagonistic (Bruland et al. 1991). For example, the cellular concentrations of Zn, Co and Cd in marine microalgae are highly interdependent because the metals have similar size/charge ratios (Zn and Co) or chemical nature (Zn and Cd) and thus can substitute with each other to carry out same biochemical functions (Price and Morel 1990; Lee et al. 1995; Yee and Morel 1996). In the case of Thalassiosira weissflogii, Zn, Co and Cd are known to substitute for each other in carbonic anhydrases which are involved in inorganic carbon acquisition (Lane and Morel 2000; Cullen et al. 1999). In contrast, when Zn¢ is depleted and Co is available, coccolithophores is able to utilize Co to reach maxiumum growth rate (Sunda and Huntsman 1995b; Yee and Morel 1996). However, preference exists if three of the metals are all available in culture medium. The Co quota of Emiliania huxleyi decreased by a factor of 2 as Zn’ increased from 1 to 25 pM (Sunda and Huntsman 1995b) and the Cd quota of Thalassiosira weissflogii decreased five fold when Zn’ increased from 10 to 100 pM (Sunda and Huntsman 2000), suggesting these two species prefer to take up Zn if available. Due to the metal substitutions, we should expect elevated cellular concentrations of Co and Cd in open ocean phytoplankton growing at very low Zn¢ concentrations. The variability of Zn, Co, and Cd quotas observed between diatoms and coccolithophores reflects in part the difference of the cellular response to metal-metal substitution (Price and Morel 1990, Morel et al. 1994, Sunda and


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Huntsman 1996). Not only for Zn, Co, and Cd, different essential metals groups express various metal-metal interaction patterns, which have been studied in details especially by Sunda and Huntsman (1992; 1995b; 1996; 1998a; 2000) and by Morel and his colleagues. These metal-metal interaction patterns were well discussed and reviewed in the studies (Bruland et al. 1991; Sunda and Huntsman 1998c; Whitfield 2001).

Major Growth Conditions (Light, Temperature, Macronutrient Availability et al.) Since cellular metal quotas are related to cellular growth rates, the major growth factors like light, macronutrients, temperature which influence growth rates can directly or indirectly influence cellular metal quotas. For example, the Fe and Mn quotas in marine algae are highly dependent on the intensity of the light regime. Light intensity affects the cellular concentration of Fe as it is an integral part of a host of electron carriers involved in photosynthesis. A change in light intensity from 50 μmol quanta m–2 s–1 to 500 μmol quanta m–2 s–1 resulted in a decrease by a factor of two in the Fe quota of the dinoflagellate Prorocentrum minimum (Sunda and Huntsman 1997). As many of the essential metals are involved in the uptake of the major nutrients (C, N, and P), the availability of the major nutrients is also an important parameter affecting cellular metal concentrations. For example, the Zn, Co and Cd quotas in diatoms also depend on the pCO2 in the growth medium due to the demand on synthesizing carbonic anhydrases. Under low pCO2 condition in culture medium, Zn, Co and Cd quotas in diatoms tend to increase, and vice versa. The concentration and chemical form of nitrogen in the medium also influence Fe requirements and quotas algal. For example, Thalassiosira weissflogii cultures growing nitrate were found to have a 60% higher Fe quota than cultures grown on ammonium (Maldonado and Price 1996). In addition, habitat and/or cell size (Sunda and Huntsman 1997) are the other important factors affecting trace metal quotas in marine algae. The laboratory culture studies showed that oceanic (smaller) isolates are able to survive under metal limitation due to their lower Fe¢, Mn¢ and Zn¢ requirement and higher cell surface area to take up the metals when compared to neritic or larger species (Brand et al. 1983; Sunda and Huntsman 1992, 1995a, 1997). External conditions can only explain part of the elemental composition variability in marine microalgae. Different physiological and biochemical characteristics among different species certainly are the other major factors causing the quota variability.


Trace Metal-Algae Studies The research groups in the laboratories of Drs William G. Sunda and Francois M.M. Morel are the pioneers in applying the trace metal buffer system to study algal trace metal concentrations in marine microalgae culture. The results of these early studies may be found in the review papers (Morel et al. 1991, Sunda and Huntsman 1998c, Whitfield 2001). Morel et al. (1991) reported that the trace metal quotas of Fe, Mn, Zn, Co, Ni, and Cd mainly based on the model coastal diatom Thalassiosira weissflogii growing at 90% of the maximum growth rate were 6.7, 6.7, 4.2, 2.5, 1.7, and 1.7 mmol/ mol C respectively, corresponding to 0.67, 0.67, 0.42, 0.25, 0.17, 0.17 mmol/ mol P if C/P=100. Later on, the systematic detailed studies carried out by Sunda and Huntsman for the model algal species Thalassiosira pseudonana, Thalassiosira oceanica, Thalassiosira weissflogii, and Emiliania huxleyi (Sunda and Huntsman 1995a, b, c; 1996; 1997; 1998a, b; 2000) quantitatively revealed many important factors affecting the metal composition and metal uptake rate as discussed above. The metal quotas obtained from the model species are certainly inadequate to reasonably estimate a representative value for the metal composition of the whole phytoplankton assemblages in the ocean. With the difficulties on choosing and culturing enough representative species, there had been no systematic culture studies focused on determining the trace metal elemental stoichiometry in various marine algal species until recently (Ho et al. 2003, 2004), which should be the most extensive study in terms of total species number used. All of the 15 species chosen in the study were grown under an identical and low Fe culture medium designed to mimic the trace metal concentration levels in natural seawater. The individual cellular metal concentrations of each species may be found in the paper. (Ho et al. 2003). It is noted that trace metal composition normalized to a major cellular element provides a cell volume independent concentration (also called quota), which presents a meaningful unit for comparison among different species. Carbon and phosphorus are the most often used denominators to calculate metal quotas in trace metal-algae studies. However, the extracellular cellulose or bio-inorganic calcium carbonate materials found in many dinoflagellates and coccolithophores species hamper the use of carbonnormalized unit among different species; also due to the traditional Redfield ratio (Redfield 1934, 1958), metal quota are usually normalized to P. Thus, phosphorus normalized elemental quotas are most often used by marine biologists and oceanographers (Broecker and Peng 1982; Bruland et al. 1991; Hecky et al. 1993; Geider and La Roche 2002; Li and Peng 2002). It should be


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pointed out that cellular P concentrations in marine microalgae may also vary plastically especially under extreme P limited condition. The studies compiled by Geider and La Roche (2002) showed that marine microalgae can exhibit large variations in the C/N/P ratios when grown under nutrient depleted conditions. The N/P ratio of some marine microalgae may vary from less than five to larger than 50 when the cells were grown under extreme N or P limited conditions. The critical N/P composition, representing the composition under N and P co-limitation condition, has been determined for several species, which ranged from 20 to 50 for the species tested (Geider and La Roche 2002). These N/P ratios under extreme nutrient depleted conditions are either far above or less than the Redfield ratio, suggesting that marine microalgae in the ocean in general are not under extreme N or P limited conditions. The relatively low variability for P content (normalized to cell volume) among the 15 species found by Ho et al. (2003) suggest that P normalized metal quotas are appropriate when the cells were grown under macronutrient replete condition. Ho et al. (2003) found that there are relatively significant variabilities for all the metal quotas among the 15 species examined (Table 8.1). The ranges of the metal quotas from 10th to 90th percentile (excluding the lowest and highest data) are within a factor of 20 in general. In most of the species tested, the cellular quotas follow the order Fe>Mn>Zn>Cu>CoªCd, from the highest Fe quota of 7.5 mmol/mol P on average to the lowest Co and Cd quotas around 0.2 mmol/mol P on average. Considering the numerous essential roles Fe plays in algae, Fe is expected to have the highest quota among the trace metals in spite of the fact that Fe¢ in natural seawater is extremely low. In contrast, the lowest Co and Cd quotas probably are due to the limited functions that Co and Cd can play in the eukaryotic species. Systematic metal quota differences were also revealed among different phyla (Fig. 8.2). It is not surprising to find significant inter-species and inter-phyla differences for metal quotas in marine microalgae. Large variabilities for macronutrient element composition (C, N, and P) have been reported when various algal species were grown under identical nutrient replete condition (Geider and La Roche 2002), suggesting there is significant variability for the elemental composition among different species and clones. In addition to the interspecific variability, by reviewing numerous culture and field studies on the elemental composition of C, N, and P of marine microalgae and suspended particles, Geider and La Roche (2002) concluded that, depending highly on the availability of the major nutrients, the C/N/P

Martin and Knauer 1973 (n=4)a Net Coastal

Martin et al. 1976 (n=6)b Net Pacific ocean

Collier and Edmond 1984 (n=2)c Net Pacific ocean

Kuss and Kremling Cullen et al. Ho et al. d e 1999 (n=9) 2003 w./wo.1 nM Fe 2003 (n=15) Centrifuge Filtration Culture Atlantic ocean Southern ocean Laboratory

Bruland and othersf Deep water Pacific ocean

Fe 7.4 (5.5) 3.6 (1.3) 4.6 (0.7) 4.6 (1.3) N.A. 7.5 (5.3) 0.33 Zn 0.86 (0.63) 1.9 (1.2) 3.0 (1.3) 1.9 (0.7) 2.9/11 0.80 (0.52) 2.7 Mn 0.39 (0.21) 0.36 (0.11) 0.34 (0.04) 1.6 (0.2) 0.70/1.6 3.8 (2.4) 0.33 Cu 0.18 (0.10) 0.38 (0.06) 0.52 (0.05) 0.37 (0.06) 0.60/1.4 0.38 (0.35) 1.7 Ni 0.21 (0.16) 0.34 (0.14) 0.86 (0.17) 1.4 (0.4) N.A. N.A. 3.0 Co N.A. N.A. N.A. 0.19 (0.02) 0.10/0.15 0.19 (0.13) 0.01 Cd 0.07 (0.02) 0.53 (0.08) 0.54 (0.10) 0.51 (0.09) 0.44/1.2 0.21 (0.22) 0.35 a Data are obtained from the group I raw data in Table 1 of Martin and Knauer (1973). Samples were collected in Monterey Bay, California, with 76 mm aperture phytoplankton net during blooming condition. Only data with Al content less than 100 mg/g (dry weight) are included. High Al concentration in the samples indicates that the data may be biased by the presence of alumino-silicate minerals (Bruland et al. 1991). The phosphorus concentration was obtained from Bruland et al. (1991). b Data are obtained from Table 7-3 in Martin et al. (1976). Samples were collected in the oligotrophic water of North Pacific open ocean with 64 mm aperture plankton net. Only data with Al less than 100 mg/g are included, i.e. data from Station 69, 73, 75, 77, 78, and 85. The data of Station 54, 81, and 88 are not included as P concentrations were too low (81, 88) or some elements (esp. Fe) were abnormally high (54). c Data are obtained from Table 3 in Collier and Edmond (1984). Samples were collected in the open ocean of North Pacific with 44 mm aperture net. Only data of MANOP C (Tow 1 and Tow 2) in the table are included. Other samples had either high Al contents (>100 mg/g) or no Al data reported. d Data are obtained from Table 3 and 4 in Kuss and Kremling (1999). Only data of ‘biogenic samples’ are included in the table are included. Suspended particulate matters were collected in the surface water of the Atlantic open ocean with pumping and centrifuge method. e Data are obtained from Table 3 and Table 2 in Cullen et al. (2003). Data with (left data) and without Fe added (right data) are both presented. The quotas obtained with 1 nM Fe added approximate the metal quotas without Fe limitation (Table 2). Suspended particulate matters were collected in the surface water of the Antartic ocean by pumping and memebrane filtration (0.45 mm). The data plotted in Figure 3 are the data with Fe added to exclude the influence of low growth rates caused by severe Fe limitation. f Reference sources: Fe (Martin and Gordon 1988); Mn (Landing and Bruland 1980); Zn, Cu, Ni, and Cd (Bruland 1980); Co (Knauer et al. 1982).

Sampling method Sampling site

Reference

Table 8.1 Comparison of trace metal composition in natural plankton assemblages, suspended particulate materials, cultured algae, and deep sea water. All metal quotas are normalized to P. Numbers in the parentheses represent one standard deviation.



1000 e

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0 10 1000 5 0 2 1000 1 0 1 1000 u

0 05 1000 o

00 05 1000

00 hloro

ras o

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Fig. 8.2 Comparison of the average trace metal quotas among different phyla. For n > 2, the error bars represent 1 s.e.; for n = 2, the error bars represent 1 average deviation. Chlorophyceae (n = 3); Prasinophyceae (n = 3); Dinophyceae (n = 4); Prymesiophyceae (n = 2); Bacillariophyceae (n = 4). Figure is modified from the Fig. 4 of Ho et al. (2003). ratios may range and vary dramatically in marine microalgae, though in general the elemental composition in most natural suspended materials collected in the ocean still follows the Redfield ratio. In spite of the significant variability found for the trace metal composition among different species or phyla, the possibility should not be excluded that the marine microalgae assemblage may still have a relatively constant trace metal composition as found in macronutrient composition in marine algae by Redfield. In fact, Redfield (1958) was aware that the elemental composition of plankton was uniform only in a ‘statistical’ sense, thus the significant

288 Algal Cultures, Analogues of Blooms and Applications variabilities of the composition among different species or phyla should exist. In culture studies, to choose a truly representative species to stand for the entire marine microalgae population in the ocean is operationally impractical. The 15 eukaryotic species chosen in the study by Ho et al. (2003) certainly should not be considered as exact representatives of the marine microalgal assemblage in the oceans. However, since the 15 species have included the major eukaryotic phyla in the ocean, if the metal quotas in culture studies can approximately reflect the cellular quotas in nature, the distribution ranges of the metal quotas represented by the species may stand for the quota range of algal species in natural planktonic assemblages; the metal quota average obtained from the 15 species might then approximate the average found in nature. Even though the M¢ chosen in the study of Ho et al. (2003) were designed to mimic the conditions in natural seawater, whether the theoretical M¢ value can reflect the conditions in nature are remained unanswered. The growth conditions like temperature or light intensity for the culture studies in the laboratory are also not exactly the same as the natural conditions in the field. Examining the metal quota variability affected by varying M¢ and other major growth conditions would thus help us understand the variability extent of the quotas affected by the external and internal factors. The question can be answered by the laboratory studies in which the quotas of a given trace metal in a given phytoplankton species are measured under a variety of growth conditions, and over a range of M¢. Such detailed studies have already been performed by Sunda and Huntsman (1995b, 1998b, 2000). The studies of Sunda and Huntsman showed that the cellular trace metal quotas in marine phytoplankton are relatively well regulated over a certain range of bioavailable metal concentrations, a range that actually embraces the natural metal concentrations in the oceans but not severely limiting the algal growth. Using Fe as an example, when the bioavailable Fe concentration (Fe¢) range roughly from 10 to 750 pM (Sunda and Huntsman 1995a), the cellular Fe quotas varied with Fe¢ are listed as the following: Fe quotas of E. huxleyi varied from 11 to 24 mmol/mol C when Fe¢ ranged from 10 to 750; Fe quotas of Pelagomonas calceolata varied from 8 to 10 mmol/mol C when Fe¢ ranged from 11 to 580; Fe quotas of Thalassiosira oceanica varied from 5 to 34 mmol/mol C when Fe¢ ranged from 10 to 760; Fe quotas of Thalassiosira pseudonana varied from 13 to 70 mmol/mol C when Fe¢ ranged from 24 to 760; Fe quotas of Thalassiosira weissflogii varied from 11 to 31 mmol/mol C when Fe¢ ranged from 28 to 750; Fe quotas of Prorocentrum minimumi varied from 11 to 37 mmol/mol C when Fe¢ ranged from 23 to 750.


289

To summarize, an increase by factors of 20 to 75 in the unchelated Fe concentration (Fe¢) only results cellular quota change by factors of 1.3 to 7 for all the algae examined, which included diatoms, coccolithophore, and dinaflagellate. Likewise, when Zn¢ increased from 15 to 2454 pM, the Zn quotas only increased by factors of 3 to 10 for the algae examined (Sunda and Huntsman 1996). Similar results were also found for other trace metals. The detailed comparison for all the metals are compiled in the study of Ho et al. (2003). In conclusion, bioavailable trace metal concentrations only modestly affect algal metal quota at the low concentration levels which are close to the natural concentrations. Trace metal quotas are generally regulated within a factor of 2 to 4 when the M¢ varies over 1 order of magnitude. Similarly, the influence of other external factors, like metal-metal interaction, the light intensity, Fe limitation and so on, can rarely change the cellular quota of a given trace metal by more than a factor of 2 to 5 in the natural growth conditions (Sunda and Huntsman 1997; Cullen et al. 2003). Thus, it can be said that the quota variabilities affected by the major external factors are much smaller than the intrinsic variabilities that are seen among the 15 species, which span one or two orders of magnitude depending on the metal. Thus, the metal quotas reported from the culture study (Ho et al. 2003) mainly reflect the intrinsic biochemical requirements for the essential trace elements and the quotas determined in the culture study should be close to the metal quotas for the species in the ocean. The range of the metal quotas observed in the laboratory study can thus be taken as a first approximation of the range expected for the trace element composition of phytoplankton in the sea. The quota average of the culture study can be taken as a first approximation of the trace metal quotas of various species of phytoplankton. The average with one standard deviation of the metal quotas in the culture study of Ho et al. (2003) yields the following trace metal stoichiometry for marine microalgae: P1000 Fe7.5±5.3 Mn3.8±2.4 Zn0.80±0.52 Cu0.38±0.35 Co0.19±0.13 Cd0.21±0.22. In other algal studies focusing on metal composition, the three model species of Thalassiosira pseudonana, Thalassiosira weissflogii, and Emiliania huxleyi have the most complete metal quota data. Two of the species Thalassiosira weissflogii and Emiliania huxleyi were already included in the study of Ho et al. (2003). The trace metal quotas of the two species obtained from Ho et al. (2003, 2004) are generally close to those reported by Sunda and Huntsman (Ho et al. 2003, 2004). The data in Ho et al. can thus well represent the value of the same species from other studies.


TRACE METAL COMPOSITION IN NATURAL PLANKTONIC ASSEMBLAGES The Average and Variability of the Metal Composition As discussed in the method section, while applying the metal data of suspended particles obtained in the field to represent the composition of plankton assemblages, it is essential to exclude the influence of abiogenic composition from the samples. Using Al and P concentrations as thresholds to evaluate the data obtained from field studies, the data sets complied in Table 8.1 can be considered to be reliable in representing the average metal composition for plankton assemblages in the sampling regions. The three earlier data sets all came from plankton tows samples, including the coastal sample (Martin and Knauer 1973) and two open ocean samples (Martin et al. 1976; Collier and Edmond 1984), collected with 76, 64, and 44 mm net apertures respectively. The data set of Kuss and Kremling (1999) came from pumping and centrifuge; compared to the data set of Cullen et al. (2003) which came from pumping plus membrane filtration. In spite of no available information of Al and Fe in the data set of Cullen et al. (2003), the data are included, as it is unlikely that the samples collected in the Antarctic region were severely influenced by lithogenic particles, where it is known to have low terrestrial dust input. In the study of Cullen et al. (2003), the metal quotas with and without Fe treated (adding 1 nM) are both included. To exclude the influence of the low growth rates due to the severe Fe limitation (Cullen et al. 2003), the metal quotas with Fe added are used for calculating the average. To visualize the difference among different data sets from different sampling methods, the data in Table 8.1 are presented in Fig. 8.3. It should be noted that some of the average metal composition value in the data sets have large standard deviations (Table 8.1). This is partially due to the limited numbers of reliable data in each data set and partially due to the influence of abiogenic particles (Table 8.1). It can be justified by larger standard deviation for Fe and Mn, and smaller standard deviation for Co and Cd. As seen in Fig. 8.3, in spite of the fact that the data were obtained from different sampling methods and sampling regions, the variability of each metal composition from all the data sets are fairly constrictive. The metal composition follow the order Fe>ZnªMn>Cu>CoªCd, from the highest Fe quota of 7.4 mmol/mol P to the lowest Co and Cd quotas around 0.1 mmol/mol P, which are similar to the metal quota distribution trends observed in the culture studies (Ho et al. 2003). The Fe quotas are remarkably consistent with each other in the field datasets, ranging from 3.6 to 7.4 mmol/mol P. The highest Fe value, 7.4 mmol/mol P, is from the solo coastal

The Trace Metal Composition of Marine Microalgae e

o 10

ol

ol o

10

u

291

1

01

1

01

Fig. 8.3 Comparison of the average trace metal quotas among field and culture studies. All of the metals are normalized to P with the units of mmol/mol P. Open symbols represent the data obtained from plankton tow sampling method (Circle: Martin and Knauer 1973; Reverse triangle: Martin et al. 1976; Square: Collier and Edmond 1984); solid symbols represent the data obtained from centrifuge or filtration methods (Circle: Kuss and Kremling 1999; Reverse triangle: Cullen et al. 2003); the culture data is labeled with the crossed diamond symbol (Ho et al. 2003; 2004). Details for how the data were obtained are described in Table 8.1. data (Martin and Knauer 1973). Neritic species are expected to have higher Fe quotas than oceanic species as culture studies have shown that neritic species have higher Fe demands to sustain maximum growth than oceanic species (Brand et al. 1983; Sunda and Huntsman 1995a). Overall, the Fe value among the field data sets only range by factors of 2. Similarly, the quotas of Zn and Cu in the data sets only vary approximately by factors of 3, ranging from 0.9 to 3.0 and from 0.2 to 0.6 mmol/mol P respectively. In both cases (Zn and Cu), the Zn and Cu value from the study of Martin and Knauer (1973) are the lowest among the data sets, using planktonic tow with a larger net aperture (76 mm) to collect plankton samples during a diatom bloom in coastal region. Compared to Fe, Zn, and Cu, the variabilities of the Mn and Ni quotas are much larger, ranging from 0.36 to 1.6; and from 0.21 to 1.4 mmol/mol P respectively. Interestingly, the Mn quotas of the three data set that came from plankton nets are almost identical to one another, and both the Mn and Ni quotas from plankton nets are all significantly lower than the other 2 data sets obtained from pumping and filtration methods, suggesting the larger particles collected by plankton tows may contain lower Mn and Ni quotas than the particles collected by the pumping and centrifuge methods. The result obtained by analyzing the 15 Mn data from Ho et al. (2003) shows that the averaged Mn quota (with one standard error) of the four largest species, with cell volume more than 1000 (mm)3, Gymnodinium chlorophorum, Thoracosphaera heimii, Ditylum brightwellii, and Thalassiosira eccentrica, is

292 Algal Cultures, Analogues of Blooms and Applications 1.8±0.2 mmol/mol P, which was much smaller than the average of the other 11 smaller species, 4.5± 0.6 mmol/mol P, indicating smaller phytoplankton may have higher Mn quotas or there are significant Mn adsorption on the cell surface. If the first situation is the case, samples collected by plankton nets would underestimate Mn quotas in phytoplankton assemblage; and the Mn quotas obtained from the samples collected by pumping and filtration should be more representative for total plankton. The Ni quotas determined in the samples collected by the plankton tows were also lower than the ones obtained from the centrifuge method. It is believed that prokaryotic algae may have kept using Ni and Co in the modern ocean and contain higher Ni and Co quotas due to the reducing conditions of early Proterozoic oceans that prokaryotic algae appeared from and the higher Ni and Co availability under anoxic conditions than other trace metals (Saito et al. 2002). Thus it is expected to see higher Ni quotas in the samples collected by the centrifuge method, which probably also included most of the prokaryotic algae (Fig. 8.3). Unfortunately, there were no Co data reported for the studies with plankton tow sampling methods. The two field Co data from pumping method were a close match for the data of the culture study, ranging from 0.1 to 0.19 mmol/mol P and remarkably close to the culture value (0.19), which is also the lowest average quota among the metals. All of the field studies listed in the Table 8.1, except Kuss and Kremling (1999), probably have undersampled the picoplankton and thus cyanobacteria. Due to their high total biomass in the open ocean, more future culture and field studies should focus on examining the trace metal composition of the bacteria. The most noteworthy features in Fig. 8.3 is the fairly high and remarkably consistent Cd quotas in the open ocean samples collected from different sampling methods, ranging from 0.44 to 0.54 mmol/mol P, which is almost one order of magnitude higher than the coastal Cd value from Martin and Knauer (1973). These high Cd values from the oceanic samples, possibly reflect extensive substitution of Cd for Zn in the algae live in the Zn-poor open ocean and intensive uptake of cd by calcareous plankton organisms. Compared to the culture data (Table 8.1), only with the notable exception of Mn, the field quotas for trace elements fall within the range of laboratory values (Fig. 8.3) and the individual field value of Fe, Zn, Cu, Co, and Cd are relatively close to one another among the different field studies and also close to the average of the culture values (Table 8.1 and Fig. 8.3). The consistency of the average metal composition of the reliable field and culture studies raises a fundamental question about why the metal composition can be relatively constant in marine phytoplankton assemblage.


293

In spite of the fact that the interspecific variability is fairly large, ranging around one order of magnitude for most of the metals, the inter-phyla variability narrows the variability down to factors of 5 or even less after averaging the metal quotas among the different species from the same phyla (Fig. 8.2). The moderate inter-phyla metal variability can be smoothed further when averaging the metal contents from the whole algal community structure. Although M¢ and other growth conditions are different in different regions, as discussed previously, the metal quotas are relatively well regulated by varying M¢ and other growth factors under natural conditions. In addition, several lines of evidence suggest that marine microalgae that survive under environments with extremely low metal concentrations, develop various mechanisms to take up the essential trace metals, which would narrow down the metal quota differences for the algae grown under high and low M¢ regions. For example, the cell surface/volume ratios are much smaller in the open oceans where the metal concentrations are extremely low and some open ocean species are able to take up organically bound Fe in natural seawater (Maldonado and Price 1999, Hutchins et al. 1999). However, it is possible that the generalities of the average metal composition may be lost under certain growth environments that are dominant by few algal species such as in the regions with Emiliania huxleyi blooming or red tide. To summarize, the average metal data with one standard deviation from the field studies yields the following trace metal stoichiometry: P1000 Fe5.1±1.6 Mn0.68±0.54 Zn2.1±0.88 Cu0.41±0.16 Ni0.70±0.54 Co0.15±0.06 Cd0.42±0.20

CONCLUSIONS Extend the Redfield ratio? The concept of the Redfield ratio may refer to the following two aspects: the first one is the consistency of the elemental composition of the plankton assemblage collected from different oceanic regions; the second one is the relationship between plankton composition and seawater composition. Can the Redfield ratio concept be usefully extended to the trace metals in terms of the two aspects? In this review, the intercomparison of the metal quotas between the culture and field datasets shows that the average metal quota from the field datasets obtained from various sampling methods and regions are relatively consistent to each other; and the average metal quotas measured in the field assemblages are also consistent with the average composition determined in culture studies. These striking similarities not

294 Algal Cultures, Analogues of Blooms and Applications only indicate that the metal composition obtained from laboratory culture studies can precisely reflect algal trace metal composition in the field but also support the data reported from the field studies listed in Table 8.1 may well represent the average metal composition for plankton assemblages. It can be said that it is very likely that there is constant trace metal composition for phytoplankton assemblages in the ocean. In this aspect, the traditional Redfield ratio may be extended to the trace metals. The overall trace metal stoichiometry obtained from the five field studies listed in Table 8.1 is: P1000 Fe5.1±1.6 Mn0.68±0.54 Zn2.1±0.88 Cu0.41±0.16 Ni0.70±0.54 Co0.15±0.06 Cd0.42±0.20 It appears more field studies are needed to confirm the consistency of the average metal quotas in the ocean. Comparing the metal composition in plankton with water metal/p ratios in the deep Pacific ocean water (Table 8.1), expressed as P1000 Fe0.33 Mn0.33 Zn2.7 Cu1.7 Ni3.0 Co0.01 Cd0.35, the result shows that the composition for Cd and Zn are similar between the phytoplankton composition and the water composition, indicating that the two metals are mainly regulated by biological processes, taken up in the surface ocean by microalgae and regenerated in the deep oceans. Relatively, it is not surprising to obtain the low metal/P value in the deep water for Fe and Mn due to their property to be scavenged in the water column of the oceans. The higher Cu/P and Ni/P ratios in the deep water than the algal composition suggest that the sources of dissolved Cu and Ni in oceanic deep water are mainly from abiogenic materials instead of the decomposition of biogenic particles. Overall, the metal stoichiometry in marine microalgae provides a basis for modeling and examining how marine microalgae influence the relative distribution and the vertical transport of the trace metals in the ocean, especially for Cd and Zn.

Final Remark Laboratory algal culture experiments observed that the essential metals at low concentrations can limit marine microalgal growth (Anderson and Morel 1982; Brand et al. 1983). Driven partially by the findings, John Martin and his colleagues eventually revealed and proved the importance of Fe on controlling algal productivity in the ocean and proposed the prominent ‘The Iron Hypothesis’ (Martin and Gordon 1988). Trace metal-algae study is a splendid example showing that marine microalgal culture study is a powerful analogue tool to understand the roles of marine microalgae play in nature.


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ACKNOWLEDGMENTS I thank Subba Rao Durvasula for his hearty encouragement while preparing this manuscript. I am deeply grateful for the valuable comments from the anonymous reviewer, which significantly enhanced the quality of this manuscript. I would like to thank Yuan-Hui Li and François M. M. Morel for their helpful comments on the manuscript.

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298 Algal Cultures, Analogues of Blooms and Applications Redfield, A.C. 1934. On the proportions of organic derivatives in sea water and their relation to the composition of plankton. pp. 176-92. In R.J. Daniel [ed.], James Johnstone Memorial Volume. Liverpool University Press, UK. Redfield, A.C. 1958. The biological control of the chemical factors in the environment. Am. Scientist. 46: 205-221. Redfield, A.C., B.H. Ketchum, and F.A. Richards. 1963. The influence of organisms on the composition of sea-water. pp. 26-77. In M.N. Hill [ed.], The Sea., Interscience Publication New York USA. Rue, E.L. and K.W. Bruland. 1995. Complexation of iron III by natural organic ligands in the Central north Pacific as determined by a new cometitive ligand equilibration/ adsorptive cathodic stripping voltammetric method. Mar. Chem. 50: 117-138. Saito, M.A. and J.W. Moffett. 2001. Complexation of cobalt by natural organic ligands in the Sargasso Sea as determined by a new high-sensitivity electrochemical cobalt speciation method suitable for open ocean work. Mar. Chem. 75: 49-68. Saito, M.A., J.W., Moffett, S.W. Chisholm, and J.B. Waterbury, 2002. Cobalt limitation and uptake in Prochlorococcus. Limnol. Oceanogr. 47: 1629-1636. Sherrell, R.M. and E.A. Boyle. 1992. The trace metal composition of suspended particles in the oceanic water column near Bermuda. Earth Planet. Sci. Lett. 111: 155-174. Sunda, W.G. and R.R.L. Guillard 1976. The relationship between cupric ion activity and the toxicity of copper to phytoplankton. J. Mar. Res. 34: 511-529. Sunda, W.G. and S.A. Huntsman. 1983. Effect of competitive interactions between manganese and copper on cellular manganese and growth in estuarine and oceanic species of the diatom Thalassiosira. Limnol. Oceanogr. 28: 924-934. Sunda, W.G. and S.A. Huntsman. 1992. Feedback interactions between zinc and phytoplankton in seawater. Limnol. Oceanogr. 37: 25-40. Sunda, W.G. and S.A. Huntsman. 1995a. Iron uptake and growth limitation in oceanic and coastal phytoplankton. Mar. Chem. 50: 189-206. Sunda, W.G. and S.A. Huntsman, 1995b. Cobalt and zinc interreplacement in marine phytoplankton: Biological and geochemical implications. Limnol. Oceanogr. 40: 14041417. Sunda, W.G. and S.A. Huntsman. 1995c. Regulation of copper concentration in the oceanic nutricline by phytoplankton uptake and regeneration cycles. Limnol. Oceanogr. 40: 132-137. Sunda, W.G. and S.A. Huntsman. 1996. Antagonisms between cadmium and zinc toxicity and manganese limitation in a coastal diatom. Limnol. Oceanogr. 41: 373-387. Sunda, W.G. and S.A. Huntsman, 1997. Interrelated influence of iron, light and cell size on marine phytoplankton growth. Nature. 390: 389-392. Sunda, W.G. and S.A. Huntsman, 1998a. Interactions among Cu2+, Zn2+, and Mn 2+ in controlling cellular Mn, Zn, and growth rate in the coastal alga Chlamydomonas. Limnol. Oceanogr. 43: 1055-1064. Sunda, W.G. and S.A. Huntsman. 1998b. Control of Cd concentrations in a coastal diatom by interactions among free ionic Cd, Zn, and Mn in seawater. Envir. Sci. Technol. 32: 2961-2968. Sunda, W.G. and S.A. Huntsman. 1998c. Processes regulating cellular metal accumulation and physiological effects: phytoplankton as model systems. Sci. of the Total Envir. 219: 165-181. Sunda, W.G. and S.A. Huntsman. 2000. Effect of Zn, Mn, and Fe on Cd accumulation in phytoplankton: Implications for oceanic Cd cycling. Limnol. Oceanogr. 45: 1501-1516.


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' Algal Cultures as a Tool to Study the Cycling of Dissolved Organic Nitrogen Deborah A. Bronk1 and Kevin J. Flynn2 1

Department of Physical Sciences, The College of William and Mary, Virginia Institute of Marine Science, Route 1208; Greate Rd., Gloucester Point, VA 23062, USA 2 Institute of Environmental Sustainability, University of Wales, Swansea SA2 8PP, UK

Abstract The dissolved organic nitrogen (DON) pool is a large heterogeneous mixture of compounds including urea, amino acids, proteins, nucleic acids, amines, amides and humic substances, among others. Traditionally, the DON pool has been viewed as largely refractory and unimportant to planktonic nutrition. This view was supported by the persistence of relatively high DON concentrations even in environments where the inorganic nitrogen forms had been driven below the level of detection by planktonic uptake. Over the past decade, however, evidence has been accumulating that indicates the DON pool is a dynamic component of marine systems with relatively high rates of uptake and release being tightly coupled. Research into DON cycling is difficult, however, because of the complex chemical nature of the pool and the myriad biotic interactions that affect it. Cultures are therefore valuable tools to study DON cycling because they allow one to simplify a complex system by controlling more variables then are possible in field studies. The objectives of this chapter are to describe the analytical tools needed to study DON cycling in cultures, to review culture and field work related to DON production and bioavailability with a focus on more recent studies, to discuss the difficulties in extrapolating culture results to the field and to present recommendations for future research.

INTRODUCTION Dissolved organic nitrogen (DON) is generally the largest pool of fixed nitrogen in most aquatic systems (reviewed in Bronk 2002). The pool consists of a wide range of organic compounds, such as urea, amino acids, combined

302 Algal Cultures, Analogues of Blooms and Applications amino acids, peptides, proteins, creatine and humic and fulvic acids. The exact composition of the pool, however, is unknown at any given time and likely changes over relatively short spatial and temporal scales. The fact that relatively high concentrations of DON persist even in oligotrophic environments believed to be nitrogen limited led to the traditional view that DON was largely refractory and therefore unimportant to phytoplankton nitrogen nutrition. Research conducted in the past decade or so, however, has challenged that belief and the DON pool is increasingly recognized as a dynamic component of aquatic nitrogen cycles. Research has shown that phytoplankton are both producers and consumers of DON. Much of what we know about algal-DON interactions has been learned through the use of cultures, particularly from work done in the 60s, 70s and 80s, and there are a number of reviews that describe DON research during this time period (Fogg 1966, Hellebust 1974, Flynn and Butler 1986, and Antia et al. 1991). The objectives of this chapter are to describe the analytical tools needed to study DON cycling, to review studies of DON production and bioavailability in cultures and in the field, to discuss the difficulties in extrapolating culture results to natural waters, and to present recommendations for promising areas of research in the future.

ANALYTICAL METHODS Culture Media and Analytical Techniques The ability to culture phytoplankton on artificial media greatly expanded the use of cultures in studies of phytoplankton physiology and growth (reviewed in Provasoli et al. 1957, Berges et al. 2001). One of the most widely used nutrient enrichment recipes is the ‘f’, or its half-strength counterpart, ‘f/2’ media developed by Guillard and Ryther (1962). Later media recipes were developed to improve autoclavability (Harrison et al. 1980) or for adaptation to specific studies such as trace metal physiology (Price et al. 1988/1989). Good reference sources on culturing techniques and protocols are Guillard (1975), Fogg and Thake (1987) and Hoffs (1999).

Types of Cultures and the Questions They can Address Batch The simplest culture system is a batch culture. Media is inoculated with a phytoplankton species and then the cells are left to grow under specific temperature and light conditions. To keep cells in suspension and to

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facilitate gas exchange, batch cultures are generally shaken, periodically swirled or aerated (reviewed in Guillard 1975). Batch cultures are ideal for asking questions relating to growth yield or growth stage, as well as to study the effect of light and temperature on cells. With respect to growth stages, batch cultures go through a lag phase, where cell numbers do not appreciably increase immediately after inoculation, exponential phase, where cell numbers increase exponentially and the culture is in steady state, stationary phase, where some resource is limiting the continued production of biomass, and, senescence, where cells begin to die.

Continuous Continuous cultures, also known as chemostats, are a nutrient limited culture held in perpetual steady state through the constant addition of fresh media (reviewed in Smith and Waltman 1995). They are especially valuable because they are more representative of cells in the open ocean where biomass may be very low but growth rates of individual cells may be high due to high rates of nutrient regeneration (analogous to the dilution rate). As fresh media is added, an equal volume of old media, with the cells it contains, flows out of the culture chamber. The growth rate of the cells in the chemostat is controlled by the delivery rate of new media such that the growth rate equals the dilution rate (i.e. flow rate/volume of culture vessel). The biomass in the culture can be expanded by increasing the concentration of nutrients in the media at a given dilution rate. Chemostats may be considered ideal for addressing questions of nutrient limitation, including the effect of growth rate, temperature, or light on the limiting nutrient, as well as to compare the response of different species to a given set of growth conditions (Nagao and Miyazaki 2002). Chemostats, however, do not enable the ready examination of near-maximum growth rates (due to the risk of washout) and there are severe logistic constraints that limit their use when large volumes of cell suspension need to be removed for sampling. They also tend to be time consuming and temperamental to maintain.

Cage cultures Cage cultures are batch or continuous culture systems where cells are grown within a dialysis bag or on one side of a filter or dialysis membrane. The membrane allows nutrients to diffuse into the culture area while simultaneously allowing excretory or release products to diffuse out (reviewed Sakshaug and Jensen 1978).


Axenic versus xenic cultures Regardless of the type of culture system used to investigate questions of phytoplankton physiology and ecology, it is best to use axenic or pure cultures. In phytoplankton cultures, the main threat to axenicity is bacterial contamination. Bacteria may out-compete phytoplankton for substrates such that their presence can severely confound experimental results, particularly with respect to measuring DON release and uptake rates. If one is measuring DON release from phytoplankton, the presence of bacteria can result in recently released DON being taken up by the contaminant bacteria – in effect, the bacteria consume the experimental signal. If one is measuring algal DON uptake, the presence of contaminant bacteria can result in the appearance of uptake by the phytoplankton, when in fact no algal uptake occurred. There are a number of techniques that can be used to isolate axenic cultures including micro-pipetting and washing (Pringshein 1949), using antibiotics (Cottrell and Suttle 1993), or a combination of both (e.g. Nagai et al. 1998). Many phytoplankters, however, are extremely difficult to render and maintain axenic, particularly cultures of cyanobacteria and colonial forms (e.g. Trichodesmium).

Methods to Measure DON Concentration and Composition DON is operationally defined as all nitrogen forms passing through a filter (generally 0.2 to 0.7 mm) that is not a form of dissolved inorganic nitrogen (DIN, ammonium, nitrate or nitrite). To measure bulk DON one must determine the concentration of total dissolved nitrogen (TDN) and then separately determine the concentration of ammonium and nitrate/nitrite. The final DON estimate, therefore, has the combined analytical error of three analyses. This makes it difficult to detect small but significant changes in concentrations of DON over time or between different environments. This lack of sensitivity is less of an issue in culture experiments because the biomass and substrate concentrations tend to be higher than those found in the environment and because substrate additions are in accordance with analytical detection limits. There are three primary methods currently used to measure TDN concentrations: persulfate oxidation, ultraviolet (UV) oxidation, and high temperature combustion (Table 9.1, reviewed in Bronk et al. 2000 and Sharp et al. 2002). A number of methods also exist to measure concentrations of individual organic nitrogen compounds including urea, dissolved free amino acids (DFAA) and dissolved combined amino acids (DCAA), humic


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Table 9.1 Methods for measuring concentrations of DON and individual organic compounds including dissolved primary amines (DPA) and dissolved free and combined amino acids (DFAA and DCAA) Analyte

Method

Reference

Bulk DON concentrations

Persulfate oxidation

Menzel and Vaccaro 1964 Valderamma 1981 Armstrong et al. 1966 Hansell et al. 1993

UV oxidation High temperature combustion Organic compounds: Urea DPA DFAA DCAA Humic substances Nucleic acids (DNA, RNA) Proteins Methylamines

Monoximine Urease Flourometric opa Fluorescamine HPLC Liquid hydrolysis/HPLC Vapor phase hydrolysis/HPLC XAD extraction

Modified bicinchoninic acid Gas chromatography

Price and Harrison 1987, Cozzi 2004 McCarthy 1970 Parsons et al. 1984 North 1975 Mopper and Lindroth 1982 Parsons et al. 1984 Keil and Kirchman 1991 Hessen and Tranvik 1998 Karl and Bailiff 1989, Sakano and Kamatani 1992, Siuda and G de 1996 Smith et al. 1985, Nguyen and Harvey 1994, Yang et al. 1993

substances, nucleic acids, proteins, and methylamines (Table 9.1), most of which exist at nanomolar to micromolar levels (summarized in Table 9.2). Concentrations of one of the most labile DON pools, free amino acids, are reported either as total dissolved primary amines (DPA), measured fluorometrically (North 1975, Parsons et al. 1984) or as individual DFAA, measured with HPLC (Mopper and Lindroth 1982). The simplest forms of Table 9.2 Range of mean concentrations of organic nitrogen compounds found in nature (data from Bronk 2002) Compound Urea DFAA DCAA Humic substances Nucleic acids: DNA RNA Purine and pteridines Methylamines

Range of mean (mM) 0 to 13.0 0.001 to 0.7 0.15 to 4.20 0.4 to 12.3 0.05 to 80.6 0.5 to 871 up to 12.6 0 to 0.62

Reference reviewed in Bronk 2002 reviewed in Bronk 2002 reviewed in Bronk 2002 reviewed in Bronk 2002 Karl and Bailiff 1989 Antia et al. 1991 Gibb et al. 1999

306 Algal Cultures, Analogues of Blooms and Applications DCAA are dipeptides, with the most complex being soluble protein. To measure the nitrogen concentration within humic compounds, often the largest component of DON in aquatic systems, the humics are removed from solution by passing the sample through an XAD-8 resin (Aiken 1988). The TDN concentration in the eluate is then measured and the difference between the TDN concentration in the original sample and the TDN concentration of the eluate is assumed to be the concentration of humic substances.

Methods to Study DON Release and Bioavailability To measure rates of DON release and uptake there are two main approaches – monitoring changes in ambient concentrations and tracer techniques. In the first, at its simplest, net flux out of or into biomass is determined by monitoring changes in concentration over time in the surrounding water. One permutation of this approach is the bioassay where a sample is spiked with a substrate to increase the magnitude of the fluxes (Flynn 1990, Carlsson et al. 1995, Seitzinger et al. 2002); if the spike is large, however, it can cause a significant perturbation to the system. The bioassay approach is ideally used in situations where only a single organism is present (i.e. axenic cultures), because in complex systems one cannot be sure biotic or abiotic transformations are not liberating nitrogen from DON as DIN, which is then the form incorporated by the phytoplankton. The most important biotic process is bacterial remineralization. Abiotic processes that can break down DON include photochemical decomposition (e.g. photochemical ammonification); under normal incubation conditions, however, cultures are generally not be exposed to the far ultraviolet range of light responsible for photochemical decomposition (reviewed in Mopper and Kieber 2002). While useful in cultures, estimating flux rates by monitoring concentration changes is of limited use in the field because biomass is generally too low to produce a measurable change in substrate concentrations over the course of hours. The approach is also problematic if uptake and release are tightly coupled. For example, substantial uptake rates could be severely underestimated if regeneration rates similar in magnitude were co-occurring. With the introduction of 15N tracer techniques to phycology and oceanography (Neess et al. 1962, Dugdale and Goering 1967) it became possible to quantify small but significant nitrogen uptake rates. Isotopes also have the advantage that uptake rates can be measured even in situations where regeneration is co-occurring. There are also isotope


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dilution approaches that allow for the simultaneous measurement of uptake and regeneration rates of ammonium (Glibert et al. 1982), urea (Hansell and Goering 1989, Slawyk et al. 1990), and amino acids (Fuhrman 1987). Isotope approaches have the advantage that they are much more sensitive than monitoring concentration changes but they are also more costly and labor intensive. The number of compounds that can be studied is also limited to a relatively few compounds that have commercially available 15N, 14C, 13C or 3 H labeled forms such as DFAA or urea (Fuhrman and Bell 1985, Hansell and Goering 1989, Wheeler and Kirchman 1986, Collos and Slawyk 1986, Bronk et al. 1998). Some organic substrates can be purchased in a number of different forms. For example, uptake of urea can be studied with 15N-labeled urea (Lomas et al. 2002), dual-labeled urea (15N and 13C or 14C; Hansell and Goering 1989, Bronk et al. 1998), urea labeled with radioactive 14C (Price and Harrison 1988a, Lisa et al. 1995), or a sulfur analog of urea, 14C-thiourea (Rees and Syrett 1979b); note that thiourea is toxic to at least one phytoplankter, Emiliania huxleyi (Palenik and Henson 1997). Bioavailability of the DON pool as a whole is difficult to study because the pool is composed of a large number of compounds and the exact composition is unknown (reviewed in Antia et al. 1991, Benner 2002, Bronk 2002). This makes it especially difficult to determine which substrates to use in uptake studies. At first glance it may seem logical to add labeled forms of DON that are present at elevated concentration in seawater, such as glycine. In reality, however, the organic substances most often seen in seawater may only be present at elevated concentrations because their uptake rates are relatively low (Schell 1974, Flynn and Butler 1986, Flynn 1990). One approach is to let the phytoplankton community in natural waters produce a realistic 15N-labeled DON tracer (Bronk et al. 1993a). In this approach a whole water sample is incubated with 15N-labeled ammonium or nitrate and then the recently released DO15N is isolated (see below, Bronk and Glibert 1993a). More recently, Veuger et al. (2004) produced 15N-labeled DON using a Skeletonema costatum culture, which was subsequently used as a tracer of DON uptake in the field. Other approaches used to study DON uptake use enzyme techniques where the hydrolysis of flourogenically tagged substrates are monitored (Pantoja et al. 1993; 1997, Pantoja and Lee 1999; Mulholland et al. 2002; Berg et al. 2002) or where enzyme activity is measured over time (e.g. Berges and Falkowski 1996). Isotopic approaches have also been used to measure release of DON (Bronk and Glibert 1991) or individual DON compounds such as urea (Hansell and Goering 1989) and amino acids (Fuhrman 1987). To measure rates of release for the bulk DON pool, the pool is isolated at the end of a 15N

308 Algal Cultures, Analogues of Blooms and Applications incubation (reviewed in Bronk 2002). Isolating the pool can be accomplished using wet chemical techniques (Axler and Reuter 1986; Slawyk and Raimbault 1995; Bronk and Ward 1999, Veuger et al. 2004) or ion retardation resin (Bronk and Glibert 1991; Nagao and Miyazaki 1999; reviewed in Bronk 2002). If both the extracellular and intracellular DON pools are isolated, one can measure a release rate as well as an intracellular transformation rate of inorganic nitrogen to organic nitrogen within the cell (Bronk and Glibert 1991, Bronk 1999).

DOM Composition and Production in Culture and in Natural Waters The mid-60s to mid-80s was a very active period in the study of algal nitrogen physiology in cultures, much of which involved the cycling of DON. Particularly common during this time were batch culture studies of nitrogen release where the accumulation of DON, or some specific organic compound, was measured over time. A number of studies demonstrate that there is appreciable production of organic matter in phytoplankton cultures and that organic matter accumulates during exponential growth with even more accumulating during the stationary phase (Fogg 1952, Allen 1956). For example, in one study the N-fixing cyanobacterium, Calothrix scopulorum, released an average of 40% of its assimilated nitrogen in culture under optimal growth conditions with the percentage increasing under suboptimal condition (Jones and Stewart 1969). In a study with continuous cultures, significant production of DON was documented in eight phytoplankton species with the release increasing when cells went from exponential to stationary growth (Newell et al. 1972). From studies such as these it was concluded by many that exponentially growing algae have low DON release rates but that at the end of exponential growth and the onset of senescence excretion of DON increases substantially. It is not always apparent from such studies, however, that the rate of release per cell changes, how the nature of limiting nutrient affects release (e.g. phosphorus, nitrogen, or silica), or to what extent other events associated with bloom termination (such as grazing or viral lysis) contribute to DON release. While it is reasonable to expect nutrient stressed, senescent and damaged cells to leak organics (Fogg 1966) there has been an active debate over the question of whether healthy cells also do so (Fogg 1977, Sharp 1977). While DOC release from autotrophic organisms living in nutrient or trace metallimited systems is easy to understand, explanations for why cells would release significant amounts of DON, are considerably more difficult to


309

rationalize, particularly under the nitrogen-limited conditions that exist in many marine systems. The contention is that appreciable DON release only occurs from damaged or dying cells and that high rates of release in an apparently healthy population is an artifact of sample handling or filtration (Sharp 1977). More recently, however, there has been a growing consensus that extracellular release is a normal function of healthy cells (reviewed in Antia et al. 1991 and Bronk 2002). For example, the highest rates of DFAA excretion in diatoms is observed during exponential growth (Myklestad et al. 1989 and references therein). In another series of culture experiments, most of the DOM produced is again released during nutrient replete conditions (Biddanda and Benner 1997). In two clones of marine Synechoccocus spp. the highest rates of DON release, both absolute and as a percentage of gross nitrogen uptake, is observed during nitrogen sufficient growth, and the release rates decrease by over a factor of four when ammonium is depleted in the medium (i.e. they reached senescence; Bronk 1999). In cultures of Scenedesmus quadricauda and Microcystis novacekii, DON release is low in nitrogen-limited cultures but significantly higher in cultures that are nitrogen-replete (Nagao and Miyazaki 2002).

DON concentrations and composition in natural waters Concentrations of bulk DON and individual organic nitrogen compounds vary widely across environments (Tables 9.2 and 9.3). A broad suite of biochemicals, including amino acids, proteins, urea, nucleic acids, and amino sugars, have been measured in seawater at low levels, but they represent only a small fraction of the total DON pool (Table 9.2, reviewed in Antia et al. 1991 and Benner 2002). The pool also includes other high molecular weight (HMW) dissolved compounds, such as humic substances. Humic substances are a class of organic compounds that are operationally defined based on their retention on hydrophobic resins, which can exist in Table 9.3 Range of mean concentrations of DON in various marine and aquatic systems (from Bronk 2002 and Berman and Bronk 2003) System Ocean – surface waters Ocean – deep waters Coastal/continental shelf Estuarine Rivers Lakes

Range of mean (mM)

Mean ± std (mM)

DON:TDN (%)

0.8 to 11.0 1.4 to 9.8 1.1 to 52.5 0.6 to 65.0 2.9 to 90.0 3.6 to 187.8

5.8 ± 2.0 4.3 ± 2.1 9.9 ± 8.1 22.5 ± 17.5 34.7 ± 20.7 38.1 ± 34.7

61.6 ± 32.9 9.9 ± 2.6 65.3 ± 30.4 68.9 ± 22.4 60.1 ± 23.5 ~66.0 ± 15.3

310 Algal Cultures, Analogues of Blooms and Applications relatively high concentrations in marine and aquatic systems (reviewed in Hessen and Tranvik 1998). The largest fraction of DON in aquatic systems, however, remains undefined (reviewed in Benner 2002). When considering the composition of the DON pool, it is important to remember that the composition is greatly affected by uptake processes. For example, the composition of the DFAA pool in natural waters is largely composed of those compounds that bacteria and phytoplankton have the lowest affinity for (Flynn and Butler 1986). Amino acids such as glutamate, alanine, serine and glycine are often present at the highest concentrations, while others such as arginine, glutamine, and asparagine are often present at the lowest concentrations, in reflection of preferential uptake

DON release in natural waters In culture, DON release is limited to direct release from algae, or possibly release due to viral infection. In the field, however, DON release (reviewed by Carlson 2002) is mediated by a number of processes including direct release from primary producers (Bronk 1999) and bacterioplankton (Ogawa et al. 2001), egestion, excretion, and sloppy feeding from micro- and mesozooplankton (Nagata and Kirchman 1991, Ward and Bronk 2001, Steinberg et al. 2002, 2004), viral lysis of bacterioplankton (Fuhrman 1992) and eukaryotic cells (Suttle 1994), and particle solubilization (Smith et al. 1992; summarized in Fig. 9.1). Here we will focus on the two release processes that are likely most important in natural waters: virus- and grazermediated release. Viral infection is important to consider with respect to DON release because in the final stages of viral infection, the phage increase to such numbers that the cell actually bursts, resulting in the release of any dissolved organics present within the cell (Wommack and Colwell 2000). Viral infection and lysis is an ongoing process within plankton communities but the quantitative contribution of this process to rates of DON release is unknown. One aspect of viral ecology that has received attention is their role in the decline of blooms. A number of investigations have uncovered either direct (Nagasaki et al. 1994, Tarutani et al. 2000), or indirect (Sieburth et al. 1988, Milligan and Cosper 1994), evidence for the involvement of viral lysis in the large-scale mortality and cessation of monospecific microalgal blooms (reviewed by Wommack and Colwell 2000 and Weinbauer 2004). Viruses have also been implicated in the decline of blooms of the common coccolithophorid Emiliania huxleyi (Bratbak et al. 1993; Brussaard et al. 1996) and the diatom Skeletonema spp. (Bratbak et al. 1990), as well as harmful algal species such as Aureococcus anophagefferens (Gobler et al. 1997).


311

Fig. 9.1 Conceptual diagram of processes involved in dissolved organic nitrogen (DON) release (Sources) and processes involved in DON utilization (Sinks) in aquatic systems (modified from Bronk 2002) Grazing by microzooplankton (e.g. flagellates and ciliates), on phytoplankton and bacteria, can release varying percentages of carbon, nitrogen or phosphorus, depending on prey type, via egestion and possibly diffusion (reviewed in Nagata 2000). This released DOM is composed of HMW and low MW (LMW) compounds (Taylor et al. 1985), DFAA (Flynn and Fielder 1989), and DCAA (Nagata and Kirchman 1991). DON is also released from micro- and macrozooplankton through excretion (Small et al. 1983; Steinberg et al. 2002) and fecal pellet dissolution (Lampitt et al. 1990, Jumars et al. 1989, Urban-Rich 1999). While there are few direct measurements, studies indicate excretion of DON by zooplankton can be a large proportion of the total nitrogen metabolized or ingested by the organism (Steinberg et al. 2000). Though ammonium is the primary nitrogen excretory product of

312 Algal Cultures, Analogues of Blooms and Applications many zooplankton, DON (i.e. urea and amino acids) is excreted at lower but significant levels (Bidigare 1983). Another important source of DON is sloppy feeding by mesozooplankton (i.e. copepods), where cells are not ingested whole but instead are broken during feeding thereby releasing intracellular dissolved compounds into the water column. A number of studies have demonstrated increased release of amino acids (Williams and Poulet 1986, Roman et al. 1988) or bulk DON (Hasagawa et al. 2000) due to zooplankton feeding.

DON BIOAVAILABILITY TO ALGAE IN CULTURE AND IN NATURAL WATERS The study of DON bioavailability is especially suited to cultures because the pool is so complex in natural waters. Here we review uptake mechanisms used to access organic nitrogen and briefly survey the bioavailability of a suite of organic nitrogen compounds. We note that more detailed reviews of DON uptake can be found in Paul (1983), Flynn and Butler (1986), Antia et al. (1991) and Bronk (2002).

Uptake Mechanisms Uptake of the smaller organic compounds, urea and amino acids, can occur by active transport or, if extracellular concentrations are extremely high (mM levels), through facilitated diffusion. For larger organic compounds such as peptides, humic acids, etc. direct uptake via transport proteins at the cytoplasmic membrane is not possible. Nitrogen associated with these larger compounds can be taken into the cell via pinocytosis or phagocytosis (cellular ingestion of substances within membrane bound vesicles) or after extracellular enzymatic cleavage of nitrogen from the larger compounds. Little is known about pino- or phagocytosis as a means of acquiring nitrogen, but uptake of a HMW (2000kDa) dextran by the dinoflagellate Alexandrium catenella has been documented (Legrand and Carlsson 1998). The uptake mechanism that has received more attention of late is extracellular exzymatic breakdown and then subsequent adsorption of the free nitrogen (Fig. 9.2). A number of phytoplankton strains have been shown to have cell surface amine oxidase enzymes that can cleave amino groups from primary amines (Palenik and Morel 1990a) including Chlamydomonas reinhardtii (Langheinrich 1995) and Gonyaulax polyedra (Sankievicz and Colepicolo 1999). The resulting alpha-keto acids or aldehydes remain in the water column forming potential carbon sources for bacteria. This scenario illustrates that, though studies with 14C-labeled organic compounds


313

Fig. 9.2 Proposed mechanism of cell-surface L-amino acid and amine oxidases (from Palenik et al. 1988). The enzyme catalyzes the decomposition of an amine at the surface of a phytoplankton cell resulting in the release of ammonium (NH4+), a keto acid, and hydrogen peroxide (H2O2). The amine is shown labeled with 14C to illustrate that in a tracer experiment, the amine will appear to be used by the bacterial fraction, while the N may actually being utilized by the phytoplankton. generally show transfer of the label to the bacterial fraction, these results cannot necessarily be extrapolated to include utilization of the associated amino nitrogen. This type of event is analogous to the role of alkaline (marine species) and acidic (freshwater species) phosphatases that develop when algae are phosphorus-stressed. Mulholland et al. (1998) quantified extracellular amino acid oxidase activity in natural waters from a number of oceanic and estuarine systems using a fluorescent analog of lysine (Pantoja and Lee 1994), and found that oxidase activity is widespread. The presence of cell surface enzymes (Palenik et al. 1988, Palenik and Morel 1990b) also raises the possibility that phytoplankton may access nitrogen from larger moieties without taking up the entire molecule. Studies with axenic cultures of Aureococcocus anophagefferens found high rates of peptide hydrolysis suggesting that this harmful bloom former can access some components of the HMW DON pool as a nitrogen source (Berg et al. 2002). Studies with cultures of Emiliania huxleyi, however, found that, while short-chained aliphatic amines were readily taken up, longer chained aliphatic amines were not used as a nitrogen source (Palenik and Henson 1997).

Uptake of Individual Compounds Urea Urea is a small neutral molecule that can move into and out of cells by passive diffusion as well as by active transport; passive diffusion, however,

314 Algal Cultures, Analogues of Blooms and Applications is unlikely to be of significance at the low concentrations typical of natural waters. The degradation of urea is catalyzed intracellularly by two enzymes – urease and ATP:urea amidolyase; generally an organism will possess one but not both enzymes. Urease catalyzes the breakdown of urea to ammonium and carbonic acid (Collier et al. 1999). The enzyme itself is a constitutive nickel-ligated metaloprotein (Sakamoto and Bryant 2001); the ability of phytoplankters to use urea may have been underestimated in culture studies prior to the realization that nickel is required in the media for urea uptake via urease (Oliveria and Antia 1986). The enzyme ATP: urea amidolyase is a HMW protein that catalyzes two reactions with the end products being ammonium and bicarbonate. In contrast to urease, ATP: urea amidolyase appears to be inducible (Syrett and Leftley 1976). The ability to breakdown urea to ammonium appears to be common such that most freshwater and marine phytoplankton species tested grow efficiently on urea as the sole nitrogen source (reviewed in Antia et al. 1991). Urea uptake kinetics have been studied in a number of species and are consistently found to be saturable and carrier-mediated (Table 9.4). A number of marine phytoplankton species have been shown to have a high affinity for urea with Ks values less than 1 mM (Table 9.4). In a comprehensive study of urea uptake by Chlamydomonas reinhardtii, uptake at concentrations less than 70 mM is mediated by a saturable transport mechanism with a Ks of 5.1 mM; at concentrations greater than 70 mM, urea transport into the cell takes place by passive diffusion (Hodson et al. 1975, Williams and Hodson 1977). These researchers also found that uptake only proceeds in the presence of light and oxygen. Urea uptake is influenced by the availability of other nitrogen substrates. Many studies have shown that urea uptake is suppressed by the presence of ammonium (Lui and Roels 1970, Kirk and Kirk 1978a and b, Horrigan and McCarthy 1982, Lund 1987, Molloy 1987, Molloy and Syrett 1988a and b, Ricketts 1988) though not in all cases (Syrett and Leftley 1976). In contrast to ammonium, nitrate generally does not inhibit urea uptake, but the presence of urea has been shown to inhibit uptake of nitrate (McCarthy and Eppley 1972, Molloy and Syrett 1988b, Ricketts 1988) though, again, not in all cases (Kirk and Kirk 1978a and b). This is consistent with a common control of ammonium, nitrate and urea uptake centering on the intracellular concentrations of glutamine and 2-oxoglutarate and hence on the availability of intracellular ammonium and carbon skeletons typically produced during photosynthesis (Flynn 1991).

Thalassiosira weisflogii Grunow Dinoflagellates Alexandrium catenella Prasinophytes Micromonas pusilla

Chlorella pyrenoidosa Chick Eudorina elegans Ehrenberg Golenkinia minutissima Iyengar et Balakrishnan Gonium pectorale M ller Pandorina morum Bory Pleodorina californica Shaw Scenedesmus obliquus Turpin Scenedesmus quadricauda Turpin Volvox carteri Iyengar Cyanobacteria Psuedoanabaena catenata Lauterborn Diatoms Ditylum brightwellii Grunow Lauderia sp. Phaeodactylum tricornutum Bohlin Skeletonema costatum Greville Thalassiosira pseudonana Hustedt

Chlorophytes Ankistrodesmus braunii N geli Chlamydomonas reinhardtii Dangeard Chlorella fusca Shihara et Krauss

Phytoplankton species

0.4 0.4 1.7 0.6– 1.0 1.4 0.4 0.5 0.5– 1.7 28.4 ± 15.0 0.38 ± 0.07

0.67 ND 0.02 ND ND ND ND ND ND ND ND ND ND 0.011 0.013 0.002– 0.02 0.015 0.008 0.08 0.024– 0.03 0.025 ± 0.008 0.054 ± 0.026

Ks (mM) 0.8 5.1 15.0 16.5 0.6 0.1 0.7 0.1 2.6 0.5 0.8 1.2 0.9

Vmax (h– 1)

Cochlan and Harrison 1991

Collos et al. 2004

McCarthy 1972 McCarthy 1972 Rees and Syrett 1979a McCarthy 1972 McCarthy 1972 Horrigan and McCarthy 1981 McCarthy 1972

Healey 1977

Kirk and Kirk 1978c Williams and Hodson 1977 Syrett and Bekheet 1977 Bekheet and Syrett 1979 Kirk and Kirk 1978c Kirk and Kirk 1978c Kirk and Kirk 1978c Kirk and Kirk 1978c Kirk and Kirk 1978c Kirk and Kirk 1978c Kirk and Kirk 1978c Healey 1977 Kirk and Kirk 1978c

Reference

M

M M M M M M M

FW

FW FW

FW FW FW FW FW FW FW FW FW FW

FW/M

D

R R D R R D R

R

R R R R R R R R R R R

N status

Table 9.4 Kinetic parameters for urea uptake in freshwater (FW) and marine (M) algae. Cultures were either nitrogen replete (R) or nitrogen depleted (D).


315


Amino acids Microalgae can simultaneously take up a number of DFAA at natural concentrations and remove them to levels below detection (Lu and Stephens 1984). This capability appears to be wide spread, though there are differences in the ability to take up DFAA between phytoplankton groups (Flynn 1990). Uptake of amino acids occurs mainly through active transport, and there appears to be at least three transport systems, each of which facilitates the uptake of one of the three types of amino acids – acidic (negatively charged), neutral (uncharged), and basic (positively charged). Multiple amino acids have been shown to share the same porter system, which is based likely on the charge of a group of amino acids (Liu and Hellebust 1974b, North and Stephens 1972). Some amino acids are taken up but they can not be used for growth. For example, the toxic dinoflagellate Alexandrium fundyense can take up a range of amino acids but nitrogen derived from amino acids is not able to support significant growth (John and Flynn 1999). It has been suggested that two intracellular pools occur in cells, a storage pool and a metabolic pool (Wheeler and Stephens 1977), although whether these are physically separate within the cell is not known. In general, amino acid uptake is subject to the regulatory process of transinhibition, a process in which the internal cellular concentration of the substrate limits its own transport across the plasma membrane. When intracellular pools of free amino acids are replete, then transport into the cell halts. In contrast to the uptake of inorganic nitrogen, the use of DFAA is promoted by nitrogenstress and/or carbon-stress, because these conditions would adversely affect the size of the intracellular pool of free amino acids. In general, amino acid uptake is controlled by a number of parameters including intracellular pools, nitrogen deprivation, light availability, and the presence of other nitrogen substrates. A number of studies have demonstrated that nitrogen deprivation increases amino acid uptake (North and Stephens, 1971, 1972, Wheeler et al. 1974, Flynn and Syrett 1985, 1986). For example, glycine uptake rates by Platymonas increase by a factor of ten when the cells become nitrogen depleted (North and Stephens 1971). Nitrogen-starvation, with or without carbon-starvation (i.e. incubation in darkness), is known to stimulate amino acid uptake (Flynn and Syrett 1986). Light deprivation can also induce amino acid uptake likely due to the need for carbon (Lewin and Hellebust 1975, 1976, 1978). The presence of ammonium has been shown to inhibit amino acid uptake (Flynn and Butler 1986), though not in all cases (North and Stephens 1971, Kirk and Kirk 1978c, Flynn and Syrett 1986). There are also studies that demonstrate


317

instances when amino acids inhibit ammonium uptake (Flynn and Wright 1986) and nitrate uptake (Bilboa et al. 1981). Amino acids can also inhibit uptake of other amino acids. For example, lysine and arginine share the same porter system and can therefore inhibit each other (reviewed in Flynn and Syrett 1986). Kinetic parameters have been determined for a number of different phytoplankton species (summarized in Table 9.5). Similar to urea, DFAA uptake appears to be saturable and carrier mediated. In more recent work, two axenic phytoplankton cultures were grown on eight different amino acids supplied at a concentration of 50 mM; Thalassiosira pseudonana is not able to grow on any of the amino acids but Emiliania huxleyi is able to grow on the neutral amino acids offered (Ietswaart et al. 1994).

Other organic compounds A wide variety of other organic nitrogen compounds have been studied as a nitrogen source for algae (reviewed in Table 9.6 and Antia et al. 1991). The amino sugar, glucosamine is used as a nitrogen source by two diatoms (McLachlan and Craigie 1966) but a third diatom, Phaeodactylum tricornutum, is not able to grow on this substrate (Hayward 1965). The cryptomonad, Hemiselmis virescens, is able to grow on glucosamine, as well as on galactosamine (Antia and Chorney 1968). The dipeptide glycineglycine is also a good nitrogen source for some phytoplankters (Turner 1979, Neilson and Larsson 1980). Purines, pyrimidines, and pteridines have been documented to serve as nitrogen sources for a number of algal species (Table 9.6). Some purines and pteridines are primary excretory products that are the end products of nitrogen catabolism (Antia et al. 1991). The major purine bases found in nucleic acids are adenine and quanine and the major pyrimidine bases are thymine, cytosine, and uracil. Both adenine and quanine are able to serve as a sole nitrogen source for Chlamydomonas reinherdtii (Lisa et al. 1995) though the substrates appear to have been added at mM concentrations in this study. Kinetic experiments estimate Ks values of 3.3 and 3.2 mM for adenine and quanine respectively (Lisa et al. 1995). Berman and Chava (1999) also provide evidence that the purines guanine and hypoxanthine can serve as nitrogen sources for phytoplankton, and uptake appeared to be inducible and took place after a lag period. This study, however, also used relatively high substrate additions (100 mM) and non-axenic monocultures, raising the possibility that the nitrogen was taken up not in purine form but as a regeneration product of bacterial purine uptake. Pettersen and Kuntsen (1974) also show that guanine can be used as a nitrogen source (Pettersen

Alanine Nitzschia laevis Hustedt Arginine Chlamydomonas reinhardtii Dangeard Cyclotella cryptica Reiman Nitzschia ovalis Arnott Volvox carteri Iyengar Aspartate Pleodorina californica Shaw Scenedesmus obliquus Turpin Scenedesmus quadricauda Turpin Volvox carteri Iyengar Glutamate Anabaena variabilis K tzing ex Gomont Cyclotella cryptica Reiman Navicula angularis Grunow Navicula laevis Hustedt Navicula pavillardi Hustedt Glutamine Anabaena variabilis K tzing ex Gomont Glycine Gymnodinium breve Davis Phaeodactylum tricornutum Bohlin Tetraselmis subcordiformis Wille


Chapman and Meeks 1983 Baden and Mende 1979 Lu and Stephens 1984 North and Stephens 1971

0.5 0.8 1.2 0.9 1.4– 100 36 20 30 20 1.1– 13.8 110.0 3.0 19.0

ND ND ND ND Cyanobacteria Diatom Diatom Diatom Diatom Cyanobacteria

Diatom

Chapman and Meeks 1983 Liu and Hellebust 1974 a, b Lewin and Hellebust 1976 Lewin and Hellebust 1978 Lewin and Hellebust 1975

Kirk and Kirk 1978c Kirk and Kirk 1978c Healey 1977 Kirk and Kirk 1978c

Kirk and Kirk 1978b Liu and Hellebust 1974 a, b North and Stephens 1972 Kirk and Kirk 1978a

3.9 3.0 2.0 3.8

Chlorophyte Diatom Diatom Chlorophyte

Lewin and Hellebust 1978

Reference

20

Ks (mM)

Diatom

Type

M M M

FW

FW M M M M

FW FW

FW

FW M M FW

M

FW/M

Contd.

R R D

R

R R R R R

R R R R

R R R R

R

N status

Table 9.5 Kinetic parameters for amino acid uptake for freshwater (FW) and marine (M) algae. Cultures were either nitrogen replete (R) or nitrogen depleted (D).


Guanine Phaeodactylum tricornutum Bohlin Chlorella fusca Shihara et Krauss Leucine Anabaena variabilis K tzing ex Gmont Anacystis nidulans Richter Ankistrodesmus braunii (Nageli) Chlorella fusca Shihara et Krauss Pandorina morum Bory Scenedesmus obliquus (Turpin) Kruger Lycine Phaeodactylum tricornutum Bohlin Phaeodactylum tricornutum Bohlin Methionine Gymnodinium breve Davis Phenylalanine Chlorella fusca Shihara et Krauss Proline Cyclotella cryptica Reiman Tyrosine Chlorella fusca Shihara et Krauss Valine Gymnodinium breve Davis Uric acid Platymonas concoutae Parke et Manton


Table 9.5 Contd.

Flynn and Syrett 1986 Flynn and Syrett 1986 Baden and Mende 1979

2.3 0.8 125.0

Diatom Diatom

Richards and Thurston 1980 Baden and Mende 1979 Douglas 1983

0.4 150.0 1.5– 3.4

Chlorophyte

Liu and Hellebust 1974 a, b

6.0

Diatom

Pedersen and Knutsen 1974

5.0

Chlorophyte

Thiel 1988 Lee-Kaden and Simonis 1982 Kirk and Kirk 1978c Richards and Thurston 1980 Kirk and Kirk 1978c Kirk and Kirk 1978c

10.8 125.0 16 2.5 52 47

Cyanobacteria Cyanobacteria Chlorophyte Chlorophyte Chlorophyte Chlorophyte

Shah and Syrett 1982 Pedersen and Knutsen 1974

Reference

0.5 0.1– 1.0

Ks (mM)

Diatom Chlorophyte

Type

M

M

FW

M

FW

M

M M

FW FW FW FW FW FW

M FW

FW/M

R

R

R

R

R D

R R R R R R

R

N status


319

320 Algal Cultures, Analogues of Blooms and Applications Table 9.6 Summary of culture studies that demonstrate whether a given freshwater (FW) or marine (M) phytoplankter can grow, Yes (Y), or is unable to grow, No (N), on a given substrate Compound Allantoic acid

Organism 1

Allantoin1

Amides Acetamide

Formamide

Nicotinamide Succinamide Amines Aniline Diethylamine Diethanolamine Ethylamine Methylamine

p-aminobenzoic acid Putrescine

Use

Chlorophyte Chrysophyte Cyanobacterium Diatoms (2) Dinoflagellate Eustigmatophyte Prymnesiophytes (3) Prymnesiophyte Chlorophycean algae (6) Microalgae (12) Microalgae (4 of 18) Prasinophyte

Y N Y N N Y Y N Y N Y N

Chlamydomonads (22 of 38) Cyanobacterium Microalgae (13 of 27) Aureococcus anophagefferens Emiliania huxleyi Pleurochrysis carterae Prymnesium parvum Thalassiosira pseudonana Prorocentrum minimum Emiliania huxleyi Pleurochrysis carterae Prymnesium parvum Thalassiosira pseudonana Prorocentrum minimum Phaeodactylum tricornutum Cryptomonad Chlamydomonads (8)

Y Y Y Y Y N N Y Y Y Y N N N Y N Y

Cyanobacterium Cyanobacterium Diatom Cyanobacterium Diatom Cyanobacterium Cryptomonad Diatom Cryptomonad Chlorophytes (2) Cryptomonad Cyanobacterium

N N Y Y Y N N Y N Y N Y

FW/M

FW FW M M

Reference Oliveira and Huynh 1990 Oliveira and Huynh 1990 Oliveira and Huynh 1990 Oliveira and Huynh 1990 Oliveira and Huynh 1990 Oliveira and Huynh 1990 Oliveira and Huynh 1990 Oliveira and Huynh 1990 Devi Prasad 1983 Birdsey and Lynch 1962 Antia et al. 1980 Edge and Ricketts 1978

FW Cain 1965 Brackish Kapp et al. 1975 Neilson and Larsson 1980 M Berg et al. 2002 M Palenik and Henson 1997 M Palenik and Henson 1997 M Palenik and Henson 1997 M Palenik and Henson 1997 M Palenik and Henson 1997 M Palenik and Henson 1997 M Palenik and Henson 1997 M Palenik and Henson 1997 M Palenik and Henson 1997 M Palenik and Henson 1997 Hayward 1965 Antia and Chorney 1968 FW Cain 1965 Kapp et al. 1975 Kapp et al. 1975 Wheeler and Hellebust 1981 Kapp et al. 1975 Wheeler and Hellebust 1981 Kapp et al. 1975 Antia and Chorney 1968 Wheeler and Hellebust 1981 Antia and Chorney 1968 Neilson and Larsson 1980 Antia and Chorney 1968 Kapp et al. 1975

Contd.


Table 9.6

321

Contd.

Compound

Amino sugars Glucosamine

Combined amino acids Glycine-glycine

Nucleic acids Salmon sperm DNA Pteridines Pterin-6-carboxylic acid Pterin Purine-Adenine

Purine-Guanine

Organism

Use

FW/M

Reference

Cyanobacterium Eustigmatophyte Rhodophyte

Y Y Y

Aphanizomenon ovalisporum Cryptomonad (achitinous) Cyclotella spp. Diatoms (2) (chiton-forming) Diatom (achitinous) Microalgae (26) Pediastrum spp.

Y2 Y Y2 Y N Y Y1

FW

Chlorophytes (2) Cryptomonad

Y N

FW M

Microalgae (5)

N

FW

Prymnesiophytes (3)

Y

M

Cyanobacterium

Y

Kapp et al. 1975

Cyanobacterium

N

Kapp et al. 1975

Cryptomonad Chlamydomonas reinhardtii Chlamydomonads (33 of 38) Chlorella protothecoides Krueger Chlorella pyrenoidosa Cryptomonad Diatom Haematococcus– related species (6) Prototheca zopfii Krueger Tetraselmis striata Butcher Emiliania huxleyi Chlorophyta Aphanizomenon ovalisporum Chlamydomonas reinhardtii Chlorophytes (6) Cryptomonad Cyclotella spp. Diatom

Y T Y Y Y Poor N Poor Y Y Y Y T Y Y Y2 Y

Kapp et al. 1975 Neilson and Larsson 1980 Neilson and Larsson 1980

FW

M FW

Berman and Chava 1999 Antia and Chorney 1968 Berman and Chava 1999 McLachlan and Craigie 1966 Hayward 1965 Berland et al. 1976 Berman and Chava 1999

Neilson and Larsson 1980 Antia and Chorney 1968 Vieira and Klaveness 1986 Antia and Chorney 1968 Vieira and Klaveness 1986 Turner 1979

FW FW

Landymore and Antia 1978 Lisa et al. 1995 Cain 1965

M M

Rose and Casselton 1983 Ammann and Lynch 1964 Antia and Chorney 1968 Shah and Syrett 1982

M

FW M M FW M

Droop 1961 Stacey and Casselton 1966 Edge and Ricketts 1978 Palenik and Henson 1997 Sarcina and Casselton 1995 Lisa et al. 1995 Shah and Syrett 1984 Antia and Chorney 1968 Berman and Chava 1999 Shah and Syrett 1982

Contd.

322 Algal Cultures, Analogues of Blooms and Applications Table 9.6

Contd.

Compound

Organism Diatom (3) Pediastrum spp. Prasinophyte Prasinophyte Prasinophyte (1) Red algae (2)

Purine-Uric acid

2

FW/M

Y Y2 Y Y Y N

M FW M M M M

Supralittoral Haematococcus (6) Y M Centric diatoms Fair-Good M Chlamydomonads (16 of 38) Y FW Chlorophytes (5 of 8) Y FW Chlorophytes Y M Cryptomonad Y M Cyanobacterium Y M Cyanobacterium N FW Diatoms (8) Y M Haematococcus – Y related species (5 of 6) Microalgae (15 of 27) Y Prasinomonad Y Prasinophytes (10) Y M Prymnesiophytes (3) Y M Supralittoral protests (4 of 5) Y M Symbiotic alga Y M

Purine-Hypoxanthine Aphanizomenon ovalisporum Cyclotella spp. Pediastrum spp. Emiliania huxleyi Purine-Xanthine Chlorophytes (5 of 8) Chrysomonad Cryptomonad Cyanobacterium Dinoflagellate Pyrimidines-Uracil Chlamydomonads (38) Cryptomonad Cyanobacterium Haematococcu s (related spp. 3 of 6) Prasinophyte Rhodophyte 1

Use

Y2 Y2 Y2 Y Y Y Y Y Y N N Poor Poor

FW FW FW M FW M M M M FW M M

N N

M

Ureides, a urea derivative. Non-axenic monocultures, substrate additions were 100 mM.

Reference Shah and Syrett 1984 Berman and Chava 1999 Gooday 1970 Edge and Ricketts 1978 Shah and Syrett 1984 Turner 1970 Shah and Syrett 1984 Droop 1961 Guillard 1963 Cain 1965 Birdsey and Lynch 1962 Turner 1979 Antia and Chorney 1968 Van Baalen and Marler 1963 Van Baalen 1965 Fisher and Cowdell 1982 Droop 1961 Neilson and Larsson 1980 Douglas 1983 Turner 1979 Turner 1979 Droop 1955 Edge and Ricketts 1978 Douglas 1983 Berman and Chava 1999

Palenik and Henson 1997 Birdsey and Lynch 1962 Mahoney and McLaughlin 1977 Antia and Chorney 1968 Kapp et al. 1975 Mahoney and McLaughlin 1977 Cain 1965 Antia and Chorney 1968 Kapp et al. 1975 Droop 1961 Edge and Ricketts 1978 Turner 1970


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and Kuntsen 1974); surprisingly concentrations of ammonium up to 1 mM did not affect the rate of guanine uptake (Pettersen 1975). As in other organics, nitrogen deprivation increases the use of guanine (Shah and Syrett 1982, 1984). Rates of uptake of methylamine, urea, guanine, lysine and arginine increases as the periods of nitrogen deprivation increased. Uric acid is another good nitrogen source for growth for a number of phytoplankton species (Guillard 1963, Birdsey and Lynch 1962, Droop 1955, 1961, Cain 1965, Antia and Chorney 1968, Turner 1979, Neilson and Larsson 1980, Fisher and Cowdell 1982, Douglas 1983). Other purines, including adenine, guanine, hypoxanthine, and xanthine, can also be used as a nitrogen source (Antia et al. 1975, Devi Prasad 1983, Shah and Syrett 1984, Kapp et al. 1975). Another group of organic compounds, humic substances, have traditionally been considered unavailable for assimilation due to their HMW and structural complexity. More recent studies of humic substances, however, indicate that they are not as refractory as once thought (Moran and Hodson 1994, Amon and Benner 1994, Gardner et al. 1996, See 2003). Recent work using 15N-labeled estuarine humic substances, formed in the laboratory, indicates that the ability to take up humic-nitrogen is widespread in cultures of coastal phytoplankton. See et al. (submitted) surveyed 17 phytoplankton species isolated from coastal waters and found that all phytoplankters tested can take up humic-nitrogen in short-term (3 h) incubations. Timecourse experiments, however, indicate that uptake of humic-nitrogen is not sustained, implying that once the fraction of bioavailable nitrogen associated with the humics is exhausted uptake ceases (See 2003, See et al. submitted).

DON uptake in natural waters Observations of high and relatively constant concentrations of DON in the ocean, where production was considered at the time to be primarily limited by the availability of DIN, combined with field studies that showed organic substrates, primarily 14C-lableled, were taken up by the small bacterial size fraction led to the dogma that DON was largely refractory in nature and therefore unimportant to phytoplankton nutrition (McCarthy et al. 1975). As a result, bacteria have traditionally been thought to be the primary users of DON in natural systems, and that direct uptake of DON into phytoplankton, without bacterial or photochemical remineralization, has been considered to be relatively minor. Evidence began to accumulate during the 1990s, however, that some organic compounds (i.e. urea) can be important to

324 Algal Cultures, Analogues of Blooms and Applications phytoplankton nutrition (Glibert and Garside 1992), that fluxes into and out of the DON pool are substantial (Bronk and Glibert 1993a, Bronk et al. 1994, Slawyk and Raimbault 1995), and that the nitrogen fraction of organic substrates need not follow the path of the carbon component of a molecule into the cell (Palenik and Morel 1990a). In natural waters DIN is still accepted as the most important form of nitrogen supporting algal growth, but it is now recognized that phytoplankton are not simply a source of DON that subsequently supports bacterial activity, but are a sink for DON as well. As we briefly review studies of DON uptake in natural waters here it is important to remember two things. First, uptake measurements in the field likely include bacterial, as well as, phytoplankton uptake. Second, once an organic substrate is added to an incubation in the field, it can be altered in a number of ways including bacterial or photochemical breakdown or abiotic adsorption such that the nitrogen form actually taken up can be very different than the one added. Field studies of DON uptake have focused on urea and amino acid uptake because there were commercially available tracers. Urea has been the one exception to the believed bacterial dominance of DON utilization. In general, phytoplankton are believed to be the primary users of urea in marine systems (Price and Harrison 1988a), though some recent studies have questioned this belief (Tamminen and Irmisch 1996). For example, in the Thames Estuary (UK), the addition of a broad prokaryotic inhibitor decreases dark uptake rates of urea by 86 ± 25% suggesting that bacterial uptake of urea is substantial (Middelburg and Niewenhuize 2000). Lomas et al. (2002) reviewed urea uptake rates for over a decade in Chesapeake Bay and found that urea is consistently an important nitrogen source for the plankton community. Additional field studies have shown urea to be an important nitrogen source in the open ocean (Price and Harrison 1988b), the coastal ocean (Probyn et al. 1990, Veuger et al. 2004), the polar ocean (Cochlan and Bronk 2001), and lakes (Gu and Alexander 1993); more detailed reviews of urea uptake studies can be found in Antia et al. (1991) and Bronk (2002). Bacteria are generally considered the primary users of DCAA and DFAA although autotrophic uptake of DFAA have been demonstrated on numerous occasions (reviewed in Flynn and Butler 1986, and Antia et al. 1991). Studies of cell-surface enzymes, however, suggest that phytoplankton use of DFAA may be greater than previously thought (see above, Palenik et al. 1988). For example, in a salt marsh phytoplankton community, addition of organic nitrogen, including glycine, glutamic acid, and an amino acid


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mixture, results in increased phytoplankton growth (Lewitus et al. 2000). Furthermore, in the Thames Estuary (UK), a study using a prokaryotic inhibitor indicated that approximately 51% of amino acid uptake is by autotrophs (Middelburg and Niewenhuize 2000). Additional studies that measure uptake of other organic nitrogen compounds such as purines, pyrimidines, and amines show that though phytoplankton and bacteria can utilize these compounds, the uptake rates are quite low (reviewed in Antia et al. 1991). It must be remembered, however, that there is no reason to expect the use of any one form of DON to match that of DIN but that the combined use of a range of different DON forms can be highly significant even when studies of the use of any one form indicates only modest uptake. Experiments in which natural humic substances, isolated from river water, are added to an assemblage of coastal phytoplankton reveal that growth and biomass formation are stimulated (Carlsson et al. 1993), though the mechanisms of the enhancement are unknown. The literature suggests that the nitrogen associated with humic substances can be removed by one of three mechanisms: through microbial activity (Müller-Wegener 1988), via excision by phytoplankton cell-surface enzymes (Fig. 9.2, Palenik and Morel 1990a) or through photodegradation to LMW compounds by exposure to ultraviolet radiation (reviewed in Mopper and Kieber 2002, summarized in Fig. 9.1). Determining uptake rates for the DON pool as a whole, however, is problematic because it is so chemically complex. Bronk and Glibert (1993a) used 15N-labeled DON produced in situ in Chesapeake Bay and found that during the decline of the spring bloom and during the summer, uptake rates of DON are comparable or higher than uptake rates of ammonium and nitrate. Although rates of DON uptake in the spring had a diel pattern and followed that of nitrate, both indications that autotrophic uptake dominated, the fate of the DON is unknown. Veuger et al. (2004) used 15 N-lableled DON produced in a Skeletonema culture to compare uptake of inorganic versus organic nitrogen uptake in a Danish fjord. They found that the organic forms (urea and DFAA) are important sources of nitrogen for the community and that DON other than urea and DFAA contributed to measured uptake.

The special case of blooms Much of the recent work on DON utilization has been tied to the potential of DON as a nitrogen source to phytoplankton that can form harmful algal

326 Algal Cultures, Analogues of Blooms and Applications blooms (HABs, Paerl 1988). There is increasing evidence linking DON additions with the increase in HABs (Berg et al. 1997, Berman 1997, 2001, Carlsson et al. 1998). Dinoflagellates, in particular, are known to use organic nutrients, such as urea and DFAAs, both directly (Butler et al. 1979, Berg et al. 1997) and indirectly, via cell-surface enzymes (Palenik and Morel 1990a and b, Pantoja and Lee 1994, Mulholland et al. 1998). DON has been implicated in initiating the A. anophagefferens (brown tide) blooms in Long Island (NY, USA, Dzurica et al. 1989, Berg et al. 1997, LaRoche et al. 1997) with particular emphasis on urea utilization (Dzurica et al. 1989, Berg et al. 1997). Berg et al. (1997) found that 70% of the total nitrogen utilized during an Aureococcus anophagefferens bloom off Long Island is organic with the largest fraction contributed by urea. Later studies indicate that the ability to use HMW DON may give Aureococcus anophagefferens a competitive advantage over co-occurring species (Berg et al. 2003). High urea levels (greater than 1.5 mM) were also found to co-occur with dinoflagellate blooms in aquaculture ponds (Glibert and Terlizzi 1999). Typical of many dinoflagellates, Karenia brevis (formerly Gymnodinium breve), can take up a variety of organic nitrogen compounds (including amino acids) as nitrogen sources for growth (Steidinger et al. 1998; Bronk et al. in press). In cultures of K. Appl. Env. Microbiol., cell yields increase dramatically when glycine, leucine and aspartic acid are added (Shimizu et al. 1995). What is not clear in most of these studies is whether the association between HABs and DON is direct or indirect. The formation of phytoplankton blooms is often associated with increases in DON concentrations. The decline of blooms are also known to be periods of significant DOM release from phytoplankton due to such processes as physiological stress or high grazing pressure (Carlson et al. 1994, Jenkinson and Biddanda 1995). DON accumulates during the year in temperate waters (Flynn and Butler 1986, Bronk et al. 1998) and HABs also become more frequent in summer months. Most HAB species are not in axenic culture, however, raising the possibility that any stimulation by the presence of DON is indirect because it may promote bacterial and other heterotrophic activities.

Problems Extrapolating Culture Results to the Field With the wealth of culture data available on algal uptake of DON, can we answer the question – Do phytoplankton in natural waters fulfill a portion of their nitrogen needs by taking up dissolved organic compounds? The answer is a definitive – probably! Though uptake and release of a wide number of organic compounds has been documented in culture, it is much


327

more difficult to address whether a given species actually uses that substrate in the environment. The answer to that question depends on many factors including the concentration of substrate in the environment, alternative sources of nitrogen that are available, and competition for the substrate by other phytoplankton species or bacteria. Here we review a number of reasons why it is difficult to extrapolate culture results to the field. First, batch cultures generally are started with nutrient concentrations many times higher than those found in nature; for example, f/2 media has a starting nitrate concentration of 883 mM (Guillard 1975). While such studies indicate the potential for growth, the high concentrations used severely limit the ability to extrapolate results to natural waters. Second, cells in batch culture will eventually reach a state of extreme nutrient starvation, which likely has no counterpart in nature where regeneration possesses are ongoing. This is particularly problematic for extrapolating results from DON release studies to the field. Third, in batch cultures cells move through the phases of growth very quickly (h) relative to natural waters (d), which allows the cells little or no time to adapt to their changing environment. On the other hand, chemostats have potential for selecting for long-term adaptations that may not be representative of nature either. Fourth, the practice of using non-axenic cultures or measuring uptake in the field using the GF/F filters (nominal pore size 0.7 mm) that are compatible with mass spectrometry results in both bacteria and phytoplankton being collected and analyzed. This practice severely limits our ability to understand algal nitrogen nutrition because results are confounded by possible bacterial uptake. Fifth, there is the question of competition with bacteria for substrates. For algae to successfully compete with bacteria for an organic substrate, they must have transport systems with kinetic parameters that can function at the low concentrations of organic substrates found in the environment. Though kinetic parameters of a given species can be measured in the field under extreme bloom conditions, where a given species overwhelmingly dominates the biomass, most kinetic work must be done in cultures. One problem with measuring kinetic parameters in cultures is that the cultures are generally maintained at concentrations many times the concentrations seen in the field. The question is whether cells that have been grown under saturating nutrient conditions for many generations (often for years, even decades) will show the same uptake kinetic characteristics of the cells in the field where substrate concentrations are very low (Paul 1983). Sixth, these are a number of processes that occur in natural waters that can alter organic substrates once they are added to an incubation (reviewed

328 Algal Cultures, Analogues of Blooms and Applications in Bronk 2002). Organic compounds can be broken down photochemically or through bacterial decomposition resulting in the release and subsequent uptake of ammonium or some other smaller labile moiety. Organics can also adsorb abiotically to particles such that they appear to be taken up. Lastly, there is the issue of multiple nitrogen substrates. If a cell will use a given organic substrate as a sole nitrogen sources it does not necessarily mean it will use that substrate in the environment when other substrates are available. Furthermore, the inability of a cell to grow on a given substrate as its sole nitrogen source does not mean it does not use that substrate in the field. For example, glutamine and arginine are more readily catabolized than histidine and yet the DON source may still be important as an augment to total nitrogen nutrition (Antia et al. 1991).

SUGGESTIONS FOR FUTURE RESEARCH Future research will in large measure be dictated, as it has in the past, by the development of new analytical techniques and approaches. There are many common practices that limit our ability to translate culture results to the field, as described above. Refining techniques and modifying protocols to circumvent these problems has the potential to dramatically advance our understanding of DON cycling in the environment. Some specific suggestions include curtailing the practice of conducting bioavailability studies with mM substrate additions if the goal of the study is to relate the findings to the environment. Expanded use of chemostats would allow cultures to be maintained at much lower nutrient and biomass concentrations thus making their results more applicable to natural waters. Greater attention to axenicity in cultures and more widespread use of techniques for separating bacteria from phytoplankton, such as flow cytometric sorting (Lipschultz 1995) has great potential to improve our understanding of algal nitrogen nutrition, particularly with respect to organic nitrogen forms that are highly desirable substrates for bacteria. Targeted studies on the affect of alternative nitrogen sources on DON uptake would be beneficial for deciphering the complex issue of substrate selection in the complex mix of organic nitrogen forms found in aquatic environments. Lastly, expanding our view from cultures and field incubation studies to define the role of the DON pool in the global carbon cycle remains one of the most challenging and important aspects of aquatic ecosystem research today. Progress in this area will require more complex mathematical models for the proper description of DON fluxes within ecosystem simulators. Currently these models typically involve a one-way flow of nitrogen from


329

algae to bacteria, which is almost certainly incorrect. Even the most complex models of phytoplankton used in such simulators currently ignore a role for DON (Flynn, this volume). Culture work can have a pivotal role in improving models by providing important baseline controls for a range of variables. The challenge will be to design culture studies of algal growth using environmentally relevant species under quasi-natural conditions.

ACKNOWLEDGMENTS The writing of this chapter was supported by NSF grant OCE-02218252 to DAB. This is contribution number 2677 of the Virginia Institute of Marine Science, The College of William and Mary.

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10 Osmotrophy in Marine Microalgae Alan J. Lewitus1 1

Belle W. Baruch Institute for Marine and Coastal Sciences, University of South Carolina, and Marine Resources Research Institute, South Carolina Department of Natural Resources Hollings Marine Laboratory, 331 Fort Johnson Road, Charleston, South Carolina, USA 29412

Abstract The widespread ability of microalgae to use dissolved organic compounds has been long recognized from culture studies, but the ecological relevance of the process traditionally has been a point of contention. In recent years, research focus on this issue has increased, due in part to a growing appreciation for the lability of dissolved organic matter (DOM) pools, and for the nutritional versatility of microalgae in natural communities. Evidence for the latter has come primarily from demonstrations of mixotrophic nutrition via phagotrophy, but more recently, evidence for the ecological importance of mixotrophic use of DOM also has accumulated. A review of microalgal culture research into the physiological ecology of osmotrophic nutrition is presented, revisiting early unresolved debates on the ecological importance of this pathway, exploring the complications in laboratory tests of this process that confound ecological application, and addressing recent discoveries that have stimulated newfound interest in microalgal heterotrophy.

INTRODUCTION The ecological relevance of microalgal heterotrophy (defined as the use of exogenous organic substrates as metabolic carbon source, Margulis et al. 1990) is historically one of the most debated issues in marine science. Use of microalgal cultures to examine this topic can serve as a classic illustration of the complexities involved in extrapolating laboratory findings to natural ecosystem function. The ability of microalgae to assimilate organic compounds is nearly ubiquitous under appropriate laboratory conditions. However, whether the conditions necessary for expression of this pathway

!"" Algal Cultures, Analogues of Blooms and Applications in culture are environmentally relevant or exceptional (i.e. a laboratory artifact) is still unresolved when applied to the trophic dynamics of most marine ecosystems. As discussed below, the case is now strong that dissolved organic substrates are used by microalgae in certain environments, for example hypereutrophic ponds, sea ice, or estuarine sediment porewater. There is growing recognition that dissolved organic nitrogen (DON) loading can promote harmful algal blooms (HABs) through direct use. The bioavailability of dissolved organic matter (DOM) is considered higher than previously thought, based on the discoveries of the ‘microbial loop’ (and other sources of organic-rich microenvironments), UV photolysis of macromolecules, and realization that some components of humic matter (high molecular weight fractions) are highly labile. Also, it is now well accepted that microalgal phagotrophy (colloquially termed ‘mixotrophy’) is widespread and can contribute significantly to overall community protist grazing. Despite these advances, inclusion of microalgal osmotrophic pathways in marine food web models is lacking, although an emphasis on mixotrophy vis-à-vis algal phagotrophy has emerged (Thingstad et al. 1996, BarettaBekker et al. 1998, Stickney et al. 2000, Quintana et al. 2002, Anderson et al. 2003, Zhang et al. 2003). Wetzel (1994) pointed out the need to incorporate ‘multiple resource utilization’ pathways into microbial loop models, stressing the importance of autotrophic and heterotrophic competition for carbon and nitrogen resources. Although bacteria-phytoplankton competition for inorganic nutrients is popularly accepted (Laws et al. 1985, Wheeler and Kirchman 1986, Caron 1994, Kirchman 1994, Kirchman and Wheeler 1998), similar considerations for organic nutrients and carbon have been slow to follow (Flynn and Butler 1986, Antia et al. 1991). This chapter addresses the role of microalgal culture experiments in advancing knowledge of the physiological ecology of marine microalgal heterotrophy. It will emphasize microalgal use of dissolved organic carbon (DOC) rather than DON, which has been reviewed recently (Bronk 2002, Berman and Bronk 2003), dissolved organic phosphorus (DOP), or particulate carbon (microalgal phagotrophy), also reviewed recently (Jones 1997, Raven 1997, Stoecker 1998, 1999). Some reference to freshwater species is included, but the focus is on marine systems. The chapter emphasizes ongoing gaps in our understanding of this issue, with a recommendation to integrate this pathway into testable hypotheses and models, and adopt previous approaches and findings using modern techniques and diverse cultures.

Osmotrophy in Marine Microalgae

!"#

A HISTORICAL PERSPECTIVE From the recognition that soil extract was requisite or stimulatory to the growth of some microalgae (Provasoli et al. 1957, Morrill and Loeblich 1979), to the multiple demonstrations of DOM enhancement of growth in the light or support of dark (chemoheterotrophic) growth, the widespread ability of microalgae to use organic growth substrates has long been realized (reviewed in Danforth 1962, Lewin 1963, Van Baalan and Pulich 1973, Droop, 1974, Neilson and Lewin 1974, Ukeles and Rose 1976, Hellebust and Lewin 1977, Sepers 1977, Antia 1980, Bonin and Maestrini 1981). As the number of examined species increased, it became apparent that the cellular mechanisms, regulation, and capacity for DOM uptake varied greatly with substrate, algal species, and environmental condition. In a classic review on heterotrophic nutrition in diatoms, Hellebust and Lewin (1977) described the group’s remarkable variability in metabolic pathways for organic substrate uptake and their environmental regulation. For example, some species could grow chemoheterotrophically, while others whose growth was stimulated by organic enrichment were obligate phototrophs. Light could stimulate DOM uptake or inhibit it. Some diatoms had inducible transport systems and others constitutive. Several specific uptake systems for DOM were documented in this group that varied widely with species. The authors surmised that “among those species studied so far, no two have responded to organic substances in quite the same way. This high degree of specificity is most remarkable, and is probably good indirect evidence for the evolutionary adaptation of species to various specific environmental habitats and situations.” Furthermore, Hellebust and Lewin (1977) pointed to the fact that several diatoms contained transport systems specific only to substrates that are commonly abundant (e.g. glucose, lactate and glutamate) as strong support for “the assumption that these species have evolved highly sophisticated and ecologically significant mechanisms for facultative heterotrophy.” Although these conclusions argued for an ecological role for this process in diatoms, Hellebust and Lewin (1977) were not suggesting that diatoms were primary users of DOM at the typically low substrate concentrations found in most aquatic environments, but rather suggested that these algae might be competitive in organically enriched environments such as those associated with sediments or in hypereutrophic systems such as sewage treatment ponds. As was recognized over a decade before, bacteria appeared to have much more efficient DOM uptake systems than those phytoplankton species tested. For example, Michaelis-Menten uptake kinetics consistently

!"$ Algal Cultures, Analogues of Blooms and Applications revealed microalgal half-saturation constants higher than in bacteria, and higher than the concentrations normally found in natural waters (Hobbie and Crawford 1969, Hellebust and Lewin 1977, Martinez and Azam 1993, Vallino et al. 1996). A seminal study (Wright and Hobbie 1965, Hobbie and Wright 1965), comparing uptake of glucose and acetate by Chlamydomonas sp. Ehrenberg and a bacterial isolate demonstrated greater substrate affinity by the bacterium. Also, whereas bacterial uptake was biphasic, indicating a high-affinity (i.e. active transport) and low-affinity (i.e. diffusion) stage, only a simple diffusion-like mechanism of uptake was suggested by the microalgal kinetic curves. In this and other laboratory studies, the DOM concentrations necessary to support dark growth or stimulate low-light growth were much higher than those typically measured in marine or estuarine waters (Barber 1968, Williams 1970, Bennett and Hobbie 1972, Antia et al 1975, Hellebust and Lewin 1977, Admiraal and Peletier 1979, Flynn and Butler 1986, Mulholland et al 1988, Lewitus and Caron 1991a,b, Martinez and Azam 1993). Wright and Hobbie’s (1965) findings had a profound influence on perception of this issue, and introduced healthy skepticism to extrapolations of culture findings on microalgal heterotrophy to nature. In contrast to Wright and Hobbie’s (1965) results with Chlamydomonas sp., several studies since have indicated complex rather than simple DOM uptake kinetics in the same Chlamydomonas species (Bennett and Hobbie 1972) and other microalgae (Hellebust 1970, Lylis and Trainor 1973, Liu and Hellebust 1974, Sheath and Hellebust 1974, Bollman and Robinson 1977, 1985, Sepers 1977, Chapman and Meeks 1983, Flynn and Butler 1986). Hellebust (1970 ) suggested that the amino acid transport systems involved in the diatom, Melosira nummuloides C. Agard, were “as complex as those reported for any other microorganism”, including active uptake at low substrate concentrations, a relatively long duration of active uptake (> 1 h), and a transport constant for arginine (0.77 mM) comparable to many bacteria. As mentioned, enzyme kinetic studies on diatoms generally have demonstrated relatively higher half-saturation constants than substrate concentrations typically associated with marine environments, with some exceptions where transport constants were < 1 mM (Bunt 1969, Hellebust 1970, Shah and Syrett 1982, Flynn and Syrett 1986a). As heterotrophic capabilities are widespread in pennate, but not centric diatoms, the case was presented that uptake systems in the former are adapted to organic-rich conditions associated with benthic or epiphytic environments (Wheeler et al. 1974, Lee et al. 1975, Ukeles and Rose 1976, Hellebust and Lewin 1977, Admiraal and Peletier 1979, Pip and Robinson 1982, Admiraal 1984, Flynn


!"%

and Butler 1986). Nilsson and Sundbäck (1996) used microautoradiography to determine pronounced uptake of 3H-labeled amino acids by benthic diatoms in sandy sediment porewaters. The potential for microalgal heterotrophy in sediment porewaters is further addressed in the ‘Sediments’ section below. The traditional thresholds used to evaluate the ecological relevance of laboratory-derived microalgal DOM uptake constants need a readjustment given the recent findings indicating higher DOM lability in sediment porewater and other aquatic systems (Geller 1985, Kieber et al. 1989, Bronk and Glibert 1993, Wetzel et al. 1995, Lindell et al. 1995, 1996, Moran and Zepp 1997, Hopkinson et al. 1998, Seitzinger and Sanders 1999, Stepanaukas et al. 1999, 2002, Cole 1999, Seitzinger et al. 2002, Berg et al. 2003). However, the argument that bacteria have a relatively greater affinity for DOM uptake than microalgae based on the preponderance of kinetic-based comparisons is more difficult to counter. As discussed in the ‘Taxonomic variability’ section, culture studies on the DOM uptake properties of microalgae include very few of the smaller nanoflagellates or picoplankton. Theoretically, these types would be expected to have a relatively high affinity for substrate uptake (i.e. smaller cell surface-to-volume ratio). Furthermore, in marine and especially freshwater systems, several cases exist where investigators demonstrated significant uptake of DOM in direct competition with bacteria (Table 10.1). The use of half-saturation constants as criteria for uptake capability under natural conditions is problematic on several grounds. These include the critical effects of incubation time (Saks and Kahn 1979, Admiraal 1984, Admiraal et al. 1984) and other experimental conditions (see ‘Considerations in culture experiments’ section below), and the need to simultaneously consider the maximum substrate velocity (Vmax) in order to encompass competitive advantages associated with luxury uptake and storage capacity (Neilson and Lewin 1974, Tilman and Kilham 1976, Saks and Kahn 1979, Healey 1980). Culture studies on the ecological role and physiological properties of microalgal heterotrophy have dwindled since the research boom on these topics in the 1960’s and 1970’s, but an interest in DON use has resurged, primarily based on supposition of its importance in relieving N limitation (reviewed in Flynn and Butler 1986, Antia et al. 1991, Bronk 2002, Berman and Bronk 2003). Flynn and Butler (1986) and Antia et al. (1991) suggested a reassessment of the importance of microalgal use of dissolved free amino acids (DFAA), citing several new or revisited bases; e.g. the ecological importance of pico- and nanophytoplankton, the use of appropriate culture maintenance conditions, the need to include AA mixtures in laboratory

Marine microbial mats Freshwater fish ponds Brackish-to-marine sediment porewater Estuarine tidal creek Open ocean Estuarine tidal creek

Lake Coastal marine Freshwater pond Lake Lake Lake Polar sea ice Polar marine planktonic, benthic, and sea ice Lake Lake Stream Coastal, open ocean

35

Glycine S-methionine FITC-dextran

14

Antibiotics Flow cytometry Laser confocal microscopy

Microautoradiography Microautoradiography Microautoradiography

Microautoradiography Microautoradiography Microautoradiography Microautoradiography

Oscillatoria rubescens Oscillatoria rubescens Periphytic Chlamydomonas Picoplankton, diatoms Cyanobacteria, diatoms Cyanobacteria, diatoms Benthic diatoms, cyanobacteria, flagellates Eukaryotes Prochlorococcus Kryptoperidinium foliaceum, Scrippsiella sp.

Microautoradiography Microautoradiography Size-fractionation Microautoradiography Microautoradiography Microautoradiography Microautoradiography Single cell micromanipulation

Cryptophytes, diatoms Diatoms, dinoflagellates Scenedesmus Chlorophytes Epiphytic microalgae Diatoms, cyanobacteria Diatoms Diatoms (7 species)

Microautoradiography

Chlorophytes, cyanobacteria

Lake

Size-fractionation Microautoradiography Antibiotics Size-fractionation

Small flagellates, epiphytes Cyanobacteria (4 species) Diatoms, cryptophytes Epiphytic diatoms

14

C-acetate, glucose C-acetate, glucose 14 C-acetate, glucose 14 C-AA mix, glucose, glycerol, mannitol, urea 3 H-acetate, AA mix, glucose, glycollate 14 C-glucose 14 C-glycine 3 H-acetate 14 C-acetate 14 C-fructose, glucose, sucrose 14 C-, 3H-glucose 14 C-serine 3 H-AA mix, glucose, glutamate, glycine, leucine 14 C-AA mix 3 H-AA mix 3 H-AA mix 14 C-AA mix, 3 H-AA mix, glucose, mannitol 3 H-AA mix, acetate, glucose N-acetyl-D-[1-3H] glucosamine 3 H-AA mix

Lake Lake Ice-covered lake Salt marsh

Method

Taxa

Labeled Substrate

14

Environment

Table 10.1 Tracer studies demonstrating DOC uptake by microalgae in mixed communities

Lewitus et al. 2000 Zubkov et al. 2003 This chapter

Paerl et al. 1993 Nedoma et al. 1994 Nilsson and Sundb ck 1996

Feuillade et al. 1988 Bourdier et al. 1989 Amblard et al. 1990 Paerl 1991

McKinley 1977 Wheeler et al. 1977 Vincent 1980 Vincent and Goldman 1980 Pip and Robinson 1982 Moll 1984 Palmisano et al. 1985 Rivkin and Putt 1987

Pollingher and Berman 1976

Allen 1971 Saunders 1972 Maeda and Ichimura 1973 Lee et al. 1973

Reference

!"& Algal Cultures, Analogues of Blooms and Applications


!"'

experiments, critical consideration of light conditions, and recognition of microalgal cell surface enzymes are addressed below. These and other rationale for the need to reconsider the ecological importance of microalgal DOC use are addressed below.

TAXONOMIC VARIABILITY Culture Bias (Lack of Information on Flagellates and Picophytoplankton) Notwithstanding the important work of Antia’s laboratory on cryptophytes and chrysophytes (reviewed in Antia 1980), it has been argued that our understanding of the physiological parameters of marine microalgal DOM use is based traditionally on diatoms, a group associated with spring blooms and with tendencies toward relatively high photosynthetic and NO3– uptake capacities (Flynn and Butler 1986, Berg et al. 1997, 2003, Smayda 1997, Doblin et al. 1999, Bronk 2002, Fan et al. 2003). Relatively little is known of the heterotrophic properties of the types of microalgae most commonly found in environments suboptimal for support of photosynthesis (e.g. the summer communities in temperate estuaries). These include flagellates (e.g. dinoflagellates) and pico-sized cyanobacteria, prochlorophytes, or eukaryotes. The relative lack of information on flagellates and picophytoplankton is partly due to the difficulty in maintaining cultures under standard protocols, and in establishing axenic cultures (Granéli et al. 1997, John and Flynn 1999, Zubkov et al. 2003). As standard protocols traditionally involved mineral media and moderate-to-high growth irradiance, culture collections in the past were probably biased against microalgal species with high heterotrophic capabilities (Bennett and Hobbie 1972, Antia et al. 1975, Flynn and Butler 1986). Although present day culture collections have greatly expanded in taxonomic diversity, the examination of heterotrophic pathways in phytoflagellates and picophytoplankton in defined laboratory studies has been extended to relatively few species.

Flagellates Lewitus and Kana (1994) tested the effects of glucose, glycerol or acetate addition on the growth rates of 16 phytoplankton species isolated from a subestuary of Chesapeake Bay (salinity 7-12 PSU) during late winter/early spring, a nanoflagellate-dominated community. Substrate additions as low as 1 mM significantly stimulated growth in 9 of the isolates (Table 10.2). The growth rate of one of the isolates, Nephroselmis sp. F. Stein, a 4-mm diameter

!# Algal Cultures, Analogues of Blooms and Applications Table 10.2 Effect of organic compound additions on growth rate of axenic cultures freshly isolated from a Chesapeake Bay subestuary (11 PSU). Acetate, glycerol, or glucose were added at £ 10 mM; irradiance = 12 mE m-2 s-1. HP designates isolated at Horn Point Laboratory; CCMP designates strains deposited at Provasoli-Guillard National Center for Culture of Marine Phytoplankton. Class

Species

Diameter (mm)

Phaeodactylum sp. HP9101 2.5 (at mid-region) Chlorella sp. HP9001 2 Ankistrodesmus sp. HP9101 1.5 (at mid-region) Cryptophyceae Chroomonas sp. HP9001 3 Chroomonas sp. HP9004 7 Hemiselmis sp. HP9001 3.5 Storeatula major HP9001 8 Dinophyceae Karlodinium micrum HP9001 8 Prorocentrum minimum HP9001 8 Euglenophyceae Eutreptia sp. HP9101 9 Pedinellophyceae Pseudopedinella pyriforme HP9001 9 Prasinophyceae Nephroselmis sp. HP9001 4 Pyramimonas sp. HP9001 5 Prymnesiophyceae Diacronema sp. HP9101 3 (CCMP 1610) 3 unidentified (CCMP 1611) 3 Raphidophyceae Heterosigma akashiwo HP9001 9 Bacillariophyceae Chlorophyceae

Significant Increase in Specific Growth Rate no yes yes no no yes yes yes yes no yes yes no yes Yes no no

prasinophyte, was stimulated > 7-fold in axenic cultures (Lewitus and Kana 1994, 1995). Glucose addition to the prasinophyte resulted in loss of chloroplast material, consistent with catabolite repression of chloroplast development (e.g. as found in Euglena gracilis G.A. Klebs, some strains of Chlorella M. Beijerinck, and Poterioochromonas malhamensis (Pringsheim) Peterfi; Pringsheim 1952, Myers and Graham 1956, App and Jagendorf 1963, Shihira-Ishikawa and Hase 1964, Harris and Kirk 1969, Monroy and Schwartzbach 1984, Lewitus and Caron 1991a). Thus, Nephroselmis sp. appeared to favor heterotrophic over autotrophic nutrition. Very small prasinophytes are common in oceanic and estuarine waters (Biegala et al. 2003, Rodriguez et al. 2003, Worden et al. 2004, Lewitus pers. observ.), yet few physiological studies with these picoeukaryotes exist. Because standard enrichment protocols for phytoplankton usually include mineral media (which may not support optimal growth for some species, Lewitus


!#

and Kana 1994), and because chloroplast loss may be mistaken for obligate heterotrophy, the prevalence of this Nephroselmis species or other flagellates with similar physiological characteristics is unknown. Chrysophytes, cryptophytes, and dinoflagellates are known to be nutritionally versatile (Antia 1980, Gaines and Elbrächter 1987). Kristiansen (1990) stated “It is doubtful if any entirely photoautotrophic chrysophytes exist”. More work has been done on freshwater than marine chrysophytes, although phagotrophy is widespread throughout this group (Sanders and Porter 1988). As mentioned, a relatively higher capacity for heterotrophic than photoautotrophic growth has been demonstrated in the freshwater chrysophyte, Poterioochromonas malhamensis, but a similar preference for heterotrophy has not been discovered in marine species. As with diatoms, growth enhancement by DOC substrate addition at high concentrations has been demonstrated for several marine flagellates (Rahat and Jahn 1965, Droop and McGill 1966, Pintner and Provasoli 1968, Antia et al. 1969, Cheng and Antia 1970, Ukeles and Rose 1976, Morrill and Loeblich 1979). In these studies, glycerol was predominantly the favored substrate, but the requirement for high concentrations to support growth (Lewitus and Caron 1991b) argues against an active uptake mechanism (Antia 1980). However, studies on enzyme kinetics for glycerol uptake are lacking for these taxa. Over the last decade, a major advance was made in understanding the importance of cell surface enzymes for DOM use by phytoflagellates. Palenik and Morel (1990a,b, 1991) documented the occurrence of cell surface amine oxidases in Prymnesium parvum N. Carter (Prymnesiophyceae), Pleurochrysis carterae (Braarud and Fagerland) Christensen (Prymnesiophyceae), and Amphidinium operculatum Claperede and Lachmann (Dinophyceae), with half-saturation constants < 0.5 mM for amino acids and primary amines. N uptake by this mechanism does not require direct assimilation of DON, but rather involves extracellular DON degradation and uptake of released ammonium. The process has since been shown to be an important route of phytoplankton N acquisition in some natural communities, including those dominated by Aureococcus anophagefferens Hargraves et Sieburth (Pelagophyceae) or Trichodesmium C.G. Ehrenberg ex Gomont (Cyanophyceae); Pantoja and Lee (1994), Mulholland et al. (1998, 2003). Cell surface or extracellular enzymatic breakdown of complex macromolecules by microalgae has received attention recently. Martinez and Azam (1993) demonstrated cell surface aminopeptidase activity in several marine Synechococcus Nägeli strains, and suggested that proteolysis was important to the N nutrition of these cyanobacteria. Stoecker and Gustafson (2003) measured cell surface leucine aminopeptidase activity in

!#


several dinoflagellate species, and speculated that the mechanism was used to enhance amino acid availability at the cell surface. The authors found a correlation between aminopeptidase activity and dinoflagellate abundance in Chesapeake Bay samples, and concluded that bloom populations might contribute significantly to overall microbial proteolytic activity. Cell surface peptide hydrolysis also was shown in axenic cultures of Aureococcus anophagefferens (Berg et al. 2002, 2003, Mulholland et al. 2002b), and Berg et al. (2003) hypothesized that this was a mechanism used to scavenge N from HMW DON in groundwater. Also recently, heterotrophic and phototrophic protists have been considered major contributors to glucosaminidase and glucosidase activities. b-glucosaminidase activity was developed as a tool to measure protist bacterivorous activity, based on its role in degrading bacterial cell wall peptidoglycan (Vrba et al. 1993, 1996). The assay has been applied using both cell extracts (vacuolar enzyme) and intact cells (extracellular enzyme); Vrba et al. (1996). Sherr and Sherr (1999), using cell extracts, found that several marine microalgal species exhibited b-glucosaminidase activity comparable to heterotrophic flagellates. Vrba et al. (1997) found an association between low-affinity b-N-acetylglucosaminidase activity and diatom biomass in a freshwater eutrophic reservoir, and suggested ectoenzyme production by diatoms as a possible explanation. A significant contribution of microalgae to extracellular a- and b-glucosidase activity in freshwater systems has been proposed recently (Sabater et al. 2003, Vrba et al. 2004).

Picophytoplankton Based on their small size (i.e. higher surface area:volume ratio), coccoid cyanobacteria such as Synechococcus may be expected to have relatively high capabilities for taking up DOM. Two decades ago, the evidence for heterotrophy in this and other marine and freshwater cyanobacteria under natural conditions was weak, but Smith (1982) suggested that understanding the natural relevance of the process was biased by the lack of pure cultures, the use of unnatural culture conditions, and a limited knowledge of allelochemical interactions. Paerl (1991) demonstrated uptake of an amino acid (AA) mixture at trace concentrations by axenic isolates of Synechococcus. Uptake of 3H-labeled AAs occurred in the dark, but was light-stimulated. Furthermore, DCMU only partially reduced the light-stimulated AA uptake but completely inhibited photosynthetic 14C uptake, suggesting direct photostimulation of heterotrophic C use. When combined with microautoradiographic evidence of AA uptake in picophytoplankton from diverse N-limited oceanic communities, the author concluded that the


!#!

process was widespread in this group, and proposed that DON use may explain high picoplankton primary production in DIN-limited waters. The existence of viable chloroplast-containing cells from aphotic oceanic depths has been recognized for decades, including samples from the deepsea (Bernard 1963, Fournier 1966, 1970, 1971, Hamilton et al. 1968, Malone et al. 1973, Platt et al. 1983). Anderson (1982) and Lewitus and Broenkow (1985) documented deep-sea fluorescence maxima associated with oxygen minimum zones in the Eastern Tropical North Pacific, and Broenkow et al. (1985) suggested that these peaks were produced by cyanobacteria, based on in situ fluorometric spectral characteristics. The existence of a metabolically active chroococcoid cyanobacterial community in aphotic/anoxic waters was later demonstrated in the Baltic Sea (Detmer et al. 1993). The authors suggested a possible role of heterotrophy in survival or growth at these depths, based on observations of high growth potential under aphotic/ anoxic conditions. More recent studies in the Eastern Tropical North Pacific and in the Arabian Sea also confirmed that subphotic oxygen minimum zone pigment layers were dominated by the cyanobacterium, Prochlorococcus marinus Chisholm et al. 2001, with some contribution from Synechococcus in the former region (Goericke et al. 2000). The authors questioned whether, at the ambient low irradiances, strictly photoautotrophic nutrition could sustain Prochlorococcus population growth in the face of protozoan grazing pressure, and suggested photoheterotrophy as a reasonable alternative. Zubkov et al. (2003) demonstrated that Prochlorococcus and Synechococcus in Arabian Sea waters incorporated 35S-methionine (at ca. natural concentrations) at rates comparable to heterotrophic bacteria. However, the estimated contribution of Prochlorococcus to daily turnover of the methionine pool was much greater than that of Synechococcus. Worden et al. (2004) hypothesized different ecological niches for these cyanobacteria genera in oceanic environments, based on nutrient preferences. Synechococcus and picoeukaryotes tend to co-exist and have relatively high NO3 uptake capabilities, while Prochlorococcus, which may not be capable of using NO3 (Moore et al. 2002), may instead use regenerated nutrients such as NH4 or DON. Tests of this intriguing hypothesis are limited by the availability of axenic Prochlorococcus cultures. As Zubkov et al. (2003) pointed out, Rippka et al. (2000) included methionine in the medium used to establish the first axenic culture of Prochlorococcus.

Harmful Algae The preponderance of recent literature on DOM use by phytoplankton has come from studies on harmful algae (reviews by Paerl 1988, 1997, Carlsson

!#" Algal Cultures, Analogues of Blooms and Applications and Granéli 1998, Anderson et al. 2002). Much of the attention has been triggered by the hypothetical relationship between anthropogenically related increased nutrient loading and an associated global increase in the prevalence and distribution of HABs (Smayda 1989, 1990, Hallegraeff 1993, Paerl 1997, Glibert et al. 2001, Anderson et al. 2002). A key proposed link is that harmful species may have a greater relative capability to use DON, a constituent of anthropogenic pollution. Rationale for categorically distinguishing harmful bloom species from phytoplankton typically viewed as supportive of higher trophic levels include the traditional view that the former cannot adequately compete for inorganic nutrients at low concentrations, and thus have evolved mechanisms for competing in alternative nutrient environments (discussed in Smayda 1997, with recognition that this may be over-generalized). These can include DIN-rich conditions that allow increased biomass of all phytoplankton, which may favor species with allelopathic capability (e.g. chemical deterrence of competitors or predators, chemical auto-stimulation), or DOM-rich conditions which would favor species capable of metabolizing exogenous organic substrates. Although recent experimental tests of DOM uptake have emphasized the role of DON in bloom promotion under DIN-limited conditions (Maestrini et al. 1999, Bronk 2002, Berman and Bronk 2003), the corresponding value of DOC as a promoting factor is not as clearly argued and has lagged in experimental examination. As discussed in the following sections, some recent studies suggest that explanations of HAB proliferation warrant consideration of heterotrophic pathways.

Dinoflagellates Dinoflagellates have been long suspected of having relatively high osmotrophic capabilities. Many species are not amenable to culturing using standard mineral media, and are difficult to grow axenically (Provasoli et al. 1957, Pant and Fogg 1976, Morrill and Loeblich 1979, Granéli et al. 1997, Doblin et al. 1999, John and Flynn 1999). Phagotrophic mixotrophs are prevalent in this group, and phagotrophic mechanisms are diverse (Gaines and Elbrächter 1987, Schnepf and Elbrächter 1992, Bockstahler and Coats 1993, Lewitus et al. 1999). Furthermore, dinoflagellate blooms have been associated with DOM-rich waters (Prakash and Rashid 1968, Mahoney and McLaughlin 1977, Granéli and Moreira 1990, Burkholder et al. 1997, Carlsson and Granéli 1998, Anderson et al. 2002). Stoecker and Gustafson (2003) demonstrated a high potential for dinoflagellates to use macromolecules by cell surface enzymatic breakdown (see above). Another HMW DOM pool that may be widely used by dinoflagellates is humic matter.


!##

Dinoflagellate blooms are commonly associated with rain-driven runoff of humic-rich water (Prakash and Rashid 1968, Glover 1978, Therriault et al. 1985, Granéli et al. 1989, Lara et al. 1993, Carlsson et al. 1993, Carlsson and Granéli 1993, 1998). A stimulatory effect of DOM on dinoflagellates has been attributed to enhanced trace metal availability (i.e. chelation), direct use of DOM as C or N source, or uptake of bacteria-mediated DOM degradation products (Anderson and Morel 1982, Granéli et al. 1985, 1986, Sunda and Huntsman 1995, Doblin et al. 1999). The bioavailability of humic matter has been a contentious issue, but recent data indicate that a much greater fraction of this pool is labile than previously thought, especially the higher molecular weight fractions (Carlsson et al. 1998, Stepanaukas et al. 1999, 2000, Bronk 2002, Stolte et al. 2002, Berman and Bronk 2003). Some culture experiments have supported the relatively high ability of dinoflagellates to use HMW DOM associated with humic matter. The addition of riverine humic substances to N-limited axenic and nonaxenic cultures of Alexandrium catenella (Whedon and Kofoid) E. Balech stimulated growth, which was attributed to direct uptake of DON (Carlsson et al. 1998). In exploring possible uptake mechanisms, the authors demonstrated direct uptake of HMW fluorescently labeled dextran, a polysaccharide. A companion study by Legrand and Carlsson (1998) demonstrated uptake of HMW (2000 kDa) but not lower molecular weight (20 kDa) dextrans. The use of HMW DOM (e.g. colloids) may be through pinocytosis or phagocytosis (Klut et al. 1987, Carlsson et al. 1998, Legrand and Carlsson 1998). Stolte et al. (2002) found that Alexandrium tamarense (Lebour) E. Balech used HMW DOM as a N source, and also suggested the possibility of phagocytotic colloidal uptake. The direct uptake of HMW DOM has been proposed as a ‘niche’ for phototrophic and heterotrophic flagellates in direct competition with bacteria (Sherr 1988, Marchant and Scott 1993, Tranvik et al. 1993, Legrand and Carlsson 1998, Schuster et al. 1998). Other dinoflagellate blooms potentially stimulated by humic matter recently were discovered in South Carolina tidal creeks. In 1999, bloom initiation of Kryptoperidinium foliaceum (Stein) Lindemann in North Inlet, a high-salinity salt marsh estuary, followed rain-driven runoff events, and over the bloom period, K. foliaceum abundance varied inversely with DOC, DON, and DOP concentrations, and positively with dissolved inorganic carbon concentrations, suggesting high respiratory activity (Lewitus et al. 2001; K. foliaceum referred to as a Scrippsiella species, but later renamed by Wolny et al. in press). K. foliaceum bloom populations were tested for dextran uptake capability. Fluorescein isothiocyanate (FITC) labeled HMW (2000 kDa) dextran was taken up rapidly by K. foliaceum bloom populations, but

CMYK

lower molecular weight dextran (3 kDa) was not (Figs. 10.1 and 10.2). Prebloom (103 cell ml-1) and peak bloom populations (6 ¥ 104 cell ml-1) ingested dextran rapidly (intracellular fluorescence was detected in > 50% of cells after 30 min). Uptake was observed in up to 74% and 67% of cells from pre-bloom and peak-bloom periods, respectively. Pronounced and rapid uptake of HMW dextran also was observed in bloom isolates of K. foliaceum, in cultures of Scrippsiella trochoidea (Stein) Loeblich III and K. foliaceum, and in natural populations of K. foliaceum and an unidentified Scrippsiella sp. (Table 10.3). Blooms of K. foliaceum and Scrippsiella sp. were commonly observed in South Carolina estuarine tidal creeks in recent years. In these same experiments, dextran uptake was not observed in monocultures of the following species isolated from South Carolina estuaries: the chrysophyte, Ochromonas sp. Wyssotski SCAEL970626, and the raphidophytes, Fibrocapsa japonica S. Toriumi and H. Takano SCC1-01, Chattonella subsalsa B. Biecheler CAAE 1662, and Heterosigma akashiwo (Y. Hada) Y. Hada ex Y. Hara et M. Chihara CAAE 1663 (data not shown). The use of colloidal humic substances by phagotrophy implies that both organic sources of N and C are assimilated, raising hypothesized roles of the process in relieving nutrient

Fig. 10.1 Laser confocal micrographs of a Kryptoperidinium foliaceum cell from a natural bloom sample at successive 0.5-mm depth intervals from “A” to “B” to “C” to “D”. The red is chlorophyll fluorescence from numerous chloroplasts, and green is (FITC) fluorescence from ingested stained dextran. Scale bar is 10 mm.

CMYK

CMYK

CMYK

!#$ Algal Cultures, Analogues of Blooms and Applications


2000

re- loo loo

a ex ra

80

h

40

ells

60

20

o

racellular

100

!#%

0

0

5

10

15

20

3

80

a ex ra

60 40 20

o

ells o

racellular

100

0

0

5 e a er

10 - ex ra

15 o

20 h

Fig. 10.2 Time-course of fluorescein isothiocyanate (FITC) labeled dextran uptake by Kryptoperidinium foliaceum populations collected during a pre-bloom (open squares) or bloom (closed circles) stage, showing % of cells with intracellular FITC fluorescence. (A) uptake of 2000 kDa dextran, (B) uptake of 3 kDa dextran. Mean and standard deviation (error bars) of triplicate samples. Table 10.3 Percent of population estimated to take up FITC-labeled high molecular weight (2000 kDa) dextran based on observations of intracellular FITC fluorescence Population Kryptoperidinium foliaceum isolate

Scrippsiella trochoidea CCMP1331

K. foliaceum UTEX1688 K. foliaceum (bloom) Scrippsiella sp. (bloom)

Antibiotics

Irradiance Condition

Incubation Time (h)

% Cells with FITC

No No No Yes

Continuous light Continuous darkness 12L:12D 12L:12D

2 2 2 2

38 ± 6 57 ± 4 34 ± 6 39 ± 10

No No No Yes No

Continuous light Continuous darkness 12L:12D 12L:12D 12L:12D

2 2 2 2 0.5 0.5 0.5

26 ± 5 37 ± 1 41 ± 10 31 ± 2 35 25 33

!#& Algal Cultures, Analogues of Blooms and Applications and/or light limitation. Adequate tests of these hypotheses under defined laboratory conditions are not yet possible for K. foliaceum and Scrippsiella sp. from South Carolina waters because isolates have not been amenable to long-term culturing.

Aureococcus Blooms of the ‘brown tide’ pelagophyte, Aureococcus anophagefferens, have recurred periodically in northeastern U.S. estuaries since first observed in 1985. Hypotheses on factors triggering bloom formation by this picoeukaryote have included consideration of heterotrophic pathways. Dzurica et al. (1989) demonstrated a relatively high ability of nonaxenic cultures to take up glucose and glutamate in comparison with several other microalgae. A potential role of DOM was reinforced by findings that bloom populations had greater growth responses and uptake abilities with DON substrates than with NO3 (Berg et al. 1997). In fact, the low NO3 utilization capability resembled properties of a closely related pelagophyte, Aureoumbra lagunensis D.A. Stockwell et al., the ‘Texas brown tide’ (DeYoe and Suttle 1994). Using axenic cultures, Berg et al. (2002) found that A. anophagefferens was able to take up a variety of DON compounds (urea, acetamide, aminopepetidase, chitobiose), with exceptionally high rates of aminopeptide hydrolysis that were greater than those of two of three heterotrophic bacteria tested. Berg et al. (2003) tested culture responses to HMW DON isolated from sediment pore water, and found that A. anophagefferens was capable of using a large fraction of this N pool. The protein fraction was most preferentially used, through cell surface enzymatic peptide hydrolysis. A high capability for DON uptake from many sources is consistent with LaRoche et al.’s (1997) hypothesis that bloom formation is inversely related to DIN-rich groundwater flow; i.e. that blooms formed during drought years when restricted groundwater input resulted in relatively high water column DON:DIN. However, Berg et al.’s (2003) finding that porewater DON was an exceptionally favorable source for growth adds a complication to the hypothesis in the potential link to groundwater DON. Whereas a high capability for DON use (including urea) may be instrumental in supporting high A. anophagefferens biomass, formation and sustenance of monospecific blooms may relate to DOC uptake. Blooms are associated with relatively high DOM C:N ratios and low irradiances (Milligan and Cosper 1997, Breuer et al. 1999, Gobler and Sañudo-Wilhelmy 2003). Gobler and SañudoWilhelmy (2001) found that A. anophagefferens bloom populations were generally stimulated by glucose, but not urea, additions, and hypothesized


!#'

that labile exudates from mixed phytoplankton blooms may select for monospecific blooms, based on DOC use under low light availability. The relative roles of DON and DOC in stimulating A. anophagefferens blooms therefore may be a function of bloom stage. Tests of this hypothesis will rely, in large part, on culture studies to compare the physiological parameters of DOC and DON assimilation and metabolic function.

APPLICATION OF CULTURE RESULTS TO MICROALGAL HETEROTROPHY IN SPECIFIC ENVIRONMENTS Hypereutrophic Environments The association of phytoplankton species exhibiting high heterotrophic abilities under laboratory conditions with their distribution in exceptionally organic-rich waters has been recognized for some time. For example, Ryther (1954) tested the effects of organic and inorganic nutrients on the growth of two small chlorophyte isolates (Nannochloris atomas Butcher, Stichococcus sp. Nägeli) that formed blooms in association with duck farm waste input to Great South Bay and Moriches Bay, New York. These chlorophytes grew equally as well on DON substrates as on DIN. Mahoney and McLaughlin (1977) examined growth responses to organic N, P and C compounds in three flagellates that dominated blooms in a hypereutrophic section of Lower New York Bay that was influenced by sewage effluent. Isolates of the dinoflagellates, Prorocentrum micans Ehrenberg and Heterocapsa rotundata (Lohmann) G. Hansen (formerly Katodinium rotundatum (Lohmann) Loeblich III), and the raphidophyte, Heterosigma akashiwo (Y. Hada) Y. Hada ex Y. Hara et M. Chihara (formerly Olisthodiscus luteus N. Carter) displayed capabilities of using most of the substrates. The authors argued that the lowest C concentrations tested (5 mg L-1) were reasonable estimates of levels in hypereutrophic systems. Iwasaki (1979) showed a growth enhancement effect of organic substrate additions to axenic cultures of several dinoflagellates and raphidophytes, and proposed that this pathway may contribute to their growth or survival in the organic-rich environments where they typically bloom (e.g. the Inland Sea of Japan, pearl oyster culture grounds, and waters impacted by pulp waste). Neilson and Larsson (1980) also found a relatively high ability of isolates from polluted environments to use DON compared to isolates typically found in unpolluted areas or to strains obtained from culture collections. Chemoheterotrophic growth and DOM-stimulated low light growth were demonstrated in axenic cultures of most of the diatom isolates from muddy sediments of the Eems-Dollard

!$ Algal Cultures, Analogues of Blooms and Applications estuary influenced by organic enrichment from wastewater discharge, but not isolates from organic-poor sandflats (Admiraal and Peletier 1979, Admiraal 1984).

Sediments The potential importance of microphytobenthos to estuarine primary production is now widely recognized (Pinckney and Zingmark 1993, MacIntyre et al. 1996). Porewaters are known to be a significant source of inorganic and organic nutrients, derived from groundwater and reminerilization of sinking particulate organic matter (Henrichs and Farrington 1979, Jørgensen 1982, Nilsson and Sundbäck 1996, Paerl 1997, Burdige and Zheng 1998, Hopkinson et al. 1998, Middleboe et al. 1998, Berg et al. 2003). Light availability at the interface between sediment and water column is widely variable and often limited by turbidity via sediment resuspension (see ‘Light’ section). The repercussions of this relatively high nutrient, low/fluctuating light environment on microphytobenthic photosynthetic properties have been studied recently (MacIntyre and Cullen 1996, MacIntyre et al. 2000), but recent studies into the potential contribution of DOC uptake by this group are lacking. The need to revisit the ecological importance of this pathway is further justified by the newfound recognition that DOC in sediment porewaters can be highly labile (Hopkinson et al. 1998, Berg et al. 2003). In fact, based on the recognized importance of DOM photolysis in enhancing bioavailability, Cole (1999) proposed that refractory DOC accumulating in aphotic layer sediments may become labile when exposed to light (e.g. during resuspension, tidal scouring, or bioturbation). Lee et al. (1975) and Hellebust and Lewin (1977) suggested the importance of microbenthic organism interaction (i.e. microenvironments) in DOC flow (see ‘Microenvironment’ section). Diatom production of flocculent layers of heteropolysaccharides may enhance DOC availability directly or following heterotrophic microbial degradation (Decho 1990, Antia et al. 1991, Arnosti et al. 1994, Underwood and Smith 1998, Staats et al. 2000). Consideration that a labile concentrated pool of DOC exists in low light environments has implications not only to potential heterotrophy of benthic microalgae, but also to overlying phytoplankton that can take advantage of access to this pool during light limiting periods. For example, in shallow waters, certain dinoflagellates may be able to access porewater DOC and/or diatom DOC exudates by exposure to resuspended sediments or through active downward migration (Horstmann 1980, Larsen 1987, Kondo et al. 1990, Nilsson and Sundbäck 1996, Smayda 1997).


!$

The presence of non-pigmented diatoms in benthic habitat is evidence that osmotrophy is a natural process in benthic diatoms (Lewin and Lewin 1967, Admiraal 1984). As mentioned in the ‘Historical perspective’ section, several studies have shown organic substrate stimulation of pennate diatom growth in limiting light that may typify the water/sediment interface. For example, Cooksey and Chansang (1976) found growth stimulation by several DOC compounds of three Amphora spp. Ehrenberg ex Kützing at 2% of the saturating light level. Saks and Kahn (1979) demonstrated growth stimulation of a salt marsh diatom, Cylindrotheca closterium (Ehrenberg) Lewin and Reimann, by low concentrations of various organic substrates in the light and dark, and concluded that, in the short term (1 h) but not a longer term (10 d), the diatom outcompeted a co-occurring bacterium, Aeromonas sp. Stanier, for uptake. Lewitus et al. (2000) demonstrated a significant increase in this same diatom species in response to glycine addition in 72-h natural community bioassays from a South Carolina salt marsh, but the stimulatory effect did not occur in the presence of antibiotics, suggesting that stimulation was mediated by bacterial breakdown of glycine. However, uptake of a mixture of 3H-labeled amino acids was demonstrated by microautoradiography to be common in microphytobenthic diatoms in a sandy bay in Sweden, including C. closterium (Nilsson and Sundbäck 1996). Nilsson and Sundbäck (1996) also observed uptake in benthic cyanobacteria (e.g. Microcrocis sp. Richter, Phormidium sp. Kützing, Merismopedia sp. Meyen) and phytoflagellates. The potential for porewater DOC uptake in these groups and others (e.g. green algae) that may be constantly in contact with marine sediment porewater or intermittently exposed (e.g. diurnal or tidally-driven vertical migration, sediment resuspension, cyst cycles) rarely has been examined experimentally using cultures (see ‘Culture bias’ section).

Microenvironments The concentration of organic matter can be elevated within microenvironments, and consideration that microalgal heterotrophy may occur at these locations has a sound theoretical basis (Riley 1963, Sloan and Strickland 1966, White 1974, Lee et al. 1975, Hellebust and Lewin 1977). One implication of this reasoning is that DOM concentration estimates from bottle samples may not be an appropriate benchmark for bioavailable DOM. Considerations of elevated DOM bioavailability in microenvironments can include a wide range of circumstances involving microbial community interactions, including excretory, grazing or cell lysing processes associated with macroaggregates (marine snow, microbial mats, flocculent masses),

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phytoplankton blooms, ‘microbial loop’ food webs or even microenvironments surrounding individual micro- or macroorganisms. Uptake of algal exudates by microalgae may explain species shifts in community dynamics. As mentioned, Gobler and Sañudo-Wilhelmy (2001, 2003) showed that glucose additions to mixed bloom assemblages selected for the growth of Aureococcus anophagefferens, and the authors hypothesized that heterotrophic use of bloom exudates by this species under limiting light may be instrumental in formation of monospecific blooms. Oligotrophic oceanic colonies of the diazotrophic cyanobacterium, Trichodesmium, contain mixtures of cells with and without N2-fixing capabilities (Bergman and Carpenter 1991, Lin et al. 1998), and both uptake and release of DON have been shown in natural populations and cultures (Capone et al. 1994, Glibert and Bronk 1994, Mulholland and Capone 1999, 2000, Mulholland et al. 1999, Berman-Frank et al. 2001). Mulholland et al. (2001) confirmed the ability of Trichodesmium cultures to fix N2 in the presence of 1 mM NH4, glutamate, or glutamine, but found nitrogenase inhibition in response to 10 mM additions. The authors concluded that simultaneous uptake of N2 and combined N was probable under ambient nutrient conditions. Based on alkaline phosphatase measurements in natural and culture populations, Mulholland et al. (2002a) concluded that DOP could be an important source of P for the cyanobacterium. DON release by Trichodesmium colonies was proposed as an important N source for associated microorganisms, including microalgae (Paerl et al. 1989, Paerl 1991, Capone et al. 1994, Glibert and Bronk 1994, Mulholland et al. 1998). Recent studies have proposed a role of Trichodesmium-produced DON as N source for supporting blooms of the dinoflagellate, Karenia brevis (C. C. Davis) G. Hansen and Ø. Moestrup (Heil et al. 2001, Lenes et al. 2001, Lester et al. 2001, Vargo et al. 2001). Lytic processes may lead to release of labile DOM through algicidal viruses or bacteria, autolysis of bacteria or algae, or ‘sloppy feeding’ by zooplankton (Fuhrman and Suttle 1993, Strom et al. 1997, Agusti et al. 1998, Ferrier-Pagés et al. 1998, Fuhrman 1999, Bronk 2002). Strom et al. (1997), using laboratory experiments with a variety of zooplankton and phytoplankton types, showed that grazing was the dominant route of ambient DOC supply relative to algal excretion. Glucose is an important photosynthetic product of microalgae, and its release during bloom senescence, grazing, or other forms of lysis may contribute to its high ambient concentrations in estuarine and marine systems (Ittekot et al. 1981, Rich et al. 1996, Skoog and Benner 1997, Biersmith and Benner 1998). The widespread ability of microalgae to use this C source may be related to its abundance as a lytic product.


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A strong case can be made for microalgal use of DOC-rich microenvironments based on chemotactic capabilities, although again this process has only been studied in a few marine species. Levandowsky and Hauser (1978) reviewed evidence of chemosensory responses of the heterotrophic dinoflagellate, Crypthecodinium cohnii (Seligo) Chatton in Grassé, to several DOC compounds. Cooksey and Cooksey (1988) demonstrated chemotaxy to sugars in the benthic diatoms, Amphora spp. Willey and Waterbury (1989) showed chemotactic behavior of Synechococcus to nanomolar levels of glycine and alanine. Lee et al. (1999) demonstrated chemotaxy to amino acids in a small (3 mm) cryptophyte (Chroomonas sp. Hansgirg HP9001, Table 10.2) when grown with glycine, but not with NO3. The authors hypothesized that this process was inducible in response to NO3 limitation.

CONSIDERATIONS IN CULTURE EXPERIMENTS Strain Variability and Genetic Bias Numerous examples exist of microalgal species that exhibit strain differences in heterotrophic capabilities. Bronk (2002) suggested that conflicting results on DON use by cultures of Emiliania huxleyi (Lohmann) Hay and Mohler (Prymnesiophyceae) may be attributed to strain variability. Yamada et al. (1983) found that one isolate of the diatom, Skeletonema costatum (Greville) Cleve, grew equally well with arginine or NO3 as sole N source, but another isolate failed to grow on arginine. In these and other examples (Palmer and Togasaki 1971, Carpenter et al. 1972, Ukeles and Rose 1976, Flynn and Butler 1986, Gaines and Elbrächter, 1987, Fruend et al. 1993), it is difficult to differentiate between true strain variability and ‘genetic biasing’ that can occur during culture maintenance. As mentioned, the standard protocols for maintaining microalgae typically involve mineral medium and adequate light, while heterotrophic bacteria are routinely cultured on organic-rich medium and without light. Therefore, the possibility that cultures become genetically altered after long-term maintenance should be seriously considered, and comparisons between microalgal and bacterial DOM uptake capabilities using such cultures may include this bias. Some possible examples of loss of heterotrophic potential over culture maintenance time include glucose uptake capability by the diatom, Cyclotella cryptica Reimann, Lewin and Guillard (White 1974), and urea uptake by the diatom, Skeletonema costatum (Carpenter et al. 1972). Also, the growth of Storeatula major D.R.A. Hill (Cryptophyceae) was enhanced by glucose addition in experiments with freshly isolated cultures (Lewitus and Kana 1994, Table 10.2). In contrast, this same isolate was not stimulated by a range of glucose

!$" Algal Cultures, Analogues of Blooms and Applications concentrations (10 mM to 10 mM) after a decade of maintenance on f/2 medium (supplied with NO3 and PO4) and moderate light (Willis and Lewitus, unpub. data). The contrasting situation in which photosynthetic ability was lost by long-term maintenance in organic-rich medium was hypothesized by Jones et al. (1996) for the freshwater chrysophyte, Poterioochromonas malhamensis.

Choice of Substrates Culture experiments examining microalgal organic substrate uptake and physiological response must be critically evaluated based on the type of substrate used. As is the case with diatoms (see ‘Historical Perspective’ section), high variability for preferred substrates exists in all taxa (Flynn and Butler 1986, Antia et al. 1991, Berg et al. 1997, Berman and Bronk 2003). Conclusions based on relatively high or low uptake of tested substrate(s) need to consider that niche differences in substrate use may be the rule in natural microalgal communities. Lylis and Trainor (1973) argued that experiments demonstrating a relatively low ability of diatom cultures to use acetate should not draw general conclusions that DOC uptake is not ecologically important in this group because other substrates such as glucose have been shown to be more favorably used. The use of DON substrates as C or N source can depend on the role of the substrate in photosynthetic metabolism. For example, some amino acids can be more readily used for C biosynthate than others, and this has been related to the substrate-specific requirement of light (i.e. photosynthate) for uptake (Liu and Hellebust 1976, Flynn and Butler 1986, Flynn and Syrett 1986b). The ease by which organic C compounds can be stored or their potential as respiratory sources also may influence uptake capability, and therefore evaluation of organic substrate effect based on short-term growth responses is not always an accurate indicator of assimilation (Pearce and Carr 1969, Bennett and Hobbie 1972, Neilsen and Lewin 1974, Hellebust and Lewin 1977, Baden and Mende 1978, Richardson and Fogg 1982, Krupka and Feuillade 1988). Mixtures of substrate additions may cause greatly different physiological responses than single compound additions. Flynn and Butler (1986) pointed out that, when inorganic and organic N are both available, amino acid use can be more efficient than when DON is supplied alone because the need for deamination and subsequent assimilation of NH4 is reduced. An interdependency between DOC and DON has been demonstrated where enzymes involved in the uptake of one class are induced by the presence of


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the other. For example, glucose or other DOC substrates have been shown to stimulate amino acid uptake, either by induction of intracellular or cell surface enzymes, or by increasing biosynthetic needs resulting from higher respiration rates (Sauer et al. 1983, Sauer 1984, Berg et al. 1997, Mullholland et al. 1998). Several amino acid uptake systems are present in symbiotic Chlorella spp. M. Beijerinck, some of which can be induced by glucose even when growing photosynthetically (Cho and Komor 1985, Komor et al. 1988). Lewin and Hellebust (1976) showed that chemoheterotrophic growth of Nitzschia angularis var. affinis (Grun.) Perag. on glucose required the presence of amino acids. Growth of some cyanobacteria on glucose was found to be enhanced by amino acids (Smith 1982). Other examples exist where the presence of one DOC compound causes induction of another (e.g. purines induce adenine and guanine uptake by Chlamydomonas reinhardtii P.A. Dangeard; Lisa et al. 1995) or the same compound will cause induction of its own uptake system (Neilsen and Lewin 1974, Saks and Kahn 1979, Combres et al. 1994). Transinhibition can also occur where, for example, one substrate inhibits the uptake system for another. Several examples have been cited where DON uptake is inhibited by DIN and vice-versa, but transinhibition of one AA for another also has been demonstrated (Flynn and Butler 1986, Ricketts 1988, Antia et al. 1991, Lisa et al. 1995, Mulholland et al. 1998). The numerous cases of inducible DOM uptake systems imply that lags in enzyme synthesis should be an important consideration in experimental design and interpretation. For example, after transfer from light to dark, DOM uptake systems may require an acclimation period to deplete intracellular AA pools, ATP, cAMP, or other photosynthetic products (North and Stephens 1972, Lylis and Trainor 1973, Neilson and Lewin 1974, Lewin and Hellebust 1975, 1976, 1978, Bollman and Robinson 1977, Sepers 1977, Admiraal 1984, Flynn and Butler 1986, Flynn and Syrett 1986a, Antia et al. 1991).

Importance of Light Conditions and the Relationship Between Heterotrophy and Phototrophy Heterotrophy refers to the use of organic substrates as C sources. However, environments most often cited as potential sites for microalgal DOC use involve light (i.e. energy) limitation rather than DIC limitation. Although DIC limitation is not as commonly addressed, it has theoretical and empirical bases for occurring, for example during blooms, in microbial mats, or in shallow turbid estuaries (Fogg 1965, Mahoney and MacLaughlin 1977, Bonin and Maestrini 1981, Admiraal et al. 1982, Bollman and Robinson

!$$ Algal Cultures, Analogues of Blooms and Applications 1985, Raven and Johnston 1991, Nilsson and Sundbäck 1996, Miao and Wu 2002). The use of organic substrates as alternative C sources may pertain in these cases, and this possibility has been understudied. Light is a source of energy and therefore with respect to its function in relieving light limitation, DOC may most accurately be considered a source of energy alternative or supplemental to light (Bonin and Maestrini 1981, Lewitus and Kana 1994, 1995, Berg et al. 1997). Based on the close interregulation of osmotrophic and photosynthetic pathways, physiological responses of microalgae to DOM use have strict dependence on light conditions; e.g. irradiance level, light/dark cycle, spectral characteristics. Several studies have shown that, when grown under limiting light conditions, DOC use can enhance microalgal growth, but the physiological repercussions of this pathway on photosynthetic metabolism can vary greatly, and include repression of synthesis of photosynthetic machinery, and reduction of, no effect on, or enhancement of photosynthetic performance and potential (Wiessner and Gaffron 1964, White 1974, Schwelitz et al. 1978, Ogawa and Aiba 1981, Saks 1983, Lewitus and Caron 1991a.b, Lewitus et al. 1991, Lewitus and Kana 1994, 1995, Kovacs et al. 2000). Under severe light limitation, the allocation of biosynthate to light harvesting machinery may be restricted such that relief of this energy limitation by DOC respiration may lead to enhanced pigmentation (Lewitus and Kana 1994). That biomass production from photoheterotrophic nutrition can exceed photoautotrophic or chemoheterotrophic nutrition at limiting light levels has been recognized for some time, and is the basis for highly active research efforts on mixotrophic cultivation in biotechnology and aquaculture applications (Day and Tsavalos 1996, Day et al. 1999, Wood et al. 1999, Garci et al. 2000, Lee 2001, Xie et al. 2001, Lebeau and Roberts 2003, Xu et al. 2004).

SUMMARY The widespread capability of microalgae to assimilate dissolved organic substrates in culture experiments has stimulated curiosity on the process’s ecological relevance for decades. Arguments for microalgal DOM uptake in the face of bacterial competition traditionally failed on kinetic grounds, and the relatively high substrate concentrations needed to solicit dark growth or low-light growth enhancement in many studies is still validly criticized. One fallout of this criticism was a loss of momentum in laboratory research on the physiological responses and environmental regulation of marine microalgal osmotrophy, so effectively gained in the 1960’s, 1970’s, and


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%$into the 1980’s. Our understanding of microalgal ecology has evolved dramatically over the last two decades, and we now recognize the importance of microenvironments such as those associated with the ‘microbial loop’, and the widespread significance of small phytoplankton to primary production and phagotrophic microalgae (‘mixotrophs’) to community grazing. With advances in DOM analytical capabilities and recognition of the important influence of photolysis, the consequence of relatively high DOM lability to microbial food web dynamics has been an active research focus. An important subtopic is the consideration that DON from runoff, atmospheric deposition, or groundwater may be more available to marine microalgae in relief of N limitation. To this end, cell surface or extracellular amino acid oxidases and more recently proteolytic enzymes have received attention in ecological contexts. At the forefront of this research is consideration that many harmful algal blooms may be stimulated by this ‘new’ DON input. With the advent of these new paradigms, the use of cultures to acquire basic knowledge of microalgal physiology under alternative nutritional states is needed to reevaluate the ecological role of microalgal heterotrophy.

ACKNOWLEDGEMENTS I am grateful to Krista DeMattio, Megan Heidenreich, and Bonnie Willis for isolating Kryptoperidinium foliaceum and Scrippsiella sp., and conducting dextran experiments. Thanks also to Patrick Brown for help on the laser confocal miscroscope. These experiments were funded by U.S. ECOHAB Program (sponsored by NOAA/NSF/EPA/NASA/ONR) grant NA16OP2796, NOAA grants NA06OA0675 and NA86RG0052 (SC Sea Grant Consortium grant). Contribution 1386 of University of South Carolina’s Belle W. Baruch Institute for Marine and Coastal Sciences, Contribution 546 of South Carolina Department of Natural Resources Marine Resources Research Institute, and ECOHAB Contribution 115.

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11 Role of the Cell Cycle in the Metabolism of Marine Microalgae Jacco C. Kromkamp1 and Pascal Claquin2 1

Centre for Estuarine and Marine Ecology, Netherlands Institute of Ecology, PO Box 140, 4400 AC Yerseke, The Netherlands 2 Laboratoire de Biologie et de Biotechnologies Marines Universit de Caen BasseNormandie Esplanade de la paix, 14032 Caen Cedex, France

Abstract In this chapter we summarize the effect of the cell cycle on nutrient uptake and metabolism and the interaction with light and the naturally occurring light-dark cycle. Different methods exist to examine the cell cycle, but the most preferred method is flow cytometric analysis of the DNA content of algal cells. Cell cycle checkpoints ‘check’ whether conditions are met to proceed to the next phase. If conditions are not met, cells stay in that particular phase. The naturally occurring light-dark cycle can induce synchronized growth in picocyanobacteria, green microalgae and dinoflagellates, but it has not been shown in diatoms. The cue for synchronization differs: in some species it is the dark/light (‘dawn’) transition whereas in other species it is the ‘dusk’ transition. The occurrence of cell cycle checkpoints can be investigated by limiting or starving cells for a particular nutrient or for light, although these methods do not always yield the same answer, for reasons not clear yet. Starvation for light or nitrogen causes most algal cells to accumulate in the G1-phase, although diatoms and Euglena also seem to have a 2nd light control point in the G2+M-phase. Diatoms have a major checkpoint in the G2+Mphase: as a consequence slow growing cells which are nutrient or light limited (Si-limitation excepted) will become heavily silicified. A 2nd Si-control point seem to be present in some diatoms in the G1-phase. Hardly any information is available for nitrogen or phosphorus, but a phosphoruscheckpoint in the S-phase for diatoms and picocyanobacteria has been described, although Prochloroccocus can also accumulate in the G2+M-phase during P-starvation, suggesting a checkpoint in these phases as well. As nutrient uptake often seems to be confined to a particular cell cycle phase we

!&$ Algal Cultures, Analogues of Blooms and Applications speculate about the role of (partly) synchronized algal communities in phytoplankton species diversity.

THE CELL CYCLE: INTRODUCTION During normal cell division algal cells go through a series of steps that together make up the cell cycle. Microscopically two phases can be easily identified: the interphase, during which the algae grow, followed by cell division. The eukaryotic cell cycle is best studied, especially in yeast and mammalian cell, as a result of direct link with cancer research, and the genes involved in controlling the cell cycle are highly conserved from simple unicellular cells such as yeast to complex metazoans (Nurse 2000). During the M-phase nuclear division (mitosis) is followed by cell division (cytokinesis) and during this latter phase the formation of two daughter cells by cell division takes place. The interphase consists of three steps: the G1phase (G stands for gap), during which most of the growth takes place, followed by the S-phase (S stands for synthesis) in which DNA replication takes place. The S-phase is followed by a second gap phase, G2, in which the cells prepare themselves for mitosis (Fig. 11.1). Although no new DNA is synthesized in either G1 or G2, repair of damaged DNA can take place in the gap phases. Although the length of all the phases is variable, the largest variability is found in G1, the major growth phase. so l

1

erph

2

ase

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Fig. 11.1 The cell cycle showing the 4 typical phases in eukaryotic cells The cell cycle is tightly regulated by a complex interplay of a family of proteins, cyclins, which are activated by cyclin dependent protein kinases (cdk’s). The activated complexes phosphorylate specific target proteins. Every phase of the cell cycle has its own family of cyclin/cdk complexes for triggering different processes in the cell cycle. G1 cyclins belong to the cyclin D family, but others cyclins (like cyclin E) can be found as well in the G1-

Role of the Cell Cycle in the Metabolism of Marine Microalgae

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phase. Cyclins A and E can be found in the S -phase, whereas cyclins B seem to be restricted to the M-phase. The cdk family is also highly specific and only bind to a specific cyclin (like cdk4 binds to cyclin D) (Nagyova et al. 2003, De Veylder et al. 2003, Nurse 2000) or http://www.biocarta.com/ genes/index.asp). Breakdown of a cyclin terminates the activity of the corresponding cdk, leading to varying cyclin concentrations during the cell cycle. The activity of the cdks can also be regulated by inhibitory proteins (e.g. p19) which cause conformational changes by inhibitory phosphorylation by cdk-dependent kinases (e.g. wee1) or activation of the ckd/cyclin complex by phosphatase (e.g. Cdc25). Prokaryotic organisms have a different way of DNA replication, and they can often divide faster than they replicate their genome. This is made possible because replication forks can start before the previous round of DNA duplication is ended. Nevertheless, cyanobacteria and prochlorophytes proceed through cell cycle phases similar to that of eukaryotic algae (Vaulot 1994), although it has been shown that several strains of Synechococcus and Prochorococcus have more than two copies of the genome (Vaulot 1994, Binder and Chisholm 1995b), and that the copy numbers of one, two, three, four, five ...) do not follow that E. coli model which predicts that the copy nr follows 2n. This suggests that DNA synthesis is loosely coupled to the cell division cycle. Nevertheless, slow growing prokaryotic phototrophs often have a defined S-phase and hardly any cells with more than two copies of the genome (Binder and Chisholm 1995b)

Cell Cycle Checkpoints The cyclin/ckd complexes also play an import role in the cell cycle checkpoints (Murray and Hunt 1993). These checkpoints play a vital role in the coordination of the cell cycle and prevent entering into the next phase when the previous phase has not been completed (Fig. 11.2). The G1-checkpoint (also called restriction point or Start) is present in all eukaryotic cells and controls whether cells are able to enter the DNA-synthesis phase. When cells pass this restriction point they are committed to DNA duplication and cell division. Cells need to exceed a critical size before they can pass the G1/S-checkpoint (De Veylder et al. 2003), and as diatoms need to exceed a certain size before they can divide the same is likely to be true for algae as well. This checkpoint also senses DNA-damage. The G2/M checkpoint serves to ensure that the S-phase has been completed and that no damaged DNA is present before the cells are allowed to proceed into the M-phase. For more information on the molecular mechanisms involved in the way these

!&& Algal Cultures, Analogues of Blooms and Applications co rol h co rol

co rol

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Fig. 11.2 Principal environmental checkpoints observed in the cell cycle of eukaryotic microalgae measured using flow cytometric (FCM) determination of the cell cycle stages. FCM analyses cannot separate the G2 and M-phase; *specific arrest points in the diatoms cell cycle. The light control checkpoint in the G2+M-phase is also described for Euglena sp. (Zachleder and Van Der Ende 1992). checkpoints work we refer to reviews by De Veylder et al. (2003) and Lisby and Rothstein (2004). Environmental factors influence progression of algal cells through the cell cycle, of which light and nutrient availability are the most important ones (Prezelin 1992). Limitation or starvation for light or a particular nutrient can be used to investigate in which phase of the cell cycle the cells will encounter a checkpoint because if a certain phase contains such a checkpoint, cells will accumulate in this particular phase. Nutrient deprivation, light and temperature conditions can entail cell cycle arrests (Brzezinski et al. 1990, Olson et al. 1986, Vaulot et al. 1986, 1987), or an increase of the duration of specific phases (Claquin et al. 2002, Olson et al. 1986). As mentioned by Pascual and Caswell (1997), this leads to a conceptual view of the cell cycle known as the transition point hypothesis, in which an environmental factor has no effect on cell progression beyond a certain point in the cycle (Spudich and Sager 1980, Vaulot et al. 1986). Deficiencies in nutrients or in light cause arrest of cells in the G1 phase in the majority of microalgae groups (Spudich and Sager 1980, Vaulot et al. 1986, Zachleder and van der Ende 1992). Diatoms and Euglenophytes appear to have a different regulation of the cell cycle, arrest of the cell cycle in G1 and G2 phases was shown for diatoms (Brzezinski et al. 1990, Olson et al. 1986, Vaulot et al. 1986, 1987). Euglena gracilis seem to have light restriction points downstream of G1 as well (Hagiwara et al. 2001, 2002). However, Jacquet et al. (2001) showed that different strains of Prochlorococcus are affected by light both in S or G1 phases and Parpais


!&'

et al. (1996), observed in Prochlorococcus spp. an arrest of cells in the S phase under phosphorus limitation as well. Before discussing the influence of cell cycle on nutrient metabolism, first the effect of light is mentioned as photoautotrophs do not only depend on light as their only or main source of energy, but because algae do not only experience variations in light intensity but also in the time they are exposed to light. As a result, the total daily light dose is dependent on the light intensity as well as the length of the light period (photophase). However, first we will describe how the different stages in the cell cycle can be measured.

Cell Cycle Measurements In order to study the cell cycle, it is necessary to determine the proportion of cells in the different cell cycle phases. Flow cytometric analysis appears to be the most efficient method to study the cell cycle as it allows a rapid analysis of a large population. The cellular DNA can be stained by various fluorochromes. Common fluorochromes specific for DNA such as ethidium bromide and propidium iodide emit photons at wavelengths which interfere with the red chlorophyll fluorescence and require chlorophyll extraction prior to DNA staining (Jochem and Meyerdierks 1999, Olson and Chisholm 1986, Van Bleijswijk and Veldhuis 1995). Other type of DNA dyes are excitable by ultraviolet (UV), like DAPI and Hoechst, which emit at blue wavelengths and therefore do not interfere with chlorophyll autofluorescence, and because of this it was the preferred dye for some time (Binder and Chisholm 1995a, Lemaire et al. 1999, Vaulot et al. 1987, Vaulot and Partensky 1992). However, a high power UV laser is required for flow cytometry using this dye, which is not available on most benchtop flow cytometers suitable for shipboard experiments (Jochem and Meyerdierks 1999). A recent generation of blue light excitable DNA stains emits in the green and thus do not overlap the spectrum of chlorophyll fluorescence emission, simplifying DNA labeling of microalgae. These dyes for DNA are PicoGreen, SYTOX (Veldhuis et al. 1997) or YOYO-1 (Jochem and Meyerdierks 1999). After flow cytometric analysis the DNA distribution obtained can be deconvoluted into the different populations of G1, S and G2+M phases (because the DNA content in the G2 and M-phase is the same, a flow cytometric analysis of stained DNA cannot distinguish between cells that are in G2 or in the M-phase). Other methods can be used to determine the cell cycle activity like microscopic observation, [3H]thymidine labelling or molecular markers.

!' Algal Cultures, Analogues of Blooms and Applications Indeed, by using microscopy the proportion of cells in mitosis and in the interphase can be estimated after labelling with simple DNA stains as carnine acid (Saburova and Polikarpov 2003), or DNA fluorochrome staining, [3H]thymidine nucleotides incorporation can be used to monitor DNA replication and the detection of gene expression by Northern-Blots can give information about the cell cycle stage (Planchais et al. 2000).

Methods of Synchronization Cells and of Measurements of Cell Cycle Activity A synchronous cell culture is characterized by a high proportion of cells proceeding to the same event of the cell cycle at the same time. Cells in unsynchronized populations progress at different rates through the cell cycle phases. In order to obtain a synchronized population several techniques can be used but the general feature is usually the same. The principle consists in reversibly arresting cells at a certain definite stage. When all cells have accumulated at this particular stage, removal of the blockade will restart their progression through the cell cycle in parallel, at least for a couple of divisions. Temperature shocks, inhibitor treatments, nutrient starvation, photoperiod variations can be used to synchronize unicellular algae. A light–dark cycle, sometimes associated with temperature cycles or nutrient starvation, is commonly used (Chisholm 1981). Studies about the cell cycle were mostly performed on few genera of green algae, like Chlorella, Chlamydomonas, Dunaliella, Scenedesmus (Lemaire et al. 1999, Senger and Bishop 1967, Spudich and Sager 1980, Strasser et al. 1999, Wegmann and Metzner 1971) and in some other groups of microalgae, like Euglena (Euglenophyta) (Winter and Brandt 1986), Emiliania huxleyi, Pavlova lutheri (Haptophyta), some Dinophyta (Gonyaulax, Amphidinium) or various diatoms (Cylindrotheca fusiformis, Skeletonema costatum, Navicula) (Chisholm 1981). Most species can be synchronized by using a light-dark cycle, however, the patterns shown by diatoms grown on light and dark cycles appear to be fundamentally different from those of other groups of microalgae and growth is frequently unsynchronized. Several methods were thus developed to synchronize diatoms. Silicon starvation coupled with light stress was used to synchronize diatoms, particularly in the species Cylindrotheca fusiformis (Busby and Lewin 1967, Darley et al. 1976, Hildebrand et al. 1998, Kröger and Wetherbee 2000, Sullivan 1979). Recently, methods which avoided the use of silicon starvation and light and dark stresses were developed. The synchronization of the population of C. fusiformis was performed using cell cycle inhibitors as nocodazole (methyl


!'

(5-[2-thienylcarbonyl]-1H-benzimidazol-2-yl) carbamate) or aphidicolin (Claquin et al. 2004). Nocodazole is an antimitotic agent that disrupts microtubules (Luduena and Roach 1991, Vasquez et al. 1997), arresting the cell cycle at the G2/M phase. Aphidicolin inhibits eukaryotic and viral DNA replication by blocking DNA polymerases (Cheng and Kuchta 1993, Spadari et al. 1985) and blocks cells in the end of the G1 phase (Planchais et al. 2000). After washing, these cell cycle arrests are reversible. By using these cell cycle inhibitors, the cells were synchronized under a light-dark cycle or under continuous light (Claquin et al. 2004). The additional benefits of these methods are that they allow to separate the processes related to the light and dark cycle from those related to the cell cycle. Planchais et al. (2000) made a short review of the cell cycle inhibitors mainly used in plant cell cycle studies. Some of them could be tested on the different microalgae groups. Others methods based on the behavior of algae to investigate synchronization can also be found in the literature: for example, Wong and Whiteley (1996) described a method based on the motility of the heterotrophic dinoflagellate Crypthecodinium cohii as a function of the cell cycle stages. Cells in the G1 phase were motile, whereas cells in S and G2+M phases were non-motile because they lost their flagella. By collecting the swimming cells they obtained only cells in the G1 phase which could then be used to follow synchronized division of the population.

Light: Influence of Light Intensity and Light Period on Growth and Photosynthesis Photoautrophs depend on light for cell growth, and the length of the G1-phase is dependent on the ambient irradiance and thus on the rate of photosynthesis (Zachleder and van den Ende 1992, Fig. 11.3). When algal cells go beyond the G1/S checkpoint the progression through the cell cycle becomes independent of the light intensity and the cells can divide in the dark. As a result of this dependence on light it is not surprising that light-dark periods will influence the cell cycle and many algal species will be entrained by a light-dark period, although it is in many cases unclear whether the ‘dawn’ or ‘dusk’ transition acts as a zeitgeber. It has been shown for field populations of both Prochlorococcus (Vaulot et al, 1987) and Synechococcus (Vaulot et al, 1996) that the cell cycle is highly synchronized to the diel cycle. Also, in most dinoflagellates the diel cycle entrains cell division, giving maximum growth rates of one per day. Van Dolah and Leighfield (1999) showed for the red tide dinoflagellate Gymnodinium breve that the dark/light transition (‘dawn’) is the cue which entrains the cells. In

Algal Cultures, Analogues of Blooms and Applications 4

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Fig. 11.3 Effect of irradiance on the absolute length of the cell cycle phases (left) and on the relative proportion of the different stages of the cell cycle (right) most of the photophase, the cells were in the dark and a delay of the L/Dtransition caused a similar delay in the onset of the S and G2+M phases. Shifting the L/D (‘dusk’) transition forward did not affect the timing of the cell cycle. Natural populations of the same species showed the same timing to the diel cycle, but estimated growth rates were approx. 0.29 divisions per day, indicating that the cells would experience 3-4 diel cycles before they could pass the G1/S checkpoint. The situation in Euglena, which will arrest in G1, S or G2+M when transferred to continuous darkness, is different (Hagiwara et al. 2001). In this case the L/D-transition (‘dusk’) is the Zeitgeber entraining the cell cycle (Fig. 11.4). If cells are entrained by the naturally occurring light dark cycle, this can induce variability in physiological processes: using synchronized cultures of the green alga Scenedesmus obliquus Strasser et al. (1999) showed that relative photosystem II (PS II) antenna size, maximum PS II efficiency and photochemical quenching coefficient were influenced by the stage of the cell cycle, although no information about the cell cycle stage was measured. Similar results were reported by Kaftan et al. (1999). Winter and Brandt (1986) showed cell cycle dependent changes in antenna size in Euglena gracilis. Claquin et al. (2004) used nocodazol to synchronize cultures of the marine diatom Cylindrotheca fusiformis and studied variation in photosynthesis and respiration as function of the cell cycle stage. They observed that the maximum rate of PS II electron transport and oxygen evolution were lowest when the percentage

Role of the Cell Cycle in the Metabolism of Marine Microalgae 24

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Fig. 11.4 Changes in the number of cells of Euglena gracilis in different phases of the cell cycle. The cells were grown in a 14:8 LD-cycle and than transferred to darkness. The light break around subjective dusk (B) produced the biggest increase in cell numbers, followed by a light break in midnight (C), midday, whereas the “dawn” transition (D) did not produce a response at all. From Hagiwara et al (2002). G2+M cells were highest (late dark, early morning period), and that it increased in the G1-phase (Fig. 11.5). That the photosynthetic activity would by highest in the G1-phase could be expected because this phase is the major growth phase, which thus needs an efficient rate of photosynthesis. The rate of total oxygen uptake followed the pattern in photosynthesis. These authors also demonstrate that light stimulated the rate of oxygen uptake, which was attributed partly to the Mehler reaction. The light stimulated rate of oxygen uptake as a fraction of the total rate of oxygen uptake in the light was, however, independent of the cell cycle.

!'" Algal Cultures, Analogues of Blooms and Applications

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Fig. 11.5 Changes in cell number, cell cycle stage and photosynthesis in synchronized cultures of the diatom Cylindrotheca fusiformis. ETR: PSII electron transport rate, PBm: maximum rate of oxygen production (photosynthetic capacity). From Claquin et al. (2004).

Influence of Light/Dark Cycle on Nutrient Metabolism As shown in the previous section on checkpoints, some nutrients are only taken up during a specific stage of the cell cycle. This was explored by Pascual and Caswell (1997) who investigated the effect of cell cycle dependent nutrient uptake using mathematical model. In their chemostat model, nutrients were only taken up during a particular phase of the cell cycle, and the cells could only proceed through the cell cycle after conditions satisfying the transition point have been met. Despite the fact that their model implicitly assumed that the cells were growing synchronously, no light-dark cycles were given. When the limiting nutrient was supplied continually in their model, stable conditions were reached at higher dilution rates, whereas at low dilution rates strong oscillations in cell numbers were observed. At a constant dilution rate oscillations occurred at relative high nutrient concentration, whereas at low nutrient concentration stable conditions developed. When a variable nutrient input with a regular interval was simulated, both periodic and aperiodic responses were observed (Fig. 11.6). When they refined their model to include nutrient storage, oscillations in cell number were again observed with frequencies different from the forcing. As under natural conditions the light-dark cycle might entrain phytoplankton species inducing synchronized growth, this also may lead to phasing in nutrient uptake kinetics. On the other hand Olson and Chisholm (1983) showed that nutrient forcing can override the

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Role of the Cell Cycle in the Metabolism of Marine Microalgae 0 60

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Fig. 11.6 Fluctuations in cell number under a periodic nutrient supply. A: transients, B: long-term dynamic. C: nutrient supply. Notice the aperiodic fluctuation in cell number in B, despite the fact that the nutrients were supplied with regular interval. From (Pascual and Caswell 1997). light-dark cycle and drive cell division. Very complex oscillatory patterns may be the outcome, which are not phased to either the LD or nutrient input cycles, making it difficult to drawn conclusions about the forcing functions behind the changing cell numbers. More research in this field is needed.

Cell Cycle and P-uptake and P-content Only a few papers on the effect of the cell cycle on phosphorus metabolism have been published. As phosphorus is an essential element not only in the energy metabolism but also as important structural component of DNA, a shortage of P may be expected to lead to a checkpoint in the DNA synthesis phase. However, Parpais et al. (1996) showed for different strains of the photosynthetic prokaryote Prochlorococcus an increase in the number of cells that were arrested in the S phase as well as in the G2+M phase during P-starvation, apart from one of the North Atlantic strains tested, which only showed an increase in the percentage of S-phase arrested cells. Interestingly, the S-phase arrested cells of Mediterranean strain CCMP 1378 did not resume growth after addition of phosphate, whereas cells arrested

!'$ Algal Cultures, Analogues of Blooms and Applications in the G1 or G2+M-phase did. An increase in the number of cells arrested in the S-phase was only demonstrated recently for the first time for a eukaryotic alga, the diatom Thalassiosira pseudonana (Table 11.1, Claquin 2002). However, when cells of this diatom were grown in steady state P-limited continuous cultures, the percentage of cells in the S-phase decreased whereas the percentage of cells in the G2+M phase increased when the growth rate decreased (i.e. when the intensity of the limitation increased) (Fig. 11.7). This increase in percentage of cells in the G2+M-phase with lower growth rates was observed also for nitrogen- and light-limited cells. Although limitation and starvation experiments generally, but not always lead to the same conclusion (Vaulot 1994), these results indeed show that starvation can lead to different behavior than limitation, an observation Table 11.1 Percentage of cells in each phase of cell cycle in the diatom Cylindrotheca fusiformis after 6 and 24 as a function of Si, N or P starvation and under unlimited conditions (Claquin 2002)

Unlimited cells Si starved cells 6 h Si starved cells 24 h P starved cells 6 h P starved cells 24 h N starved cells 6 h N starved cells 24 h

%G1

%S

%G2+M

67.5 39.8 39.7 65.8 17.9 68.8 88.1

6.4 7.0 8.5 5.5 66.2 10.6 6.0

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Fig. 11.7 Duration (left) and fraction of cells (right) of the diatom Thalassiosira pseudonana in different phases of the cell cycle when grown in steady state P-limited continuous cultures. Notice the large increase in percentage of G2+M-phase cells and the decrease in percentage of S-phase cells with low growth rates. Data from Claquin et al (2002).


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also made by Brzezinski et al. (1990) for the Si-limited cells of the diatom Thalassiosira weisflogii, which arrests in G1 and G2 or M after Si-starvation, but does not change the length of the M-phase during Si-limitation. The results of the cultures studies on Prochloroccus strains by Parpais et al. (1996) suggested that P-limitation could only be induced with N:P-ratios exceeding 50, making it difficult to actually have P-limited Prochloroccus strains in nature. Vaulot et al (1996) studied the addition of low concentrations of phosphate to Synechococcus in the surface waters of the Mediterranean waters in summer. The cells were highly synchronized to the diel cycle with most cells in G1 during the day and DNA synthesis starting around 16.00 with a maximum of cells in the G2+M-phase around 21:00 h. Addition of P or N+P, but not of N, shortened the G1-phase and decreased the % of cells in G2+M around 18:00 and increased the number of cells in G2+M around noon the next day, demonstrating that the Synechococcus were indeed P-limited, and dose response curves suggested that the limitation was stronger at the end of June than in the middle of July, thus demonstrating that some oligotrophic regions maybe P-limited rather than N-limited.

Cell Cycle and Si-uptake and Si-content The main particularity of diatoms is the silicified cell wall called a frustule, which essentially consists of hydrated amorphous silica. The silicon metabolism consequently plays a fundamental role in diatoms (see MartinJézéquel et al. 2000, for a review), vegetative division cannot occur without formation of the valves of the daughter cells and cell growth cannot occur without girdle band formation (Gordon and Drum 1994, Pickett-Heaps et al. 1990, Volcani 1981). Silicic acid is mainly present in the seawater at pH 8.0 in the following two forms: as Si(OH)4, which is the prevailing form representing 97% of dissolved silica; and as SiO(OH)3– that composes the remaining 3% (Ingri 1978, Stumm and Morgan 1981). Most diatom species consume the prevailing form (Si(OH)4) (Del Amo and Brzezinski 1999), however, it was shown that Phaeodactylum tricornutum could also use the minority form (Riedel and Nelson 1985, Del Amo and Brzezinski 1999). However, it is important to note that Phaeodactylum tricornutum is a special species which can grow and perform divisions in the absence of silicic acid (Round et al. 1990). The assimilation of silicic acid is done by the means of active sodium dependent transport in marine diatoms (Paasche 1973a, b, Azam et al. 1974, Bhattacharyya and Volcani 1980) and it may be coupled with potassium in

!'& Algal Cultures, Analogues of Blooms and Applications freshwater species (Sullivan 1976). This transport uses sodium/silicic acid symporters (Bhattacharyya and Volcani 1980, Hildebrand et al. 1997). Hildebrand et al. (1997) characterized these silicon transporters in Cylindrotheca fusiformis, and they proved the existence of five genes (Silicic acid transporter (SIT) genes) coding for these transporters (Hildebrand et al. 1998). The presence of these genes could be generalized for other pennate or centric, marine or freshwater diatom species (Hildebrand et al. 1998). Hildebrand et al. (1998) observed that the rate of mRNA expression of SIT genes was multiplied four-fold just before the maximal phase of the synthesis of a frustule (Martin-Jézéquel et al. 2000). Silicon uptake and deposition appear to be associated with the formation of new siliceous valves just prior to cell division, and thus mainly seem to be confined to the G2 and M period between cytokinesis and daughter cell separation (Hildebrand 2000, Sullivan 1986, Sullivan and Volcani 1981). The silicon metabolism in diatoms is then strictly linked to the cell cycle (MartinJézéquel et al. 2000). However, some species can assimilate silicon before this phase and stock it (Chisholm et al. 1978, Brzezinski and Conley 1994). The girdle bands are formed after the valves and can represent a significant part of the total biogenic silica of the cell in some species (Round 1972). Chiappino and Volcani (1977) showed that the formation of the first and often of the second girdle bands follow the formation of new valves in Navicula pelliculosa, whereas the formation of other bands can proceed till the last moment before the mitosis. The Si-uptake does not require photosynthetic energy, but does require energy from respiration (Raven 1983, Sullivan 1976, Sullivan 1980), consequently the silicic acid uptake can take place in the dark as well as in the light (Chisholm 1981, Martin-Jézéquel et al. 2000). The models of Brzezinski (1992) and Flynn and Martin-Jézéquel (2000) based on a large amount of data about the relation of the cell cycle and the silicon metabolism (Blank et al. 1986, Blank and Sullivan 1979, Brzezinski and Conley 1994, Darley and Volcani 1969, Okita and Volcani 1978, 1980, Sullivan 1976, 1977) suggested that the increase of the length of the G2 phase should have entailed an increase of the silicate uptake, this hypothesis developed by Martin-Jézéquel et al. (2000) was confirmed by Claquin et al. (2002). They observed in continuous cultures of the marine diatom Thalassiosira pseudonana that the increase of the length of the G2 phase, which was due to a decrease of the growth rate under stronger light, nitrogen or phosphorus limitation, entailed an augmentation of the silification of the cells. Thus it appears that the cellular silicon content and the frustule thickness (i.e. BSi per cell surface) are regulated by the total amount of Si


!''

taken up which is directly driven by the length of the cell cycle (i.e. by the growth rate). Hence, the silicon content variations seem not to be linked to the type of the limitation but to the intensity of the limitation. This model is not appropriate under silicon limitation; in this case the cellular amount of BSi decreases with lower growth rate (Martin-Jézéquel et al. 2000). As mention before, the strict link between the silicon metabolism and the cell cycle associated with a low respiratory energy requirement independent of photosynthesis (Löbel et al. 1996, Martin-Jézéquel et al. 2000, Raven 1983) allows to explain the uncoupling between the carbon and silicon metabolisms (Claquin 2002, Claquin and Martin-Jézéquel 2005). This uncoupling controls the Si/C or Si/N ratio variations frequently observed in connection with growth conditions changes due to light intensity, temperature or nutrient limitation (Brzezinski 1985, Claquin et al. 2002, Davis 1976, Harrison et al. 1976, 1977, Paasche 1980).

Cell Cycle and DIN-uptake and N-content Nitrogen is a major element in the control of microalgae growth. Microalgae can assimilate both forms of inorganic nitrogen, nitrate and ammonium and several algae can take up organic nitrogen in the form of urea or amino acids. Nitrogen and C-metabolism are tightly coupled because protein synthesis needs both, and as such it is logical that nitrogen metabolism depends on photosynthesis. As the reduction of nitrate via nitrite to ammonia is light dependent, nitrate uptake takes place in the light only (Dortch 1990) and this can cause patterns of diel variation in nitrogen metabolism which is thus connected to photosynthesis (Turpin 1991, Turpin et al. 1988, Vergera et al. 1998). Influence of the cell cycle on nitrogen metabolism is shown by the fact that the few algae investigated arrest mainly in the G1-phase (Hildebrand and Dahlin 2000, Olson et al. 1986, Vaulot et al. 1987), like light-limited algae, although several diatoms and cyanobacteria also show an arrestpoint in G2+M (Vaulot, 1994). Hildebrand and Dahlin (2000) showed in Cylindrotheca fusiformis a relationship between the nitrate transporter (NAT) mRNA levels and the cell cycle. They observed high levels of NAT mRNA in early G1 phase, a decrease during the remainder of G1, then an increase during the S and into the G2 phase, and finally a decrease after the M phase. Olson et al. (1986) observed in the diatom Thalassiosira weissflogii and in the coccolithophore Hymenomonas carterae an exclusive lengthening of the G1 phase by nitrogen limitation. They concluded that the G1 phase is more nitrogen dependent than the other phases, but in Thalassiosira pseudonana a major nitrogen-dependent segment in the G2+M phase was shown (Claquin et al. 2002).

" Algal Cultures, Analogues of Blooms and Applications Claquin et al. (2002) showed that even if the nitrate uptake is linked to the cell cycle, the length phase variations do not necessarily affect the global metabolism of nitrogen because they observed that the cellular nitrogen content did not change significantly with growth rate under either light or phosphorus limitation. Numerous studies proved that the nitrogen uptake is uncoupled from assimilation (Boyd and Gradmann 1999, Collos 1982, Collos and Slawyk 1976, Raimbault and Mingazzini 1987) and the assimilation of nitrogen depends on the regulation of different processes mainly linked with the carbon metabolism (Cresswell and Syrett 1979, Dortch et al. 1979, Huppe and Turpin 1994, Turpin 1991).

CONCLUSIONS In this overview we summarized the effects of the influence of a particular cell cycle stage on the physiological behavior of different microalgae and how the cell cycle is influenced by changes in the environment. Limitation for light of a particular nutrient will lengthen the duration of all cycle stages, but especially the G1-phase, the major growth phase, seems most affected. Due to the periodic nature of light, the cell cycle of algae is often entrained by the natural occurring light-dark cycle: picocyanobacteria like Synechococcus sp or Prochloroccocus, dinoflagellates and some green algae seem to synchronize their cell cycle to the light-dark cycle, whereas diatoms do not seem to do this. A result of this entrainment to the L:D-cycle is that the maximum growth rate will be maximally one division per day. At present it is difficult to generalize because synchronized growth has not been investigated in many species and it is therefore impossible to say if all green algae or all cyanobacteria show synchronized cell division as a result of the natural occurring light-dark cycle. Also with respect to the ‘cue’ causing the synchronization variability between groups exist: the dinoflagellate Gymnodinium uses the dawn transitions as zeitgeber (van Dolah and Leighfield 1999), whereas Euglena uses dusk as zeitgeber (Hagiwara et al. 2001). The role of the various checkpoints in the cell needs further investigations. If a particular nutrient is taken up only during the G1 phase, as seems the case for nitrogen, than the G1/S cell cycle checkpoints determines whether a cell will proceed to the next phase. In unsynchronized cultures this will mean that only a part of the cells is actually involved in the uptake of the nutrient, and determination of nutrient uptake kinetics will then lead to underestimates of the true kinetic parameters. If nutrients are taken up during a particular phase only, this can also potentially lead to large oscillations in cell number, and combined with nutrient storage the


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oscillations might have a periodicity different from the forcing, as shown by a modelling exercise by Pascual and Caswell (1997). In our view this mechanism can also explain part of the diversity of the phytoplankton: if two populations of different algae are synchronized by the L:D cycle but take up the same limiting nutrient in a different phase of the cell cycle, competition for the same nutrient will not lead to exclusion because the competition for the limiting nutrient is temporary separated. Alternatively, competition for a limiting nutrient between synchronized species like green microalgae and unsynchronized species like diatoms might also lead to stable coexistence if the synchronized species has a higher affinity for the nutrient because of the difference in timing of nutrient consumption: only during the time in which the green algae are in the cell cycle phase in which nutrient uptake takes place are the diatoms in disadvantage. Thus, the effect of competition on the coexistence depends on the nutrient availability, whether the limiting nutrient is taken up only during a particular phase of the cell cycle and the degree of synchronization. Unfortunately only little experimental evidence is available in the literature to test these ideas. More work on different algae where nutrients are only taken up in a particular phase of the cell and on the influence cell cycle checkpoints is needed. Also, the molecular mechanisms behind the cell cycle checkpoints need further investigation.

ACKNOWLEDGEMENTS We like to thank the van-Gogh exchange programme. This is publication 3596 of the NIOO-CEME.

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Nutritional Value of Microalgae and Applications

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Nutritional Value of Microalgae and Applications John K. Volkman and Malcolm R. Brown CSIRO Marine Research and Aquafin CRC, GPO Box 1538, Hobart, Tasmania 7001, Australia

Abstract Microalgae provide a well balanced mixture of nutrients to organisms higher in the food web. Indeed life in the oceans is ultimately dependent on the organic matter produced by microalgae through photosynthesis. These aquatic food-chains are mimicked in aquaculture where microalgae are used as live feeds for a variety of animals including molluscs, larval stages of crustaceans and some fish species, and as food for zooplankton which in turn are used as live feeds for other animals. Microalgae contain high, but variable, percentages of the key macronutrients: typically 25 to 40% protein (of DW), 5 to 30% carbohydrate and 10 to 30% lipid. The proportions of each nutrient can be modified by careful selection of growth conditions or by harvesting the microalgae at different growth stages. All species have similar amino acid compositions and are rich in the essential amino acids. The sugar composition of polysaccharides is more variable, but most have high proportions of glucose (21–87%). Most microalgae are rich sources of one or both of the essential polyunsaturated fatty acids eicosapentaenoic acid 20:5(n-3) and docosahexaenoic acid 22:6(n-3). These fatty acids are essential for the growth of marine fish larvae. Green microalgae tend to be poor sources of these polyunsaturated fatty acids: prasinophytes have moderate amounts of 22:6(n-3) (4-10%), whereas chlorophytes are deficient in both acids (0-3%). Microalgae also contain significant quantities of micronutrients and antioxidants such as the vitamins ascorbic acid (1–16 mg g–1 DW), riboflavin (20–40 mg g–1) and tocopherols as well as different carotenoids and a surprising variety of novel lipids. Details of the nutritional values of the different classes of microalgae, based on their biochemical composition, are discussed in this review plus comments on the applications of microalgae to aquaculture nutrition. Uses of microalgae also extend well beyond

408 Algal Cultures, Analogues of Blooms and Applications aquaculture to include commercial sources of carotenoid pigments, single cell oils having a high content of polyunsaturated fatty acids, dietary supplements such as ‘Spirulina’ tablets, biodiesel and many others.

INTRODUCTION Microalgae have an important role to play in aquaculture. They are the primary food source for larval and juvenile bivalves, and for the larvae of some crustaceans and fish species. Microalgae are also used to enhance the biochemical composition of zooplankton such as rotifers and copepods that are used as food for late-larval and juvenile crustaceans and fish (Reitan et al. 1997). Commercially important marine animals that rely on cultured algae as food for part of their life cycle include edible oysters (e.g. Crassostrea gigas and Ostrea edulis), pearl oyster (Pinctada maxima), abalone (Haliotis spp.), and shrimp (Penaeus spp.). Juvenile fish such as Atlantic halibut (Hippoglossus hippoglossus) and barramundi (Lates calcarifer) are fed on commercially reared zooplankton, before being introduced to a pelleted diet. Size is an important criterion for choosing appropriate microalgal species for aquaculture. Larval animals are usually fed microalgae in the nanoplankton size range (2–20 mm). Shrimp larvae can use larger species such as diatom chains (e.g. Skeletonema spp. which can be up to 60 mm long), whereas larger ‘sticky’ pennate diatoms such as Nitzschia spp. and Navicula spp. (> 20 mm) are readily grazed by abalone. The usefulness of a microalga for aquaculture also depends on its nutritional quality and on how easy it is to culture which in turn is reflected in its tolerance to temperature, salinity and light conditions, especially if grown in outdoor tanks or ponds (Jeffrey et al. 1992). It is perhaps not surprising that aquaculturists worldwide have chosen to work with a limited number of microalgal species that grow well even though the biochemical composition of these species may not be ideally matched to the nutritional requirements of aquatic animals. Information on the gross composition, amino acids, lipid classes, fatty acids, sugars and vitamins of many microalgal species is available in the literature, although most studies do not provide data for all biochemical classes. An exception is the work of Brown et al. (1997) which presents biochemical data for about 40 species from seven algal classes to assess how composition relates to differences in the nutritional value of the species. Representatives of most of the major algal taxa were examined: diatoms, haptophytes, prasinophytes, chlorophytes, eustigmatophytes, cryptomonads and a rhodophyte.


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METHODS Algal Culture Microalgae are grown in mass culture for aquaculture operations, but most biochemical data in the literature have been derived from small scale cultures. These data can provide a useful guide as long as it is appreciated that culturing conditions can markedly affect the relative proportions of the biochemical classes, and to a lesser extent affect the distribution of compounds within a biochemical class. Important considerations are the choice of culture medium, light intensity (e.g. 70–80 mmol photons m–2 s–1 white fluorescent light is often used), light:dark cycle, temperature, extent of aeration, CO2 supplementation, pH and stage of culture at both the inoculum and at harvest. A completely chemically defined medium is preferred. A common one is the f/2 medium of Guillard and Ryther (1962) (see Guillard 1975 for a review of culture media), the widespread use of which has dramatically advanced algal aquaculture (Wikfors and Ohno 2001).

Procedures for Biochemical Analyses Sensitive and specific analytical methods are required to measure the nutrient concentrations in the small quantities of microalgal samples typically available from small-scale culturing (£ 20 mg DW). Cells are usually harvested by centrifugation or by filtering onto glass fiber filters. Cells should be washed with sufficient volumes of 0.5 M ammonium formate (e.g. 3 ¥ 5 mL for filters) to remove residual salts from the seawater medium prior to drying for dry weight determinations. Many microalgae are rapidly lysed on harvest with release of lipolytic enzymes which can result in a high proportion of free fatty acids in the extract. Some authors recommend boiling cells before analysis to inactivate these enzymes (Budge and Parrish 1999). Note that samples should contain a large enough mass of total lipids (> 4% DW) to overcome the absorption of polar lipids on glassware (Bergen et al. 2000). For amino acids, algal samples are hydrolysed in hydrolysis-tubes at 110°C for between 12 to 48 h with either 6 M HCl (Enright et al. 1986b, Daume et al. 2003) or 4 M methanesulphonic acid containing 0.02% tryptamine (MSA reagent) (Brown 1991). Hydrolysates from 6 M HCl can be freeze-dried, and derivatised directly prior to analysis, though this process gives a poor recovery of tryptophan. Residual lipids and carbohydrate can interfere with the derivatisation process (Cohen et al. 1988). The MSA

410 Algal Cultures, Analogues of Blooms and Applications reagent provides a good recovery of tryptophan, though the hydrolysate must be passed through a cation-exchange resin (AG 50W-X8; Bio-Rad, Ca, USA) to remove the reagent (Lazarus 1973, Brown 1991). However, an advantage is that this process separates amino acids from lipids and carbohydrates, producing a purified amino acid fraction for derivatisation and analysis. Common reagents used for amino acid derivatisation include phenylisothiocyanate (Bidlingmeyer et al. 1984) and 9-fluorenylmethoxycarbonyl chloride (Daume et al. 2003). The anhydroamino acid residues can be summed to give an estimate of the protein content. Most studies of lipids in microalgae use some variation of the original extraction method of Bligh and Dyer (1959) designed for analysis of fish lipids which uses successive extractions with a chloroform-methanol-water mixture (1:2:0.8; v/v/v), although good yields can also be obtained with other solvents such as ethanol and hexane-ethanol (1:2.5) (Molina Grima et al. 1994). Higher contents of methanol have been shown to give better yields of the more-polar lipids such as phospholipids (Smedes and Askland 1999). The resultant extracts are combined, and separated into chloroform and aqueous-methanol layers by the addition of water and chloroform. The chloroform layers are then concentrated by rotary evaporation to provide a total lipid extract. Note that there can be some loss to the aqueous phase or water-solvent interface of more polar lipid classes that contain watersoluble moieties such as sugars. Lipid classes can be separated into biochemical classes by HPLC (Kato et al. 1996, Silversand and Haux 1997, Nordbäch et al. 1998), or by open column or thin-layer chromatography (Christie 1982). The use of Ag+-impregnated supports can be very useful in separating lipids with different degrees of unsaturation (Dobson et al. 1995). Quantitative data can be determined by thin-layer chromatography with flame ionization detection (Iatroscan TLC-FID) (Volkman and Nichols 1991, Parrish et al. 1992, Bergen et al. 2000) or using a light-scattering detector with HPLC (Christie 1998, Nordbäch et al. 1998). Total lipids are often transesterified or saponified to produce total fatty acids which are analyzed as methyl esters by capillary GC using polar and non-polar capillary columns and GC-mass spectrometry (Volkman et al. 1989). The aqueous-methanol layers from a lipid extraction contain monoand oligo-saccharides, and these can be assayed for carbohydrate by the phenol-sulphuric acid method (Dubois et al. 1956). The residues remaining after lipid extraction contain polysaccharide which can be hydrolysed with 0.5 M H2SO4 at 100°C and analyzed for carbohydrate (Dubois et al.


411

1956). Constituent sugars may be converted to alditol acetate derivatives (Blakeney et al. 1983) and quantified by gas chromatography (Whyte 1987, Brown 1991). Total cell carbohydrate is determined as the sum of the mono-, oligosaccharide and polysaccharide carbohydrate. Alternatively, the method of Dubois et al. (1956) can be applied directly to the cell pellet or freeze-dried biomass to estimate total carbohydrate (Brown et al. 1993a). A range of specific and sensitive assays are available for analysis of vitamins in microalgae. For example, ascorbic acid, a-tocopherol, riboflavin and thiamin can be assayed by reverse phase HPLC with fluorimetric detection (Brown et al. 1999), whereas biotin and other B group vitamins are assayed by microbiological methods (Seguineau et al. 1996). GC methods used for sterol analysis are also suitable for the analysis of lipid-soluble vitamins such as a-tocopherol (reviewed by Rupérez et al. 2001). Note that atocopherol elutes near cholesterol on non-polar GC phases.

Biochemical Composition Proximate Composition The proximate composition, i.e. the relative percentages of protein, carbohydrate, lipid and mineral (or ash) can vary substantially between microalgae species. Under standard conditions (i.e. where nutrients are not limited), microalgae typically contain between 10 to 40% of DW as protein, 10 to 30% as lipid, 5 to 30% as carbohydrate and 10 to 40% as ash (Whyte 1987, Renaud et al. 1999, Knuckey et al. 2002) (Table 12.1). Due to this variability it is difficult to categorize algal classes based on proximate composition alone. Nevertheless, cryptophytes are generally richer in protein than other microalgae (McCausland et al. 1999, Renaud et al. 1999), whereas diatoms have higher concentrations of ash (Brown and Jeffrey 1995, Renaud et al. 1999) (Table 12.1). The reported percentages of proximate components in microalgae generally sum to less than 100%. In two independent studies that examined a total of 28 species or strains Renaud et al. (1999) found an average value of 77% (range: 63 to 92%), whereas Knuckey et al. (2002) found an average value of 96% (range: 78 to 117%). Aside from potential analytical error, reasons for these discrepancies relate to: 1) other nutrient components unaccounted for when using a specific assay, and 2) methodologies that under/over-estimate nutrients within certain algal species. With respect to the former, residual moisture can account for up to 8% of proximate weight in microalgae after freeze-drying (Table 4 in Rebolloso Fuentes et al. 2000).

Data from individual studies: Chlorophytes D. tertiolecta Dunalliella sp. Chlorella spp. (CS-247 and CS-195), Stichococcus sp. Prasinophytes Tetraselmis sp. CS-362 Tetraselmis spp. (NT18 and TEQL01), Nephroselmis sp. Cryptophytes Rhodomonas salina Cryptomonas sp., Rhodomonas sp. Diatoms Amphora coffeaformis, Chaetoceros sp., Fragilaria pinnata, Nitzschia sp., N. cf. frustulum, Skeletonema sp., S. costatum, unidentified chain diatom Attheya septentrionalis, Entomoneis sp., Extubocellulus spinifera, Minidiscus spp., Thalassiosira oceanica, T. pseudonana, Nitzschia spp., Papiliocellulus simplex T. pseudonana, Chaetoceros sp., C. calcitrans Cylindrotheca fusiformis, Navicula jeffreyi, Nitzschia closterium, Lauderia annulata

Algal species/Class studied

28 13 6-16 (12) 26 9-14 (11) 19 4-9 (6) 4-15 (7)

9-30 (19)

8-17 (12) 5-12 (8)

30 26-32 (30) 59 29-47 (38) 22-37 (26)

12-38 (24)

21-23 (22) 16-38 (29)

Carbohydrate

39 64 15-23 (19)

Protein

10-16 (12) 18-20 (19)

14-42 (25)

13-20 (15)

19 19-22 (20)

16 10-14 (12)

23 23 9-17 (14)

Lipid

27-38 (33) 9-35 (20)

9-43 (29)

21-39 (30)

10 15-17 (16)

17 11-17 (14)

18 12 n.a.

Ash

Whyte (1987) Brown and Jeffrey (1995)

Knuckey et al. (2002)

Renaud et al. (1999)

McCausland et al. (1999) Renaud et al. (1999)

Brown et al. (1998a) Renaud et al. (1999)

McCausland et al. (1999) Thomas et al. (1984) Brown and Jeffrey (1992)

Reference

Contd.

Table 12.1 Proximate composition of microalgal species and classes, based on logarithmic phase cultures. Data are expressed as % of DW showing the range of values from selected studies, with mean values shown in parentheses. n.a. denotes not analyzed.


Skeletonema sp., S. costatum Rhodophytes Rhodosorus sp. Porphyridium cruentum Eustigmatophytes Nannochloropsis-like sp. Nannochloropsis salina Prymnesiophytes Pavlova pinguis Isochrysis sp. NT14, 3 unidentified spp. (NT19, CS-260 and QLD-B2) Isochrysis sp. (T.ISO), P. lutheri Isochyrsis sp. (T.ISO), I. galbana Summary Data for Classes: Chlorophytes Prasinophytes Cryptophytes Diatoms Rhodophytes Eustigmatophytes Prymnesiophytes

Algal species/Class studied

Table 12.1 Contd.

20 32 23 14 41 7-16 (12) 12-27 (19) 8-9 (9) 6-28 (15) 9-26 (16) 4-19 (11) 5-30 (12) 20-32 (26) 14-23 (19) 7-41 (16)

17 41 33 24-32 (28) 22-29 (25) 28-33 (31) 15-64 (32) 26-32 (30) 29-59 (42) 12-38 (25) 32-34 (33) 17-41 (29) 22-33 (29)

Carbohydrate

32 34

Protein

9-23 (18) 10-16 (13) 19-22 (20) 10-42 (17) 5-7 (6) 21-26 (24) 13-37 (23)

13-23 (18) 32-37 (34) 21-25 (23)

19

26 21

5 7

Lipid

12-18 (15) 11-17 (15) 10-17 (14) 9-43 (28) 16-20 (18) 8-16 (12) 4-20 (12)

13-20 (16) 4-5 (5) 9-12 (10)

12

16 8

16 20

Ash

Renaud et al. (1999) Knuckey et al. (2002) Whyte (1987)

Brown et al. (1998a)

Brown et al. (1998a) Thomas et al. (1984)

Renaud et al. (1999) Rebolloso Fuentes et al. (2000)

Reference


413

414 Algal Cultures, Analogues of Blooms and Applications Crude fiber, not normally accounted for in standard carbohydrate assays using acid hydrolysis, may be associated with cell wall structures (e.g. cellulose matrix, chitan and pectic polysaccharides in scales surrounding cells; Vesk et al. 1990) and can comprise more than 10% of total carbohydrate (Parsons et al. 1961). Some microalgae also contain a few percent of polymeric non-extractable lipidic ‘algaenans’ (Gelin et al. 1997, 1999). The choice of methodologies can also lead to errors. Lipid can be difficult to extract from microalgae with tough cell walls (e.g. Nannochloris sp., Nannochloropsis spp., Tetraselmis spp. and Chlorella spp.) and hence can be underestimated (Volkman et al. 1989). Carbohydrate assays are usually based on colorimetric assays with glucose as the standard (e.g. phenolsulfuric acid method; Dubois et al. 1956) and are accurate if glucose is the predominant sugar, but may under- or over-estimate true carbohydrate if there are high proportions of other sugars that are either more or less reactive than glucose. Protein can be estimated by summation of individual amino acids from cell hydrolysates and is the most accurate method (Brown 1991). However, less specific colorimetric assays (Clayton et al. 1988) generally give comparable results (M.R. Brown, unpublished data). ‘Crude protein’ is often reported as total % N ¥ 6.25, but this method can overestimate protein by up to 30% if cells have particularly high concentrations of non-protein N (e.g. from nucleic acids, hexosamines and amides) (Lee and Picard 1982). Variation in proximate compositions also occurs within the same species under different culture conditions, for example light, temperature and nutrient status (Harrison et al. 1990, Thompson et al. 1992) though not in a consistent way across all species. Knowledge of how species respond under different environments allows the production of nutrients of interest to be optimized. For example, when nitrate is limiting the levels of carbohydrate double at the expense of protein in species such as Isochrysis sp. (T.ISO) and Chaetoceros calcitrans (Harrison et al. 1990, Brown et al. 1993b), whereas high light intensity stimulates the accumulation of carbohydrate in Thalassiosira pseudonana (Brown et al. 1996). In general, microalgae are richer in polar lipids during logarithmic phase, but accumulate triacylglycerols during stationary phase (Dunstan et al. 1993). The molar N:P ratio of microalgae and cyanobacteria can be quite variable in nutrient-limited cells, ranging from less than five when phosphate is greatly in excess to greater than 100 when inorganic N is greatly in excess (Geider and La Roche 2002). Under nutrient-replete growth conditions the cellular N:P ratio is more restricted, typically ranging from 5 to 19, with most observations below the Redfield ratio of 16. Lowest values of N:P are


415

associated with nitrate- and phosphate-replete conditions in both field samples and in culture (Geider and La Roche 2002). The C:N ratio is also quite variable with the average C:N ratios of nutrient-replete phytoplankton cultures and oceanic particulate matter slightly greater than the Redfield ratio of 6.6. The use of N and P in the cosmopolitan marine species Emiliania huxleyi has been extensively studied by Riegman et al. (2000). This work showed that E. huxleyi has an exceptional P assimilation capability. Its uptake rate of nitrate in the dark is 70% lower than in the light, while N-limited cells are smaller than P-limited cells and contain 50% less organic and inorganic carbon. As a consequence of its high affinity for inorganic phosphate, and the presence of two different types of alkaline phosphatase, E. huxleyi is able to perform well in ecosystems where productivity is controlled by P availability.

Amino acids Most of the amino acids within microalgae are incorporated as protein. The content of free amino acids can increase significantly during the stationary phase (e.g. from to 2 to 40 times their concentration during exponential phase; Flynn 1990); an analysis of five species found concentrations ranging from 3 to 12% of total DW (Derrien et al. 1998). While the majority of the free amino acids are the same as those incorporated in protein, microalgae also contain other amino acids as intermediary metabolites, e.g. ornithine, 4-aminobutryic acid and hyroxy-proline and collectively they can constitute up to 1 to 2% of the DW (Brown 1991, Derrien et al. 1998). Many microalgae algae also contain trace amounts of mycosporine-like derivatives of amino acids that efficiently absorb UV-light (Jeffrey et al. 1999). These compounds are transferred through the food chain and may have a UV-protective role in natural ecosystems (Newman et al. 2000, Tartarotti et al. 2004). Accurate quantitative data are lacking on their concentration in algae, though surface bloom-forming dinoflagellates, cryptomonads, haptophytes and raphidophytes seem to have the highest concentrations (Jeffrey et al. 1999). Dense laboratory cultures of Phaeocystis antarctica contained one derivative, mycosporine-glycine:valine in concentrations of up to 500 mg ml–1 of culture, though data were not expressed on a DW basis (Newman et al. 2000). Most amino acid analyses of microalgae have assessed profiles from cell hydrolysates, thus assessing total (i.e. free plus protein-bound) amino acids. In analyses of 37 microalgae, Brown and co-workers found that the amino acid compositions were remarkably similar (Brown 1991, Brown

416 Algal Cultures, Analogues of Blooms and Applications et al. 1997) (Table 12.2). Aspartate and glutamate were present in highest concentrations (typically 8 to 12% of total amino acids); cystine, methionine, tryptophan and histidine were in lowest concentrations (typically 0.5 to 3%) and average values of other amino acids ranged from 4 to 8%. Also, there were no class-specific differences in composition although species from the genus Tetraselmis (T. suecica and T. chui) contained twice the arginine content of other species (Brown 1991). Other studies have shown that the amino acid compositions of microalgae are relatively unaffected by light and nutrient conditions (Brown et al. 1993a,b, Daume et al. 2003). Table 12.2 The amino acid composition (weight % of total amino acids) of microalgae, showing range and mean data from 37 species. Data compiled from Brown (1991), Brown and Jeffrey (1992), Volkman et al. (1993), Dunstan et al. (1994), and M. R. Brown unpublished. Data are compared to those of oyster larvae, Crassostrea gigas. Amino acid Essential: arginine histidine isoleucine leucine lysine methionine phenylalanine threonine tryptophan valine Non-Essential: alanine aspartate cystine glutamate glycine proline serine tyrosine

Range

Mean ± s.d.

Crassostrea gigas

4.8 1.4 3.4 6.5 5.0 1.4 4.5 4.0 0.4 4.4

– 15.3 – 2.7 – 5.9 – 10.4 – 8.6 – 3.2 – 7.7 – 7.3 – 3.1 – 6.8

7.3 2.0 4.6 8.1 6.2 2.2 6.2 5.3 1.6 5.8

± ± ± ± ± ± ± ± ± ±

2.1 0.3 0.6 1.0 0.8 0.4 0.8 0.7 0.5 0.6

7.7 1.7 4.5 7.0 8.4 1.3 5.3 5.2 1.6 5.5

5.3 7.5 0.4 9.5 5.2 4.1 4.5 3.3

– 9.1 – 10.9 – 2.0 – 12.6 – 7.7 – 13.3 – 8.4 – 6.9

7.6 9.1 0.8 10.9 6.0 6.1 5.7 4.8

± ± ± ± ± ± ± ±

0.9 0.9 0.4 0.8 0.5 1.9 0.8 0.8

5.7 10.2 0.7 12.5 9.3 4.5 5.1 4.6

Sugars and carbohydrates Carbohydrates in microalgae are distributed between polymeric fractions and the soluble fractions of simple sugars, i.e. the mono-, di- and oligo-


417

saccharides. Most carbohydrate within the polymeric fraction is readilyhydrolysable polysaccharide, though some microalgae can have significant amounts of fibrous material such as cellulose (e.g. Tetraselmis spp.; Parsons et al. 1961) or chitan (e.g. Thalassiosira spp.; Falk et al. 1966). Polysaccharide typically comprises 80 to 95% of the total carbohydrate in microalgae, excluding the fibre (Whyte 1987, Brown 1991). Several studies have examined the constituent sugar composition of algal polysaccharide following acid hydrolysis to characterize the polysaccharide types and determine phylogenetic differences in profiles (Chu et al. 1982, Whyte 1987, Brown 1991). The sugar composition of microalgae varies appreciably. Glucose is the predominant sugar, ranging from 20 to 90% of total sugars, and its percentage increases in most microalgae, except for diatoms, with culture age (Chu et al. 1982, Whyte 1987). These observations are consistent with glucan (polysaccharides rich in glucose) being the major food reserves in microalgae (Handa and Yanagi 1969). In a survey of 16 microalgae, Brown (1991) found galactose (1 to 20% of polysaccharide sugars) and mannose (2 to 46%) were also common, with arabinose, fucose, rhamnose, ribose and xylose found in varying proportions (0 to 17%). Haptophytes contained more arabinose, on average, than microalgae from other classes, whereas diatoms had higher percentages of galactose and mannose than most other species. Diatoms can also excrete up to 50% of their polysaccharide into the culture medium during the stationary phase (Mykelstad 1974). Red microalgae are encapsulated within a sulfated polysaccharide rich in xylose, glucose and galactose, and which dissolves into the growth medium (Geresh and Arad 1991). The prasinophyte Prasinococcus capsulatus forms a copious, and unique, sulfated and carboxylated polyanionic polysaccharide named capsulan. This polysaccharide capsule is presumed to originate in the Golgi body and is secreted through a crown of 10 pores in the cell wall, the ‘decapore’ (Sieburth et al. 1999). Large scale production of the red microalga Porphyridium is being investigated as a source of polysaccharides for the cosmetic, drugs and health food markets (Singh et al. 2000).

Lipids Lipids are an important source of storage energy for microalgae. They also function as insulators of delicate internal organs as membrane constituents, and they provide hormones and other bioactive components that regulate cell biochemistry. Lipids also have a vital role in tolerance to several physiological stressors in a variety of organisms including cyanobacteria (Singh

418 Algal Cultures, Analogues of Blooms and Applications et al. 2002). The major lipid classes in microalgae are triacylglycerols (TAG; an energy store), phospholipids (e.g. phosphatidylcholine PC, phosphatidylglycerol PG, and phosphatidylethanolamine PE) and glycolipids (e.g. monogalactosyldiacylglycerol MGDG, digalactosyldiacylglycerol DGDG, and sulfoquinovosyldiacylglycerol SQDG) (Okuyama et al. 1992, Budge and Parrish 1999). Betaine lipids have been reported in only a few species (Eichenberger 1993, Eichenberger et al. 1996), but it may be that they are more widely distributed. Diacylglyceryltrimethylhomoserine (DGTS) and hydroxymethyl-N,N,N-trimethyl-b-alanine (DGTA) occur in the cryptophyte Chroomonas salina (Eichenberger et al. 1996). DGTA, diacylglyceryl carboxyhydroxymethylcholine (DGCC) and diacylglyceryl glucuronide (DGGA) occur as major lipid components in some haptophytes such as Pavlova (Eichenberger and Gribi 1997). Both DGTA and DGTS are produced in the chrysophyte Ochromonas and the cryptomonad Cryptomonas; but in the presence of either DGTS or DGTA, the phospholipid PC is often not produced (Eichenberger 1993). All microalgae contain sterols (Volkman 1986, Volkman et al. 1998) and these can occur either as the free sterol (most common) or as a fatty acid ester, glycoside or sulfate. The distributions can be exceedingly simple (e.g. eustigmatophytes usually contain just cholesterol with traces of other sterols) to highly complex (dinoflagellates can contain 20 or more 4-desmethyl and 4-methyl sterols) (Volkman 1986). A few microalgae also contain unusual lipids as discussed later.

Fatty acids Fatty acids are denoted by a short-hand nomenclature x:y(n-z) where x is the number of carbon atoms, y is the number of double bonds and z is the position of the double bond closest to the methyl (w) end of the molecule. All double bonds are assumed to be cis (Z) and methylene-interrupted unless otherwise stated. Thus the polyunsaturated fatty acid (PUFA) arachidonic acid (AA) is denoted by 20:4(n-6), eicosapentaenoic acid (EPA) is denoted by 20:5(n-3), and docosahexaenoic acid (DHA) is denoted by 22:6(n-3). In the older literature these are often referred to as 20:4w6, 20:5w3 and 22:6w3 respectively, hence the common expression ‘omega-3’ and ‘omega-6’ fatty acids. EPA and DHA are essential dietary components for a variety of mollusc, shrimp and fish larvae, and may also be essential for other marine animals (Langdon and Waldock 1981, Castell et al. 1986, Volkman et al. 1992, Sargent et al. 1997). AA may be important for fish larvae (Tocher and Sargent 1984). Good sources of AA include diatoms, eustigmatophytes and rhodophytes (Fig. 12.1).

Nutritional Value of Microalgae and Applications 30

419

22 6 -3 20 5 -3

25

20 4 -6 20 % of total 15 fatty acids 10 5

0 es

ph

o

or

hl

ph

s

ra

es

o

r

p

o

ph

es a

o

s ph

ho

es

o

us

es

es h es o op p h a es sp op p s ss a r e s r rlo r a r oph s ph

Fig. 12.1 Average percentage compositions of the long-chain PUFAs docosahexaenoic acid (DHA; 22:6n-3), eicosapentaenoic acid (EPA; 20:5n-3) and arachidonic acid (AA, 20:4n-6) of microalgae commonly used in aquaculture. Data compiled from over 40 species analysed at CSIRO Marine Research. Microalgae contain a great variety of fatty acids and their distributions are often distinctly different between algal classes (Volkman et al. 1989). Diatoms, eustigmatophytes, cryptomonads, rhodophytes and some haptophytes (e.g. Pavlova species) are excellent sources of EPA (from 7–34% of total fatty acids) (Volkman et al. 1989, 1991, 1993, Dunstan et al. 1992, 1994). Many representatives from these classes have been used successfully as food in larval culture (reviewed by Brown et al. 1997). For example the diatom Skeletonema costatum provides a good diet for molluscs, due to its high proportion of PUFA such as EPA (Berge et al. 1995) Many cryptomonads and haptophytes are relatively rich in DHA (0.2–11%), whereas eustigmatophytes, rhodophytes and diatoms contain small amounts of AA (0–4%). Chlorophytes are deficient in both C20 and C22 PUFAs, although some species have small amounts of 20:5(n-3) (up to 3.2%). Chlorophytes generally have low nutritional value and are not suitable as a single species diet (Brown et al. 1997). Prasinophytes contain significant proportions of C20 and C22 PUFAs (but rarely both) and Tetraselmis species have been used successfully for shrimp and mollusc culture (Wikfors et al. 1996, Brown et al. 1997).

420 Algal Cultures, Analogues of Blooms and Applications The C18 PUFAs, 18:2(n-6) and/or 18:3(n-3) are essential for many freshwater fish (Castell et al. 1986, Brett and Müller-Navarra 1997). The dietary C18 PUFA can be elongated and desaturated to form longer chain PUFA more efficiently by freshwater fish than by marine fish, although dietary EPA and DHA, when available, are preferentially incorporated into fish tissues. Significant levels of 18:2(n-6) and 18:3(n-3) are found in most microalgal groups, except for the diatoms and eustigmatophytes which contain very low levels (Volkman et al. 1989, 1993, Dunstan et al. 1994). The major C18 PUFA in prasinophytes and haptophytes is 18:4(n-3) (Volkman et al. 1989, Dunstan et al. 1992). The importance of these polyunsaturated C18 PUFA in marine finfish diets is not known. A common feature of all microalgal fatty acid distributions is a high proportion of the saturated C16 fatty acid palmitic acid (16:0). Diatoms and haptophytes can also contain significant amounts of 14:0, and diatoms contain very high proportions of palmitoleic acid [16:1(n-7)] which is often the major fatty acid present. These acids are not usually considered as specific nutritional factors, but they are readily utilized for energy (Thompson et al. 1993). Haptophytes generally contain the highest proportions of saturated fats (ca. 33%), followed by diatoms and eustigmatophytes (ca. 27%), chlorophytes and prasinophytes (ca. 23%) and cryptomonads (ca. 18%) (Volkman et al. 1989, 1991, 1993; Dunstan et al. 1992, 1994). An unusual feature of the fatty acids of some dinoflagellates is the presence of very long-chain C28 highly unsaturated fatty acids [28:7(n-6)] and [28:8(n-3)] (Mansour et al. 1999, Leblond and Chapman 2000). The biological function of these fatty acids is yet to be elucidated, but it is known that they are associated with phospholipids and hence may be membrane constituents synthesized in the cytoplasm (Leblond and Chapman 2000). The effects of these unusual fatty acids being present in a diet are unknown. The pathways by which polyunsaturated fatty acids are biosynthesized by microalgae combine features found in both plants and animals (Moreno et al. 1979). Thus, in the n-6 pathway, 18:2 is desaturated to 18:3(n-6), elongated to 20:3(n-6), and subsequently desaturated to 20:4 (n-6) and then to 20:5(n-3). In the n-3 pathway, 18:2 is first desaturated to 18:3(n-3) which is then sequentially converted, apparently by the same enzymatic sequence of the n-6 pathway to 18:4(n-3), 20:4(n-3) and 20:5(n-3) (Shiran et al. 1996). Specific aspects of the pathways are now being reinvestigated. For example, it appears quite possible, based on animal studies, that 22:6(n-3) in microalgae is formed by chain-shortening of 24:6(n-3) (Voss et al. 1991,


421

Buzzi et al. 1997, Sprecher et al. 1999), rather than elongation of 20:5(n-3) and further desaturation. However, there are no reports of this long-chain PUFA in microalgae to date. The high abundance of 18:5(n-3) in some dinoflagellates, prasinophytes, haptophytes and raphidophytes (see Bell et al. 1997 for references) may also be due to a similar chain-shortening mechanism from 20:5(n-3) as originally proposed by Joseph (1975). Chainshortening of fatty acids by b-oxidation in Phaeodactylum tricornutum was confirmed in the 14C-labelling studies of Moreno et al. (1979). The fatty acid compositions of the triacylglycerols and different polar lipids in an alga can be very different. For example, Leblond and Chapman (2000) found that the phospholipid fractions in dinoflagellates contained the majority (over 75% in 12 of 16 strains) of the DHA present. In contrast, the highly unsaturated C18 fatty acids 18:4(n-3) and 18:5(n-3) were primarily recovered from a chloroplast-associated glycolipid fraction comprised of MGDG, DGDG and SQDG. These lipids have different physiological functions and thus their proportions (and that of their constituent fatty acids) can vary significantly in response to changes in light intensity, salinity and most importantly nutrient levels. In contrast to these results, Meireles et al. (2003) found that EPA in the haptophyte Pavlova lutheri was especially concentrated in MGDG (ca. 45%) and TAG (ca. 33%), whereas DHA was dispersed through the various classes, especially within TAG (ca. 27%), diphosphatidylglycerols (DPG, ca. 22%), and betaine lipids (21%). Fatty acids from microalgae may be efficiently transferred to higher trophic levels such as fish larvae via intermediary zooplankton (Watanabe et al. 1983, Brett and Müller-Navarra 1997, Reitan et al. 1997). This process is often termed ‘trophic upgrading’. The most used microalgae for boosting zooplankton with PUFA are those which contain high levels of EPA (e.g. Nannochloropsis oculata), DHA (e.g. Isochrysis sp. clone T-ISO), or both (e.g. Pavlova lutheri). Rotifers (Brachionus plicatilis) readily ingest Pavlova cells from which they can accumulate high concentrations of EPA, DHA and other PUFA within a few hours (Nichols et al. 1989). After just three hours of feeding on Pavlova, the distribution of fatty acids in the rotifer becomes almost indistinguishable from its algal food.

Effect of Culture Conditions on Lipid Compositions Environmental conditions can lead to significant changes in the lipid composition of microalgae (Shifrin and Chisholm 1981). Sterol distributions are usually little affected, but environmental conditions can have a dramatic effect on fatty acid distributions (Dunstan et al. 1993). Other studies have

422 Algal Cultures, Analogues of Blooms and Applications demonstrated changes in fatty acid composition associated with light intensity (Thompson et al. 1990, 1993, Brown et al. 1993b), culture media (Ben-Amotz et al. 1985), temperature (James et al. 1989, Thompson et al. 1992) and pH (James et al. 1988, Guckert and Cooksey 1990), but many of the changes were species-specific. Such changes are a common response by microalgae to a particular environmental stress. In part this may simply be due to the changes in growth rate and physiological state rather than a specific response caused by environmental influences. Many studies have not corrected for changes in growth rate and thus the algae are not harvested at the same physiological state.

Growth stage at harvest in batch culture Lipids of microalgae are often rich in polar lipids during the logarithmic phase growth, but many species accumulate triacylglycerol during the stationary phase when nitrogen is limiting (Dunstan et al. 1993). The fatty acid amount and composition of microalgae is dependent on both growth conditions and stage of harvest (Siron et al. 1989). For example, Nannochloropsis oculata contains more PUFA per cell in the logarithmic phase than in the stationary phase, whereas the reverse is true for Pavlova lutheri (Dunstan et al. 1993). In the diatom Phaeodactylum tricornutum the proportion of 20:5(n-3) first increased and then decreased with culture age, whereas 16:1(n-7) continued to increase (Siron et al. 1989). A comprehensive study of effects of growth phase on the lipid content and composition of a dinoflagellate was carried out by Mansour et al. (2003). The lipid content per cell increased two fold from late logarithmic to linear growth phase and then decreased at the stationary phase. Changes in fatty acid content mirrored these changes, while the sterol content continued to increase with culture age. The largest changes occurred in the proportions of lipid classes. Triacylglycerols increased from an initial 8% (of total lipids) to 30% at the late stationary phase, with a concomitant decrease in the polar lipid fraction. The proportions of 16:0 and 22:6(n-3) increased while those of 18:5(n-3) and 20:5(n-3) decreased with increasing culture age. Donato et al. (2003) observed significant changes in the amounts of fatty acids, sterols, carotenoids and a-tocopherol with culture age in the haptophyte Diacronema vlkianum. Sterol content increased up to the stationary phase and then declined in the decay phase, whereas a-tocopherol contents kept increasing perhaps reflecting the need for more anti-oxidants as the cells aged. The distribution of individual sterols only showed small changes. The highest content of PUFA was observed in the stationary phase cultures.


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Semi-continuous and continuous culture Semi-continuous culture is often used in aquaculture where a portion of the culture is used as feedstock and the volume is then topped up with fresh medium. Fábregas et al. (2001) examined the food value of the marine prasinophyte Tetraselmis suecica for Artemia when the alga was grown semi-continuously with renewal rates between 10% and 50%. At the low renewal rate the alga’s biochemical composition was similar to that of the stationary-phase cultures commonly used in aquaculture and this produced poor growth and survival in the Artemia and low food-conversion efficiency compared to cultures maintained with a high renewal rate. The gross biochemical composition of the Artemia resembled that of the microalgal food except for total lipid content. Higher renewal rates resulted in higher lipid percentages in the microalga, but in Artemia the percentage of lipids decreased from 19% of the organic weight with a renewal rate of 10%, to 13% with a renewal rate of 50%. The proportion of protein in Artemia expanded with increasing renewal rates in the microalgal cultures from 45% to 65% of the organic weight, while the carbohydrate percentage decreased under the same conditions. These results emphasize the importance of controlling microalgal nutritional value for the success of aquaculture food chains in which filter feeders are involved. If high rates of renewal are used (i.e. continuous culture), microalgae of a less variable biochemical composition can be obtained. For the diatom Phaeodactylum tricornutum grown in continuous culture for 1.5 to 7 d (LópezAlonso et al. 2000), culture age had almost no influence on the fatty acid content (which remained around 11% of DW) and very little effect on the composition. The culture age had a greater impact on the proportions of lipid classes: TAG ranged from 43% to 69%, and galactolipids oscillated between 20% and 40%. In general, the content of polar lipids of the biomass decreased with culture age.

Effects of light intensity and light-dark cycle Changes in fatty acid composition associated with light intensity have been demonstrated by Thompson et al. (1990, 1993) and Brown et al. (1993b). Logarithmic phase cultures of the diatom Thalassiosira pseudonana grown under a 12:12 h light-dark cycle (100 mE m–2s–1) contained a 25% greater proportion of 20:5(n-3) than similar cultures grown under continuous light (50 and 100 mE m–2s–1) (Brown et al. 1996). The content of saturated fats in microalgae can also be improved by culturing under high light conditions (Thompson et al. 1993).


Growth temperature Reducing the growth temperature usually brings about an increase in the proportion of those fatty acids having a greater number of double bonds. Fatty acid compositions can respond rapidly to changes in temperature, with significant changes seen within two hours (Rousch et al. 2003). This is much faster than effects due to changes in other culturing factors such as nutrient status which may take several days to become significant. Okuyama et al. (1992) showed that a decrease in growth temperature from 5°C to 2°C was accompanied by a significant increase in levels of 18:5(n-3) and 18:4(n-3) in an un-named haptophyte. Intriguingly, the level of 22:6(n-3) changed little. These data led the authors to conclude that C18 PUFA with more than three double bonds, 18:5(n-3) in particular, serve as modulators of membrane fluidity. The effects of temperature appear to be species-specific. For example, in the chlorophyte Chlorella the proportion of C18 PUFA increased with a decrease in temperature, whereas the proportion of 20:5(n-3) in the eustigmatophyte Nannochloropsis increased with temperature up to 25ºC and then declined greatly at 30ºC (James et al. 1989). Note that species of Chlorella contain very low contents of C20 and C22 PUFA, in contrast to the “marine Chlorella” used in the early days of aquaculture in Japan which is actually a eustigmatophyte (Maruyama et al. 1986).

Effect of nutrient regime Changes in fatty acid composition due to the choice of culture media have been shown by Ben-Amotz et al. (1985). However, sterol distributions are usually fairly robust and distributional variations with changes in environmental conditions are usually small (Hallegraeff et al. 1991), except for a few noted exceptions (Piretti et al. 1997). Parrish et al (1999) showed that Isochrysis galbana grown in 85-lite cage culture turbidostats under conditions of nitrogen limitation had a significantly higher total lipid content than when grown under nutrient-replete conditions. This was due mainly to a doubling in the amount of less unsaturated triacylglycerol in the cells. Increasing CO2 availability can also enhance the production of lipids and fatty acids. Hu and Gao (2003) found that photoautotrophic conditions with enriched CO2 (2800 mL CO2 L–1) and aeration gave the highest biomass yield, total lipid content (9% of DW), total fatty acids and polyunsaturated fatty acids (35% of total fatty acids) in the picoplankton Nannochloropsis sp.


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Mixotrophic cultures gave a greater protein content but less carbohydrates. Adding sodium acetate (2 mM) decreased the amounts of the total fatty acids and EPA. Nitrogen content is an important parameter, since in its absence proteins cannot be synthesized. Hence, López-Alonzo et al. (2000) found that saturated and monounsaturated fatty acids accumulated when the nitrogen concentration was decreased in continuous cultures of P. tricornutum. As N was lowered, TAG increased from 69% to 75%, neutral lipids from 6% to 8% and phospholipids from 73% to 79%. These changes were compensated for by a decrease in galactolipids from 21% to 12%. Increased fatty acid synthesis has been shown to occur when phosphorus is limited (Siron et al. 1989). When Phaeodactylum tricornutum was cultured in a P-deficient medium the composition of fatty acids was much like that observed in a senescent batch culture (Siron et al. 1989).

Lipid Oxidation and Toxic Fatty Acids PUFA are particularly prone to oxidation and these oxidation products may have toxic effects (Hardwick et al. 1997). Autoxidation of fish oils (and by implication other marine oils) can also lead to polymer formation (Burkow and Henderson 1991). The stability of algal oils depends on their fatty acid composition, the physical and colloidal states of the lipids, the content of antioxidants, and the presence and activity of transition metals (Frankel et al. 2002). For example, the relatively high oxidative stability of an algal oil containing 42% DHA was completely lost after tocopherols and other antioxidants were removed (Frankel et al. 2002). Many complex lipids are readily hydrolysed to yield free fatty acids and several studies have suggested that specific free fatty acids can be toxic (see below). The formation of free fatty acids due to autolysis on cell rupture seems to be a particular problem with diatoms (Berge et al. 1995). For this reason, it has been suggested that cells should be immediately placed in boiling water to deactivate lipolytic enzymes before lipid extraction (Budge and Parrish 1999). Octadecapentaenoic acid [(all-cis D3,6,9,12,15-18:5; 18:5(n-3)] is an unusual fatty acid found in marine dinoflagellates, haptophytes, raphidophytes and prasinophytes (Joseph 1975; Dunstan et al. 1992; Bell et al. 1997; Parrish et al. 1998; Sellem et al. 2000), but only rarely is it found at higher trophic levels in the marine food web. Ghioni et al. (2001) found that 18:5(n-3) was readily metabolised by cell lines derived from turbot, gilthead sea bream and

426 Algal Cultures, Analogues of Blooms and Applications Atlantic salmon to 18:4(n-3 ) via a 2-trans-18:5. This intermediate seems to be involved in the b-oxidation of both 18:5(n-3) and 18:4(n-3) and is thought to be generated by a D3,D2-enoyl-CoA-isomerase acting on 18:5(n-3). The toxicity of the main PUFA in microalgae has been investigated using a range of bioassays including Microtox, diatom growth inhibition, and sea urchin gamete and embryo bioassays (Arzul et al.1998, Sellem et al. 2000). Long-chain PUFA with five or six double bonds are the most active. The 18:5(n-3) fatty acid from the toxic dinoflagellate Gymnodinium cf. mikimotoi delays or inhibits first cleavage of sea urchin (Paracentrotus lividus) eggs and provokes abnormalities in embryonic development (Sellem et al. 2000). This PUFA also causes toxic effects in the gills and intestine of the sea bass Dicentrarchus labrax leading to strong mucus production in the gills and alteration of ionocytes (Sola et al. 1999). Glycoglycerolipids containing 18:5(n-3) derived from microalgae can also be biologically active and potentially haemolytic (Parrish et al. 1998; Arzul et al. 1998). A mixture containing the C16 PUFA 16:2, 16:3 and 16:4 as well as 18:4 and 14:0 (inactive) from the diatom Phaeodactylum tricornutum inhibited growth of Bacillus subtilis and Vibrio parahaeomolyticus (Cooper et al. 1985).

Vitamins Microalgae play an important role as the primary source of many essential vitamins that are passed up the food chain. In microalgae commonly used in aquaculture and grown under standard phototrophic conditions, ascorbic acid generally occurs in highest concentrations and shows the greatest variation (1 to 16 mg g–1 DW; Brown and Miller 1992). Other vitamins typically show a two- to four-fold difference between species (Table 12.3; Seguineau et al. 1996; Brown et al. 1999). Limited information has been published on the effects of culture condition on vitamin content (Donato et al. 2003). Concentrations of a-tocopherol, riboflavin and thiamin can increase by up to two to three-fold during the stationary phase in phototrophic cultures (Brown and Farmer 1994, Seguineau et al. 1996, Brown et al. 1999). In another study, a-tocopherol in Euglena gracilis was increased from 400 mg g–1 (phototrophic cultures) to 1700 mg g–1 through light and nutrient optimization and including a heterotrophic culture phase (Ogbonna et al. 1999). Heterotrophic culture, together with strain mutagenesis has also been used to optimize extracellular production of ascorbic acid in Chlorella pyrenoidosa to concentrations between 1 to 2 g L–1 (Running et al. 1994).


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Table 23.3 Range of vitamin content of microalgae (mg g– 1 DW) used in aquaculture. Combined data from Brown et al. (1999), Seguineau et al. (1996) and Brown and Miller (1992). Requirements of shrimp from Conklin (1997) and marine fish from Tacon (1991) and NRC (1993). Retinol requirements of fish can be met through pro-vitamin A metabolites such as b– carotene. Vitamin

Concentration range (mg g– 1)

Requirements of shrimp

Requirements of marine fish

Ascorbic acid (C) b– carotene niacin a– tocopherol (E) Thiamine (B1) Riboflavin (B2) pantothenic acid Folates Pyridoxine (B6) Cobalamin (B12) Biotin Retinol (A) Ergocalciferol plus Cholecalciferol (D2, D3)

1,000 – 16,000 500 – 1,200 110 – 470 70 – 350 30 – 110 25 – 50 14 – 38 7 – 24 4 – 17 1.7 – 7.4 0.7 – 1.9 C22) acyclic lipids are not common in microalgae, but studies over the past decade have indicated a number of interesting exceptions. Volkman et al. (1992) identified C30-C32 alcohols and diols in marine eustigmatophytes from the genus Nannochloropsis. At the time of their discovery, the function of such compounds was not known, but studies by


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Gelin et al. (1999) have shown that these diols are building blocks for novel highly aliphatic biopolymers produced by these microalgae. Long-chain mid-chain hydroxy acids are also present in these microalgae and may be involved in the formation of these biopolymers (Gelin et al. 1997). Recent work has established that similar alkyl diols and hydroxyl fatty acids occur in some diatoms (Sinninghe Damsté et al. 2003). Certain species of haptophytes including the genera Emiliania, Gephyrocapsa, Isochrysis and Chrysotila contain a suite of unusual very long-chain (C37-C40) unsaturated ketones termed alkenones (Volkman et al. 1980a, 1995, Conte et al. 1995, 1998, Rontani et al. 2004), as well as related alcohols, esters and alkenes (Conte et al. 1994, Rontani et al. 2004). Geochemists have shown considerable interest in these compounds since they are well preserved in sediments and the proportions of tri- and diunsaturated C37 alkenones is strongly correlated with growth temperature (Brassell et al. 1986) so these compound distributions can be used as a paleothermometer. Some of the species used as aquaculture feeds (Isochrysis) contain alkenones, and a nutritional role has been suggested (Ben-Amotz and Fischler 1990), but this remains unproven. Rather, it appears that many animals are unable to effectively utilize these compounds and thus excrete them relatively unchanged (Volkman et al. 1980b, Knauer et al. 1999).

Other Bioactive Compounds There has been increasing interest in tapping the genetic variation of microalgae and cyanobacteria as a source of new products, such as pharmaceuticals and pesticides (Borowitzka 1995). While this screening has identified many compounds with biological activity, commercial development is yet to occur, although the pace may quicken if the techniques of molecular biology for genetic manipulation are applied (Allnutt 1996). Ohta et al. (1998) tested 106 microalgae and cyanobacteria for anti-Herpes simplex virus (HSV-1) activity. Methanol extracts of Dunaliella bioculata and D. primolecta and the cyanobacteria Lyngbya sp. and Lyngbya aerugineo-coerulea all had inhibitory activity, but the green alga, D. primolecta, had the highest anti-HSV-1 activity (10 mg mL–1 of extract completely inhibited the cytopathic effect). The active substances were proposed to be pheophorbide-like compounds. Water-soluble (but not lipid-soluble) extracts of the marine microalgae Chlorella stigmatophora and Phaeodactylum tricornutum show significant anti-inflammatory, analgesic and free radical scavenging activity (Guzman et al. 2001).

442 Algal Cultures, Analogues of Blooms and Applications Toxins initially isolated from fish or shellfish (e.g. paralytic shellfish poisoning - PSP; diarrhetic shellfish poisoning, DSP) are now known to originate from certain microalgae, especially dinoflagellates (Yasumoto and Satake 1998). These toxins have been used to investigate the structure and function of ion channels on cell membranes and to elucidate the mechanism of tumor promotion. Some cyanobacteria also contain similar toxins and a few diatoms such as Pseudonitzschia contain the amnesic toxin domoic acid (Douglas and Bates 1992, Bates 2000).

Other Applications The future promise in microalgal biotechnology seems to be developing at the crossroads of molecular biology and novel aspects of cellular metabolism. (Allnutt 1996, Dunahay et al. 1996, Zaslavskaia et al. 2000, Stephanopoulos and Kelleher 2001). New products have also been developed for pharmaceutical and research applications. These include stable isotope biochemicals produced by algae in closed-system photobioreactors and as a source of extremely bright fluorescent pigments. Cryopreservation has also had a tremendous impact on the ability of strains to be maintained for long periods of time at low cost and maintenance while preserving genetic stability (Apt and Behrens 1999). Specific microalgae can be used to improve water quality, help in the treatment of sewage and produce biomass. They can be used to produce hydrogen and biodiesel as alternative fuels (Wyman and Goodman 1993), fix N2 for use as a biofertilizer, restore metal-damaged ecosystems, reducing CO2 loads, and reclaim saline or alkaline infertile lands (Rai et al. 2000). Existing and potential uses of diatom biomass have been discussed by Lebeau and Robert (2003). One of the more unusual applications they mention is the use of silica from diatoms in the nanotechnology area.

Comparisons of Cultures with Microalgal Blooms Biochemical data obtained from algal cultures seems to be a useful guide to the composition of the microalgae in natural environments even though environmental conditions may be very different. Environmental extremes may be infrequent in aquatic ecosystems, but they can occur when microalgae form large blooms that can strip the water of the major inorganic nutrients. The lipid compositional variations seen with batch cultures may provide a guide to the variations possible in nature and must be considered when comparing algal and sediment lipid data (Volkman et al. 1998).


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Leblond et al. (2003) compared the fatty acid and sterol compositions of the harmful marine dinoflagellate, Karenia brevis, from a natural bloom with the same species in culture. A close correspondence was found between the natural and cultured samples. The fatty acid compositions in membrane phospholipids, chloroplast-associated glycolipids or storage triacylglycerols varied significantly. The glycolipid fraction contained abundant 18:5(n-3) while the phospholipid fraction contained small amounts of 28:8(n-3) and octacosaheptaenoic acid 28:7(n-6). The sterols occurred mainly in the non-esterified form and consisted of two main constituents. Diatoms are readily ingested by zooplankton and have generally been considered a major source of food for marine food webs in productive regions. Recently, laboratory studies have demonstrated that the hatching success of copepods feeding on diatoms is dramatically impaired. Field data have now confirmed these observations (Miralto et al. 1999): in a diatomdominated bloom only 12% of eggs hatched compared with 90% under postbloom conditions. This effect has been linked to the presence of three triunsaturated C10 aldehydes (Miralto et al. 1999). Intriguingly, copepods can reproduce successfully on a diet of dinoflagellates that contain the potent neurotoxin saxitoxin and will accumulate the toxin making them toxic to fish (White 1981).

CONCLUSIONS Microalgae still have a strong role to play in aquaculture as a preferred source of live and preserved feeds. The protein quality of all microalgae is high, but the sugar content and composition are variable, and in some instances may affect the nutritional value. The essential PUFAs 20:5(n-3) and 22:6(n-3) are key nutrients in animal nutrition, and most algae are rich in one or both of these acids. Chlorophytes, however, lack these acids and this contributes to their low food value. Microalgae are generally a rich source of vitamins, especially ascorbic acid, but species are variable in composition and in some instances this could contribute to differences in nutritional value in aquatic ecosystems. Because particular microalgae may be limiting in one or more of the key nutrients, mixed-algal diets provide a better balance and normally are used in mariculture. The biochemical composition of microalgae can be manipulated readily by changing the growth conditions, but the effects vary from one species to another. Knowledge of how species respond to different environments is of practical use to aquaculturists, who may then grow the algae to

444 Algal Cultures, Analogues of Blooms and Applications optimize the level of specific nutrients needed by the animal. Microalgae are an important zooplankton food since important algal nutrients (fatty acids and vitamins) may be transferred to higher trophic levels via the zooplankton intermediates. Microalgae have been used in many other applications such as single cell oils and dietary supplements, but until their cost of production can be reduced this will tend to limit their use to speciality products.

ACKNOWLEDGEMENTS We thank Jeannie-Marie LeRoi, Kelly Miller, Suzanne Norwood, Christine Farmer, Stephanie Barrett, Graeme Dunstan, Peter Mansour, Dion Frampton, Ian Jameson and Daniel Holdsworth for assistance with algal cultures and/or biochemical analyses. Dion Frampton and Ian Jameson provided helpful reviews of the manuscript. Our collaborators Dr S.W. Jeffrey and S.I. Blackburn are thanked for many valuable discussions on algal culture and access to cultures from the CSIRO Algal Culture Collection. Our early research was supported by Grants 86/81, 88/69, 90/63 and 91/ 59 from the Fisheries Research and Development Corporation, Grant A18831836 from the Australian Research Council and a Rural Credits Development Grant. We gratefully acknowledge funding from the CRC for Aquaculture and the Aquafin CRC for funding our studies.

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Algal Cultures Analogues of Blooms and Applications Volume 2


Algal Cultures, Analogues of Blooms and Applications Volume 12

Editor

D.V. Subba Rao Bedford Institute of Oceanography Dartmouth, NS Canada

Science Publishers Enfield (NH), USA

Plymouth, UK

Photos of microalgal species Front cover: Emiliania huxleyi a coccolithophore (Credit: Dr. S.W. Jeffrey, CSIRO, Australia) Color plate as frontispiece 1. Dinophysis norvegica 2. Biddulphia sp. 3. Thalassiosira sp. 4. Pseudo-nitzschia pungens f. multiseries 5. Anabaena circinalis 6. Small volume cultures 7. Skeletonema costatum 8. Dunaliella tertiolecta, 9. Rhodomonas salina 10. Chaetoceros sp. (Credits: #1. Dr. Rajashree Gouda # 2, 4. and 10. Subba Rao and # 3,5,6,7,8,9 Dr. S.W. Jeffrey) Black and white plate on the reverse of frontispiece 1. Bacteriastrum sp. 2. Paralia sulcata 3. Chaetoceros lascinosum 4. Gymnodinium catenatum 5. Picoplankters 6. Naviculoid diatom 7. Ornithocercus magnificus 8. Cryptophyte 9. Dinophysis fortii 10. Planktoniella sol. (Credits: #1, 2, 3, 4, 6, 7, 8, 9,10 Dr. S.W. Jeffrey and #5 Subba Rao)

SCIENCE PUBLISHERS An imprint of Edenbridge Ltd., British Channel Islands. Post Office Box 699 Enfield, New Hampshire 03748 United States of America Internet site: http://www.scipub.net sales @ scipub.net (marketing department) editor@ scipub.net (editorial department) [email protected] (for all other enquiries) Library of Congress Cataloging-in-Publication Data Algal cultures, analogues of blooms and applications / editors, D.V. Subba Rao. p. cm. Includes bibliographical references (p.). ISBN 1-57808-393-1 1. Algae—Cultures and culture media. 2. Algal blooms. I. Subba Rao, D.V. QK565.2.A438 2005 579.8--dc22

2005051701

ISBN (Set) 1-57808-393-1 ISBN (Vol. 1) 1-57808-392-3 ISBN (Vol. 2) 1-57808-394-X © 2006, Copyright Reserved All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior written permission. This book is sold subject to the condition that it shall not, by way of trade or otherwise, be lent, re-sold, hierd out, otherwise circulated without the publisher’s prior consent in any form of binding or cover other than that in which it is published and without a similar condition including this condition being imposed on the subsequent purchaser. Published by Science Publishers, Enfield, NH, USA An imprint of Edenbridge Ltd. Printed in India.



Preface

Marine phytoplankters, the free-floating photosynthetic life, play a crucial role in the production of oxygen and in food web dynamics in the seas. Their diversity in taxonomy, morphology, size, and nutritional requirements continue to fascinate biological oceanographers. The spatial and temporal variations of algae are enormous. To obtain a steady supply of algae for biochemical and physiological experimentation, it soon became necessary to culture the algae under defined laboratory conditions. Although studies on natural assemblages of marine phytoplankton and laboratory cultured algae were initiated about the same time (1893), unlike the former, culture studies did not progress till the 1960s as rapidly as one would have wished. Due to the development of tracer carbon-14 technique in 1952, interest in studying marine micro algal cultures has been growing rapidly and many unexpected and exciting discoveries have already emerged. Four examples that involve marine micro algae may be cited: Discovery of photosynthetic picoplankton, UV light and climate, Geoengineering and climate and genetic engineering. New techniques have been developed in recent years and rapid advances have been made relating measurements of primary organic production of marine micro algae to their photosynthetic pigments, evident from the plethora of papers and reviews published. In general, principles of terrestrial plant physiology and biochemistry have been extended to study the physiological ecology of marine micro algae. Algal cultures are being used as excellent experimental materials to model growth, nutrient kinetics, physiological ecology, pollution research, phycotoxin research, remote sensing and climatic studies. However, most of the cultures are isolated from temperate seas and very few from other regions. It is to be noted that utility of algal cultures in tropics is mostly limited mariculture and to species isolated elsewhere but not native to their seas.

LEEE Algal Cultures, Analogues of Blooms and Applications Despite some differences, several similarities exist between the data obtained on blooms and cultures. As a result of the ease with which some of the algae can be cultured, considerable interest is currently evinced to see if cultures could be used to gain insights to understand some of the ecological principles such as species succession, periodicities, physiological adaptations. Additionally, research is focused on the application of algae in mariculture operations, marine biotechnology, in space research for waste recycling systems, and as source of natural compounds such as antiviral and antifungal compounds and pharmaceuticals. Of the 5000 confirmed taxa of marine micro algae, about 300 species contribute to blooms, both benign and toxigenic. More and more of novel nuisance phytoplankton blooms are recorded and the connection between their global expansion and human activities is actively sought. About 500 species, mostly from the temperate seas, are brought into culture and about 30 species are studied in considerable detail. A few important toxigenic dinoflagellate taxa still baffle any attempts to culture. Needless to add that research on algae will be actively pursued in the decades to come. When Science Publishers, New Hampshire approached me to collate and edit a volume on marine micro algae, I thought it would be useful to broaden the range of topics and bring out a thematic volume with recent developments in micro algal research. This book contributed by colleagues from 14 nations encompasses numerous scientific disciplines. I tried to involve serious researchers who have made excellent contributions on marine micro algae. They were requested to incorporate the latest findings specifically to address how best algal cultures can be utilized as analogues of natural blooms, their utility in understanding the ecological principles and their applications in biotechnology. Each chapter is contributed by an expert or group of experts, reviewed internally by colleagues and by outside referees as well. I am grateful to each of the contributors for their high level of professional and scholarly efforts, cordial, and prompt cooperation extended to me. I have gained from their efforts but the omissions and commissions are mine. The scientific opinions expressed in this book are those of the authors and not that of any institution. This book is not intended to be a compendium of everything worth knowing about marine micro algae given the fact that the knowledge base is constantly expanding. It is hoped that this volume will be useful to our colleagues in biological oceanography as well as other scientists, advanced undergraduate and graduate students as a summary of current thoughts in physiological ecology.

Contents

EN

Acknowledgements

Special thanks are due to Dr. Shirley Jeffrey for the generous assistance with most pictures of algae, and to Dr. Rajashree Gouda. My sincere thanks are extended to Mr. Arthur Cosgrove, Technographics, Bedford Institute of Oceanography, for his artistic skills in the design of the cover and the plates of algae. For her infinite patience, excellent help with the formatting, correspondence and unstinting support I am most grateful to my wife Bala T. Durvasula.


Contents

Preface Acknowledgements List of Contributors

vii ix xv

Volume 1 Chapter

1

Why Study Algae in Culture? D.V. Subba Rao

Chapter

2

Photosynthetic Pigments in Marine Microalgae: Insights from Cultures and the Sea S.W. Jeffrey and S.W. Wright

Chapter

131

5

Algal Blooms and Bacterial Interactions Bopaiah Biddanda, Paulo Abreu and Clarisse Odebrecht

Chapter

91

4

Allelopathic Interactions Among Marine Microalgae Genevi ve Arzul and Patrick Gentien

Chapter

33

3

Phases, Stages and Shifts in the Life Cycles of Marine Phytoplankton Marina Montresor and Jane Lewis

Chapter

1

163

6

Viral Infection in Marine Eucaryotic Microalgae Keizo Nagasaki

189

NEE Algal Cultures, Analogues of Blooms and Applications

Chapter

7

Autecology of Bloom-Forming Microalgae: Extrapolation of Laboratory Results to Field Populations and the Redfield-Braarud Debate Revisited Theodore J. Smayda

Chapter

8

The Trace Metal Composition of Marine Microalgae in Cultures and Natural Assemblages Tung-Yuan Ho

Chapter

343

11

Role of the Cell Cycle in the Metabolism of Marine Microalgae Jacco C. Kromkamp and Pascal Claquin

Chapter

301

10

Osmotrophy in Marine Microalgae Alan J. Lewitus

Chapter

271

9

Algal Cultures as a Tool to Study the Cycling of Dissolved Organic Nitrogen Deborah A. Bronk and Kevin J. Flynn

Chapter

215

385

12

Nutritional Value of Microalgae and Applications John K. Volkman and Malcolm R. Brown

407

Volume 2 Chapter

13

Effects of Small-Scale Turbulence on the Physiological Functioning of Marine Microalgae Elisa Berdalet and Marta Estrada

Chapter

14

Mechanistic Models of Algal Physiology Kevin J. Flynn

Chapter

459

501

15

Competition of Aquatic Microalgae in Variable Environments: The Disturbance Effect Sabine Fl der and Ulrich Sommer

533

Contents

Chapter

16

Effects of Temperature and Irradiance on Marine Microalgal Growth and Physiology Peter Thompson

Chapter

801

23

From Microscope to Magnet: Probing Phytoplankton Population Structure and Physiology Using Mammalian Antibodies Louis Peperzak and Sonya T. Dyhrman

Chapter

769

22

Biotechnology of Immobilized Micro Algae: A Culture Technique for the Future? Thierry Lebeau and Jean-Michel Robert

Chapter

715

21

Molecular Biology and Genetic Engineering in Microalgae Oliver Kilian and Peter G. Kroth

Chapter

685

20

Effects of Ultraviolet Radiation on Microalgal Growth, Survival and Production Andrew T. Davidson

Chapter

671

19

Absorption, Fluorescence Excitation and Photoacclimation Egil Sakshaug and Geir Johnsen

Chapter

639

18

Photosynthetic Response and Acclimation of Microalgae to Light Fluctuations Johan U. Grobbelaar

Chapter

571

17

Photosynthesis—Irradiance Relationships in Marine Microalgae Pedro Duarte

Chapter

NEEE

839

24

Prospects for Paratransgenic Applications to Commercial Mariculture using Genetically Engineered Algae Ravi V. Durvasula, Ranjini K. Sundaram, Scott K. Matthews, Pazhani Sundaram and D.V. Subba Rao

865

NEL Algal Cultures, Analogues of Blooms and Applications

Chapter

25

Development of Statistical Models for Prediction of the Neurotoxin Domoic Acid Levels in the Pennate Diatom Pseudo-nitzschia pungens f. multiseries Utilizing Data from Cultures and Natural Blooms Ilya Blum, D.V. Subba Rao, Youlian Pan, S. Swaminathan and N.G. Adams

891

Appendix 1 Algal cultures D.V. Subba Rao

917

Appendix 2 Algal culture centers D.V. Subba Rao

931

About the Contributors

933

Acknowledgements to Reviewers

947

Index

949

Contents

List of Contributors

Paulo Abreu Departmento de Oceanografia Fundação Universidade Federal do Rio Grande (FURG) Caixa Postal 474, 96201-900 Rio Grande, RS, Brazil. Nicholas G. Adams National Marine Fisheries Service, NFSC 2725 Montlake Blvd., East Seattle, Washington 98112, USA. Genevi ve Arzul Ifremer, DEL-PC, BP 70 F-29280 Plouzané, France. Elisa Berdalet Institut de Ciències del Mar Centre Mediterrani d’Investigacions Marines i Ambients (CSIC) Pg. Marítim, 37-49 E-08003 Barcelona, Catalunya, Spain. Bopaiah Biddanda Annis Water Resources Institute and Lake Michigan Center Grand Valley State University 740 W Shoreline Drive, Muskegon, MI 49441, USA. Email: [email protected] Ilya Blum Department of Mathematics Mount Saint Vincent University Halifax, N.S. Canada, B3M2J6

NL

NLE Algal Cultures, Analogues of Blooms and Applications Deborah A. Bronk Department of Physical Sciences The College of William and Mary Virginia Institute of Marine Science Route 1208; Greate Rd. Gloucester Point, VA 23062, USA. Email: [email protected] Malcolm R. Brown CSIRO Marine Research and Aquafin CRC GPO Box 1538 Hobart, Tasmania 7001 Australia. Pascal Claquin Laboratoire de Biologie et de Biotechnologies Marines Université de Caen Basse-Normandie Esplanade de la paix, 14032 Caen Cedex, France. Andrew T. Davidson Australian Antarctic Division Channel Highway, Kingston Tasmania 7050, Australia. Pedro Duarte CEMAS – University Fernando Pessoa Praça 9 de Abril, 349, 4249-004 Porto Portugal. Ravi V. Durvasula Department of Epidemiology and Public Health Yale University School of Medicine, New Haven, CT, USA. Email: [email protected] Sonya T. Dyhrman Biology Department MS #32, Woods Hole Oceanographic Institution Woods Hole, Massachusetts 02543 USA.


Marta Estrada Institut de Ciències del Mar Centre Mediterrani d’Investigacions Marines i Ambients (CSIC) Pg. Marítim, 37-49 E-08003 Barcelona, Catalunya, Spain. Sabine Fl der Institut für Botanik Universität zu Köln Gyrhofstr. 15 50931 Köln Germany. Email [email protected] Kevin J. Flynn Institute of Environmental Sustainability University of Wales Swansea, Singleton Park SA2 8PP, UK. Patrick Gentien Ifremer, CREMA, F-17137 L’Houmeau, France. Geir Johnsen Biological institute Norwegian University of Science and Technology N-7491 Trondheim, Norway. Johan U. Grobbelaar Department of Plant Sciences Botany, University of the Free State Bloemfontein 9300, South Africa. Tung-Yuan Ho Department of Earth and Environmental Sciences National Chung Cheng University, Ming-Hsiung, 621, Chia-Yi, Taiwan. Email: [email protected]

NLEE

NLEEE Algal Cultures, Analogues of Blooms and Applications S.W. Jeffrey CSIRO Marine Research GPO Box 1538, Hobart Tasmania, 7001, Australia. Oliver Kilian Fachbereich Biologie Universität Konstanz 78457 Konstanz, Germany. Jacco C Kromkamp Centre for Estuarine and Marine Ecology Netherlands Institute of Ecology PO box 140 4400 AC Yerseke, the Netherlands. Email: [email protected] Peter G. Kroth Fachbereich Biologie Universität Konstanz 78457 Konstanz, Germany. Thierry Lebeau, Laboratoire de Biologie Marine (UPRES EA 2663), Institut des Substances et des Organismes de la Mer (ISOmer) Université de Nantes, 2, rue de la Houssinière, BP 92208, 44322 Nantes cedex 3, France. Jane Lewis Research Centre Director School of Biosciences University of Westminster 115, New Cavendish Street London W1W 6UW UK. Alan J. Lewitus Belle W. Baruch Institute for Marine and Coastal Sciences University of South Carolina, and Marine Resources Research Institute


NEN

South Carolina Department of Natural Resources P.O. Box 12559, Charleston, South Carolina, USA. USA 29422-2559. Scott K. Matthews Department of Epidemiology and Public Health Yale University School of Medicine, New Haven, CT, USA. Marina Montresor Stazione Zoologica ‘A. Dohrn’ Villa Comunale 80121 - Naples, Italy. Keizo Nagasaki Harmful Algae Control Section, Harmful Algal Bloom Division, National Research Institute of Fisheries and Environment of Inland Sea 2-17-5 Maruishi, Ohno, Saeki, Hiroshima 739-0452, Japan. Email: [email protected] Clarisse Odebrecht Departmento de Oceanografia Fundação Universidade Federal do Rio Grande (FURG) Caixa Postal 474, 96201-900 Rio Grande, RS, Brazil. Youlian Pan Institute for Information Technology National Research Council of Canada 1200 Montreal Road, Building M50 Ottawa Canada, KIA OR6 Louis Peperzak National Institute for Coastal and Marine Management/RIKZ P.O. Box 8039 NL-4330 EA Middelburg The Netherlands. Jean-Michel Robert Laboratoire de Biologie Marine (UPRES EA 2663), Institut des Substances et des Organismes de la Mer (ISOmer) Université de Nantes, 2, rue de la Houssinière, BP 92208, 44322 Nantes cedex 3, France.


13 Effects of Small-Scale Turbulence on the Physiological Functioning of Marine Microalgae Elisa Berdalet and Marta Estrada Institut de Ciències del Mar, Centre Mediterrani d’Investigacions Marines i Ambients (CSIC), Pg. Marítim, 37-49, E-08003 Barcelona, Catalunya, Spain.

Abstract Marine microalgae are typically smaller than 200 μm. At this small scale, the direct effects of water turbulence on plankton are of potential ecophysiological importance. Turbulence interacts with the transport of molecules in and out of the cells, affects contact rates (and hence coagulation and predation processes) between suspended plankters and may cause mechanical cell damage, morphological changes and alterations of cell division and growth. In the case of motile cells, their trajectories in the water column are modulated by both organism swimming behaviour and water motion. Here, we explore the available theoretical and experimental information on the ecophysiological effects of small-scale turbulence on phytoplankton and present a brief outline of the relevant fluid dynamics concepts. Overall, dinoflagellates appear to be a specially sensitive group to small-scale turbulence. However, comparison among different studies is not straightforward due to the variety of setups and the lack of quantification of the turbulent kinetic energy applied in many of the experiments. Furthermore, direct extrapolation to natural conditions should be done with caution. In nature, the dynamics of plankton communities is driven by the interaction between physical forcing at various scales, the ecophysiological responses to small scale turbulence and the effects of other biological and environmental factors.

INTRODUCTION The dynamics of planktonic communities is markedly driven by the physical properties of the aquatic ecosystems. Physical forcing not only shapes the structure of the pelagic environment but also affects biological processes in

"$ Algal Cultures, Analogues of Blooms and Applications many direct and indirect ways (Mann and Lazier 1991, Kiørboe 1993). At the scale of a few to tens of meters, advective and turbulent flows transport organisms around the water column. Autotrophic organisms experience changes in inorganic nutrients and light regimes which influence their physiological process. Further, development and maintenance of plankton communities clearly depend on circulation patterns which transport cells and influence sedimentation and resuspension. At small scale, there may be direct and indirect effects of water motion on phytoplankton life. Turbulence may exert direct effects of potential ecophysiological importance on plankton, namely, through interaction with the transport of molecules in and out of the cells (Karp-Boss et al. 1996), causing mechanical cell damage (Thomas et al. 1997 and references there in) and morphological adaptations (Zirbel et al. 2000), or affecting contact rates (and hence coagulation and predation processes) between suspended plankters (Rothschild and Osborn 1988, Kiørboe 1997). In the case of motile cells, swimming behavior combines with water motion to modulate the trajectories of the organisms in the water column (Kamykowski et al. 1998 and references there in). The aim of this study is to review information about the direct effects of small-scale turbulence on the ecophysiology of phytoplankton. First, some relevant fluid dynamics concepts and a brief description of the most common experimental setups used to generate turbulence in the laboratory is presented. The main body of this review includes sections on boundary layer related effects of small-scale turbulence (e.g. nutrient uptake) and, on direct effects of turbulence on phytoplankton cells and colonies (shape, behavior, motility, bioluminescence, growth and cell division), with, given their particular response, particular mention of dinoflagellates. We will finish our account trying to build a bridge between laboratory and nature. Without it, all the research described would have only limited meaning. We will consider some clues about the implications of small-scale turbulence for the phytoplankton in its natural environment, including cell growth in low-nutrient enviroments and the dynamics of algal blooms.

BASIC FLUID DYNAMICS CONCEPTS TO UNDERSTAND THE TURBULENT ENVIRONMENT OF THE PHYTOPLANKTON CELLS What is Turbulence? Massaguer (1997) stated that “a flow is said to be turbulent when it has no simple structure, neither in time nor in space. This is a negative definition, and thus a source of confusion”. There is indeed no generally accepted

Effects of Small-Scale Turbulence

"$

definition of turbulence. Turbulent flow patterns have been characterized by expressions like “a subtle mixture of order and chaos” (Nelkin 1992) or “a puzzling blend of order and disorder” (Vassilikos 1995). Turbulence is a property of the motion, not of the fluid, and two of its characteristics are randomness and diffusivity (Tennekes and Lumley 1972). Several theoretical approaches have proved useful for our understanding of turbulence and its effects, but this subject continues to be an unsolved problem, in the sense that there is not a clear understanding of the observed phenomena (Nelkin 1992). However, recent improvements in computer performance have allowed realistic direct simulation of turbulent flows at low Reynolds numbers. Appropriate references for more information on turbulence theory or its ecological implications are Tennekes and Lumley (1972) and Mann and Lazier (1991).

The Dissipation of Kinetic Energy. Viscosity Kinetic energy is imparted to the world ocean by forcing such as heat exchanges, wind and tides, at various spatio-temporal scales. The energy of large eddies is transmitted progressively to smaller and smaller scales until the motion is resisted by the molecular viscosity of the water. A common assumption, as long as the eddies are large, is that through this process, called the energy cascade, no energy is dissipated. Eventually, when the eddies reach a small enough size, the Kolmogorov length scale, molecular viscosity becomes important and acts to resist and damp the gradients in velocity. Through this smoothing of the flow by viscosity, the turbulent energy is finally converted to heat and dissipated. The amount of energy transferred down the cascade is equal to the energy dissipated by viscosity, which can be expressed by the rate of turbulent energy dissipation, e, [L2T–3].

Inertial and Viscous Forces: The Reynolds Number The relative importance of inertial versus viscous forces in a flow is often represented by the Reynolds number. The magnitudes of interest are the mean speed of the fluid, u, a characteristic length scale, d, and the kinematic viscosity of the fluid, n (which represents the viscous force). For water, nª10–6m2s–1. A flow becomes turbulent above a critical value of the Reynolds number, which must be determined for specific cases. The Reynolds number works out to be the velocity u times a characteristic dimension of the flow, d, divided by n: Re = ud/n

"$


The Reynolds numbers designed initially for fluid elements can be applied to organisms in the water. The inertial force that is necessary to accelerate the body to its present velocity u, or to stop the body travelling at a constant speed under its own inertia. High Reynolds numbers indicate that inertial forces dominate their life and that viscosity can be ignored; the opposite is reflected by low Reynolds values. What are the Reynolds numbers estimated at the size scale of phytoplankton? Vogel (1981) and Okubo (1987) calculated Re for living organisms from bacteria to large mammals, considering the length scale and the corresponding sinking or swimming velocity of the organism (Fig. 13.1). For example, a small fish with d = 10 cm swimming at u = 1 ms–1, has a Re = 105 and hence inertial forces dominate. However, at the phytoplankton cell domain, a 50 mm long organism swimming at 10 mms–1, has a Re of 10–4 indicating that inertial forces can be ignored. 10 M

8

M M M F H F F

log Re

6 4

A A A A Z

2

A

Z

0

Z R

–2

P P

P

–4 B

P

–6 –6 –4 –2 0 2 log l (cm)

4

Fig. 13.1 Variation of the Reynolds number (Re) with organism size. M: mammals, F: fish, A: amphipod, Z: zooplankton, R: protozoa, P: phytoplankton, B: bacteria, H: man. Redrawn from Okubo (1987), with permission.

Kolmogorov Length Scales Which is the size of the smallest turbulent eddies? Again it is determined by the relative importance of the two competing forces (Mann and Lazier 1991): - the force due to viscosity, which works to remove fluctuations in velocity and is represented by n


"$!

- the inertial force associated with the turbulent motions, which tends to create velocity fluctuations; it is represented by the turbulent kinetic energy dissipation rate, e (W kg–1 or m2s–3) The smallest length scale of the energy cascade (where there is virtually no energy), known by the Kolmogorov lenght scale (ln) is expressed as: ln = C(n3/e)1/4 The factor C cannot be determined by dimensional analysis. It is often taken as 1, but 2P appears to be a more realistic value (Mann and Lazier 1991, Gargett 1997). In natural aquatic media, the smallest values of ln ª (n3/e)1/4 are of the order of a mm (Table 13.1). The large scales are very important from Table 13.1 A: Rates of turbulent energy dissipation (e), Kolmogorov length scales (lv) and strain rates (g) for natural (A1) and experimental (A2) systems. B: Average turbulent energy dissipation in the upper 10 m, generated by wind (taken from Kiørboe and Saiz 1995; calculated according to the model of MacKenzie and Legget 1993). References (Ref): 1. Reynolds (1994); 2. Kiørboe and Saiz (1995), with data compiled by Granata and Dickey (1991) and MacKenzie and Legget (1993); 3. Lakhotia and Papoutsakis (1992); 4. Thomas and Gibson (1990); 5. Dempsey (1982). A1. Natural systems

e (cm2 s–3)

lv (cm)

g (s–1)

Ref.

Lakes Open ocean Shelf Coastal zone Tidal front Tidal estuary (Severn)

0.014 10–2–4 10–2 10–6–10–2 10–3 –10–2 10–3–100 10–1

0.29 –0.07 1–0.10 0.18–0.10 0.18–0.03 0.06

0.1–2.1 0.01–1 0.32–1 1.0 –10 3.16

1 2 2 2 2

5 10–2 –5.5

0.07 –0.02

2.2 –2.4

1

A2. Experimental systems

e (cm s )

lv (cm)

g (s )

Ref.

Animal cell cultures Couette cylinders Range Effect threshold Paddle stirrer

0.6–1

0.036–0.031

7.7–11

3

0.045–164 0.18 0.096–0.14

0.008–0.066 0.048 0.062–0.052

2.2–132 4.4 3.1–3.7

4 5

B. Model Wind speed (m s–1)

e (cm2 s–3)

lv (cm)

g (s–1)

5 10 15 20

1.7 10–3 1.5 10–2 4.9 10–2 8.4 10–2

0.16 0.09 0.07 0.06

0.4 1.2 2.2 2.9

2 –3

–1

"$" Algal Cultures, Analogues of Blooms and Applications the point of view of transport, but direct effects on phytoplankton can be expected to occur at small scales. It has been shown by measurements that the maximum shear resides at relatively small scales of the order of (3 - 4)ln (Gargett 1997). In general, mathematical treatment of turbulence considers Newtonian fluids, with constant molecular viscosity. However, viscosity and elasticity of sea water may vary in response to biological factors such as mucus excretion by phytoplankton. These changes could have theoretical and ecological implications (Jenkinson and Biddanda 1995). Manipulation of fluid viscosity has also been used for experimental purposes and for protecting cell cultures against hydrodynamic stress (García Camacho et al. 2001). It has also been pointed out that movement of animals could increase turbulent dissipation rates (Farmer et al. 1987).

The Turbulent Environment of Phytoplankton Cells If phytoplankton are smaller than the Kolmogorov length scale, what are the relevant characteristics of the velocity field, concerning direct effects on algae? At spatial scales of the size of phytoplankton organisms (< 200 mm), which are typically smaller than the Kolmogorov length scale (Table 13.1), velocity fluctuations have been suppressed by viscosity and the velocity field should present a nearly linear (laminar) shear (velocity gradient) varying randomly in time with a typical shear magnitude determined by (Lazier and Mann 1989, Jiménez 1997, Gargett 1997): du/dz = Csh(e/n)1/2 where du/dz is the velocity gradient and Csh is a proportionality factor. According to this mathematical expression, the effects of shear will depend not only upon the cell size, which determines the length scales for which velocity gradients will be relevant for the organism, but also on turbulent kinetic energy dissipation rate, e. In terms of classic fluid dynamic parameters (Tennekes and Lumley 1972), the main characteristics of the velocity field, concerning direct effects on algae, can be described by: • the turbulent kinetic energy dissipation rate, e [L2 T–3]. • the rate of strain parameter, g [T–1], which is proportional to a velocity gradient or shear and represents the magnitude of the deformation rate due to mean velocity gradients in the flow fields. g is dimensionally equivalent to (e/n)1/2. • the shear stress, t ª mdu/dz (MLT–2), where m= rn is the dynamic viscosity (Thomas and Gibson 1990).


"$#

An important feature of the oceanic turbulence is its intermittency, which implies that the turbulent shear is not evenly distributed in space (Jou 1997). Indeed, strong gradients are more common than they should be in the case of a Gaussian distribution. At small scales, intermittency is related to the presence of strong coherent vortices, with diameters of the order of ten times the Kolmogorov scale, but much longer lengths. From the point of view of plankton, these events should be very intense but calculations show that their probability is small. At relatively large scales, intermittency may be induced by factors like variable wind forcing and breaking of surface waves.

EXPERIMENTAL STUDY OF THE EFFECTS OF SMALL-SCALE TURBULENCE Quantification of Turbulence It is not easy to quantify turbulence either in nature or in the laboratory. A thorough review of this subject, which is out of the scope of this work, can be found in Peters and Redondo (1997) and a practical example in Estrada and Peters (2002). One of the relevant parameters in terms of quantification of turbulence is e, the turbulent kinetic energy dissipation rate [L2T–3], although some important properties of natural turbulence spectra are not captured by this single parameter. Useful conversion factors for e are 1 W Kg–1 = 10000 cm2s–3 = 1025 W m–3 (approx. for seawater of salinity 35 and 20ºC). e is usually estimated by a variety of methods, including mathematical models which derive turbulent energy dissipation from wind speed, image analysis techniques (particle tracking, Particle Image Velocimetry) and spectral analysis of flow velocity fluctuations. Time series of velocity can be obtained by means of laser Doppler velocimeters and acoustic Doppler velocimeters, among others.

Experimental Setups to Generate Turbulence The observation and adequate sampling of natural phenomena constitute the base to build models and hypotheses that often can only be tested in the laboratory. The increased availability of reliable sensors (Gargett 1997) has allowed a dramatic improvement of our understanding of the distribution of microscale velocity variations at sea. However, many biological variables of interest cannot still be measured with enough spatio-temporal resolution to match the physical data. Direct or indirect (e.g., using wind velocity) estimations of turbulence intensity along with biological variables have revealed the links between turbulence and biological phenomena such as red tides (Berman and Shteinman 1998), enhanced feeding rates between

"$$ Algal Cultures, Analogues of Blooms and Applications cod larvae and their prey (Ottersen and Sundby 1995) or vertical distribution of dinoflagellate taxa (Sullivan et al. 2003). However, given the difficulties of controlling turbulence in situ, testing of the physiological and behavioral effects of fluid motion on plankton, has been mostly carried out in the laboratory, using different approaches (e.g. Table 13.2). In any case, it must be noted that it is virtually impossible to recreate oceanic turbulence in the laboratory. One basic aspect is that turbulence is generated externally at sea and internally in experimental containers. Other problems are that the size of the experimental vessels introduces differences in the integral length scales, and that artificially generated turbulence does not approximate the temporal intermittency found in natural conditions (Peters and Redondo 1997, Sanford 1997). Often, the experimentally generated turbulence levels are higher than in situ values. Thus, the limitations of each experimental setup must be kept in mind and any extrapolation to natural conditions must be done with caution. Some comments about the setups most commonly used to generate turbulence in the studies conducted on phytoplankton (Table 13.2) are given next: (a) Oscillating grids. An example of this experimental setup, designed by Dr. F. Peters (Estrada and Peters 2002), is illustrated in Fig. 13.2a. Turbulence is generated by vertically oscillating grids within a plexyglass cylindrical vessel. The grids are made of stainless steel bars (0.38 cm thick) coated with a plastic polyamide; they have a diameter of 12.9 cm, and a mesh size of 1.42 cm. Movement is provided by 4 independently controlled variable speed AC gear-head motors, each acting on 2 grids. This allows for simultaneous assessment of 4 oscillating frequencies in 8 vessels. For instance, using 0.5, 1.5, 3.5 and 15 rpm frequencies and corresponding stroke radii of 2, 5, 7, and 7 cm in 2-L vessels, would result in 4 different turbulence levels with turbulent kinetic energy dissipation rates (e) of 0.0001, 0.005, 0.05, and 1 cm2 s–3, respectively (calculated according to Peters and Gross 1994). These values would correspond to naturally occurring turbulence levels in the upper 10 m of the ocean in situations of almost no wind, moderate breeze, moderate gale, and storm, respectively (MacKenzie and Leggett 1993, Kiørboe and Saiz 1995, Petersen et al. 1998). Recently, this experimental setup, applied to 2 or 15-L containers, has been used within the research project NTAP (http://www.icm.csic.es/bio/projects/ntap). (b) Shakers. For convenience, shakers were the preferred devices in early biological experiments (e.g. Table 13.2). The shaking motion can be orbital or reciprocal (back and forth movement). Turbulence is produced by wall


"

%$Table 13.2 Literature on effects of turbulence on different species of phytoplankton, most of them dinoflagellates. The organism size is the one indicated in each particular study. When the authors did not provide it, a cell size range obtained from taxonomic guides (Fogg et al. 1973, Tomas 1997) is indicated in brackets, except when no available data (n.a.) have been found. Effects are coded as: no effect (=), increased growth (›), decreased growth (fl), growth inhibition (flfl), and cell death (ƒ). Species Non–dinoflagellate algae Anabaena cylindrica Anabaena spiroides Asterionellopsis glacialis Brachiomonas submarina Chaetoceros curvisetus Ditylum brightwellii Dunaliella tertiolecta Dunaliella viridis Isochrysis galbana I. galbana Olisthodiscus lutheus Phaeodactylum tricornutum Scenedesmus sp. Scenedesmus quadricauda Skeletonema costatum S. costatum Spirulina Stichococcus sp. Thalassiosira sp. Thalassiosira weissflogii

Reference

Size (mm)

Fogg and Than-Tun 1960 [7] Volk and Phinney 1968 n.a. Thomas et al. 1997 [8–12 ¥ 30–150] Savidge 1981 27.5 Schöne 1970 [4–20] Pasciak and Gavis 1975 [25–100 ¥ 80–130] Berdalet and Estrada 1993 9 Aguilera et al. 1994 13 Berdalet and Estrada 1993 6 Havskum 2003 6 Berdalet and Estrada 1993 15 Savidge 1981 20 Bakus 1973 n.a. Hondzo et al. 1998 16 Schöne 1970 n.a. Thomas et al. 1997 n.a. Richmond and Voshak 1978 [7] Bakus 1973 n.a. Berdalet and Estrada 1993 8 Berdalet and Estrada 1993 8

Dinoflagellate

Reference

Gonyaulacales Alexandrium catenella Alexandrium fundyense A. fundyense Alexandrium minutum A. minutum A. minutum Alexandrium tamarense A. tamarense Ceratium fusus Ceratium tripos CL1 C. tripos CL3 C. tripos Ceratocorys horrida Crypthecodinium cohnii C. cohnii C. cohnii

Sullivan and Swift 2003 Juhl et al. 2001 Sullivan and Swift 2003 Berdalet and Estrada 1993 Chen et al. 1998 Berdalet et al. 2001 Sullivan and Swift 2003 White 1976 Sullivan and Swift 2003 Sullivan and Swift 2003 Sullivan and Swift 2003 Havskum et al. 2004 Zirbel et al. 2000 Tuttle and Loeblich 1975 Tuttle and Loeblich 1975 Yeung and Wong 2003

Size (mm) 30 30 17 [30] 17 30 [30 – 35] 150–300 150–300 150–300 200 [150 – 200] [15 – 20] [15 – 20] 15 – 20

Setup

Effect

Shaker

fl fl › fl fl fl = › = =/› = › ›/fl fl › › › fl/› › ›

Plankton wheel Oscillating grid Bubbling Couette cylinder Shaker Bubbling Shaker Oscillating grid Shaker Oscillating grid Bubbling Couette cylinder Bubbling Plankton wheel Paddle wheels Bubbling Shaker Shaker

Setup Oscillating rod Couette cylinder Oscillating rod Oscillating grid Couette cylinder Shaker Oscillating rod Shaker Oscillating rod Oscillating rod Oscillating rod Oscillating grid Shaker Shaker Stirring Shaker

Effect = fl › = fl fl = fl fl flfl flfl =/fl fl ƒ = flfl

Contd.

"$& Algal Cultures, Analogues of Blooms and Applications Table 13.2 Contd. Size (mm)

Dinoflagelletes

Reference

Fragilidium subglobosum Heterocapsa triquetra H. triquetra Lingulodinium polyedrum L. polyedrum L. polyedrum L. polyedrum L. polyedrum L. polyedrum L. polyedrum L. polyedrum Oxyrrhis marina Peridinium gatunense (as P. cinctum f. westii) Pyrocystis fusiformis Pyrocystis noctiluca Scrippsiella trochoidea Gymnodiniales Akashiwo sanguinea A. sanguinea A. sanguinea A. sanguinea Amphidinium carterae Gymnodinium sp. Gymnodinium catenatum Gyrodinium sp. Prorocentrales Prorocentrum micans P. micans Prorocentrum triestinum P. triestinum

Havskum et al. 2004 Dempsey 1982 Yeung and Wong 2003 Gibson and Thomas 1995 Juhl et al. 2000 Juhl et al. 2000 Juhl and Latz 2002 Sullivan and Swift 2003 Thomas and Gibson 1990 Thomas and Gibson 1990 Thomas et al. 1995 Havskum 2003 Pollingher and Zemel 1981

15 – 17 [30 – 35] 35 35 35 30 [30 – 35] [30 – 35] [30 – 35] 16 50

Sullivan and Swift 2003 Sullivan and Swift 2003 Berdalet and Estrada 1993

500 [150 – 350] 20

Berdalet 1992 Berdalet 1992 Thomas and Gibson 1992 Tynan 1993 Galleron 1976 Berdalet et al. 2001 Sullivan and Swift 2003 Sullivan and Swift 2003 Berdalet and Estrada 1993 Tynan 1993 Berdalet and Estrada 1993 Berdalet et al. 2001

Setup

Effect

Oscillating grid Paddle Shaker Couette cylinder Couette cylinder Shaker Shaker, Couette Oscillating rod Couette cylinder Shaker Couette cylinder Oscillating grid Shaker

=/›

Oscillating rod Oscillating rod Shaker

= flfl fl

36 36 [40 – 75] 35 [10 – 15] 9.8 60 60

Oscillating grid Shaker Couette cylinder Couette cylinder Shaker or bubbling Shaker Oscillating rod Oscillating rod

ƒ flfl fl flfl ƒ = › =

18 [30 – 50] 11 13

Shaker Couette cylinder Shaker Shaker

fl flfl fl fl

40 – 60

flfl flfl fl fl fl › flfl flfl flfl =/› fl

effects in the flask (Peters and Redondo 1997). The types of vortices shed by an oscillating boundary layer (such as a flask with water inside) are symmetrical, advancing into the body of the fluid from the boundaries. Therefore, the level of turbulence is stronger near the walls of the containers. In practice, this setup is used for capped vessels of volumes smaller than 4-L. Unfortunately, the turbulence intensity generated by orbital shakers is not easy to estimate or measure, and was not determined in most of the studies that are reported in Table 13.2. Only Zirbel et al. (2000) used


"$'

Front View

Fig. 13.2a Examples of two commonly used setups to generate turbulence in the laboratory. vertically oscillating grid. sophisticated digital particle image velocimetry techniques to estimate turbulence in their particular set-up. Recently, measurements with an acoustic Doppler velocimer indicated that the e used by Berdalet (1992), Berdalet and Estrada (1993) and Berdalet et al. (2001) would have been about 1–2 cm2s–3 (F. Peters and O. Guadayol, personal communication). (c) Couette cylinder (Fig. 13.2b). This device consists of two coaxial cylinders which leave a small gap that is filled with the fluid. In practice, the gap between the two cylinders does not exceed 5 mm and the total volume of the algal medium is < 1-L. The two cylinders (outer and inner with radii, respectively, ro and ri in Fig. 13.2b) are rotated at different angular speeds (Wo and Wi, respectively), giving shear rates which increase with the difference in angular speeds and can be easily calculated. Under appropriate dimensions and speeds, this setup creates a laminar shear field between the two sheets, although the whole set of flow regimes that can be obtained is complex (Tritton 1988). Theoretical reasoning indicates that, for lk typical of non-stormy conditions, most planktonic organisms are too small to be influenced by the random fluctuations of the velocity field and are only affected by the remaining laminar shear field (Lazier and Mann 1989,

"% Algal Cultures, Analogues of Blooms and Applications

ro

riWi ri

roWo

h

O

Fig. 13.2b Examples of two commonly used setups to generate turbulence in the laboratory. Couette cylinders: the relevant parameters and variables to calculate e are the radii of inner and outer cylinders (ri and ro, respectively) and their angular velocities (Wi and Wo, respectively) and, the height of the cylinder (h). Thomas and Gibson 1990, Shimeta et al. 1995). Thus, the use of Couette cylinders in experiments with plankton has been popularized by the uniformity of its flow conditions and by the ready calculation of shear parameters for different settings without the need to do any physical measurements. Detailed information about the characterization of the turbulence generated by different experimental setups, their advantages, limitations and uses can be found in Peters and Redondo (1997).

DIRECT EFFECTS ON NUTRIENT UPTAKE Background. The Problem of the Boundary Layers As any solid surface submerged in water, phytoplankton cells generate around them a layer of reduced fluid movement or boundary layer, due to the stress or drag effect exerted by the solid. Viscosity causes the average speed of the flow and the size of the turbulent eddies to decrease from their values


"%

in the open water to zero at the boundary. This creates problems for organisms which require the transport of nutrients and metabolites to and from the cells, including gas exchanges. At the small scale domain of the phytoplankton cells (£ 1 mm), those fluxes are controlled by molecular diffusion, which is the slow mixing caused by random motion of molecules and is much slower than turbulent diffusion over long distances. Thus, if nutrients cannot be appropriately supplied by molecular diffusion, the possibility of diffusion limitation of nutrient uptake increases (Fig. 13.3). The

Fig. 13.3 Scheme of the distribution of nutrient concentrations around the cell under two different flow regimes: stagnant water (above) and uniform flow created when cells sink (from X to–X) in stagnant water (below) according to Karp-Boss et al. (1996). When the cell is moving from right to left, the flow is from left to right. The concentration field at distances (in the three spatial dimmensions, X/r0, Y/r0 or Z/r0) up to 10 cell radii from the center of the cell is illustrated in the left panels, and at shorter distances of 2 cell radii in the right ones. In each flow regime, the Péclet (Pe) and Sherwood (Sh) numbers have been obtained with analytical approaches described by Karp-Boss et al. (1996). The extent of the diffusive boundary layer reaches up to 9 cell radii from the cell surface in absence of fluid motion (above). The boundary layer is distorted by the uniform (or shear, not shown) flow and the concentration gradient steepens in certain regions. Given that transport is controlled by the thinnest zones of the boundary layer, nutrient fluxes can be enhanced by the swimming-generated uniform (or shear, not shown) flow compared to the diffusion limitation experienced by non-motile cells in stagnant water. Redrawn with permission of the authors.

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thickness of a boundary layer is reduced in proportion to the speed of the water moving past it. Relative motion of cell and fluid, either originated by active swimming or sinking of the organism, or by motion of the fluid, will have an effect on renewing the depleted zone. Given this conceptual scenario, what will experience a motionless phytoplankton cell suspended in the water and taking up nutrients? Can cell sinking or swimming help to overcome nutrient limitation in nature? Can turbulence affect nutrient uptake? These subjects have been mostly considered at a theoretical level, rather than experimentally.

Sinking and Swimming. Theoretical Studies A first and classical study was conducted by Munk and Riley (1952). These authors derived formulae for the rates of nutrient absorption by phytoplankton cells approximating the shapes of spheres, discs, cylinders and plates and considered the effect of the movement of the water relative to sinking cells. They concluded that for phytoplankton cells measuring ≥ 20 mm in diameter, absorbtion was favored by a rapid sinking rate. Later, Pasciak and Gavis (1974) and Gavis (1976) discussed diffusion limitation in conjunction with conventional Michaelis-Menten nutrient dynamics, specifically, considering the maximum uptake rate (Vm, mmol cell–1 h–1) and the half saturation constant (K, mM). For small cells and nutrient concentrations in the environment not many times greater than K, they found that diffusion can exert a severe limitation on cell metabolism. Gavis (1976) introduced the effect of motion relative to the water into the calculations. Using published rates of sinking of phytoplankton cells, he concluded that an appreciable increase in nutrient uptake rate would occur only for relatively large cells (100 mm) and low nutrient concentrations. In the optimum case, sinking would only alleviate about 30% of diffusion limitation. With respect to swimming, only large cells (100 mm) at a speed of about 3 body lengths per second could slightly increase their nutrient uptake. Gavis (1976) concluded that motion (either swimming or sinking) could reduce diffusion transport limitation, but not eliminate it completely. However, motion might just be sufficient to give one species a competitive edge over another. The effectiveness of swimming or sinking was also approached theoretically by Berg and Purcell (1977) and Purcell (1977). Their conceptual model was combined by Sommer (1988) with data on size and swimming velocities of 19 species of flagellates. He concluded that swimming could increase the nutrient flux by only 5–30% in the case of cells £ 5 mm, and


"%!

double or even triple it in the case of flagellates ≥ 50 mm in diameter. Sinking (without swimming) would not benefit the nutrient uptake of the small cell and would increase the nutrient flux to the large cell only by about 30–40%. Mann and Lazier (1991), who summarized the available studies at that time, concluded that viscosity dominated the world of small organisms in the 1–10 mm range, so that diffusion was faster than water movement in supplying nutrients through their boundary layer. Thus, these organisms would move in order to find better concentrations of nutrients in the environment rather than to reduce their diffusion limitation. Swimming could be effective to increase nutrient uptake for flagellates > 10 μm. The advantage was greater for large cells (> 50 mm) because their smaller ratio of surface area to volume caused stronger nutrient limitation. The question was revisited by Karp-Boss et al. (1996), who proposed improvements and extensions of previous solutions. Their approach was based on deriving functional relationships between two non-dimensional parameters: the Péclet and the Sherwood numbers (Fig. 13.4). The Péclet number, Pe, indicates the effectiveness of advective (i.e. due to the body movement relative to the water) transport compared with diffusive transport through the fluid over the specified length scale: - Lu/D Pe ~ where L and u are, respectively, the characteristic dimension (e.g. cell radius) and velocity (sinking and swimming velocities in the case of motile cells), and D is the diffusion coefficient of the nutrient (Table 13.3). Diffusion dominates at Pe < 1, and advection dominates when Pe > 1. The Sherwood number, Sh, which indicates the relative enhancement of nutrient delivery due to advection, is the ratio between the solute flux arriving at the cell surface in the presence of fluid motion (Q) and the purely diffusional flux (QD): - Q/QD Sh ~ From the proposed relationships between Sh and Pe, Karp-Boss et al. (1996) concluded that for both swimming and sinking, non-dimensional mass transfer to the cell depended directly (but not necessarily linearly) on cell size and cell velocity relative to the water and inversely on the diffusion coefficient. From their calculations on steady flows and spherical cells, a minimum cell radius of about 20 mm was found to be necessary in order for sinking or swimming to produce a relevant increase of nutrient flux with respect to the stagnant-water case; a 60 mm diameter cell would experience a 50% increase in nutrient flux when moving. In contrast to Gavis (1976), Karp-Boss et al. (1996) pointed out that it was not easy to discriminate

"%" Algal Cultures, Analogues of Blooms and Applications 20 A. Steady, Uniform flow (sinking) 10 Eq. 17 Sh

5

Eq. 18 Eq. 16 1

0.001

0.01

0.1

1 Pe

10

100

1000

20

10

B. Sub-kolmogorov shear from dissipating turbulence (neutrally buoyant, non-motile cells) Eq. 49

Sh

5

IP-a Eq. 48 1

0.001

IP-b

0.01

0.1

1 Pe

10

100

1000

Fig. 13.4 Sherwood number (Sh) as a function of Péclet number (Pe) in different flow situations calculated by Karp-Boss et al. (1996). In their work, the authors used different equations (Eq. 16, 17, 18, 48 and 49) and interpolations (IPa and IPb) to generate the different curves, depending on the range values of Pe. We address the reader to the original reference for the whole understanding of the study. whether swimming was more beneficial than sinking in enhancing flux because many other factors such as the cost of swimming, the cell size and even cell rotation could be implied in the process.

Effects of Turbulence. Theoretical Studies In their already mentioned study, Munk and Riley (1952) introduced also the effect of turbulence on diffusive layers around small organisms and


"%#

Table 13.3 Reynolds (Re) and Péclet (Pe) numbers for swimming and sinking phytoplankton (from Karp-Boss et al. 1996). References (Ref.): 1. Kamykowski et al. 1992; 2. Sommer 1988; 3. Smayda 1970. Organism

Prorocentrum mariae-labouriae Gyrodinium dorsum Dinophysis acuta Coccolithus huxleyii Coscinodiscus wailesii

Cell radius Swimming Sinking (µm) speed speed –1 (µm s ) (µm s–1)

Re

Pe

Ref.

6

171

0

1 ¥ 10–3

1

1

16.5 33 5 to 6.5

330 500 0

0 0 3 to 15

0

81 to 347

5.4 16.5 1.5 ¥ 10–2 to 9.7 ¥ 0–2 5.7 to 24

1 2 3

70

5.4 ¥ 10–3 1.6 ¥ 10–2 1.5 ¥ 10–5 to 9.7 ¥ 10–5 5.7 ¥ 10–3 to 2.4 ¥ 10–2

3

concluded that it did not essentially modify their results for laminar flows. Lazier and Mann (1989) and Mann and Lazier (1991) reviewed these investigations taking into account that turbulent motions in natural aquatic environments, across distances of £1 mm, approximate a linear velocity gradient or shear. Using the experimental results of Purcell (1978), which relate shear to the increase in the diffussive flux, Lazier and Mann (1989) concluded that strong turbulence (dissipation rate 10–6 W kg–1) would produce only a ca. 2% increase in flux for motion-less cells 100 mm in diameter. In weak turbulence, only cells ≥ 1 mm will be affected. Fig. 13.5 shows the hypothetical profiles of the velocity fields at the organisms scale that inspired their calculations.

Fig. 13.5 (a) Hypothetical velocity profile through 30 mm of the ocean showing the approximate size of the smaller fluctuations. (b) A magnification of the profile in (a) to illustrate the approximate constancy of the shear over scales of ca. 1 mm. (c) A further magnification of the velocity profile to illustrate the velocity field close to a spherical organism of radius 1 mm. From Mann and Lazier 1989, (with permission).

"%$ Algal Cultures, Analogues of Blooms and Applications The effects of fluid motion on nutrient fluxes to planktonic cells were revised again by Karp-Boss et al. (1996), who found that turbulence effects could be an order of magnitude greater than previously postulated. Chains of diatoms or filamentous cyanobacteria, with lengths approaching the Kolmogorov scale could be expected to experience enhanced nutrient flux with turbulence. Another conclusion, of potential importance with respect to motile cells such as dinoflagellates, was that rotation, whether caused by active swimming or by shear from fluid motion, would reduce the rate of nutrient transfer relative to a non-rotating cell, except when the axis of rotation was parallel to the direction of flow (i.e. the direction of swimming or of shear). Shape of the cells was another important factor, and its dependence on cell size varied for different motion environments. Magar et al. (2003) added a new aspect considering in their model the hydrodynamic interaction between swimming cells which also interferes with nutrient diffusion. Very recently, Jumars et al. (2004) performed an step forward considering that molecular diffusion times depends nonlinearly on diffusion distances. Using new numerical and analytical approaches they found that, contrary to their previous study (Karp-Boss et al. 1996) steadily swimming cells will in general experience more shear thinning than sinking cells at the same speed of translation, and thus experience increased nutrient fluxes. The relative importance of different diatom shapes and chain structures on nutrient uptake under turbulent conditions was considered by Pahlow et al. (1997). Their model predicted that diatom chains with gaps would obtain greater nutrient supply than compact chains and solitary cells. Although these studies still require experimental testing, they may provide support for suggestions concerning the possible ecophysiological role of spines, horns and other complex shapes. As has been pointed out by Karp-Boss et al. (1996), up to now, most theoretical work has been carried out assuming spherical cells under steady flows. The extension to other shapes and more realistic flow fields is still an open line of research.

Sinking and Swimming. Experimental Studies There have been very few experimental studies of the effect of fluid motion on nutrient uptake by phytoplankton. Pasciak and Gavis (1975), using a Couette device, found that nitrate uptake by Ditylum brightwellii, a relatively large diatom, increased about 5% at 5 rad sec–1. Canelli and Fuhs (1976) examined the effect of the sinking rate of two diatoms (Thalassiosira spp.) on


"%%

phosphorus uptake using, as an analogy, a constant flow of medium through a filter in which the cells were held. However, according to KarpBoss et al. (1996), this approach could not be valid to derive conclusions concerning natural fluid motions in the water column. Savidge (1981) examined the effect of turbulence, obtained by means of an oscillating grid, on nutrient uptake by the diatom Phaeodactylum tricornutum. In this case, nitrate uptake was enhanced by stirring, but phosphate uptake and carbon fixation (as measured by the 14C method) decreased. These results show that effects of turbulence on nutrient uptake may be more complex than predicted by relatively simple theoretical considerations and emphasize the need of experimental research.

Gas Exchange The concept of boundary layer limitation has been mostly applied to major inorganic nutrients but it is also relevant for gas exchange. Gavis and Ferguson (1975) suggested that it may concern CO2 utilization. Evidence for the relevance of CO2 transport limitation was provided by Riebesell et al. (1993) and Chen and Durbin (1994). Wolf-Gladrow and Riebesell (1997) derived a simple model simulating diffusional transport and reactions within the carbonate system around microplankton. Considering an spherical geometry and the size scale of microalgae, these authors concluded that the flux of CO2 through the boundary layer to the microalgal cell surface was entirely dominated by diffusional transport, with reactions within the carbonate system generally contributing less than 5% of the total CO2 supply.

DIRECT EFFECTS OF SHEAR ON CELLS AND COLONIES Effects on Cell Shape The shape of phytoplankton cells and colonies may interact with small scale water turbulence and other environmental properties in a variety of ways (Margalef 1978). However, apart of some basic concepts, related, for example to the effects of organism size or motility, there is little solid ground to interpret the functional significance of the unique and elaborate forms of many phytoplankton taxa (Sournia 1982). In the case of dinoflagellates, physical and chemical factors hypothesized to affect dinoflagellate cell morphology include nutrient conditions (Peters 1934, Margalef 1997) and water temperature (Dowidar 1972). Morphological specializations such as

"%& Algal Cultures, Analogues of Blooms and Applications horns or spines constitute an anti-predation strategy, but they can also increase form drag and reduce the cell sinking (Walsby and Xypolyta 1977, Takahashi and Allan 1984, Alexander 1990). Evidence of flow-induced morphological transformation in the marine photosynthetic dinoflagellate Ceratocorys horrida, a cosmopolitan thecate species found in low nutrient, warm surface waters, was provided by Zirbel et al. (2000). Cells cultured under still conditions possess six large spines, each almost one cell diameter in length. When gently agitated on an orbital shaker, population growth was inhibited and a smaller, short-spined cell type appeared. The reduction in cell size was likely related to the 39% mean decrease in vacuole size. Short-spined cells were first observed after 1 h of agitation at 20°C; after 8 to 12 d of continuous agitation, long-spined cells were no longer present. The morphological change was completely reversible. At the highest level of turbulence tested, there was negative population growth in C. horrida cultures, indicating that fluid motion caused cell mortality. Under still conditions, the sinking rate of individual long-spined cells was significantly less than that of short-spined cells, even though the former are larger and have a higher cell density, suggesting that the long spines of C. horrida reduce cell sinking. Given that oceanic turbulence can inhibit dinoflagellate cell swimming, as suggested by laboratory studies (Pollingher and Zemel 1981, Thomas and Gibson 1990, Zirbel et al. 2000), the authors hypothesized that cells may sink from near-surface turbulent layers into more quiescent deeper water. Shorter spines and reduced swimming would allow cells to sink away from turbulent surface conditions more rapidly. Thus, the ecological importance of the morphological change could be to avoid conditions that inhibit population growth and cause cell damage.

Behavior and Motility In nature, phytoplankton cells regulate their position in the water column by active swimming or buoyancy control (Kamykowski et al. 1988). Motility and migratory behavior up and down the water column may allow a control of both, light regime and nutrient uptake. In addition, migration may be linked to other physiological aspects such as biosynthetic processes, cell cycle and division, predator-prey interactions and cell aggregation. As seen earlier, sinking and swimming may contribute to the nutrient advective flux towards the cells, which can also be affected by turbulence-generated shear.


"%'

Phytoplankton motion and trajectories can also be altered by turbulence, because it can cause the phytoplankton cells or colonies to translate and rotate. Karp-Boss and Jumars (1998) documented the movement of two large chain-forming diatoms, Skeletonema costatum and Thalassiosira nordenskiöeldii, in steady flow. Both taxa underwent periodic rotation upon exposure to simple shear flow, but each with specific trends that differed somewhat from model predictions. The work evidenced that variations in cell densities, morphology and size would affect both the nutrient fluxes and the collision frequencies experienced by the diatom chains. Several studies about the interactions between small-scale turbulence and swimming behaviour have been conducted on dinoflagellates, which have a fast speed capacity compared to the rest of phytoflagellates (Kamykowski and McCollum 1986, Levandowski and Kaneta 1987, Raven and Richardson 1984). Estrada et al. (1987) observed changes in vertical distribution of Prorocentrum micans cells between stirred and unstirred cylindrical vessels (no turbulence measurements were done in that study). Also, Thomas and Gibson (1990) described qualitative changes in swimming behavior of Lingulodinium polyedrum (=Gonyaulax polyedra) upon exposure to simple shear flow (that exceeded natural levels) in Couette cylinders. Whereas cells in the control swam forward vigorously, those under shear lost their trailing flagellum and seemed to spin in place. Chen et al. (1998) observed a reduction of about a 50% in the swimming velocity of Alexandrium minutum when exposed to a turbulent shear of > 7 rad s–1 (also generated with Couette cylinders; corresponding e of ca. 0.4 cm2s–3). Representative modeling efforts that include behaviour of organisms exposed to small-scale (Yamazaki and Kamykowski 1991, Franks and Marra 1995, Kamykowski 1995) and mesoscale turbulence demonstrate complex, often non-intuitive, patterns in space and time. Kamykowski and coauthors combined experimental observations with numerical models (Yamazaki and Kamykowski 1991, Kamykowski 1995, Kamykowski et al. 1988) that simulated the effect of turbulence, light, temperature, gravity and metabolic state on swimming trajectories of dinoflagellates In their approach, these authors calculated the cell trajectories by superimposing swimming velocities of dinoflagellates on a vector model of turbulence. However, Karp-Boss et al. (2000) provided experimental evidence that relatively high shear rates (generated by Couette cylinders) caused changes of swimming trajectories in single cells of Glenodinium foliaceum and both single cells and chains of Alexandrium catenella. Due to the dinoflagellate response to shear, the resulting trajectories could not be predicted by simple

"& Algal Cultures, Analogues of Blooms and Applications superimposition of the ambient flow field and inherent swimming behavior. From their calculations, Karp-Boss et al. (2000) concluded that, under moderate and weak turbulence, cells would be able to reorient themselves and that strong turbulence will be required to overwhelm swimming efforts of A. catenella and G. foliaceum. On larger scales, dinoflagellates would not experiment significant interference from moderate or weak turbulence and could still maintain a preferred orientation under strong vertical mixing of the water column. A 1D numerical model study exploring the interaction between tidal mixing in an estuary and phytoplankton motility was carried out by Ross (2002).

Effects on Phytoplankton Growth and Cell Division Growth of mass algal cultures is favored by agitation through aeration, paddle wheels, magnetic stirrers or orbital shakers (Aguilera et al. 1994, Grobbelaar 1994, Guillard 1975). Below certain threshold, mixing should improve light, gas exchange through the water-air interface (involving pH effects) and nutrient transport regimes. However, inhibition has also been observed in different phytoplankters at diverse turbulence levels. Available literature focusing on the subject is summarized in Table 13.2. The growth of the cyanobacterium Anabaena cylindrica was stimulated up to a shaking rate of 90 oscillations per minute, but decreased at higher shaking rates (Fogg and Than-Tun 1960). Inhibitory effects of agitation on the growth of A. spiroides were reported by Volk and Phinney (1968). Savidge (1981) reported some kind of interaction between nutrient availability and turbulence in the diatom Phaeodactylum tricornutum: cell division times decreased with increasing agitation of phosphate-limited cultures, but they increased in the nitrate-limited ones. Schöne (1979) showed that intensity of the motion of the sea surface (in a categorical scale) was inversely related to chain length of several colony-forming diatoms like Chaetoceros curvisetus and Skeletonema costatum; mechanical breaking-up of chains appeared to be the reason for the diminution of chain length with increasing motion. In the laboratory, air bubbling produced similar effects on S. costatum cultures. Other effects of agitation were more subtle, such as the synchronization of cell division in Skeletonema costatum populations, after only 5 min of agitation per day. Bakus (1973) carried out competition experiments between Scenedesmus spp. (chlorophyte) and Stichococcus spp. (generally classified as a chlorophyte) under varying conditions of irradiance and aeration and found that the advantage in the final yield of Scenedesmus spp. over Stichococcus spp. could be reversed in the presence of a combination of


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turbulent water movements and decreased illumination. Using Couette cylinders and oscillating grids, Hondzo et al. (1998) and Hondzo and Lyn (1999) found that the growth of Scenedesmus quadricauda (measured in terms of chlorophyll concentration), was maximum in stagnant cultures and was inhibited at high Reynolds numbers. The flocs comprised of dead and living cells of algae formed as a result of shear flow. The study proposed a model relating algal growth as a function of Re and e. The main body of the literature about direct effects of turbulence concerns dinoflagellates, in part due to their reported sensitivity and in part because of their link to harmful bloom events. Tuttle and Loeblich (1975) and Galleron (1976) found inhibitory effects of aeration on dinoflagellates although, in contrast, Siegelman and Kycia (1979) could grow large-scale cultures of dinoflagellates under strong aeration. Death and disintegration of Alexandrium fundyense (= A. tamarense) cells when exposed to continuous rotary shaking at ≥ 125 rpm was described by White (1976). Intermittent shaking, even for only 30 min d–1 also caused growth inhibition. Pollingher and Zemel (1981) reported division rate inhibition of Peridinium gatunense (= Peridinium cinctum forma westii) in Lake Kinneret when wind episodes (speed exceeding 3.5 ms–1) occurred between 18:00 and 02:00 h, a time period which corresponded to the premitotic and mitotic phases of cell division. In contrast, intense winds blowing during the day had no effect in the population development. In the laboratory, the relationship between water turbulence and division rate of P. gatunense was tested experimentally using rotary shaking at 100 rpm. Continuous shaking increased cell mortality and decreased division rates with respect to the controls. Intermittent shaking of 2 h d–1 during the dark inhibited cell division, while shaking during the light period did not affect the division process. Berdalet (1992) placed Akashiwo sanguinea (=Gymnodinium nelsonii) cultures on an orbital shaker at 100 rpm and showed that, relative to cells in still control vessels, cell division was prevented, cellular volume increased up to 1.5 times, nuclear morphology was modified and RNA and DNA concentrations per cell increased up to 10 times. The effects of shaking were reversible after 10d of treatment, but after 20 d there was total cell death and disintegration. The same experimental design was tested further with Alexandrium minutum and Prorocentrum triestinum (Berdalet et al. 2001), which showed a similar but not as dramatic response as A. sanguinea. In contrast, a small Gymnodinium sp. was not affected by that shaking intensity. Berdalet and Estrada (1993), studied the response of dinoflagellates and representatives of other phytoplankton groups to orbital shaking using the same experimental design for all species. No significant differences were

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found between cultures of Dunaliella tertiolecta (chlorophyte), Isochrysis galbana (chrysophyte), Olisthodiscus luteus (raphidophyte) and Thalassiosira weissflogii (diatom) unshaken or shaken at speeds between 100–130 rpm. In contrast, shaking at similar intensities caused growth inhibition of several dinoflagellates, namely, Prorocentrum micans, P. triestinum and Scrippsiella trochoidea. Dempsey (1982), using a six paddle stirrer, found that Heterocapsa triquetra and Skeletonema costatum appeared to show some inhibition of growth rate at turbulence levels of the order of 10–2 watts m–3. This study constitutes one of the early attempts to reproduce natural levels of turbulence in the laboratory and measure their effects on phytoplankton, although the author had several difficulties with the apparatus, ranging from toxic effects of the stainless steel paddles to problems in maintaining the low rotation rates needed (6–7 rpm). In order to mix the cells prior to sampling, Dempsey had to apply, for a short time, rotation speeds that were more than 10 times greater than the experimental ones. She applied a presumably similar agitation to the control cultures, using a magnetic stirrer, but was unable to make precise comparisons. This kind of problem may plague many experimental studies. Thomas and Gibson (1990, 1992) used Couette devices to expose cultures of Lingulodinium polyedrum (= Gonyaulax polyedra) to known shear rates. They set the growth inhibitory levels of e, rate of strain (g) and shear stress (t) at about 0.18 cm2 s–3, 4.4 rad s–1 (outer cylinder rotating frequency) and 0.02 dyn cm–2, respectively. Further, motile cells in the shear-inhibited cultures lost their longitudinal flagella. Thomas et al. (1995) used the inhibition response of L. polyedrum to compare the turbulence level in culture bottles with that in Couette devices; the conclusion was that photosynthesis was less sensitive than cell division. With the inner Couette cylinder rotating at > 7 rpm, Chen et al. (1998) observed growth rate reduction (but not cell mortality) of A. minutum. To account for possible effects of turbulence intermittency, Gibson and Thomas (1995) subjected cultures of L. polyedrum in Couette devices, to various rotation rates maintained during different fractions of the day. The daily averaged rate of strain threshold for zero growth, was reduced by two orders of magnitude under intermittency of the simulated turbulence. Juhl and Latz (2002) found that the reduced net growth rate of L. polyedrum could result from either or both decreased cell division and cell mortality, depending on the shear intensity and growth conditions. From their calculations, they predicted that cell division in L. polyedrum populations could be inhibited by levels of turbulence common for near-surface waters,


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and that shear-induced mortality could only be expected for unusually high shear-stress levels or for cellular conditions typical of late exponential/ stationary phase cultures. Sullivan and Swift (2003) and Sullivan et al. (2003) performed a comparative study on the effect of small-scale turbulence on the net growth rate and size of 10 species of marine dinoflagellates. Their experimental setup consisted on 20-L rectangular tanks with turbulence generated by vertically oscillating cylindrical rods and e estimations were performed with an acoustic Doppler velocimeter. Dinoflagellates were exposed to two turbulent levels (e ca. 1 or 10–4 cm2s–3) and a still control. As summarized in Table 13.2, the tested species showed a variety of responses. Some of them (Alexandrium catenella, A. tamarense, Pyrocystis fusiformis, Gyrodinium sp.) appeared relatively unaffected by stirring. Growth under turbulence decreased (with different degrees) in the case of Ceratium fusus, C. tripos and Pyrocystis noctiluca but it was favored in A. fundyense, L. polyedrum and Gymnodinium catenatum. As happened with A. sanguinea (Berdalet 1992), the cross-sectional area in C. fusus increased when net growth rate decreased, while no changes of this parameter were observed in the rest of species studied by Sullivan and Swift (2003). In the particular case of L. polyedrum, the results of these authors were clearly in contrast with those obtained by Thomas and Gibson (1990), Juhl et al. (2000) and Juhl and Latz (2002) in Couette experiments, perhaps because of the two different mechanisms used to generate turbulence in each study. Direct comparison among the studies reported in Table 13.2 is not possible, given that, in general, only the studies of Sullivan and Swift (2003) and those carried out with Couette devices provided a quantification of turbulence intensity. It is likely, however, that the culture conditions in such devices lead to some artifacts. A revision of experimental data by Peters and Redondo (1997) and Peters and Marrasé (2000) indicated that the turbulence levels used in the laboratory were, on average, higher than those found in natural situations. However, given the pulsing and intermittent character of natural turbulence and the difficulties of measuring it, meaningful comparisons between nature and laboratory are still an open question.

Other Effects A study reporting an effect of fluid shear on toxin production was performed by Juhl et al. (2001). Cultures of the dinoflagellate A. fundyense, a producer of toxins responsible for paralytic shellfish poisoning, were exposed to a shear stress of 0.003 N m–2 (ca. 0.1 cm2s–3, similar to levels expected in near-surface

"&" Algal Cultures, Analogues of Blooms and Applications waters on a windy day, Table 13.1) for 1-24 h d–1 over 6-8 d. The Couette flow was generated by the rotation of the outer cylinder at 4.5 rpm. Net population growth decreased with shear exposures > 1 d–1 and became negative with exposures > 12 h d–1. At the end of the experiment, the cellular toxin content of the cultures sheared > 1 h d–1 was up to three times that of the unsheared control ones. The authors speculated that oceanic turbulence may inhibit population growth and increase cellular toxin content of A. fundyense, although in natural populations it would be difficult to distinguish the effect of turbulence from other influences on toxin variability, particularly if volume- or mass-specific, rather than cell-specific, measures of toxin were used. Another study (Wolfe et al. 2002), in which turbulence was not quantified, showed that dimethylsulfoniopropionate (DMSP) cleavage could be induced by shear stress (created by either bubbling or shaking) on the haptophyte Emiliania huxleyi and the dinoflagellate Alexandrium spp. Many dinoflagellates are a source of oceanic bioluminescence which, in this group, has been interpreted as a mechanism of reducing predation (Buskey et al. 1983, Mensinger and Case 1992, Abrahams and Townsend 1993). Spontaneous bioluminescence has been reported in L. polyedrum (Sweeney and Hastings 1958), Pyrodinium bahamense and Pyrocystis lunula (Biggley et al. 1969), P. elegans and P. acuta (Hardeland 1982), and C. horrida (Latz and Lee 1995). In addition, bioluminescence can be stimulated by different chemical and mechanical stresses. In some studies, bioluminescence was triggered by changes in shear, acceleration or pressure. For instance, Anderson et al. (1988) observed that L. polyedrum produced bioluminescence at the entrance of a capillary tube, where, presumably, the change in diameter caused a transition from laminar to turbulent flow. In contrast, Latz et al. (1994) and Latz and Rohr (1999) reported bioluminescence stimulation by a constant shear generated by Couette cylinders above an e threshold of 0.3 N m–2. However, using a similar experimental setup and stationary homogeneous shear, P. noctiluca did not produce bioluminescence unless the cells experienced transitions from laminar to turbulent flow (Cussatlegras and Le Gal 2004). Speciesspecific variability and differences in either physiological conditions of the cultures or details of the experimental setups used may be the cause of the diversity of responses observed. In most cases, these experiments were conducted at relatively high levels of fluid shear.


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Mechanisms of Cell Damage. Dinoflagellates: A Particular Case? As already mentioned, there are important constraints when trying to compare different experimental studies. Peters and Marrasé (2000) searched for general trends in the available information at that time. They compiled data from different taxa, organism size and experimental setups, standardized the quantification of turbulence based on e, and defined a normalized turbulence growth rate to allow comparison among studies (Fig. 13.6). Peters and Marrasé (2000) concluded that, on average (Fig. 13.6a), turbulence had a negative effect on growth rates, although this result was notably influenced by the high amount of data from dinoflagellates (Fig. 13.6b), a group that appeared especially sensitive to the small-scale turbulence. A quick look at Table 13.2, seems to corroborate this finding. One could then ask whether dinoflagellates are a particular case, and if so, why. White (1976) suggested that small-scale turbulence could mechanically damage the dinoflagellate cell integrity, influence physiological processes like cell division or interfere with cell orientation and migration. It has been speculated that the peculiar sensitivity of dinoflagellates to water motion and their adaptations for living in stratified water columns are related to their evolutive history of diversification in mesozoic seas, supposed to have been less turbulent than those in later periods (Kennett 1982). The observation that nucleic acid content increases and cells become larger under inhibitory turbulence levels (Berdalet 1992) indicates that processes linked to nutrient uptake, biosynthetic metabolism and cell division are affected in different degrees by water turbulence. The DNA concentration increase in shaken cells suggested an impairment of chromosome separation after the duplication of DNA, i.e. at the G2/M phase of the cell cycle (Berdalet 1992). Dinoflagellate mitosis is extra-nuclear and characterized by an entirely cytoplasmatic spindle (Raikov 1995), which, in free-living forms, is subdivided into bundles enclosed in cytoplasmatic tunnels piercing the nucleus. These tunnels contain both kinetochore and non-kinetochore microtubules (Raikov 1995). It has been hypothesized that the blockage of dinoflagellate division by shaking could be caused by physical disturbance of the microtubule assemblage and/or the mechanisms responsible for chromosome separation (Karentz 1987, Berdalet 1992). A comparable impairment of chromosome separation has been observed in mutants of the yeast Schizosaccharomyces plombe subjected to starvation or sub-lethal temperature (Broeck et al. 1991). However, Yeung and Wong (2003) found that in the case of the heterotrophic dinoflagellate Crypthecodinium

"&$ Algal Cultures, Analogues of Blooms and Applications 200 mm

2

0

–2 10–4

10–2

100

102

104

10–2

100

102

104

b

standardized rate

4

2

0

–2 10–4

e (cm2s–3)

Fig. 13.6 Normalized growth rate ([rateturbulence – ratestill]/ratestill) related parameters plotted against the turbulence kinetic energy dissipation rate (when the original source did not provide it, the authors estimated e with particular assumptions) obtained in different experiments with plankton. The turbulence intensity of some studies specially those performed on orbital shakers) may have been overestimated (Peters and Guadayol, personal communication). (a) All data shown, grouped according to three plankton size classes, showing on average a negative effect of turbulence. (b) Dinoflagellate data excluded; note that the trend in (a) is now reversed. From Peters and Marrasé (2000, with permission of the authors). cohnii and the photosynthetic dinoflagellate Heterocapsa triquetra, mechanical agitation (at intensities apparently similar to those used by Berdalet 1992 and Berdalet et al. 2001) induced transient cell cycle arrest at G1 phase. Juhl and Latz (2002) found that, in Couette experiments under a shear stress of 0.004 N m–2 for 1–4 h d–1, the daily percentage of dividing L. polyedrum cells


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decreased due to an extension of, or an arrest in G1. Their interpretation was that the cells were responding to the treatment of the previous day. In contrast, continuous shear exposure appeared to increase the percentage of daytime G2 phase cells. From evidence that cyclin and cyclin-dependent kinases play a regulatory role in the dinoflagellate cell cycle (Van Dolah and Leighfield 1999) and from a comparison of their results with observations on animal cells in culture, Juhl and Latz (2002) suggested that induction of a cyclin-dependent kinase inhibitor could perhaps be responsible for the observed shear effects. None of their treatments induced increased DNA per cell, in contrast with the findings of Berdalet (1992). The discrepancy, as mentioned by Juhl and Latz (2002), could be due to the use of different species, flow conditions or DNA staining procedures. In relation with the mortality observed in L. polyedrum for exponential phase cells under high shear stress, and late exponential or stationary phase cells under low shear stress, Juhl and Latz (2002) indicated that the possibility of shear induced apoptosis should be considered. The interesting controversies surrounding small-scale turbulence effects on dinoflagellate cells point towards a stimulating line of research that may require molecular and cytological and/or ultrastructural studies. Recent progresses (Gillespie and Walker 2001) in mechanotransduction (conversion of mechanical stimulus into electrical signals) and molecular regulation mechanisms may open some of the approaches. Superimposed to cell damage or impaired cell division, more subtle mechanisms including alteration of swimming patterns, phototaxis and migration (White 1976, Estrada et al. 1987, Karp-Boss et al. 1996, 2000), and interaction with other biological (e.g. growth phase, cell shape) and environmental aspects (e.g. temperature, pH, CO2 diffusion) modulate the observed response of the organisms. Finally, as shown by the few comparative studies (Berdalet et al. 2001, Sullivan and Swift 2003), the response to small-scale turbulence of dinoflagellates may also be species-specific.

ENCOUNTER PROCESSES It has been hypothesized that turbulent shear may increase particle encounter rates and enhance aggregation of small living or non-living particles into larger and presumably faster sinking ones (Kiørboe 1997 and references therein). Jackson (1990) applied coagulation theory to study the formation of aggregates by collision between suspended phytoplankton cells; he showed that coagulation rates increased rapidly above a cell concentration threshold which was inversely related to fluid shear (whether laminar or turbulent),

"&& Algal Cultures, Analogues of Blooms and Applications algal size, and a so-called coefficient of stickiness, which could be particularly important for diatoms provided of spines or setae (a correction of this formula is given in Jackson and Burd 1998). As pointed out by Jackson (1990) and Kiørboe et al. (1990), physical coagulation processes could place an upper bound on the accumulation of algal blooms. In situ formation of marine aggregates composed by diatoms was documented by, for instance, Alldredge and Gotschalk (1989) and Kiørboe et al. (1998), while Riebesell (1993) studied the aggregation dynamics of Phaeocystis (prymnesiophyceae). The interaction between shear and stickiness in diatom aggregation was investigated, in situ and in the laboratory by Kiørboe et al. (1994) and Hansen and Kiørboe (1997). An example of aggregation of dead or living cells caused by turbulent flow was reported in experiments with the green algae Scenedesmus quadricauda (Hondzo et al. 1998, Hondzo and Lyn 1999). Predator-prey interactions among planktonic organisms may be influenced by the existence of relative motion between predator and prey or by other mechanisms related to water turbulence (Alcaraz et al. 1988, Rothschild and Osborn 1988, Marrasé et al. 1990). A simple model of predator-prey encounter rates was formulated by Kiørboe and Saiz (1995), who concluded that the effect of turbulence was minor for suspension feeding copepods but benefited ambush feeding ones; Kiørboe and MacKenzie (1995) used a similar approach to show that larval fish behavior and size could lead to substantial differences in encounter rates with the prey. Osborn (1996) proposed an alternative model of copepod feeding based on the diffusion of food particles towards the predators and concluded that feeding currents interacted with turbulence to increase the flux of food. In a general way, zooplankton predation can be expected to show a dome-shaped response to turbulence (Saiz et al. 2003). Moderate levels may increase predation efficiency, but too strong turbulence may decrease predation rates due to mechanisms such as interference with feeding currents or erosion of hydromechanical signals (Visser 2001 and references therein). Turbulence has also been found to increase grazing rates of microzooplankton on bacteria (Peters et al. 1996) and other protists. Thus, the patchy distribution of Ceratium tripos caused by aggregation-induced turbulence increased the ingestion rate of its mixotrophic predator Fragilidium subglobosum (Havskum et al. 2004). However, similar levels of e (Havskum 2003) had no effect on ingestion rates of Oxyrrhis marina feeding on Isochrysis sp. As a detailed survey of the influence of turbulence on encounter processes in the planktonic community is outside the scope of this chapter, the reader is referred to more focused studies (Kiørboe 1997) for further information.


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IMPLICATIONS FOR NATURAL SITUATIONS Microzones and Marine Snow In order to explain relatively high growth rates and lack of evidence of nutrient limitation in phytoplankton from oligotrophic open ocean waters, authors like McCarthy and Goldman (1979) hypothesized that phytoplankton could take up nutrients from the so called microzones, i.e. small areas of high nutrient concentration created by mechanisms such as zooplankton excretion or bacterial decomposition of particulate organic matter. Since its postulation, the subject was controversial. Jackson (1980) argued first against the hypothesis. He combined data from the North Pacific Gyre with experimental measurements of growth rates of the diatom Thalassiosira pseudonana and excretion rates of herbivorous ciliates and calculated that the excreted nutrients would disperse by molecular diffusion too fast to be used by the phytoplankton. In the same direction, Williams and Muir (1981), based on a model of ammonium excretion rates by Oithona and Clausocalanus, concluded that primary productivity in the oligotrophic ocean could not be maintained by such excretion patches. Further, the experimental studies of Lehman and Scavia (1982a, b) that appeared to support the importance of microzones were later questioned by Currie (1984). This author noted that the Lehman and Scavia experiments had been conducted with high phytoplankton concentrations, and thus, their results could not be extrapolated to oligotrophic waters. Currie (1984) stated, as Jackson (1980), that slow continuous phosphorus uptake from low concentrations was sufficient to sustain most phytoplankton populations. In their revision of the subject, Mann and Lazier (1991) concluded that the micronutrient patches may be too few and disperse too fast to be of great importance for phytoplankton. In contrast, according to Blackburn et al. (1998) microscale nutrient patches of a few millimeters in diameter could sustain swarms of bacteria for a short time period (ca. 10 min). At those small time and spatial scales, it is difficult to evaluate how shear would influence patch dynamics. Small-scale turbulence may also play an important role in relationship with the potential ecological relevance of microscopic and macroscopic (marine snow) aggregates for sustaining microbial food web activity (Azam and Long 2001). These aggregates provide favorable conditions for bacteria and other microbes and may be centers of diffusion for excreted metabolites (Simon et al. 2002 and references therein). The occurrence of relatively persistent microenvironments may be more likely in connection with macroscopic particles. Alldredge and Cohen (1987) used microelectrodes to

"' Algal Cultures, Analogues of Blooms and Applications demonstrate the existence of persistent oxygen and pH gradients around marine snow and concluded that these microzones could have important implications for nutrient recycling in the sea. An interesting experimental demonstration of the influence of cell size on the development of microenvironments has been reported by Richardson and Stolzenbach (1995), who investigated the development of extracellular micro-environments of pH produced by individual photosynthesizing phytoplankton. These authors showed that extracellular oxidation of Mn (II) to Mn Ox, detected by means of a blue dye, was only found around cells larger than 20 mm. Finally, it was hypothesized that small-scale turbulence could explain the low rate of N2 fixation by cyanobacteria in estuaries, coastal marine waters and many open-ocean waters. Carpenter and Price (1976) suggested that turbulence could disrupt the colonies of Trichodesmium. Paerl and Bebout (1988) proposed that higher turbulence and lower dissolved organic matter in marine environments relative to fresh-water ones reduced the likelihood of oxygen-depleted microzones around cyanobacterial cells, increasing the possibility of nitrogenase inactivation. However, this hypothesis has been the object of a lively discussion (Paerl et al. 1995, Howarth et al. 1995).

Red Tides It is difficult to ascertain to what extent laboratory results showing dinoflagellate growth inhibition by turbulence are significant for natural conditions. On one hand, experimentally generated turbulence is difficult to characterize in fluid dynamics terms and its spatio-temporal scales are not representative of field conditions. On the other hand, in natural ecosystems, the effects of turbulence on dinoflagellate growth cannot be easily separated from those by other factors such as light and nutrients. Meso- and microcosms (Eppley et al. 1978, Estrada et al. 1987) enclosing natural water samples have provided a useful experimental tool for studies at the community level, but this approach is not free from problems related to turbulence generation (Sanford 1997, Petersen et al. 1998), scaling of physical and biological variables (Platt et al. 1981) and other difficulties. Our opinion is that in spite of the possibility of inhibitory effects, smallscale turbulence is not likely to be per se a major factor controlling bloom formation. Algal blooms can be better explained as a result of the combination of mesoscale circulation features with physiological responses of the organisms, involving basically their motility (Pincemin 1969, Wyatt 1975,


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Margalef et al. 1979). Bloom termination is often associated with storms or strong winds inducing circulation patterns that tend to disperse the cells. Under these weather conditions, and with cells in conditions probably resembling those of late stationary phase cultures (Juhl and Latz 2002), bloom disappearance could be favored by direct effects of turbulence on the organisms. However, in natural environments, it is difficult to distinguish direct effects of turbulence from those related to organism transport. Similarly, the relationship between the dominance of major life-forms like diatoms or dinoflagellates can be basically explained through the effects of turbulent water motion at inertial scales and on entrainment within the water column, organism motility and responses to nutrient and light availability (Margalef 1978).

CONCLUDING REMARKS The summarized information illustrates Ambühl’s statement: “There is no life without water, and there is no life in water without turbulence in water” (Ambühl 1960). Since the origin of life, turbulence constituted a key environmental factor that interacted with microorganisms. Both the physiology and morphology of phytoplankton adapted to different scales and characteristics of the water motion. At small scales, we have seen the complexity of the mechanisms of interaction between turbulence and phytoplankton cell physiology, including nutrient incorporation, motility, growth, cell division, bioluminescence and toxin production. Motility and cell shape and size, are not passive characteristics, but play a decisive role in the physiological response of the phytoplankton to small-scale turbulence. However, there is still a marked unbalance between our theoretical scenarios of comprehension and the experimental evidences. The convergence of conceptual and experimental approaches requires an important effort in technology to improve the quantification of fluid dynamics and sampling at the small scales of the organisms, and to perform experiments under realistic levels of turbulence. Luckily, recent technological developments and interdisciplinary research have opened new doors for future research in this direction. The coupling between hydrography and phytoplankton communities dynamics at meso and large scales in nature was not the focus of this review. However, improving our knowledge of physical-biological interactions at these scales, is essential for understanding natural phenomena such as food web relationships and harmful algal blooms. These investigations also

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require the improvement of biological sampling methodologies with spatiotemporal resolutions similar to those used in physics.

ACKNOWLEDGEMENTS We specially dedicate our work to the kind memory of Prof. Dr. Ramon Margalef, who inspired worldwide the investigation of the interactions between turbulence and marine life. We acknowledge the help received from our colleagues, Dr. C. Marrasé, Dr. F. Peters and C. Roldán and the comprehension and patience of Rafael, Joaquim and Judit. This study was supported by the Spanish Project TURFI (REN2002-01591/MAR) and the European funded project NTAP (Nutrient dynamics mediated through Turbulence And Plankton interactions, contract EVK3-CT-2000-00022) within the framework of the EU RTD Programme “Environment and Sustainable Development” that forms part of the ELOISE projects cluster. It is ELOISE contribution No. 498/40.

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# Algal Cultures, Analogues of Blooms and Applications Visser, A.W. 2001. Hydromechanical signals in the plankton. Mar. Ecol. Porg. Ser. 222: 1-24. Vogel, S. 1981. Life in moving fluids: the physical biology of flow. Willard Grant Press, Boston, MA, USA. Volk, S.L. and H.K. Phinney. 1968. Mineral requirements for the growth of Anabaena spiroides in vitro. Can. J. Bot. 46: 619-630. Walsby, A.E. and A. Xypolyta. 1977. The form resistance of xytane fibres attached to the cells of Thalassiosira fluivatilis Hutstedt. Br. Phycol. J. 12: 215-223. White, A.W. 1976. Growth inhibition caused by turbulence in the toxic marine dinoflagellate Gonyaulax excavata. J. Fish. Res. Board Can. 33: 2598-2602. Williams, P.J. Le B. and L.R. Muir. 1981. Diffusion as a constraint on the biological importance of microzones in the sea. pp. 209-218. In J.C. Nihoul [ed.]. Ecodynamics. Elsevier, Amsterdam, The Netherlands. Wolf-Gladrow, D. and U. Riebesell. 1997. Diffusion and reactions in the vicinity of plankton: a refined model for inorganic carbon transport. Mar. Chem. 59: 17-34. Wolfe, G.V., S.L. Strom, J.L. Holmes, T. Radzio and M.B. Olson. 2002. Dimethylsulfoniopropionate cleavage by marine phytoplankton in response to mechanical, chemical, or dark stress. J. Phycol. 38: 948-960. Wyatt, T. 1975. The limitations of physical models for red tides. pp 81-93. In V.R. Lo Cicero [ed.]. Proceedings of the First International Conference on Toxic Dinoflagellate Blooms. MIT, Wakefield USA. Yamazaki, H. and D. Kamykowski. 1991. The vertical trajectories of motile phytoplankton in a wind-mixed water column. Deep-Sea Res. 38: 219-241. Yeung, P.K.K. and J.T.Y. Wong. 2003. Inhibition of cell proliferation by mechanical agitation involves transient cell cycle arrest a G1 phase in dinoflagellates. Protoplasma 220: 173-178. Zirbel, M.J., F. Veron and M.I. Latz. 2000. The reversible effect of flow on the morphology of Ceratocorys horrida (Peridiniales, Dinophyta). J. Phycol. 36: 46-58.

Mechanistic Models of Algal Physiology

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" Mechanistic Models of Algal Physiology Kevin J. Flynn Wallace Building, University of Wales Swansea, Singleton Park, Swansea SA2 8PP, UK

Abstract The use of models of algal physiology are discussed together with a consideration of the advantages and disadvantages of model types. While Monod and quota style models abound in the literature they have significant short comings; for a description of multi-nutrient interactions the Monod structure is inappropriate and care must be taken when using quota models. Mechanistic models of algal physiology are introduced both as a tool in their own right but also as a focus for experimental studies of algal physiology. A major impediment to the development and testing of models is the absence of suitable data from environmentally important organisms grown under various multi-factorial conditions. A multi-nutrient model is described for light-ammonium-nitrate-phosphorus-iron-silicon interactions that is simple enough for construction using readily available modeling software. This type of model is run to show the implications of growing algae in different culture systems, the dangers of measuring growth rate by changes in Chl concentration and growth under different N:P nutrient ratios.

INTRODUCTION Models can provide mathematical descriptions of algal physiology. The subjects of this chapter are dynamic models that respond to changes in external factors, typically nutrient and light availability. The construction and parameterization of such models is a supreme test of our knowledge. Contrary to the impression that one may gain from studying charts of biochemical pathways, in reality we know little about the rates and dynamic controls for processes controlling phytoplankton growth. The application of models allows the implications of rate processes to be appreciated, that it is not the amount of a substance or organism that alone defines its importance,

502 Algal Cultures, Analogues of Blooms and Applications it is the rate of flux or activity. This is so whether one is considering simple batch culture growth dynamics, at one extreme, or the oceanography of the Southern Ocean in response to iron fertilization at the other. Biological science has developed rapidly over the last decades, perhaps most notably in molecular biology. However, molecular biology, for all its uses, currently does little to contribute directly to our understanding of the rate processes that describe the ecology of Earth. We need mathematics for that purpose. Further, while molecular biology provides a tool enabling us to gain information on the individual organism or to divide groups, modeling very often does the opposite. Models integrate knowledge across, and places organisms into, functional groups. Modeling thus requires an overview of the subject; models could be considered to be dynamic reviews of knowledge expressed as mathematical equations. Unfortunately, mathematicians rarely have a sound grasp of the complexities of biology (a science that, in total contrast with mathematics, is almost totally devoid of rules), while biologists all too often have a poor appreciation of mathematics other than statistics. Fortunately, to combine biological knowledge, which may be rich in qualitative understanding but often lacks quantitative detail, with mathematics to develop models that behave in sensible ways is now much easier to undertake thanks to the development of graphic user interface (GUI) programs that remove much of the debugging work. This enables the biologist to truly become involved in the modeling process. Programs, such as Stella® (ISEE Systems), have done much to promote the development of mathematical modeling, to such an extent that the journal Ecological Modelling can have complete issues devoted to Stella programs, and standard text books also make reference to GUI platforms (Haefner 1996). If this seems all too easy, it must not be forgotten that modeling is not just about getting a simulation output that fits to a data set. Models that are essentially curve-fitting exercises (empirical models) add little to understanding. For a model to enhance our understanding of biology the basis of critical parts of its construction is preferably mechanistic in its form. Mechanistic models employ features such as feedback loops mimicking real physiological processes in their regulatory functions. This is important in another way because it increases the likelihood that the model will behave sensibly when operated under conditions laying outside of those in the data sets used for their tuning. Usually overall growth in terms of biomass or cell number, and major processes such as C-fixation, are the targets for models of algal physiology but even for these data are remarkably sparse and often available for only a


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few species. Typically those organisms (e.g. Dunaliella, Phaeodactylum, Prorocentrum) are also of minor ecological consequence, contributing little if anything to biogeochemical processes. In short, models often contain simple descriptions of those processes that the modeler considers to be the most important, characterized with data supplied by physiologists who primarily study organisms that are easiest to grow. This is another reason to use model structures that are general in their behavior, conforming to what we, as algal physiologists, would expect to happen, rather than being specific to a particular data set. The aim of this chapter is to provide an overview of modeling algal physiology. The reader is referred to other publications for specific model structures; of these the review by Flynn (2003a) gives a critical review of alternative approaches to modeling algal growth in response to nutrient limitations. In this chapter, a simplified version of the Model of Algal Physiology (MAP1) described by Flynn (2001) is described. This model, MAP2, is simple enough and computationally cheap enough, for a student to construct and use using a GUI modeling package.

Model Structures The simplest models of algal growth assume, as do most experimenters, that growth rate limitation is by only one factor. As we will see, even if growth is so limited, the behavior of algae with respect to other factors can have important ramifications, quite possibly leading to colimitation or even to a change in limiting factors.

Monod Model The most basic model is the Monod model (Monod 1942, 1949) developed to describe the steady-state growth of bacteria in a chemostat. For the description of algal growth the assumption (quite likely incorrect) is made that light is either non-limiting or that at least the level of limitation is invariant. In its simplest form the Monod model uses a single rectangular hyperbolic function (Eq. i) to describe a growth quotient (uXi) as a function of availability of one external nutrient (Xi) with reference to a half saturation constant for growth (KgXi). mXi =

Xi X i + Kg Xi

m = mmax ◊ uXi

(Eq. i) (Eq. ii)

504 Algal Cultures, Analogues of Blooms and Applications The intrinsic growth rate (m) is given by reference to a maximum growth rate (mmax; Eq. ii); a value of m=0.693 d–1 (0.693 being Ln(2)) equates to a doubling of population size in a day (assuming no loss). Even this simple model can be used to demonstrate the competitive advantages in having a high value of mmax but perhaps a poor substrate affinity (i.e. high KgXi) in a eutrophic situation versus a high affinity coupled with a low mmax in oligotrophic waters.

Quota Models In reality, for most nutrient limitations, growth (i.e. increase in biomass) does not stop dead on exhaustion of the external nutrient source, but continues for a while afterwards until the internal nutrient has been distributed as thinly as possible within cells. It is not the external nutrient concentration per se that limits growth, it is the concentration available for biochemical processes within the cells that is of primary importance. Of course one would expect external and internal concentrations to correlate, and at a steady state they do. However, in the transients seen in the real world that correlation is more than likely to fail. It is important to appreciate that reference to nutrients ‘within’ cells does not simply refer to pools of inorganic nutrient, but rather to all mobilizable material. Thus a cell that is deprived of an external source of P will redistribute what P it has within it (inorganic and organic), dividing this amongst daughter cells until each of those cells has the barest minimum quantity for survival. For P, much of that original ‘excess’ P may well be accumulated in the form of inorganic P (polyphosphate). However, it should be noted that phytoplankton are unable to accumulate sufficient internal inorganic N to support significant growth (Joseph and Villareal 1998, Richardson et al. 1998). The issue of dark-N assimilation by phytoplankton has been considered using models by Flynn et al. (2002) and within vertical migration by Flynn and Fasham (2002), with events such as the release of nitrite following incomplete nitrate reduction by Flynn and Flynn (1998). At the other extreme it is not possible to redistribute previously accumulated silicon at all; Si for diatom cell division must come afresh from the environment (Martin-Jézéquel et al. 2000). This control of cell growth by the availability of internal nutrients, the ‘cell-quota’, is the basis of what is usually termed the quota model. The concept was developed separately by Droop (1968, 1973) and Caperon and Meyer (1972). In essence it makes the growth rate a hyperbolic function of the nutrient quota, described either as a cell-quota (e.g. gN cell–1), or perhaps more usefully as a C-quota (e.g. gN g–1C). Maximum growth is attained, or


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attainable, when the quota (Q) is maximal (Q = Qmax) and no growth occurs when the quota is minimal (Q = Q0). Q0 is also termed the subsistence quota because it is the minimum amount of a nutrient that must be present within the organism for that organism simply to exist as a living entity. The shape of the curve relating Q to m varies; for some nutrients such as N it may be nearly linear, while for micronutrients such as vitamin B12 it may be steeply hyperbolic. For Si, it is inappropriate to use the quota model at all, for reasons already explained above. There are various formulae describing the relationship between Q and m. The original forms employed a constant mmax that was often much higher than the real attainable maximum growth rate because it defined the value of m at infinite Q. In reality, Q is constrained within certain limits. Flynn (2002a) proposed a normalized form of the quota equation (Eq. iii). XC is the C-quota (Q) for nutrient X, XC0 and XCm the minimum and maximum quotas respectively, KQX is a curve fitting constant akin to the half saturation constant in Eq. i, and XCu is the resultant quotient describing the relative growth rate (akin to uXi in Eq. i) XCu =

(1 + KQX) ◊ (XC - XC 0 ) (XC - XC 0 ) + KQX ◊ (XC m - XC0 )

(Eq. iii)

The C-specific growth rate (Cu) is given by mmax·XCu (Cf. Eq. ii). While the quota model was developed as an empirical curve fit to experimental data, the algal physiologist will recognize the logic, and hence a mechanistic argument, for the basis of a relationship between Q and m. However, as we shall see, the relationship between nutrient uptake and quota- controlled growth used in most original quota structures is controlled by an approach that is clearly unrealistic. The rate of uptake of nutrient required to support maximum growth is the product of mmax and Qmax (i.e. mmax·XCm) and we assume that the relationship between uptake and the external nutrient concentration is described by a rectangular hyperbola like that described in Eq. i. Note here that the half saturation constant for growth is replaced by an analogues parameter for uptake (KuX). The rate of change in XC is thus given according to Eq. iv. When X is non-limiting the rectangular hyperbolic term approaches unity, XC approaches XCm and hence XCu (from Eq. iii) also approaches unity. d/dt·XC thus becomes zero.

FG H

IJ K

d X - XCu ◊ XC ◊ XC = m max ◊ XC m ◊ X + Ku X dt

(Eq. iv)

506 Algal Cultures, Analogues of Blooms and Applications The regulation of uptake in such a model is thus passive; uptake cannot exceed demand because the maximum rate of uptake matches the maximum demand. Put another way, this says that at non-limiting nutrient concentrations, the process that controls growth of the whole organism is the rate of uptake into the cell. That is not how uptake is controlled in reality; feedback from cellular biochemical processes control uptake. Nutrient transport can demonstrate so-called surge uptake rates an order of magnitude or so greater than the highest steady-state requirements (Flynn 1998). This is not a too much of a problem with the model as long as only one nutrient is considered but in multi-nutrient scenarios uptake of the non, or lesser, limiting nutrient cannot be controlled using this passive approach alone. It is important to remember as well that the quota model was, like the Monod model, developed to describe steady-state conditions.

Multi-nutrient Interactions and Mechanistic Models Flynn (2003a) discusses in some detail alternative approaches for simulating various nutrient-limited growth processes. There is no simple universal model for accomplishing this. The form of model also varies depending on the role intended for it; the non-mechanistic PROTECH model of Reynolds et al. (2001) was designed specifically as a tool for predicting phytoplankton growth in lakes and reservoirs. There is no ‘right’ way to model algal physiology, though there are without doubt wrong ways. These wrong, inappropriate or dysfunctional, approaches include Monod-type methods in which it is assumed that cellular nutrient ratios (e.g. C:N:P) are invariant or change in consort. The latter include the Shuter (1979) model, in which N:P may be held constant though C:(N:P) varies. The whole subject of multinutrient interactions is one for which relatively few experimental data on C:N:P (etc.) exist. This is unfortunate as P-limitation in general may be expected to have many impacts because of the multiplicity of roles for P, not only within cell structure but also, critically, in biochemical regulatory processes. In the absence of other information, the usual approach to simulating multi-nutrient interactions has been to combine the quotients for nutrient status for individual nutrients. This may be accomplished by multiplying these quotients, or more usually by taking the minimum quotient as the controlling one (the so called threshold limitation approach). Hence, a multinutrient variant of the Monod model would describe growth according to Eq. v, replacing Eq. ii. m = mmax ◊ min(uX1, uX2, …)

(Eq. v)


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Likewise for a quota structure – m = mmax ◊ min(X1Cu, X2Cu, …)

(Eq. vi)

The reader is referred to Flynn (2003a) for alternative methods for the control of the uptake of the non or lesser limiting nutrients. Eq. vii gives one such approach that provides considerable flexibility. d ◊ X i C = mmax ◊ surge ◊ XiCm ◊ [{XiCu > XCumin} ◊ qb + {XiCu = XCumin}] dt

FG 1 - X C IJ H XC K ◊ FG 1 - X C IJ H XC K i

◊

Xi X i + Ku Xi

i

i

i

abs

X i Ch

abs X i Ch

- Cu ◊ X i C

(Eq. vii)

+ K Xi

This equation contains two Boolean logic terms that test for whether nutrient Xi is present within the organism as the limiting quota; this limiting quota is then responsible for slowing the rate of uptake of the non-limiting nutrient(s). [Boolean logic terms take the value 0 if false, and 1 if true; they can be replaced with If-Then-Else constructions if preferred.] The most important part of the equation, however, is a feedback term that slows and then halts uptake of Xi as the quota XiC attains a stated maximum value. This feedback uses a sigmoidal function. XiCm is (as before) the upper value of XiC that affects growth rate. XiCabs is the absolute maximum quota, at which transport is halted using a sigmoidal function described with constants KXi and XiCh. For nutrients such as P and Fe, XiCabs may be very much higher than XiCm. The rate at which XiC approaches XiCabs is modulated by the power constant b (set at a value XCumin). q itself usually takes the value of XCumin (i.e. the minimum XiCu) but if a simulation of light limitation is included then for X = Si q takes the value of the relative growth rate (Cu/mmax) (Flynn 2003a). The parameter surge allows an additional control over the rate of uptake so that now, in contrast with Eq. iv, the internal nutrient status controls uptake rather than uptake controlling growth physiology in the presence of excess external nutrient. Parameter surge could be made a variable function of XiC in recognition of the typical enhanced nutrient uptake capability seen in nutrient-stressed algae (Gotham and Rhee 1981, Terry 1982, Ikeya et al. 1997, Flynn et al. 1999). Complexity in mechanistic model has no bounds, and such constructs are not new (Lehman et al. 1975), but it has taken the ready availability of

508 Algal Cultures, Analogues of Blooms and Applications computing power to revolutionize our ability to implement such models readily. Potentially one could make a model that represents the true complexity as seen in biochemical pathway charts. That would be a rather pointless activity though. We lack the information to attempt such a task, and the resultant model would be far too expensive in computational terms to fulfill any useful role. Multiple-pool models (Maske 1982) may contain much biochemical detail, but usually they are allied to more general (e.g. quota-type) constructs. This approach enables the fidelity of the model to be enhanced for specific purposes without overstretching computational demands. Thus in the Ammonium-Nitrate Interaction Model (ANIM) of Flynn et al. (1997), the internal pools of nitrate, ammonium and glutamine are described explicitly, together with the activity of key enzymes in Nassimilation, while growth itself is control by a NC-quota function. An important feature of real cells is the delay in reacting to external stimuli, such as recovery of cells from stationary phase. In complex models, with multiple internal nutrient pools, delays are inbuilt but in simple models explicit delay functions may be required. This is especially so if one is considering modeling cell numbers as well as, or instead of, biomass. Davidson and Cunningham (1996) describe such an approach for introducing lags into these models.

Some Aspects of Simulating Rate Limiting Processes Temperature Temperature has an over-riding effect on all other processes. At the most basic level this can be modeled simply assuming all metabolic processes are affected in the same way; in reality that is not so (Lomas and Glibert 1999). Whether one wants to, or can, consider the complexity of modeling differential temperature effects depends in part on whether the model structure in question can readily incorporate the required modifications. While mechanistic models can be modified (Flynn 1999), Monod and quota models cannot. Flynn (2001) describes how a mechanistic model can interrelate temperature and nutrient interactions with cell size and growth rate.

Light-limitation Of all ‘nutrients’, light is probably the most likely to be rate limiting in nature, if only because of the light-dark cycle. Light limitation itself may be simulated using one of a number of growth-light functions (Jassby and Platt 1976). It should be noted, however, that most functions and most experimental


509

data series are actually for the description of the photosynthesis-light (PE curve) interaction rather than for growth-light. Because of the importance attributed to Chl.a both in a biochemical sense and as an index of algal biomass, there has been interest in how best to simulate the relationship between Chl:C, light, and (non-light) nutrient stress (Flynn 2003b, and references therein). In reality, cellular physiology acclimates to nutrient and light availability to, effectively, optimize Chl:C and hence photosynthesis and growth. Models may achieve a similar aim either through the solving of simultaneous equations (Laws and Chalup 1990), or through a mechanistic approach in which the synthesis and degradation of Chl:C (Geider et al. 1998) or Chl:cell (Zonneveld 1998) is simulated directly. This mechanistic approach has the advantage that the dynamics of photoacclimation may be simulated, and the disadvantage that the level of model complexity rises. There are also surprisingly few data sets with which to fully parameterize photoacclimation and more specifically the dynamics of the event with respect not only to changes in light but also to nutrient stress. The modeling of Chl:C content is discussed in Flynn et al. (2001) and Flynn (2003b). The latter paper considers whether the use of mechanistic models for photoacclimation is justified in terms of computational expense, concluding that for many ecosystem scenarios they are unnecessary.

Nitrogen and Phosphorus The model description of N and P limitations appear relatively straightforward. However, even here the problem is in the detail. For N there is the issue of N-source selection and the ‘preference’ for ammonium over nitrate (Syrett 1981, Dortch 1990). The simplest way of handling this (and hence also describing the relative importance of oxidized, ‘new’ forms of N; i.e. the f-ratio, Dugdale and Goering 1967) is through the use of an inhibition term that results in the suppression of nitrate use by the external ammonium concentration (Fasham et al. 1990). That approach is not only biologically incorrect but the output from inclusion of such a term can give incorrect simulation outputs at high nitrate concentrations because ammonium consumption is suppressed to near zero by the nitrate concentration. What we still do not describe adequately is the way in which P-stress must impact on N-source acquisition and use, and vice versa. This needs attention at all levels. While one may suspect that the more costly N-sources (e.g. nitrate and N2) would be affected more by P-stress than the use of ammonium, experimental evidence is lacking. That freshwater algae are commonly suspected of being P-limited, while marine algae are N-limited,

510 Algal Cultures, Analogues of Blooms and Applications seems to have adversely affected studies of co-limitation. Interestingly, attempts to include descriptions of N2-fixation in models are few and far between. Describing interactions between diazotrophic activity and the use of fixed-N appears only to have been described in one mechanistic model (Stephens et al. 2003). What is perhaps even more surprising is the lack of experimental data required to tune model output for what is ecologically an extremely important process. In short, the interaction between N and P in cell physiology has been poorly studied in the context of generating data for the construction and tuning of mechanistic models. Given the importance of N:P nutrient ratios in the control of species succession and the formation of noxious blooms (Granéli et al. 1998) this is rather unfortunate. As a result usually the interaction between these nutrients is described using the minimum threshold approach described in Eq. vi. The value of internal (cellular) N:P that corresponds to equal co-limitation by N and P, the critical (Rcrit) or optimum ratio at which NCu = PCu, has been used to infer the competitive advantage of phytoplankton growing in P-impoverished systems (Rhee and Gotham 1980, Elrifi and Turpin 1985, Turpin 1986). However, it is not just the internal kinetics of nutrient usage that defines a competitive advantage; the kinetics of nutrient acquisition are equally if not more important (Flynn 2002a). In the traditional quota model, the maximum nutrient uptake rate is limited to the product of the maximum growth rate and Qmax. As discussed above, (associated with Eq. vii) this is wrong. Often surge nutrient uptake may be an order of magnitude greater than steady-state demands. This feature, plus the shape of the curve relating the quota to growth rate (defined in Eq. iii by KQX), affects the concentration of external nutrient supporting half maximum growth rate (Kg). However, the transport (uptake) kinetics are much more powerful in affecting Kg than the value of the quota constants (Flynn 2002a). The traditional quota approach not only fails to describe this, it also does not reflect the competitive advantage in nutrient ‘storage’. This is a lesson in the application of models that do not reflect biological reality, that it is dangerous to over simplify models. The advantage in the accumulation of surplus nutrient within microalgae is that it supports growth (i.e. increase in C-biomass) in the absence of external nutrient. For P, which is accumulated as inorganic polyphosphate, that support may be for a few generations of cell division (modeled by John and Flynn 2000). For N however, there is little advantage. The amount of nitrate that can be accumulated, for example, amounts to only 5-10% of total cell-N; this is insufficient to support even a fraction of a cell division. Models


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can be used to explore the implications of such processes. John and Flynn (2002) considered the costs and benefits of the synthesis of paralytic shellfish poisons (PSP) in the dinoflagellate Alexandrium; their model showed that the amount of N in PSP was trivial, that the toxins could not constitute a N-reserve compound, and the synthesis costs are likely also to be trivial, especially as the toxins are only made in N-replete cells.

Iron The simplest way to include Fe-limitation is to assign another quota quotient and use the standard threshold approach to assign limitation to the nutrient with the lowest quotient. However, this approach does not acknowledge the involvement of Fe in cell energetics. The need of an algal cell for Fe may be split between demands for respiration, photosynthesis and nitrogen reduction. Of these, respiration and N-source reduction (i.e. N2-fixation, nitrate and nitrite reduction to ammonium) are linked closely to cell growth rates. The link to photosynthesis is different. In essence the lower the available irradiance the greater the need for photosystems (through photoacclimation) and hence the greater the need for Fe. For light-limitation, then, one may expect an inverse relationship between Fe demand and growth rate. Fe and light limitations thus interact. The resultant balancing act between growth rate and the distribution of Fe between cellular demands may be modeled as just that, by the solving of simultaneous equations (Armstrong 1999). Alternatively it can be described mechanistically (Flynn and Hipkin 1999) using a model that constrains photoacclimation and hence growth according to Fe availability and its distribution within the cell. The link between Chl:C and Fe:C may be included in simple empirical models, coupled with a threshold approach to relate Fe and N limitation, (Flynn 2003b), though it is not possible to fully simulate the dynamics of Ferefeeding (i.e. supply of Fe to Fe-limited cells) with such an abbreviated model.

Silicon Si nutrition for diatoms exhibits some sharp contrasts with other nutritional requirements. The most striking of these is that the application of a quotagrowth relationship for Si is incorrect. Si that has been acquired and incorporated by the cell is not available for redistribution between daughter cells in the way in which N, P or Fe may be. For this reason, while a quotatype approach may be incorporated to account for changes in Si:C, the relationship with growth rate must be coupled to the external (not internal)

512 Algal Cultures, Analogues of Blooms and Applications concentration of Si. In essence then, Monod growth dynamics apply for Si-limitation. The variability in Si content is important. Only with Si-stress does the value of Si:C decline (Martin-Jézéquel et al. 2000). With other limitations the value of Si:C increases. It is for this reason that a diatom bloom that exhausts the N-source will likely also exhaust the external Si; as N-stress develops the diatoms take up more Si thus depleting the water column of this nutrient as well. Likewise, Fe-stress or low light may be expected to promote elevated Si:C; Antarctic diatoms are more heavily salicified than those growing in higher-Fe and better illuminated waters though this may not be a direct cause and effect relationship.

Mixotrophy Many microalgae, especially dinoflagellates and prymnesiophytes, display various levels of mixotrophy, the ability to augment phototrophy with heterotrophy either through consumption of dissolved organic compounds or phagotrophy. Ecologically this is an important activity because not only does it relieve the organisms of nutrient stress but it applies predatory pressure on bacteria and protists affecting the flow of elements through the plankton food web. In oceanographic models the activity of mixotrophs is split into its functional component parts and thus apportioned into photo(auto)trophy and heterotrophy. The combination of a phototroph model with that of a microzooplankton is a logical way to progress but this requires commonality in model structure. While there may appear to be great variety in the structures of algal models, there is still greater commonality between these algal models than there are between models of microzooplankton. Although, again, there is a lack of good data for parameterization of such a model, it would provide an interesting exercise to construct one.

A multi-nutrient model of algal physiology MAP2 This section gives a description of a simplified version of the Model of Algal Physiology (MAP1) described by Flynn (2001). The component parts of MAP2 were introduced in Flynn (2003a) but this is the first complete description. Table 14.1 places MAP2 within the series of mechanistic models developed by the author. The description of MAP2 is given in a form that will enable a ready coding into a modeling platform (hence the use in equations given here of operators such as * and ^, and ASCII-text parameter names with no super or subscripts). The model contains state variables for C-biomass plus N:C, P:C, Si:C (omitted for non-diatom), Fe:C and Chl:C. In essence it is a multiple

513


Table 14.1 A comparison of the mechanistic models of Flynn et al. All state variables are intracellular C-specific. Gln, glutamine; NRNiR, nitrate reductase + nitrite reductase activity. Model

Reference

State Variables NH4 + NO3– NO2– Gln NRNiR N P Si* Fe** Chl C

ANIM SHANIM NANIM MAP1 MAP2 MAP3

Flynn et al. 1997 Flynn & Fasham 1997 Flynn & Flynn 1998 Flynn 2001 Flynn 2003a; present work Flynn 2003b

ü

ü

ü

ü

ü

ü ü ü ü

ü ü

ü ü ü ü ü ü ü ü ü ü ü ü

ü ü ü

ü ü ü ***

ü ü ü ü ü ü

* Inclusion of Si related physiology introduced in Flynn and Martin-Jézéquel (2000) ** Inclusion of Fe related physiology introduced in Flynn and Hipkin (1999) *** MAP3 contains a non-photoacclimative description of Chl:C and interaction with Fe.

quota structure coupled with a full photoacclimation function linked to Fe availability. MAP2 differs from MAP1 in not having a state variable describing the internal cellular glutamine pool and it thus handles the ammonium-nitrate interaction in a more simple fashion. This deletion increases processing speed considerably by allowing a larger integration step to be employed. A further increase in processing speed can be attained by deletion of the mechanistic photoacclimative component by a simple series of empirical terms as achieved in MAP3 (Flynn 2003b). However this also results in a loss of ability to describe the dynamics of photoacclimation and Fe assimiliation. The equations for MAP2 are given in Table 14.2, with parameter descriptions in Table 14.3, and are explained below. Table 14.2

Equations for MAP2 (see also text and Table 14.3)

Nitrogen (1) (2) (3) (4) (5) (6) (7)

NVP AVP NV AV frat sumNs NCu

(8) Nup

= = = = = = = =

Npref*NO3/(NO3+NKt)+N2pref*NO3/(NO3+N2Kt) Apref*NH4/(NH4+AKt) NVP/(AVP+NVP) AVP/(AVP+NVP) NV/(NV+AV+1e-6) NH4+NO3 (NC=NCm) Um*NCm*surge*((NCu>NPSCu)*NPSCu^beta+(NCu=NPSCu))* (NCNPSCu)*NPSCu^beta+(PCu=NPSCu))* (PCSCo)*((VS>=Um*SCo)+VS/(Um*SCo)*(VSNPSCu)+(SCu=NPSCu=1))* ((Cu>0)*Cu/Um)^betaSi+(SCu=NPSCu)*(SCuFtot)*(FC-Ftot)/(FC-Ftot+FKq)

(25) Pqm (26) PS (27) dChlC

= = =

(28) ChlC

=

(Um+basres+NCm*Um*(redco+Ncost))*NPSCu+1e-6 Pqm*(1-EXP(-alpha*ChlC*E/Pqm)) Um*ChlCm*M*NPSCu*Fcon*(1-PS/Pqm)* (1-ChlC/ChlCm)/(1-ChlC/ChlCm+0.05)-ChlC*(Cu+(1-NCu)*Um) ChlC+dt*dChlC

Iron (20) (21) (22) (23) (24)

Fup dFC FC Ftot Fcon

Chlorophyll

Quota threshold, respiration and growth (29) NPSCu = (30) basres = (31) Cu

=

MIN(NCu,SCu,PCu) Um*0.05*1.01*((NCm-NC)/(NCm-NCo))/((NCm-NC)/(NCm-NCo)+0.01)* (NCNKt) and higher maximum transport rate (i.e. N2pref>Npref). The second system is only of consequence at high nitrate concentrations (ca. > 20 mM). The existence of the second system in algae has not been demonstrated unequivocally but it would explain why some algae appear to retain a relatively high f-ratio in the presence of high ammonium and high nitrate (Collos et al. 1997). The second system may be deleted, as required. This transport function may be parameterized using data from experiments on nutrient transport (Flynn 1998, 1999) Eq. 2) This is the ammonium version of Eq. 1, with only a single transporter type. Apref may exceed Npref by perhaps an order of magnitude (Flynn et al 1999). Eqs. 3,4) These use the potential transport rates for nitrate (Eq. 1) and ammonium (Eq. 2) to derive the relative rates of use for nitrate (NV, Eq. 3) and ammonium (AV, Eq. 4). Eq. 5) The f-ratio is calculated with reference to NV and AV. The inclusion of the number 1e-6 simply prevents a division by zero error if NV and AV are zero. Eq. 6) The sum of N-source concentrations is derived prior to the calculation of N-source uptake in Eq. 8. If additional (e.g. organic, DON) N-sources are to be included then addition ‘VP (e.g. Eq. 2) terms are computed and the ‘V terms altered to reflect additional sources (e.g. Eq. 4 would become AV=AVP/(AVP+NVP+DONVP)). Eq. 6 would then become sumNs=NH4+NO3+DON. Eq. 7) The quotient describing N-sufficiency (NCu) is calculated here using the dimensionless quota formulation of Flynn (2002a). If NC exceeds the value NCm, then NCu is limited to 1 by Boolean logic terms. Eq. 8) The uptake of N-sources is a rectangular hyperbolic function of the total N-source availability (sumNS, Eq. 6) and its accompanying half saturation constant (NKu). The maximum rate is set by the product of Um and NCm but only attains that value if N is the limiting nutrient (i.e. NCu=NPSCu), otherwise it is restricted by the value of NPSCu^beta. The value of surge (see Eq. vii) could be made a function of the nutrient status, thus enhancing uptake when nutrient stressed, as seen in reality (Riegman and Mur 1984, Flynn et al. 1999). Uptake is also increasingly restricted, using a sigmoidal function with a Hill number (power) of Qh, as NC approaches the maximum possible value set by NCabs. The

518 Algal Cultures, Analogues of Blooms and Applications presence of the value 0.01 is part of a trap to prevent numeric overload (Tuning and Running, below). Eq. 9) This corrects NC with the change in C content described by Cu (see Flynn and Fasham 1997 for explanation). Eq. 10) The change in NC with time (dt) is given by uptake (Nup, Eq. 8) minus the correction term dNC (Eq. 9) Eqs. 11-14) These describe the P processes in analogy with those for N given by Eqs. 7-10 respectively. If a diatom is not being simulated then Eqs. 15 to 19 are skipped. Eq. 15) The potential rate of Si uptake is limited by the product of Um and SCm, with reference to a hyperbolic function for the external Si concentration (S) with half saturation SKu. Eq. 16) SCu is not calculated by reference to the quota (as are NCu and PCu, Eqs. 7 and 11) because the current content of Si has no bearing on growth rate. SCu makes reference to whether the value of VS is high enough to support Um*SCo. As long as that condition is met then Si is not limiting. Eq. 17) The uptake of Si, like those for N (Eq. 8) and P (Eq. 12) is a function of the external nutrient concentration and is increasing limited as SC approaches the absolute maximum value (set by SCabs) using a sigmoidal function. However, any factor limiting growth (including light) other than Si leads to an enhanced accumulation of Si (elevation in SC) as the duration of cell cycle component G2 lengthens (see Flynn and Martin-Jézéquel 2000 for a description of a full model, complete with inclusion of the cell cycle). This is achieved by reference to the Boolean logic terms that test what is limiting growth. If Si is limiting then SCu=NPSCu and is also NPSCu), or if light is limiting (i.e. SCu=NPSCu=1; C now being limiting) then Si uptake is decreased according to (Cu/Um)^betaSi, assuming that Cu is positive and hence that growth is occurring. While in theory at least P could be accumulated as polyphosphate even in cells that are not growing, Si uptake only occurs concurrently with diatom growth (i.e. cell division). Hence the differences between this equation for Sup and that for Pup (Eq. 12). Eqs. 18,19) These terms are analogous to those for N (Eqs. 9,10) and P (Eqs. 13,14). Eq. 20) Uptake of Fe is, like that of the other nutrients, a hyperbolic function of the external nutrient concentration (F) and limited to a product of Um and the maximum quota FCm (but see comment about surge for Eq. 8,


519

above). Uptake is increasingly restricted as FC approaches FCm. There is no reference to FCu, as the control of growth Fe is indirect, operating via photosynthesis. Eqs. 21,22) These are analogous to those for N (Eqs. 9,10), P (Eqs. 13,14) and Si (Eqs. 18,19). Eq. 23) The total Fe associated with metabolism is calculated here. The first part derives the amount of Fe to support growth, making reference to costs for basal respiration and that associated with N-assimilation. The second part accounts for Fe associated with nitrate and nitrite reduction (hence reference to the amount of nitrate being consumed as Nup*frat). The final part accounts for Fe associated with the photosystems. Here this assumes a PSU size of 500 against a reference size of 900 (see Flynn and Hipkin 1999 for further explanation). Eq. 24) Fcon is in some ways analogous to NCu, PCu and SCu. It is a quotient describing the availability of free Fe (i.e. the difference between the total FC and that attributed to physiology by Ftot, Eq. 23). In this simple model Fcon is only used to control the synthesis of ChlC (Eq. 27) though it could be used to modify nitrate usage and hence the f-ratio (Flynn and Hipkin 1999). Eq. 25) This calculates the maximum (i.e. gross) photosynthesis rate required to support the growth rate described by Um, with the attendant basal and metabolic respiratory costs assuming growth on nitrate. This value is restricted by the value of NPSCu (Eq. 29) on the argument that as the nutrient status deteriorates then the maximum rate of photosynthesis must be restricted else C will continue to be fixed beyond the cell need. However, this process is obviously not that efficient in reality, as evidenced by the production of extracellular DOC by P-limited cells. One may expect P-limitation to be more likely to have such an impact on regulation as a lack of P is likely to adversely affect biochemical reactions and signaling. The inclusion of the number 1e-6 prevents a division by zero error if growth halts because NPSCu becomes zero. Eq. 26) Photosynthesis is described here by a simple exponential function, though other forms could be substituted (Jassby and Platt 1976). Eqs. 27,28) The rate of Chl (i.e. ChlC) synthesis is, like NC and PC, limited by a product of Um and the maximum quota (ChlCm). Parameter M is akin to surge (see text for Eq. 8), and enables the rate of photoacclimation to be adjusted (Flynn et al. 2001). The ability to synthesize Chl is restricted by the limitation of growth by N, P or Si (via NPSCu, Eq. 29) and/or by Fe (via Fcon, Eq. 24), and enhanced by the rate of photosynthesis relative to

520 Algal Cultures, Analogues of Blooms and Applications the maximum achievable rate (i.e. PS/Pqm). As ChlC approaches its maximum value (ChlCm), synthesis is restricted by a normalized hyperbolic function with half saturation of 0.05. ChlC not only decreases with Cu (as do NC, PC, SC and FC as described by Eqs. 9,13,18,21) but it is removed in response to poor N-status (hence reference to NCu). This rate of bleaching could be modified readily. The overall change in ChlC is given by Eq. 28. Eq. 29) This calculates the minimum (threshold) of the quotients describing nutrient sufficiency for N (Eq. 7), P (Eq. 11) and Si (Eq. 16) thus establishing which of these nutrients is most limiting. If a non-diatom is modeled then SCu (Eq. 16) is omitted, or its value set at 1. Eq. 30) The basal respiration rate is calculated assuming, arbitrarily, it equates to 5% (0.05) of the net maximum growth rate (Um). However, in the absence of incoming C from photosynthesis (PS, Eq. 26), surplus C will be exhausted in time. At some point basal respiration must halt. This is achieved by reference to N:C (NC) and maximum NC (NCm). This event could be allied to a death or encystment function if required. Eq. 31) The growth rate is calculated by reference to the photosynthesis rate and respiration associated with terms for nitrate reduction (redco*Nup*frat), N-assimilation into protein (Nup*Ncost) and basal respiration (basres, Eq. 30). Eq. 32) This gives the population C-biomass. This declines if Cu (Eq. 31) is negative because respiratory costs exceed C input from photosynthesis. Biologically this is a nonsense as the implication is that the whole population biomass acts as a C-reserve. In reality, once the individual cell respires its surplus (‘reserve’) C it has to either encyst or die (see also text for Eq. 30).

Tuning and Running Initial values for state variables other than C-biomass, should be set between their minimum and maximum values. The full model contains 34 constants that could be tuned (adjusted) to optimize model output against data. In reality, less than a third of these are worth considering in the first instance. These are constants for the maximum growth rate, the half saturation constants for nutrient uptake and the value of alpha for photosynthesis. The quota constants are relatively easy to parameterize, while the model is robust enough in its behavior that some of the constants, such as those for feedback (Qh and Kxi) do not need to be considered for tuning at all. It


521

should also be remembered that this model is not simple; it is a multinutrient model handling six nutrient interactions. The model described needs to be placed within a virtual physical environment, in which nutrients are consumed, for example. In common with the formal descriptions of most models, mathematical traps have not been indicated (except for example Eq. 8). These traps prevent mathematical errors, such as the removal of nutrients to below zero or the momentary increase of NC above NCabs. These errors typically happen if the integration step is too large. However, a balance has to be struck between using a small step size and the computation time; often simple traps can save a great deal of time without causing other problems such as loss of mass balance (i.e. all the N in the system, external and internal nutrients, should be constant). It must be said that traps must only be used for this purpose and not to correct aberrant model behavior due to flaws in construction.

Some Applications The applications of models of phytoplankton are too numerous to consider exhaustively here. The important thing is to learn from the act of model construction as well has from the subsequent operation of the model. In essence models can provide focal points for data collection as part of an experiment-model iteration to enhance general understanding, and they can be used as tools in ‘what-if?’ studies. The latter should not be considered until the former has at least converged to a consensus on what the ‘typical’ organism of this type does in response to the external forcings applied. This section will consider four topics; i) relating data from the real world to models, ii) steady-state vs non-steady-state cultures, iii) applications of models to demonstrate problems in dynamic versus steady-state experiments, and iv) applications for considering impacts of the nutrient N:P ratio on growth and competition.

Data collection and culturing systems In the above discussion frequent mention has been made of the paucity of data for modeling, and yet literature abounds with reports from experiments conducted on every facet of microalgal physiology going back over the best part of half a century. So what is the problem? The main problem is that most papers fail to report a suite of parameters adequate for modeling. Too often cell growth is reported only in terms of cell number or pigment. The worst is when the pigment is reported as in vivo Chl.a fluorescence rather than actual pigment content. Cell size (and hence

522 Algal Cultures, Analogues of Blooms and Applications the number of cells per unit of C), pigment content, and especially in vivo Chl.a fluorescence (Lippemeier et al. 2001, Parkhill et al. 2001), vary widely with physiological status. Nutrient concentrations and changes in elemental composition are also not always reported. Biochemical investigations often employ very high nutrient concentrations, orders of magnitude above environmentally relevant levels. It is also unfortunate that a lot of work has been conducted using cells grown in high-nitrate marine media; because phosphate precipitates quite readily in seawater the nutrient N:P cannot be maintained. To add to these problems are the usual ones concerning the peculiarities of the species strain employed, the growth temperature and the light conditions. For the latter, not only are the incident photon flux density and photoperiod of importance, but if the nutrient concentrations are elevated then the culture is not optically thin and hence photoacclimation interacts with nutrient status during culture growth. It is thus quite possible that algal growth in these media becomes co-rate limited by N, P and also by light. The source of the algae is another problem. Field experiments inevitably make use of phytoplankton with an unknown nutrient history and hence any study of their physiology may be against a background of nutrient up- or down-shock. In addition there is the presence of, and hence interaction with, other microbes. In the laboratory there is in addition to the issue of axenic (bacteria-free) or non-axenic cultures, the controversy over the use of continuous versus batch cultures. To sum it up, as mentioned earlier, the vast bulk of experiments on algal physiology have been conducted on very few species, and these are often unrepresentative of environmentally important organisms. In view of all these issues, the author argues for the construction of models that accord with biological knowledge rather than to specific data sets. Of course the model should be capable of simulations matching data from the real world (Page et al. 1999) but in the absence of such data sets mechanistic models, or at least models constructed with accord to how biological systems work, should still be able to generate simulation outputs that could fool an expert in algal physiology. Such models can then act as a stimulus and focal point for future experimental studies.

Chemostat, Batch and Stretched-batch Culture Systems The subject of culture systems is important both with respect to the usefulness of the data derived from their use and also, because of the logistic demands for culture volume for chemical analyses.


523

Both the Monod and quota models were developed to describe the growth of microbes in steady-state chemostat cultures. That they are poor at describing the dynamics of up- or down-shock from steady-state is well known (Burmaster 1979), yet they are used most widely to describe growth in dynamic situations (Tett and Droop 1988). From a data collection point of view in some ways the chemostat is ideal because repeated measurements can be taken of the same physiological status. On the minus side, depending on the culture size and the rate of dilution, the actual amount of material available for removal and hence for different analyses is limited. Aside from the logistic problems, growth is never in the presence of unlimited nutrients. It is every bit as important to get the simulation of the dynamics of the entry and exit from steady-state as it is to get the steady-state relationship between nutrient concentration and growth rate. The simple fact is that in nature steady-state environmental conditions do not exist for any significant period of time; note that cells in the constant environment of a chemostat still take many generations to come to an internal physiological steady-state. Simply within a light-dark cycle the concept of a steady-state is questionable. However, the batch culture also presents problems, primarily associated with the brief period of time before nutrients are exhausted, and with the fact that as the cell density increases, the light attenuation develops (just as it does in a natural bloom). The removal of volumes for sampling results in the decline in the culture volume, which is also likely to impact on organism growth. A compromise is what Page et al. (1999) call a ‘stretched-batch’ culture. Here the system is treated like a batch culture with a small inoculum into a large culture vessel volume. However, there is a continuous (or perhaps discontinuous, e.g. once daily) input of fresh medium at a rate equivalent to, say, 5-10% of maximum growth rate. An equal volume of culture is removed over the course of the day. This approach provides a significant amount of culture for daily sampling maintaining the culture volume, enables the dynamics of growth into and then from nutrient-unlimited growth, and defaults eventually to a nutrient rate limited continuous culture. A similar approach is used for mesocosms where a proportion of the bag contents are replaced periodically. Growth dynamics are also similar to the development of a bloom in nature, where there is a continuous rate of attrition due to grazing and also of nutrient input from regeneration (associated with grazing and bacterial activity) and physical processes (such as upwelling and internal waves).


Steady-state vs Dynamic Here we shall consider the issue with reference to output from MAP2, specifically considering the problems of measuring growth rates in batch cultures and the advantages of using ‘stretch-batch’ systems. The implications of using batch culture systems to study N-limited growth of an organism with mmax = 1 d–1 (such as a diatom) are shown in Fig. 14.1. An inoculum equivalent to 10% of the final C-biomass gives a very short period of nutrient-replete exponential growth. Even a 1% inoculum gives only five days of such growth. Note also that the first day is dominated by the up-shock associated with recovery from N-stress. The use of a stretch-batch approach, with a dilution rate equal to 10% of mmax bringing in fresh medium and removing old medium plus biomass, stretches the growth period and also results in a steady-state (chemostat-style) terminal phase.

dilution 0.1 inoculum 10% dilution 0.1 inoculum 1% dilution 0 inoculum 10% dilution 0 inoculum 1%

Nm (d–1)

2

1

0 0

2

4

6

8

10

Time (d)

Fig. 14.1 Simulated changes in N-specific growth rate (Nm) during growth in N-limited culture systems with no dilution (simple batch) and a dilution rate of 0.1 d–1 (10% of mmax), starting with an inoculum of 10% or 1% from the previous cycle. Optical depth of 0.1 m. N-source 100 mM. The implications of following growth by measuring different parameters, in this instance C, Chl or N, are shown in Fig. 14.2. In steady-state these growth rate values measured in an asynchronous culture in continuous light will be equal, but in batch type systems, and also in nature, there can be highly significant differences. In particular, growth rates determined by Chl are not robust. They show sharp transients not only to nutrient up-shock, but also to down-shock. The extent of that latter problem will depend on the rate of Chl bleaching on nutrient limitation. However, as shown by the lower panels in Fig. 14.2, the situation becomes more complex again if the optical depth (or volume of the culture flask) is more significant. Here, as the

5 4 3 2 1 0 –1

525

dilution 0.1; inoculum 10%

dilution 0; inoculum 10% 2 C Chl N

m (d–1)

m (d–1)


1

C Chl N

0 0 2 4 6 8 10 12 14 Time (d)

0 2 4 6 8 10 12 14 Time (d)

dilution 0; inoculum 1%

dilution 0.1; inoculum 1%

3 2 1 0

C Chl N

2 m(d–1)

m(d–1)

4

1 0

–1 0 2 4 6 8 10 12 14 Time (d)

0 2 4 6 8 10 12 14 Time (d) dilution 0.1; inoculum 1%; 0.5 m OD

dilution 0; inoculum 1%; 0.5 m OD 2 C Chl N

m(d–1)

m(d–1)

2 1

C Chl N

1

C Chl N

0

0 0 2 4 6 8 10 12 14 Time (d)

0 2 4 6 8 10 12 14 Time (d)

Fig. 14.2 Simulated changes in C, Chl and N-specific growth rates (m) during growth in N-limited culture systems with no dilution (simple batch) and a dilution rate of 0.1 d–1 (10% of mmax), starting with an inoculum of 10% or 1% from the previous cycle. Optical depth of 0.1 m, or (as indicated) 0.5 m. N-source 100 mM. biomass concentration increases so self-shading becomes increasingly significant, not only prompting photoacclimation with an attendant increase in Chl:C (hence Chlm increases faster than Cm and Nm) but ultimately rate limiting growth by light availability when ChlC = ChlCmax. The potential dangers of using Chl as a biomass index for phytoplankton, and for computing phytoplankton growth, are clear. The situation is made worse when the pigment is estimated using in vivo fluorescence as the relationship between the fluorescence signal and Chl depends also on the nutrients status (Lippemeier et al. 2001). It should be noted also, that these simulations were run in continuous illumination. Imposition of a light-dark

526 Algal Cultures, Analogues of Blooms and Applications cycle (day-night), even without the natural changes of light over a day, also impinge on growth rate processes. In short, while it may be convenient to use steady-state culture systems it is more important that we understand the implications of non-steady-state growth. Models can help in focusing such studies.

Nutrient ratios Continuing with this theme, Fig. 14.3 shows simulated growth of a diatom under different nutrient regimes (nutrient ratios of N:P:Si). It can be seen here that non-Si limitation results in a rapid decrease in external Si; this is because cellular Si:C increases under such conditions. The implications of growth with different nutrient N:P can be considered using the model. LowN : LowP

LowN : HighP 1.0

Stress

0.5

0.5

0.0

0.0

100

100

10

10

mM

mM

Stress

1.0

1 N P Si

0.1 0.01 0

N P Si

1 0.1

0.01 5 10 Time (d)

0

15

Stress

1.0 0.5

0.0

0.0

100

100

10

10

1

mM

Stress mM

0.5

N P Si

0.01 0

15

HighN : HighP

HighN : LowP 1.0

0.1

5 10 Time (d)

N P Si

1 0.1 0.01

5 10 Time (d)

15

0

5 10 Time (d)

15

Fig. 14.3 Changes in nutrient status (i.e. NCu, PSu and SCu, value of 0 indicating maximum stress) and different external nutrient concentrations during simulated diatom growth in culture systems with a dilution rate of 0.1 d–1 (10% of mmax), starting with an inoculum of 1% from the previous cycle. Optical depth of 0.1 m. Nutrient N:P in lowN:lowP is the same as in highN:highP.


527

N:P alone is not the major factor; it is the combination of the absolute concentrations and this ratio. Low concentrations limit growth, leading to nutrient stress that may promote toxin or mucus production. High nutrient concentrations promote growth of biomass densities that attenuate light causing C-stress to compound the nutrient stress that follows, with potential for mass death and deoxygenation. The danger of following the development of a natural bloom of phytoplankton using Chl analysis has been indicated in Fig. 14.2. The utility of having suitable models parameterized to represent particular events can be readily appreciated. As an example, the mechanistic model of the paralytic shellfish poison (PSP) producing dinoflagellate Alexandrium developed by John and Flynn (2002) was placed in a physical scenario in which the phytoplankton could perform vertical migration (developed by Flynn and Fasham 2002). The resultant simulations (Flynn 2002b) suggested that toxicity could develop more rapidly in cells exposed to a sequence of natural conditions than one may contemplate when considering only the results from culture experiments. A continuing matter of concern is the degree to which multi-nutrient limitations interact. While the model predicts various levels of stress, only that with the greatest level (lowest value in the plots) is thought to, and in the model actually does, rate limit growth. This is an area where molecular biology may help, by the detection of stress-proteins. However, arguably nitrate reductase is a stress-protein; it is only synthesized in response to stress (lack of sufficient external ammonium to give a N-status that suppresses NR synthesis). We can immediately see a problem with terminology here; stress does not necessarily indicate growth limitation. The question is whether the stress can be countered, by for example photoacclimation or use of an alternative nutrient source (nitrate, organic sources etc.). Again models can alert us to these issues by making us think. There are endless permutations of such simulations, using different ratios and different concentrations, with inclusion of a nondiatom competitor, with different growth rates for the organisms, different light regimes etc. To study such a range experimentally would be a monumental task. Modeling, provided that one accepts that the model is not dysfunctional, can at the least indicate which combinations of conditions may be particularly worthwhile studying. Models also enable theoretical considerations of nutrient assimilation dynamics (Flynn and Berry 1999) and competition between two organisms that only differ with respect to a single parameter (e.g. affinity for one nutrient). Flynn (2002a) used such an approach to conduct an examination of the arguments surrounding the critical N:P ratio (Rhee and Gotham 1980, Turpin 1986), concluding that much of the former

528 Algal Cultures, Analogues of Blooms and Applications conclusions on this issue appeared to be an artifact of using inappropriate modeling methods.

CONCLUSIONS When constructing a model, and parameterizing it, we become all too aware of just how little we actually know about organisms whose physiology has been studied for over 100 years. Models act as a focus for what we know and reveal what we need to know more about. Over the past 20 years tools have become available, most notably the personal computer, that have revolutionized modeling. Ultimately, however, the usefulness of models can only reflect the validity of the concepts upon which they have been constructed. That requires real cooperation between ecophysiologists and mathematicians and a willingness to go beyond simple empirical curvefitting approaches.

ACKNOWLEDGEMENTS This work was supported in part by The Leverhulme Trust / Royal Society (UK).

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Richardson, T.L., J.J. Cullen, D.E. Kelley and M.R. Lewis 1998. Potential contributions of vertically migrating Rhizosolenia to nutrient cycling and new production in the open ocean. J. Plankton Res. 20: 219-241 Riegman, R. and L.R. Mur. 1984. Regulation of phosphate uptake in Oscillatoria agardhii. Arch. Microbiol. 139: 28-32. Shuter, B. 1979. A model of physiological adaptation in unicellular algae. J. Theoret. Biol. 78: 519-552. Stephens, N., K.J. Flynn and J.R. Gallon. 2003. Interrelationships between the pathways of inorganic nitrogen assimilation in the cyanobacterium Gloeothece can be described using a mechanistic mathematical model. New Phytol. 160: 545-555 Syrett, P.J. 1981. Nitrogen metabolism of microalgae. Can. Bull. Fish. Aquat. Sci. 210: 182-210 Terry, K.L. 1982. Nitrate and phosphate uptake interactions in a marine prymnesiophyte. J. Phycol. 18: 79-86. Tett, P. and M.R. Droop. 1988. Cell quota models and planktonic primary production. pp. 77-233. In: Handbook of laboratory systems for microbial ecosystems 2. J.W.T Wimpenny, (ed). CRC press, Florida, USA. Turpin, D.H. 1986. Growth rate dependent optimum ratios in Selenastrum minutum (Chlorophyta): implications for competition, coexistence and stability in phytoplankton communities. J. Phycol. 22: 94-102. Zonneveld, C. 1998. A cell-based model for the chlorophyll a to carbon ratio in phytoplankton. Ecol. Model. 113: 55-70.

15 Competition of Aquatic Microalgae in Variable Environments: The Disturbance Effect Sabine Flöder1 and Ulrich Sommer2 1 2

Botanical Institute, University of Cologne, Gyrhofstrasse 15, 50931 Köln, Germany IFM-Geomar, Leibniz-Institute of Marine Science, Düsternbrooker Weg 20, 24105 Kiel, Germany

Abstract Global diversity loss has renewed the interest in the underlying mechanisms that explain species diversity and its maintenance in natural systems. This gave the impetus to significant new development in competition research. The aim of this chapter is to review important research results on competition among microalgae and on the effects of environmental fluctuation or disturbance on community structure. It covers mechanistic competition theory as well as new approaches questioning the central theorem of the classical theory. Recent results of competition experiments and bio essays are included, that have shown how factors other than macro-nutrients may affect the structure of microalgal communities. To explain the influence of disturbance on community structure, we report different approaches to define disturbance and explain the meaning of disturbance within the framework of this review. Intermediate disturbance hypothesis and dynamic equilibrium theory build the theoretical background for discussing competition in variable environments. Experimental results, models and simulations show how environmental fluctuation influences community structure and how different mechanisms may interact.

INTRODUCTION For a long time, one of the key questions in ecology has been: “Why are there so many species?” With today’s rapid diversity loss, this old question may be one of the most relevant ones in modern ecology. The apparent contrast between the competitive exclusion principle and the incredible species

#!" Algal Cultures, Analogues of Blooms and Applications richness usually found in local ecosystems, has been discussed in relation to mechanisms that prevent the competitive exclusion of species. Tending to interrupt or to revert successional processes, disturbance or environmental fluctuation has been recognized as mechanism maintaining diversity. Recently, considerable progress has been made researching competition as well as the effects of disturbance: New models have questioned central assumptions of the competition theory, demonstrating that competition for resources may be much more complex than previously assumed. Competition experiments and bioessays have shown that light and trace elements may be important factors structuring microalgal communities in marine as well as in limnetic systems. Environmental fluctuation and disturbance have been shown to influence competition and biodiversity in local ecosystems. With an emphasis on marine and freshwater microalgae, this chapter reviews the development in competition research and in studies on disturbance effects on community structure. We start out with a section on the difficulty of defining disturbance, where we discuss different approaches to define disturbance and explain the meaning of disturbance in the context of this chapter. Some examples of disturbances relevant to microalgal communities are given in a subsection. Competition theory and the effect of disturbance are explained in the section ‘Coexistence as a competition result’. The following subsection ‘Empirical analysis of competition: constant and fluctuating conditions’ includes important examples of laboratory and field experiments on competition as well as model approaches and simulations. A focus of this chapter is on direct increases of resource availability and the effect of nutrient and light fluctuations on competition, succession and diversity of marine and freshwater microalgae. Changes in the physical environment may affect the competitive ability of species, and consequently influence competition of microalgae in an indirect way. As an example of fluctuations of the physical environment of microalgae, saline intrusions in coastal aquatic ecosystems are discussed. Consumers affect microalgal communities in various ways. Within the framework of this chapter, the discussion of consumer effects is restricted to the aspects of grazing affecting species diversity, competition among microalgae and on opposing effects of grazing and resource availability on microalgal communities. For an excellent review on biodiversity in aquatic food webs, refer to Hillebrand and Shurin (2005). Interacting effects ‘Relations of disturbance, productivity and diversity of microalgae’ are discussed in the last section of this chapter. Virus infections of algae are subject of chapter 6. For effects of turbulence on nutrient availability and microalgal growth (see Chapter 13, Berdalet and Estrada).

Competition of Aquatic Microalgae in Variable EnLironments

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Defining Disturbance A large number of community theories, ecological studies and environmental investigations focus on the effects of disturbance on certain species, communities or ecosystems. However, although the mechanisms of disturbance are frequently under investigation, attempts to define disturbance have been rare. Intuitively, it may be clear which factor might act as a disturbance in a certain community or system – defining disturbance is a complex task. Environmental factors acting as disturbance vary not only between the systems studied; variation may also occur within the trophic level being under investigation (Pickett et al. 1989). Depending on the object under investigation, disturbance mechanisms have been termed for example perturbance, catastrophe, disaster or mortality. Different intensities of disturbance have been categorized as ranging from disturbance, to disaster, to catastrophe or from stress to disturbance. These suggestions have not always been helpful in clarifying the concept of disturbance. Consequently, there is some disagreement among scientists about the correct use of the term ‘disturbance’ (Laska 2001). Grime (1979) defined disturbance as ‘mechanisms which limit the plant biomass by causing its partial or total destruction’. Kilar and McLachlan (1989) extended this definition by those mechanisms Grime (1979) defined as stress. They described disturbance as consisting ‘of physical and biological mechanisms which limit the abundance or distribution of organisms by causing their destruction or restricting their growth and reproduction’. Following Sousa (1984) and White and Picket (1985), Townsend and Hildrew (1994) defined “disturbance as any relatively discrete event in time that removes organisms and opens space or other resources that can be utilized by individuals of the same or different species”. One basic conceptual problem of disturbance remains disregarded by the definitions cited above. Disturbance may appear at any organizational level of an ecosystem, and mechanisms and consequences of disturbance may be different at each of these levels. Moving up the hierarchy can absorb disturbance, at any level of ecosystem hierarchy1 . An external factor, causing a disturbance at a certain level of ecosystem organization may be part of a higher level (Rykiel 1985). For example, grazing of Littorina littorea (Gastropoda) may be a disturbance to benthic microalgae populations inhabiting a boulder in the Baltic Sea. Heavy grazing may have a noticeable 1

The concepts of hierarchy theory are fully developed in: Allen, T.E.H. and T.W. Hoekstra. 1992. Toward a unified ecology. Columbia University Press. New York.

#!$ Algal Cultures, Analogues of Blooms and Applications impact on the benthic community of the rocky intertidal, which these mesograzers are an important part of. Focusing on the ecosystem ocean, grazing by Littorina may have no detectable effect, but it is still a disturbance to benthic microalgae. Thus, the application of the disturbance concept depends on the perception of the ecosystem structure under examination, and on the level of organization that is of interest to the researcher (Reynolds et al. 1993). To address this problem, Pickett et al. (1989) suggested the following definition: “Disturbance is a change in the minimal structure (of an ecological unit) caused by a factor external to the level of interest”, where minimal structure means a system of subunits being necessary for the maintenance of the unit of interest. The term external refers to any action that originates outside the unit in question, including the action of the higherlevel unit. In order to gain consensus in a specific research field, working definitions may be helpful. Concerning the seasonal succession of phytoplankton species composition, the following definition was proposed: “disturbances (are) primarily non-biotic, stochastic events that result in distinct and abrupt changes in the composition (of species) and which interfere with internallydriven progress towards self-organization and ecological equilibrium; such events are understood to operate through the medium of (e.g.) weather and at the frequency scale of algal generation times” (Reynolds et al. 1993). It has to be noted however, that this definition is based on the controversial concept of autogenic succession. In the context of microalgal succession, Flöder and Sommer (1999) defined “disturbance as externally forced, episodic environmental variability”. Within this chapter, we will use the term disturbance according to this definition. Disturbance affects phytoplankton communities by interrupting its succession (temporal change in species composition of a community) and by resetting it back to an earlier state or shifting it to an alternative pathway (Reynolds 1987, 1988). Any kind of environmental fluctuation that makes resources available for growth and reproduction of species may cause such resets and shifts in phytoplankton succession. Consequently, environmental fluctuation may act as a disturbance to microalgal communities, provided its influence is strong enough to interrupt and to set back successional sequences. Resource availability may be increased not only by resource fluctuation (direct increase). By removing organisms or reducing growth rates of certain species, also variations of the physical environment or consumer abundance can increase resource availability for remaining or unaffected species (indirect increase). See Table 15.1 for examples of factors that may act as disturbances to microalgal communities.


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Table 15.1 Examples of factors that increase resource availability Direct increase of resource availability: Resource fluctuations o Limiting nutrients: phosphorus, nitrogen, silicate for silicfied forms of microalgae o Trace elements: e.g. iron, boron o Light

Indirect increase of resource availability: Removal of organisms, reduction of growth rates Physical environment o Turbulence o Dilutions o Salinity, pH o Toxins

Consumer abundance o Grazing o Parasitism: e.g. viruses, fungi

In benthic as well as in terrestrial systems, the availability of space is often discussed as an important resource. Plants need space to germinate, so that they can access water and nutrient supply from the ground and light from above. In aquatic systems, acquisition of nutrients functions differently. Macronutrients and trace elements are dissolved in the medium the organisms live in, and uptake takes place via the cell surface of the microalgae. However, in spite of these differences concerning nutrient uptake, resource limitation dictates the shortage of space in both, aquatic and terrestrial systems. In other words, the space an organism needs for growth and reproduction is a function of resource availability. Of course, space in its three dimensional sense is an important factor when discussing spatial heterogeneity and patch dynamics – changes in patchiness as patch formation, alternations of position and shape of existing patches or patch structure (Pickett et al. 1997). Although an extensive discussion of patch dynamics1 and its implications for marine microalgae would be beyond the scope of this review, it is important to note the different aspects of the term space when discussing competition in variable environments.

Examples of disturbance to phytoplankton communities The classic example for factors causing disruptions in phytoplankton succession is the increase of mixing depth by stormy weather (Reynolds 1984). In temperate regions of the northern hemisphere, for example, the grand mean of the occurrence of spells of bad weather is 5-15 d (Harris 1986). The extent of vertical mixing in lakes and seas depends on surface cooling and convection, on wind action on the surface, on basin morphometry and its effects of return currents and internal wave generation, on deflection due to the rotation of the earth (Coriolis’ forces) and, where relevant, on tidal 1

For disturbance - patch dynamics relations, please see Platt and Connell. 2003. Natural disturbances and directional replacement of species. Ecol. Monogr. 73: 507-522, and Roxburgh et al. 2004. The intermediate disturbance hypothesis: patch dynamics and mechanisms of species coexistence. Ecology 85: 359-371.

#!& Algal Cultures, Analogues of Blooms and Applications surge (Reynolds 1997). Assuming constant wind conditions, an increase in mixing depth of about 9 m for each increment in wind forcing of 1 m s–1 is expected for relatively large lakes like Lake Constance and the open ocean (Reynolds 1997). Increases in mixing depth lead to dilutions of pelagic phytoplankton populations, to increases in nutrient concentration and to changes in the vertical mean of light intensity. Disruptions in phytoplankton succession caused by storm-induced mixing events have been reported from several natural lakes and the famous experimental lake systems (Lund and Reynolds 1982) in Blelham Tarn, GB. Mixing events caused either resets of species composition to earlier successional stages or shifts to alternative pathways of succession. In Crose Mere for example, phytoplankton succession shifted from an Asterionella and Stephanodiscus astraea dominated community to Eudorina-Volvox domination, which was then replaced by Anabaena and Aphanizomenon. Wind-induced mixing resulted in the phytoplankton community to revert from Anabaena-Aphanizomenon domination back to the Eudorina-Volvox dominated stage, which again shifted to an Anabaena-Aphanizomenon dominated community. In response to another mixing event, the phytoplankton shifted to an alternative stage dominated by Aulacoseira granulata, Fragilaria and Closterium. Succession progressed to Ceratium domination, which was reset to the Aulacoseira granulata, Fragilaria and Closterium stage by another wind induced mixing event. These disruptions and the reversions of the phytoplankton succession by enhanced vertical mixing have been recorded in Crose Mere in two consecutive years (1973, 1974). A similar community reversion from Anabaena-Aphanizomenon domination to Eudorina-Volvox domination was induced by experimental phosphate fertilization to Lund Tube B in 1976 (Reynolds 1980, 1984). This indicated that increasing phosphorus limitation caused the shift from EudorinaVolvox to Anabaena-Aphanizomenon dominated communities. The release from phosphorus limitation might have been responsible for the reversion back to the Eudorina-Volvox state. In aquatic systems, lakes as well as oceans, phytoplankton growth may be limited by light and the macronutrients phosphorus, nitrogen and silicate. Most limnetic systems tend to phosphorus limitation, whereas nitrogen is a limiting nutrient in vast regions of the ocean. For silicfied plankton algae like diatoms, silico-flagellates and synurophytes, silicate is an important resource, which often becomes limiting in both, marine and freshwater systems. Limnetic enclosure experiments with natural plankton assemblages performed in Lake Erken (Sweden) have shown that cyanobacteria might be limited by the trace elements boron and iron (Hyenstrand et al. 1999, 2001).


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In certain oceanic regions, phytoplankton growth appears to be traceelement limited rather than nutrient limited. High nutrient–low chlorophyll (HNLC) areas (Minas et al. 1986) of the polar and sub polar regions, and equatorial upwelling zones are characterized by low phytoplankton densities although concentrations of major nutrients are high throughout the year (Chrisholm and Morel 1991). Nitrate, phosphate and silicate are not limiting in this areas and, as addition experiments showed, neither are the trace nutrients Mn, Zn and Co (Buma et al. 1991, Coale 1991, Scharek et al. 1997). According to the iron hypothesis, limitation by iron and light and losses by zooplankton grazing controls phytoplankton production in HNLC areas. Confirming the iron hypothesis, several laboratory, shipboard and in situ enrichment experiments (reviewed in (Boyd 2002a, 2002b, de Baar and Boyd 2000) showed that iron enrichment in this regions leads to significant increases in phytoplankton biomass. In most cases, enrichment induced a shift in the phytoplankton community from small pico- or nanophytoplankton towards large diatom species. Aeolian input of continental aerosols and their partial dissolution, input from below by vertical mixing as well as upwelling and lateral transport of dissolved iron from ocean-margin sediment sources are thought to be major sources of iron replenishment. Iron disturbance in marine phytoplankton communities and its implications for marine food webs may be relevant with regard to global warming (Falkowski et al. 1988). Increasing storm events and wind velocities are predicted effects of global climate change (IPCC 2001). These would increase the Aeolian input as well as upwelling and vertical mixing (de Baar and Boyd 2000). The importance of iron inputs may change over longer climatic or geological time scales. During the last ice age, for example, the Aeolian input of continental dust may have been higher due to arid periods and increased wind speeds (Kumar et al. 1995, Martin 1990, Murray et al. 1995). Besides environmental fluctuations that involve immediate (direct) increases in the concentration of limiting resources, physical disturbance may affect phytoplankton communities. Salinity is regarded as an important factor affecting phytoplankton growth and distribution (Day et al. 1989, Hammer 1986). In coastal aquatic ecosystems e.g. estuaries and lowland freshwater systems, saline intrusions may act as physical disturbances, affecting the phytoplankton communities. A salinity of circa 5 ppt forms a lethal barrier for most estuarine planktonic algae; freshwater as well as marine species suffer severe osmotic stresses at this salinity level (Kies 1997). Consequently, diversity and species number are reduced in brackish systems, compared to freshwater and marine systems (Remane and

#" Algal Cultures, Analogues of Blooms and Applications Schlieper 1971, Hartog 1967, Schallenberg et al. 2003). An investigation of a series of water bodies of different salinity (0.2-4.2ppt) on the Dungeness shingle (south coast of Kent, GB) established salinity as an important factor explaining inter site differences of the phytoplankton communities (Chapman et al. 1998). If saline intrusions cause an aquatic system to shift from a freshwater to a brackish state, the increased salinity acts as a disturbance because of the osmotic stress it forces upon the phytoplankton. Osmotic stress is likely to affect the conditions for competition among species. A comparison of growth rates among phytoplankton species from a tidally influenced shallow lake with predominantly freshwater conditions (Lake Waihola, NZ) that had been exposed to freshwater and different levels of oligohaline conditions (0.5 to 5 Psu) showed species-specific differences (Flöder and Burns unpubl. data). Saline intrusions significantly influenced the phytoplankton community of this lake and reduced its biodiversity (Flöder and Burns 2004).

Coexistence as a Competition Result The models of Lotka (cited from Lotka 1956) and Volterra (1926) were the first to imply the impossibility of two or more species to coexisting on the same limiting resource for unlimited time. These models built the theoretical basis for Gause’s (1934) competition experiments, that tested this hypothesis with different Paramecium species competing for the resource yeast. The idea was adopted by Lack (cited: from Lack 1954) and Huxley (1943) and led to the formulation of the competitive exclusion principle (Hardin 1960), according to which competition selects for the fittest species and leads to the exclusion of all the others. This development resulted in the theorem “only as many species can coexist, as there are limiting resources”. Competition experiments conducted by Tilman (1977) and others verified the competitive exclusion principle (see Sommer 2002 for an overview). When competing for one resource under constant culture conditions, as realized in the chemostat technique, the species with the lowest demand of the resource will win against the other species. Competition for two limiting resources can result in the coexistence of two species. Competition studies led to the formulation of the mechanistic resource competition theory (Tilman 1982). The basic innovation of this theory was the definition of the equilibrium value (R*) of the free concentration of the limiting nutrient. R* is a species-specific parameter, describing the competitive ability of a species for a nutrient. This threshold value determines the minimum demand of a resource at which (at given specific loss rates and physical conditions) reproduction and losses of


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a population are at equilibrium. By determining R* for the species under investigation, it is possible to predict the outcome of competition experiments. Competition of two species, A and B, for a single essential resource is shown in Fig. 15.1. Expressing reproduction rates of two species A and B as a function of resource concentration (Fig. 15.1a), species A displays a higher reproduction rate and a higher loss rate as species B. Accordingly, R*B is lower than R*A . Species B will therefore win the competition (Fig. 15.1b) by reducing R* to values below the minimum demand of species A. In a graphical representation of two species competing for two essential resources (Fig. 15.2), the relation between R* of resource 1 and R* of resource 2 is expressed by zero net growth isoclines (ZNGI). If species A has a lower R* for both resources (Fig. 15.2a), this species will win the competition as long as the resource supply point (certain combination of resource concentrations available to the species) is within the region marked by the ZNGIs of species A. Stable coexistence is possible, if the ZNGIs of species A and species B cross each other (Fig. 15.2b). This means species A has a lower R* for resource 1, whereas species B has a lower R* for resource a Reproduction rate

A B loss - species A loss - species B

R*B R*A

Resource concentration

Population density

b B R

A R*B Time

Fig. 15.1 Competition of two species for a single resource. a. Dependency of reproduction rate on resource concentration and determination of the equilibrium value (R*). b. Time course of species abundances and resource concentration. Thick line: species A, thin line: species B, dashed line: resource concentration (after Tilman 1982).

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2. Being the better competitor for resource 1, species A will win, if both species compete for resource 1. This is the case for all resource supply points that fall into the region a in Fig. 15.2b. Species B is the better competitor for resource 2 and will dominate when competing for this resource (region b). The intersection of the ZNGIs describes an equilibrium point at which coexistence of species A and species B is possible. The region of coexistence (a + b) of species A and B is marked by their consumption vectors (CA and CB). The slopes of the consumption vectors indicate, whether stable or unstable coexistence equilibrium can be assumed. If, like in Fig. 15.2b, the consumption

Concentration of resource 2

a A B

a

a

a Concentration of resource 1


b A B

a

a

a+b

b CB

b CA


Fig. 15.2 Competition of two species for two essential resources. Solid vertical and horizontal lines: zero net growth isoclines (ZNGI) of species A, dashed vertical and horizontal lines: ZNGI of species B, solid arrow (CA): consume vector of species A, dashed arrow (CB): consume vector of species B. ZNGIs and consume vectors form the boundaries of regions, which indicate the outcome of competition. Depending on the region a resource supply point falls into, one species will dominate or both species will coexist. a: Species A wins, b: species B wins, a + b: species A and species B coexist. a. Species A always wins. b. Species A and Species B coexist stably if resource supply points fall into the region (a + b) marked by both consume vectors (after Tilman 1982).


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vector (CA) of species A is steeper than the one of species B, each species consumes relatively more of the resource that limits its own reproduction rate. This indicates stable coexistence equilibrium. Unstable coexistence equilibrium will result, if each species consumes relatively more of the resource that limits the reproduction rate of its competitor. In this case, the consumption vector of species B will be steeper as the one of species A. At equilibrium, even a slight derivation from the equilibrium point will result in one of the species becoming dominant. Theoretical and empirical investigations of competitive exclusion, species oscillations and chaos (see below) are based on the assumption of equilibrium conditions. This approach brought valuable insight into the processes of resource competition, but since the equilibrium assumption does not apply for natural communities, these results cannot be directly transferred to natural conditions (Harris 1986). Natural systems are characterized by environmental fluctuations of a broad range of frequencies and intensities. Due to these environmental fluctuations, resource competition in natural systems may rarely reach its final stage. The exclusion process takes time – about 25 generation times under constant environmental conditions, according to the competition experiments cited above. Considering for example the occurrence of storm events in the northern hemisphere (every 5-15 d) and the average generation time of phytoplankton of about two d (Sommer 1991), environmental conditions in the pelagic zone are likely to change three to ten times before competitive exclusion is likely to occur. Environmental fluctuations have long been suggested as an explanation for species diversity and its maintenance in natural systems. The apparent conflict between the exclusion principle and the high diversity existing in natural systems was expressed as ‘paradox of the plankton’: Pelagic habitats may be regarded as being rather homogeneous with only light and a few nutrients limiting. Even though, there are usually more than 30 coexisting species even in a small sample (Hutchinson 1961). To solve this paradox Hutchinson (1961) suggested that neither too short nor too long periods of stable environmental conditions can prevent competitive exclusion. The Intermediate Disturbance Hypothesis (IDH) (Connell 1978, Paine and Vadas 1969) followed a similar approach: According to the IDH, a maximum in species richness and diversity within a community occurs at intermediate frequencies and intensities of disturbance: • In the absence of disturbance, competitive exclusion following resource depletion would reduce diversity to very low levels.

#"" Algal Cultures, Analogues of Blooms and Applications • Under very intense or frequent disturbance, only few pioneer species could get established. This would also lead to a minimum in diversity. • Disturbance of intermediate frequency and/or intensity would provide repeated opportunities for pioneer populations, that otherwise would become extinct and successful competitors could survive the disturbance without getting dominant (Connell 1978, Paine and Vadas 1969). Transferring the IDH to plankton communities, Reynolds (1988) explained that disturbance frequencies in the order of hours (less than one generation time) cause physiological responses in plankton algae. At a low frequency (disturbance events 10 or more days apart), each disturbance may initiate a successional sequence. Smaller, intermediate cycles of disturbance (with an interval of 20-200 h) interact with growth rates and therefore tend to maintain diversity.

Empirical analysis of competition: constant and fluctuating conditions The mechanistic resource competition theory was developed with cultures of freshwater diatoms competing for the macronutrients silicate and phosphorus and Tilman’s (1977) experiments were the first to demonstrate two species coexisting on two limiting resources at a stable equilibrium. The theory has been transferred to zooplankton (Rothhaupt 1988) using different ratios of Monoraphidium and Chlamydomonas abundance as resources for the rotifer species Brachionus plicatilis and Brachionus calyciflorus. Grover (2000) investigated competition for dissolved organic carbon (DOC) and Phosphorus among heterotrophic (bacteria), mixotrophic (Ochromonas danica) and autotrophic (Scenedesmus quadricauda) plankton. A study using a cyanobacterium and a diatom competing for nitrogen and phosphorus (Hu and Zhang 1993) provided evidence for the existence of unstable equilibria. The result of competition in these experiments depended on the initial population density of the competing species and thus, agree with the mechanistic resource competition theory (Passarge and Huisman 2002). Huisman and Weissing (1994) modified Tilman’s (1982) mechanistic competition theory and proved its applicability for light (Huisman et al. 1999). Phytoplankton species differ in their critical light intensity. Similar to R* in nutrient competition (Tilman 1982), the critical light intensity (Icrit) is the threshold level at which a species is able to maintain a stable population at a certain loss rate and certain environmental conditions. Under stable environmental conditions the species with the lowest critical light intensity


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will exclude all the other species. Competition of phytoplankton species for light and nutrients has been investigated in a theoretical approach (Huisman and Weissing 1995), the model still awaits experimental verification. According to the publications available so far, no one seems to have taken on the challenge and investigated competition for iron. Unraveling competition for iron is a complex task. Trace elements like the iron are often essential components of enzymes or serve as cofactors for catalytic processes. Nitrate and nitrite reductase need iron for the reduction from nitrate via nitrite to ammonium (Timmermans et al. 1994). Therefore, iron and nitrate have interactive effects on microalgae (Maldonado and Price 1996, Price and Paffenhöfer 1985) and Liebig’s law of the minimum does not apply. Another difficulty lies in the iron availability. Particulate or colloidal iron (III) oxide constitutes the major iron source in the open ocean, but may be unavailable for the phytoplankton in this form. However, since iron has shown to be an important factor limiting phytoplankton growth in far regions of the world oceans (see above), analyzing iron competition among marine microalgae might provide important insight into mechanisms structuring the planktonic communities of these areas. Based on the results from competition experiments, the theorem “the number of limiting resources equals the number or species coexisting at equilibrium” was broadly accepted, although there is only one study available in the literature (Sommer 1998), that actually investigated competition among phytoplankton for three essential resources. In this experiment, natural phytoplankton from the Indian Ocean was supplied with a stoichiometrical N:P ratio close to the optimal ratio of the average phytoplankton (15:1) and a variable Si concentration. While two flagellates and one diatom coexisted at Si:N = 0.18:1, four species appeared to coexist at Si:N = 0.30:1 and Si:N = 0.55:1. Four species coexisting at three resources was attributed to slow exclusion in this study. Huisman and Weissing (1999) demonstrated in a theoretical approach, that competition for three or more nutrients does not always result in the corresponding number of coexisting phytoplankton species at a stable equilibrium. According to their model, periodic oscillations and deterministic chaos allows for the coexistence of more species than limiting resources. Basis of the model is the assumption, that the competing species displace each other in a cyclic manner. Assuming three species competing for three resources this would mean: species 1 is the better competitor for resource 1 and potentially limited by resource 2, species 2 is the better competitor for resource 2 and potentially limited by resource 3, species 3 is the better competitor for resource 3 and potentially limited by resource 1. Such a competing cycle leads to abundance

#"$ Algal Cultures, Analogues of Blooms and Applications oscillations of the three species involved (Fig. 15.3a). These oscillations, internally generated by the competition process, permit the coexistence of up to six species. Competition for five resources may lead to competitive chaos with irregular species fluctuations (Fig. 15.3b). Competitive chaos on three resources (Fig. 15.3c) is possible, if one species is part of two competing cycles of three species each. Thus, the two cycles are linked by this species and the system switches between the cycles in a chaotic manner (Huisman and Weissing 2001). Depending on parameter settings, competitive chaos may be a transient stage, which leads to oscillations of one of the competing cycles. Transient chaos is unpredictable. Even the smallest differences in the initial abundances of the species involved may change the outcome of multi species competition (Huisman and Weissing 2001). Huisman and Weissing’s (1999, 2001) chaos theory is still to be experimentally verified. The ecological relevance of competing cycles is unknown so far. With regard to the timescales of competitive chaos in the model, it is an interesting question, whether competitive behavior is still chaotic if superimposed by external fluctuations (e.g. weather fluctuations, seasonal changes). Based on a combined theoretical and empirical approach, the results from Roelke et al. (2003) indicate, that competitive chaos may be suppressed by disturbance. As shown in Huisman and Weissing’s (2001) study, competition of five phytoplankton species on three resources resulted in transient chaos and in oscillations of one of the competing cycles when continuous conditions were simulated. Under the assumption of discontinuous conditions (hydraulic flushing of growth medium, three-nine-d pulses), the same parameter settings led to aperiodic population shifts (3-d pulses) or to classic competitive exclusion behavior (5, 7, 9-d pulses) with either one of the competing cycles (3, 5, 7-d pulses) or a single species winning (9-d pulses). The outcome of competition depended on the initial abundances of the competitors. In contrast to continuous conditions, competition under discontinuous conditions was highly predictable. However, regardless whether competitive chaos is a regular phenomenon in natural environments or restricted to rare phases of stability combined with the occurrence of competitors with certain physiological characteristics, Huisman and Weissing’s (1999, 2001) chaos theory proves that species competition for more than two resources can be far more complex than previously assumed. Competition under pulsed nutrient supply has been investigated in multi-species laboratory experiments using mixtures of laboratory strains (Ducobu et al. 1998, Grover 1988, 1991a, Robinson and Sandgren 1983, Spijkerman and Coesel 1996) and with natural phytoplankton communities

CMYK Competition of Aquatic Microalgae in Variable EnLironments

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Fig. 15.3 Abundance oscillations and chaos of species competing for more than two resources, A: oscillations of three species on three resources, b: competitive chaos - five species competing for five resources, c: competitive chaos – five species coexisting on three resources (cited from Huisman and Weissing (2001)) CMYK

CMYK

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#"& Algal Cultures, Analogues of Blooms and Applications (Grover 1989, Sommer 1984, 1985, 1995). Temporal variation of resource supply tends to promote coexistence among species. This has been proved by chemostat experiments with natural assemblages of freshwater phytoplankton (Sommer 1985). Instead of supplying phosphorus and silicate with the continuous flow of the culture medium, these nutrients were injected into the culture suspension in weekly intervals creating repeated temporal gradients in nutrient supply. The pulsed nutrient supply increased the number of coexisting species to values far above the number of limiting resources. Five to seven species coexisted in cultures that experienced weekly pulses of phosphorus. A pulsed supply of both, phosphorus and silicate led to the coexistence of six to ten species. Coexisting species reached a cyclic equilibrium under weekly nutrient pulses (Fig. 15.4). At equilibrium, some species reached relatively constant population densities, whereas the population density of others oscillated periodically. An increase in density followed each nutrient pulse. Later in the period between pulses population density decreased until the next pulse was given. Different types of physiological specialization regarding nutrient uptake and storage explained the different patterns of population densities at equilibrium:

Stable species

Pulsed P & Si; Si:P = 20:1

A

Mougeotia Staurastrum log10 biomass ( m m3 ml–1)

Aphanizomenon Pediastrum

Oscillating

Scenedesmus Chlorella Synedra Nitzschia

Excluded

Asterionella

456

Cryptomonas Fragilaria 0

2

4 Weeks

6

8

Fig. 15.4 Coexistence in a continuous culture experiment with pulsed P and Si supply (Si:P = 20:1). Time course of species biomass (after Sommer 1985).


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• Periodical density oscillations are the characteristic pattern of growth specialists or r-strategists. These have high maximal growth rates but no adaptation to nutrient shortage. Growth specialists use resource pulses for immediate growth, which compensates for losses during nutrient shortage. Small green algae (e.g. Chlorella minutissima) showed this growth pattern was most clearly. • Affinity specialists or competitors (Tilman 1982) have a high competitive ability with regard to a given nutrient. In chemostat experiments, these species are able to reduce the equilibrium concentration of the dissolved limiting nutrient (R*) to very low levels. Therefore, affinity specialists avoid negative net growth rates during periods of nutrient shortage and display only little temporal abundance variation. Pennate diatoms (Synedra, Asterionella) represented this strategy best, when pulsing only phosphorus. Also under steady state conditions, these species are known to be successful P-competitors at high Si:P ratios. • Storage specialists use nutrient pulses to build up intracellular storage pools and use these to support growth under nutrient shortage. The physiological basis for this strategy is a maximal uptake rate (vmax) and a high quotient of maximum to minimum cell quota (qmax/q0) of the limiting nutrient. Since >95% of diatom silicate is bound in cell walls and metabolically accessible storage pools are minimal, storage of silicate is not possible. As for competitors, the abundance of storage specialists should vary little when exposed to a variable nutrient regime. They are usually large celled and have been represented by the large colonial cyanobacteria and large green algae in the experiments by Sommer (1985). Species coexistence as a consequence of fluctuating nutrient supply as shown in laboratory experiments using natural phytoplankton communities was rarely found, when laboratory strains of phytoplankton were used. In competition experiments with cultures of the diatoms Fragilaria crotonensis and Synedra sp. for example, Synedra out competed Fragilaria under phosphorus pulses (8-d interval) as well as under constant culture conditions. However, the variability in phosphorus supply significantly reduced the rate at which Synedra excluded Fragilaria (Grover 1988). Experiments using the desmids Cosmarium abbreviatum and Staurastrum chaetoceros yielded similar results. Biweekly phosphorus pulses reduced the exclusion rate of Cosmarium, whereas under daily phosphorus pulses, stable coexistence of both species occurred (Spijkerman and Coesel 1996). Ducobu

## Algal Cultures, Analogues of Blooms and Applications et al. (1998) investigated the competition of Prochlorotrix hollandica and Planktothrix agardhhii under constant conditions and under phosphorus pulses with 4-d and 8-d intervals. Planktothrix was excluded in all cases but in cultures that had experienced the 8-d interval, the exclusion process took longer than in cultures with a 4-d interval. According to Sommer (2002), the inconsistency among results from competition experiments may be due to the very restricted parameter space both, in terms of algal physiology and in terms of experimental conditions that permit coexistence as a consequence of temporal variability in nutrient supply (Grover 1991b). With a two species inoculum it might be quite unlikely to find the appropriate combination of algal properties and experimental conditions. In a natural multi species inoculum however, there is a high probability to find a group of species displaying the necessary combination of physiological specialization to persist under the experimental conditions. How different frequencies and intensities of disturbance affect the diversity of multi species cultures of phytoplankton was investigated using semi-continuous culture technique (Gaedeke and Sommer 1986). The disturbance in this study comprised of dilution events of different frequencies (1 to 14 d). Each replacement of culture suspension with a new medium had two aspects of disturbance: a decrease of phytoplankton density and an increase in nutrient concentration. To achieve the same longterm average of the dilution rates in all treatments, the intensity of disturbance increased with decreasing frequency (diagonal design). In cultures with daily dilution as well as with a 2-d interval, one species became dominant over the other species, whereas in cultures with less frequent dilution events, several species were able to coexist. As in the experiment with weekly pulses in nutrient supply and a natural assemblage of freshwater phytoplankton (Sommer 1985), coexisting species displayed characteristic growth and decline patterns in the time period between dilution events. Diversity index (Shannon and Weaver’s H’) showed a unimodal response to the interval length between dilutions and peaked at the intermediate interval of 7 d. Similarly, Robinson and Sandgren (1983) investigated the effect of semicontinuous culture conditions on a mixture of 14 common freshwater phytoplankton species. The interval between dilutions in this experiment was also negatively correlated to its magnitude. Intervals between dilution events were 1, 7 and 28 d. Species richness in this experiment peaked at an interval of 7 d, whereas the highest diversity index (Shannon and Weaver’s H’) was found in treatments that had experienced an interval length of 28 d


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between dilution events; thus, only partly providing evidence for the strength of the IDH. Differences in the interval length connected with a high diversity index were probably due to differences in the dilution rates of the chemostat systems. A low dilution rate results in longer generation times at steady state. Using marine phytoplankton species, Sommer (1995) compared the effects of fluctuating nutrient supply on species diversity in experiments of different design. One set of experiments had a similar diagonal design as the in Gaedeke and Sommer (1986) and Robinson and Sandgren (1983), while in another set the magnitude of dilutions were independent of the interval length (baseline design). A marked peak in the diversity index (Shannon and Weaver’s H’) was found only in experiments with diagonal design, providing evidence for Connell’s (1978) prediction that intermediate frequency as well as intermediate intensity of disturbance are important to maintain a high species diversity. Further evidence for the effects of resource fluctuations on species diversity comes from enclosure studies, which provide an experimental setup that is much closer to natural conditions than the semi-continuous culture approaches discussed above, but are less controllable than these. The first enclosure experiment that investigated how artificial destratification during summer stratification phase affects phytoplankton succession, was conducted by Reynolds et al. (1983). One of the Lund Tubes (Lund Tube C, 1630 m² area, 9.9 m mean depth, for a detailed description the design of the Lund Tubes see (Lund and Reynolds 1982), was destratified by airlifting hypolimnetic waters from a depth of ca. 8 m and discharging it at the surface. During mixing events, the hypolimnetic water was pumped to the epilimnion for 8 h per day for a period of three to eight days. The artificial enlargement of the epilimnion was carried out at four periods during the stratification phase of the lake. This experiment had no replication and no undisturbed treatment was available, but the results were compared to successional sequences under undisturbed conditions. After each artificial mixing, stratification was rapidly re-established with a structure closely resembling the pre-mixed condition. The normal successional sequence, from autumn-winter-spring diatom assemblages, to summer r-selected species, to summer K-strategists and back to autumn-winter-spring diatom assemblages is related to the seasonal stratification cycle of the water column. Intermittent destratification led to changes in the ‘normal’ sequence of phytoplankton succession. During each phase of artificial mixing, the phytoplankton community shifted from summer r-selected species (Sphaerocystis, Dinobryon, Uroglena) to autumn-winter-spring diatom assemblages (diatoms and Oscillatoria) and reverted to r-selected species

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when the column re-stratified. On more K-selected species, destratification had the effect to temporarily impede their increase and delay their “eventual attainment of a dominant biomass through the intervening periods of quiescence”. The experimental manipulation of mixing hypolimnetic water into the epilimnion was adopted for a replicated enclosure study (Flöder and Sommer 1999), that was designed as an experimental test of the Intermediate Disturbance Hypothesis (IDH). Two experiments were carried out in Lake Plußsee, a eutrophic headwater lake in northern Germany. Due to its maximal depth (29 m) in relation to a rather small surface area and its sheltered location in a valley, the stratification phase of the lake (mixing depth: 4 m) is very stable in summer. This made the lake an ideal choice for investigating the impact of artificial destratification in an enclosure study. Compared to the Lund tube used by Reynolds et al. (1983), the enclosures of the IDH study (diameter: 1 m, depth: 10 m, 15 m) were rather small. Therefore, the duration of the experiment (36 d) had to be a compromise. On one hand, there was the idea of simulating as many mixing events as possible, on the other hand the effects of periphyton growth on the inner surfaces of the enclosure walls may be considerable after several weeks, which is due to the large surface to volume ratio of the enclosures. To ensure that the inoculum of the enclosures consisted of the natural plankton community present at the starting time of the experiment, the elapsed enclosures were lowered to the depth that equaled their length and then filled by slowly pulling the upper ring of the enclosure towards the lake surface. While undisturbed enclosures served as controls, destratification was imposed by breaking the thermocline with compressed air (1 bar, 3 min) at different frequencies (2-, 4-, 6-, 8-, 10- and 12-day intervals). The natural mixing depth of the lake (4 m) was increased to 6 m and 9 m respectively. Thermal stratification in the enclosures was restored to its original state within a few hours after disturbance. The addition of hypolimnetic water led to pulse-like increases in nutrient concentration. Although the bottom of the enclosures consisted of 100 μm mesh size netting and provided for some exchange with nutrient rich hypolimnetic water, the different intervals of mixing resulted in a gradient of disturbance intensities. Smallest nutrient pulses were connected to 2-d intervals and the largest pulses to 12-d intervals of mixing. Thus, the enclosure study had a similar diagonal design as the laboratory approaches cited above (Gaedeke and Sommer 1986, Robinson and Sandgren 1983, Sommer 1995). Phytoplankton succession was similar in both experiments. As shown for experiment 1 (artificial mixing depth: 9 m), the phytoplankton community consisted mainly of


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Cyanodictyon, Oocystis and Chrysochromulina (Fig. 15.5), when the experiment was started. Cyanodictyon became highly abundant in the enclosure disturbed every second day and in the undisturbed enclosure. Under a

Fig. 15.5 Examples of the time course of phytoplankton abundance (based on organism density): Experiment 1. 2 d, 6 d, 12 d: length of the disturbance interval in days, und.: undisturbed treatment, x.1: no. of the replicate. Mean between replicate difference of dominant species was 21% (±16.2 SD). Species with an abundance of >10% at least once during the experiment are included. The other species are summarized as “rest”. Ana spir: Anabaena spiroides, Chryso: Chrysochromulina parva, Cyanod: Cyanodictyon imperfectum, O parva: Oocystis parva (cited from Flöder and Sommer 1999).

##" Algal Cultures, Analogues of Blooms and Applications 6-d mixing regime, algal abundances were more balanced and Anabaena occurred in addition to the initially common species. Species diversity (Shannon and Weaver’s H’) peaked at the 6-d interval in both experiments (Fig. 15.6) and statistical analyses revealed unimodal relations between diversity index and both, frequency and intensity of disturbance.

Diversity index

3.0

Exp. 1

Exp. 2

2.5 2.0 1.5 1.0 2 6 10 und. 2 6 10 und. Disturbance interval (d)

Fig. 15.6 Diversity index (Shannon and Weaver’s H’) the end of the experiments. 2 d,..., 12 d: length of the disturbance interval in d, und.: undisturbed treatment. The dashed lines serve for orientation only. Experiment 1: 225% increase of the natural mixing depth (4m); experiment 2: 150% increase of the natural mixing depth (cited from Flöder and Sommer 1999). Contrasting results were found in a comparable enclosure study (Beisner 2001), which was conducted in an oligotrophic system (Placid Lake, British Columbia, Canada). In these experiments, phytoplankton diversity was affected by artificial destratification only in the absence of herbivorous grazers and under nutrient addition (nutrient rich conditions nutrient enrichment shifted the experimental system from oligotrophic towards meso-eutrophic conditions). Mixing events that were induced with a 3-d interval were connected to a low diversity index (H’: 0.7), while a mixing interval of 21 d resulted in an intermediate H’ (1.2). The highest H’ was found under undisturbed conditions (H’: 1.5). Beisner (2001) explained the high diversity in unmixed enriched systems with spatial heterogeneity. Spatial structuring would be more important in mediating coexistence among competitors than temporal heterogeneity in nutrient pulses alone. Unfortunately, the data set presented does not provide any proof for the existence of vertical thin layers or small-scale patchiness nor for species actually coexisting by this form of niche separation. Therefore, this very interesting hypothesis needs further investigation. In a theoretical analysis, Litchman and Klausmeyer (2001) investigated the influence of light fluctuations over a wide range of time scales, and hypothesized that stable coexistence among phytoplankton species


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Average H’ at steady state

competing for light is possible only under slow (T > 100 h) light fluctuations. This hypothesis was confirmed in an experiment that investigated the influence of different time scales of fluctuating light on phytoplankton diversity (Flöder et al. 2002). A natural assemblage of phytoplankton was held under semi-continuous culture conditions for 49 d. Superimposed on a diel light to dark cycle (16:8 h), light conditions in the experiment were either periodically changed from high intensity (100 mmol photons m–2 s–1) to low intensity (20 mmol photons m–2 s–1) at intervals of 1, 3 and 12 d, or fixed to constant intensities (permanent high and low light levels). Cultures were supplied with an excess of phosphorus, nitrogen, bicarbonate and silicate in order to exclude limitation by these nutrients. Total phytoplankton abundance increased and reached a saturation level in the treatment with permanent high-light supply and in treatments with periodically changing light intensities. Compared to the treatments with constant light intensities, the average diversity index (Shannon and Weaver’s H’) at steady state was increased in the treatments with fluctuating light supply (Fig. 15.7). This increase in phytoplankton diversity was attributed to a combined effect of the interval of light fluctuation and self-shading effects of the plankton algae. a

0.8

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Fig. 15.7 Average diversity index at steady state, a under constant light intensity, b under fluctuating light intensity. PH permanent high light intensity (100 mmol photons m–2 s–1), PL permanent low light intensity (20 mmol photons m–2 s–1). Bars represent the standard error (cited from Flöder et al. 2002). In competition experiments that investigated the effect of light fluctuation as day length and vertical mixing may cause it, fluctuating light with interval lengths of less than one d caused the coexistence of species. Van Gemerden (1974) investigated competition among Chromatium weissei and

##$ Algal Cultures, Analogues of Blooms and Applications Chr. vinosum. These purple sulfur bacteria could coexist on the same substrate, if light fluctuated at a light:dark cycle of 4:8 or 6:6 h. In two-species competition experiments, the freshwater species Phormidium luridum and Nitzschia sp. coexisted under light fluctuating at one-h and at eight-h intervals. In multi species experiments this kind of light fluctuation promoted diversity only at high average light intensities (Litchman 1998). Phormidium luridum and Anabaena flos-aquae coexisted at light:dark cycles of 12:12 h as well as at high:low light cycles of 12:12 and 4:4 h (Litchman 2003). Thalassiosira rotula and Chaetoceros sp., two marine diatom species, coexisted at a day-night cycle of 10:14 h under ammonium limitation (Brzezinski and Nelson 1988). Regular periodicities in nutrient uptake and cell division caused by the photo-cycle were hypothesized as the reason for this coexistence. Periodic changes in the nutrient uptake rate may cause fluctuations in the concentration of the limiting nutrient, and enable species to coexist by temporal separation in the use of limiting nutrients. It has to be stated however, that this type of coexistence is brought about by niche separation through species-specific differences in the physiological response to regular light fluctuations like vertical mixing or the day-night cycle. The number of species that can coexist through this mechanism may be limited (Flöder et al. 2002). To investigate the influence of periodic salinity intrusions in coastal aquatic ecosystems, natural phytoplankton assemblages from a tidally influenced shallow lake were exposed to salinity changes of different frequency (3.5 - 14 d). These were compared to assemblages that experienced constant freshwater conditions (Flöder and Burns 2004). Two effects of saline intrusions on diversity index (Shannon and Weaver’s H’) and species number could be distinguished (Fig. 15.8). The first salinity pulse caused the transition from a fresh to a brackish state, and led to immediate and strong decreases in diversity index and species number in all cultures. After that, the interval length of salinity changes affected the diversity loss per unit time. This loss over time was highest under constant freshwater conditions and lowest in cultures that had experienced salinity pulses with an interval of 3.5 d. Together, both effects determined the development of diversity index and species number of the phytoplankton communities, resulting in a Ushaped response at the end of the experiment. Under the influence of weekly and fortnightly salinity pulses, diversity index and species number were clearly lower than under constant freshwater conditions and salinity pulses with an interval of 3.5 d. This finding suggests that saline intrusions into predominantly fresh coastal aquatic ecosystems might lead to decreases in


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Time course (d)

Fig. 15.8 Diversity index (H’) and species number (Scorr) in the course of the experiment. 3.5 d, 7 d, 14 d: length of the interval between salinity pulses in days; fresh: freshwater treatment. Symbols (=, O, 6) denote different replicates. Regression lines are significant at the 5 % (*) or 0.1 % (***) level (cited from Flöder and Burns 2004). diversity of the phytoplankton communities if they are caused by spring tides. IPCC (2001) predicted that global warming would lead to increasing sea levels and to a higher frequency of storm tides. This could have severe effects on coastal aquatic ecosystems, if saline intrusions reached formerly freshwater systems. The presence of grazers may have several effects on planktonic and benthic algal communities. Grazing afflicts mortality on aquatic microalgae, which can affect species diversity in different ways. Potentially, increased

##& Algal Cultures, Analogues of Blooms and Applications grazing leads to local extinctions of species, which results in decreasing species diversity. On the other hand grazing may prevent competitive exclusion and therefore maintain diversity (Hillebrand and Shurin: 2005). For both of these diverging effects of grazing there is evidence in the literature – consumer effects on prey diversity have been reported to be negative, positive or absent (Chase et al. 2002). Grazers of microalgae may be rather general in their feeding behavior or they may be specialized feeders, the degree of specialization ranging from mechanical preference of certain food types to active selection on food items (Hillebrand and Shurin 2005). Selective grazing may increase species diversity and stabilize community structure, if grazers have density dependent functional responses and select for the most abundant species (switching). Cropping of the most abundant algae would allow less abundant species to increase until those species themselves become the object of selective grazing (Chesson 2000, Porter 1977). If selective grazers are highly specialized and consumers feeding on abundant species are more common than those feeding on rare species, this may also tend to maintain species diversity (Hillebrand and Shurin 2005, Pacala and Crawley 1992). Selective grazers affect species diversity in a negative way, only if rare species are chosen and eliminated by intense feeding. Diversity may also decrease due to non-selective grazing, if intense grazing leads to the extinction of rare species (Porter 1977). In benthic communities, certain types of herbivory (e.g. the grazing of Littorina littorea) may create an increased spatial heterogeneity of micro-algal cells, which leads to an increase in diversity (Sommer 2000). The reduction of algal biomass may increase resource availability for the remaining organisms. Under resource limitation, this aspect of grazing may therefore affect competition among microalgae. Firstly by simply sharing out available resources among less organisms, and secondly by regeneration of resources via grazer excretions (Axler et al. 1981, Ejsmont-Karabin et al. 1983, Lehman 1980). Assuming all competitors are grazed upon equally, sharing resources among fewer organisms might slow down the competition process. If competitive superior species rather than competitive inferior ones are grazed upon (keystone predation), species may coexist that would otherwise exclude each other (Leibold 1996, Lubchenco 1978, Paine 1966). Keystone predation therefore tends to increase or maintain diversity. Keystone predators may be actively selecting for their prey organisms. If the quality of being the dominant competitor species correlates with higher susceptibility to grazing due to growth form, exposition or chemical constitution, keystone predation may also be a non selective process (Chase et al. 2002, Hillebrand and Shurin 2005). Recycling of nutrients by grazing depends on the grazers’


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nutrient demands and on the stoichiometry of the food items ingested. Regeneration of nutrients by grazers may alter ratio of nutrients available to the microalgae and thereby change the conditions of competition (Elser and Urabe 1999). This might be a process by which a competitively inferior species becomes superior and actually affect the outcome of competition.

RELATIONS OF DISTURBANCE, PRODUCTIVITY AND DIVERSITY OF MICROALGAE As a non-equilibrium theory, the Intermediate Disturbance Hypothesis is based on resource competition and the exclusion principle and therefore applicable only to competitive systems. Huston (1979, 1994) accounted for this premise by adding another dimension to the theory. The dynamic equilibrium theory (Huston 1979, 1994) predicts species diversity to be at maximum if frequency and intensity of disturbance as well as the productivity of a system are at intermediate levels. The productivity dimension reaches from systems with low productivity that are highly resource limited to highly productive systems, where resource limitation is rare. A comparison of data sets covering a variety of ecosystems showed that the effect of disturbance on biodiversity can be either negative or positive, depending on the productivity of the ecosystem (Proulx and Mazumder 1998). Proulx and Mazumder’s (1998) meta-analysis suggested that plant-species richness enhances with increasing grazing pressure in a nutrient-rich environment but decreases in a nutrient-poor environment. Kondoh (2001) investigated this relationship of diversity to productivity and disturbance in a modeling approach and showed how the effects of disturbance and productivity level on diversity may be interrelated. The model is based on the patch-occupancy model (Hastings 1980, Tilman 1994), which assumes that the environment consists of a large number of patches. These patches are either empty, or occupied by one of n species. The proportion of patches occupied by each species changes due to colonization and extinction processes. Multispecies coexistence is possible if a trade-off between competitive ability and colonization or extinction rate is assumed: a superior competitor has a lower colonization rate or a higher extinction rate as an inferior competitor. To investigate the impact of productivity and disturbance, an increase in productivity enhances the colonization rate and an increase in disturbance increases the extinction rate of all species by a constant in Kondoh’s (2001) modified model. According to this model, the relationship between disturbance and species richness as well as the relationship between productivity and species richness are unimodal, but the level of one factor at

#$ Algal Cultures, Analogues of Blooms and Applications which species richness is at maximum, depends on the level of the other factor. At balanced levels of disturbance and productivity, the trade-off between the species allows more species to coexist leading to maximum diversity (Fig. 13.9). The model predicts a positive correlation between disturbance and diversity when productivity is high, a negative correlation when productivity is low and a unimodal pattern at moderate productivity. (b)

3–5 6–8 9–11 0.24

Disturbance level (D)

0–2

12–14

0.19

15–17 18–20

0.14 0.09

15–17

0.04

12–14 9–11

0 1

7

13

19

25

31

37

6–8 3–5 43 0–2

Preoductivity level (R)

Fig. 15.9 Effects of disturbance and productivity on species richness according to simulations using a modified patch occopancy-modell, the number of coexisting species are denoted in the parameter space of disturbance and productivity levels, n = 20 (cited from Kondoh 2001). In their field study, Agard et al. (1996) investigated the influence of disturbance and primary productivity originating from the Amazon and Orinoco Rivers discharge on the biodiversity of Caribbean phytoplankton, and could confirm Huston’s (1979, 1994) theory. Highest diatom diversities (Margalef’s d) were found at intermediate levels of disturbance and primary productivity (Fig. 15.10). However, using species richness or Shannon and Weaver’s H’ as diversity measure, diversity was highest along a diagonal ridge across the diagram. Thus, in the spatially more or less homogenous environment of the pelagic, patterns were found that are similar to those Kondoh (2001) derived from his modeling approach for spatially structured environments. A field study on benthic microalgal communities in streams (Cardinale and Hillebrand, unpublished data) is the first study that provided evidence that a pattern, similar to the one described by Kondoh’s (2001) model, can be found in spatially structured environments. Experimental evidence for the validity of the prediction that disturbance decreases diversity at low productivity but increases diversity at high productivity (Huston 1979, 1994, Kondoh 2001) comes from factorial field


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Fig. 15.10 Contour diagram showing variation of diatom diversity with silicate concentration (as measure of salinity disturbance originating from the Amazon discharge) and primary productivity. A: number of diatom species; B: Margalef’s d, C: Shannon and Weaver’s H’ (cited from Agard et al. 1996). experiments that investigated the diversity of benthic algal communities (Worm et al. 2002). Nutrient supply and grazing pressure were manipulated in these experiments, run in the Baltic Sea and the NW Atlantic Ocean. Experimental sites had similar physical characteristics and a similar species pool, but differed in nutrient supply and productivity. While the Baltic Sea site was highly productive (net primary productivity: 4,900 g C m–2y–1),

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the NW Atlantic site had a very low productivity (net primary productivity: 600 g C m–2y–1). Nutrient enrichment increased algal coverage and height in both experiments. Algal diversity (Shannon and Weaver’s H’) increased with the nutrient enrichment in the low productivity environment of the NW Atlantic, but decreased with enrichment at the highly productive Baltic site. In the NW Atlantic, more species colonized following nutrient enrichment, whereas in the Baltic Sea, enrichment led to the dominance of Enteromorpha sp., a fast growing species with high nutrient demand (Fig. 15.11a). Grazing decreased diversity in the NW Atlantic, where only the most resistant species withstood consumption by grazers. In the Baltic Sea grazing reduced the abundance of Enteromorpha, and increased diversity by creating open space for colonization by other species (Fig. 15.11b). The reversed grazing effect on algal diversity that Worm et al. (2002) observed in their experiment is in close agreement with Kondoh’s (2001) model. These results on interacting effects of productivity and disturbance by grazing may give an example of how coexistence is the result of many different processes and interactions between mechanisms (Roxburgh et al. 2004).

Fig. 15.11 Reversing effects of grazers (A) and in situ enrichment (B) on species diversity in a low- productivity (NW Atlantic) and a high-productivity (Baltic Sea) environment (cited from Worm and Karez 2002) With this discussion of Huston’s (1979, 1994) dynamic equilibrium theory we end this chapter, being aware that only a part of the factors that may influence competition, succession and diversity of aquatic microalgae were presented. This chapter was restricted to competition processes in local


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communities. These were treated as isolated units although in nature local communities are likely to be connected to different degrees (forming ‘meta communities’) and invasion may significantly influence competitive processes in local communities (Leibold et al. 2004, Hillebrand and Blenckner 2002, Menge et al. 2003, Mouquet and Loreau 2002, Shurin et al. 2000). In the introduction of this chapter we asked the old question “Why are there so many species”? Mechanisms that may maintain diversity within a system were discussed. However the causes of species diversity are only one aspect of the question. Another aspect would be the ecological function of biodiversity. Biodiversity may affect system stability and ecosystem functioning; the diversity-stability and the diversity-productivity debates currently are hot spots in ecology. For further reading we would like to recommend Allison (2004) Dodson et al. (2000), Loreau et al. (2001), McCann (2000) and Steiner and Liebold (2004).

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Volterra, V. 1926. Fluctuations in the abundance of a species considered mathematically. Nature 118: 558-560. White, P.S. and S.T.A. Pickett. 1985. Natural disturbance and patch dynamics: an introduction. pp. 3-13. In S.T.A. Pickett and P. S. White. [eds.] The ecology of natural disturbance and patch dynamics. Academic Press, Orlando, USA. Worm, B. and R. Karez. 2002. Competition, coexistence and diversity on rocky shores. pp. 133-163. In U. Sommer and B. Worm. [eds.] Competition and Coexistence. SpringerVerlag, Berlin, Heidelberg. Worm, B., H.K. Lotze, H. Hillebrand and U. Sommer. 2002. Consumer versus resource control of species diversity and ecosystem functioning. Nature 417: 848-851.

16 Effects of Temperature and Irradiance on Marine Microalgal Growth and Physiology Peter Thompson Principal Research Scientist, CSIRO Marine Research, Hobart, Tasmania, 7001, Australia

Abstract Investigations that have used primarily laboratory cultures to understand how different species of microalgae respond to variation in light and temperature are reviewed. The review indicates that suitable empirical models that quantify microalgal growth as a function of irradiance can be borrowed from those developed to quantify primary production. A model originally developed to describe rate responses in arthropods provides an improved describe of the responses of microalgae to temperature at the species level in comparison with the more generic models. These models are, or can be easily made, flexible enough to describe the range of observed responses under steady state conditions. At the species level and under steady state conditions a relatively large number of studies have revealed a high degree of variability in physiological responses to irradiance and temperature. For example at the same temperature and over a similar range in irradiance the chlorophyll a quota may change by a factor of > 10 in one species or hardly at all in another. In some cases the lack of photoacclimation in chlorophyll a quota may represent an obligate niche (high or low light adapted species) but in others it seems to indicate alternative methods of coping with excess light energy. Improved technology has made it possible to relative rapidly analyze for a wider range of pigments but there are relatively few studies that document the contribution of these pigments to improved fitness. Variation in many other cellular constituents has been proposed to have predictable responses to variation in temperature and irradiance yet the evidence suggests that while some species may have very significant changes in carbon, protein, lipid or carbohydrate content as a function of irradiance the response is not universal that such a generalization is potentially misleading. Molecular techniques are greatly improving our capacity to relate physiological responses to environmental conditions but the range of microalgal species

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Algal Cultures, Analogues of Blooms and Applications investigated remains small. For fatty acids (FA) in heterotrophic cells there is a well developed conceptual frame work for predicting the response to temperature in terms of homeoviscosity of membranes and some information on molecular signaling but it is worth noting that the whole cell FA composition of autotrophic cells may be affected differently than that of their membranes due to the simultaneous change in energy status that normally accompanies a reduction in temperature. There is a need for more laboratory studies on transients and cycles at time scales appropriate to those encountered in the field. Field based experiments have demonstrated species-specific shifts in growth rates associated with fluctuating versus static light regimes suggesting that temporal variability may be an important parameter that defines a particular niche space. Clearly ecological success requires exploitation of a series of cyclic and sometimes noncyclic transients in irradiance and temperature and yet we have relatively little understanding of the capacity of different species to benefit under fluctuating regimes. Individual species are likely to have different strategies for exploitation of variable conditions, potentially different depending upon the time scale of the fluctuation or cycle. For example the capacity for nonophotochemical quenching varies between species, potentially conferring considerable advantage when exposed to short cycles of excess irradiance while different life history stages may allow some species to exploit longer cycles of variation in temperature, irradiance or nutrient availability. Laboratory studies that would allow us to better understand how species may gain ecological advantages by exploiting these cycles in environmental forcing lag behind the field based experiments that have demonstrated they are important. In conclusion, laboratory studies that have investigated microalgal responses to steady state growth under a range of fixed temperatures, irradiances and nutrients have been the primary basis of making phytoplankton ecology a predictive science at the level of biomass. Improvements to this situation may result from research that underpins dynamic resource allocation models for phytoplankton eventually leading to ecological models that include spatial and temporal variation in the most important forcing factors to predict the responses of species or functional groups. In this context the capacity to exploit a variable light field is suggested as an important determinant of a species’ niche space.

INTRODUCTION Like many organisms microalgae respond to temperature and light in such a way that understanding these responses is essential to the study of microalgal ecology. A great deal has been achieved in terms of microalgal physiology and ecology over the last few decades that we are approaching a

Effects of Temperature and Irradiance

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basic level of understanding from which more complex questions can be posed. We have reached the point where generalizations in terms of responses to light and temperature seem both possible and potentially useful (Clarke 2003). While genotypic adaptations that allow species to exploit certain niches (growth strategies) can be behavioral (Smayda 2002) many manifest themselves as physiological responses to light and temperature. Certainly with the increasing desire to make ecology a more predictive science microalgal ecophysiologists will need to continue the advance from hypotheses testing to conceptual models to mechanistic and process-based models that parameterize responses and quantify the uncertainties. Research on responses to light and temperature will be critical to predictive ecology whether it be predicting the onset of the spring bloom in a particular bay or changes in carbon cycling in response to global warming. Research using microalgal cultures are and will continue to be the major tool used to quantify responses to variation in irradiance and temperature. This chapter sets out to review microalgae responses to temperature and irradiance, seeking commonalities, while still acknowledging the uncertainties or weaknesses of the resulting generalities. Extrapolation of results from laboratory studies into nature requires careful consideration of how well our experimental systems mimic the forcing functions in nature and their biological responses. For example, in microalgal nutrient studies this debate has previously centred on whether chemostats, turbidostats or batch cultures are better simulated in natural conditions (Jannaasch 1974). For similar experiments on microalgal responses to light and/or temperature the issue is more about choosing the correct amplitude and frequency of the cycles. The influences of temperature and irradiance on microalgal physiology are profoundly linked yet fundamentally different. This dichotomy occurs because the myriad of physiological reactions that can be simply summed up as “growth” are temperature dependent, yet the initial step of capturing the energy to drive those reactions is not (Raven and Geider 1988). Every photosynthetic organism must somehow balance this fundamental problem: that the availability of light energy and its capture may be uncoupled from the capacity to productively use that energy. For microalgae with their very limited capacity to store metabolic intermediates the consequences can be rapid (on a human time scale) and the photodestructive damage can be fatal. Complicating the organism’s need to balance photon capture and growth over a range of irradiances and temperatures is the fact that natural cycles in the amplitude and frequency of these two driving forces are increasingly different as you approach temperate latitudes. Appreciation of this dichotomy

#%" Algal Cultures, Analogues of Blooms and Applications between the need to capture, and the capacity to use, light energy while exposed to cycles of irradiance and temperature that varying in their timing, absolute and relative magnitude will greatly facilitate the understanding of the material in this chapter. The amplitude of the annual cycle of surface temperatures in the Pacific and Atlantic oceans tend to peak (maximum difference between monthly maximum and monthly minimum) at mid latitudes (about 35°S and 40°N) at 8°C (Sverdrup et al. 1942). For irradiance the mean monthly average may range from zero to 45 MJ m–2 d–1 at latitudes > 80° but remain nearly constant at 38±10% MJ m–2 d–1 near the equator (Kirk 1994). The frequency of other irradiance cycles tends to be shorter, and their amplitude greater than temperature cycles either on an absolute basis or a biological basis. For example, at latitudes < 60° diel cycles of surface irradiance may vary from ~ zero to 2000 mmol photons m–2 s–1 of photosynthetically active radiation (PAR, 400 – 700 nm) while temperature may vary ~ 1°C diurnally. Thus diel shifts in temperature are unlikely to reach a biological zero (herein defined as the temperature where growth is constrained to zero) or the maximum tolerable temperature. Yet for most species of microalgae the diel change in surface irradiance is sufficient to alter photosynthetic processes from near zero to maximum. The complexity of the cycles in even these, the simplest of forcing functions, means that studies from natural ecosystems are difficult to interpret for cause and effect so that laboratory studies are necessary if phytoplankton ecology is to advance to become a predictive science. The consequences of these differences in the frequency and amplitude of light and temperature cycles are considerable in terms of physiological responses by microalgae. Furthermore the cycles can be useful in terms of determining the most pertinent physiological research issues of relevance to ecological questions. For example, there is probably little need to know the effects of cycling temperature from 0 to 30° on a diel cycle if our interest is the ecology of microalgae in a large body of water. Conversely we will need to know a significant amount about the effect of relatively large diel cycles in irradiance. Ultimately we might like to know enough about the physiological responses to these (and other) forcing factors to predict annual cycles of biomass and species composition in natural communities. There are, already, models that predict biomass and increasingly Class composition with reasonable accuracy (Lee et al. 2002). Whether these models are truly predictive (i.e. correctly predict a microalgal response to an unusual variation in the forcing function) is not very clear. This chapter will examine the physiological responses of phytoplankton to variation in light and temperature over a range of time and space scales starting at the


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small and short end of the spectrum and working towards the limits of extrapolation, e.g. towards phytoplankton community effects at the ecosystem level. Thus for the purpose of this chapter the discussion will be constrained to consider only temporal periods suitable for physiological acclimation (focus on light cycles > 10 min) or long enough to influence growth rate, species competition, succession and within the major habitat types (excluding small or ephemeral water bodies).

Microalgal Responses to Temperature: Resource Saturated1 Growth Temperature exerts a fundamental control over microalgae because growth is only possible within some relatively limited range. Although survival is possible outside this range there are no growth thresholds at approximately –2 and 80°C. Over the temperature range where growth is possible the relationship between temperature and growth has been described by a variety of linear and exponential equations the selection of which seems to primarily depend upon whether the authors were physicists, chemists or biologists (Ahlgren 1987). For microalgae we are interested in an equation that describes biological reactions (such as growth) from ~ -2 to 80°C, a relatively modest range of temperatures. For individual species the temperature range over which growth is possible is much less with cosmopolitan, marine, temperate species typically having a range of ~ 30°C (e.g. zero growth at 5°C and zero growth at 35°C) while for some polar species the range may be < 10°C. Our capacity to quantitatively predict phytoplankton community responses to changes in temperature requires knowledge of the upper and lower thresholds and between these thresholds the nature of the relationship between temperature and growth for the species in the ecosystem under investigation. We have known for a long time that most uncatalyzed chemical reactions increase exponentially with temperature as described by Arrhenius (1889): k=

- Ea A eRT

(Eq. 1.0)

where k is the rate constant, Ae is the encounter rate, Ea the activation energy, R is the gas constant and T is temperature in degrees Kelvin. While the Arrhenius equation can be used to predict the response of microalgal growth to variation in temperature, there are a number of complications. There are many processes associated with growth that depend upon diffusivity, 1

Nutrients are available in excess.

#%$ Algal Cultures, Analogues of Blooms and Applications solubility and fluidity; physical factors that are variously influenced by temperature. Many of the important reactions for growth are enzyme mediated (catalyzed) with lower activation energies and different Q10s than equivalent uncatalyzed reactions. The upper temperature thresholds of most enzyme catalyzed reactions are determined by the thermal stability of their proteins. Many species will physiologically acclimate in response to variation in temperature, for example adjusting their membrane lipid composition, thus increasing their capacity to grow. The interaction of photochemistry, chemistry, biochemistry, organelles and cells in a liquid medium produces a complex relationship between growth and temperature. This section examines efforts by phycologists to quantify the relationship between microalgal growth and temperature while seeking to provide a physiological understanding of the basis for these relationships. The goal of the first section of this chapter is to review and recommend an empirical relationship between temperature and growth. Relative to irradiance, there is relatively little information on microalgal responses to temperature. There have been some studies on the physiological responses of individual species but very few authors have experimented with a range of Classes or species. A few authors have gathered together a multitude of studies by different researchers to try and discern generalities. Consider, for example, the substantial work by Eppley (1972). From the data available at the time and using a simplified version of the Van’t Hoff’s 1884 equation for chemical reaction rates Eppley (1972) parameterized a general model for the response of microalgal growth to temperature: log10m = 0.0275t – 0.070

(Eq. 1.1)

mmax = 0.851(1.066)t

(Eq. 1.2)

or where t = temperature (°C) and mmax = the maximum specific growth rate (t–1). This model describes the upper edge of the envelope of microalgal responses derived from a large number of species. Even though it is more than 30 years old this study is the most thorough in the field and the equations are used in many models that attempt to describe the effects of global warming. Yet as noted by Eppley (1972) the model will over predict growth at low and high temperatures for almost all individual species, furthermore its application to a community with a changing species composition remains untested. The work by Moisman et al. (2003) clearly demonstrates the problems associated with using the generic Eppley model to characterize the response of individual species and parameterize models of global primary production.


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The Q10 parameter is a simple summary commonly used to consider the relative response of different species to variation in temperature. Over the range of temperatures from -2 to 80°C it is essentially equivalent to many exponential equations that relate reaction rate to temperature (Ahlgren 1987) such as Berthelot (1862), Van’t Hoff (1884) and Arrehenius (1889). It is calculated thus: Q10 = v2 (at t + 10°C)/v1 (at t)

(Eq. 1.3)

Q10 = (v2/v1) exp[10/(t2-t1)]

(Eq. 1.4)

or where v1 is the velocity of the process at temperature t1 and where v2 is the velocity of the process at temperature t2. The parameter, Q10, is ~ 2 in many chemical and enzyme mediated reactions. In short term experiments photosynthetic responses to temperature will have a Q10 of ~ 2 (Davison 1991). Growth, however, is the summation of many such reactions and processes for which the Q10 values have been estimated to range from 1.0 to 6.6. As mentioned in the introduction photochemical reactions are essentially independent of temperature (Q10 = 1), low Q10s are also reported for hydrolysis of some compounds as well as diffusion of some solutes in water. Membrane permeation of some compounds is very sensitive to temperature and has high Q10s (Raven and Geider 1988). Given the range of Q10s for reactions associated with growth the Q10 for growth is best determined experimentally by measuring growth at two different temperatures and replacing v1 and v2 in Equations 1.3 or 1.4 with growth rates. A species with a higher Q10 is increasing its growth rate faster over the specified temperature range than one with a lower Q10. With all other things being equal the species with a higher Q10 should achieve an ecological advantage during warming periods. A significant advance in the empirical description of microalgal growth was made by Alghren (1987) after the thorough review of temperature responses and the following was deemed most suitable for predicting the growth response of individual species of microalgae to variation in temperature: m = a(t-a)b (Eq. 1.5) where m is the growth rate (velocity of the process, reciprocal of time, eg. growth d–1 ), a and b are constants, t = temperature, and a is a biological zero (Ahlgren 1987). Since different species can stop growing at widely different lower temperature thresholds the use of a species specific constant for a biological zero (a) was an important improvement over Q10s or the simple,

#%& Algal Cultures, Analogues of Blooms and Applications but more generic model of Eppley (1972). Unfortunately at this time there are relatively few reports of a for microalgal species. It seems there is reasonable agreement that the shape of the generic curve for growth versus temperature is exponential (Li and Morris 1982) but for some species under certain conditions the relationship between m and T appears linear (Montagnes and Franklin 2001). An unknown fraction of this variability in the m versus T relationship probably arises from the fact that many species shift from light-saturated to light-limited as temperatures increase (Collins and Boylen 1982), an experimental artifact that probably impacts on many laboratory studies. Unfortunately it is difficult to statistically separate the linear versus exponential models when the range of temperatures over which a species grows is relatively small. Species with strong exponential relationships between T and m also tend to have a very small step from the temperature where growth rate is maximal and death occurs (Li and Morris 1982). Because of this abrupt transition from maximal growth rate to death the optimal temperature for growth is hard to determine and small oscillations at these high temperatures can significantly depress growth rates and viability. This abrupt transition from mmax to death is the likely cause of the very large rise in endosymbiotic dinoflagellate death (coral beaching) associated with a relatively small increase in water temperature (Hoegh-Guldberg 1999). The abrupt transition may also be an important factor in the usefulness of dinoflagellate cysts to reconstruct previous changes in sea surface temperatures (Brinkhuis et al. 1998). Where there are sufficient data to examine this transition from maximal growth rate to death the transition tends to be abrupt and can be modeled using:

LM N

mT = a 1e(bT ) - e bTm -

F (T - T) I OP H DT K Q m

(Eq. 1.6)

where mT is the resource saturated, maximal growth rate at temperature T, T is temperature and a1 is the rate of temperature dependent processes at a basal temperature, and b is equivalent to a composite Q10 (both a1 and b are species specific constants), Tm is the species specific upper temperature threshold, DT is the temperature range where the curve plateaus (Logan et al 1976). The model (Equation 1.6) has some significant benefits over Equations 1.3 or 1.5. Explicit inclusion of an upper temperature threshold provides a better overall parameterization of the growth of microalgae in response to temperature than the other models. The various published data sets on the relationship between growth rate and temperature for Skeletonema costatum suggests considerable intraspecific variability but the amalgam of these data demonstrates the capacity of the model to cope with a low Q10 and a


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relatively broad range of temperatures where growth rate remains high. The broad plateau (DT) is commonly observed in cyanobacteria and chlorophytes (Payer et al. 1980). Alternatively some cryptophyte data from Ojala (1993) shows a high Q10 and an abrupt upper temperature threshold. Both types of responses are well described by the model (Eqn. 1.6). Both Equations 1.5 and 1.6 are significant improvements since they can be used to estimate the lower temperature threshold at which growth rate is zero (a in Equation 1.5). Problems with Equation 1.6 include its greater complexity, but that seems necessary because of the variable width of the plateau in the growth versus temperature relationship that can be observed in different species (Fig. 16.1). Other problems include the greater amount of data, relative to the simpler models, required to estimate the four parameters and its lack of rigorous testing across a range of species. 2.5

model (Crypto) Ojala Falkowski Sakshaug Yoder Jorgesen model (Skele)

2.0 1.5 1.0 0.5 0.0 0

10

20

30

Temperature (°C)

Fig. 16.1 Growth versus temperature data for Skeletonema costatum from several sources (see Legend) and for two cryptophytes (∑) from Ojala (1993). Both data sets fit to same model (Equation 1.6) using different parameters. In conclusion, while estimates of Q10 and a are useful, the model of Logan et al. (1976) is judged to provide the best parameterization of the empirical relationship between temperature and microalgal growth. The parameters (a1 & b) and the two constants (Tm & DT) should be determined and reported for more species so that we can improve our capacity to predict species level responses to variation in temperature.

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Separating the Effects of Temperature from Those of Irradiance Designing an experiment to completely separate temperature effects from those of irradiance is complicated because increasing temperature and holding irradiance constant can shift a species from light-saturated to light-limited. In this situation the observed acclimations may be more a response to the physiological limitation by irradiance rather than the greater temperature. Ideally a more sophisticated experimental design where the growth versus irradiance relationship is predetermined for each temperature and then the cultures are held at physiologically equivalent irradiances for each temperature would be used. For example, Isat is an exponential function of temperature (Collins and Boylen 1982) and can be expressed as: Isat = D e(0.078T) (Eq. 1.7) where Isat is the irradiance (mmole of photons m–2 s–1) required to saturate photosynthetic growth rate, D is a species specific constant (32 mmole of photons m–2 s–1 in Collins and Boylen 1982) and T is the temperature (°C). The significance of this relationship is that to hold irradiance physiologically constant while investigating the effects of temperature would involve growing cells at Isat (for Anabaena variabilis from 70 to 725 mmol photons m–2 s–1) while increasing temperatures from 10 to 40°C (Fig. 16.2). In this manner the physiological and biochemical effects of temperature, separate Isat (mmoles of photons m–2 s–1)

600 500

Isat = 208e0.0341T – 241

400

r = 0.997

2

300 200 100 0 0

10

20 30 Temperature (C)

40

Fig. 16.2 Redrawn from Collins and Boylen (1982) with permission of the Journal of Phycology. The influence of temperature on the irradiance (Isat) required to achieve the maximal photosynthetic rate in Anabaena variabilis. Irradiances from 70 to 725 mmol photons m–2 s–1 while temperatures varied from 10 to 40°C, T = temperature.


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from those of irradiance, might be deduced. Since there do not seem to be any growth experiments that have adopted this approach it is worth bearing in mind that the relative degree of energy saturation is likely to fall as temperature increases and that data from some experimental designs may be flawed in this regard. The best available data probably comes from experiments where irradiance was set above saturation (> 2Ek) at the greatest temperature used and would therefore be saturating at all lower temperatures.

Microalgal Responses to Temperature: Specific Physiological and Biochemical Acclimations Carbon It has been reported that the carbon content per cell (carbon quota, Qc) has a U shaped relationship with temperature (Goldman 1979, Goldman and Mann 1980). The tendency of cells to increase carbon at both high and low temperatures would suggest a need for a minimal content of some cellular components at intermediate temperatures but similar experiments demonstrated that this proposed U shaped relationship is not widespread (Ahlgren 1987, Thompson et al. 1991). The conclusions in Goldman and Mann (1980) regarding cellular quotas and temperature may be compromised due to the co-variation of light and temperature in the experimental outdoor ponds, it is, however, intrinsically appealing that at some optimal temperature the cellular machinery necessary for the ‘dark’ processing of CO2 and other building blocks of the cell should be minimal. There is quite a bit of data to show that carbon quota increases at lower temperatures across a range of species, both flagellates and diatoms (Durbin 1977, Rhee and Gotham 1981, Thompson 1999, Montages and Franklin 2001). These results are consistent with the hypothesis that at low temperatures cells should acclimate by producing more of the enzymatic machinery to process CO2 (Morris and Glover 1974, Raven and Geider1988). While there is a strong body of evidence relating cellular quota to growth rate under nutrient limitation (Droop 1973), unfortunately, data on C, N, P quotas as a function of temperature are rare. Where these data are available (Scenedesmus in Rhee and Gotham 1981) it seems that all three are likely to increase at low temperatures (Li 1980). There does not seem to be strong evidence for a rise in Qc as temperature exceeds some optimum.

Chlorophyll a quota If growth results from a balance between energy capture and metabolism, and that balance is reasonably stable, then as growth rate rises so must the

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rate of energy capture. Therefore in species where mmax is an exponential function of temperature, irradiance is constant and sufficient to saturate growth, then it is reasonable to assume that the quotas of photosynthetically active pigments should also be an exponential function of temperature. Laboratory studies have reported a wide range of relationships between chlorophyll a quota and temperature from exponential (Fig. 16.3a) to linear to no significant change (Coles and Jones 2000). The lack of a consistent response may reflect either a change in the degree of resource (irradiance) saturation during experimentation or fundamental differences in the physiological acclimation to temperature. Given that the dominant temperature cycle in nature is annual and that the time scales required for acclimation of chlorophyll a quota are much shorter (Post et al. 1985) cells should be near or at ‘steady state’ in terms of temperature and chlorophyll content. Using short term experiments it is possible to show that the biochemical trigger and physiological adjustments to temperature are similar to those for irradiance. A reduction in temperature increases the proportion of reduced plastiquinone leading to a redox mediated feedback on cab (chlorophyll a and b) messenger RNA abundance (an acclimation process identical to that induced by increasing irradiance, Maxwell et al. 1994, 1995) and resulting in a reduction in the synthesis of chlorophyll. This generic response is perfectly consistent with the interpretation that the primary consequence of reduced temperature or increased irradiance for an algal cell is too much light harvesting capacity and the oversupply of excited electrons to plastiquinone. What has become increasingly apparent is that some species have different ‘strategies’ for coping with excess light energy and that those differences are most apparent at low temperatures. 450 400 350 300 250 200 150 100 50 8

10

12

14

16

18

20

22

24

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Temperature (°C)

Fig. 16.3a Chlorophyll a quota (femtograms cell–1) for Thalassiosira pseudonana grown at 220 mmoles of photons m–2 s–1 (data from Thompson et al. 1992a).


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Physiological differences that may obscure the expected exponential relationship between chlorophyll a quota and temperature may occur due to maximal limits on chlorophyll a quota, differences in photosynthetic efficiency, or package effects, shifts in accessory pigments, different mechanisms for coping with the capture of excess light energy or species where growth rate is not an exponential function of temperature. It should also be anticipated that at extreme temperatures, supra-optimal temperatures (> Tm, Eqn 1.6) or below a (Eqn. 1.5), the relationship between chlorophyll a quota and growth rate may not be consistent with the relationship at temperatures between a and Tm. Cells are expected to become rapidly chlorotic at temperatures < a or > Tm. To compare across species it can be necessary to standardize across the huge range in cell sizes present in microalgae. The most commonly used standardization is by carbon quota, as chlorophyll a: carbon (q–1) or carbon:chlorophyll a (q). Predicting q is of particular importance to quantitative phytoplankton ecology because it is fundamental to many models of phytoplankton growth (Cullen 1990). The relationship of q with temperature has received some attention (Geider 1987) and has been described by one generalized equation originally proposed to be applicable to all microalgae: C:chla = 43.4 - 1.14T + 1.85Ie –0.126T

(Eq. 1.8)

where C is the cellular carbon quota, or biomass of carbon (weight), chla is the cellular chlorophyll a quota or biomass of chlorophyll a (weight), T is temperature (°C) and I is irradiance in mmol photons m–2 s–1 and e is the base of the natural log (= 2.718). Equation 1.8 would appear, however, to be flawed in that it seriously overestimates the observed ratios for many species at low temperatures (Figs. 16.3b, c). Given that there are considerable Class differences in the structure of the photosynthetic apparatus, accessory pigments, ratios of accessory pigments to chlorophyll a and the use of carbon or silicon as a structural element it may be that a general unified model for C:chla is not feasible. Efforts to replace the empirical model (Eqn. 1.8) with a dynamic cell-based model (Geider et al. 1997, Zonneveld 1998) may be a useful improvement but will still need to consider a greater range of possible physiological responses. For example, some species such as Dunalliela tertiolecta, have little change in this ratio with temperature (Morris and Glover 1974, Thompson et al. 1992a, Sosik and Mitchell 1994) apparently using a different strategy to cope with the problem of excess light energy capture. It is hypothesized that interspecific variation in q will converge

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carbon: chlorophyll a ratio

1000 Eqn. 1.8 T.antarctica C.furcellatus N.delicatissima

800

600

400

200

0 0

100

200

300

400

500

Irradiance (mmol photons m–2 s–1 )

Fig. 16.3b C:chla ratios from three species (Thalassiosira antarctica, Chaetoceros furcellatus and Nitzschia delicatissima, from Hegseth 1989) and the model from Geider (1987) all at ~ 0°C.

carbon: chlorophyll a ratio

140 Eqn. 1.8 Thompson et al. 1992a

120 100 80 60 40 20 8

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16

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20

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Temperature (°C)

Fig. 16.3c Means and standard deviations for carbon:chlorophyll a ratios from 8 species of temperate phytoplankton (Thompson et al. 1992a) and the model (Geider 1987) all at 220 mmol photons m–2 s–1. as temperature approaches optimal (Raven 1984). For eight temperate species the mean and the standard deviation in the C:chla ratio fell 50% as temperature rose from 10 to 25 °C suggesting that interspecific variability may decline with increasing temperature (Fig. 16.3c).


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The wide range in C:chla observed in different species, especially at low temperature and high irradiances, implies different ‘strategies’ for coping with the exponential decline in the rate of enzymatic process that reduce carbon (carbon fixation or photochemical quenching) at lower temperatures (Huner et al. 1998). It seems likely that species with low qs at low temperatures must have well developed or alternative mechanisms of photooxidative protection relative to those with high qs. For phytoplankton the various strategies of pigment acclimation to temperature are likely to be even more important then in terrestrial species (Savitch et al. 2002) with their relatively simple pigment composition.

Lipids and Fatty acids Many higher plants increase their storage products in response to lower temperatures or higher irradiances (Stitt and Hurry 2002) and phytoplankton may respond similarly although they have relatively little storage capacity. In terms of lipids and fatty acids three major acclimations are considered here: storage of energy, a shift in the relative balance between components needed to capture light versus those needed to process chemical energy and maintenance of homoviscosity. Field based investigations have been inconsistent, some reporting considerable increase in lipids synthesis at low temperatures (Smith and Morris 1980) but others have not observed this (Li and Platt 1982). Potentially it is the combination of low temperature and low nitrate concentrations that results in large increase in triglycerides and saturated fatty acids of some Antarctic diatoms that need a sink for excess light energy in the absence of a NO3 pool (Mock and Kroon 2002). Laboratory research has demonstrated a 50% increase in total lipid relative to dry weight and an increase in triacylglycerides at 8 versus 20°C in the cryptomonad Chroomonas salina (Henderson and Mackinlay 1989). Studies involving multiple species have reported that some species increase total lipid at low temperature while others do not (Renaud et al. 1995, Thompson et al. 1992b). Lipid classes have also been reported to vary with temperature (Lynch and Thompson 1982, Thompson 1989) with lower temperatures increasing triacylglycerols but it is unclear whether this is a generalized response. At the level of specific fatty acids responses to variation in temperature were inconsistent across all eight species. Phaeodactylum tricornutum and Pavlova lutheri had 50% increases in the percentage of 16:0 at low temperatures while other species such as Chaetoceros simplex, Nitzschia paleacea and Isochrysis galbana had an opposite responses of a similar magnitude in their proportions of either 16:0 or 14:0 (Renaud et al. 1995, Thompson et al. 1992b).

#&$ Algal Cultures, Analogues of Blooms and Applications For PUFA there is evidence that certain polyunsaturated fatty acids such as 16:4n1 and 18:4n3 may exhibit general trends by increasing on a whole cell basis at lower temperatures in a number of species (Thompson et al. 1992b, Henderson and Mackinlay 1989). From the available information it can be concluded that some species increase their whole cell composition of lipids and/or triacylglycerol and/or saturated fatty acids in response to low temperature. It is suggested that these acclimations probably represent a strategy to store excess light energy at low temperatures. The lipid and fatty acid composition of whole cells versus that of membranes may respond differently to variation in temperature and to examine this possibility there is a need to separate and analyze membranes from the various cellular constituents (Lynch and Thompson 1982, Uemura and Steponkus 1997) or possibly the fatty acids of particular lipids. There are significant differences in the fatty acid composition of chloroplast lipids between psychrophilic and mesophilic species of the genus Chlamydomonas that can be related to heat sensitivity (Morgan-Kiss et al. 2002). If temperature acclimation results in a shift in the relative balance between the cellular components needed to capture light versus those needed to process chemical energy (Raven and Geider 1988) then it may manifest itself as a reduction in membrane lipids associated with chloroplasts. Given that many species show a reduction in chlorophyll a content at low temperatures they could also manifest changes in chloroplast number, size or density of thylakoid membranes. A rise in the proportion of phospholipids and a (not significant) decline in galactolipids was observed in Chroomonas salina grown at 8 versus 20°C (Henderson and Mackinlay 1989). It is widely reported that many organisms increase their proportion of unsaturated fatty acids or the degree of unsaturation in the fatty acids in response to lower temperatures (Hochachka and Somero 1984, Hadley 1985). The accepted reason for a shift in the degree of FA saturation in response to temperature is the need for biological membranes to adjust their composition to keep their fluidity relatively constant (Sinensky 1974) and prevent ‘chilling injuries’ (Greer and Laing 1988). Responses of some FW cyanobacteria to low temperatures suggest adjustments in lipids and fatty acids but whether these experiments achieved separation of effects due to temperature versus changes in energy status is not clear. For FW cyanobacteria there is also evidence that thylakoid membranes substantially change the temperature at which they undergo phase transitions depending upon growth temperature and their lipid plus fatty acid composition (Chaloub et al. 2003). For eurythermal species changes in fatty acid composition and sterol content are hypothesized to allow the maintenance


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of membrane homoviscosity that is thought to be required for normal physiological functioning and growth at different temperatures (Wada and Murata 1990). Indeed variation in membrane viscosity may act as the signal to enhance the expression of various desaturase genes, mRNA and lead to the increased synthesis of specific desaturation enzymes (Los et al. 1993). A number of researchers have demonstrated that a particular species will show a shift in the degree of unsaturation of fatty acids extracted from the whole cell when grown at lower temperatures (Mortensen et al. 1988) but this trend does not seem to be very consistent (Renaud et al. 1995, Thompson et al. 1992b). The mechanism of acclimation to low temperatures reported for Synechocyctis 6803 involved increasing mRNA for some, D6, D12 and w3, but not all desaturases (Glatz et al. 1999) suggesting that increased amounts of specific PUFAs perhaps associated with the increased desaturation of specific membranes may be the mechanism used by microalgae to achieve sufficient homoviscosity for growth at low temperatures.

Protein For some species total cellular protein has been reported to reach a minimum at intermediate temperatures (Cook 1963) while other studies have seen no trend (Li and Morris 1982) suggesting no universal response. The most generic response might be the formation of heat stress proteins widely reported in higher plants and in some microalgae (Kloppstech and Ohad 1986). A rapidly increasing number of organisms including cyanobacteria have been shown to produce both heat shock proteins and chaperones in response to supraoptimal temperatures (Glatz et al. 1999). For some specific proteins, such as the D1 and D2 proteins from the PS II complex, there is an increase in turnover associated with higher temperatures (Krause 1994). Of course a simultaneous increase in synthesis and degradation (turnover) will not change cellular protein quotas although it may represent a more widespread response to higher temperatures. Recent reports of cold shock proteins provides a conceptual framework for the hypothesis that total protein quota might rise at either temperature extreme.

Enzymes Cells may mitigate the negative effects of lower temperatures on many physiological processes by either making more of an enzyme or using a different form of the enzyme with a greater kinetic efficiency. Raven and Geider (1988) hypothesized that algal cells may have greater capacity than terrestrial plants to increase their content of ‘catalysts’ (e.g. enzymes) in response to decreasing temperatures, estimating this capacity as a factor of ~

#&& Algal Cultures, Analogues of Blooms and Applications 2, although they also recognized the lack of supporting data. Given that the rate of some processes required for growth may increase by > 6 times for a 10° rise in temperature while others may only manage a factor of ~ 1 (Raven and Geider 1988) it seems likely that requirements for temperature induced biosynthesis may be highly specific. This review will focus on two enzymes: Ribulose-1,5-biphosphate carboxlase/oxygenase (RUBISCO) and nitrate reductase (NR) because of their fundamental role in the supply of the raw materials for growth. More enzyme or a different form with a lower thermal response are the two major strategies of acclimation. For Arctic phytoplankton photosynthetic activity covaried closely with RUBSICO activation energy suggesting that RUBISCO activation determines the photosynthetic response of this community to variation in temperature (Li et al. 1984). Obligate psychrophiles may out-compete mesophiles at low temperatures by having enzymes that have greater kinetic efficiency (Marshall 1997). Similarly it can be speculated that stenothermal organisms may out-compete their eurythermal competitors by having optimized enzyme systems for maximum growth over a narrow temperature range (potentially resulting in a greater Q10, Feller et al. 1996) that are metabolically cheaper to build and maintain (Clarke 2003). Reports of increased capability to photosynthesize after acclimation to lower temperatures (Jorgensen 1968) implied either a significant increase in RUBISCO and the other cellular enzymes required to reduce carbon or a greater affinity (kinetic efficiency). This interpretation is now in doubt as a possible artifact of batch cultures (Morris and Glover 1974). Investigations into the efficiency of growth can be confounded by a variety factors that may have nothing to do with temperature, physiological acclimations or genetic differences in RUBISCO such as the age of a batch culture (Morris and Glover 1974). For example, the change in solubility of CO2 in response to temperature is different to that for O2 and combined with the dual carboxylase/oxygenase activity of RUBISCO could appear to improve enzyme efficiency at low temperatures. The demonstration of a difference in temperature optimum for RUBSICO between temperate and polar phytoplankton species was achieved in 1987. RUBISCO reached maximum activity at 4.5°C and a minimum in terms of the K1/2 for the substrate ribulose bisphosphate (RUBP) at 3∞C in three polar species as apposed to at 18∞C in temperate species (Descolas-Gros and de Billy 1987). These results are in sharp contrast to those for a psycrophilic chlorophyte of the Chloromonas genus where RUBISCO activity and temperature optimum were similar to the mesophilic control (Devos et al. 1998). It would seem this is another area where additional investigation is required to understand the physiological


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acclimation potentials of different phytoplankton species. RUBSICO is an enzyme that has a catalytic chaperone. This accompanying protein is important in maintaining RUBSICO’s function under variable irradiance and especially at higher temperatures (Portis 2003). The presence and functioning of this chaperone appears to be interspecifically variable. The information available for RUBISCO suggests that species are likely to manifest different strategies to temperature acclimation and given the range of physiological responses reported within some species (e.g. Skeletonema costatum), it seems likely that regional differences are also likely to have evolved within the species complexes of some of our more cosmopolitan microalgae. Of the three main N sources available for phytoplankton (NO3, NH4 and N2) we know that low temperature inhibits nitrate more than ammonium uptake (Reay et al. 1999) and that diazotrophs have a possible temperature limitation related to their sensitivity to O2 (Staal 2003). Growth on nitrate is energetically more expensive that on NH4 and the NR enzyme has a high turnover rate so there is high maintenance cost for growth on this substrate. It is possible that overcoming the physiological difficulties of growth at low temperatures using nitrate is a physiological specialty of diatoms. While there are structural differences in NR between Classes (Iwamoto and Shiraiwa 2003) the physiological effects of these differences are still being elucidated. It has been reported that microalgae from the chlorophyll c containing Classes have nitrate reductase (NR) with a temperature optimum of 10 to 20°C while chlorophyll b containing Classes and terrestrial plants have NR with a temperature optimum above 30°C (Gao et al. 2000). However it has also been demonstrated that nitrate reductase had a 20∞C difference in optimal temperatures between a psychrophilic and a mesophilic species of the chlorophyte genera Chloromonas (Loppes et al. 1996), a result that seems to make an overall Class distinction in NR capability doubtful. There are also reported differences in NR between strains, for example Anabaena isolated from the Antarctic was different from tropical isolates in optimum temperature and activation energy for NR (Shukla and Kashyap 1999). In diatoms (Raimbault 1984) but not in most flagellates (Lomas and Gilbert 2000) nitrate uptake and nitrate reduction show different kinetic responses to temperature resulting in uncoupling of uptake and assimilation producing large internal nitrate ‘pools’ in some diatom species at low temperatures. The accumulation of large internal nitrate pools has been hypothesized to provide a sink for energy during transient exposure to excess light in cold water diatoms (Lomas and Gilbert 1999). Accumulation of nitrate is interspecifically variable in diatoms (Dortch et al. 1984,

#' Algal Cultures, Analogues of Blooms and Applications Raimbault and Mingazzini 1987) and so is the excretion of nitrite (Parslow et al. 1984, Raimbault 1986). The later process would effectively export energy. These observations certainly suggest that a species’ capacity to take up, accumulate and/or assimilate nitrate at low temperatures may be important factors determining its capability to achieve community dominance in low temperature (Reay et al 2001) and fluctuating light regimes.

Physiological Mechanisms that Improve Growth or Survival at Temperature Extremes Temperature range The greatest extremes are most likely encountered by algae living in the top millimeters of desert soils (Kidron et al. 2000). These eurythermal species may survive a temperature range of 120°C. Life history changes such as cyst production may be triggered by temperature extremes and this survival mechanism is considered briefly later. Cellular mechanisms such as protein and lipid modifications that allow survival under heat cold and light stress are in photosynthetic cyanobacteria were previously reviewed in Glatz et al. (1999).

(a) Minimum temperatures The ‘marine’ polar microalgal community can be divided between the largely fresh water species that live on the upper surface of the permanent snow and the ice flora that grows on the undersurface of the pack ice. Upper surface snow algae occupy a unique habitat where they are exposed to some of the most extreme environmental conditions. Under these cold conditions microalgae are under extreme photo-oxidative stress (Huner et al. 1998). In response some microalgae accumulate an astaxanthin ester producing the characteristic red pigmentation found in some snow algae (e.g. Chlamydomonas nivalis (Bauer) Wille) to reduce photodamage under conditions of high irradiance including high UV (Gorton and Vogelmann 2003). The esterification of astaxanthin with fatty acids represents an unusual physiological adaptation allowing this chromophore to be concentrated within cytoplasmic globules maximizing its photoprotective efficiency (Bidigare et al. 1993). There are also reports of green and black snow associated with dense algal blooms in or originating from snow fields and being transported by high winds. Photoprotective mechanisms include carotenoids acting to quench triplet state chlorophyll or excited singlet state 1 O2 (Owens 1994, Foyer and Harbinson 1994). Some pigments that have


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been reported and seem likely to be important mechanisms for coping with excess irradiance under low temperature or nutrient stress include: 1. b-carotene and a-carotene (Orset and Young 1999) ¨ zeaxanthin cycle (Hager 1980) 2. violaxanthin Æ ¨ diatoxanthin cycle (Arsalane et al. 1994) 3. Diadinoxanthin Æ The microalgal communities found in or under the polar ice are largely diatom dominated (Kirst and Wiencke 1995). The ice flora can grow very dense (concentrations of 37.4 g C/m2 or exceeding 6500 mg chl a/m3, Arrigo et al. 1995) in an inverted benthic habitat, on the underside of the pack ice. Inspite of the low temperatures these phytoplankton generally have high chlorophyll a quotas presumably because they are really light limited (Cota et al. 1991) as the irradiance on the underside of the ice is often less than 1% of irradiance on the surface. Some species have very high pigment content and thus very low irradiance is required to saturate photosynthesis and growth. The optimal temperatures for growth may be 4-10°C although transient maxima in photosynthetic rates may occur at higher temperatures. Factors other than photo-oxidation damage can cause low temperature death of cells (Russell 1992). As water becomes solid it causes severe dessication and can result in internal damage in many plants (Pearce 2001). For example, cell membranes can be ruptured by ice crystal formation. Some species are very resistant to this by excluding water and accumulating ‘antifreeze compounds’ or cryoprotectants, a process much studied in terrestrial crops (Collins and Chandorkar 1983, Santarius 1973). Such compounds are normally low molecular weight solutes and sugars. In marine micro and macro algae both glycerol and b-dimethylsulphonioproprionate (DMSP) have been suggested as cryoprotectants and aids to osmoregulation (Kirst et al. 1991, Kirst 1990). In most cases these substances are nominated as cryoprotectants because of increases in their cellular quotas in samples collected from colder environments, or in association with colder seasonal weather. From laboratory research we also know that increasing the external concentrations of many sugars and b-dimethylsulphonoxide (DMSO) results in increased tolerance to low temperatures in some microalgae (McGrath and Daggett 1977, Mortain-Bertrand et al. 1996). From laboratory studies there is increasing evidence that cellular DMSP content increases at low temperature in some species (Rijssel and Gieskes 2002) although there is some debate about whether the internal concentrations of DMSO are sufficient to impart increased temperature tolerance in polar microalgae (Lee et al. 2001).

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Unlike responses to irradiance there are very few tabulated data sets of the minimum temperature at which net growth will occur (a from Eqn. 1.5 or a1 from Eqn. 1.6) or accurate measures of the temperature at which growth rate is maximal or the temperature at which death occurs. This lack of data persists even though a species temperature tolerance and Q10 have long been recognized as important determinants of ecological success (Goldman and Carpenter 1974). A limited data set from Ahlgren (1987) indicated that a varied from 0 to 12°C depending upon the species.

(b) Maximum temperatures Some species are obligate psychrophiles where temperatures above 8 – 10°C are fatal (Fiala and Oriol 1989). Microalgal psychrophiles are poorly studied for the causes of death at these relatively low temperatures and, in general, the upper temperature limits are relatively poorly studied in microalgae. Recently increased interest in mass cultivation and ballast water treatment has seen more research on this question but primarily applied to eurythermal and temperate species. It has been suggested that the upper temperature limits for growth are set by protein and membrane stability. Both hypotheses are better studied in terrestrial plants than marine microalgae (Sung et al. 2003). The terrestrial plant research on this subject tends to focus on the effects of short term heat or cold shocks which are a serious economic problem for crop plants but not especially relevant to aquatic autotrophs where the temperature buffering capacity of any sizeable water body means that short term fluctuations are greatly reduced. It is known that although PS II repair is a function of temperature chloroplasts and their membranes appear to be relatively easily damaged by heat (Glatz et al. 1999). In particular photosystem II is damaged by lipid disruption, manganese release and protein ‘failure’ (synthesis and repair of proteins cannot keep up with damage, especially for protein D1, Krause 1994) in association with higher temperatures (Quinn 1988). Marine habitats where water temperatures exceed the ~ 30°C that is tolerated by many temperate species are rare albeit quite important. They contain some tropical seas, some benthic microalgae on illuminated sediments and some tide pools. Like most extreme environments they tend to have reduced biodiversity but may be important zones of speciation. Some extreme hot or cold aquatic environments (c.f. freshwater) tend to be dominated by cyanobacteria. Although marine cyanobacteria are relatively poorly studied from their freshwater relatives we know they can grow and photosynthesize at constant temperatures up to 74°C (Meeks and Castenholz 1971).


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Ecological impacts of temperature In spite of the shortcomings of Q10 as a measure of microalgal response to variation in temperature (see earlier) there are a large number Q10s available for different species. In a single study of eight temperate species Q10 ranged from 1.8 to 3.1 (mean and standard deviation = 2.4 ± 0.43, Thompson et al. 1992a) while in eight Antarctic species Q10 ranged from 1.16 to 5.8 (mean and standard deviation = 2.7 ± 1.67, Fiala and Oriol 1989). Growth versus temperature data for six strains of freshwater Anabaena circinalis isolated from around Australia showed a broader temperature range suitable for growth relative to the marine temperate and polar species and had a mean Q10 of 1.16 ± 0.31 (Fig. 16.4). The greater variation in Q10s for polar relative to temperate species suggests the intriguing potential of more significant niche separation associated with relatively small temperature variation in some polar environments. Based on these data it seems reasonable to speculate that more thermally stable environments might favor the evolution of some stenothermal species (relatively small DT) with high Q10s. Such species would require catalytic systems (enzymes) with efficiencies that increase rapidly with temperature as a method to achieve ecological advantage Psychrophilic

Mesophilic 10

–1

Growth rate (d )

10

1

1

0.1

0.1

0.01

0.01

0

10

20

30

40

Temperature (°C)

Fig. 16.4 Growth rates for phytoplankton species versus growth temperature. Triangles are psychrophilic (data kindly provided by M. Fiala, (data from Fiala and Oriol 1989); circles are eight temperate species (adapted from Thompson et al. 1992a); while squares are unpublished growth rates for six strains of freshwater Anabaena circinalis isolated from around Australia. Solid line is Eppley’s (1972) upper envelope.

#'" Algal Cultures, Analogues of Blooms and Applications and quite possibly these highly efficient catalysts have a more limited operational temperature range. Many field studies have shown associations between temperature and phytoplankton ecology but experiments that vary only one factor and have controls are rare. One such study using mesocosms demonstrated a 5.5°C temperature rise accelerated succession resulting in greater dominance by dinoflagellates (Carpenter 1973). Thus temperature can affect the outcome of competition between species or between Classes of marine microphotoautotrophs with the most likely effects being the replacement of diatoms with dinoflagellates, then chlorophytes (Goldman and Ryther 1976) and finally cyanobacteria (Goldman 1977) if temperature rises much above 35-40°C. For example, in outdoor trials it was found that at low temperatures diatoms dominated, with clear species shifts as temperature rose (Goldman and Ryther 1976). Above 25°C pennate diatoms increased their dominance until chlorophytes took over at 30°C. The trials did not extend further but it seems likely that above 30°C cyanobacteria might dominate. It has recently been hypothesized that the dominance of N2 fixing non-heterocystous cyanobacteria rather than heterocystous cyanobacteria in tropical oceans is a temperature mediated response (Staal et al. 2003). The researchers provide a model and data to support the hypothesis that heterocysts can be disadvantageous at certain temperatures because of the differences in the temperature dependence of three key reactions: the O2 flux necessary to support dark respiration; ATP production; and N2 fixation in the dark. The proposed underlying mechanism is similar to that reviewed in Raven and Geider (1988), that diffusion is a largely physical process with a Q10 ~ 1 while the biochemical reactions will have Q10s ~2 , and their interactions are important in determining growth. Staal et al. (2003) hypothesize that heterocystous cyanobacteria can only increase their efficiency by increasing dark N2 fixation, a process requiring more ATP the production of which is limited by the diffusion of O2 at higher temperatures. Thus these species of cyanobacteria must increase the permeability of the heterocyst envelope, potentially to the point where maintaining it is no longer a benefit but a detrimental cost, reducing any competitive advantage over non heterocyctous cyanobacteria. Given that laboratory studies may be biased due to the small subset of species used or the limitations of scale, what emphasis should be placed upon field studies with their multiplicity of covarying factors? At the Class level cyanobacteria dominate the marine tropics (Wright et al. 1996) and the polar fresh water environments (Tang et al. 1997) such that the only


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generalization that seems feasible is a great tolerance of temperature and salinity. While there are many physiological differences between cyanobacteria and eukaryotic microalgae, it is tempting to speculate that it is a consequence of the less structured nature of prokaryotic life that limits Q10s for growth and favors sternothermal (high DT, Eqn. 1.6) or euryhaline species that tolerate a greater range of environmental extremes. Diatoms dominate the polar oceans (Hasle 1969) and there would appear to be fewer (relative to diatoms) psychrophilic dinoflagellates, particularly photosynthetic marine ones (Kirst and Wiencke 1995, Archer et al. 1996). This comparison may be too simplistic since there are more diatom species than dinoflagellates and there are some notable psychrophilic forms: Amphidinium cryophilum (Schiller 1954, Wedemayer et al. 1982), a few polar Ceratium species (Nordli 1957), Polarella glaciais which dominates in some transient high salinity, low temperature Antarctic environments (Stoecker et al.1992) and dinoflagellates as snow algae (Gerrath and Nicholls 1974). In temperate ecosystems dinoflagellates species often peak later in the year than diatoms, during the warmer months (Taylor 1987, Smayda 2002). This seasonal succession may have more to do with temperature induced stratification resulting in the vertical separation of light and nutrient supply providing an ecological advantage to vertical movement by dinoflagellates rather than a Class difference in physiological responses to temperature. Both the continuing eutrophication of the coastal zone and global warming (Peperzak 2003) should be considered potential causes for the hypothesized increase in harmful algal blooms (HABs).

Irradiance, Daylength and Growth Introduction The relationship between irradiance and growth can be broken down into several components; light-energy capture, energy conversion to short term storage products and growth. The first two components are often considered together as photosynthesis. Excellent and extensive reviews of photosynthesis exist (Falkowski and Raven 1997 and Larkum et al. 2003) and this chapter will not seek to repeat that information here. It will consider effects of irradiance on the growth of microalgae and review only the selected processes that are most relevant. Under steady state plus nutrient replete conditions photosynthesis and growth are highly related physiological processes so that much of the recent review by MacIntyre et al (2002) is relevant. The major difference between experiments that determine ‘growth versus irradiance’ relative to ‘photosynthesis versus irradiance’ is

#'$ Algal Cultures, Analogues of Blooms and Applications that the latter typically uses cells that are acclimated to a single irradiance and measures short term carbon uptake or oxygen evolution over a range of irradiances while the former determines a growth rate after acclimation to each irradiance. There are considerable data on microalgal responses to irradiance and substantial consensus within the international community regarding methodology so that results from various laboratories are often compatible. As a consequence our capacity to compare across taxonomic groups is relatively high allowing considerable overall synthesis.

Light as an energy source The sun emits electromagnetic energy across a large spectrum but many of the emitted wavelengths are differentially absorbed by chemicals in the Earth’s atmosphere so that the energy reaching the surface of the water is primarily within the 400 – 700 nm wavelengths. For photosynthesis the range of wavelengths absorbed by plants and used directly for photosynthesis is often considered to be 400 – 700 nm (= photosynthetically active radiation = PAR). Under clear atmospheric conditions an irradiance of ~ 2000 mmol photons m–2 s–1 PAR can reach the surface of the ocean, sufficient to saturate photosynthesis or growth in all characterized species of microalgae. Variation in atmospheric ozone means that UV reaching the surface does not always covary with PAR and the spatial extent of an ecological impact associated with variation in UV is more limited than for PAR. In terms of photosynthesis or growth, quanta (∫photons) from high to low energy (400 to 700 nm or blue to red) are fundamentally equal. For this reason photobiologists need to measure irradiance in quanta rather than as energy (as preferred by engineers). Yet the ~ 50% greater energy of a blue photon must be dissipated somehow and this has physiological consequences. Also irradiance outside that which is photosynthetically useable radiation (PUR), such as UV, may still impact on growth and physiology.

The underwater light field: some basics When the sun approaches its zenith about 2% of the incident light is reflected from calm, flat water, rising sharply to be nearly 100% reflected as the sun approaches the horizon (Kirk 1994). Waves decrease reflectance in a manner that is a nonlinear function of wind speed and fetch. For example, with the sun on the horizon reflectance declines from ~ 100% to ~ 30% as wind speed rises from 0 to 16 m s–1 (assumes wind waves are fully developed). In contrast if the sun is more than 20° above the horizon then


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winds of up to 16 m s–1 are reported to have negligible effects on reflectance (Kirk 1994). Light penetrating into the water is also selectively absorbed and scattered. The absorption is often expressed as an extinction co-efficient (k): Id = I0e–kd where Id is the energy at depth, I0 is the energy at the surface, d = depth, natural log e. Ultra violet and infra red wavelengths disappear most rapidly. In pure water absorption of infra red is high but UV is actually absorbed by the gases (primarily O2) dissolved in water (Kirk 1994). Red light (~ 600 nm) is also absorbed quickly so that after a few meters there is little left but bluegreen (peak about 480 nm). In very clear water significant blue-green light can penetrate to considerable depth (100-200 meters). In more turbid coastal waters blue can be more scattered and absorbed so that the peak wavelengths are shifted towards green. For algal populations this means there is both a decline in irradiance and a shift in wavelength with depth. Effects of variation in irradiance quantity are much better studied than variation in quality. Recent emphasis on UV has demonstrated that bottle composition can have a significant impact on the wavelengths of light energy within a container relative to outside potentially biasing experimental results. Other problems with experimental data include the scattering and absorption effects associated with cells that are, almost always, magnified many times in microalgal cultures. Essentially the effects of tens of meters of coastal water can be compressed into tens of centimeters in dense cultures. This situation can lead to problems with irradiance measurements and subsequent interpretation of the results especially between laboratories. In an ideal experimental system irradiance would be equal upon all cells. Very few culture systems achieve this goal, but some are noticeably better than others. Systems that are dimensionally thin and optically thin (low cell numbers) with a uniform light field provide data that are most suitable for investigations into microalgal physiology. Some laboratories have reported irradiances on the illuminated side of very dense cultures. Under these circumstances it is not possible to estimate the mean irradiance and thus impossible to incorporate the results into a more general framework of physiological responses. Algal culture and experimentation most frequently relies on artificial lighting. Certainly artificial lighting makes it much easier to standardize irradiance and apply the scientific method to microalgal research. Most light sources are, unfortunately, rather different in spectral output than natural daylight and often even more different from the ‘normal’ underwater light field. This problem can be at least partially rectified by using ‘full spectrum’

#'& Algal Cultures, Analogues of Blooms and Applications light sources or filters between the light source and the culture. Copper sulphate solutions, colored glass and various tinted plastics have all been used to more closely mimic the underwater light regime. It would make experimental results more comparable if photosynthetically usable radiation (after Morel 1978) was routinely determined especially when the experiment’s spectral composition is unusual. Not only does the availability and quality of irradiance vary with depth but it also varies in time. Long term (relative to phytoplankton generation times) total daily insolation to the outer atmosphere varies with latitude and time of year. Seasonal weather and associated cloud cover can provide additional long to medium term variability. The daily L:D cycle has a large magnitude (range 0-2000 mmoles of photons m–2 s–1) with intervals of illumination varying from 0 to 24 h. Changes in the diurnal cycle can be slow relatively to the less predictable variations associated with individual clouds, variable reflection due to waves and focusing through waves. This variability is combined with variation in the vertical position of the phytoplankton due to circulation or motility to create complicated patterns of spatial and temporal variability in irradiance (light incident to the cell).

The architecture of the photosynthetic apparatus The functioning of the photosynthetic apparatus is very sensitive to light and temperature. It has a complex architecture on molecular to membrane scales. It varies dramatically across the various phylogenic groups found in the phytoplankton, from the single membrane (thylakoid) in cyanobacteria to the multiple stacks of thylakoid membranes (grana) inside chlorophyte chloroplasts (Anderson 1999). The pigment molecules are organized into light harvesting complexes (LHCs) that act like antennae around the two types of reaction centers (PSI and PSII). The arrangement of reaction centers varies with Class and is dependent upon thylakoid arrangement. Both reaction centers and thylakoid arrangement are dynamic in response to irradiance, especially in chlorophytes (Anderson 1999). Pigment types also vary across phylogentic groups (Jeffrey et al. 1997). It seems reasonable to hypothesize that these phylogenic differences in thylakoids and pigments may be related to differences in phylogenetic differences in growth versus irradiance (Richardson et al. 1983). Within the LHC and reaction centers pigment molecules exist in a tightly controlled physical orientation with regard to each other, their apoproteins, and the other components of the photosynthetic apparatus (Lawlor 1987). Very detailed physical information on their structure comes from crystallographic studies of the reaction centers from purple sulfur bacteria that are resolved at 2.3 Å (Hoff and Deisenhofer


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1997). Reaction center structure varies phyogenetically. On a time scale of hours to days the size and pigment composition of the antenna-like light harvesting complexes around PSI and PSII can change in response to variation in irradiance or temperature. The capacity to make these acclimations are important in determining growth responses to variation in irradiance and temperature. Light energy captured by a pigment molecule excites the molecule and needs to be dissipated or quenched to prevent eventual cell damage. Various pathways are possible for quenching; for example by carbon reduction (photochemical quenching) or fluorescence emission or transfer to other pigments such as carotenoids and dissipation as heat (nonphotochemical quenching). Photosynthetic growth requires highly precise photosynthetic architecture to allow efficient energy capture and flow and highly regulated feedback loops to maintain those functions under variable environmental conditions.

Phytoplankton Responses to Light Growth and photosynthesis The overall process of photosynthesis is one of energy capture and conversion so that a chemical in low energy state + light energy Æ (photosynthetic organism) Æ chemical in higher energy state. Using this definition and ignoring the more transient intermediates (ATP, NADPH) the following reactions (Table 16.1) would be considered the major primary photosynthetic processes and their relative energetic costs for balanced growth by autotrophic microalgae on nitrate. Table 16.1 1973)

Approximate energy costs of major ‘photosynthetic’ processes (after Healey

Substrate

Electron cost

‘Initial’ product

Atomic proportions in cells C:N:S (sum =1)

Electrons per mole

Percent of energy

CO 2 NO 3 SO4

4 8 8

CH 2O NH 4 HS

0.8972 0.0979 0.0048

3.5888 0.7832 0.0384

81.4 17.8 0.9

Of course there are many energy consuming reactions to convert these initial products of photosynthesis into all the subsequent products and all other physiological processes that are part of growth such as maintenance, reproduction, respiration, photorespiration, excretion of reduced compounds and swimming that all these require energy derived from sunlight. As a

$ Algal Cultures, Analogues of Blooms and Applications starting point we can see that of the products of photosynthesis carbon reduction consumes ~ 81% of the energy, followed by 18% for nitrate reduction and ~1% for sulfate reduction (Table 16.1). The relationship between growth rate and irradiance is fundamental to predictive microalgal ecology. This section examines the relationship between irradiance and growth rate for cells that are nutrient sufficient and acclimated to their temperature and irradiance (steady state). Under these conditions it is presumed that growth rate can be determined from the change in biomass over time where biomass can be cell numbers, the weight of cells or their constituents, or fluorescence. Under resource saturated conditions microalgal growth is primarily by vegetative reproduction and exponential such that the instantaneous growth rate (m) can be calculated: m = (ln Nt2 – ln Nt1)/(t2–t1)

(Eq. 2.1)

where m = instantaneous growth rate with units of time–1, ln Nt2 = the natural log of the biomass at time t2, ln Nt1 = the natural log of the biomass at time t1. Growth results from the net sum of many physiological reactions, some that take place primarily in the light (e.g. carbon fixation, water splitting, oxygen evolution, photorespiration, respiration and exudation) and some of which also occur in the dark (respiration and exudation). While experiments using inorganic carbon and oxygen, labeled (C14, O18) or not, have yielded great insights to these physiological processes they are traditionally conducted upon cells acclimated to one irradiance and exposed for short periods to a range of irradiances (i.e. transient conditions). The combination of irradiance transients with the lack of a suitable short term technique to measure dark reactions places some fundamental constraints on estimates of growth from measurements of photosynthesis. Bearing in mind these differences it is still true that the relationship between growth rate (m) and acclimated irradiance (E) is similar in form to the relationship between photosynthetic rate (P) and short term irradiance (I). The various photosynthesis versus irradiance relationships proposed for phytoplankton are all saturating curves that can be described as a ‘rectangular hyperbola’ of various types (reviewed by Jassby and Platt 1976). Of the various forms the classic rectangular hyperbolic curve (Michaelis and Menten 1913, Baly 1935) does not plateau sharply enough to characterize the growth rate versus irradiance relationship observed at long day lengths, although it seems adequate at shorter day lengths (Thompson 1999): m=

m max E Ek + E

(Eq. 2.2)


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where m = growth rate, mmax = maximum growth rate, E = irradiance and Ek = the half saturation constant for growth. At longer daylengths this model results in an overestimate of mmax, by up to about 30% (Fig. 16.5) and an underestimate of m at irradiances ~ 2Ek. Several of the other models have been developed with recent emphasis upon the exponential model: m = mmax(1 – e–bE )

(Eq. 2.3)

where m = growth rate, mmax = the maximum growth rate, b = a species specific parameter, E = irradiance. The mmax parameter estimated by Equation 2.3 was better constrained and considerably closer to the observed maximum growth rate (Fig. 16.5, Table 16.1) than Eqn. 2.2. Equation 2.3 can be reconfigured as a photosynthesis versus irradiance model (Falkowski and Raven 1997) that is compatible with our current conceptual model for the photobiology of photosynthesis as a cumulative ‘one hit’ Poisson function with a turnover time (Falkowski et al. 1985): m = mmax(1 – e–aE/mmax)

(Eq. 2.4)

where m = growth rate, mmax = the maximum growth rate, a = the initial slope, E = irradiance. To complete the transfer of this empirical model to a physiological model it is necessary to replace aE with the equivalents sPSII t E (where sPSII is the optical absorption cross section of PSII (Mauzerall 1978), t = the maximum turnover rate (=1/the frequency of flashes that saturates photosynthesis, (Falkowski et al. 1985). Both models (Eqns. 2.3 or 2.4) fit growth versus irradiance data significantly better than Eqn. 2.2 but could still be improved by the incorporation of a parameter for the acclimation of target size (i.e. an increase in chlorophyll a quota). As discussed later in the region (~ 2Ek: or as in the example ~ 60 mmoles of photons m–2 s–1) where growth rate can be kept high by elevating chlorophyll a quota that these models underestimate growth rate. Under some circumstances the hyperbolic tangent model provides a better empirical fit to the data (Table 16.2) in spite of its lack of a physiological basis: m = m max tanh

FG - aE IJ Hm K

(Eq. 2.5)

max

symbols as in Eqn. 2.4. Relative to the other equations tested here the hyperbolic tangent model provided an estimate of mmax that was more tightly constrained and slightly closer to the observed maximum (Table 16.2) For growth and photosynthetic models, particularly when applied to flagellates and especially dinoflagellates, all these models can be improved

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Table 16.2 Estimates of the maximum growth rate from various models for data from Thalassiosira pseudonana grown under continuous light. Source

mmax d–1

Standard error of mmax as a percentage %

Observed Equation 2.2 Equation 2.3 Equation 2.5

1.74 2.23 1.83 1.80

10.9 7.1 2.2

by adding another parameter, to estimate the compensation irradiance (Ec), thus: m = mmax(1 – e–a(E+Ec)/mmax)

(Eq. 2.6)

The important consideration for these empirical models is the quantification of some defining features of the responses of the algae to irradiance, for example the generalized curve (Fig 14.5) shows: (1) the initial slope; (a) the higher the initial slope the more efficient the algae for a given amount of light

2.0

Ec Ek μmax

net

1.0

gross

Growth rate (d–1)

1.5

0.5

0.0

0

25

50 75 100 125 150 175 Irradiance (mmol photons m–2 s–1)

200

Fig. 16.5 Growth rate (m) versus irradiance (E) for the marine diatom Thalasiosira pseudonana grown under continuous light at 18ºC (adapted from Thompson 1999). Dashed line is data fit to Equation 2.2, dotted line is data fit to Equation 2.3; both modified to allow negative y-intercepts.


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(2) mmax the maximal growth rate or Pmax; the maximal rate of photosynthesis (3) Ec the irradiance at which growth = respiration or (Ic) the irradiance at which photosynthesis balances respiration (4) Ek the irradiance at which the initial slope intercepts mmax or Ik the irradiance at which the initial slope intercepts Pmax. (5) net growth or Pn = net photosynthesis (6) gross growth or Pg = gross photosynthesis It is possible to assess the estimates of mmax, Ek and Ec obtained under steady state growth conditions for differences between Classes once they have been corrected for cell size (Banse 1976, 1982). Generalized differences between algal Classes in photosynthetic capabilities have been known for some time (Ryther 1956). In terms of growth chlorophytes reportedly have greater Ec and Ek values than dinoflagellates and diatoms (Richardson et al. 1983) while coccolithophorids are reported intermediate (Brand and Guillard 1981) although these generalizations could be improved by examining more species. It is clear that many diatoms (e.g. Phaeodactylum tricornutum [Geider et al. 1985], Skeletonema costatum [Falkowski and Owens 1980] and Thalassiosira pseudonana [Thompson 1999]) have Ec values of ~ 1 mmol photons m–2 s–1 while dinoflagellates often have greater Ec and Ek values (Landgon 1986, Chan 1978). It appears that for many species of Dinophyta the intercept of the growth versus irradiance curve (Ec) is greater than for diatom species of a similar size, presumably due to their greater respiration rates (Langdon 1986). Parameters derived from m vs E curve often indicate significant difference between species in mmax, alpha, Ec, and Ek , can be used to predict growth rates under a wide range of possible irradiances and daylengths (Sakshaug and Andresen 1986) and predict the outcome of species competition both in theory (Litchman and Klausmeier 2001) and in practice (Huisman et al. 1999). In nature the availability of many resources is highly variable in both time and space. Many ecological models already have temporal variation in light, temperature and multiple nutrient limitations incorporated but parameterization of the phytoplankton responses remains very simplistic. That some species have a significant improvement in growth rate under fluctuating rather than static irradiance (Mitrovic et al. 2003) suggests that advances in this field will come from both examining more species and intelligently exploring transients or variable environmental conditions.

$" Algal Cultures, Analogues of Blooms and Applications

Microalgal responses to irradiance: Specific physiological and biochemical acclimations There have been many papers that describe the physiological acclimations that individual species of microalgae undergo to achieve a particular physiological condition under steady state, nutrient saturated conditions but very few that synthesize by exploring the diversity and generalities (Richardson et al. 1983). A change in chlorophyll a quota is characteristic of most phytoplankton cells acclimatized to different irradiances yet not commonly found in higher plants (Huner et al. 1998). The primary biochemical feedback control mechanism on cellular chlorophyll a content is an increased proportion of reduced plastiquinone leading to a redox mediated feedback on cab (chlorophyll a and b) messenger RNA abundance in response to increased irradiance (Escoubas et al. 1995, Durnford and Falkowski 1997) and resulting in a reduction in the synthesis of chlorophyll. The net results appear to be quite variable across phytoplankton species in that the relationship between irradiance and chlorophyll a quota has been described as exponential (Yoder 1979), linear (Rhee and Gotham 1981, Geider et al. 1997) or nonlinear (Brown and Richardson 1968). Laboratory experiments have demonstrated that when exposed to light transients the time required for phytoplankton cells to achieve a reduction in chlorophyll a quota and thereby reduce their light harvesting capacity is relatively long, 24 to ~ 100 h (Post et al. 1984). Since the time to reach acclimation in terms of light harvesting pigments is longer than experimental or natural diel irradiance cycles most species could be expected to be continuously synthesizing or degrading chlorophyll. Therefore the relationship between chlorophyll a quota and irradiance will be complicated by the timing and magnitude of the irradiance dose prior to sampling. As suggested previously studies of individual species do not often have a sufficient range of irradiance to allow accurate assessment of the shape of this curve and standardization by carbon quota is often used to allow multiple species data sets to be compared (Geider 1987). If carbon quota does not vary systematically with irradiance (see later) then the relationship of carbon:chlorophyll a (q) with irradiance should be similar to that of chlorophyll a quota (Q) with irradiance. Unfortunately q has also been reported as linear (Geider 1987), nonlinear (Sakshaug and Andresen 1986), or exponential (Thompson 1999). In spite of this apparent variability I will argue that there is a general model of acclimation in chlorophyll a quota to variation in irradiance. I would propose that at irradiances greater than 3 Ek most species will have a relatively slow and linear decline in chlorophyll a quota. In the range


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of irradiances between 2Ek and 3 Ek cells can keep their growth rates near to mmax by increasing light capture (= compensation). In this range (2Æ3 Ek) most of the capacity to increase chlorophyll a quota will be manifest (Fig. 16.6). Only once chlorophyll a quota has reached maximum and further compensation is no longer possible will growth rate start to fall with decreasing irradiance (< 2EK). There are a number of published data sets where the intervals of irradiance used in the experiments were sufficient short to perceive this general pattern, for Skeletonema costatum, (Falkowski 1980), Dunaliella tertiolecta (Falkowski and Owens 1980), Synechoococcus (Kana and Glibert 1987) and Thalassiosira pseudonana (Thompson et al. 1989). In this range of irradiances below 2Ek chlorophyll a quota should always be maximal although there are some indications that at very low irradiances some cells may become slightly chlorotic (Falkowski 1980, Fig. 16.7A). It is suggested that other pigments that form part of the light harvesting complex or antenna that capture light to be photochemically quenched (i.e. used for NADPH and ATP production) and may also show similar patterns of variation. Assuming general applicability then the relationship between irradiance and chlorophyll a quota could appear linear, nonlinear, or exponential depending upon the number and spacing of irradiance treatments. 1Ek

2Ek

3Ek

2.5

0.30 2.0 0.25 1.5

0.20 0.15

1.0

0.10

Growth rate (d–1)

Chlorophyll a quota (pg cell–1)

0.35

0.5 0.05 0.00

0.0 0

50

100

150

200

Irradiance (mmoles photons m–2 s–1)

Fig. 16.6 Chlorophyll a quota (=) and growth rate ( ) for Thalassiosira pseudonana grown on a 24:0 L:D cycle (adapted from Thompson et al. 1989). Data from Hegseth (1989) clearly demonstrate that interspecific acclimation strategies for chlorophyll a quota as a function of irradiance

$$ Algal Cultures, Analogues of Blooms and Applications

20

A

0.30

B

0.25

0.20

0.15

0.10

15

10

nonphotochemical quenching

low

Chlorophyll a Q/Qmin

Chlorophyll a quota (pg cell–1)

vary substantially (Fig. 16.7B). At –0.5∞C over the range of irradiances from 10 to 450 mmole of photons m–2 s–1 chlorophyll a quotas varied from near no change in Chaetoceros furcellatus to a seven times decrease in Thalassiosira antarctica. For these three species and over this range of irradiances the relationships between irradiance and chlorophyll a quota (Q) were all exponential but with difference slopes. Species with little change in chlorophyll a quota must either be poorly acclimated over most of the irradiance range or have well developed capabilities for nonphotochemical quenching. Although a quantitative assessment of the variability in pigment quota (max/min) would require standardization for irradiance and temperature the unstandardized reported range varies from small (< 2) to large (a factor of 20 times for phycoerytherin in Synechococcus, Kana and Glibert 1987) and seems likely to be major factor in a species’ capacity to maintain a high growth rate when irradiance is variable on time scales longer than acclimation rates.

5

0.05

1

0

10 -2

0.1 -1

high

1

10 -2

-1

Fig. 16.7 (A) Chlorophyll a quota for Thalassiosira pseudonana (circle indicates chlorotic cells, adapted from Thompson 1999) and (B) the ratio of observed chlorophyll a quota:minimum chlorophyll a quota (Q/Qmin) for three species of polar diatoms; Chaetoceros furcellatus (¡), Nitzschia delicatissima (D), Thalassiosira antarctica (*) (from Hegseth 1989). The degree of nonphotochemical quenching (arrow) is hypothesized to be greater in those with low Q/Qmin chlorophyll a ratios. Solid lines represent the hypothesized range of Q/Qmin. Phytoplankton have evolved multiple mechanisms to maximize their fitness under different conditions of irradiance. The type and ratio of pigments varies from Class to Class, species to species and depending upon growth conditions (Prezelin 1981). Reported photo-acclimation responses


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include shifts in chlorophyll a:c or chlorophyll a/b ratios; shifts in the ratio of PSI:PSII or shifts in chlorophylls:reaction center (Falkowski and Owens 1980, Prezelin 1981, Richardson et al. 1983). Where these photoacclimation responses involve pigments that are solely involved in capturing energy and transferring it to a reaction center for photosynthesis (photochemical quenching) their cellular quotas should vary in a similar manner to chlorophyll a quota (Kana and Glibert 1987) and therefore should increase at irradiances < 3 Ek. More complex patterns of variation in response to irradiance could be expected in pigments that have multiple roles: acting as accessory pigments, as antioxidants, quenching triplet chlorophyll or singlet oxygen and involved in other processes that result in absorbed light energy being dissipated as heat (nonphotochemical quenching). Where irradiance changes faster than cells can acclimate the dissipation of excess light energy as heat can be an important photoprotective mechanism. Species that dominate in environments with short term fluctuations in irradiance or species with little capability to vary light harvesting can be presumed to have considerable capability for nonphotochemical quenching (NPQ) and the need for NPQ is likely to rise exponentially with the decline in temperature. A number of pigments, other light absorbing compounds and other processes are involved in photoprotection or nonphotochemical quenching (Demming-Adams and Adams 1992, Voronova et al. 2002) and these often increase with irradiance for example: 1. Transthylakoid D pH, H+ build up in the thylakoid lumen has two roles, activation of the violaxanthin ¨ Æ zeaxanthin cycle and reconfiguration of the antennae to allow efficient NPQ (dissipating excess light energy it as heat, Gentile and Blanch 2001). 2. Astaxanthin can accumulate in lipid bodies outside the chloroplast possibly shading pigments associated with PSII (Gorton and Vogelmann 2003). ¨ diatoxanthin cycling and D pH may be important 3. Diadinoxanthin Æ for NPQ in diatoms especially under fluctuating light (Lavaud et al. 2002). 4. be-carotene can reach 8-12% of dry weight accumulating in globules within the chloroplasts interthylakoid space and provide photoprotection (Orset and Young 1999). 5. Lutein quota increased with irradiance in a prasinophyte and a chlorophyte suggesting a possible photoprotective role (Henriksen et al. 2002).

$& Algal Cultures, Analogues of Blooms and Applications 6. A variety of UV absorbing compounds including pigments (Dillion et al. 2002) and mycosporine-like amino acids that act as photoprotectants (Carreto et al. 2001). In chlorophytes the violaxanthin:zeaxanthin ratio is proportional to the chlorophyll a:b ratio and inversely proportional to granal stacking (Anderson and Aro 1994) and all three have a role in determining the ratio of photosynthesis:nonphotochemical quenching (Horton 1999). Investigation of the physiological roles of various carotenoids and xanthophylls has been aided considerably by the development of various mutant forms of higher plants and algae deficient in genes for specific pigments (Niyogi et al. 2001). The deficiency of specific pigments commonly accepted to have a role in photoprotection has not always resulted in mutants that are more sensitive to high light stress suggesting the responses of mutants can provide insights into the physiological roles of various pigments in terms of photoprotection. Carotenoids and xanthophylls can be important as photoprotective mechanisms in some species when undergoing nutrient stress (Ben-Amotz and Avron 1983).

Carbon Ideally experiments to investigate the relationship between irradiance and carbon quota would use continuous light as acclimation strategies to various light:dark cycles may induce a shift in carbon sequestration and allocation (Chisholm 1981). For example some phased cell division will occur under most L:D cycles which can lead to an accumulation of a large proportion of the cells undergoing division synchronously. Samples from cultures of synchronously dividing cells may show significant variation in cellular quotas of a number of constituents over a diel cycle. For carbon this diel variation in Qc may be a factor of two or nearly the same as the maximal response to irradiance in many species thus potentially confounding Qc versus irradiance experiments. At higher growth rates even for asynchronously dividing cultures a greater proportion of the cells will be in the process of dividing (D phase) where carbon quota and cell volume are approximately twice those in the DNA synthesis (S) phase. For diatoms cell size and carbon quota can vary in response to sexual reproduction events which may occur on time scales that are longer than the experiment (Furnas 1978). There are quite a few reports of Qc increasing with irradiance (Meeson and Sweeney 1982, Thompson et al. 1991, and tabulated references). Increasing Qc as irradiance approaches or exceeds saturation may be a


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simple strategy to accumulate additional reduced carbon (Claustre and Gostan 1987), as a sink for excess light energy or potentially for use during periods of darkness (Foy 1983). This was well demonstrated by Foy (1983) for two Oscillatoria species and by Post el al. (1985) for the diatom Thalassiosira weisflogii where carbohydrate quota increased from 24 to 137 pg cell–1 with increasing irradiance. In some diatoms increased volume under conditions of greater irradiance was achieved by adding intercalary bands (Joint et al. 1987) a response that has been observed during irradiance transients in Thalassiosira pseudonana (Thompson et al. 1991). From experiments using turbidostats under continuous light to grow Heterosigma akwashio it is clear that this species has an exponential increase in cell size and carbon content by a factor of three with increasing irradiance (Fig. 16.8). Two species of dinoflagellates had different strategies, one decreasing size at low irradiances and the other not (Swift and Meunier 1976). 450 2.5

–1

Carbon quota (pg cell )

400

2.0 300

1.5

250

200

Cell volume (cm3)

350

1.0 150

100 10

100

Irradiance (µmol photons m-2 s-1)

Fig. 16.8 Carbon quota (=) and cell volume (*) for Heterosigma akwashio as a function of irradiance when grown in turbidostats (after Thompson et al. 1991) Acclimating by increasing carbon quota under increasingly light-limited conditions (e.g. Skeletonema costatum by Falkowski and Owens [1980] and Thalassiosira antarctica by [Hegseth 1989]) imposes a significant penalty on growth rate and must give some other important benefit(s). Electron micrographs indicate that cells synthesize more thylakoid membranes (Jeffrey and Vesk 1977) and more light harvesting pigments (Falkowski 1980) when exposed to lower irradiances but what fraction of the increase in Qc these constituents are responsible has not been adequately resolved.

$ Algal Cultures, Analogues of Blooms and Applications Unusually large variations in carbon quota (Qmax/Qmin > 17) were reported for Skeletonema costatum under short daylength and low irradiance conditions (Shakshaug and Andresen 1986). These particular conditions are similar to those encountered during winter at higher latitudes suggesting the possibility that increases in Qc may be a strategy used by some species to overwinter. The dinoflagellate Gonyaulax polyedra has been reported to survive over winter at the expense of cell carbon (Rivkin et al. 1982). The relationship between carbon per cell (Qc) and irradiance does not appear to be consistent across a wide range of species or Classes yet carbon sequestration is a likely strategy that should give considerable competitive advantage under certain environmental conditions. Since both strategies (E≠= QcØ Qc≠) are evident in some species greater investigation of their benefits would seem appropriate, noting that different species may use one, or the other, a mixture of these as acclimation or ecological strategies. It seems entirely possible that the interaction of temperature, irradiance and daylength may contribute to which of these acclimation strategies are manifested. In addition increases in cellular carbon quota may accompany exposure to high or low irradiance where these are stimuli for the transition in life cycle, e.g. the formation of cysts from vegetative cells.

Carbon:chlorophyll a ratio (q q) Given that there are a range of acclimation strategies that species may use to cope with varying irradiance and significant interspecific variability in the range of chlorophyll a quotas plus the fact that chlorophyll a may be up to 10% of total cell carbon it should be no surprise that different authors have reported different relationships of the C:chla ratio with irradiance. For most species, however, pigment content is more variable than carbon content and q tends to rise with increasing irradiance (Fig. 16.9). The relationship between q and irradiance can be approximated as exponential curve.

Carbon allocation Short term experiments on carbon allocation to various pools; protein, lipid and carbohydrate have shown significant effects of irradiance intensity (Furgal et al. 1998) and spectral quality (Miyatchi et al.1978) although most research has examined the effects of nutrient deprivation (Chisholm and Shifrin1981). Electron micrographs indicate that cells synthesis more chloroplasts and more thylakoid membranes when exposed to lower irradiance (Rosen and Lowe 1984) and, for at least one species, under equivalent blue light (Jeffrey and Vesk 1977). These membranes are reported to have a greater content of polyunsaturated fatty acids (PUFAs) than other


$

Carbon: chlorophyll a (g:g)

100 90 80 70 60 50 40 30

20

10 1

10

Irradiance -2

-1

Fig. 16.9 Carbon: chlorophyll a ratios plotted versus growth irradiance for Thalassiosira pseudonana grown at 10°C (open symbols) and 18°C (closed symbols) (after Thompson 1999) cellular organelles so that the relative PUFA content should increase at low irradiances (Mortensen et al. 1988) although this response seems to be quite species specific and was not always observed in whole cell extracts. The increase in PUFA at low irradiances can result in less yield as production per unit time declines (Ahern et al. 1983, Cohen et al. 1988). In some species large increases in PUFA occurs at low irradiances (Sicko-Goad et al. 1988), conversely more saturated fatty acids can be found at high irradiances (Brown et al. 1996). The production of more saturated fatty acids in response to more light energy (Orcutt and Paterson 1974) would seem like an intuitively obvious acclimation strategy to store more reduced carbon as triacylglycerides (Constanopoulos and Bloch 1967) when photosynthesis is not light limited, a strategy commonly induced in greens and in some diatoms in response to nutrient limitation (Chisholm and Shifrin 1981). Carbohydrate storage is another typical response to excess irradiance (Cook 1963, Cuhel and Lean 1987) one that is especially important in terms of buoyancy for cyanobacteria (Brooks et al. 2001). Protein content has been reported to respond to spectral quality by increasing under blue light (Kowallik 1970, Wallen and Geen 1971, Voskresenskaya 1972).

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Light cycles Light cycles of different durations have been studied including very short cycles (0.1 s or 100Hz, Mouget et al. 1995a), short cycles (< 60 s, Grobbelaar 1989), various cycles ranging up to 24 h (Hobson 1974, Marra 1978a, Marra 1978b, Cosper 1982, Thompson 1999) and continuous light (Brand and Guillard 1981). The shortest cycles had small effects on PSII activity Pmax, alpha, pigments, enzyme activities, fatty acid, amino acid composition (Mouget et al. 1995a, 1995b). In theory if the total daily photon flux did not change and as long as the photon density was relatively low and the flash frequency longer than the total turnover time for the number of photons captured by a light harvesting complex (LHC) then variation in the timing of very short cycles should not cause significant change in physiological condition or acclimation. Particular combinations of flash intensity and duration when tuned to the photobiology can give an improvement in quantum yield (Terry 1986) but in general the impact of short term ( 10 min to £ d).

Hour to day cycles and rhythms Circadian, synchronous and phased behaviors (or processes) represent three different types of physiological periodicity. Circadian rhythms have ~ 24 h periodicities that persist without exposure to external stimuli.


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Circadian rhythms have been shown for many physiological processes, for example: photosynthesis, pigment synthesis, vertical migration and cell division (Nelson and Brand 1979, Chisholm 1981). Exposure to an external cycle in the temporal supply of a limiting resource (e.g. a L:D cycle) can entrain circadian or other behaviors so that the cells synchronously undergo the same process or behavior at the same time (Sournia 1974, Chisholm et al. 1980, Chisholm 1981, Prezelin 1992). The degree of synchrony can increase in a population by increasing the proportion of cells that are phased during successive L:D cycles. Studies with mutant and wild type Synechococcus have demonstrated that the presence of physiological periodicity can confer a competitive advantage (Johnson et al. 1998). The application of this result to other species and the possible basis for a competitive advantage from physiological periodicities are considered further below. As demonstrated for Oscillatoria redekei at low irradiances growth rate can be a linear function of dose (irradiance * time) regardless of the nature of the L:D cycle (Gibson and Foy 1983). That growth can be a linear function of dose at subsaturating irradiances indicates that lengthening the dark period does not necessarily create a greater respiration debt. Only two mechanisms are available to achieve this (1) the storage of extra energy (reductant, ATP or reduced carbon) during light periods and use that to maintain a high growth rate during the dark periods, (2) respiration rates that are ~ zero during the dark periods. From the research of Foy (1983) it is apparent that two species of Oscillatoria use strategy #1, accumulate sufficient carbohydrate while illuminated to maintain respiration throughout the dark period without incurring a penalty in growth rate even at subsaturating irradiances. The very low compensation irradiance (Ec < 1 mmol photons m–2 s–1) of Phaeodactylum tricornutum (Geider et al. 1985) strongly suggests very low respiration rates (strategy #2) and may provide the explanation for the linear growth rate versus dose response found for this species (Fawley 1984). At a Class level it might be anticipated that dinoflagellates with their relatively high respiration rates are unlikely to achieve a linear irradiance dose versus growth rate. In contrast to the strategy of Oscillatoria where growth rate was a linear function of irradiance dose at subsaturating irradiances (Gibson and Foy 1983) at saturating irradiances and continuous light the growth rates of some species are reduced relative to those observed at shorter daylengths. For example, the growth rate of Ditylum brightwellii under saturating

$" Algal Cultures, Analogues of Blooms and Applications irradiance declined > 50% as daylength was increased from 16 to 24 h (Paasche 1968). For 7 of 22 temperate species the growth rates were reduced under continuous light relative to a 14:10 L:D cycle while four species would not grow under continuous light (Brand and Guillard 1981). The discovery that some temperate species cannot grow at long daylengths suggests they have lost this capability. There did not seem to be a phylogenic basis to growth rate depression at long daylengths although growth rates of oceanic species were more sensitive than coastal species (Brand and Guillard 1981). Brand and Guillard (1981) hypothesized that this difference in sensitivity to long daylengths exists across the oceanic-coastal gradient because of differences in vertical mixing rates resulting in greater exposure to irradiance cycles in the coastal zone. It also seems reasonable to anticipate that a greater number of specialist strategies that maximize growth rates at a range of daylengths should occur in polar relative to tropical species. Assuming the ecological niche influences the range of selection strategies then the polar environment should have some species that specialize either short or long daylengths and some generalists that cope with both. For 10 polar diatoms (Gilstad and Sakshaug 1990) and some temperate diatoms the observed relationship between daylight and light saturated growth rate was a rectangular hyperbola (Fig. 16.10). This type of response, where the growth rate increases progressively less with increasing daylength, implies that the cells are somehow more efficient under short daylengths. This efficiency could be achieved by diel periodicity in the accumulation rate of reductant (NADPH), ATP or carbon (Harding et al 1981). Hypothetically a relatively short burst of photosynthesis during the 4 h of illumination would allow cells from a 4:20 L:D cycle to grow more than 2.5

mmax (d–1)

2.0 1.5 1.0 0.5 0.0 0

6

12

18

24

Day length (h)

Fig. 16.10 The relationship between daylength and light saturated growth rate for Thalassiosira pseudonana. Solid line is a rectangular hyperbola, dotted line a linear regression (redrawn from Thompson 1999)


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1/6th as fast as those under 24:0. The results (Gilstad and Sakshaug 1990) also suggest that the capacity of cells to uncouple energy acquisition from growth is a function of daylength. Figure 14.10 can be reproduced by assuming energy capture is proportional to growth rate using a simple model that gives a factor of six to the diel periodicity of energy capture (or carbon fixation, Harding et al. 1981) for cells under a four h day length and assuming a 50% fall in periodicity for each additional four h of illumination per day. In this model the periodicity of energy capture is assumed to be negligible under continuous irradiance and respiration is assumed to be a constant hourly rate consuming 10% of the total daily energy budget. Based on these observations and the model it seems reasonable to hypothesize that the capacity to uncouple energy capture from growth rate (enhanced rates of ATP, NADPH or reduced carbon formation) should be well developed in species adapted to exploit short daylengths. This characteristic may be particularly important in determining the dominance of temperate or polar diatoms during spring. A number of researchers have investigated the physiological effects of multiple light cycles within one d (Marra 1978a, Marra 1978b, Cosper 1982, Beardall et al. 1994) while others have focused on the outcome of competitive experiments (Litchman 1998, Nicklisch and Fietz 2001, McCausland et al. 2002, Mitrovic et al. 2003). These experiments are designed to recreate the sort of irradiance field that cells in the surface mixed layer might experience when wind driven Langmuir circulation is active (Cullen and Lewis 1988). The results have not been unequivocal especially for the three on Skeletonema costatum (Marra 1978a, Cosper 1982, Mitrovic et al. 2003) possibly due to the range of cycles plus variation in the maximum and minimum irradiances studied. From the competition experiments there is increasing evidence, however, that some species will do better exposed to multiple light cycles per day relative to more constant light (Litchman 1998, Nicklisch and Fietz 2001, McCausland et al. 2002, Mitrovic et al. 2003). As a result of these experiments there is a growing body of evidence that some diatom species may perform better under fluctuating light possibly due to their greater capacity to uncouple energy acquisition and growth (Terry et al. 1983). As with short daylengths there is potential to achieve an enhanced growth rate if the species is physiologically capable of accumulating ‘extra’ energy during a short light period. It can be hypothesized that very short light cycles (~ min) might favor species that can accumulate extra reductant or ATP while longer light cycles (~ h) might favour those that can accumulate extra reduced carbon. The risk associated with a strategy of maintaining sufficient light harvesting capability so that short term energy accumulation

$$ Algal Cultures, Analogues of Blooms and Applications >> growth is that exposure to a greater irradiance dose could result in more photodamage.

Photoinhibition and Photodamage: Short Term Exposure to Excessive Irradiance Photosynthetic efficiency can be reduced in response to irradiances that are greater than those recently experienced, or acclimated to, and are reviewed by Harris (1978) and Long et al. (1994). Advances since then are mainly in the understanding of the impacts of photoinhibition at the molecular level, gene expression and transcriptional responses (Chow 1994). The fundamental molecular basis for the reduction in photosynthetic efficiency caused by excess visible light is damage to PSII and the relatively slow replacement of the D1 protein. The reduction in photosynthetic efficiency increases with: 1. increasing intensity of exposure (irradiance) 2. increasing duration of exposure 3. decreasing temperature but tends to be slowly reversible (historical review in Ball and Wild [1993]) as cells acclimate to the increased irradiance or decreased temperature (Melis 1999). Again molecular biology and mutant microalgae are important tools in the quest to understand photoinhibition. For example mutants where the D1 protein is only a few amino acids different from wild types results in very significant changes in the cell’s resistance to photodamage (Forster et al. 1999). The small change required in DNA implies that mutations with variable capacity to resist photodamage may occur commonly in nature. Differences in the frequency and amplitude of temperature versus irradiance cycles relative to the acclimation rates of phytoplankton means that photoinhibition is largely a transient response to variation in irradiance not temperature. Strategies for coping with a decline in temperature which could result in insufficient catalytic capacity to convert incident light into reduced carbon should be different from those needed to cope with the more rapid variations in irradiance. In most cases, in large water bodies, the former could be relatively slow allowing many days for a cell to implement appropriate acclimation strategies (e.g. astaxanthin accumulation in lipid bodies, degradation of light harvesting pigments) while the latter would need to be much quicker (e.g. violaxanthin ¨ Æ zeaxanthin cycling and D pH). It would seem that the greatest risk is for the cell to incur some photodamage


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that is slow to repair and results in a significant reduction in growth rate or competitive ability. Classic photoinhibition, the temporary reduction in carbon fixation, has been observed in laboratory cultures of low light adapted cells from a broad range of species when they are exposed to high irradiances. It is also found when deep (~1% of surface irradiance) samples from stable water columns are incubated at surface irradiances. This sort of photoinhibition may not be ecologically significant since events or environments that keep cells at low irradiances long enough to acclimate and then quickly expose them to greater irradiances may not be common. Species that live in environments with high rates of vertical mixing (e.g. turbulent waters) are most likely to be exposed to short term fluctuation in, or possibly stochastic, light regimes. If these cells are photoadapted to the mean or median irradiance they are likely to be temporarily exposed to excess irradiance as they approach the surface. Consider a cell moving between the 1% light depth and the surface, at the surface it will be exposed to seven times the mean or > 60 times the median irradiance. Species that seek to gain a competitive advantage by keeping their capacity to capture light energy greater than that required to achieve the maximum possible growth at the mean or median irradiance could be expected to have a greater need for dealing with excess light energy. Options for coping with excess light energy are to either utilize or dissipate it safely (Falkowski et al. 1994). Enhanced capability to use excess light energy in a variable light regime is a strategy that can be used to achieve a competitive advantage. The niche exists (Litchman 1998, Nicklisch and Fietz 2001, McCausland et al. 2002, Mitrovic et al. 2003) but the physiological basis for exploitation remain hypothetical. Some methods used to safely dissipate excess light energy via nonphotosynthetic quenching (dissipation as heat) were discussed earlier. Other mechanisms that have been reported to be important in the mitigation of excess light energy: 1. differential excitation of PSI vs PSII: ratio of cyclic to noncyclic electron transport and state transitions, 2. migration of chlorophyll a + protein complex from PSII to PSI (Krause and Weiss 1991) 3. PS II reaction centre quenching (Ivanov et al. 2003) 4. chloroplast shading (Jeong et al. 2002) 5. nitrate reduction (hypothesized as very significant for polar diatoms, Lomas and Gilbert [1999]) and nitrite excretion 6. grana stacking (mostly applies to chlorophytes, Brugnoli et al. 1998)

$& Algal Cultures, Analogues of Blooms and Applications 7. photorespiration and reduce carbon (often glycolate) excretion (Wingler et al. 2000) 8. superoxide radical anion O2– formation and enzymatic (superoxide dismutase) conversion to H2O2 and eventually to water, potentially a large energy sink at high light (Ort and Baker 2002) 9. fluorescence (probably always small ~ 1 or 2% of absorbed light) The relative importance of most of these mechanisms is not known for an individual species and the capability for species or Classes of phytoplankton to gain a competitive advantage through one or another is largely hypothetical. It is, however, well known that the number and size of chloroplasts varies between species and Classes. The evolution of considerable interspecific variability suggests a possible selective pressure or competitive advantage. For many years it has been possible to observe chloroplast movements and hypothesize that they should serve to optimize photosynthetic efficiency and prevent photodamage (Wada et al. 2003). While the number of chloroplasts can vary between species so can the capacity to move them in response to irradiance (Chen and Li 1991). Recent evidence from mutant Arabidopsis has finally demonstrated that fewer large chloroplasts impose a penalty on growth (Jeong et al. 2002). Strains with equivalent photobiology to wild types but varying only in the number and size of chloroplasts had significant differences in growth at both high and low irradiances. Therefore while large chloroplasts can move around within cells it seems their capacity for photoprotection or optimization of photosynthetic efficiency is reduced. Many small chloroplasts may improve absorption efficiency and be advantageous at low irradiances but what fraction of a cell’s photoprotection may arise from chloroplast shading or whether species with many small mobile chloroplasts are better able to exploit variable light fields has not been adequately determined and may prove a profitable field of investigation.

Energy losses via respiration and photorespiration In simple terms the gap between the quantity of light energy captured and that used for growth is ‘inefficiency’ that could reduce fitness (i.e. growth rate). Respiration and photorespiration are the major physiological processes that divert energy or reduced carbon away from growth. They have very different physiological roles (Raven and Beardall 1981). Respiration can occur in the light and the dark. As the oxidation of reduced carbon compounds to generate NADPH, ATP or new carbon skeletons respiration is essential for growth and maintenance. Yet high respiration relative to photosynthesis


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under conditions of subsaturating irradiance would reduce growth. Photorespiration is the light dependent consumption of O2 through two reactions: 1. via the direct transfer of excited electrons in PSI to O2 Æ H2O2 (a) via catalase to O2 (Mehler reaction) (b) via ascorbate peroxidase to O2 (water-water cycle, Asada 1999) 2. the RUBISCO catalyzed synthesis of phosphoglycolate. Thus it would seem that the main role of photorespiration must be associated with short term energy management in a photoprotective sense. Energy may be lost through photorespiration but these pathways should function only when necessary for photoprotection. As all of the photorespiratory pathways are enzyme dependent it seems less likely that they will be particularly important in low temperature environments, even under high irradiance conditions. Photorespiration is likely as a response to warm and transient high light conditions, often in association with nutrient limitation (Beardall 1989). Photorespiration involving phosphoglycolate synthesis is also sensitive to the relative concentration of CO2:O2 at the RUBSICO binding site so that conditions of low CO2 or high O2 result in more phosphoglycolate production. Leakage of glycolate produced largely from photorespiration into the medium has been demonstrated to represent up to 20% of carbon fixation in diatoms and chlorophytes under nitrogen limiting conditions. Significant interspecific differences in the initial slope of m vs E relationships, the irradiance required to saturate growth rate and respiration rates have been documented in many laboratory studies (Falkowski and Owens 1978). Therefore it is reasonable to hypothesize that they can be used to predict community composition when light is the limiting resource. Single parameters that encapsulate the balance between photosynthesis and respiration include photosynthetic quotients and net growth efficiency. The ratio of photosynthesis:respiration is often measured as the ratio of oxygen evolved:carbon fixed in the light and expressed as the photosynthetic quotient (PQ). PQs can be determined in short term experiments using an oxygen electrode and 14C and should suit researchers concerned with the possible uncoupling of fixation from respiration under transient conditions. PQs are reported to range from 0.5 to 3.5 (Williams and Robertson 1991). PQ values of ~ 1.2 to 1.7 are often considered normal assuming balanced growth. The greater values are commonly associated with the extra energy required for nitrate reduction (PQ ~ 1.7) relative to growth on ammonium

$ Algal Cultures, Analogues of Blooms and Applications (PQ ~ 1.2). PQs below one indicate the capacity for carbon fixation to be greater than oxygen evolution, possibly as a result of oxygen consumption via photorespiration. PQ values > 1.7 have been associated with a transient increases in: 1) reductant use in processes such as nitrogen reduction or assimilation, 2) excretion of reduced carbon under conditions of excess irradiance or nutrient stress. From studies using a mass spectrophotometer, 13C and 18O it is possible to demonstrate that respiration increases under illuminated conditions and decreases after the transition to darkness (Weger et al. 1989). The difference in respiration rates in the light versus dark appears to be related to the fact that respiration must be both a catabolic and anabolic process (Weger and Turpin 1989). The amount of carbon catabolized in the dark declines with time, for example during the dark portion of the light:dark cycle (Weger et al. 1989) or during prolonged darkness (Waite et al. 1992). Respiration in the dark can also be temporally elevated after a short period of irradiance (Beardall et al. 1994). Rates of respiration measured in the dark are frequently reported to be proportional to growth rate (Verity 1982b) or photosynthetic rate (Verity 1982a) and growth irradiance (Nielsen 1997, Verity 1982a). They also tend to rise exponentially with temperature (Collins and Boylen 1982) or not (Gibson and Foy 1989), and increase under nutrient stress. Given the requirement for carbon skeletons of various sorts and the turnover rates of various carbon pools the rates of respiration in the light are estimated to average 20 to 30% of photosynthetic rates (Geider and Osborne 1989). There are however important taxa based differences in respiration capacity. The lowest relative capacities for respiration in the light are recorded from the cyanobacteria where the rates may be only 5% of photosynthesis (Kratz and Myers 1955) while dinoflagellates species often have high PQs or low net growth efficiencies (Chan 1978, Langdon 1986, Langdon 1988, Langdon 1993). The capacity for respiration in the dinoflagellate Gonyaulax polyhedra was greater than 100% of the photosynthetic rate (Prezelin and Sweeny 1978). Species from other Classes have respiratory capacities between these extremes, ranging from 30 to 50% of photosynthesis (Raven 1976). Laboratory base experimentation has not demonstrated any compelling evidence for a selective advantage to a high respiratory capacity. Hypothetically it could represent a strategy to exploit environments where the need for respiratory products is uncoupled from irradiance. The measure of net growth efficiency (C retention:C fixation) provides a useful parameter for comparing species and predicting the outcome of competitive interactions (Falkowski et al. 1985). In a very good example of laboratory experimentation that is relevant to ecological issues Adolf


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et al. (2003) demonstrated that under low irradiance the dinoflagellate Karlodinium micrum had a lower net growth efficiency than the cryptophyte Storeatula major. Species like Karlodinium micrum, that have relatively high respiration rates or that use relatively more respiration derived reductant even during the light period, often seem to have low maximal growth rates. These traits, high respiration, low net growth efficiency and low mmax all seem more common among dinoflagellates than diatoms (Falkowski and Owens 1978). Species with these characteristics may also have less capacity to photoacclimate or ‘up regulate’ and increase their growth rate to take advantage of greater irradiances (Adolf et al. 2003). Yet dinoflagellates often bloom achieving overwhelming community dominance. It is possible that the vertical separation of essential resources (light and nutrients) combined with motility provides the niche that these species can successfully exploit. In such a niche the greater respiratory capacity should allow more nutrient assimilation in the dark and thus may provide an ecological advantage, but this hypothesis is not well tested.

Life histories Comprehensive studies on the influence of light and temperature on the initiation of a change in life cycle stage are available for only a small number of species. Elucidating the physiological basis for the induction of a shift in life history stage is only possible in laboratory cultures sometimes working with a few isolated cells. High and low temperature, irradiance and spectral quality have all been implicated individually or in combination in either cyst formation or excystment (Bravo and Anderson 1994, Rengefors and Anderson 1998) but a comprehensive review of this research is beyond the scope of this chapter. Cyst formation is often induced by conditions that are highly unfavorable for growth (e.g. under cold, dark, nutrient or resource depleted conditions, high grazing pressure) such that the net impact on population growth rate is difficult to estimate. Two of the major benefits are proposed to be genetic recombination (Walker 1984) and survival through difficult conditions (Smetacek 1985, Edlund and Stoermer 1997). It will be very difficult to appropriately account for life history strategies in predictive phytoplankton ecology.

Light-Temperature Interactions Several researchers have suggested the initial slope (aE) of the relationship between irradiance and growth rate might vary as a function of temperature based on theoretical assessments of cell physiology (Raven and Geider

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1988). Unfortunately experimental data (Foy 1983, Fawley 1984, Thompson 1999) have shown no indication of this intriguing possibility. It can be argued that species should have evolved to avoid capturing excess photons because it results in photodamage or death. Given that the magnitude and frequency of the dominant (day:night) light cycle and variation caused by other factors (e.g. mixing, clouds) is too rapid for cells to achieve the ‘perfect’ pigment content at some time during most days cells will be exposed to the ‘wrong’ irradiance. In response it would seem that different species have evolved a range of strategies from a conservative, low risk strategy of maintaining a low capacity to capture photons to a high-risk strategy of increasing their capacity. Clearly both strategies can be successful. In terms of the interaction of light and temperature the high-risk strategy of a high pigment content provides a likely contrast in mitigation strategies. When exposed to excess irradiance at higher temperatures highrisk species might rely more on rapid photochemical quenching (carbon fixation, ATP and reductant generation) and well developed capacities for efficient, enzyme-dependent nonphotochemical quenching (NPQ) such as photorespiration. High-risk species at low temperatures should have alternative mechanisms for NPQ when exposed to excess irradiance such as state transitions, photoprotective pigments, structural changes in PSII due to the buildup of protons in the presence of zeaxanthin so the NPQ efficiency ¨ zeaxanthin; diadinoxanthin ¨ increases, pigment cycles (violaxanthin Æ Æ diatoxanthin) and chloroplast movement. In general temperature will set the upper limit on growth rate and resource supply (light or nutrients) may only act to reduce m. Exceptions that were noted include long daylengths, where the growth rates of some species did not respond predictably to increasing irradiance dose. It is hypothesized that a greater proportion of species from oceanic and tropical to temperate environments may have an obligate requirement for a L:D cycle. Simplifying the light-temperature interactions by assuming only day:night and seasonal temporal cycles in both and ignoring complications caused by other factors such as nutrients and grazing it is possible to summarize their interactions in terms of species characteristics that are proposed to be beneficial as environmental conditions approach the various extremes (Fig. 16.11).

CONCLUSIONS This chapter has proposed widely applicable empirical relationships to predict growth rate from irradiance, daylength and temperature all derived from laboratory research with algal cultures. The physiological responses of


Irradiance

High NPQ

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High mmax

Membrane fluidity,

Heat stress proteins

Increased catalysts, Increased light cryoprotectants Harvesting pigments Variation in life history

Buoyancy Motility Large cells (overwinter) High pigment quota Cysts (overwinter) High net growth efficiency

Temperature

Fig. 16.11 A conceptual map of species characteristics that could improve fitness across interacting gradients of light and temperature; it is assumed that day:night and seasonal and temporal cycles exist in both and ignored complications caused by shorter term cycles or other factors such as grazing pressure and nutrient availability. phytoplankton to temperature, daylength and irradiance were discussed in relation to their contribution to growth. Of the relationships examined only the response of growth rate to daylength could be linked to species from a particular environment. Only a few physiological characteristics were associated with particular taxon. For example, low net growth efficiency seems to be more prevalent in dinoflagellates than other Classes. The variation in growth rate model parameters seems to be high within specific environments and within taxa. For these reasons the model parameters need to be determined for the species of interest if the models are to be useful in predicting community composition. Thus much of the detailed knowledge required to turn phytoplankton ecology into a predictive science must come from laboratory studies. Simple empirical models, as considered here, do not adequately account for the potential of physiological responses in a dynamic environment to improve growth rate. The diversity of physiological responses to temperature and irradiance implies a wide diversity of successful strategies. Models that include a dynamic physiological component are likely to be necessary for predictive ecology in environments with fluctuations on time scales longer and shorter than acclimation rates. Robust dynamic and process-based physiological growth rate models are rapidly improving but they appear to be overgeneralized. If over generalized then they will have little chance of correctly predicting harmful algal blooms caused by individual species. It is proposed that resolving the paradox of the phytoplankton is really about recognizing the broad spectrum of temporal

$ " Algal Cultures, Analogues of Blooms and Applications and spatial variability in resource availability that phyoplankton have evolved strategies to exploit.

ACKNOWLEDGEMENTS My thanks to Michel Fiala (Laboratorie Arago, Universite Pierre et Marie Curie, France) and Ian Jameson (CSIRO Marine Research, Hobart, Australia) for kindly providing additional data. Constructive comments by an anonymous reviewer significantly improved this chapter.

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Photosynthesis-Irradiance Relationships in Marine Microalgae

639

% PhotosynthesisIrradiance Relationships in Marine Microalgae Pedro Duarte CEMAS – University Fernando Pessoa, Praça 9 de Abril, 349, 4249-004 Porto, Portugal

Abstract The photosynthesis-irradiance (P-E) relationship has been one of the most used concepts in ecology for many decades, as a tool to calculate primary production integrated over several temporal and spatial scales. Its use and misuse has been subject to a lot of debate in scientific literature. In this work the P-E relationship is discussed in the light of findings both from natural and laboratory phytoplankton assemblages. Several aspects are discussed, including the variety of mathematical formulations available, classified herein as empirical versus mechanistic and static versus dynamic. The experimental approaches to the P-E relationship, the importance of incorporating dynamic aspects of photosynthesis into existing formulations and its utilization in ecological models are also discussed. The analysis of available results and studies suggests that the traditional way of obtaining P-E curves may lead to biased estimates of water column integrated primary production. Emphasis should be given to dynamic analysis of the curves mentioned above, considering the time dependent response of phytoplankton photosynthesis to light intensity through mechanisms of photoresponse and photoacclimation. Whereas there is a reasonable understanding of short time (min to hs) photoacclimations, the same does not apply to longer time scales, where more factors play a significant role.

INTRODUCTION The relationship between photosynthesis and irradiance (P-E relationship) is perhaps one of the most widely used in phytoplankton physiology and marine ecology to evaluate the effects of photoacclimation, nutrient limitation, UV damage, genetic regulation (Johnson and Barber 2003) and as a tool

640 Algal Cultures, Analogues of Blooms and Applications to predict photosynthetic response to irradiance and production at various temporal and spatial scales (Falkowski and Raven 1997). It is fundamental to simulate phytoplankton growth in most mathematical models, where biomass changes are computed from physiologic processes such as photosynthesis, respiration and exudation (Jørgensen and Bendoricchio 2001). It has particular relevance in recent attempts to predict fluxes of carbon in the world’s oceans (Sakshaug et al. 1997). It has been studied both under natural and laboratory conditions, using real communities and monocultures. It has been described by various mathematical formulations and found to be closely dependent on the way phytoplankton primary productivity is measured (Steele 1962, Marra and Heinemann 1982, Parsons et al. 1984, Eilers and Peeters 1988, Falkowski and Raven 1997, Jørgensen and Bendoricchio 2001, Rubio et al. 2003).

P- E Relationship–Historical Perspective and Definitions Over one hundred years, several equations were used to describe the P-E relationship. These ranged from simple linear functions, with a level asymptote (Blackman 1919), through formulations of a Monod type or response curves with a maximum, till more complex ones with time dependent parameters (see Table 17.1 for several examples). Most of the above mentioned formulations are empirical, capable of describing geometrically the observed results, not being based on physiologic processes (Equations 1 – 11 in Table 17.1). Some assume a saturation curve (Equations 1 to 5 in Table 17.1), whereas others consider photoinhibition (i.e. the decline in carbon fixation at high irradiance) (Equations 6 to 9 in Table 17.1). Models such as those presented by Fasham and Platt (1983), Eilers and Peeters (1988, 1993) (Equations 12 and 13 in Table 17.1), Megard et al. (1984) (similar to Eilers’ model), Han (2001a, b) and Rubio et al. (2003) are based on known sequences of metabolic transformations. The models shown in Table 17.1 are just a sample of those available in the literature. The general aim of P-E equations is not a complete description of all the fine-controlled processes of photosynthesis, but rather a least sufficient representation of the main features of the P-E relationship. In most cases, these equations are merely used for predictive purposes and underlying physiologic processes are viewed as a ‘black box’. Some parameters are common to almost all models or can be derived from the models themselves, namely initial slope or photosynthetic efficiency (a), optimal light intensity or the light level that maximizes photosynthesis under given nutrient and temperature conditions (Eopt), the light level at

P = Pmax 1 - exp 1 -

P = Pmax tanh(a E/Pmax)

4

5

8

7

F H E Ek

LM N

FG H

Eopt =

where

FG H

IJ K

aE Ps

Ps a +b ln a b

P = Ps 1- exp -

nπ1

I OP KQ

m=1

LM I exp FG 1- E IJ OPn N Eopt H Eopt K Q

LM N

(1 + (E /E k )m )1/ m

IJ OP exp FG bE IJ K Q H Ps K

n = 1 (n empirical integer)

P = Pmax

m=2

3

6

P = Pmax

2

E /E k

P=

RSa E , E < Pmax /a TPmax

Equation

1

No

Photoinhibition models

Saturation models

Category

Empirical and static

Type

Platt et al. (1980)

Parker (1974)

Steele (1962)

Jassby and Platt (1976)

Webb et al. (1974)

Smith (1936)

Baly (1935)

Blackman (1919)

Source

Contd.

Table 17.1 Some of the available formulations for the P-E relationship. P – photosynthetic rate (usually expressed as mg C mg Chl a–1 h–1); E – light intensity (usually expressed as mmol quanta m–2 s–1); Ek - light saturation index (for details on the parameters see also Table 17.2 and text).


641

11

10

9

Pmax = Ps

8

FG a IJ FG b IJ b /a Ha + bK Ha + b K

(1 + (E /E k )u )1 +n )/ u

E /E k

z

1 t 1/ a E dt g 0

I JK

(1 - exp ( - t/tr )) tanh (E /E k ) 1 + (1 - exp ( - t /ti )) K t i (E - Ecrit )

Ecrit – Critical light level above which photoihnibition may occur; tr – response time to changing light; t – exposure time to a particular light level; ti – photoinhibition development time

P(E, t) =

g – timescale for photoinhibition a – controls the degree of nonlinearity of the rate responseto light intensity

Pt(E, t) = Pmax(E, t) exp -

F GH

P(E, t) = Pt(E, t tanh (aE/Pt(E, t)) where,

different combinations of u and v produce saturation or inhibition curves

P = Pmax

b – empirical coefficient that determines photoinhibition

Equation

No

Table 17.1 Contd.


Saturation model


Category

Empirical and dynamic

Empirical and dynamic

Empirical and static

Type

Contd.

Pahl-Wostl and Imboden (1990)

Franks and Marra (1994)

Iwakuma and Yasuno (1983)

Source


14

13

12

No

1 a

Pmax

1

-

1 a Eopt 2

2 a Eopt

P(E, t) =

FG 1 - exp FG - t IJ IJ aE 2 + bE + c H H ti K K

E

P 2 – mP + (c exp(bE) + a) EP + amE = 0 where, m is a parameter proportional to oxygen liberation from water; c is a parameter that controls the extension of the saturation plateau;b is the photoinhibition parameter

c=

b=

a=

2

E aE + bE + c

where,

P=

Equation

Table 17.1 Contd.


Category

Mechanistic and dynamic

Mechanistic and static

Type

Duarte and Ferreira (1997) derived from Eilers andPeeters (1988) and Pahl-Wostl and Imbodem (1990) models (see above)

Fasham and Plant (1983)

Eilers and Peeters (1988)

Source


643

644 Algal Cultures, Analogues of Blooms and Applications which the linear part of the P-E curve intercepts a plateau (light saturation index - Ek) and the maximal production rate or photosynthetic capacity (Pmax) (Fig. 17.1). Parameter a may be obtained by calculating the limit of the derivative of P in relation to E as E approaches zero. In inhibition models, Eopt may be determined by calculating the light intensity that maximizes the same derivative. Different models may yield quite different predictions of the P-E curve parameters (Frenette et al. 1993), which are characterized by a

Initial slope a

h

(mg C mg Chla–1h–1)

Photosynthetic rate

Pmax

Respiration rate

Ek Ec

Fig. 17.1 text).

Eopt

mmol quanta m –2 s –1

P-E curve and respective parameters (adapted from Parsons et al. 1984) (see

Table 17.2

P-E curve parameters and common reported ranges (see text)

Parameter

Experimental conditions

Ranges

Units

References

Pmax photosynthetic capacity

Algal cultures

1.1 – 6.2

mg C mg Chl a–1 h–1

Natural populations

0.1 – 22.6

mg C mg Chl a–1 h–1

Natural populations Natural populations Natural populations

0.07 – 0.259

Parsons et al. 1984 MacIntyre and Geider 1996 Parsons et al. 1984 Jørgensen et al. 1991 Macedo et al. 2001 Shaw and Purdie 2001 Macedo et al. 1998 Shaw and Purdie 2001 Macedo et al. 1998 Macedo et al. 2001 Macedo et al. 1998 Macedo et al. 2001

a - initial slope

Eopt – optimal light intensity Ek – light saturation index

300 – 992

mg C mg Chl a–1 h–1 (mmol quanta m–2 s–1) –1 mmol quanta m–2 s–1

5.2 – 474

mmol quanta m–2 s–1


645

large variability (Table 17.2). The values presented in Table 17.2 are just a sample of those found in the literature, whether measured in laboratory or natural phytoplankton assemblages. It is well established, since at least the 80s, that the parameters used in the P-E curves change at different time scales, suggesting that the dynamic behavior of the P-E curve parameters should be included in models (Falkowski and Wirick 1981, Marra and Heinemann 1982, Marra et al. 1985). Under natural conditions Pmax and Ek are constantly changing due to factors such as irradiance, temperature and species composition (Falkowski 1981, Côté and Platt 1983, Falkowski and Raven 1997). This dynamic behavior suggests an optimization of net photosynthesis and growth (Geider et al. 1996, 1998). Some P-E parameters have well defined biological meanings, for example a may be expressed as the quantity of oxygen produced or carbon consumed per unit light intensity per unit of chlorophyll mass per unit of time; Pmax is a function of dark reactions when no environmental factors are depressing photosynthesis (Parsons et al. 1984), with a maximum theoretical value of 25 mg C mg Chl a–1 h–1 (Falkowski 1981). The choice of different equations to model the P-E relationship should depend on the objectives of the study, taking also into account the light regime to which phytoplankton is exposed. Laboratory cultures are commonly exposed to stable light intensity and photoperiod, whereas natural phytoplankton assemblages are generally exposed to a varying light regime. The need for dynamic formulations is therefore clearer in the latter case. Although some phytoplankton assemblages do not seem to exhibit photoinhibition (Macedo et al. 2001), the opposite is true in most circumstances (Parsons et al. 1984). In the absence of other criteria, mechanistic formulations should be preferred to empirical ones due to their clear linkage to phytoplankton physiology, but ultimately the most important feature is the predictive capability of the equation chosen.

Methods to Obtain P- E Curves – Field and Laboratory Studies Typically, P-E curves are obtained by either incubating phytoplankton samples in the field or in the laboratory at various light intensities for a length of time. To guarantee different light levels in the field, samples are kept at different depths or incubation bottles covered with neutral density filters or PVC nets. In the laboratory similar light-filtering devices may be used with phytoplankton exposed to ‘natural light’ tungsten or halogen

646 Algal Cultures, Analogues of Blooms and Applications lamps. With the former it is easier to obtain higher light intensities, but infrared filtering may be necessary to avoid excess heating of the incubated samples or the cultures. It is important to measure light intensity with a quantum light sensor as frequently as possible, especially when working in the field in cloudy days, when light intensity varies the most. Phytoplankton photosynthesis is evaluated by measuring oxygen differences using the light-dark bottle technique or continuous automated measurements or, more often, by the 14C technique (Vollenweider 1974, ICES 1996). Non-linear regression analysis (e.g. the Gauss-Newton method) may be used to fit a P-E equation to measured data, estimating the usual photosynthetic parameters. Statistically it is desirable to have as many different light levels as possible. Significant fits may be obtained with as few as six or seven light levels. Generally, all parameters are assumed to be constant and the corresponding P-E equations and curves known as static. Alternatively, P-E curves may be obtained under varying light regimes and exposure times in order to mimic natural environment (Macedo et al. 2002). These are known as dynamic curves, for which dynamic P-E equations should be used where some equation parameters are related to exposure time to different light intensities. Examples of studies where dynamic curves were obtained go back at least to Marra (1978a, b) who conducted experiments in the field with variable light intensity, by circulating incubation bottles over different depths during the incubation period, and in the laboratory, with monocultures, simulating different light regimes. The measurement of photosynthetic rate has limitations due to the following general constraints: (i) Sensitivity and accuracy of the method employed. (ii) Experimental artifacts that result from the enclosure of organisms in a bottle such as, changes in pH, nutrient and oxygen concentrations and modifications in the light field induced by the incubation bottles. These artifacts are more important under high phytoplankton concentrations and/or large incubation periods. (iii) Lack of realism of environmental conditions that results from keeping the incubation bottles under a stable light regime for periods of up to four hours, among other things. More specific limitations depend on the method employed i.e. the oxygen or the 14C. The low sensitivity of the former limits its application under low chlorophyll concentrations (< 1 mg m–3) (Vollenweider 1974). The 14C method allows the measurement of carbon fixation even at very low


647

production rates, although its accuracy may be compromised due to the differential uptake of 14C /12C and the uncertainty of measuring gross or net photosynthesis – this problem may be reduced if the incubation time is small enough to prevent the partial consumption of carbon fixed during photosynthesis (ICES 1996). Further, there may be incorporation of 14C in reactions not connected to photosynthesis and there may be losses though soluble products of photosynthesis released from cells (Vollenweider 1974). One of the advantages of the oxygen method is that net, gross photosynthesis and respiration may be determined. However, respiration rates in the light and in the dark may differ, compromising the accuracy of the method to estimate gross photosynthesis (Vollenweider 1974, Parsons et al. 1984, Falkowski and Raven 1997). Problems may also arise in the conversion of oxygen produced to carbon fixed, because the photosynthetic quotient (P.Q.) depends on: (i) The type of organic material produced during photosynthesis-ranging from unity, when carbohydrates are the principal products to as high as 3.0, when fats are being synthesized (Vollenweider 1974). (ii) The oxidation state of the nitrogen source. If nitrogen is supplied as nitrate it must be reduced to ammonium from the respiratory oxidation of carbohydrate and from the photosynthetic oxidation of water. In both cases, nitrate is reduced at the expense of CO2 reduction (Falkowski and Raven 1997). (iii) Processes such as denitrification, sulphate reduction/oxidation and calcium carbonate solution (Oviatt et al. 1986) that may occur within the incubation bottles. In laboratory cultures, where phytoplankton concentrations may be very high, the oxygen method may often be employed. Ideally, both the 14C and the oxygen method should be used for an estimation of the P.Q.. A possible way to overcome the lack of sensitivity of the oxygen method under field conditions is the concentration of phytoplankton, as described by Macedo et al. (1998). This method has the disadvantage of changing species composition, due to differential filtration, and concentrating microzooplankton. In experiments carried out in the Tagus estuary, the method was successfully employed without significant damage of the filtered cells and without any apparent filtration effect on final production rates. One of the major difficulties with field experiments is the changing nature of phytoplankton communities. Different species compositions may produce different results even under similar environmental conditions. This

648 Algal Cultures, Analogues of Blooms and Applications is one of the reasons why working with laboratory cultures is so important to understand the response mechanisms to light intensity variability. Cultures may also help to analyze the role of different species in community photosynthesis and to understand the variability of photosynthetic parameters as a function of environmental factors.

Use of P-E equations in ecological models P-E equations play a central role in ecosystem models as the link between light energy and ecosystem functioning. In ecological modeling literature it is possible to find some of the equations depicted in Table 17.1 (Jørgensen and Bendoricchio 2001), where they are used to compute primary productivity as part of a mass balance describing algal growth. It is important to integrate these equations over the depth interval where primary productivity is to be calculated in order to account for the exponential decay in light intensity (following the Lambert-Beer law). Ideally, analytical integrations should be performed for greater accuracy and computing speed. Models have also been used to study the performance of several P-E functions, namely to compare static and dynamic formulations (Duarte and Ferreira 1997). Therefore, the choice of equations and respective parameters may be crucial for the model outcome. Static P-E equations have been used in most mathematical models of ecosystems (Baretta et al. 1988, Fasham et al. 1990, Herman 1993, Taylor 1993, Raillard and Ménesguen 1994). Dynamic equations have been used mostly in vertically resolved models of the mixed layer (Falkowski and Wirick 1981, Lande and Lewis, 1989, Janowitz and Kamykowski 1991, Franks and Marra 1994, Kamykowski et al. 1996, MacIntyre and Geider 1996, Duarte and Ferreira 1997). These differences may be explained by the spatial resolution and focus of different modeling approaches.

Objectives The objectives of this work are to: 1) Synthesize some of the available knowledge on the dynamic aspects of the P-E relationship; 2) Compare static and dynamic formulations of the P-E relationship; 3) Propose some recommendations on the elaboration of P-E curves; 4) Describe how to include dynamic formulations of photosynthesis in ecosystem models.


649

CONCEPTUALS The Dynamic Nature of the P-E Relationship Experimental data obtained from natural assemblages and laboratory cultures of phytoplankton indicate that photosynthesis depends on light intensity in a dynamic way and that phytoplankton can maintain high rates of photosynthesis during the first minutes after exposure to saturating or inhibiting irradiance (Harris and Lott 1973, Harris and Piccinin 1977; Marra 1978a, b, Marra and Heinemann 1982, Marra et al. 1985, Macedo et al. 1998). Therefore, static P-E curves may not be adequate to describe the photosynthesis of algal cells in the natural environment, where mixing can cause a complex pattern of light variation. The need to express the P-E relationship with time-dependent parameters was suggested by Falkowski and Wirick (1981), Marra and Heinemann (1982) and Marra et al. (1985). Marra (1978b) performed field experiments using ‘classic’ incubations, with bottles suspended at fixed depths over the mixed layer and incubations where bottles were cycled across the same depth range. Estimates of integral photosynthesis obtained with the latter incubations were 19 to 87% higher than those obtained with the former on sunny days. Differences were negligible on cloudy days. In the same work, experimental results with laboratory cultures confirmed the time-dependent behavior of the P-E relationship, with photosynthetic capacity decreasing with an increase in exposure time to high-light intensities. Laboratory results from Marra (1978a) and Marra and Heinemann (1982) demonstrate the occurrence of a morning maximum and an afternoon depression in photosynthesis of cultures exposed to a diurnally varying light regime and, sometimes, the existence of a morning and an afternoon maximum with the later being lower than the former. Several workers studied the effects of light fluctuations on productivity (Flameling and Kromkamp 1977, Gallegos and Platt 1985, Mallin and Pearl 1992, Franks and Marra 1994, MacIntyre and Geider 1996). One central issue is the occurrence of photoinhibition in response to high irradiance levels. There is abundant evidence that photoinhibition occurs in natural phytoplankton communities (Prasil et al. 1992, Kirk 1994, Long et al. 1994, Falkowski and Raven 1997, Han et al. 2001b) and that it depends not only on exposure to a critical light level, but also on the exposure time (Takahashi et al. 1971, Harris and Lott 1973, Pahl-Wostl and Imboden 1990). Photoinhibition is related to photodamage to photosystem – II (PSII) and has been largely studied over the last decades (Han 2001b). Macedo et al. (2002) conducted

650 Algal Cultures, Analogues of Blooms and Applications experimental and modeling studies in several ecosystems – a coastal lagoon, an estuary and a rocky shore. The results obtained by these authors and shown in Figs. 17.2 a and c give a strong evidence for the poor development of photoinhibition in samples incubated at various light intensities for a period of 30 and 45 min, respectively. When incubation time is increased to 120 min photoinhibition is apparent. The presented results for the Arrábida and Tagus estuary follow a similar pattern as the ones observed experimentally by Marra (1978a, b), predicted by the DYPHORA model (Pahl-Wostl and Imboden, 1990) and by the model of Duarte and Ferreira (1997). The initial slope of the P-E curves was not affected by the incubation

P (mg C mg Chla–1 h–1)

40 35 30 25 20 15 10 5 0

45 min. 120 min.

a) 14C 120 min

12

14C 120 min 14C 45 min

O2 120 min

10 8 6

O2 120 min O2 45 min

O2 45 min

4 14C 45 min

2 0 16 14 12 10 8 6 4 2 0

b)

30 min 120 min

c)

0

100 200 300 400 500 600 700 800 900 E (mmol quanta m–2 s–1)

Fig. 17.2 P-E curves obtained in a rocky shore (Arrábida, Portugal) a), in Summer, in St. André coastal lagoon (SW, Portugal) b), in summer, and in the Tagus estuary (Portugal) c), in spring. All measurements based on the light dark bottle oxygen technique, except for St. André lagoon, where the 14C method was also employed (adapted from Macedo et al. 2002). Continuous lines were obtained by fitting Equation (14 Table 17.1) to measurements (see text).


651

time while Pmax and Eopt changed significantly. Neale and Marra (1985) pointed out that the variation of Pmax should be considered as the primary source of time-dependence and Franks and Marra (1994) presented a non linear time-dependent Pmax. There is some evidence that fluctuating light regimes may enhance photosynthesis. Higher yields were observed in laboratory under pulsing light (Rabinowitch 1956). Gallegos et al. (1977) proposed a model in time domain for production with a term adding productivity under pulsing conditions. Some processes in photosynthesis are very fast and may therefore reach steady state almost instantaneously. The absorption of photons by the photosystems, their transition to the excited state and water photolysis have time scales in the order of tens to thousands of ms (Fasham and Platt 1983). However, the activation and deactivation of Rubisco in response to an increase or decrease in irradiance have time scales in the order of minutes and the rate at which photosynthesis is induced is limited by the kinetics of Rubisco’s activation (MacIntyre and Geider 1996). According to these authors, static P-E models may overestimate photosynthetic rates by assuming that photosynthesis reaches a steady state instantaneously after an increase in irradiance, mostly in situations of high turbidity and mixing rates. Moreover, full development of photoinhibition may take from 0.5 to 1.5 hs (Pahl-Wostl and Imboden 1990), comparable to typical mixing time scales as estimated by Macedo et al. (2002). This should be the limiting process for photosynthesis to reach the steady state under constant saturating light. Therefore, it is expected that photoinhibition will not fully develop if its time scale is larger than the mixing time scale of phytoplankton cells and if cells are entrained into the mixed layer below a depth where light intensity is below the critical value for photoinhibition to develop (photoinhibition depth). In these conditions, incubation of phytoplankton at constant depths or light regimes for periods larger than the mixing time scales, in order to derive P-E curves, may lead to overestimation of real photoinhibition and underestimation of real productivity, contrary to the bias expected from ignoring Rubisco’s activation (see above). Even under stratified conditions, phytoplankton cells are not fixed at a specific depth. Considering a surface layer in a stratified water column, photoinhibition may be reduced if the layer depth is larger than the photoinhibition depth. In spite of the overwhelming evidence about the temporal dynamics of photosynthesis described in the previous paragraphs, there are situations where a static behavior seems to be the rule. The results obtained by Macedo et al. (2002) for the St. André coastal lagoon (Fig. 17.2b) did not exhibit a clear

652 Algal Cultures, Analogues of Blooms and Applications time-dependent behavior. This lagoon is a shallow water ecosystem, where phytoplankton cells are under light intensities over 300 mmol quanta m–2 s– 1 most of the time. One possible explanation is that cells are photoacclimated to high-light and therefore less sensitive to photoinhibition. A decrease in the susceptibility to photoinhibition and enhanced photosynthetic capacity (Pmax) in response to increasing irradiance are common phenomena in planktonic algae (Richardson et al., 1983; Sukenik et al., 1990; Flameling and Kromkamp, 1997). Similar results were observed in other ecosystems, where a time-dependent response was observed in some places and not detected in others (Lizon and Lagadeuc 1998).

Dynamic Modeling of the P-E Relationship The reaction of cells to changing light may be divided into photoresponse (Pahl-Wostl and Imboden 1990) and photocclimation (Falkowski and Raven 1997). Photoresponse has typical time scales between a few min and a few h and corresponds to the time it takes for photosynthesis to reach a steady state response to light. Photoacclimation occurs at time scales of several h to d and corresponds to changes in cell composition, as chlorophyll contents per cell. These changes may also contribute to the dynamic nature of the P-E parameters. The focus of this work is primarily on photoresponse, since this is more directly linked to the P-E relationship, whereas photoacclimation is more dependent on other factors. It may depend on nutrient levels and, at longer time-scales, on species composition. Falkowski and Wirick (1981) developed a mixed layer model in which photosynthetic response was adjusted according to within-day variations in chl:carbon ratios, by either influencing a or Pmax. These variations followed a first-order kinetics. The rationale behind the model was that sun-adapted cells should have lower quantum efficiencies but higher photosynthetic capacities than shadeadapted ones. Photoinhibition was not considered. The authors concluded that variations in light regimes due to turbulence have little influence on primary productivity in a mixed layer. However, this conclusion might be biased by the fact that neither photoresponse adaptations, that occur at smaller time-scales, neither photoinhibition were considered. The chl: carbon or the inverse ratio was also used as a photoacclimation variable by Geider and Platt (1986). The DYPHORA model by Pahl-Wostl and Imboden (1990) combines the saturation model of Jassby and Platt (1976) with photoresponse timescales (Equation 11, Table 17.1). The authors were able to reproduce daily variability of photosynthesis observed in Marra (1978a) experiments, suggesting that at


653

within day-time scales photoresponsive adjustments are appropriate to describe P-E dynamics. In this model, productivity and photoinhibition are viewed as time dependent parameters through first-order kinetics:

c

dPt 1 P t - Peq = tr dt

h

(Eq. 15)

df 1 = K(E - E crit ) - j dt ti

(Eq. 16)

where, Pt - time dependent productivity; P eq = tanh(E/Ek) – static non-inhibited productivity f - Time dependent inhibition function; K – Inhibition growth constant (see Table 17.1 for a definition of the remaining terms). The solutions to Equations 15 and 16 are the numerator of Equation 11 (Table 17.1), and the second term on the denominator of the same equation, respectively:

F H

FH t IK I tr K

Pt(t) = Peq 1 - exp -

f t (E,t) = (1 – exp(–t/ti)) Kti(E – Ecrit)

(Eq. 17) (Eq. 18)

It is assumed that equation 17 holds for Pt < Peq, being Pt constant for greater values. This equation accounts for the response time of photosynthesis to changes in irradiance. MacIntyre and Geider (1996) used the same approach to simulate photosynthesis induction by changing irradiance. The term Kti (E – Ecrit) is assumed to be zero when E < Ecrit, i.e. photoinhibition is time dependent and occurs only above a critical light level, whereas recovery from photoinhibition occurs instantaneously. The DYPHORA model produces curves similar to those shown in Figs. 17.2 and 17.3, showing the time-dependent nature of P-E parameters and the timecourse of photosynthesis over a day, with morning maximum and afternoon depression, or with two peaks – one in the morning and another in the afternoon, as observed by several authors (Marra 1978a and b, Marra and Heinemann 1982, Marra et al. 1985, Macedo et al. 1998). Using a similar approach Franks and Marra (1994) combined equation 5 (Table 17.1) with a time and light dependent Pmax :

dPmax (I, t ) E 1 a (P0 - Pt ) = dt g where, P0 – asymptote for Pt (see also Equation 10, Table 17.1).

(Eq. 19)

654 Algal Cultures, Analogues of Blooms and Applications According to Equation 10, Pmax decreases with time as a function of ‘accumulated’ irradiance. A typical plot of photosynthetic rate as a function of time over a daily period is shown in Fig. 17.3 (curve b). Photoinhibition is not considered in this model and since the equation does not predict recovery from accumulated irradiance, the model does not allow to simulate situations like two peaks in photosynthetic activity occurring over a daily 1

Light intensity (relative units)

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0

2

4

6 Time (h)

8

10

12

10

12

70 60

Photosynthetic rate

c 50 40 b 30 a

20 10 0 0

2

4

6 Time (h)

8

Fig. 17.3 Top - Light intensity (in relative units) over a photoperiod of 12 h. Bottom Photosynthetic response (in arbitrary units) as a function of time: a) Morning and afternoon peaks, mediated by photoinhibition; b) Morning maximum and afternoon depression; c) Midday maximum, in the absence of strong photoinhibition, under low light levels. These curves may be obtained by the DYPHORA model, Equation 14 (Table 17.1) and the Eiler’s dynamic model depicted in Figs. 17.4 and 17.5 (see text).


655

period (Fig. 17.3, curve a), as observed earlier (Fig. 17.1 of Marra and Heinemann [1982]). Franks and Marra (1994) coupled Jassby and Platt’s static equation (Equation 5, Table 17.1) and equation 10 (Table 17.1) with a Lagrangian mixed layer model. They were able to show that vertically integrated productivity estimated with the former model, gave lower results than when estimated with the latter, when mixing was considered, in accordance with previous experimental results (Marra 1978b). The effect of mixing is to redistribute cells within the mixing layer. When cells from deeper waters (lower irradiance), that had not accumulated as much irradiance as cells nearer the surface, are mixed upward, their productivity is higher. Similar results were obtained by Duarte and Ferreira (1997) using Equation 14 (Table 17.1) coupled with an Eulerian mixed layer model. The Eilers and Peeters (1988, 1993) model is based on the physiology of photosynthesis and its structure allows both a stable (steady state) and a dynamic analysis of light effects on photosynthesis and on the development of photoinhibition. The model is a mechanistic formulation of the P-E relationship. A mechanistic model can be more useful for testing the effects of different factors on model parameters and characteristics of the production curves (Fasham and Platt 1983). Eilers’ model describes the photosynthetic processes and those connected with photoinhibition, producing P-E curves similar to those obtained with the models of Vollenweider (1965) and Platt et al. (1980). Eilers and Peeters (1988) considered three possible states for the absorbing pigments - ‘photosynthetic factories’ (PSF) sensu Crill (1977): Fundamental, excited and inhibited. The transitions from the fundamental to the excited state and from the excited to the inhibited state are proportional to E and to the rate constants k1 and k2 (Fig. 17.4). In Eilers’ model, the recovery rate or probability of the excited PSF returning to the fundamental state (k3 in Fig. 17.4) is assumed to be dependent on how fast energy is used in the dark-temperature dependent reactions. This may be not entirely correct because recovery of the excited to the fundamental state may occur without release of oxygen from the water (Megard et al. 1984) and consequent energy generation to the dark reactions. The consequences of these differences on the temperature effects on photosynthesis are discussed in Duarte (1995) and will not be analyzed further here. The recovery rate of the inhibited to the fundamental state (k5 in Fig. 17.4) corresponds to repair from photoinhibition. Recently it has been demonstrated that photoinhibition is related to protein damage in the electron transfer system PSII (Han et al. 2000).

656 Algal Cultures, Analogues of Blooms and Applications From the diagram shown in Fig. 17.4 it is possible to obtain a steady state solution.

Fig. 17.4 States and transition rates between the three possible pigmentary states fundamental, excited and inhibited (Q1, Q2 and Q3, respectively) following the energy-circuit language (Odum, 1975); f(E) represents the light effects and f(t) is a Arrhenius function - exp(d – e/t) (adapted from Eilers and Peeters (1988) (see text for explanation). Let Q1, Q2 and Q3 represent the quantities of PSF at the different states and P the photosynthetic gross productivity. From the possible transitions under steady state-conditions it follows (Eilers and Peeters 1988):

dQ 1 = – k2Q1E + k3Q2 + k5Q3 = 0 dt

(Eq. 20)

dQ 2 = k2Q1E – k3Q2 – k4Q2E = 0 dt

(Eq. 21)

dQ 3 = k4Q2E – k5Q3 = 0 dt

(Eq. 22)

Q1 + Q2 + Q3 = k

(Eq. 23)

These equations can be solved explicitly. Solving for Q2 the result is: Q2 =

kk 2 k 5 E 2

k 2 k 4 E + k 5 (k 2 + k 4 ) E + k 5 k 3

(Eq. 24)

The rate of photosynthetic production is proportional to the quantity Q2: P = k1Q2 =

k 1kk 2 k 5 E k 2 k 4 E 2 + k 5 (k 2 + k 4 ) E + k 5 k 3

(Eq. 25)


657

To reduce the number of parameters, the numerator and the denominator are divided by k1kk2k5 and a, b and c introduced: a=

k2k 4 k 1kk 2 k 5

(Eq. 26)

b=

k 5 (k 2 + k 4 ) k 1kk 2 k 5

(Eq. 27)

c=

k 5k 3 k 1kk 2 k 5

(Eq. 28)

P=

E aE + bE + c

(Eq. 29)

The final result is: 2

As shown in Table 17.1, the parameters a, b and c may be expressed in terms of a, Eopt and Pmax. The parameter a is responsible for photoinhibition. If it is null a saturation curve is obtained. This would correspond to assume only two states in the diagram of Fig. 17.4 – the fundamental and the excited ones. In this situation, the first term of the denominator in Equation 29 would be zero. Photoinhibition is more pronounced for larger values of a. Eilers and Peeters (1993) present a dynamic analysis of the previously described model, providing some important results that can help clarify the influence of light intensity, variability and incubation time in the analysis of productivity experiments. The authors also discuss part of the non-steady solutions of equations 20, 21 and 22. In the present work these equations were solved analytically and numerically, using MatLab and one of its extensions – Simulink (MatLab 2000, Simulink 2002). The analytical solution for Q2 may be found in Eilers and Peeters (1993). The complete solution may be obtained using MatLab. The non-steady state form of equations 20, 21 and 22 is a system of stiff equations, because there are two very different scales of the parameters that regulate the changes of the dependent variables (Press et al. 1992). The fluxes between Q1 and Q2 are much faster than between those and Q3, with k2 >>1000 k4 and k3 >>1000 k5 (Eilers and Peeters 1993). Therefore, proper numerical methods are required like some of the ordinary differential equation solvers for stiff equations, with variable step size, available in MatLab and Simulink. The usage of the analytical solutions is not so suitable for routine calculations, because of the computing time involved. In Fig. 17.5, the Simulink model diagram used in the numerical simulations is shown. The diagram includes the same fluxes and variables of Fig. 17.4,

Fig. 17.5 Simulink model diagram of the system represented in Fig. 17.4. The system is forced by light intensity. The quantities Q1, Q2 and Q3 are calculated (in the integrators) by numerical integration of the sum of all input and ouput fluxes. The sums of the fluxes are performed within the circles, with positive signs for inputs and negative for outputs. Each flux is driven by the donnor variable, the transfer coefficient (Ks) and, in some cases, by light intensity. The product of these quantities is calculated within the multiplier symbols (see text).



659

except those related to the dark reactions. In Fig. 17.3 the type of curves obtained with the model for a simulation of 12 h, under a normal light regime are shown. The model was initialized with Q1 = 100, Q2 = 0 and Q3= 0 (the number of PSFs in each state expressed as a percentage). Constant values are assumed for all Ks (k2 = 50000 h–1, k3 = 10000 h–1, k4 = 0.3 or 5 h–1 and k5 = 0.2 or 5 h–1). These values were chosen to guarantee, on one hand, an almost instantaneous transition from the fundamental to the excited state and a fast recovery from the excited state and, on the other hand, a photoinhibition development and recovery time above one hour. Light was expressed in relative units and photosynthetic rate assumed proportional to Q2 (equation 25). When the higher values of k4 and k5 are chosen, a curve with two peaks is obtained – one in the morning and another in the afternoon (Fig. 17.3 curve a). Higher values of these coefficients imply faster development of photoinibition and respective recovery, producing larger variability in photosynthetic response. When the lower values are chosen curve b is obtained, showing a morning maximum and afternoon depression. Lower rates did not allow afternoon repair from photoinhibition. Finally, when light intensity is halved, a maximum is observed at midday. In Fig. 17.6a the temporal variation of Q2 is shown under several stable light intensities, as predicted by the Simulink model with the higher Ks. A higher light intensity leads to a higher initial increase in the excited state. Further, the higher final and stable values do not correspond to the higher light intensities. Similarly, in Fig. 17.6b experimental photosynthetic rates measured by Macedo et al. (1998) as a function of time under different stable light intensities are shown. The patterns are the same shown in Fig. 17.6b if it is assumed that the photosynthetic rate is proportional to Q2 (equation 25) – a decrease in photosynthesis over time, stabilizing after approximately one hour and a half, with higher values corresponding to intermediate light levels. In Fig. 17.7 the results of several simulations with pulsing light are shown. The frequency of light pulses varied from 0.04 to 10 h–1. Their duration was chosen to guarantee the same average light for all simulations. The results obtained suggest that photosynthetic rate may be maximized if light frequency is increased from the normal daily pattern to a value between one and two hours. The results are merely speculative, but it may be hypothesized that increasing light pulse frequency to time scales comparable to those required for full development of photoinhibition, may prevent it from occurring, leading to an improvement in light utilization efficiency. This is in contradiction with the experimental results of Marra (1978a). This author did not observe any significant improvement in light

660 Algal Cultures, Analogues of Blooms and Applications 100 90 80

E=1

70 60

E = 0.5

E=2 50

E = 0.2

40 30

E = 0.1

20

a)

10 0

0

0.1

0.2

0.3

0.4

0.5

P (mg C.mg Chl a–1h–1)

Time (hours) 100 170 300 500 1445

35 b)

30 25 20

Light intensities (mmol quanta m–2s–1)

15 10 5 0 0.5

1

1.5

2

2.5

Time (hours)

Fig. 17.6 (a) Temporal variation of Q2 under several stable light intensities predicted by the Simulink model with the higher Ks. (b) Photosynthetic rates measured as a function of time under five stable light intensities (adapted from Macedo et al. 1998) (see text). utilization efficiency in experiments where diurnal light pulses were compared to higher-frequency pulses. However, the highest frequency used was around 0.33 h–1, corresponding to four pulses of three hours each. In the Simulink model, it is possible to include a switch to limit the photoinhibition flux from Q2 to Q3 to light levels above a critical limit, as in DYPHORA model. This change reduces the relative importance of photoinhibition without affecting any of the patterns depicted in Fig. 17.3 and Fig. 17.6. Without the switch, a certain degree of photoinhibition always develops, even under low light, because there are always some transitions


Light intensity (relative units)

1.2

661

a)

1 0.8 0.6 0.4 0.2 0 0

5

10

15

20

Time (hours) 55

Number of PSFs

50 45

Q1 Q2 Q3

40 35

b)

30 25 20

0

2

4

6

8

10 12 14 16 18 20 22 24

Duration of the light pulses (hours)

Fig. 17.7 (a) Example of the light pulses used to force the model and obtain the results shown in b). Average light intensity was kept constant. Only the frequency and duration of the pulses changed. (b) Temporal variation of Q1, Q2 and Q3 under pulsing light predicted by the Simulink model. between the excited and the inhibited state, unless K4 = 0 (see above). Fig. 17.8 shows the analytical solution of the Eilers and Peeters model, depicting the dependence of the number of PSF in the excited state (Q2) on light intensity and light exposure time. Q2 decreases with time faster at higher light intensities. In practice, it is difficult to estimate the values of the coefficients and variables of Fig. 17.4. A possible alternative is to use Equation 14 (Table 17.1) that results from combining Eilers’ model with the same type of photoinhibition time dependent function of DYPHORA’s model. This function is multiplied by parameter a of Eilers’ model, that is responsible for

662 Algal Cultures, Analogues of Blooms and Applications photoinhibition. It is also possible to multiply Equation 14 by a term of the type

F1 - exp F - t I I (cf. equation 17) H t rK K H

to account for photosynthesis light induction time, following the approach of MacIntyre and Geider (1996). With this equation it is also possible to obtain the patterns described before for the rate of photosynthesis – morning and afternoon maximums, morning maximum and afternoon depression and midday maximum (Fig. 17.3). Moreover, the dependence between light intensity and light exposure time is similar to that shown in Fig. 17.8, with saturation type curves evolving to inhibition curves as the light exposure time increases (Eilers and Peeters 1993). These results emphasize the bias that may result from measurements of photosynthetic rates under stable light intensities for unrealistically large time periods, when the type of P-E curve depends on exposure time to light and when photoinhibition is overestimated, leading to an underestimation of photosynthetic rates. Different authors emphasize different photoadaptive parameters – Pmax, a, the photoinihibition parameter or Ek. Experimental data with cultures from Marra (1978b) suggests that Pmax is the most variable parameter at time

Q2 (%)

Time (hours) Relative light intensity Fig. 17.8 Analytical solution of the Eilers and Peeters model, obtained with MatLab, as a function of light intensity (relative units) and time (hours).


663

scales of hours. The same applies to the results of Macedo et al. (1998, 2002), with field data, although a time dependent a and photoinhibition were also observed. It is noteworthy that these parameters may be reciprocally related as suggested by the parameter equations of Eilers’ model (Table 17.1). Therefore, it may be difficult to separate their variance. For example, a negative slope in relation to light is explained in the DYPHORA model by an increase in photoinhibition, whereas in the model of Franks and Marra (1994) it is explained merely as a reduction in Pmax. The P-E saturation and inhibition models described in Han (2001a) and Han (2001b), respectively, are similar to that of Eilers and Peeters (1988, 1993). Han’s models are also based on the existence of two (saturation P-E curve) or three (inhibition P-E curve) states for the photosynthetic units (PSU) (PSF - photosynthetic factories in the terminology of Eilers and Peeters [1988], see above) – open (reactive), closed (activated) and inhibited. However, transitions from the open to the closed and from the closed to the inhibited states are dependent on the cross-section of PSII, instead of different constants as in Eiler’s model. Rubio et al. (2003) presented a model that accounts for photoinhibition and photoacclimation. In this model, the light-capture process and the subsequent enzyme-mediated reactions are considered explicitly. The former (faster process) is described as a first order function of light intensity and the quantity of PSU in the resting state, whilst the latter is described as a Michaelis-Menten function. Regardless of the energy harvested by the photosynthetic apparatus, the rates of carbon fixation in the Calvin cycle are the limiting process. This is an important difference from previously discussed models. For example, in Eilers’ model, photosynthetic rate is limited by the abundance of PSFs in the exited state, without any subsequent limitation due to biochemical processes. The model of Rubio et al. (2003) includes also photoinhibition, which is considered proportional to the square root of light intensity, from the assumption of a 0.5 order deactivation kinetics of the PSUs. Another important aspect of this model is that it seems potentially capable of including photoacclimation effects, like changes in the number and size of the PSUs. According to the authors, this model is primarily intended for use in the analysis of photobioreactors although it seems to have potential to be applied to natural conditions.

General Recommendations to Obtain P- E Curves The first step in approaching the determination of P-E curves is to know which are the temporal and spatial scales of interest for the given problem.

664 Algal Cultures, Analogues of Blooms and Applications Whether in culture or field conditions, time-dependent phenomena should be investigated prior to the final design of primary production experiments, in order to avoid the potential bias of static incubations. This may be done by testing several incubation times under saturating light intensities. Whenever P-E time-dependent responses are observed, incubations should include not only several light intensities but also incubation times. These should range from a few minutes to three to four hours. The lower limit is determined by the detection limits of the available methods and also operational limitations. If possible, the temporal range used in the incubations, should cover the mixing time scales of the phytoplankton under study, across surface depths, where photoinhibition is most likely to occur. Prior to incubation, phytoplankton should be kept in subdued light to allow cells to recover from any previous photoinhibition. Ideally, P-E equations should be validated at longer time-scales than those used in the incubations. This may not be possible in field conditions, but it is feasible in the laboratory. If the objective is to describe the P-E relationship at short temporal scales (up to a few h) it seems appropriate to concentrate on photoresponse phenomena and look for the time dependences of usual parameters P-E (see Table 17.1 for examples of time dependent models and Table 17.2 for P-E parameters). As larger temporal scales are chosen, photoacclimation processes should be studied and incorporated in the models, following approaches similar to those suggested by Falkowski and Wirick (1981) and Geider and Platt (1986). The recent model by Rubio et al. (2003) may be an important advance in this area. If studies are conducted in field conditions and as temporal scales of interest increase, the probability of having significant nutrient, temperature and other limiting environmental effects also increases. Furthermore, community changes are more likely to occur. Therefore, it is important to incorporate the effects of this variability in P-E dynamics when primary productivity is to be integrated over seasonal time scales. As a fist approach this may be done by looking at statistical relationships between environmental variables and photosynthetic parameters. It seems very difficult at the present state of knowledge to predict the variability of P-E curve parameters at monthly, seasonal or larger time scales, even if species composition is well known. This is an area where working with cultures may help to understand the physiologic differences between species and understand which factors are the limiting over the course of time.


665

Seasonal variations of physiological parameters were investigated across the North Atlantic by Kyewalyanga et al. (1998). The authors managed to explain a significant fraction of the temporal variability of a, Pmax and the maximum quantum yield of photosynthesis in different oceanic provinces as a function of several environmental variables such as: temperature, sampling depth, nutrients, optical properties of water and chlorophyll. In a study conducted at St. André Coastal Lagoon, Macedo et al. (2001) obtained monthly P-E curves and studied environmental variables and phytoplankton species composition. Pmax values ranged from 2 to 22.6 mg C mg Chl a–1 h–1 and Ek from 5.2 to 334.8 mmol quanta m–2 s–1. It was possible to describe the variability of both parameters with respect to temperature, with an Arrhenius function, obtaining a significant fit between observed and predicted values, only when the community was dominated by dinoflagellate species – Prorocentrum minimum – in six out of thirteen months: 1 ln(Pmax) = 3.527 – 20.999 (r = 0.632, p < 0.05) T 1 ln(Ek) = 7.162 – 36.858 (r = 0.752, p < 0.01) T

(Eq. 30) (Eq. 31)

Coupling P-E Dynamics with Hydrodynamic Models In recent years, there has been an increasing trend to couple physical and biogeochemical models. This coupling generally implies that both models are computed over the same spatial grid and temporal scale. Biogeochemical processes are computed at each grid cell, giving place to local changes in pelagic variables, such as phytoplankton concentration, which are then transported over the model grid by the hydrodynamic model. The idea of using P-E dynamic formulations within the scope of a coupled model implies the resolution of adaptive equations, nested within the calculation of local changes in phytoplankton biomass. However, as phytoplankton biomass is transported across the model grid, so should their ‘adaptive status’ be transported and mixed with the existing phytoplankton biomass and respective ‘adaptive status’. This means that after each model iteration, not only the resulting biomass at each model grid cell will depend on local and transport processes, but also the resulting ‘adaptive status’. It is conceivable that in a Eulerian vertically resolved mixed layer model, photoinhibited cells at the surface layers may be convected downward, changing the average phytoplankton properties of the destination layers. In such a situation, photoinhibition may develop further downward if

666 Algal Cultures, Analogues of Blooms and Applications the downflux speed is faster than the recovery from photoinhibition, as suggested by the models of Franks and Marra (1994) and Duarte and Ferreira (1997). An opposite situation may result from the upwelling of noninhibited cells, ‘diluting’ surface inhibited phytoplankton and increasing productivity. Lande and Lewis (1989) compared an Eulerian model similar to the one implemented by the previous authors with a Lagrangian model, that simulated the trajectories of individual cells and respective photoacclimation, retaining information on individual cells, and obtained very similar photosynthetic rates with depth. The small differences observed (< 1%) were due to nonlinear dependences of photosynthesis on the P-E parameters, in conjunction with the variance and covariance of these traits among cells at given depths.

CONCLUSIONS The main conclusions emerging from the results and studies discussed insofar are: 1) The importance of studying the response of the P-E relationship to time; 2) The importance of describing the parameters of the P-E relationship as a function of time and light intensity, when dynamic P-E behavior is observed; 3) The need to incorporate P-E dynamic equations in ecosystem models, specially in mixed layer resolved models; 4) That primary production may be biased when based on static productivity measurements. Furthermore, it does not seem appropriate to recommend one particular P-E model among those available in the literature. As shown earlier, different models may yield good predictions and the choice of one of them may be done by trial and error. It seems appropriate with the present level of knowledge to incorporate photocclimation mechanisms in P-E models in order to predict photosynthetic rates at temporal scales of several hours or even days in culture conditions.

ACKNOWLEDGEMENTS The author wishes to thank Maria João Guerreiro and an anonymous referee for the review of the manuscript and their helpful comments.


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668 Algal Cultures, Analogues of Blooms and Applications Geider, R.J., H.L. MacIntyre and T.M. Kana. 1998. A dynamic regulatory model of phytoplankton acclimation to light, nutrients, and temperature. Limnol. Oceanogr. 43: 679-694. Harris G.P. and J.N.A. Lott. 1973. Light intensity and photosynthetic rates in phytoplankton. J. Fish. Res. Bd. Can. 30: 1771-1778. Harris G.P. and B.B. Piccinin. 1977. Photosynthesis by natural phytoplankton populations. Arch. Hydrobiol. 59: 405-457. Han, B.-P. 2001a. Photosynthesis-irradiance response at physiological level: a mechanistic model. J. theor. Biol. 213: 121-127. Han, B.-P. 2001b. A mechanistic model of algal photoinhibition induced by photodamage to phosystem-II. J. theor. Biol. 214: 519-527. Han, B.P., and M. Virtanen, J. Koponen and M. Straskraba. 2000. Effect of photoinhibition on algal photosynthesis: a dynamic model. J. Plankton Res. 22: 865-885. Herman, M.J. 1993. A set of models to investigate the role of benthic suspension feeders in estuarine ecosystems. pp. 421-454. In R. Dame. [ed.]. Bivalve filter-feeders in estuarine and coastal ecosystems. NATO ASI Series, Series G, Vol. 33. SpringerVerlag, New York, USA. ICES CM. 1996/L:3. Biological Oceanography Committee. Report of the working group on phytoplankton ecology. Ref: C+E+Env. 28-30. Iwakuma, T. and M. Yasuno. 1983. A comparison of several mathematical equations describing photosynthesis-light curve for natural phytoplankton populations. Arch. Hydrobiol. 97: 208-226. Janowitz, G.S. and D. Kamykowski. 1991. An Eulerian model of phytoplankton photosynthetic response in the upper mixed layer. J. Plankton Res. 13: 988-1002. Jassby, A.D. and T. Platt. 1976. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21: 540-547. Johnson, Z. and R.T. Barber. 2003. The low-light reduction in the quantum yield of photosynthesis: potential errors and biases when calculating the maximum quantum yield. Photos. Res. 75: 85-95. Jørgensen, S.E., S. Nielsen and L. Jørgensen. 1991. Handbook of Ecological Parameters and Ecotoxicology. Elsevier, Amsterdam, the Netherlands. Jørgensen, S.E. and G. Bendoricchio. 2001. Fundamentals of ecological modelling. Developments in Ecological Modelling 21. Elsevier, Amsterdam, the Netherlands. Kamykowski, D., G.S. Janowitz, G.J Kirkpatrick and R.E. Reed. 1996. A study of timedependent primary productivity in a natural upper-ocean mixed layer using a biophysical model. J. Plankton Res. 18: 1295-1322. Kirk, J.T.O. 1994. Light and photosynthesis in aquatic ecosystems, 2nd ed. Cambridge Univ. Press, New York, USA. Kyewalyanga, M.N., T. Platt, S. Sathyendranath, V.A. Lutz and V. Stuart. 1998. Seasonal variations in physiological parameters of phytoplankton across the North Atlantic. J. Plankton Res. 20: 17-42. Lizon, F. and Y. Lagadeuc. 1998. Comparisons of primary production values estimated from different incubation times in a coastal area. J. Plankton Res. 20: 371-381. Lande, R. and M.R. Lewis. 1989. Models of photoadaptation and photosysnthesis by algal cells in a turbulent mixed layer. Deep-Sea Res. 36: 1161-1175. Long S.P., S. Humphries and P.G. Falkowski. 1994. Photoinhibition of photosynthesis in nature. Ann. Rev. Plant Mol. Biol. 45: 655-662.


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Macedo, M.F., J.G. Ferreira and P. Duarte. 1998. Dynamic behavior of photosynthesisirradiance curves determined from oxygen production during variable incubation periods. Marine Ecology Progress Series 165: 31-43. Macedo, M.F., P. Duarte, P. Mendes and J.G. Ferreira. 2001. Annual variation of environmental variables, phytoplankton species composition and photosynthetic parameters in a coastal lagoon. J. Plankton Res. 23: 719-732. Macedo, M.F., P. Duarte and J.G. Ferreira. 2002. The influence of incubation periods on photosynthesis-irradiance curves. J. Exp. Mar. Biol. Ecol. 274: 101-120. MacIntyre, H.L. R.J. Geider.1996. Regulation of Rubisco activity and its potential effect on photosynthesis during mixing in a turbid estuary. Mar. Ecol. Prog. Ser. 144: 247-264. Mallin, M.A. and H.W. Pearl. 1992. Effects of variable irradiance on phytoplankton productivity in shallow estuaries. Limnol. Oceanogr. 37: 54-62. Marra, J. 1978a. Effect of short-term variations in light intensity on photosynthesis of a marine phytoplankter. Mar Biol 46: 191-202. Marra, J. 1978b. Phytoplankton photosynthetic response to vertical movement in a mixed layer. Mar Biol 46:203-208. Marra, J. and K. Heinemann. 1982. Photosysnthesis response by phytoplankton to sunlight variability. Limnol. Oceanog. 27: 1141-1153. Marra, J., K. Heinemann and G. Landriau, J. 1985. Observed and predicted measurements of photosynthesis in a phytoplankton culture exposed to natural irradiance. Mar. Ecol. Prog. Ser. 24: 43-50. Matlab. 2000. Matlab, The language of technical computing, Using Matlab. The Mathworks, Inc., Natick, MA, EUA. Megard, R.O., D.W. Tonkyn and W.H. Senft. 1984. Kinetics of oxygenic photosynthesis in planktonic algae. J. Plankton Res. 6: 325-337. Neale, P.J. and J. Marra. 1985. Short-tem variations of Pmax under natural irradiance conditions: a model and its implications. Mar. Ecol. Prog. Ser. 26: 113-124. Odum, H.T. 1975. An energy circuit language for ecological and social systems: Its physical basis. pp. 139-211. In B.C. Patten. [ed.], Systems analysis and simulation in ecology. Vol. II. Academic Press, London, UK, pp. 139-211. Oviatt, C.A., D.T. Rudnick, A.A. Keller, P.A. Sampou and G.T. Almquist. 1986. A comparison of system (O2 and CO2) and C-14 measurements of metabolism in estuarine mesocosms. Mar. Ecol. Prog. Ser. 28: 57-67. Pahl-Wostl, C. and D. M. Imboden. 1990. DYPHORA - a dynamic model for the rate of photosynthesis of algae. J. Plankton Res. 12: 1207-1221. Parker, R.A. 1974. Empirical functions relating metabolic processes in aquatic systems to environmental variables. J. Fish. Res. Bd. Canada 31: 1150-1552. Parsons, T.R., M. Takahashi and B. Hargrave. 1984. Biological oceanographic processes. Pergamon Press, Oxford, UK. Platt, T., C.L. Gallegos and W.G. Harrison. 1980. Photoinhibition of photosynthesis in natural assemblages of marine phytoplankton. J. Mar. Res. 38: 687-701. Prasil, O., N. Adir and I. Ohad. 1992. Dynamics of photosystem II. Mechanism of photoinhibition and recovery processes. pp. 295-348. In: J.R. Barber. [ed.]. The Photosystems: Structure, Function and Molecular Biology. Elsevier, New York, USA. Press, W.H., S.A. Teukolsky, W.T. Vetterling and B.P. Flannery. 1992. Numerical recipes in C. The art of scientific computing. Cambridge University Press, New York, USA.

670 Algal Cultures, Analogues of Blooms and Applications Rabinowitch, E.T. 1956. Photosynthesis, Vol. II, Interscience, New York, USA. Raillard, O. and A. Ménesguen. 1994. An ecosystem box model for estimating the carrying capacity of a macrotidal shellfish system. Mar. Ecol. Prog. Ser. 115: 117-130. Richardson, K., J. Beardall and J.A. Raven. 1983. Adaptation of unicellular algae to irradiance: an analysis of stategies. New Phytol. 93: 157-191. Rubio, F.C., F.G. Camacho, J.M.F. Sevilla, Y.Chisti and E. Molina. 2003. A mechanistic model of photosynthesis in microalgae. Biotecnology and Bioengineering 81: 459-473. Shaw, P.J. and D.A. Purdie. 2001. Phytoplankton photosynthesis-irradiance parameters in the near-shore UK coastal waters of the North Sea: temporal variation and environmental control. Mar. Ecol. Prog. Ser. 216: 83-94. Sakshaug, E., A. Bricaud, Y. Dandonneau, P.G. Falkowski, D.A. Kiefer, L. Legendre, A. Morel, J. Parslow and M. Takahashi. 1997. Parameters of photosynthesis: definitions, theory and interpretation of results. J. Plank. Res. 19: 1637-1670. Simulink. 2002. Simulink, Dynamic system simulation for MatLab, Using Simulink. The Mathworks, Inc., Natick, MA, EUA. Smith, E.L. 1936. Photosynthesis in relation to light and carbon dioxide. Proc. Natl. Acad. Sci. 22: 504-511. Steele, J.H. 1962. Environmental control of photosynthesis in the sea. Limnol. Oceanogr. 7: 137-150. Sukenik, A., J. Bennet, A. Mortain-Bertrand and P.G. Falkowski. 1990. Adaptation of the photosynthetic apparatus to irradiance in Dunaliella tertiolecta. Plant. Physiol. 92: 891898. Takahashi, M., S. Shimura, Y. Yamaguchi and Y. Fujita. 1971. Photoinhibition of phytoplankton photosynthesis as a function of the exposure time. J. Oceanogr. Soc. Japan 27:43-50. Taylor, A. 1993. Modelling climatic interactions of the marine biota. pp. 373-413 In J.Willebrand and D.L.T. Anderson. [eds.]. Modelling Oceanic Climate Interactions. NATO ASI Series, Series I, Vol. 11. Springer-Verlag, New York, USA. Vollenweider, R.A. 1965. Calculation models of photosynthesis – depth curves and some implications regarding daily estimates in primary production measurements. Mem. Ist. Ital. Idrobiol. 18: 425-427. Vollenweider, R.A., 1974. A manual on methods for measuring primary productivity in aquatic environments. Blackwell scientific publications, Oxford, UK. Webb, W.L. and M. Newton and D. Starr. 1974. Carbon dioxide exchange of Alnus rubra: A mathematical model. Oecologia 17: 281-291.

Photosynthetic Response and Acclimation

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& Photosynthetic Response and Acclimation of Microalgae to Light Fluctuations Johan U. Grobbelaar Department of Plant Sciences, Botany, University of the Free State, Bloemfontein 9300, South Africa

Abstract Most microalgae experience light fluctuations and consequently they are constantly competing for this resource. They are subjected to intensity changes of up to two orders of magnitude as well as frequency variations of up to 10 orders. An array of photosynthetic and physiological responses have been reported including varying growth rates, pigment concentrations, and also phytoplankton community composition and diversity. The capacity to adjust to fluctuating light regimes is a hallmark in photosynthesis, without which life on earth would have been very different. This acclimation is not only to constant light but also to fluctuating light, where the shorter the light/dark frequencies the more the microalgae adjust to high light acclimated properties. They are constantly re-adjusting their responses depending on the prevailing conditions. The capture of light energy is controlled by the primary electron acceptor of Photosystem II and the transfer rate of electrons down the electron transport chain to Photosystem I. Stress and the overall physiological condition have a marked effect on the fate of the captured energy. Photoinhibition is checked in intermittent light and at high L/D-frequencies no indication of photoinhibition was found. Light fluctuations are largely dependent on water movement (turbulence) and this affects the boundary layer of the microalgae and consequently the uptake rates of nutrients and release of metabolites.

THE DYNAMIC LIGHT ENVIRONMENT Most microalgae are subjected to the heterogeneity of a light gradient and in fact are constantly competing for resources, of which light is one of

672 Algal Cultures, Analogues of Blooms and Applications the most important. Not only are they subjected to intensity changes in the range of up to two orders of magnitude, but also frequency variations of up to 10 orders. As Grobbelaar (1989b) pointed out, irrespective of where algae grow they are influenced essentially by three ranges of intermittent light, i.e.: 1. low-frequency cycles of hours to days and years. 2. medium-frequency fluctuations of seconds to minutes, and 3. high-frequency fluctuations of 100 ms (10 Hz) and less. The first range is determined mainly by diurnal and seasonal variations, influencing mainly the periodicity of cell division (Sorokin 1957) and circadian rhythms in metabolism such as photosynthesis (Doty and Oguri 1957). Low-frequency fluctuations have also been shown to influence growth rates, pigment concentrations, and also phytoplankton community composition and diversity (Flöder et al. 2002, Litchman 1998, Nicklisch and Fietz 2001). Medium-frequency fluctuations are the dominant range to which most algae in culture and nature are exposed too. There have been suggestions that light:dark (L/D) fluctuations in this range could be beneficial, where on the one hand it is suggested that turbulence resulting in these frequencies, enhances productivity, whilst on the other hand no evidence of such stimulatory effect could be found (Grobbelaar 1989). A further complication as suggested by Legendre et al. (1986) is that microalgae can adapt to the dominant frequencies of their environment. According to them, high and low light (shade) acclimation can occur in the range of medium to long term light fluctuations. The photosynthetic versus irradiance response curve (P/I) has been used extensively to describe the response of algae to light environments (Fig. 18.1). Three distinct regions are discernable; i.e. an initial lightlimited region at low-light intensities where photosynthetic rates expand with increasing irradiance, a light saturated region where photosynthetic rates are independent on irradiance, and a region of photoinhibition in which photosynthetic rates decreases with an increase in irradiance. In the light limited region the rate of photon absorption is correlated with the rate of electron transport from water to CO2, with the liberation of O2. The initial slope is usually donated by the symbol a (Jassby and Platt 1976) and when normalized with chlorophyll a or biomass it represents the maximum quantum yield of photosynthesis. In optically thin cultures or phytoplankton populations obviously only the chlorophyll a absorbed light should be used (Falkowski and Raven 1997). However, it is possible to


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Photosynthesis

Pmax

a 0 Dark Respiration

Rd 0 Ic

Ik

Light intensity

Ii

Fig. 18.1 Light response curve of photosynthesis versus light intensity (P/I curve). Pmax, the maximum photosynthetic rate measured at saturated I; Rd, dark respiration; a, maximum photosynthetic efficiency; Ik, the transition light intensity between light dependant and light saturated photosynthesis; and Pi, photoinhibition. Solid arrows indicate the affect of dark acclimation and open arrows the response to light acclimation. compensate in the calculations for the non-photosynthetic absorption of light that is common in natural systems. The transition between light-limited and light-saturated photosynthesis could be gradual or abrupt (Leverenz 1987), implying a non-linearity between absorbed light and photosynthetic rates. At light saturation photosynthetic rates reach a maximum (Pmax) without changing even though the light intensity increases. This transition between light-limited and light-saturated photosynthesis is donated by Ik and it is defined as: Ik =

Pmax a

(Eq. 1)

At light saturation, the rate of photon absorption exceeds the rate of electron turnover in PS II. At high irradiancies light-induced depression of photosynthesis occurs commonly referred to as photoinhibition. It is important to consider the time scales when photoinhibition manifests itself. Over the short term, light induced photoinactivation of PS II could be viewed as a survival strategy by reducing the number of redundant PS II units. However, if PS II reaction centers are not repaired through the continuous replacement of the D1 protein, then the damage would become permanent resulting in PS II inactivation (Prasil et al. 1992). As stated the lightdependent inactivation may be reversible or irreversible where the latter would require de novo protein synthesis. It has been found that Pmax was

674 Algal Cultures, Analogues of Blooms and Applications unaffected even at a reduction in PS II reaction centers of almost 50%, due to increased electron turnover in the remaining functional PSII centers (Behrenfeld et al. 1998). The reason for this is that the acceptor side of photosynthesis is limited by the capacity of the Calvin-cycle, and the impact of photo-inhibition would only become apparent when the numbers of reaction centers are reduced to such a level that the capacity of the Calvincycle cannot be met. Photoprotection could take place during prolonged exposure to high irradiancies and the distinction or interaction between damage and photoprotection is difficult (Demmig-Adams and Adams 1992). Synthesis of the photoprotective pigments such as b-carotene and zeaxanthin is common in algae allowing them to tolerate high irradiances, which is typical of highlight acclimated algae. Stress could severely influence the over-excitation of PS II and consequently the depression of photosynthetic rates, e.g. high and low temperatures (Bongi and Long 1987, Vonshak et al. 2001), and nitrogen deficiency (Herzig and Falkowski 1989).

Light Acclimation Algae (and plants) have developed several mechanisms to cope with changes in the quality and intensity of light. In essence the aim of the plant is to balance the light and dark photosynthetic reactions. Sukenik et al. (1987) indicated that Rubisco levels remained relatively constant under varying light regimes, suggesting that the major regulation occurs in the light reactions, especially of PS II. Regulation of PS II is possible through modulation of the light-harvesting capacity and/or changes in the number of PS II reaction centers. The amount of light harvesting pigments per cell increases under light limiting conditions, implying an increase in the number of photosynthetic units and the size of the light harvesting antennae. Also a decrease in the accessory pigments b-carotene and zeaxanthin is seen under light limiting conditions (Falkowski et al. 1994), which is characteristic of low-light acclimated cells. The reverse is true for cells exposed to high-light intensities. Such high-light acclimated cells typically have less photosynthetic units, lower chlorophyll a and high concentrations of accessory pigments. The time scales within which the algal cells must photoacclimate range from seconds to hours depending on the parameters measured. Cullen and Lewis (1988), determined that the photoadaptive response for Thalassiosira, of up to 50 % of the full response, could take place within 10 min to 21 h. Paradoxically it appears as if the tendency is for algae to mostly be in the


675

low-light acclimated state. This is the case in all high density algal cultures (Grobbelaar et al. 1995). The implication of this is that algae can effectively utilize low-light intensities, but on the other hand they often have to deal with excess energy. Mostly the excess energy is handled in PS II through either state transitions or dissipation, and these happen within seconds to minutes. Structural and biochemical changes that take place require longer time scales. The effect of light acclimation on the P/I curve is shown in Figure 18.1, where the open arrows show the direction of the response due to high-light and the solid arrows the response due to low-light acclimation. At high-light Pmax increases, while at low-light it decreases. Alpha (a) increases with lowlight acclimation and decreases under high-light exposure. The onset of photoinhibition occurs at lower light intensities for low-light acclimated algae compared to high-light acclimated algae that can tolerate much higher light intensities. Ik is lower for low-light acclimated algae than for high-light acclimated algae. Even dark respiration (Rd) varies considerably depending on the light history (Grobbelaar and Soeder 1985), where high-light acclimated algae will have higher dark respiration rates than low-light acclimated one’s. The interplay between high-light and low-light acclimation is dynamic and ever changing in algal populations, being natural or cultured. Algae will acclimate to the average light intensities in their environs, not only in the photic zones, but the entire optical depth.

Photosynthesis in a Dynamic Light/Dark Environment A well mixed optically dense culture implies that the individual cells are subject to ever changing light intensities. The dynamics of such a light/dark (L/D) environment is determined by (Fig. 18.2): (1) Culture depth or optical cross section, with the knowledge that at any depth exceeding 100 mm, the water (nutrient medium) would progressively absorb penetrating light more and more (Kroon et al. 1989), rendering it unavailable for photosynthesis. The optical depth also has a direct influence on the time duration of the L/D variations, where the deeper the system the longer will the time-scales be and vice versa. (2) Turbulence, where through various means and techniques mixing rates and patterns could be altered. The higher the turbulence the shorter will the L/D time-scales be and vice versa. (3) Biomass concentration and areal density (Grobbelaar et al. 1990). The influence of the areal density on areal productivities (Fig. 18.2) has

676 Algal Cultures, Analogues of Blooms and Applications L/D cycles

I%

100

a

Areal Density/Productivity

Irradiance/Depth I % 100 0

Optimal

z Productivity

0

b

b

a c

40 - 60

c

Areal Density (g(dw L-1)

t

Fig. 18.2 Light/Dark fluctuations as affected by different areal densities at a fixed optical path length. a, represents low areal density; b, the optimal areal density; and c, a high areal density. been known for many years (Vonshak et al. 1982, Grobbelaar et al. 1990). In essence the intermittent light is determined by the areal density, the optical depth of the bioreactor and the degree of mixing (turbulence). The areal density determines the quantity of light per cell in the reactor and several studies have shown that there is a ‘bellshaped’ relationship between productivity and areal density, with a definite optimum for maximum productivity (Fig. 18.2). Below the optimal areal density all the cells are exposed to light, depending on their position in the optically dense medium. Above the optimal areal density a portion of the culture is in the dark and depending on the areal density different L/D ratios can manifest itself, with the implication is that the cells move in and out of the photic volume. The influence of areal density with the consequent light attenuation with depth and the resultant L/D cycles is shown in Fig. 18.2. Obviously this is a simplification, because particle movement in a mixed solution is random and uniform sinusoidal cycles would not be found. However, for clarification it is assumed that the mixing is uniform, moving the cells from the bottom to the surface of the reactor and back. Furthermore, it is assumed that light attenuation is only from actively photosynthesizing biomass. In nature a significant attenuation could result from dissolved colored substances (e.g. gilvin) and/or non-photosynthesizing suspended particulate materials (e.g. detritus and clay) (Kirk 1983). Condition (a) in Fig. 18.2 is typical of low biomass densities where a significant quantity of


677

light reaches the bottom of the reactor. All cells are exposed to light and mixing causes cells to be exposed to maximum or a little less light and represents the conditions in a culture immediately following inoculation. Under these conditions they become high-light acclimated (Grobbelaar et al. 1995) and overall areal productivity is dependent on the biomass concentration. When light is attenuated such that it decreases from 100% to 0 % over the depth profile, under uniform conditions the cells will be exposed to sinusoidal cycles ranging in light intensities from 100 % to 0 % and back (condition b in Fig. 18.2). This has also been termed the optimal areal density (Grobbelaar et al. 1984, Hartig et al. 1988) and effectively means that all light is absorbed that impinges on the culture surface by photosynthesizing biomass if no other light absorbing materials are present. At areal densities exceeding the optimal, a portion of the cultures is in the dark, depending on the actual biomass concentration (condition c in Fig. 18.2). Under uniform condition the cells are exposed to L/D cycles where the dark period is determined by the areal density, being longer at higher areal densities and vice versa. The reason for the decrease in areal productivity at biomass concentrations higher than the optimal is mainly due to respiratory losses as modeled by Grobbelaar et al. (1984). In systems where all the available impinging light energy is absorbed by photosynthesizing algal biomass, areal density, turbulence (mixing) and the optical light path length interact to determine the eventual light exposure regime the algal cells are exposed too. In systems where light is attenuated by non-photosynthesizing material (most natural systems), essentially the same principals applies, however, a portion of light energy is dissipated resulting in no biomass production. L/D cycles could be enhanced by increasing the degree of mixing, but this becomes impractical when the culture depth becomes too deep (greater then 100 mm). Richmond (2004) stated that the most powerful means by which to increase L/D-cycle frequencies is by reducing the optical path length of a mixed culture depth. This also applies to natural shallow systems (Grobbelaar 1989a). The enhancement of photosynthesis by ‘flashing light’ (both the rate and the efficiency) has been known for many years (Kok, 1953). Richmond and Vonshak (1978) reported increased growth rates of Spirulina when the stirring speeds of their mass cultures were doubled. They ascribed the increase to a more favorable dark/light cycle with increased turbulence. Laws et al. (1983) introduced wing-shaped foils into the flow path of their stirred mass cultures, which created vortex circulations of about 0.5 - 1 Hz. This resulted in an almost doubling of productivity.

678 Algal Cultures, Analogues of Blooms and Applications According to Terry (1986), two conditions are necessary for the enhancement of photosynthesis in a modulating light field, i.e. a longer dark period than the light period and relatively high photon flux densities. In effect cells that are exposed to intermittent high-light intensities experience a dilution of the light, because over a given time span they receive less light. An important prerequisite, however, is that the L/D frequencies be sufficiently short in order to match the turnover time of QA (the primary electron acceptor of PS II) re-oxidation, otherwise the excess energy will be dissipated. This could explain why in several studies, both in natural environments and in mass cultivation, no enhancement of production rates were measured (Falkowski and Wirick 1981, Grobbelaar, 1989b, Jewson and Wood 1975) where it was established that the L/D cycles fell within the medium range (minutes to hours). Enhancement of photosynthesis in L/D environments depends on a number of conditions, according to the findings of Grobbelaar et al. (1996), the most important is that the L/D-cycles be less than 1 Hz. Under such conditions photosynthetic rates and light utilization efficiencies are increased as already pointed out by Kok (1953). The enhanced rates in photosynthesis are exponential with increasing L/D frequencies (Fig. 18.3), but varied markedly between the different treatments and the light history of the algae (Grobbelaar et al. 1996). In general large variations in the photosynthetic rates were found, both in the response to increased light/ dark frequencies as well as to the ratio of light/dark periods. Three main conclusions were made from the results of Grobbelaar et al. (1996), i.e.: 350 300

y = -16.3 ln(x) + 281.7

250

y = -11.3 ln(x) + 244.0

1L/2D 1L/1D 2L/1D 1L/2D 1L/1D 2L/1D

y = -22.5 ln(x) + 263.5

200 150 100 50 0 0.1

1

10

100

1000

10000

Fig. 18.3 Photosynthetic rates against different L/D frequencies and different L/D ratio’s


679

1) Photosynthetic rates enhanced exponentially with increasing light/ dark frequencies, 2) A longer dark period in relation to the light period does not necessary lead to higher photosynthetic rates (efficiencies), and 3) Acclimation following subjecting algae to L/D-frequencies of 0.05, 0.5 and 50 Hz for longer periods, did not take place. Grobbelaar et al. (1996) also found that low L/D-frequencies resulted in the algae becoming low-light acclimated, while the opposite was true for algae exposed to high L/D-frequencies. The results shown in Fig. 18.3, using Scenedesmus obliquus (TURP.) Kütz. strain 206, clearly show how L/D-cycles affect photosynthetic rates. These were measured using a small volume gas exchange chamber as described in by Bartos et al. (1975). The experimental setup was arranged such that the chamber was illuminated from both sides with arrays of forty high bright red LED’s (light emitting diodes) each mounted on a disc and the light focused using magnifying glasses into the chamber. The maximum intensity at continuous illumination was about 600 m mol photons/m2/s inside the chamber. The LED’s were switched ‘on’ and ‘off’ using a digital pulse generator in conjunction with a regulated power supply (see Grobbelaar et al. 1996 for more details). The effect of altering the L/D-ratio was clearly evident, where at any given L/D-frequency the highest photosynthetic rates were measured at a ratio of 1 (1L/1D). Increasing the light portion (2L/1D) had little effect, while a longer dark period mostly resulted in lower photosynthetic rates. As Grobbelaar et al. (1996) found, photosynthetic rates decreased by a third at a 1L/2D ratio indicating that it is a function of total light exposure. The actual L/D-frequencies played a role in the response where the lowest photosynthetic enhancement over the frequencies tested was seen where the light period was longer than the dark period (2L/1D). The findings shown in Fig. 18.3 have important practical implications where the 2L/1D represents (a) in Fig. 18.2, 1L/1D (b) in Fig. 18.2 and 1L/2D the condition (c) in Fig. 18.2. This also supports the optimal areal density situation where the L/D-cycles will have a ratio of 1. From the slope of the 1L/1D it is clear that photosynthetic rates were most stimulated over the range of frequencies given in Fig. 18.3. Nedbal et al. (1996) found for green algae and cyanobacteria that the rates of photosynthetic oxygen evolution normalized to equal mean irradiance were lower or equal in intermittent light compared to the maximum rate found in an equal optimal continuous regime. However, they found that the growth rates were always higher in intermittent light compared to that in equivalent continuous light. Nedbal et

680 Algal Cultures, Analogues of Blooms and Applications al. (1996) made the point that enhancement is only possible, when the lighton period is 1 ms the electron transfer capacity of e.g. the plastoquinone (PQ) pool will be exhausted. However, they mainly ascribe the differences in growth rates to the role that photoinhibition plays, where this down-regulation process is vastly different in continuous light, compared to intermittent light, being significantly less in the latter. They ascribed differences in the dynamics of the redox state of the PQ pool to be responsible for the low photoinhibition rates observed in intermittent light.

Intermittent Light and Water Movement (Turbulence) Fluctuating light regimes in aquatic environments is for most dependent on water movement and to a lesser degree on surface waves and climatic conditions. Depending on certain conditions photosynthetic rates and growth is enhanced in intermittent light. However, water movement or mixing also affects the mass transfer rates of e.g. nutrients from the growth medium to the algae and removal of metabolites from the cell surfaces of the algae, to the growth medium. The increased mass transfer rates affect the boundary layer, being smaller at high turbulences, resulting in higher nutrient uptake rates and vice versa. Comparing photosynthetic rates at different turbulences, L/D-cycles and ratios, Grobbelaar (1994) found a synergistic affect, where increased turbulence enhances the exchange rates of nutrients and metabolites between the cells and their growth medium and these, together with the increased light/dark frequencies, would increase productivity and photosynthetic efficiency. However, above Reynolds Numbers of 5000, photosynthetic rates were suppressed and this was ascribed to excessive shear stress, pressure releases and cavitation (Merchuk 1988).

CONCLUSIONS The capacity to adjust to fluctuating light regimes is a hallmark in photosynthesis, without which life on earth would have been very different. Photosynthesizing organisms acclimate to the average light conditions in their environs and constantly readjust as conditions change. Physiologically several processes are involved each with specific response and reaction times. Bio-physically the capture of light energy is controlled by the primary electron acceptor of Photosystem II and the transfer rate of electrons down the electron transport chain to Photosystem I. Photoinhibition is


681

checked in intermittent light and at high L/D-frequencies no indication of photoinhibition could be found. Light fluctuations also imply movement of the cells in the aquatic environment and this affects the boundary layer directly influencing uptake of nutrients and release of metabolites, resulting in a synergistic increase in growth rates.

REFERENCES Bartos, J., E. Berkova, and I. Setlik. 1975. A versatile chamber for gas exchange measurements in suspensions of algae and chloroplasts. Photosynthetica 9: 395-406. Behrenfeld, M.J., O. Prasil, Z.S. Kolber, M. Babin, and P.G. Falkowski. 1998. Compensatory changes in Photosystem II electron turnover rates protect photosynthesis from photoinhibition. Photosynthesis Res. 58: 259-268. Bongi, G. and S.P. Long. 1987. Light-dependant damage to photosynthesis in olive leaves during chilling and high temperature stress. Plant Cell Environ. 10: 241-249. Cullen, J.J. and M.R. Lewis. 1988. The kinetics of algal photoadaptation in the context of vertical mixing. J. Plankton Res. 10: 1039-1063. Demmig-Adams, B. and W.W. Adams. 1992. Photoprotection and other responses of plants to high-light stress. Annu. Rev. Plant Physiol., Plant Mol. Biol. 43: 590-626. Doty, M.S. and M. Oguri. 1957. Evidence for a photosynthetic daily periodicity. Limnol. Oceanogr. 2: 37-40. Falkowski, P.G. and C.D. Wirick. 1981. A simulation model of the effects of vertical mixing on primary productivity. Mar. Biol. 45: 289-295. Falkowski, P.G., R. Greene, and Z. Kolber. 1994. Light utilization and photoinhibition of photosynthesis in marine phytoplankton. pp. 407-432. In N.R. Baker and J.R. Bower. [eds.] Photoinhibition of Photosynthesis. Bios Sci. Publ. Bethesda, Maryland, USA. Falkowski, P.G. and J.A. Raven JA. [eds.]. 1997. Aquatic Photosynthesis. Blackwell Science, Oxford, UK. Flöder, S., J. Urable and Z. Kawabata. 2002. The influence of fluctuating light intensities on species composition and diversity of natural phytoplankton communities. Oecologia 133:395-401. Grobbelaar, J.U. 1989a. The contribution of phytoplankton productivity in turbid freshwaters to their trophic status. Hydrobiologia. 173: 127-133. Grobbelaar, J.U. 1989b. Do light/dark cycles of medium frequency enhance phytoplankton productivity? J. Applied Phycology. 1: 333-340. Grobbelaar, J.U. 1994. Turbulence in mass algal cultures and the role of light/dark fluctuations. J. appl. Phycol. 6: 331-335. Grobbelaar, J.U. and C.J. Soeder. 1985. Respiration losses in planktonic green algae cultivated in raceway ponds. J. Plankton Res., 7(4): 497-506. Grobbelaar, J.U., C.J. Soeder, and E. Stengel. 1984. Modelling algal productivity and oxygen production in large outdoor cultures. Spezielle Berichte der Kernforschungsanlage Jülich, Germany, No 282. Grobbelaar, J.U., C.J. Soeder and E. Stengel. 1990. Modelling algal productivity in large outdoor cultures and waste treatment systems. Biomass 21: 297-314. Grobbelaar, J.U., L. Nedbal, V. Tichy and I. Setlik. 1995. Variations in some photosynthetic characteristics of microalgae cultured in outdoor thin-layered sloping reactors. J. appl. Phycol. 7: 243-260.

682 Algal Cultures, Analogues of Blooms and Applications Grobbelaar, J.U., L. Nedbal, and V. Tichy. 1996. Influence of high frequency light/dark fluctuations on photosynthetic characteristics of microalgae photoacclimated to different light intensities and implications for mass algal cultivation. J. appl. Phycol. 8(4-5): 335-343. Hartig, P., J.U. Grobbelaar, C.J. Soeder, and J. Groeneweg. 1988. On the mass culture of microalgae: Areal density as an important factor for achieving maximal productivity. Biomass. 15: 211-221. Herzig, R. and P.G. Falkowski. 1989. Nitrogen limitation in Isochrysis galbana. 1. Photosynthetic energy conversion and growth efficiencies. J. Phycol. 25: 462-471. Jassby, A.D. and T. Platt. 1976. Mathematical formulation of the relationship between photosynthesisand ligh for phytoplankton. Limnol. Oceanogr. 21: 540-547. Jewson, D.H. and R.B. Wood. 1975. Some effects on integral photosynthesis of artificial circulation of phytoplankton through light gradients. Verh. int. Verein Limnol. 19: 1037-1044. Kirk, J.T.O. 1983. Light and photosynthesis in aquatic ecosystems. Cambridge University Press, Cambridge, UK. Kok, B. 1953. Experiments on photosynthesis by Chlorella in flashing light. pp. 63-158. In B. Burlew [ed.] Algal Cultures from Laboratory to Pilot Plant. Carnegie Institution of Washington Publication 600, Washington DC., USA. Kroon, B.M.A., H.A.M. Ketelaars, H.J. Fallowfield and L.R. Mur. 1989. Modelling high rate algal pond productivity and photosynthesis-irradiance parameters. Hydrobiologia 238: 79-88. Laws, E.A., K.L. Terry, J. Wickman and M.S. Challup. 1983. A simple algal production system designed to utilize the flashing light effect. Biotechnol. Bioeng. 25: 2319-2335. Legendre, L., M. Rochet and S. Demers. 1986. Sea-ice microalgae to test the hypothesis of photosynthetic adaptation to high frequency light fluctuations. J. exp. mar. Biol. Ecol. 97: 321-326. Leverenz, J.W. 1987. Chlorophyll content and the light response curve of shade-adapted conifer needles. Physiol. Plant 71: 20-29. Litchman, E. 1998. Population and community responses of phytoplankton to fluctuating light. Oecologia. 117:247-257. Merchuk, J.C. 1988. Shear effects on suspended cells. Advances in Biochem. Eng. Biotech. 44: 65-95. Nedbal, L., V. Tichy, F. Xiong, and J.U. Grobbelaar. 1996. Microscopic green algae and cyanobacteria in high-frequency intermittent light. J. appl. Phycol. 8(4-5): 325-333. Nicklisch, A. and S. Fietz. 2001. The influence of light fluctuations on growth and photosynthesis of Stephanodiscus neoastrea (diatom) and Planktothrix agardhii (cyanobacterium). Arch. Hydrobiol. 151:141-156. Prasil, O., N. Adir and I. Ohad. 1992. Dynamics of photosystem II: mechanisms of photoinhibition and recovery processes. Vol. 11, pp. 295-348. In J. Barber [ed.]. Topics in Photosynthesis, The Photosystems: Structure, Function and Molecular Biology. Elsevier, Amsterdam. Richmond, A, and A. Vonshak. 1978. Spirulina culture in Israel. Arch. Hydrobiol. Beih., Ergebn. Limnol. 11: 274-280. Richmond, A. 2004. Biological principals of mass cultivation. pp. 125-177. In A. Richmond [ed.]. Handbook of microalgal culture: Biotechnology and Applied Phycology. Blackwell Publishing, Oxford. 125-177.


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Sorokin, C. 1957. Changes in photosynthetic activity in the course of cell development in Chlorella. Physiol. Pl. 10: 659-666. Sukenik, A., J. Bennett and P.G. Falkowski. 1987. Light-saturated photosynthesis – limitation by electron transport or carbon fixation. Biochim. Biophys. Acta 891: 205215. Terry, K.L. 1986. Photosynthesis in modulated light: quantitative dependence of photosynthetic enhancement on flashing rate. Biotechnol. Bioeng. 28: 988-995. Vonshak, A., A. Abeliovich, S. Boussiba and A. Richmond. 1982. Production of Spirulina biomass: Effects of environmental factors and population density. Biomass 2: 175. Vonshak, A., G. Torzillo, J. Masojidek and S. Boussiba. 2001. Sub-optimal morning temperature induces photoinhibition in dense outdoor cultures of the alga Monodus subterraneus (Eustigmatophyta). Plant Cell Environ. 24: 1113-1118.


19 Absorption, Fluorescence Excitation and Photoacclimation Egil Sakshaug and Geir Johnsen Dept. of Biology, Norwegian University of Science and Technology, N-7491 Trondheim, Norway

Abstract Among the wide variety of photosynthetic pigments, chlorophyll a plays a central role both as light collector and receiver of energy absorbed by auxiliary chloroplast pigments (fat-soluble carotenoids and chlorophylls, water-soluble biliproteins), and by transferring the energy to the photosystems (PS I, PS II) of the photosynthetic apparatus. Yet some carotenoids protect the photosystems against harmful doses of light. The absorption spectrum of living cells is important input in bio-optical models of primary production and also yields important taxonomic information (the pigment diversity of microalgae dwarfs that of higher plants). The optical signature of living cells, however, is radically different from that of the corresponding pigment extracts because the pigments in living cells are bonded to pigment-specific proteins in the thylakoid membranes of the chloroplast. Moreover, living cells scatter a significant fraction of the incident radiation, introducing grave errors in commonly used methods for obtaining absorption spectra of pigment extracts unless serious counter-efforts are undertaken. In vivo chlorophyll a emission is generated by PS II (+5% from PS I at ordinary temperature). Thus fluorescence excitation spectra differs from absorption spectra by being independent on the photoprotective pigments. Given proper scaling (a non-trivial undertaking), such spectra can serve as proxies for the action spectrum of oxygenic photosynthesis and the spectrum of light available to PS II. As the pigments of living cells are subject to photoacclimation, absorption spectra and fluorescence excitation spectra obtained from shade-acclimated (pigment-rich) cells differ from those from cells acclimated to strong light. One of the reasons for this is stronger self-shading inside and between the

$&$ Algal Cultures, Analogues of Blooms and Applications chloroplasts (the packaging effect) in the pigment-rich cells (especially pronounced in large-celled species). This in effect lowers the Chl a-normalized absorption efficiency. The effect is carried over to the P vs. E parameters a and Ek, in which the absorption spectrum is implicit. In Chl a-normalized a and Ek, however, the largest source of variation nevertheless seems to be the highly variable chlorophyll a content of the cells. This chapter focuses on the above topics with emphasis on studies of laboratory cultures of phytoplankton. Information from such studies is surprisingly sparse compared to the large mass of absorption data from natural waters, yet it is paramount for interpretation of field data in a physiological and productivity context, and for generating more realistic bio-optical models.

INTRODUCTION Photosynthetic eucaryotic plankton are optically active because they possess a variety of pigments bonded to the thylakoids inside the chloroplasts, each pigment with different optical signatures. Also the photosynthetic procaryotes, the Cyanobacteria have their pigments stacked in thylakoid membranes but lack chloroplasts. The distribution of algal pigments is related to the taxonomic group or species in question thus also the optical signature of phytoplankton. Whether derived from living cells or pigment extracts, the optical signature above all is characterized by the predominance of chlorophyll a, which is ubiquitous in photosynthetic organisms. It exhibits a peak in the blue wavelength band, at ~435 nm and in the red wavelength band, at ~676 nm. Other chloroplast pigments absorb light especially in bands in which chlorophyll a absorbs inefficiently. These pigments include the chlorophylls b and c, carotenoids, biliproteins and the UV-absorbing mycosporine-like amino acids (Liaaen-Jensen 1978, Jeffrey 1980, Rowan 1989, Jeffrey et al. 1997, Neale et al. 1998, Bissett et al. 2001). This chapter focuses on results from laboratory cultures of phytoplankton and deals with the optical signature of pigments in living cells, including absorption, the fraction of light received by PS II, and Chl a-specific fluorescence excitation spectra. The bio-optical characteristics in turn are compared with the pigment composition of the cells and are used to evaluate and interpret photoacclimation and photosynthetic parameters. Although a lot of information has been amassed on absorption coefficients in natural waters, bio-optical information from cultures is sparse. In natural waters, bio-optical characteristics are difficult to interpret in a physiological context, partly because natural communities usually comprise several

Absorption, Fluorescence Excitation and Photoacclimation

$&%

quantitatively important species, partly because dead organic matter interferes with the measurements. Earlier reviews on these and similar topics have been presented earlier (Yentsch 1980, Prézelin and Bozcar 1986, Geider and Osborne 1991, Falkowski and Woodhead 1992, Kirk 1994, Falkowski and Raven 1997, Jeffrey et al. 1997, Sakshaug et al. 1997, IOCCG 2000). Both the chlorophylls and the carotenoids are soluble in fat and organic solvents. In contrast, the biliproteins of cyanobacteria, red algae, and cryptophytes (the pink phycoerythrin, for instance) are water-soluble (Rowan 1989). Absorption spectra of extracted mixtures of plant pigment are easy to study with conventional methods, but are not identical to the corresponding spectra of living cells. The main causes of this are that solvents break the bonds of the pigment-protein associations characteristic for living cells and in addition, introduce wavelength shifts and significant changes in the weight-specific absorption coefficients (Johnsen et al. 1994a, b). The pigment-protein complexes in living cells are aligned in assemblages that form Light-Harvesting Complexes (LHCs) in the thylakoid membranes. The LHCs are associated with the two photosystems, PSI and PS II, to which they transfer the energy of captured photons by inductive resonance (Falkowski and Raven 1997, Jovine et al. 1995). It is therefore the LHCs that determine the absorption and light utilization characteristics of living cells (Prézelin and Boczar 1986). Chlorophyll a emits fluorescence in the red wavelength band with a peak emission at 685 nm, which is only 10 nm higher than the red peak of absorption in living cells. In vivo fluorescence arises largely from PS II where oxygen is released. Emission from chlorophyll a associated with PS I contributes only 5% of the total emission from chlorophyll a at ordinary temperature (–2 to 30°C) yet is strong at –196°C (liquid nitrogen) as calculated by Butler (1978) and verified for phytoplankton by Johnsen et al. (1997). Given weak light, phytoplankton can become light-limited and, in the water column and extremely dense cultures worsen the light field for themselves by self-shading. As microalgae absorb light in a spectrally discriminating fashion, they modify the spectral composition of the underwater light field considerably by leaving mainly “junk light”, that is light which is inefficiently absorbed. Thus chromophytes and chlorophytes, absorbing blue and red light efficiently, leave mainly green light. The Cyanobacteria, in contras absorb green and yellow light efficiently and blue light inefficiently, leaving blue light in the water column. The

$&& Algal Cultures, Analogues of Blooms and Applications spectral discrimination by algae on top of that by the water itself affects the spectral composition of the back-radiation from the sea surface to outer space. This in turn forms the basis for studying phytoplankton distribution in the uppermost few meters of the water column over vast areas using satellite or airplane-mounted sensors for ocean color (Fig. 19.1).

Fig. 19.1 Remote sensing of different optical properties in phytoplankton. Left panel: Bloom of the coccolithophorid, Emiliania huxleyi off the coast of Western Norway, characterized by strong scattering originating from the coccolith cover of the cells. SeaWiFS, Goddard Space Flight Center, NASA. Right panel: Bloom of the thecate dinoflagellate, Prorocentrum micans in Oslo Harbor, characterized by the red-brown carotenoid peridinin. Fjellanger-Wideroe/Karl Tangen. Given a priori assumptions about the underwater light field and the temperature-controlled maximum photosynthetic rate, satellite-based data for the chlorophyll a concentration can be used in combination with so-called ‘Photosynthesis versus Irradiance’ (P vs. E) functions to calculate rates of primary production. Although dependent on photoacclimation (adjustments to changes in light on a scale from a few seconds to a few days; Falkowski and Owens 1980, Prézelin and Boczar 1986, Sakshaug et al. 1997), the P vs. E coefficients are treated as parameters (constants) in most models. Some recent models pioneered by Geider et al. (1996), however, do include photoacclimation. Photoacclimation essentially ensures that a change in the growth rate, given a change in irradiance and/or day length, is relatively small (Sakshaug and Holm-Hansen 1986). In fact, many microalgal species can grow at about the same rate for a wide interval around the optimum irradiance (Gilstad and Sakshaug 1990). Yet photoacclimation cannot prevent the growth rate from falling given a sufficiently large decrease in

Absorption, Fluorescence Excitation and Photoacclimation

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irradiance, but the growth rate will decrease with the logarithm of the irradiance rather than linearly (Sakshaug and Andresen 1986).

Absorption in the Sea Given solar elevation >10°, only 3-5% of the light entering the sea is likely to be reflected (Yentsch 1980). Thus, nearly all of the light hitting the sea surface is absorbed. At very low solar elevations, however, reflection losses can be considerable, especially when the surface is calm. The by far largest fraction of the light that enters the water column is absorbed by the water itself, leaving a relatively small fraction to be absorbed by phytoplankton and other optically active components such as colored (non-phytoplankton) matter. In fact, light used for photosynthesis in the water-column is generally 100°C) to allow the polymerization. After mixing the micro algal suspension with the polymer and cooling the temperature is not conducive for algal cell viability. Indeed, several micro algal cultures of technological interest are incubated at temperatures around 15°C, whereas commercial agar polymerises at temperature below 35-40°C. However, due to its ion tropic nature, the mechanical stability of the alginate gel depends greatly on the chelating agents in the culture medium, such as phosphate, a common and major nutrient ion in wastewater (Kierstan and Coughlan 1985). The cation mainly used to chelate Na-alginate is Ca2+, leading to the gel dissolution with phosphate (Robinson et al. 1985, Travieso Còrdoba et al. 1995, Lau et al. 1997). To avoid this drawback, calcium may be replaced by barium (Santos-Rosa et al. 1989b), although Vílchez et al. (2001) showed that beads with barium were not more stable than those with calcium, and may be toxic to photosynthesis (León and Galván 1995, Vílchez et al. 2001). In fact, Vílchez et al. (2001) showed that light-dependent oxygen evolution in Chlamydomonas reinhardtii was 20% lower due to barium toxicity.

&$ Algal Cultures, Analogues of Blooms and Applications Alginate is mainly used for beads but also for screens (Kaya and Picard 1995, Kaya et al. 1996) and for microcapsules (Joo et al. 2001). The low thickness of the latter did not affect the growth of the immobilized micro algae because of the very low diffusion limitation. This resulted in a higher cell concentration in the capsule than those of immobilized micro algae in beads and several times higher than that of free cells. These same authors demonstrated that micro encapsulation was more effective for the cultivation in the bubble column bioreactor than in a stirrer tank. Other polymers such as carrageenan (Chevalier and de la Noüe 1985, 1988, Cañizares et al. 1994, Travieso et al. 1996, Lau et al. 1997 and 1998) and agar films (Lebeau et al. 1999 and 2000) are rather fragile and may constitute a drawback in continuously stirred reactors (Rao and Hall 1984). Included in the usage are agarose (Robinson and Wilkinson 1994), disks of sponge of Luffa cylindrica for removal of Ni from aqueous solution by Chlorella sorokiniana (Akhtar et al. 2003a, 2003b), quartz microfibre filter for monitoring herbicides (Védrine et al. 2003), palm petiolar felt-sheath (Iqbal and Zafar 1997), a cheap reticulate fibrous network found as a dense patting around the bases of young leaves of palm, and dried seaweeds with Sargassum (Ulva) (da Costa and de França 2003) and Chitosan, a low-cost natural product which was found in the skeletons of shrimp, crabs and other shellfish (de la Noué and Proulx 1988, Kaya and Picard 1996), which displayed a long term performance. Chitosan (2% m/v) improved the stability of the gel towards phosphate salts (1 M Na3PO4) and sodium pyrophosphate was the best chelating agent that did not affect the diffusion of inorganic nutrients (Kaya and Picard 1996). Concerning artificial matrices, polyurethane foams are not expensive compared with natural polymers (Travieso et al. 1996) and are versatile because of their broad range of porosities and mechanical properties (Bailliez et al. 1985). Although the polymer itself shows no cytotoxic activity (Rao and Hall 1984), it is made of toxic water-soluble isocyanates that may be encountered in media and was shown to reduce photosynthetic activity and hydrocarbon production of Botryococcus braunii (Gudin and Thomas 1981, Bailliez et al. 1988). Conversely, in the case of Anabaena azollae, the filaments penetrated readily into the pores of the foams, revealing no cell toxicity (Kannaiyan et al. 1994). Immobilization of B. Braunii in polyurethane cubes showed better behavior as support for the immobilization of micro algae than sodium alginate (Travieso Còrdoba et al. 1995) and Chetsumon et al. (1993) described a photo bioreactor with anchored polyurethane foam blocks which showed best results in both

Biotechnology of Immobilized Micro Algae

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growth and antibiotic production when compared with free-floating polyurethane foam blocks. Polyvinyl foams were also used (Gil and Serra 1993, Garbisu et al. 1994) for Phormidium immobilization, with similar results as compared with polyurethane matrices.

Immobilization Onto a Matrix (Adsorption, Covalent Coupling) Loss of viability has been observed when using covalent immobilization (Jirku 1999). For this reason, adsorption is the most widely used immobilization technique. It is about preformed polyurethane and polyvinyl foams (Orvain et al. 2003) with an adsorption, which was enhanced following nitrogen starvation. Floating coal fly ash blocks were used (Matsumoto et al. 2000) with cells of the cyanobacterium Synechococcus sp. NKBG 040607 to achieve 2 ¥ 108 cells per cm2. Zeolitic product (Zecer-56) was used for immobilization of Chaetoceros mulleri (Lopez-Ruiz et al. 1999), filter paper disks with Scenedesmus subspicatus, which was immobilized by filtration and then covered with a thin alginate layer (Frense et al. 1998). The aerial cyanobacteria Trentepohlia aurea produced itself exopolymers and formed a bio film (Abe et al. 2003), hollow fibres (Chen and Johns 1996, Ferreira et al. 1998), supports of silica types, such as sepiolite and montmorillonite (Minguez-Mosquera et al. 1991), and removable membrane (Naessens et al. 2000). In the latter, Chlorella vulgaris was immobilized on removable membrane placed in front of the tip of an optical fibre bundle inside a micro cell for the detection of some herbicides.

PHOTO BIOREACTORS DEVOTED TO IMMOBILLIZED MICRO ALGAL CELLS Cultivation of a small number of micro algal species (e.g. Chlorella, Dunaliella, Spirulina) can be performed in shallow open ponds, since they grow in highly selective conditions with little contamination by other micro algae and protozoa. Most of the micro algal species require, nevertheless, axenic and well controled conditions. Besides the synthesis of high-value products from micro algae for applications in pharmacy and cosmetics appears to be feasible only on the basis of closed photo bioreactors, and with good manufacturing practices recommended by ISO and EC guidelines (Pulz 2001). As a consequence, for future applications open ponds systems seem to have a lower innovative potential than closed systems.

&& Algal Cultures, Analogues of Blooms and Applications Due to the high production costs of micro algae, considerable improvement to reactor productivity is required. Heterotrophic algal cultures have gained more and more attention in the last few years. Indeed, heterotrophic cultivations eliminate the requirement of light and hence offer the possibility of enhancing cell density, by using high cell density culture techniques, as currently used in fermentation, as well as less sophisticated bioreactors (Zaslavskaia et al. 2001). For controling productivity of photosynthetic cultures, the main major factors are availability and intensity of the light, turbulence and inorganic carbon (Robinson et al. 1986a, Tredicci 1999). Pulz showed (2001) that closed photo bioreactors of a tubular configuration or of the compact-plate type as well or a combination of these two designs may prove better as the distribution of light would be even over an enlarged surface area. In addition, vertical arrangements of horizontal running tubes or plates seems to be preferred for reasons of light distribution and appropriate flow. While several studies concerning free algal cells aimed to test new designs of photo bioreactors (Richmond 2003), most of the photo bioreactors for culturing immobilized microorganisms are derived from standard bioreactors. Mallick (2002) while reviewing biotechnological potential of immobilized algae for wastewater treatment, made an inventory of five types of bioreactors with immobilized cells: i) fluidized bed bioreactors, ii) packed bed bioreactors, which suffer from the difficulties of poor light penetration, mixing and gaseous flux, iii) parallel-plate bioreactors which are suitable for the effective utilization of sunlight, but are difficult to construct, iv) airlift bioreactors (the stream of air or other gases that are pneumatically promoted may lead to improve the exchange between the gas phase and the medium. Conversely, the main problem of this procedure is the low cell stocking density and, v) hollow fibre bioreactor. These bioreactors are physically stable and can be cleaned and restocked with new biomass. Cell leakage is not a major problem with hollow fibre cartridges. With immobilized Chlorella vulgaris Travieso et al. (1992) reported the best growth with fluidized bed compared to packed bed for sewage treatment. However, Travieso et al. (1999) and Santos-Rosa et al. (1989) showed the contrary. Airlift bioreactor, which was also tested in this latter study yielded promising results for ammonium production from nitrite, using Chlamydomonas reinhardtii entrapped in barium alginate beads. Although most of the immobilized-cells photo bioreactors were not designed for this purpose, to optimize the culture conditions with immobilized cells several variables were studied. These include: i) the cell density (Robinson and


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Wilkinson 1994), ii) the flow rate (Travieso et al. 1992, Robinson and Wilkinson 1994, Aksu et al. 1999, Khattar et al. 1999), iii) the medium recycle (Robinson and Wilkinson 1994, Travieso et al. 1996), iv) the CO2 supply (Ferreira et al. 1998, Chetsumon et al. 1993, Kaya et al. 1996), and the light intensity (Travieso et al. 1992, Lebeau et al. 2002). Few photo bioreactors were especially designed for growing immobilized photosynthetic cells. Two photo bioreactors were applied to marennine production by H. ostrearia (Fig. 22.3a). The first one tubular in shape (Fig. 3a), consisted of an agar gel layer. The mixture of agar and cell suspension was introduced in a mould contained by an inner glass cylinder directly in contact with the barrel of optical fibre. An outer cylinder with a stainlesssteel grid was in contact with the culture medium (Junter et al. 1989, Lebeau et al. 2000). Cultures of H. ostrearia were characterized by the sedimentation of micro algae at the bottom of the culture vessel during incubation, and grown under limited light (Rincé et al. 1999). Immobilization of algae in this tubular agar layer provided a surface: volume ratio up to 3.3 m2/m3, which facilitates exchange of nutrients and harvesting light. The second photo bioreactor (Fig. 22.3b) consisted of a small plate module, which was sandwiched between two neon lights; it had an inlet and an outlet in diametrically opposite positions (Lebeau et al. 2002). The module was filled either with an agar gel layer of 3 mm thickness or with a monolayer of alginate beads. Fluidization was prevented by means of a grid placed over the beads. Cell growth and marennine production were higher with alginate beads than that obtained with the agar layer, which was partly explained by the better solute diffusion in the former case. This second photo bioreactor seemed more convenient at the time of scaling-up, as compared with the first one because several modules could be connected in parallel without any change the volume of each module. Chetsumon et al. (1993) described also a new photo bioreactor with anchored polyurethane foam strips with immobilized Scytonema sp., which yielded better growth and antibiotic production than the free-floating polyurethane foam blocks. For wastewater treatment Kaya and Picard (1995) suggested an original new photo bioreactor with immobilized Scenedesmus bicellularis on parallel alginate screens and starved in moist air. Starvation in air required low light energy consumption and the light source was not requisite in the wastewater reactor due to the short incubation time of immobilized cells. To remove CO, Travieso et al. (2002) suggested a pilot scale bioreactor (BIOALGA), which is a rotary bio film reactor for immobilization of algae. The rotary drum covered by a polyurethane band immobilized micro algae on the surface.

Top of the photo bioreactor

& Algal Cultures, Analogues of Blooms and Applications

Nutrient medium

} Culture medium (10 liters) Optical-fibre barrel Tubular agar gel layer (0.55 l)

External microporous membrane (optional)

Optical-fibre barrel

a

Front view

Marennine solution

Peristaltic pump a

The extremity of optical fibre is inserted in a hole made in the barrel

(a) Taps Nutrient medium

Neon lighting

Marennin solution Agar or alginate beads layer

Neon lighting

Recirculation loop

Peristaltic pump

(b) Fig. 22.3 Photo bioreactors used for marennine production synthesized by the diatom Haslea ostrearia. The micro algae were immobilized i) in an agar gel layer, tubular in shape, introduced into a photo bioreactor with internal illumination (from Lebeau et al. 2000) or ii) in an agar gel layer or a monolayer of alginate beads introduced in a plate-photo bioreactor with a tangential flow (from Lebeau et al. 2002).


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It is important to note that in all photo bioreactors, the diffusionreaction-growth approach must be taken into account to optimize the process. Lin et al. (2003) used a kinetic model to describe inorganic carbon utilization by micro algae bio film, which could be employed for the design of a flat-plate photo bioreactor to treat CO2 by microbial bio film in a fossilfuel power plant. The model includes fundamental mechanisms of diffusive mass transport and biological reaction of inorganic carbon by micro algal bio film. For instance, it is considered that the use of hollow fibre modules lead to an increase in the biomass productivity of Nannochloropsis because of a higher mass transfer and a recirculation of unused CO (Carvalho and Malcata 2001). Ferreira et al. (1998) showed an enhancement of the CO2 transfer per area of membrane surface by a factor 10, in comparison to operation with silicone tubing. The biomass of Isochrysis galbana obtained in hollow fibre photo bioreactor was approximately three-fold greater (Burgess et al. 1993) than biomass productivity obtained using free cells culture (Molina et al. 1992).

ARTIFICIAL IMMOBILIZATION APPLIED TO MICRO ALGAE: A SURVEY OF THE BIOTECHNOLOGICAL APPLICATIONS New trends in biotechnological applications of micro algae There is a growing interest in micro algae due to their potential utilization in various fields. The idea of utilizing algal culture systems for improving the well being of humans and for solving environment problems related to anthropic activities has been laudable and an important objective of applied phycological research. However many researchers have realized that some of these ideas for example: solving malnutrition, reduction of global carbon dioxide by using large scale micro algal culture systems, production of industrial energy (bio-hydrogen) appear to be economically unrealistic (Borowitzka and Borowitzka 1992, Borowitzka 1995, Richmond 2003). Nevertheless for several interesting and promising specific purposes such as aquaculture, production of bioactive compounds and health products, improvement of water quality, intensive micro algal cultures can play an important role. Micro algal biotechnology as we understand here is a mixture of the ‘old’ and the ‘modern’ biotechnologies because some of these organisms have been utlilized in a wide range of industries for example from large scale cultures to characterization of biologically active compounds and to genetic engineering. The recent progress of biotechnologies by using molecular biology protocols continue to advance our understanding of

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production of bioactive molecules, their biosynthesis and physiological functions. Not many marine micro algae are studied and their application in biotechnology should be explored. This line of research holds a bright future.

Synthesis of metabolites While micro algae-mediated chemicals are numerous (Vílchez et al. 1997a), such as amino acids, lipids, pharmaceuticals, pigments, polyols, carbohydrates, polysaccharides, primary alcohols and vitamins, a few biotechnological applications utilize immobilized micro algal cells. Micro algal immobilization was applied to polysaccharides synthesis, which are used as viscosifiers (the largest proportion of algal polysaccharides used commercially), flocculation agents and lubricants. Porphyridium cruentum is the only species used for this application (Thépenier et al. 1985, Robinson et al. 1986b; Iqbal and Zafar 1997) and was immobilized in matrices such as polyurethane foams. Robinson et al. (1986b) reported production of considerable amount of sulphate capsular polysaccharides for over 17 months. In the field of energy, hydrocarbons were produced with Botryococcus braunii (Bailliez et al. 1983, 1985, Frenz et al. 1989, Singh 2003) and with Botryococcus protuberans (Singh 2003) immobilized in alginate beads or polyurethane foams. Besides, Frenz et al. (1989) succeeded in recovering hydrocarbon via short contact with hexane of algae, without impairing growth or hydrocarbon production. Micro algae were also used for hydrogen production, e.g., immobilized cells of Anabaena (Kayano et al. 1981, Brouers and Hall 1986). While this micro algae naturally produce ammonium from atmospheric nitrogen, this cyanobacteria can also be adapted to photo produce H2, by growing the cells in nitrogen-free cultures (Rao and Hall 1984). Ammonium was produced by Chlamydomonas reinhardtii immobilized in Ba-alginate (Brouers and Hall 1986, Santos-Rosa et al. 1989a,b). Polyols were synthesized as glycerol, which is needed as raw material in different industries such as explosives, cosmetics, lubricants, and printing ink production. Its biosynthesis is an alternative to chemical processes from petrol and to saponification of fats, and was performed using Dunaliella salina (Thakur and Kumar 1999) and Dunaliella parva (Hatanaka et al. 1999) immobilized in natural polymers, which are able to accumulate high rate of glycerol, an important osmoregulatory solute, in response to rapid change of salt concentration. Chlamydomonas reinhardtii, which was immobilized in alginate beads (León and Galván 1995), differs in its response from


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Dunaliella as it excretes the majority of glycerol into the medium. Thus, it is advantageous both from a biotechnological point of view, and because of the lower cost of recovery. In the field of health applications, it was reported a-keto acids synthesis from amino acids by immobilized Anacystis and Chlorella (Wikstrøm et al. 1982). These molecules may be used directly in pharmaceuticals for therapy on chronic uraemia or as synthons, or as substrate for subsequent biotransformation, e.g, with L-amino dehydrogenase (reductive amination). Morphine from externally fed-codeine was also reported with immobilized cells of Spirulina platensis in Ca-alginate beads (Rao et al. 1999). More recently, Lebeau et al. (1999, 2000, 2002) studied the synthesis of mareninne, a blue-green pigment of biotechnological interest, with in vitro and in vivo activities against human lung cancer and anti-HIV effects (Carbonelle et al. 1999). Artificial immobilization is of particular interest for benthic diatoms that are sensitive to disturbance and whose products of interest may be naturally over expressed during the benthic stage where algal cells are immobilized in their own exopolysaccharides.

Wastewater treatment and cleaning-up processes Micro algae in immobilized forms are widely used for cleaning-up purposes. In a review Mallick (2002) emphasizes that 50% of all reports dealing with immobilized micro algae involve their use in wastewater treatment, including metal removal. It may be partly explained by the practical limitation in algal treatment systems, which is harvesting or separation of algal biomass from the treated water discharge. Thus, cell immobilization may circumvent this harvest problem. Another important aspect is that ammonium, nitrate, nitrite, orthophosphate or phosphorus which are important regulatory nutrients for the growth of micro algae can be removed from wastewater by algal treatment systems. The micro algae mainly utilized in the removal of nitrogen, phosphorus and metals in the order of decreasing efficacy (Mallick 2002): Chlorella, Scenedesmus, Phormidium, Anabaena, Chlamydomonas, Dunaliella and Spirulina. In general with immobilized micro algal cells, removal of phosphate was a much slower process than that for nitrogen and that a gradual decline occurred in efficiency from the first to subsequent cycles (Mallick 2002). In the case of metal removal, metal biosorption is influenced by pH, temperature and cell density but identical biosorption by alive and dead cells may be also noticed. Finally, choice of the matrix also influences the volatilization rate for Hg removal (Mallick 2002).

& " Algal Cultures, Analogues of Blooms and Applications Micro algae were also immobilized for the mineralization of organic compounds, e.g., decolorization of dye by immobilized cells of Chlorella pyrenoidosa was reported by Huang et al. (2000), immobilization and degradation of butyltin by Chlorella emersonii (Zhang et al. 1998), an organotin compound with a large spectrum of industrial applications, e.g., wood and textile preservatives, fungicides and pesticides and antifouling paint on ships and fishing equipment.

Biosensor and bioassay in ecotoxicology studies Biosensors using whole cells are of ecotoxicity interest because these bioreceptors are the targets of numerous toxins. Micro algae-based biosensors devoted to herbicide monitoring in water are based on photosynthesis inhibition and use the chlorophyll fluorescence as the measurable signal. Biosensors immobilizing Scenedesmus subspicatus (Frense et al. 1998) and Chlorella vulgaris (Naessens et al. 2000, Védrine et al. 2003) were developed to indirectly monitor photosystem II (PS II) inhibiting herbicides such as endrine, atrazine, simazine, isoproturon, diuron and other target site photosynthesis with alachlor and glyphosate (Naessens et al. 2000). In the latter case, it could also be used for determination of Dinitroo-Cresol, which is a poor inhibitor of PSII electron transport. Detection of PSII herbicides was achieved at sub-ppb concentration level. Védrine at al. (2003) reported that optimization of the bioreceptor was achieved with a continuous cultivation technique, which facilitates the determination of optimal operating conditions and results in an improvement of pollutant detection. One of the advantages of immobilized cells is the maintenance of the cell viability over a long period without significant loss of fluorescence properties. Thus, from a practical point of view, the immobilized cells preparations can be supplied to the operators who use biosensor in field investigations (Frense et al. 1998). Also on the basis of the photosynthetic activity, Naessens and Tran-Minh (1999) constructed a biosensor, immobilizing Chlorella, capable to respond to a toxic volatile organic compound such as perchloroethylene present in the form of aerosol. This biosensor allowed direct determination in the gas phase without previous trapping the pollutant in a solution. For determination of toxic metals, Durrieu and Tran-Minh (2002) tested a biosensor using inhibition of alkaline phosphatase present on the external membrane of Chlorella vulgaris. Micro algal cells were immobilized on removable membranes placed in front of the tip on an optical fiber bundle.


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Assessment of eutrophication of running waters also led to in situ biomonitoring using alginate immobilized Scenedesmus subspicatus (Twist et al. 1997). It was demonstrated that micro algal cells entrapment heightened the rate of response to growth and increased nutrient supply compared to free cells, although this effect was not significant at environmentally relevant nutrient concentrations. While evaluating the potential of an in situ algal bioassay for routine toxicity estimates of potentially contaminated estuarine environments, Santos et al. (2002) reported large differences between the laboratory and in situ responses in the case of immobilized cells of Phaeodactylum tricornutum. It was attributed to temperature and light conditions that were less favorable for algal growth in the field.

Micro algal stock culture management It has been shown by several authors that micro algae immobilization allows storage of cells for a longer period without any loss of viability compared to free cells. Tamponnet et al. (1985) showed that immobilized cells of Euglena gracilis retain 90% of their chlorophyll content after 3 months of incubation, whereas free cells displayed pheophytinization only after 7 days, which is comparable to Haslea ostrearia cultures (unpublished data). Diatoms such as H. ostrearia (Lebeau et al. 1998), Amphidinium carterae, Chaetoceros ceratosporum, Emiliana huxleyi, Phaeodactylum tricornutum, Skeletonema costatum, Thalassiosira pseudonana, (Hertzberg and Jensen 1989), Nitzschia obtusa (Kannapiran et al. 1997), and others micro algae such as Scenedesmus subspicatus (Frense et al. 1998), Euglena gracilis (Tamponnet et al. 1985), Pseudanabaena galeata (Romo and Perez-Martinez 1997) and Isochrysis galbana (Chen 2003) were stored in alginate matrices for several months. Chen (2001) showed that cells of Scenedesmus quadricauda were alive and maintained their physiological activities even after three years of storage. Hirata et al. (1996) first applied encapsulation and dehydration for cryopreservation of Dunaliella tertiolecta, using alginate encapsulation and dehydration. Optimization of dehydration was a critical factor in ensuring survival, and the method was subsequently applied to a wider range of micro algae, including cyanobacteria. Chen (2001, 2003), Hertzberg and Jensen (1989) and Lebeau et al. (1998) cultivated the immobilized micro algae at 4°C with reduced light or in absolute darkness, with or without a liquid medium, to reduce the metabolic activity during the storage. In the case of entrapped S. quadricauda (Chen 2001) and Isochrysis galbana (Chen 2003), cells reduced their pyrenoid size

& $ Algal Cultures, Analogues of Blooms and Applications after long-term storage compared with the normal free-living cells. When the algal cells were recultivated, the pyrenoids were reconstructed which suggests that entrapped algal cells remained alive probably by consuming the reserve of pyrenoids during storage. After the period of storage, the ability of cells to serve as inocula is crucial in culture studies. Lebeau et al. (1998) demonstrated that the cells were viable following two months storage, depending on the culture medium. To recover the immobilized micro algae from the matrix, two strategies were suggested. As a consequence of algal growth when the beads are incubated under favorable conditions after the period of storage, the cells release from the matrix and multiplied in liquid medium. In H. ostrearia, the release/ immobilized cells ratio was higher at 15°C as compared with 4°C and was also higher with alginate by comparison with agar gel layer (Lebeau et al. 1999). In the case of alginate, the cell release also depended on the concentration and type of multivalent cation (Dainty et al. 1986). Another strategy consists of dissolving the alginate beads to release all the cells immobilized into the matrix. Lebeau et al. (1998) showed that the contact between the cells and EDTA or Na2-citrate should not exceed 45 to avoid viability loss. Long-term storage of immobilized cells has various practical applications. In Haslea ostrearia (Lebeau et al. 1998), practically no growth was observed during the storage at low temperature and irradiance. Due to the absence of growth, it allowed cultivation of diatom cells with identical size, contrary to what occurs in standard stock management where the size of the cells decreases at each division. Chen (2003) observed that the storage technique reduces the cost of producing live food (Isochrysis galbana) and to control the water quality in clam culture. In the case of biosensors, the immobilized cell preparations can be supplied to the operators who use biosensor in field investigations (Frense et al. 1998). Compared to free cell cultures that need periodic sub culturing, immobilized cell cultures because of their low maintenance save time and money.

Co-immobilization The aim of the co-immobilization is mainly to ensure the close proximity of two or more micro organisms (Gonzalez and Bashan 2000) for two main purposes: i) co-immobilization of micro algae and plant-growthpromoting bacteria (PGPB) with growth effect on the host because of producing plant hormones, mainly auxin (Gonzalez and Bashan 2000, Gonzalez-Bashan et al. 2000) and ii) oxygen generated by micro algae for the


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accompanying micro-organisms involves compound transformation (Chevalier and de la Noüe 1988, Khang et al. 1988, O’Reilly and Scott 1995). In the first case, bacteria are considered as ‘helpers’ for micro algae, unlike those in the second. For plant-growth-promoting bacteria (PGPB), the most widely studied examples are the co-immobilization of micro algae and Azospirillum brasilense, a member of the group of plant rhizosphere, which promotes the growth of many terrestrial plants and crop yield with an enormous potential in agriculture. Although Chlorella vulgaris is not known to harbor any beneficial bacteria its association with Azospirillum sp. a terrestrial PGPB, resulted in a significant increase in the growth of the micro algae (Gonzalez and Bashan 2000). These authors demonstrated that C. vulgaris and A. brasilense colonized the same cavities inside the immobilization matrix. The micro algae tended to concentrate in the more aerated periphery while the bacteria colonized the entire bead. As a consequence, these bacteria may also prove useful for increasing the production of micro algae that have important applications in aquaculture, environmental cleanup, and human food and animal feed industries. De-Bashan et al. (2002b) tested this co-immobilization for the removal of ammonium ions, which was higher compared to immobilization of the micro algae alone. For example following two days incubation 91% of the ammonium was eliminated in the co-immobilized culture compared to only 59% in the immobilized. Significant removal of phosphorus occurred only in semi-continuous culture conditions and decreased strongly after the first cycle. More recently de-Bashan et al. (2004) showed that C. vulgaris or Chlorella sorokiniana was co-immobilized with the type strain A. brasilense Cd, which was considered as a micro algae growth-promoting bacterium because of promoting many growth parameters of C. vulgaris, such as cytology, lipids and pigment production. As a consequence, co-immobilization was more efficient in the removal of up to 100% ammonium, 15% nitrate, 36% phosphorus within six days compared to 75% ammonium, 6% nitrate and 19% phosphorus with the micro algae alone. Another field of application for co-immobilization of micro algae with bacteria or fungi concerns oxygen supply and to overcome oxygen diffusion problems. It was applied with Bacillus subtilis, a strictly aerobic organism that was co-immobilized with Scenedesmus obliquus (Chevalier and de la Noüe 1988) for the synthesis of alpha-amylase. These authors observed increase of the alpha-amylase activity up to 26% than with immobilized B. subtilis alone. O’Reilly and Scott (1995) reported several other examples

& & Algal Cultures, Analogues of Blooms and Applications with positive effect of co-immobilization: C. vulgaris with the bacterial species Providencia for a-keto isocaproic acid from leucin, Chlorella pyrenoidosa with Gluconobacter oxydans for the production of dihydroxyacetone from glycerol, and C. pyrenoidosa with the fungus Cephalosporium acremonium for the production of Cephalosporin C. Other co-immobilization systems were tested of which the mutual benefits are not always explained. Mak and Trevan (1988) co-immobilized urease with Chlorella emersonii to enhance urea hydrolysis, as a source of nitrogen and carbon. As a consequence, higher biomass levels were attained and lower cell leakage and homogeneous cell growth in the Ca-alginate beads. Finally, Huang et al. (2000) showed that Penicillium co-immobilized with C. pyrenoidosa led to enhanced dye degradation, as a consequence of the anabolic activity of the micro algae. Co-immobilization led to a decolorization rate of 92.9% in 4 days as compared to 80.9% with micro algae immobilized alone during the same time.

PERFORMANCES OF FREE AND IMMOBILIZED MICRO ALGAL CELL SYSTEMS The above presentation shows immobilization in micro algae can lead to changes in metabolism, in some cases, with higher yield or productivity as compared to free cells. This increase concerns nitrogen and phosphorus removal from wastewater contains Cd, Mn or Zn (Table 21.3), which may be explained by the protective effect of the matrix. Degradation of organic compounds such as butyldin or dye also enhanced, and immobilization has a positive effect on the cell survival during long-term storage with higher viability. In the case of the synthesized metabolites, higher production with immobilized cells was observed for hydrocarbon, hydrogen, nitrogen bio fertilizers, glycolate and glycerol. It is of interest that identical or even lower productions were observed in respect of pharmaceuticals, propanediol and keto-acids. It is not clear or conclusive whether immobilization increases or decreases the rate of heavy metal removal. Higher removal rates were observed for Cr and Cu and lower for Ni, while it depends on the study for Cd (Table 21.3). In the case of heavy metal removal, it is more difficult to conclude the effect of immobilization on the cells since the matrices can strongly biosorb heavy metals. In general, performances of free and immobilized cell systems are rarely compared on the same basis, which does not allow concluding easily about the most appropriate culture technique. To gain direct access to the


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microbial metabolism, the specific production or productivity is an accurate variable, which may be used to compare several kinds of photo bioreactors with free or immobilized cells. Unfortunately, at present most studies are limited to understand the functioning of the photo bioreactors, i.e., yield, volume and productivity.

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&!$ Algal Cultures, Analogues of Blooms and Applications Santos-Rosa, F. and F. Galván. 1989. Ammonium photoproduction by free and immobilized cells of Chlamydomonas reinhardtii. Appl. Microbiol. Biotechnol. 31: 55-58. Santos-Rosa, F., F. Galván and J.M. Vega. 1989a. Biological viability of Chlamydomonas reinhardtii cells entrapped in alginate beads for ammonium photoproduction. J. Biotechnol. 9: 209-220. Santos-Rosa, F., F. Galván and J.M. Vega. 1989b. Photoproduction of ammonium by Chlamydomonas reinhardtii cells immobilized in barium alginate: a reactor feasibility study. Appl. Microbiol. Biotechnol. 32: 285-290. Schügerl, K. 2000. Integrated processing of biotechnology products. Biotechnol Adv 18: 581-599. Scott, C.D. 1987. Immobilized cells: a review of recent literature. Enzyme Microb. Technol. 9: 66-73. Shirai, Y., K., Hashimoto, H. Yamaji and H. Kawahara. 1988. Oxygen uptake rate of immobilized growing hybridoma cells. Appl. Microbiol. Biotechnol. 29: 113-118. Shuler, M.L. 1985. Immobilized whole cell bioreactors: potential tools for directing cellular metabolism. World Biotech. Rep. 2: 231-239. Singh Y. 2003. Photosynthetic activity, and lipid and hydrocarbon production by alginateimmobilized cells of Botryococcus in relation to growth phase. J. Microbiol. Biotechnol. 13: 687-691. Tam, N.F.Y., Y.S. Wong and C.G. Simpson. 1998. Repeated removal of copper by alginate beads and the enhancement by micro algae. Biotechnol. Tech. 12: 187-190. Tampion, J. and M.D. Tampion. 1987. Immobilized cells: Principles and Applications. Cambridge University Press, Cambridge, UK. Tamponnet, C., C. Gudin and D. Thomas. 1985. Euglena gracilis cells entrapped in calcium alginate gel. Physiol. Plant. 63: 277-283. Thakur, A. and H.D. Kumar. 1999. Use of natural polymers as immobilizing agents and effects on the growth of Dunaliella salina and its glycerol production. Acta Biotechnol. 19: 37-44. Thépenier, C., C. Gudin and D. Thomas. 1985. Immobilization of Porphyridium cruentum in polyurethane foams for the production of polysaccharide. Biomass 7: 225-240. Travieso Còrdoba, L., E.P. Sànchez Hernàndez and P. Weiland. 1995. Final treatment for cattle manure using immobilized micro algae. I. Study of the support media. Resour. Conserv. Recy. 13: 167-175. Travieso, L., F. Benitez, P. Weiland and R. Dupeyron. 1992. Sewage treatment using immobilized micro algae. Bioresour Technol. 40: 183-187. Travieso, L., F. Benitez, P. Weiland, E. Sànchez, R. Dupeyron and A.R. Dominguez. 1996. Experiments on immobilization of micro algae for nutrient removal in wastewater treatments. Bioresour. Technol. 55: 181-186. Travieso, L., R.O. Cañizares, R. Borja, F., Benitez, A.R. Dominuez, R. Dupeyron and Y.V. Valiente. 1999. Heavy metal removal by micro algae. Bull. Environ. Contam. Toxicol. 62: 144-151. Travieso, L., F. Pellón, F. Benítez, E. Sánchez, R. Borja, N. O’Farrill and P. Weiland. 2002. BIOALGA reactor: preliminary studies for heavy metal removal. Biochem. Eng. J. 12: 87-91. Tredicci, M.R. 1999. Bioreactors, photo. pp. 395-419. In M.C. Flickinger and S.W. Drew [eds.]. Encyclopedia of bioprocess technology: fermentation, biocatalysis and bioseparation, vol 1. Wiley, New York, USA.


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Trevan, M.D. 1980. Immobilized enzymes: An Introduction and Applications in Biotechnology. John Wiley and Sons, New York, UK. Twist, H., A.C. Edwards and A. Codd. 1997. A novel in-situ biomonitor using alginate immobilised algae (Scenedesmus subspicatus) for the assessment of eutrophisation in flowing surface waters. Wat. Res. 31: 2066-2072. Urrutia, I., J.L. Serra and M.J. Llama. 1995. Nitrate removal from water by Scenedesmus obliquus immobilized in polymeric foams. Enzyme Microb. Technol. 17: 200-205. Vandanjon, L., N. Rossignol, P. Jaouen, J.M. Robert and F. Quemeneur. 1999. Effects of shear on two micro algae species. Contribution of pumps and valves in tangential flow filtration systems. Biotechnol. Bioeng. 63:1-9. Védrine, C., J.C. Leclerc, C. Durrieu and C. Tran-Minh. 2003. Optical whole-cell biosensor using Chlorella vulgaris designed for monitoring herbicides. Biosens. Bioelectron. 18: 457-463. Vijayalakshmi, M., A. Marcipar, E. Segard and G.B. Broun. 1979. Matrix-bound transition metal for continuous fermentation tower packing. Ann. N. Y. Acad. Sci. 326: 249-254. Vílchez, M.J., I. Garbayo, M.V. Lobato and J.M. Vega. 1997a. Micro algae-mediated chemicals production and wastes removal. Enzyme Microb. Technol. 20: 562-572. Vílchez, M.J., I. Garbayo, E. Markvicheva, Galván and R. León. 2001. Studies on the suitability of alginate-entrapped Chlamydomonas reinhardtii cells for sustaining nitrate consumption processes. Bioresour. Technol. 78: 55-61. Vílchez, C. and J.M. Vega. 1995. Nitrite uptake by immobilized Chlamydomonas reinhardtii cells growing in airlift reactors. Enzyme Microb. Technol. 17: 386-390. Vílchez, M.J., J. Vigara, I. Garbayo and C. Vílchez. 1997b. Electron microscopic studies on immobilized growing Chlamydomonas reinhardtii cells. Enzyme Microb. Technol. 21: 45-47. Wada, M., J. Kato and I. Chibata. 1979. A new immobilisation of microbial cells. Immobilised growing cells using carrageenan gel and their properties. Eur. J. Appl. Microbiol. Biotechnol. 8: 241-247. Walsh, P.K. and D.M. Malone. 1995. Cell growth in immobilization matrices. Biotechnol. Adv. 13: 13-43. Webb, C., H. Fukuda and B. Alkinson. 1986. The production of cellulase in a spouted bed fermentor using cells immobilized in biomass support particles. Biotechnol. Bioeng. 28: 41-50. Wikstrøm, P., E. Swajcer, P., Brodelius, K. Nilson and K. Mosbach. 1982. Formation of á-keto acids from amino acids using immobilized bacteria and algae. Biotechnol. Lett. 4: 153-158. Wong, M.H. and D.C. Pak. 1992. Removal of Cu and Ni by free and immobilized micro algae. Biomed. Environ. Sci. 5: 99-108. Zaslavskaia, L.A., J.C. Lippmeier, C. Shih, D. Ehrhardt, A.R. Grossman and K.E. Apt. 2001. Trophic conversion of an obligate photoautotrophic organism through metabolic engineering. Science 292: 2073-2075. Zhang, L., G. Huang and Y. Yu. 1998. Immobilization of micro algae for biosorption and degradation of butyltin chlorides. Art. Cells. Blood Subs. And Immob. Biotech. 26: 399-410.


23 From Microscope to Magnet: Probing Phytoplankton Population Structure and Physiology Using Mammalian Antibodies Louis Peperzak1 and Sonya T. Dyhrman2 1

National Institute for Coastal and Marine Management/RIKZ, P.O. Box 8039, NL-4330 EA Middelburg, The Netherlands, 2 Biology Department MS #32, Woods Hole Oceanographic Institution Woods Hole, Massachusetts 02543, USA

Abstract Mammalian antibodies, developed from mono-specific algal cultures, have been used with great success to study the distributions, and to a lesser extent the physiology, of phytoplankton in natural environments. This chapter discusses the applications and methodological considerations of antibodies in algal research and includes a comprehensive list of the currently available poly- and monoclonal phytoplankton antibodies. Several technologies to detect antibody-probes, including epifluorescence microscopy, flow cytometry, and solid phase cytometry are also outlined. As the use of antibodies in algal research is constantly evolving, this chapter also highlights new developments such as isolating intact cells from complex natural samples using a magnet: immunomagnetic bead separation. Magnetically isolated cells can be used to assess the physiological status of a population with traditional techniques in vitro. As an alternative, the theoretically feasible simultaneous application and combination of a speciesspecific probe and physiological probes in a single sample remains a future challenge. Clearly, the use of mammalian antibodies for phycological studies is a rich and ever expanding area of research.

INTRODUCTION Phycological applications of mammalian antibodies primarily focus on identification of target species or focus on studies of physiologically

&" Algal Cultures, Analogues of Blooms and Applications important proteins. Studying the abundance, population structure, and physiology of phytoplankton in natural waters is challenging because field populations are often complex mixtures of several algal species and of other living and dead particles. In contrast, mono-specific algal cultures contain just one species, although culture conditions may not, and probably do not, adequately reflect the natural environment. Mammalian antibodies have proven very useful in studies of field populations. For example, the need to count and identify small prokaryotic and eukaryotic algae, and to distinguish toxic algae from morphologically comparable non-toxic species in natural samples, has spurred the development of antibody probes since the late 1980s. Competition for nutrients and light is a fundamental process in algal ecology and one of the major factors determining the structure of phytoplankton communities. To measure algal physiology, antibodies have been produced for elucidating the location and abundance of enzymes such as Rubisco (carbon-physiology) and alkaline phosphatase (phosphorusphysiology). With newly evolving antibody applications it is now possible to use species-specific antibodies coupled to magnetic beads, to perform standard physiological tests on algae separated from their natural environment and isolated from their competitors, thereby solving the problem of testing phytoplankton under ‘unnatural’ culture conditions. A number of good reviews on antibodies are already available. One of the first books exploring the applicability of antibody probes is by Yentsch et al. (1988). Polyclonal and monoclonal probes for detecting harmful algae were reviewed by Vrieling and Anderson (1996) and a compilation, in conjunction with flow cytometric techniques, was given by Peperzak et al. (2000). Application protocols for antibody probes can be found in Scholin et al. (2003). Here we focus on the state of the field with particular focus on antibody techniques or technologies that have been applied to label and detect phytoplankton algae and to probe their physiological status.

Antigen-Antibody Binding and Detection Background When a mammal such as a mouse or a rabbit is infected with a microorganism, for instance a bacterium, one of the animal’s immune system responses is the production of antibodies. These antibodies are small proteins that bind specifically to molecules on the infecting bacterium’s outer cell wall. The molecules or sites to which the antibody binds are called antigens or epitopes. It is this immune response that is exploited in the production of polyclonal and monoclonal antibodies for phytoplankton

From Microscope to Magnet

&"

research. In this case, instead of a bacterium, phytoplankton cells from a mono-specific algal culture are injected into a mammal: usually a rabbit for the production of polyclonal antibodies and a mouse when monoclonal antibodies are required. When cells are injected into an animal its immune system is confronted with a large number of epitopes, for example the different antigens on the algal cell wall. Some of these antigens may be part of structural or functional parts of the cell wall that are not species-specific. Other antigens may be specific for a group of taxonomically related algae and some may react with an antigen found on only one algal species. Although all of these antibodies may be important, such species-specific antibodies can be especially valuable tools in phytoplankton research.

Polyclonal antibodies Polyclonal antibodies are found in the serum of immunized animals and they are relatively easy and inexpensive to produce. Furthermore, polyclonals bind to several epitopes giving strong binding complexes. Polyclonal antibodies (polyclonals – PAb) are often produced in rabbits because these animals yield more serum than a mouse. A disadvantage is that the animal can only be bled a few times after which this source of antibodies is retired. The production of polyclonal antibodies to mammalian and algal proteins is also successful in chickens (Gassman et al. 1990, Dyhrman and Palenik 2001). In this case, the animal does not have to be bled because polyclonal antibodies can be isolated from the chicken eggs. Furthermore, chicken protocols for antibody production typically have 2-10 fold higher antibody yields, in a shorter period of time, compared to standard rabbit protocols (Narat 2003). Despite their obvious utility, polyclonal chicken antibodies are an underused resource. The reasons for this are unclear, but may largely be a function of historical preferences, and a lack of information about chicken antibodies (Narat 2003, and references therein).

Monoclonal antibodies Monoclonal antibodies (monoclonals – MAb) are produced in a more complicated way. For example, an immunized mouse is sacrificed and its spleen cells are hybridized (fused) with carcinoma cells so that immortal spleen-carcinoma cells are formed. Single hybridoma colonies are isolated and cultured so that each new culture originates from one single cell. These hybridomas can be cultured endlessly in flasks and the antibodies are

&"


secreted into the culture fluid. Each hybridoma cell only makes one specific antibody, resulting in a continuous supply of specific monoclonal antibodies. A drawback is that all hybridomas have to be tested until one is found that produces the desired antibody. Producing monoclonals involves an ingenious procedure but it is, therefore, also more costly then making polyclonals. Moreover, screening the antibodies for the desired specificity can be time consuming. When, however, large amounts of antibodies are needed, as in phytoplankton monitoring programs, the cost and time to produce them could outweigh the problem of low yield associated with polyclonals. Although mice are the primary source of monoclonals it is important to again point out that there are other options. Namely, there has been recent success in developing chicken monoclonals to antigens from organisms such as limpets, humans, and cows (Narat 2003, and references therein).

Secondary antibodies and reporter systems Once a polyclonal or monoclonal antibody is produced, a convenient method of detection must be chosen. In the case of fluorescence-based detection, a fluorescent reporter molecule is typically attached to the antibody. One way of doing this is direct linkage of the antibody and the reporter. A more widely used method involves linking the antibody to the reporter with a secondary antibody (Fig. 23.1). Secondary antibodies are made by injecting a primary antibody, which usually belongs to the IgG or IgM class of antibodies (IgY in the case of chickens), into other animals such as goats or sheep. This leads to commercially available secondary antibodies such as Goat-Anti-Mouse IgG (GAM). Also readily available are secondary antibodies conjugated to fluorescent molecules (e.g. fluorescein isothiocyanate, FITC), enzymes (e.g. horseradish peroxidase, HRP), gold particles or to magnetic beads (Fig. 23.1). In other words, once a suitable MAb or PAb has been found, there are numerous ways to link that primary antibody, and hence the algal cell of interest, to a reporter molecule or paramagnetic bead. Of these detection options, fluorescent reporters are the most commonly used in phycology research. Fluorescent reporters are widely used because of their high quantum yield and narrow wavelength emission compared to enzymatic color reactions. However, a problem that is regularly encountered in fluorescence techniques is autofluorescence. Algae can be especially challenging in this regard because chlorophyll-containing cells emit a red fluorescence when excited with blue or green light. Accessory pigments may enhance the


&"!

a. Direct fluorescence

Target cell b. Indirect fluorescence Primary antibody Primary Ab with fluor c. Enhanced fluorescence or magnetic bead attachment

Secondary Ab with fluor Secondary Ab with biotin Streptavidin with fluor

or

Streptavidin with magnetic bead

Fig. 23.1 Immunolabeling procedures. Direct method (a): the species-specific primary antibody conjugated to a fluorescence reporter molecule (fluor) is attached directly to the target cell. In the indirect method (b) the fluor is conjugated to a secondary antibody that binds to the primary antibody-target cell complex. Instead of a fluor, the secondary antibody may carry gold particles, an enzyme or magnetic beads. The enhanced method (c) takes advantage of the strong binding of streptavidin with biotin. Multiple biotin molecules on the secondary antibody bind streptavidin. Streptavidin is coupled to the reporter, e.g. fluors or magnetic beads (e.g. Fig. 23.3). problem by emitting orange or far-red fluorescence and cell walls can fluoresce in the green wavelengths. As such, investigators typically select fluorescent reporters that emit at wavelengths other than autofluorescent cell compounds. In addition, various fluorescent reporters are now commercially available that differ in quantum yield, sensitivity to pH, excitation and emission bandwidth and self-quenching. For instance, Oregon Green, BODIPY FL or Alexa Fluor 488 may replace widely used FITC. Different reporters and also different filter settings of the optical system can reduce interference by autofluorescence. A further option involves bleaching autofluorescence by filtered sunlight or ‘cool’ halogen lamps (Kingsley et al. 2001, Peperzak, unpublished). Even if autofluorescence is not a problem, a low number of epitopes may prohibit the binding of a sufficient number of fluorescent antibodies to yield a suitably high signal. There are several ways to increase the number of emitted photons per epitope. One is the application of a secondary biotinylated antibody (Vrieling et al. 1993b): one secondary antibody molecule has several biotin moieties capable of binding an avidin or streptavidin molecule. The avidin or streptavidin can subsequently be linked to a fluorescent marker in the third labeling step (Fig. 23.1). The second method is the enzymatic-induced enhancer TSA (Tyramid Signal

&"" Algal Cultures, Analogues of Blooms and Applications Amplification). In this example, the enzyme horseradish peroxidase is first bound immunologically to an epitope where it activates fluorescenttyramid molecules. These activated molecules bind rapidly to proteins near the epitope thereby increasing the number of fluorescent reporters (e.g., fluorescein). TSA enhancement has been used, for instance, in labeling mammalian tissue using up to three different antibodies (Wang et al. 1999), labeling bacteria associated with dinoflagellates (Biegala et al. 2002), but not with antibodies in whole cell assays as yet. Another method involves the use of quantum dots. Quantum dots (QDs) are highly quantum-specific highintensity nanocrystals and their use was anticipated several years ago (Peperzak et al. 2000). QDs are preferably excited with short wavelength irradiance (UV) but they emit fluorescence in a narrow band at high, normally used wavelengths (e.g. FITC-green). A disadvantage is that if UV instead of more common blue or green excitation light is used, new filter sets for microscopes or flow cytometers have to be installed. However, the high quantum yield of QDs and the fact that UV is used to excite them, leading to reduced sample autofluorescence, should provide a much higher signal: noise ratio then traditional fluorochromes. QDs conjugated to streptavidin for use with biotinylated antibodies have only recently become commercially available, but they show good potential for phycological applications where autofluorescence is a primary concern.

Detection methods There are numerous detection methods available that suit different targets and applications including microscopy and cytometry. Among several ways to detect labeled cells, cell organelles, or functional sites (Fig. 23.2), microscopic inspection of immunogold labeling has the advantage of detecting very small sub-cellular components although it cannot be used to rapidly process large numbers of cells. Perhaps most common is fluorescence microscopy of labeled samples on a filter support (epifluorescence microscopy or EFM). EFM is quite useful for counting cells and for taxonomic identification of field samples, however, quantitative measurements of label intensity requires image analysis. Image analysis on many cells or samples can be laborious. Alternatively, a flow cytometer is able to speedily count and measure labeled cells suspended in fluid. Flow cytometry (FCM) can be used for rapidly counting species-specifically labeled cells in field samples although it can be difficult to identify target cells in natural samples when non-target particles may have autofluorescence, e.g. overlapping with FITC emission (Peperzak et al.


Immunofluorescent marker

Cell

&"#

Optical: EFM SPC FCM EM EFM CLSM

Cell count Size Surface intensity Intracellular location Intensity Chemical:

Immunomagnetic bead

FCM–s IMBS

Chemistry Physiology

Fig. 23.2 Immunolabeling detection methods for cells. An immunofluorescent cell can be examined optically with epifluorescence microscopy (EFM), solid phase cytometry (SPC) or flow cytometry (FCM). Intracellular labeled organelles or molecules can be visualized using immunogold or immunofluorescence by electron microscopy (EM), EFM or Confocal Laser Scanning Microscopy (CLSM). Fluorescence intensity indicates epitope quantity. When the cell is labeled with immunomagnetic beads it can be separated (IMBS) from contaminating particles and its chemistry and physiology can be measured in vitro using standard techniques. Alternatively, immunofluorescent cells can be separated using FCM-sorting. For practical reasons, all immunolabels in this figure are depicted as direct methods. 1998). Solid phase cytometry (SPC) is a new technique that combines some of the qualities of each of the previously described methods. In SPC cells are immobilized on a membrane filter, probed and scanned by a laser beam. SPC has the advantage of rapidly counting whole filters with even low cell numbers and simultaneously measuring label intensity. The position of each cell on the filter is stored so that re-examination of cells with a microscope is possible (Mignon-Godefroy et al. 1997, Lemarchand et al. 2001, West et al. 2002, West 2004).

Application: Species Detection and Physiological Analysis Species detection There is a rich history of studies using antibodies to identify and detect marine algae. The application of antibodies to label phytoplankton cells becomes necessary when species in natural samples lack morphological detail. Often, they are either very small or they resemble non-target species so closely that misidentification with bright field microscopy is an unacceptable risk. An example of small cells in marine waters includes cyanobacteria of the genus Synechococcus (£ 1 mm). Due to their small size

&"$ Algal Cultures, Analogues of Blooms and Applications and characteristic autofluorescent accessory pigments, either phycoerythrin or phycocyanin, cyanobacterial groups are fairly easily identified and counted via a flow cytometer. Genetic and physiological differences among isolated Synechococcus strains were reported in the 1980s but microscopic identification to the strain/species level was not possible due to a lack of morphological distinction. After Fliermans and Schmidt (1977) used immunological techniques to identify freshwater cyanobacteria, the approach quickly moved to marine populations of cyanobacteria (Campbell et al. 1983). Recent work with strain-specific antibodies (Toledo and Palenik 2003) has emphasized the physiological heterogeneity between strains and how they segregate in the water column. For example, the abundance of Synechococcus strain CC9605 was identified off the coast of California using a whole-cell assay with polyclonal antiserum (Toledo and Palenik 2003). This strain was preferentially found in shallow depths, suggesting that Synechococcus strains are not uniformly distributed (Toledo and Palenik 2003). Small eukaryote cells belonging to the pelagophytes and the prasinophytes were the next targeted for antibody-based studies (Anderson et al. 1989, Shapiro et al. 1989, see Table 23.1). The small -2 mm- pelagophyte Aureococcus anophagefferens is difficult to discriminate against other species when its abundance is low. Using a polyclonal antibody Anderson et al. (1989) succeeded to detect A. anophagefferens at less then 10 cells per ml. Hiroishi et al. (1988) followed this work with monoclonal antibodies for the large but morphologically comparable harmful raphidophytes Chattonella antiqua and Chattonella marina. The study of harmful algae inspired the production of many other polyclonal and monoclonal antibodies (Table 23.1). Because algal-specific antibodies are not available commercially they should be obtained directly from their scientific manufacturer (Table 23.1). In addition to antibody-based detection systems, there are several whole-cell, nucleic acid-based detection systems that also show promise for identifying algae of interest. Fluorescent In Situ Hybridization (FISH) of phytoplankton cells with oligonucleotide probes is a promising technique that emerged in the 1990s. Oligonucleotide probes, once the correct sequence has been found, are easy and fast to produce with the same range of fluorescent reporters as for antibodies. However, the necessary cell wall permeabilization for FISH makes it unsuitable for delicate cells (e.g. Fibrocapsa japonica) and in contrast to antibody probes, oligonucleotide probe intensities can be dependent on the growth rate or physiological

culture/Wadden Sea culture culture/Wadden Sea culture/Atlantic Ocean

culture Atlantic Ocean

culture

culture/Atlantic and Pacific oceans, Mediterranean

culture/Japanese coast culture/Gulf of Maine culture/Spanish coast culture/Spanish coast culture/New Zealand coast



Pseudo-nitzschia pungens

Thalassiosira oceanica

Dunaliella tertiolecta

B6125 (unidentified)

Chroomonas salina

Synechococcus spp.

Alexandrium catenella

Alexandrium fundyense

Alexandrium lusitanicum

Alexandrium minutum

Alexandrium minutum

bacillariophyceae

cryptophyceae

cyanophyceae

dinophyceae

chlorophyceae

Culture/Field application

Species

Class

Table 23.1 Marine plankton species identification with immunological techniques

Bates et al. 1993, Vrieling et al. 1996b

PAbFITC

Mendoza et al. 1995 Mendoza et al. 1995, Costas and Lopez-Rodas 1996 Chang et al. 1999

PAbFITC PAbFITC, PAbFITC

MAb

Contd.

Sako et al. 1996

MAbFITC

Aguilera et al. 1996, 1998

Campbell et al. 1987, Campbell 1988, Acosta Pomar et al.1998, Toledo and Palenik 2003

PAbFITC, Alexa 488

beads

Shapiro et al. 1989

Campbell et al. 1994

Shapiro et al. 1989

PAbFITC

PAB

FITC

PAbFITC

Shapiro et al. 1989, Campbell et al. 1994

L. Peperzak and B. Sandee, unpublished

MAbAPC PAb

Bates et al. 1993, Vrieling et al. 1996b

PAbFITC

FITC

Reference

PAb/MAbc


&"%

Class

Table 23.1 Contd.

culture/Japanese coast North Sea culture culture/Spanish coast

culture/central North Sea

Alexandrium tamarense


Cochlodinium polykrikoides

Gymnodinium catenatum

Gymnodiniun mikimotoia

culture culture culture/Spanish coast culture culture culture culture culture culture

Gymnodinium simplex

Gyrodinium sp.

Prorocentrum micans

Prorocentrum lima

Prorocentrum micans

Prorocentrum minimum

Prorocentrum rostratum

Prorocentrum triestinum

North Sea

Gymnodium nagasakiensef

G. mikimotoi

a

G. mikimotoi St. Lawrence Bay

Japanese and Thai cultures


a

culture

Alexandrium tamarense g


Species

FITC

PAb

López-Rodas et al. 1998


PAbFITC FITC


López-Rodas et al. 1998 PAbFITC

PAb


PAbFITC FITC

Vrieling et al. 1993a + b

Mendoza et al. 1995 PAbFITC

PAb

Vrieling et al. 1993b

PAbFITC FITC

Nagasaki et al. 1991

Peperzak et al. 1998/ L. Peperzak and B. Sandee, unpubl.

Blasco et al. 1996

Vrieling et al. 1994, 1995a, 1996a

Mendoza et al. 1995

Cho and Costas 2004

Peperzak et al. 1998

Sako et al. 1996

Adachi et al. 1993

MAbFITC

MAb

FITC,APC

MAb

FITC

MAbFITC

PAbFITC

PAb

FITC

MAbFITC,b

MAb

MAb

Taylor and Lewis 1995

PAbFITC FITC

Reference

PAb/MAbc

Contd.

&"& Algal Cultures, Analogues of Blooms and Applications

culture Atlantic Ocean

culture North Sea/Atlantic Ocean

culture/ North and Wadden Sea culture

Micromonas pusilla

Pycnococcus provasolii (W4823)

Emiliania huxleyi

Emiliania huxleyi type A and B

Chattonella antiqua

Chattonella marina

Shapiro et al. 1989, Campbell et al. 1994

Shapiro et al. 1989 Shapiro et al. 1989, Campbell et al. 1994

Shapiro et al. 1989, Campbell et al. 1994 van Bleijswijk et al. 1991

Hiroishi et al. 1988, Vrieling et al. 1995b Hiroishi et al. 1988

PAbFITC PAbFITC PAbFITC PAbFITC MAbFITC MAbFITC

López-Barreiro et al. 1998

PAbFITC

PAb

Anderson et al. 1989, 1993

PAbFITC FITC

Reference

PAb/MAbc

b

present name Karenia mikimotoi, all are immunologically identical to Gyrodinium aureolum and Gymnodinium nagasakiense unsuccesfully applied in field samples c PAb / MAb = Polyclonal / Monoclonal Antibody with fluorophore in superscript: FITC=conjugated using Fluorescein Isothiocyanate; APC = AlloPhycoCyanin d EPICS‚ = Coulter, FACS‚ = Becton Dickinson, OPA = Optical Plankton Analyser (RIKZ), EurOPA = European Optical Plankton Analyser (RIKZ) e Texas Brown Tide organism f MAb GN-89 did not label a Portuguese strain of G. cf. nagasakiense (Gyrodinium cf. aureolum) g several strain-specific MAb’s

a

raphidophyceae

prymnesiophyceae

prasinophyceae

culture/Atlantic Ocean

Pelagococcus subviridis

Aureoumbra lagunensis USA SE coast

USA NE coast

Aureococcus anophagefferens

pelagophyceae e


Species

Class

Table 23.1 Contd.


&"'

&# Algal Cultures, Analogues of Blooms and Applications status of the cells. For example, cells that have stopped growing may have very low fluorescence intensity (Anderson et al. 1999, Peperzak et al. 2001). For the diatom genus Pseudo-nitzschia a way to restore fluorescence intensity is to spike the cells with nutrients and incubate the sample for 24 under semi-natural conditions before fixing and probing (Peperzak et al. 2002). Another detection system worth highlighting is in situ PCR. This method involves PCR-based amplification of a nucleic acid target sequence inside permeabilized cells (Hodson et al. 1995). With subsequent color or fluorescence detection of the PCR product, individual cells can be identified with a microscope (Hodson et al. 1995). This mitigates signal problems, but would still be difficult to implement with fragile cell types.

Physiological probe development In contrast to the ‘trial and error’ method for finding a species-specific antibody, the production of physiological probes is performed in a more targeted fashion, first by identifying a target protein of interest and then by isolating this target protein for antibody production. Target proteins can be identified using assorted techniques. In some cases investigators have a priori knowledge of a particular protein of interest, but more often, they need to screen algal cultures for protein targets that are regulated in the desired manner. In general, the presence or absence, or the change in abundance or activity of a physiological marker (e.g. a key enzyme in a metabolic pathway), is measured using standard molecular biology methods. For example, cellular protein composition due to environmental changes can be assessed by homogenizing the cells and separating the proteins based on their molecular weight with sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). Protein expression patterns in the gel can be visualized with a wide variety of sensitive stains (e.g. silver staining). Additional cell fractionation, protein labeling and protein separation and detection techniques can increase sensitivity and aid screening of protein expression patterns. For example, 2-dimensional gel electrophoresis (2-DE) combines a first dimension separation based on protein isoelectric point with a second dimension separation using SDS-PAGE. The increased resolution of 2-DE relative to other separation mechanisms has been used to study protein expression in freshwater algae such as Chlamydomonas (Hippler et al. 2001). Another mechanism for detailed screening of protein expression patterns involves tagging cell-surface associated proteins with biotin. Palenik and Koke (1995) used the biotin-containing derivatizing reagent succinimidyl-6(biotinamido) hexanoate to tag cell-surface proteins of the prymnesiophyte


&#

Emiliania huxleyi and then looked for proteins that were regulated by either nitrogen or phosphorus availability. These marker proteins, tagged with biotin, were visualized after SDS-PAGE on Western blots using avidin-HRP. In this way, a nitrogen-regulated protein (a N-stress biomarker) was identified that was absent during exponential growth or under phosphate-limitation but was present under nitrogen-limitation and during growth on urea (Palenik and Koke 1995). The biotinylation technique can also be applied to other important marine groups such as diatoms and dinoflagellates (Palenik and Koke 1995, Dyhrman and Palenik 1997, Dyhrman and Palenik 2003). Once proteins of interest have been identified they need to be purified, directly sequenced for antibody development, or tested for cross-reactivity to existing commercially available antibodies using Western blots. If antibodies are not already available, the proteins of interest can be prepared by SDSPAGE, 2-DE, electroelution, or through other purification and separation mechanisms and then used to immunize a mouse or rabbit. A SDS-PAGE based approach was used by Scanlan et al. (1993) to raise a polyclonal antibody to a protein (PstS) that is synthesized by the cyanobacterium Synechococcus when grown under phosphorus-limiting conditions. The antibody showed reactivity with PstS of both Synechococcus and Prochlorococcus, as such, antibody probing of culture and field populations for PstS expression can identify cells from these genera that may be experiencing phosphorus stress (Scanlan et al. 1997; Scanlan and Wilson 1999). This is a very important tool considering the important role picocyanobacteria play in low phosphorus environments such as the Sargasso Sea, and the North Pacific Subtropical Gyre (Scanlan and Wilson 1999). In another example, polyclonal antibodies have also been used to examine phosphorus stress in the marine dinoflagellate Prorocentrum minimum. Using the biotin tagging approach discussed above, Dyhrman and Palenik (1997, 2001) identified, purified and raised specific, polyclonal antiserum to a putative alkaline phosphatase that is up-regulated in the dinoflagellate P. minimum under low phosphorus conditions. Application of this immunoprobe in the field indicated that the population dynamics of this harmful species in Narragansett Bay (USA) were influenced by phosphorus supply (Dyhrman and Palenik 2001).

Physiological probe application Biochemical techniques using cell homogenates are commonly used to study protein expression patterns between nutrient-limited and nutrientreplete phytoplankton cultures (Scanlan and Wilson 1999). However, in

&#


natural samples, when there is a need to identify the phytoplankton species, or when the localization of a marker protein is to be studied, a method is needed that leaves the cell wall and organelles intact. The classical approach is to fix phytoplankton cells, embed them in a resin, and to make ultra thin sections with a microtome. The sections are treated with a primary antibody followed by a secondary -immunogold- antigen. Using a transmission electron microscope the localization of the gold particles reveals the molecules to which the primary antigen has bound (Figs. 23.1 and 23.2). This immuno-localization technique has been used to identify the pigment phycoerythrin, the nitrogen-fixing enzyme nitrogenase, and the CO2binding enzyme RUBISCO inside individual algal cells (Janson et al. 1995 a and b, Table 23.2 references). When phytoplankton cells can be permeabilized, allowing the primary and secondary antibody to enter the cell and its organelles, a whole cell assay can be developed. A great advantage is that the cells can then be identified under a microscope, or the label intensity of individual cells can be measured by flow cytometry. For example, iron-stress in whole diatom cells has been detected using a polyclonal antiserum against diatom flavodoxin (La Roche et al. 1996). Also, polyclonal antiserum to a highaffinity phosphate binding protein was used to detect P-stress in whole, single Synechococcus cells, both in algal cultures and in natural samples (Scanlan et al. 1997). However, just as with oligonucleotide probes, the need for permeabilization excludes species with delicate cell walls from intracellular antibody labeling. One way to meet this challenge is to target proteins associated with the surface of the cell so that whole cell labeling can be accomplished without permeabilization. This approach was used to develop a whole cell assay for the dinoflagellate P. minimum by targeting a putative cell-surface alkaline phosphatase (Dyhrman and Palenik 2001). Although, this is not a fragile cell-type, the method serves as model for the development of antibody-based assays with more delicate cells because it targets a surface antigen. La Roche et al. (1999) and Scanlan and Wilson (1999) provide extensive reviews of antibodies and their use in the study of algal physiology, these reviews should be consulted for more detailed information.

Special and Future Applications Detection and physiology: pre- and post-probing In the previous sections the development of species-specific and physiological probes using algal cultures has been described. However, in natural

PCNAc PCNAc c

MAbPE

MAbFITC FITC

PAbFITC FITC

g

no

Phosphorus limitation

Rubisco

PAbno

Nitrogen limitation

PAbFITC PstS phosphatebinding protein

LHCP

dinophyceae

several species

PAb

several species

Nitrate reductase

PAbFITC

Nitrate utilization

Synechoccus, Prochlorococcus

Isochrysis galbana

Isochrysis galbana


flavodoxin

PAbno

Iron limitation

Cyclotella sp.

Thymine dimers

MAbFITC



Ethmodiscus rex

Rhizosolenia sp.

Skeletonema costatum

Thalassiosira weisflogii



Species

DNA damage

p34cdc2

MAbFITC

PAb

PCNAc p34cdc2c c

PCNAc

MAbFITC

PCNA

PCNAc

MAbFITC

Cell cycle

MAb

Directed against

PAb/ MAbd

Measurementa

Table 23.2 Whole cell physiological measurements with immunotechniques

cyanobacteria

prymnesiophyceae

prymnesiophyceae

bacillariophyceae

chlorophyceae

bacillariophyceae

dinophyceae

chlorophyceae

bacillariophyceae

bacillariophyceae

bacillariophyceae

bacillariophyceae

chlorophyceae

chlorophyceae

Class

Scanlan et al. 1993, 1997

Falkowski et al. 1989

Balch et al. 1988

La Roche et al. 1993

Buma et al. 1995

Lin et al. 1996a

Lin et al. 1996b

Lin et al. 1995, 1996a

Reference

Contd.


&#!

bacillariophyceae bacillariophyceae bacillariophyceae pelagophyceae

Thalassiosira weisflogii Thalassiosira oceanica Skeletonema costatum Aureococcus anophagefferens

Rubisco Rubisco Rubisco Rubisco Rubisco Rubisco

PAbFITC

MabFITC

RUBISCO distribution

Toxin

Okadaic acid

prymnesiophyceae

Isochrysis galbana

Rubisco

PAbFITC

RUBISCO concentration

dinophyceae

prymnesiophyceae

Isochrysis galbanae

Prorocentrum lima

bacillariophyceae

Thalassiosira weisflogiib

b

for whole cells after permeabilization probe reacted positively with 38 species of algae using western immunoblotting c Proliferating Cell Nuclear Antigen (PCNA) and p34cdc2 kinase are cell cycle proteins d fluorophore used: FITC = Fluorescein Isothiocyanate, PE = PhycoErythrin, no = no fluorescent probe e best fixative was 96% EtOH f microscopical tests on field samples were performed g light harvesting Chl a/ c/ fucoxanthin complex

a

chlorophyceae


Rubisco

PAbFITC

dinophyceae

Photosynthetic rate

Prorocentrum minimum

Alkaline phosphatase

Class

PAbPE

Species

Directed against

PAb/ MAbd

Measurementa

Table 23.2 Whole cell physiological measurements with immunotechniques

Costas et al. 1995, Abad et al. 2002

Lin and Carpenter 1996a

Lin and Carpenter 1997a+b

Orellana and Perry 1995f

Orellana and Perry 1992

Dyhrman and Palenik 2001

Reference

&#" Algal Cultures, Analogues of Blooms and Applications


&##

samples with many species and possibly different species or group specific physiological adaptations, it is of interest to assess physiological state in a targeted manner. Physiological measurements on species or groups in communities comprised of many species can be accomplished in several ways. The first is pre-probing: the application of a physiological probe on the whole community after which the cells of interest are identified and assayed. As the cells are isolated afterwards, the probe must not interfere with the isolation or identification procedure. The second approach is post-probing: the target cells are isolated first, after which the probe is applied. Such a probe can react either internally or externally (e.g. reveal the activity of an extracellular enzyme), because interfering species have been removed.

Pre-probing Substantial effort has gone into developing pre-probing methods, but specific application in marine phytoplankton is still limited. To test the hypothesis that less-viable cells have the same photosynthetic activity as viable, exponentially growing cells, Veldhuis et al. (2001) used the fluorescent dye SYTOX Green to identify and sort flow cytometrically viable and less-viable cells from phytoplankton cultures that had been incubated with NaH14CO3. SYTOX Green penetrates cells with a compromised cell membrane but it will not cross the membrane of living cells. The sorted cells were collected on filters and their 14C uptake was subsequently measured in a scintillation counter (Veldhuis et al. 2001). Li (1995) incubated samples taken in the North Atlantic Ocean with NaH14CO3 to measure carbon uptake rates. Since the phytoplankton community consisted of three natural flow cytometrically distinguishable groups of picoplankton (£ 2 mm), the 14C uptake by each group could be measured after flow cytometric sorting. Li (1995) and Veldhuis et al. (2001) did not have to use a species-specific probing technique to sort their phytoplankton (sub)-populations. However, coastal waters may be dominated by diatoms, prymnesiophytes and dinoflagellates, groups that cannot easily be identified and separated using standard flow cytometer settings (Hofstraat et al. 1994). In such complex samples a species-specific immunological probe (Table 23.1) could be applied after the physiological probe to measure or sort the target species and compare its values to the bulk phytoplankton. To our knowledge, this pre-probe antibody-sorting technique has not been used with marine phytoplankton. Alternatively, a physiological immunoprobe (Table 23.2) can be applied to a whole sample in order to detect cells with certain capabilities and in a

&#$ Algal Cultures, Analogues of Blooms and Applications second (dual label) step the species of interest could be labeled using a species-specific immunolabel (Table 23.2). Theoretically, species-specific rRNA probes or lectins can also be applied. Physiological immunoprobes have been developed to investigate phytoplankton cell cycles, DNA damage, iron-, nitrogen- and phosphorus-limitation, photosynthesis and toxin concentration (Table 23.2). Most of these studies were conducted in cultures although some probes have been tested in field samples (Peperzak et al. 2000). A special example of pre-probing includes the antibody labeling and quantification of toxic compounds in algal species that are harmful to other biota (Peperzak et al. 2000). Although progress has been made in recent years on labeling the toxin okadaic acid in the dinoflagellate Prorocentrum (Costas et al. 1995, Zhou and Fritz 1994, Abad et al. 2002), at present no wellestablished whole-cell method exists to detect intracellular toxins in field samples. A major problem with automated flow cytometry detection and counting of toxic cells is the highly green autofluorescent cell wall of dinoflagellates. Possible solutions lie in the use of special fluorochromes and appropriate filter sets (Abad et al. 2002). Whole-cell toxin methods will be useful, not only in early-warning systems for toxic algal blooms, but also in the study of genetic, cell cycle and environmental effects on toxin production in algal cells. When physiological probes, such as those highlighted above, are used in field samples, microscope techniques or flow cytometric sorting based on intrinsic fluorescence are typically used to identify cells and to measure probe intensities (Balch et al. 1988, Lin et al. 1995, Orellana and Perry 1995, Lin et al. 1996a, Dyhrman and Palenik 1999). It is important to note that combined applications of species-specific and physiological probes have not yet been achieved.

Post-probing When the use of a physiological immunoprobe in a natural community is not possible (no probe available) or impractical (dual labeling necessary), the order of probing-identifying/sorting may be reversed. In this case the species of interest is identified and sorted first, and then physiological probing using standard biochemical techniques is performed. Although fluorescent immunoprobing and sorting by flow cytometry is possible, it has not been applied widely in field populations. A different approach involves isolating a target population with immunomagnetic bead separation (IMBS, Fig. 23.2). IMBS is used to separate a target population of cells or proteins from a mixed sample. The first phycological application was for isolating toxic dinoflagellates of the genus Alexandrium by Aguilera et al. (1996, 2002)

CMYK From Microscope to Magnet

&#%

CMYK

CMYK

and Costas and Lopez-Rodas (1996) (Table 23.1). The IMBS method involves coupling immunomagnetic beads to a target cell through a species-, or genus-specific antibody (Figs. 23.1 and 23.3). By placing a solution of cell-coupled beads next to a magnet the target population can be separated from the rest of the community. The success of target cell recovery is heavily influenced by the choice of cell preservation (no fixative, acid, etc.), the antibody specificity, the bead type (for example, M-280 [2.8 mm diameter] sheep anti-mouse IgM), or the coupling mechanism (direct versus indirect) (Aguilera et al. 1996, 2002). For example, fewer beads are associated with live cells than with fixed cells, and live IMBS results in 30% of recovered cells with lost integrity, whereas with fixed cells, all the cells maintained their integrity (Aguilera et al. 2002).

Fig. 23.3 A photomicrograph of Alexandrium fundyense labeled with dynabeads (Dyhrman and Anderson 2003) after immunomagnetic bead separation (IMBS) of a live sample. Arrows indicate the 2.8 mm beads. A number of physiological measurements have been tested on IMBS, i.e. post-probing, samples. Aguilera et al. (2002) found that the isolation of live Alexandrium fundyense cells with IMBS did not significantly alter the cell quota of nucleic acids, protein, chlorophyll a, C-2 toxin, saxitoxin, or neosaxitoxin relative to controls. Differences between controls and IMBS samples were found for cellular C, N, and the stable isotopes 13C/12C and 15 N/14N (Aguilera et al. 2002). However, Poulton (2001) found that IMBS influence on the cellular C and N measurements could be mitigated by sieving out the extra uncoupled beads. To date these aforementioned physiological measurements have not been combined with IMBS in field populations of Alexandrium or other genera. This is certainly a fruitful area of further research, and there is one recent example of IMBS and its use with field populations of dinoflagellates. In this case, IMBS was used to concentrate A. fundyense from different surface stations during a field study CMYK

&#& Algal Cultures, Analogues of Blooms and Applications in the Gulf of Maine (Dyhrman and Anderson 2003). Although A. fundyense is the causative agent of PSP in the Gulf of Maine, A. fundyense cells typically only make up a small component of the total phytoplankton community, thus necessitating IMBS post-probing efforts. The application of IMBS resulted in Gulf of Maine samples that were substantially enriched in A. fundyense, with few contaminating cells of other taxa (Dyhrman and Anderson 2003). Subsequently, the IMBS samples were assayed for urease activity to determine the extent to which A. fundyense field populations were hydrolyzing urea. It was found that A. fundyense expressed higher urease activities after a decline in average inorganic nitrogen concentration (Dyhrman and Anderson 2003). As urease is a common enzyme, it is critical to separate the activity of toxic species from the rest of the community. In summary, IMBS utility is a function of species or genus specific antibodies. As the suite of antibodies specific for different phytoplankton groups grows, the application of IMBS to different taxa and research questions will likely expand and provide a valuable tool for studying key species in their natural environment.

CONCLUSIONS Over the last decade a large number of mammalian antibodies have been developed for studies of phytoplankton population structure and physiology. The application of these tools has allowed researchers to study niche adaptation, monitor the dynamics of harmful populations, and characterize the physiology of key species or groups in the field. These advances have improved our understanding of phytoplankton dynamics in marine systems. Clearly, antibodies have expanded our analytical capabilities, and as new approaches such as IMBS and SPC continue to improve and be applied, we anticipate that the use of antibodies in phytoplankton research will grow.

ACKNOWLEDGMENTS SD would like to thank Sheean Haley for helpful comments on the manuscript and also acknowledge the support of the Frank and Lisina Hoch Endowed Fund and the J. Lamar Worzel Assistant Scientist Fund.

REFERENCES Abad, J.P., L. Peperzak, J. Puente, B. Sandee, E. Ruiz, A. Aguilera and I. Marin. 2002. Immunological methods for the detection of okadaic acid in Prorocentrum lima cells, 10th Conference on Harmful Algae, Florida USA. (abstract)


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Acosta Pomar, M.L.C., G. Caruso, T.L. Maugeri, R. Scarfò and R. Zaccone. 1998. Distribution of Synechococcus spp. determined by immunofluorescent assay. J. Appl. Microbiol. 84: 493-500. Adachi, M., S. Sako and Y. Ishida. 1993. The identification of conspecific dinoflagellates Alexandrium tamarense, from Japan and Thailand by monoclonal antibodies. Nippon Suisan Gakkaishi 59: 327-332. Aguilera, A., S. Gonzales-Gil and D.M. Anderson. 1996. Immunomagnetic separation of cells of the toxic dinoflagellate Alexandrium fundyense from natural plankton samples. Mar. Ecol. Prog. Ser. 143: 255-269. Aguilera, A., S.B.A. Gonzalez-Gil, D.M. Keafer and Anderson. 1998. Isolation of the toxic marine dinoflagellate Alexandrium fundyense from unpreserved cultures by magnetic affinity cell sorting pp. 258-239. In B. Reguera, J. Blanco, M.L. Fernandez and T. Wyatt (eds.): Harmful Algae. Xunta de Galicia and Intergovernmental Oceanographic Commission of UNESCO, Vigo. Spain. Aguilera, A., B.A. Keafer, G.H. Rau and D.M. Anderson. 2002. Immunomagnetic isolation of live and preserved Alexandrium fundyense cells: species-specific physiological, chemical, and isotopic analyses. Mar. Ecol. Prog. Ser. 237: 65-78. Anderson, D.M., D.M. Kulis and E.M. Cosper. 1989. Immunofluorescent detection of the brown tide organism, Aureococcus anophagefferens pp213-228 In: E.M. Cosper, V.M. Bricelj and E.J. Carpenter (eds.): Novel Phytoplankton Blooms, Springer Verlag, Berlin, Germany. Anderson, D.M., B.A. Keafer, D.M. Kulis, R.M. Waters and R. Nuzzi. 1993. An immunofluorescent survey of the brown tide chrysophyte Aureococcus anophagefferens along the northeast coast of the United States. J. Plankton Res. 15: 563-580. Anderson, D.M., D.M. Kulis, B.A. Keafer and E. Berdalet. 1999. Detection of the toxic dinoflagellate Alexandrium fundyense with oligonucleotide and antibody probes: variability in intensity with physiological condition. J. Phycol. 35: 870-883. Balch, W.M., C.M. Yentsch, B. Reguera and W. Campbell. 1988. Examining nitrate reduction by phytoplankton with an immunoassay pp263-276 In C.M. Yentsch, F.C. Mague and P.K. Horan (eds.): Immunochemical approaches to coastal, estuarine and oceanographic questions, Springer-Verlag, Berlin, Germany. Bates, S.S., C. Léger, B.A. Keafer and D.M. Anderson. 1993. Discrimination between domoic-acid-producing and non-toxic forms of the diatom Pseudonitzschia pungens using immunofluorescence. Mar. Ecol. Prog. Ser. 100: 185-195. Biegala, I.C., G. Kennaway, E. Alverca, J-F. Lennon, D. Vaulot and N. Somin. 2002. Identification of bacteria associated with dinoflagellates (Dinophyceae) Alexandrium spp. using Tyramid Signal Amplification-Fluorescent In-Situ Hybridisation and Confocal Microscopy. J.Phycol. 38: 404-411. Blasco, D., L. M. Berard-Therriault, Levasseur and E.G. Vrieling. 1996. Temporal and spatial distribution of the ichthyotoxic dinoflagellate Gyrodinium aureolum Hulburt in the St Lawrence, Canada. J. Plankton Res. 18: 1917-1930. Buma, A.G.J., E.J. van Hannen, L. Roza, M.J.W. Veldhuis and W.W.C. Gieskes. 1995. Monitoring ultraviolet-b-induced DNA damage in individual diatom cells by immunofluorescent thymine dimer detection. J. Phycol. 31: 314-321. Campbell, L., E.J. Carpenter and V.J. Ianoco. 1983. Identification and enumeration of marine Chroococcoid cyanobacteria by immunofluorescence. Appl. Environ. Microbiol. 46: 553-559. Campbell, L. and E.J. Carpenter. 1987. Characterization of phycoerythrin-containing Synechococcus spp. Populations by immunofluorescence. J. Plankton Res. 9: 1167-1181.

&$ Algal Cultures, Analogues of Blooms and Applications Campbell, L. 1988. Identification of marine chroococcoid cyanobacteria by immunofluorescence. In: Immunological approaches to coastal, estuarine and oceanographic questions. C.M. Yentsch, F.C. Mague and P.K. Horan (Eds.), SpringerVerlag, New York, U.S.A. Campbell, L., L.P. Shapiro and E.M. Haugen. 1994. Immunochemical characterization of eukaryotic ultraplankton from the Atlantic and Pacific oceans. J. Plankton Res. 16: 35-51. Chang, F.H., I. Garthwaite, D.M. Anderson, N. Towers, R. Stewart and L. MacKenzie. 1999. Immunofluorescent detection of a PSP-producing dinoflagellate, Alexandrium minutum, from Bay of Plenty, New Zealand, New Zealand J. Mar. Fresh. Res. 33: 533-543. Cho, E.S. and E. Costas. 2004. Rapid monitoring for the potentially ichthytoxic dinoflagellate Cochlodinium polykrikoides in Korean coastal waters using fluorescent probe tools. J. Plankton Res.26: 175-180. Costas, E., M.I. San Andrés, S. González-Gil, A. Aguilera and V. López-Rodas. 1995. A procedure to estimate okadaic acid in whole dinoflagellate cells using immunological techniques. J. Appl. Phycol. 7: 407-411. Costas, E. and V. Lopez Rodas. 1996. Enumeration and separation of the toxic dinoflagellate Alexandrium minutum from natural samples using immunological procedures with blocking antibodies. J. Exp. Mar. Biol. Ecol. 198: 81-87 Dyhrman, S.T. and B. Palenik. 1997. The identification and purification of a cell-surface alkaline phosphatase from the dinoflagellate Prorocentrum minimum (Dinophyceae). ). J. Phycol. 33: 602-612. Dyhrman, S.T. and B. Palenik. 1999. Phosphate stress in cultures and field populations of the dinoflagellate Prorocentrum minimum (Dinophyceae) detected by a single-cell alkaline phosphatase assay. Appl. Environ. Microbiol. 34: 3205-3212. Dyhrman, S.T. and B. Palenik. 2001. A single-cell immunoassay for phosphate stress in the dinoflagellate Prorocentrum minimum (Dinophyceae). J. Phycol. 41: 400-410. Dyhrman, S.T. and D.M. Anderson, 2003. Urease activity in cultures and field populations of the toxic dinoflagellate Alexandrium. Limnol. Oceanogr. 48: 647-655. Dyhrman, S.T. and B. Palenik. 2003. A characterization of ectoenzyme activity and phosphate regulated proteins in the coccolithophore Emiliania huxleyi. J. Plank. Res. 25: 1-11. Falkowski, P.G., A. Sukenik and R. Herzig. 1989. Nitrogen limitation in Isochrysis galbana (Haptophyceae) II. Relative abundance of chloroplast proteins. J. Phycol. 25: 471-478. Fliermans, C.B. and E.L. Schmidt. 1977. Immunofluorescence for autecological study of a unicellular bluegreen alga. J. Phycol. 13: 364-368. Gassman, M., P. Thoemmes, T. Weiser and U. Huebscher. 1990. Efficient production of chicken egg yolk antibodies against a conserved mammalian protein. FASEB J. 4: 2528-2532. Hippler, M., J. Klein, A. Fink, T. Allinger and P. Hoerth. 2001. Towards functional proteomics of membrane protein complexes: analysis of thylakoid membranes from Chlamydomonas reinhardtii. The Plant journal: for cell and molecular biology. 28: 595606. Hiroishi, S., A. Uchida, K. Nagasaki and Y. Ishida. 1988. A new method for identification of inter- and intra-species of the red tide algae Chattonella antiqua and Chatonella marina (Raphidophyceae) by means of monoclonal antibodies. J. Phycol. 24: 442-444.


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Hodson, R.E., W.A. Dustman, R.P. Garg and M.A. Moran. 1995. In situ PCR for visualization of microscale distribution of specific genes and gene products in prokaryotic communities. Appl. Environ, Microbiol. 61: 4074-4082. Hofstraat, J.W., W.J.M. van Zeijl, M.E.J. de Vreeze, J.C.H. Peeters, L. Peperzak, F. Colijn and M. Rademaker. 1994. Phytoplankton monitoring by flow cytometry. J. Plankton Res. 16: 1197-1224. Janson, S., E.J. Carpenter and B. Bergman. 1995a. Immunolabeling of phycoerythrin, ribulose 1,5-biphosphate carboxylase/oxygenase and nitrogenase in the unicellular cyanobionts of Ornitocercus spp. (Dinophyceae) Phycologia 34: 171-176. Janson, S., A.N. Rai and B. Bergman. 1995b. Intracellular cyanobiont Richelia intracellularis: ultrastructure and immuno-localisation of phycoerythrin, nitrogenase, Rubisco and glutamine synthetase. Mar. Biol. 124: 1-8. Kingsley, K., K. Carroll, J.L. Huff and G.E. Plopper. 2001. Photobleaching of arterial autofluorescence for immunofluorescence applications. Biotechniques 30: 794-7. La Roche, J., R.J. Geider, L.M. Graziano, H. Murray and K. Lewis. 1993. Induction of specific proteins in eukaryotic algae grown under iron-, phosphorus-, or nitrogendeficient conditions. J. Phycol. 29: 767-777. La Roche, J., P.W. Boyd, M.L. McKay and R.J. Geider. 1996. Flavodoxin as an in situ marker for iron stress in phytoplankton. Nature 382: 802-805. La Roche, J., R.M.L. McKay and P. Boyd. 1999. Immunological and molecular probes to detect phytoplankton responses to environmental stress in nature. In Molecular Ecology of Aquatic Communities. J.P. Zehr and M.A. Voytek (eds.) Hydrobiologia 401: 177-198. Lemarchand, K., N. Parthuisot, P. Catala and P. Lebaron. 2001. Comparative assessment of epifluorescence microscopy, flow cytometry and solid-phase cytometry used in the enumeration of specific bacteria in water. Aquat. Microbiol. Ecol. 25: 301-309. Li, W.K.W. 1995. Composition of ultraphytoplankton in the central North Atlantic. Mar. Ecol. Prog. Ser. 122: 1-8. Lin, S., J. Chang and E.J. Carpenter. 1995. Growth characteristics of phytoplankton determined by cell cycle proteins: PCNA immunostaining of Dunaliella tertiolecta (Chlorophyceae). J. Phycol. 31: 388-395. Lin, S. and E.J. Carpenter. 1996a. An empirical protocol for whole-cell immunofluorescence of marine phytoplankton. J. Phycol. 32: 1083-1094. Lin, S., E.J. Carpenter and J. Chang. 1996b. Detection of p34cdc2- and cyclin B-like proteins in Dunaliella tertiolecta (Chlorophyceae). Mar. Biol. 125: 603-610. Lin, S. and E.J. Carpenter. 1997a. Rubisco of Dunaliella tertiolecta is redistributed between the pyrenoid and the stroma as a light/shade response. Mar. Biol. 127: 521-529. Lin, S. and E.J. Carpenter. 1997b. Pyrenoid localization of Rubisco in relation to the cell cycle and growth phase of Dunaliella tertiolecta (Chlorophyceae). Phycologia 36: 24-31. Lopez Barreiro, T., T.A. Villareal and S.L. Morton. 1998. Development of an antibody against the Texas brown tide (Aureoumbra lagunensis) pp. 263-265. In B. Reguera, J. Blanco, M.L. Fernandez and T. Wyatt (eds.): Harmful Algae. Xunta de Galicia and Intergovernmental Oceanographic Commission of UNESCO, Vigo. Spain. Lopez Rodas, V., A. Aguilera, S. Gonzales-Gil and E. Costas. 1998. Immunogenetical analysis of Prorocentrum species by antibodies pp. 280-282. In B. Reguera, J. Blanco, M.L. Fernandez and T. Wyatt (eds.): Harmful Algae. Xunta de Galicia and Intergovernmental Oceanographic Commission of UNESCO, Vigo. Spain.

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Mendoza, H., V. Lopez-Rodas, S. Gonzales-Gil, A. Aguilera and E. Costas. 1995. The use of polyclonal antisera and blocking of antibodies in the identification of marine dinoflagellates: species-specific and clone-specific antisera against Gymnodinium and Alexandrium. J. Exp. Mar. Biol. Ecol. 186: 103-115. Mignon-Godefroy, K., J-G. Guillet and C. Butor. 1997. Solid phase cytometry for detection of rare events. Cytometry 27: 336-344. Nagasaki, K., A. Uchida and Y. Ishida. 1991. A monoclonal antibody which recognizes the cell surface of red tide alga Gymnodinium nagasakiense. Nippon Suisan Gakkaishi 57: 1211-1214. Narat, M. 2003. Production of antibodies in chickens. Food Technol. Biotechnol. 41: 259267. Orellana, M.V. and M.J. Perry. 1992. An immunoprobe to measure Rubisco concentrations and maximal photosynthetic rates of individual phytoplankton cells. Limnol. Oceanogr. 37: 478-490. Orellana, M.V. and M.J. Perry. 1995. Optimization of an immunofluorescent assay of the internal enzyme ribulose-1,5-bisphoshate carboxylase (Rubisco) in single phytoplankton cells. J. Phycol. 31: 785-794. Palenik, B. and J.A. Koke. 1995. Characterization of a nitrogen-regulated protein identified by cell surface biotinylation of a marine phytoplankton. Appl. Environ. Microbiol. 61: 3311-3315. Peperzak, L., W.H. van de Poll, R. Koeman, E.G. Vrieling and L.P.M.J. Wetsteyn. 1998 Monitoring toxic phytoplankton: comparison of immunofluorescence assays with conventional light microscopical techniques pp. 260-262. In B. Reguera, J. Blanco, M.L. Fernandez and T. Wyatt (eds.): Harmful Algae, Xunta de Galicia and Intergovernmental Oceanographic Commission of UNESCO, Vigo. Spain. Peperzak, L., E.G. Vrieling, B. Sandee and T. Rutten. 2000. Immuno flow cytometry in marine phytoplankton research. Sci. Mar. 64: 165-181. Peperzak, L., B. Sandee, C. Scholin, P. Miller and L. van Nieuwerburgh. 2001. Application and flow cytometric determination of antibody and rRNA probes to Gymnodinium mikimotoi (Dinophyceae) and Pseudo-nitzschia multiseries (Bacillariophyceae). In G.M. Hallegraeff, S.I. Blackburn, C.J. Bolch and R.J. Lewis [eds.]. Harmful Algal Blooms 2000. IOC, UNESCO: 206-209. Peperzak, L., H. Bouma, B. Sandee, A. Hofman and C. Scholin. 2002. Probing Pseudonitzschia: a simple method for rRNA-probing of nitrogen- or phosphate-limited cells tested on 4 species of this diatom genus, 10th Conference on Harmful Algae, Florida USA. (abstract). Poulton, N. 2001. Physiological and behavioral diagnostics of nitrogen limitation for the toxic dinoflagellate Alexandrium fundyense. Thesis. Joint Program in Oceanography/ Applied Ocean Science and Engineering. MIT. MA., USA. Sako, Y., M. Adachi and Y. Ishida. 1993. Preparation and characterization of monoclonal antibodies to Alexandrium species pp. 87-93. In T.J. Smayda and Y. Shimizu (eds.): Toxic Phytoplankton Blooms in the Sea, Elsevier, Amsterdam, the Netherlands. Sako, Y., T. Murakami, M. Adachi, A. Uchida, Y. Ishida, M. Yamaguchi and T. Takeuchi. 1996. Detection of the toxic dinoflagellate Alexandrium species by flow cytometry using a monoclonal antibody pp. 463-466. In T. Yasumoto, Y. Oshima and Y. Fukuyo (eds.) Harmful and Toxic Algal Blooms, IOC-UNESCO, Sendai, Japan. Scanlan, D.J., N.H. Mann and N.G. Carr. 1993. The response of the picoplanktonic marine cyanobacterium Synechococcus PCC7803 to phosphate starvation involves a protein


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homologous to the periplasmatic phosphate-binding protein of Escherichia coli. Arch. Microbiol. 152: 224-228. Scanlan, D.J., N.J. Silman, K.M. Donald, W.H. Wilson, N.G. Carr, I. Joint and N.H. Mann. 1997. An immunological approach to detect phosphate stress in populations and single cells of photosynthetic picoplankton. Appl. Environ. Microbiol. 63: 2411-2420. Scanlan, D.J. and W.H. Wilson. 1999. Application of molecular techniques to addressing the role of P as a key effector in marine ecosystems. In J.P. Zehr and M.A. Voytek (eds.) Molecular Ecology of Aquatic Communities. Hydrobiologia 401: 149-175. Scholin, C., E. Vrieling, L. Peperzak, L. Rhodes and P. Rublee. 2003. Detection of HAB species using lectin, antibody and DNA probes. pp. 131-163 In: G.M., Hallegraeff, D.M. Anderson, and A.D. Cembella, [eds.]. Manual on Harmful Marine Microalgae. UNESCO Publishing Copenhagen, Denmark. Shapiro, L.P., L. Campbell and E.M. Haugen. 1989. Immunochemical recognition of phytoplankton species. Mar. Ecol. Prog. Ser. 57: 219-224. Taylor, J.A. and J. Lewis. 1995. Immunofluorescence of Alexandrium species from the United Kingdom. pp. 89-94. In: P. Lassus, G. Arzul, E. Erard, P. Gentien and C. Marcaillou (eds.): Harmful Marine Algal Blooms, Lavoisier. France. Toledo, G. and B. Palenik. 2003. A Synechococcus serotype is found preferentially in surface marine waters. Limnol. Oceanogr. 48:1744-1755. van Bleijswijk, J., P. van der Wal, R. Kempers, M. Veldhuis, J.R. Young, G. Muyzer, E. de Vrind-de Jong and P. Westbroek. 1991. Distribution of two types of Emiliania huxleyi (Prymnesiophyceae) in the northeast Atlantic region as determined by immunofluorescence and coccolith morphology. J. Phycol. 27: 566-570 Veldhuis, M.J.W., G.W. Kraay and K.R. Timmerman. 2001. Cell death in phytoplankton: correlation between changes in membrane permeability, photosynthetic activity, pigmentation and growth. Eur. J. Phycol. 36: 167-177. Vrieling, E.G., W.W.C. Gieskes, F. Colijn, J.W. Hofstraat, L. Peperzak and M. Veenhuis. 1993a. Immunochemical identification of toxic marine algae: first results with Prorocentrum micans as a model organism. pp. 925-931. In T.J Smayda and Y. Shimizu (eds.): Toxic Phytoplankton Blooms in the Sea, Elsevier, Amsterdam, the Netherlands. Vrieling, E.G., A. Draaijer, W.J.M. van Zeijl, L. Peperzak, W.W.C. Gieskes and M. Veenhuis. 1993b. The effect of labeling intensity, estimated by real-time confocal laser scanning microscopy, on flow cytometric appearance and identification of immunochemically labeled marine dinoflagellates. J. Phycol. 29: 180-188. Vrieling, E.G., L. Peperzak, W.W.C. Gieskes and M. Veenhuis. 1994. Detection of the ichtyotoxic dinoflagellate Gyrodinium (cf.) aureolum and morphologically related Gymnodinium species using monoclonal antibodies: a specific immunological tool. Mar. Ecol. Prog. Ser. 103: 165-174. Vrieling, E.G., W.W.C. Gieskes, M. Rademaker, G.H. Vriezekolk, L. Peperzak and M. Veenhuis. 1995a. Flow cytometric identification of the ichthytoxic dinoflagellate Gyrodinium aureolum in the central North Sea. pp. 743-748. In P. Lassus, G. Arzul, E. Erard, P. Gentien and C. Marcaillou (eds.): Harmful Marine Algal Blooms, Lavoisier, France. Vrieling, E.G., R.P.T. Koeman, K. Nagasaki, Y. Ishida, L. Peperzak, W.W.C. Gieskes and M. Veenhuis. 1995b. Chattonella and Fibrocapsa (Raphidophyceae): first observations of, potentially harmful, red tide organisms in Dutch coastal waters. Neth. J. Sea Res. 33: 183-191. Vrieling, E.G. and D.M. Anderson. 1996. Immunofluorescence in phytoplankton research: applications and potential. J. Phycol. 32: 1-16.

&$" Algal Cultures, Analogues of Blooms and Applications Vrieling, E.G., G.H. Vriezekolk, W.W.C. Gieskes, M. Veenhuis and W. Harder. 1996a. Immuno-flow cytometric identification and enumeration of the ichthyotoxic dinoflagellate Gyrodinium aureolum Hulburt in artificially mixed algal populations. J. Plankton Res. 18: 1503-1512. Vrieling, E.G., R.P.T Koeman, C.A. Scholin, P. Scheerman, L. Peperzak, M. Veenhuis and W.W.C. Gieskes. 1996b. Identification of a domoic acid-producing Pseudo-nitzschia species (Bacillariophyceae) in the Dutch Wadden Sea with electron microscopy and molecular probes. Eur. J. Phycol. 31: 333-340. Wang, G., C.L. Achim, R.L. Hamilton, C.A. Wiley and V. Soontornniyomkij. 1999. Tyramide Signal Amplification method in multiple-label immunofluorescence confocal microscopy. Methods 18: 459-464. West, N., R. Bacchieri and H. Moreau. 2002. Immunofluorescent detection of toxic algal species belonging to the genera Alexandrium and Prymnesium. 10th Conference on Harmful Algae, Florida USA. (Abstract). West, N. 2004. Rapid detection of Toxic Prymnesium sp. using specific monoclonal antibodies and solid phase cytometry. Appl. Environ. Microbiol. Submitted. Yentsch, C.M., F.C. Mague and P.K. Horan. 1988. Immunochemical approaches to coastal, estuarine and oceanographic questions. Lecture Notes on Coastal and Estuarine Studies 25. Springer-Verlag, Berlin, Germany. Yentsch, C.M., F.C. Mague and P.K. Horan. 1988. Immunological approaches to coastal, estuarine and oceanographic questions. Springer-Verlag, New York, U.S.A. Zhou, J. and L. Fritz 1994. Okadaic acid antibody localizes to chloroplasts in the DSPtoxin-producing dinoflagellates Prorocentrum lima and Prorocentrum maculosum. Phycologia 33: 455-461.

Prospects for Paratransgenic Applications

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" Prospects for Paratransgenic Applications to Commercial Mariculture Using Genetically Engineered Algae Ravi V. Durvasula1, Ranjini K. Sundaram1, Scott K. Matthews1, Pazhani Sundaram2 and D.V. Subba Rao3 1

Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, USA 2 Recombinant Technologies, LLC, Science Park, New Haven, CT, USA 3 Bedford Institute of Oceanography, Dartmouth, NS, Canada Corresponding author: Ravi V. Durvasula: [email protected]

Abstract Infectious diseases affecting commercial mariculture remain a leading concern around the world. Outbreaks of bacterial and viral diseases caused by organisms such as Vibrio and White Spot Virus can destroy farmed shrimp and fish operations and result in billions of dollars of loss annually. Use of antibiotics in such settings is fraught with problems of toxicity, evolution of pathogen resistance and cost. Novel approaches to control of these infections are needed. We have developed a strategy for control of disease transmission termed paratransgenesis. This approach involves expression of recombinant molecules that block pathogen transmission by genetically modified bacteria. The bacteria, which are closely linked to host animals, act as a ‘Trojan Horse’ to deliver neutralizing peptides and antibody fragments to the site of pathogen transmission. We have validated this method in the arthropod vectors of Chagas disease. We are currently developing the paratransgenic approach for application to commercial mariculture. In a model system, we have transformed the cyanobacterium, Synechococcus bacillarus, to express a biologically active single chain (VHkappa) antibody fragment. This study is a precursor to future applications that will involve expression of peptides and antibody fragments that target key epitopes of pathogens of commercial mariculture. We aim to develop the paratransgenic strategy in several algal and cyanobacterial species which are important food sources for shrimp, shellfish and farmed fish. We present

866 Algal Cultures, Analogues of Blooms and Applications an overview of (1) commercially important marine algae and their application to mariculture (2) advances in algal biotechnology (3) paratransgenic systems in general and (4) our progress to date with genetically modified S. bacillarus.

INTRODUCTION The application of transgenic technologies to marine and freshwater algae, diatoms and cyanobacteria is a new and rapidly evolving field. Whereas the genetic composition of some of these organisms is well characterized, application of recombinant DNA technologies to generate biologically enhanced or augmented forms is at a nascent stage. Several reports indicate that algae such as Chlamydomonas reinhardtii (Mayfield 2003) and Phaeodactylum tricornutum (Zaslavskaia 2001) may be genetically manipulated to express heterologous proteins. Using a chloroplast transformation system, Mayfield et al. demonstrated the expression of a functional large single-chain (lsc) antibody in C. reinhardtii. The antibody, directed against glycoprotein D of human herpes simplex virus, was produced in solubilized form by the alga and assembled into higher order complexes in vivo. In an earlier study, Zaslavskaia et al. engineered P. tricornutum with either a human (glut 1) or Chlorella (hup 1) glucose transporter gene. The resulting conversion of a photosynthetic autotroph to a heterotroph capable of obtaining exogenous glucose in the absence of light energy was a significant advance in algal biotechnology. The expression of foreign, biologically active molecules by genetically modified algae offers great potential for large-scale and economical production of many proteins of commercial and therapeutic significance. Here, we present another potential application of genetically modified algae and cyanobacteria. We have developed a strategy for control of certain infectious diseases, termed paratransgenesis, which involves delivery of engineered single chain antibodies or immune peptides by genetically modified bacteria. The bacteria, which serve as symbionts or commensals at mucosal sites of pathogen transmission, act as ‘Trojan Horse’ vehicles to export molecules that disrupt cycles of pathogen transmission. We have validated this approach in the reduviid bug vectors of American Trypanosomiasis, or Chagas disease. The symbiotic bacterium, Rhodococcus rhodnii, which resides in the gut lumen of the Chagas disease vector, Rhodnius prolixus, has been transformed to export a variety of molecules that target the parasite, Trypanosoma cruzi, the causative agent of Chagas disease. Delivery of engineered symbiotic bacteria to field populations of R. prolixus,


867

a long-range goal of this program, might reduce transmission of this disease in endemic regions of the world. Other applications of the paratransgenic strategy to control of infectious diseases are also presented in this review. We are currently developing the paratransgenic approach for application to commercial mariculture. In a model system, we have transformed the cyanobacterium, Synechococcus bacillarus, to express a biologically active single chain (VH-kappa) antibody fragment that binds progesterone. This study is a precursor to future applications that will involve expression of peptides and antibody fragments that target key epitopes of pathogens of commercial mariculture. We aim to develop the paratransgenic strategy in several algal and cyanobacterial species which are important food sources for shrimp, shellfish and farmed fish. We present an overview of (1) commercially important marine algae and their application to mariculture (2) advances in algal biotechnology (3) paratransgenic systems in general and (4) our progress to date with genetically modified S. bacillarus. Of the total 40,000 species of micro algae, 4500 are marine species of which 250 are known to grow rapidly leading to either seasonal or atypical bloom formation. A study of the blooms is important not only for their contribution to trophodynamics of the ecosystem but also due to mass mortalities of several biota associated with anoxic conditions resulting from disintegrating organic mass. There are approximately 100 micro-algae that produce specific toxins (Fogg 2002). Of these, about 60 dinoflagellates are known to cause red tides and some produce toxins causing the Diarrhetic Shellfish Poisoning (DSP), Paralytic Shellfish Poisoning (PSP), Neurotoxin poisoning (NSP) and Ciguatera. As some of these algae are consumed either as food or passively filtered and retained by the commercially important shellfish, bioaccumulation of toxins takes place in the marine food web. Red tide organisms are known to cause severe economic losses and set back in human health. Globally the economic losses could be as high as US$ 20 billion and 3.5 to 7 million disability adjusted life-years (Table 24.1), much in excess of those caused by Chagas disease (GESAMP 2001) and are comparable to those caused by epidemics such as malaria, and diabetes. Additionally bacterial and viral contamination of the water may cause considerable mortality to larvae of commercially important species.

Algal Cultures and Applications Microalgae are amenable for culturing in the laboratory in defined culture media. Several media (F, F/2, FE, Aquil, Alk, ASP, ESWA) are utilized to grow batch, continuous steady state, mass cultures of algae in an axenic

868 Algal Cultures, Analogues of Blooms and Applications Table 24.1 Comparison of global annual economic losses from Red tides, contamination and exotic introductions with those due to human diseases Category

Date

Red tides

1989-1990

Puget Sound, WA

1991 1991 1996 1998 1988 1988 1999

Washington Korea Texas Hong Kong Canada Canada Kuwait

Contamination Shellfish Bathing water Exotic introductions Zebra mussel Nemiopsis Human Diseases Tuberculosis Malaria Diabetes

Location

Species Salmon Rainbow trout Oyster Farm fish Oyster Farmed fish PSP, DSP ASP Sea bream and mullet

Loss 106 US $ 4–5 15–20 133 24 32 1.318 1.390 7

10000–20000 1200–2400 Great Lakes Black Sea

1000 250 115000 95000 35000

state (Subba Rao 2002). Selected algal species are brought into culture and utilized in several biotechnological applications (Borowitzka and Borowitzka 1992) namely to study the nature and production dynamics of toxins, as sources of a wide range of chemicals, in health food, pharmaceutical industries and wastewater treatment (Table 24.2). Although microalgal biotechnology has the potential for the production of several biologically active compounds, culturing marine microalgae was not cost effective in the initial stages. But with the advent of fermentors, photobioreactors, tubular photobioreactors, flat photobioreactors and commercial scale photobioreactors cost effective mass culture of microalgae is possible as shown (Table 24.3). The estimated cost of producing one kg of Chlorella biomass is US $8.36 under phototrophic growth (Radmer and Parker 1994). However using fermentors and closed heterotrophic growth, production could be < US $1 kg-1 of biomass on a dry weight basis (Crueger and Crueger 1989). More recently using the genetically engineered diatom Phaeodactylum tricornutum in fermentation-based systems, 10 to 50-fold greater yields were obtained in the absence of light (Zaslavskia et al. 2001) which could reduce biomass production costs further.


869

Table 24.2 Microalgae, their derivatives and application in biotechnology Application

Product or compound

Examples of microalgae

Aquaculture

Algal cultures for cultivation of shellfish, larvae etc.

Growth promoters of yeasts Biofertilizers Production of health foods

Aqueous extracts Nitrogen fixers Protein Beta carotene Polyunsaturated fatty acid Arachidonic acid Astaxanthin Hydrocarbons Alkylguanidine Agar, agarose, polysaccharides and lipids Antiviral, antifungal compounds and pharmaceuticals Removal of nutrients, heavy metals, total dissolved solids, pathogens, bacteria, refractory organics and odor Biological hydrogen

Isochrysis*,Pavlova*, Nannochloropsis*, Thalassiosira psuedonana* Chlorella and Scenedesmus Cyanobacterium Cyanobacterium Spirulina Dunaliella* Phaeodactylum tricornutum* Red alga Porphydium cruentum Haematococcus Botryococcus Gymnodinium sp*; Gonyaulax * Chlamydomonas

Pharmaceuticals Synthesis of high value products Antibiotics Purification of wastewater

New energy sources

Biologically active compounds

Replacement for serum for animal cell tissue culture medium

Cyanobacteria Scytonema Mat-forming cyanobacteria Oscillatoria

Botrycoccus braunii, Chlamydomonas reinhardtii Chlorella pyrenoidosa Scenedesmus and Spirulina

*Denotes marine algae

Algal cultures are also used as feed organisms in mariculture operations. About 50 species of algal cultures belonging to 10 classes are scaled up and utilized as feed organisms for raising commercially important shrimp, crustaceans, shellfish and fishes (Muller-Feuga et al. 2003, 2004).

Mariculture: Economics Currently, a total of 70% of the world’s conventional fish species are fully exploited (Halvorson and Quezada 1999). To increase production, intensive high-density aquaculture of finfish, shellfish and crustacean fisheries is promoted. Biotechnology in a broad sense, i.e., selection, cross breeding, hybridization and production, plays a crucial role as in ‘designer tilapia or shrimp’. About 100 gene constructs are currently employed in fish species.

870 Algal Cultures, Analogues of Blooms and Applications Table 24.3 Cost of mass cultivating microalgae Species T-isochrysis Skeletonema sp. Pavlova lutheri Nannochloropsis sp. Nannochloropsis sp. Tetraselmis sp. Cyclotella cryptica Chlorella Chlorella Phaeodactylum tricornutum*

Culture system

Production dry biomass g l-1 day-1

Cost US$ kg -1

Tanks

0.02

1000

Benemann 1992

Photobioreactors Fermentors

0.5 -1.7

100

Chini Zittelli et al 1999

100-200

10

Day et al. 1991 Glaude and Maxey 1994 Barclay et al. 1994 Cruger and Cruger 1989 Zaslavskaia et al. 2001

Fermentors Fermentors

1

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