Stock Identification Methods: Applications in Fishery Science

Stock Identification Methods Applications in Fishery Science

This Page Intentionally Left Blank

Stock Identification Methods Applications in Fishery Science Edited by

Steven X. Cadrin Kevin D. Friedland John R. Waldman

AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier

Editorial Coordinator: Kelly Sonnack Publishing Services Manager: Andre Cuello Project Manager: Justin Palmeiro Cover Design: Eric DeCicco Composition: Best-Set Text Printer: Maple Press Cover Printer: Phoenix Color Elsevier Academic Press 200 Wheeler Road, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, California 92101-4495, USA 84 Theobald’s Road, London WC1X 8RR, UK This book is printed on acid-free paper. Copyright © 2005, Elsevier Inc. All rights reserved. Except Chapter 21; Copyright © 2005, Mark B. Bain No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone: (+44) 1865 843830, fax: (+44) 1865 853333, e-mail: [email protected]. You may also complete your request on-line via the Elsevier homepage (http://elsevier.com), by selecting “Customer Support” and then “Obtaining Permissions.” Library of Congress: Application submitted. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 0-12-154351-X For all information on all Academic Press publications visit our Web site at www.books.elsevier.com Printed in the United States of America 04 05 06 07 08 09 9 8 7 6 5 4 3 2 1

ANNOTATED TABLE OF CONTENTS

Foreword by Michael Sissenwine

xi

Contributors

xiii

PART

I

Introduction 1. Stock Identification Methods: An Overview, by Steven X. Cadrin, Kevin D. Friedland, and John R. Waldman A brief introduction to stock identification, including the historical development of the ICES Working Group. 2. Definition of Stocks: An Evolving Concept, by John R. Waldman A more detailed introduction to the field, including technical definitions. 3. Fish Migration and the Unit Stock: Three Formative Debates, by D. H. Secor A review of ecological and historical issues related to stock connectivity and metapopulations. 4. Environmental and Genetic Influences on Stock Identification Characters, by Douglas P. Swain, Jeffrey A. Hutchings, and Chris J. Foote An overview on the major categories of stock identification approaches and their relative strengths for identifying stocks.

3

7

17

45

v

vi

Annotated Table of Contents

PART

II

Life History Traits 5. The Use of Early Life Stages in Stock Identification Studies, by Jonathan A. Hare A description of methods that examine geographic range and distribution during early life history, including planktonic stages as well as juvenile and adult stages. 6. Life History Parameters, by Gavin A. Begg A review and critique of approaches that use differences in ontogenetic rates to distinguish stocks.

89

119

PART

III

Natural Marks—Morphological Analyses 7. Morphometric Landmarks, by Steven X. Cadrin A description of techniques used to measure and analyze general morphometry, including traditional multivariate morphometrics and more advanced geometric analyses. 8. Morphometric Outlines, by Steven X. Cadrin and Kevin D. Friedland A review and critique of methods that describe shape of outlines for structures such as scales and otoliths, and how methods are used to distinguish individuals with differently shaped features. 9. Analyses of Calcified Structures: Texture and Spacing Patterns, by Kevin D. Friedland and Steven X. Cadrin A review of methods used to analyze spacing patterns of circuli on scales, otoliths, and vertebrae through image analysis, including digital photomicrograph examples. 10. Meristics, by John R. Waldman An evaluation of using the number of discrete morphological elements (e.g., number of vertebrae, fin rays) for identifying stocks, with illustrative examples.

153

173

185

197

vii

Annotated Table of Contents

PART

IV

Natural Marks—Environmental Signals 11. Parasites as Biological Tags, by K. MacKenzie and P. Abaunza A summary of how parasitological analysis has been used to discriminate stocks.

211

12. Otolith Elemental Composition as a Natural Marker of Fish Stocks, by Steven E. Campana A review of rapidly developing techniques that use chemical composition of secreted hard parts to identify environmental differences and individuals that inhabited different habitats throughout their life history.

227

13. Fatty Acid Profiles as Natural Marks for Stock Identification, by O. Grahl-Nielsen A description of a relatively new method for determining different populations according to fatty acids in tissues, with demonstrations on finfish and marine mammals.

247

PART

V

Natural Marks—Genetic Analyses 14. Chromosome Morphology, by Ruth B. Phillips A review and critique of techniques for detecting differences among stocks through inspection of chromosome form, including several example photomicrographs.

273

15. Genetic Analysis: Allozymes, by M.-L. Koljonen and R. Wilmot A description of traditional electrophoretic methods, with many examples of stock identification applications.

295

16. Mitochondrial DNA, by Antonios Magoulas A comprehensive review and protocol for detecting mitochondrial genetic characters and analyzing stock differences.

311

viii

Annotated Table of Contents

17. Use of Nuclear DNA in Stock Identification: Single-Copy and Repetitive Sequence Markers, by Isaac Wirgin and John R. Waldman A review and critique of methods using single-copy, coding and noncoding, repetitive nuclear DNA for stock identification.

18. Random Amplified Polymorphic DNA (RAPD), by P. J. Smith A review of polymerase chain reaction and RAPD techniques, which have had a rapidly increased application for stock identification in recent years.

19. Amplified Fragment Length Polymorphism (AFLP), by Zhanjiang (John) Liu A description of a relatively new technique with great potential for stock identification, including a comparative review with other genetic approaches.

331

371

389

PART

VI

Applied Marks 20. Internal and External Tags, by J. A. Jacobsen and L. P. Hansen A summary and critique of conventional tagging methods and their application for identifying stocks.

415

21. Electronic Tags, by Mark B. Bain A description of rapidly developing techniques involving telemetry and archival tags.

435

22. Otolith Thermal Marking, by Eric C. Volk, Steven L. Schroder, and Jeffrey J. Grimm A description of relatively new methods involving thermal signatures on fish otoliths, with photomicrographs illustrating their application for stock identification.

447

ix

Annotated Table of Contents

PART

VII

Stock Identification Data Analysis 23. Experimental Design and Sampling Strategies for Mixed-Stock Analysis, by Mary C. Fabrizio A protocol for sampling and a description of how sampling issues affect precision and accuracy of stock composition analysis.

467

24. An Introduction to Statistical Algorithms Useful in Stock Composition Analysis, by Michael H. Prager and Kyle W. Shertzer An evaluation of methods used to determine the contributions of different stocks in mixed-stock samples.

499

25. Classical Discriminant Analysis, Classification of Individuals, and Source Population Composition of Mixtures, by Jerome Pella and Michele Masuda A description of linear discriminant analysis, with focus on stock identification applications.

517

26. Neural Networks Used in Classification with Emphasis on Biological Populations, by Saul B. Saila An introduction to a relatively new method of data analysis, with illustrative examples for identifying stocks. 27. Maximum Likelihood Estimation of Stock Composition, by Jon Brodziak A review of stock identification applications using maximum likelihood to estimate contributions of different stocks in mixed-stock samples. 28. Estimation of Movement from Tagging Data, by Carl James Schwarz A description of analytical methods used to examine mark-recapture data for identifying stocks and quantifying interchange rates among stocks.

553

571

591

x

Annotated Table of Contents

PART

VIII

Application of Stock Identification Data in Resource Management 29. Stock Identification for Conservation of Threatened or Endangered Species, by Michael A. Banks A summary of how information on stock structure is used in resource management decisions. 30. The Role of Stock Identification in Formulating Fishery Management Advice, by Cornelius Hammer and Christopher Zimmermann A description of how information on stock structure is considered in advice on stock status and management alternatives. 31. Identifying Fish Farm Escapees, by Peder Fiske, Roar A. Lund, and Lars. P. Hansen A review of the issue of escaped fish from aquaculture operations and a protocol for monitoring methods.

Index

609

631

659

681

FOREWORD

Fisheries scientists and managers use the term stock so frequently that you would think we know all there is to know about it. We speak of stock assessments, stock structure, spawning stock biomass, stock-recruitment relationships, stock complexes, stock production models, and so forth. I think it is fair to say that most scientists and managers take “stock” for granted as if it were sufficient to rely on the old adage that “we’ll know one when we see it.” The truth is that its impossible to know how many scientific conclusions or fishery management decisions may have been led astray by assuming we were seeing a stock that wasn’t! Information on the stocks is needed to meet objectives of fisheries management to achieve sustainable yield, avoid recruitment failures, rebuild overfished stocks, as well as to conserve threatened and endangered species. With growing acceptance of the need to conserve biodiversity (including genetic diversity), apply a precautionary approach, and operationalize the ecosystem approach (which places greater emphasis on spatial distributions and place-based management, such as MPAs), know what is and is not, a stock has never been more important. For more than a decade, the International Council for Exploration of the Sea, Stock Identification Methods Working Group has been promoting standard protocols for sampling, data processing, and analytical methods, for data being generated by both traditional methods (e.g., meristics and morphometrics, traditional tags, parasites as natural tags) and new technologies (otolith chemistry, molecular genetics, electronic tags). The result is this comprehensive volume prepared by an outstanding team of international scientists. It addresses the stock concepts, historical development, applications to fisheries science and management, use of natural marks (some traditional and some recently developed techniques), genetics, recent advances in tagging technology, and analytical methods. Although this volume will be a valuable reference for years to come, I think that we should all be excited by the prospect of innovative advances in the near future that surely will render some of the conclusions in the book out of date. xi

xii

Foreword

The scientists responsible for the volume, and ICES as the sponsor of the Working Group, do not want to rest on their laurels. Advances in biochemistry, analytical chemistry, and electronics (including microtechnology with nanotechnology on the horizon) foretell a very productive era unfolding when it comes to stock information in support of better science and better resource management. Speaking as both the President of ICES and the Chief Science Advisor for the U.S. National Marine Fisheries Service, I look forward to important and exciting discoveries in the future. Michael Sissenwine

CONTRIBUTORS

P. ABAUNZA, Instituto Espanol de Oceangrafia, Santander, Spain MARK B. BAIN, Center for the Environment, Cornell University, Ithaca, New York, USA MICHAEL A. BANKS, Coastal Oregon Marine Experiment Station, Hatfield Marine Science Center, Department of Fisheries and Wildlife, Oregon State University, Newport, Oregon, USA GAVIN A. BEGG, CRC Reef Research Centre, James Cook University, Townsville, Queensland, Australia JON BRODZIAK, National Marine Fisheries Service, Woods Hole, Massachusetts, USA STEVEN X. CADRIN, National Marine Fisheries Service, Woods Hole, Massachusetts, USA STEVEN E. CAMPANA, Marine Fish Division, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada MARY C. FABRIZIO, National Marine Fisheries Service, Highlands, New Jersey, USA PEDER FISKE, Norwegian Institute for Nature Research, Trondheim, Norway CHRIS J. FOOTE, Department of Fisheries and Aquaculture, Malaspina University-College, Nanaimo, British Columbia, Canada O. GRAHL-NIELSEN, Department of Chemistry, University of Bergen, Bergen, Norway JEFFREY J. GRIMM, Washington Department of Fish and Wildlife, Olympia, Washington, USA xiii

xiv

Contributors

EVIN D. FRIEDLAND, NOAA Cooperative Marine Education and Research Program, University of Massachusetts, Amherst, Massachusetts, USA CORNELIUS HAMMER, Federal Research Centre for Fisheries, Institute for Baltic Sea Fisheries, Rostock, Germany M.-L. KOLJONEN, Finnish Game and Fisheries Research Institute, Helsinki, Finland L. P. HANSEN, Norwegian Institute for Nature Research, Oslo, Norway JONATHAN A. HARE, NOAA National Ocean Service, Center for Coastal Fisheries and Habitat Research, Beaufort, North Carolina, USA JEFFREY A. HUTCHINGS, Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada J. A. JACOBSEN, Faroese Fisheries Laboratory, Tórshavn, Faroe Islands ZHANJIANG (JOHN) LIU, The Fish Molecular Genetics and Biotechnology Laboratory, Department of Fisheries and Allied Aquacultures and Program of Cell and Molecular Biosciences, Aquatic Genomics Unit, Auburn University, Auburn, Alabama, USA ROAR A. LUND, Norwegian Institute for Nature Research, Trondheim, Norway K. MACKENZIE, School of Biological Sciences, Department of Zoology, The University of Aberdeen, Aberdeen, Scotland, United Kingdom ANTONIOS MAGOULAS, Hellenic Centre for Marine Research, Institute of Marine Biology and Genetics, Heraklion, Crete, Greece MICHELE MASUDA, National Marine Fisheries Service, Auke Bay, Alaska, USA JEROME PELLA, National Marine Fisheries Service, Auke Bay, Alaska, USA RUTH B. PHILLIPS, Washington State University, Vancouver, Washington, USA MICHAEL H. PRAGER, National Marine Fisheries Service, Beaufort, North Carolina, USA SAUL B. SAILA, University of Rhode Island, Graduate School of Oceanography, Narragansett, Rhode Island, USA STEVEN L. SCHRODER, Washington Department of Fish and Wildlife, Olympia, Washington, USA CARL JAMES SCHWARZ, Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada

Contributors

xv

D. H. SECOR, Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, Solomons, Maryland, USA KYLE W. SHERTZER, National Marine Fisheries Service, Beaufort, North Carolina, USA P. J. SMITH, National Institute of Water and Atmospheric Research Ltd., Wellington, New Zealand DOUGLAS P. SWAIN, Department of Fisheries and Oceans, Gulf Fisheries Centre, Moncton, New Brunswick, Canada ERIC C. VOLK, Washington Department of Fish and Wildlife, Olympia, Washington, USA JOHN WALDMAN, Hudson River Foundation for Science and Environmental. Research, New York, York; currently, Biology Department., Queens College, The City University of New York, New York, New York, USA R. WILMOT, National Marine Fisheries Service, Juneau, Alaska, USA ISAAC WIRGIN, Department of Environmental Medicine, New York University School of Medicine, Tuxedo, New York, USA CHRISTOPHER ZIMMERMAN, Institute for Sea Fisheries, Hamburg, Germany

PART

Introduction

I

This Page Intentionally Left Blank

CHAPTER

1

Stock Identification Methods— An Overview STEVEN X. CADRIN,* KEVIN D. FRIEDLAND,† AND JOHN R. WALDMAN‡ *National Marine Fisheries Service, Woods Hole Massachusetts, USA †University of Massachusetts, Amherst, Massachusetts, USA ‡Queens College, The City University of New York, New York, New York, USA

References

Stock identification is an interdisciplinary field that involves the recognition of self-sustaining components within natural populations and is a central theme in fisheries science and management. The obvious role of stock identification is as a prerequisite for the tasks of stock assessment and population dynamics, because most population models assume that the group of individuals has homogeneous vital rates (e.g., growth, maturity, and mortality) and a closed life cycle in which young fish in the group were produced by previous generations in the same group. Because stock structure and delineation are uncertain, the reliability of stock assessments, and therefore the effectiveness of fishery management, is severely limited for many fishery resources. There are also roles for stock identification in fishery science that may be equally important but less obvious. Any study that wishes to represent a living resource through field sampling, or even laboratory studies, should consider the species’ population structure in the sampling and analytical design. Whether the research concerns general life history, growth, physiology, or diet, the population of inference and its stock components should be identified. Therefore, stock identification can be viewed as a prerequisite for any fishery analysis, just as population structure is considered a basic element of conservation biology (Thorpe et al., 1995). Despite its importance, stock identification remains one of the most confusing subjects in fisheries science for nonspecialists, with a wide variety of approaches and conflicting terminologies and interpretations. There have been some excellent reviews of stock identification research, including concise overviews (Simon and Larkin, 1972; Templeman, 1982; Pawson and Jennings, 1996) and conference proceedings that include various case studies (Ihssen et al., Stock Identification Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

3

4

Steven X. Cadrin et al.

1981; Kumpf et al., 1987; Begg et al., 1999). However, a synthetic overview of stock identification was not previously available, and a focus on application of stock identification results to fishery science and management was generally lacking. Many of the case studies on stock identification are result-oriented and narrowly focused, and overview perspectives lack the detail needed to guide researchers. Furthermore, in recent years significant advances have been made in many approaches to stock identification. In 1992, the International Council for the Exploration of the Sea (ICES) established a “Study Group on Stock Identification Protocols for Finfish and Shellfish Stocks,” chaired by Kevin Friedland, to review methodologies of stock identification and develop a protocol for the application of stock identification results. The Study Group was organized in an open format to invite a wide participation of experts on stock identification to summarize the various approaches. Over the next decade, the group expanded to the “Stock Identification Methods Working Group,” currently co-chaired by Kevin Friedland, John Waldman, and Steve Cadrin, and developed a volume of contributions that is aimed at synthesizing the many disciplines involved in stock identification. An outline of major stock identification approaches and applications was formed and authors were solicited to draft chapters that review each specific method, with emphasis on recent advances, review of benchmark case studies, critique of strengths and weaknesses, and guidance for effective protocol. The support of this work by the ICES community reflects the continuing leadership of ICES in oceanographic and fishery research. After all, it was an early ICES committee that first promoted stock identification as an important consideration for fishery science in the late 1800s (Smith, 1994). This book introduces a wide variety of methods and provides guidance and example applications. As described in the introductory chapters, the definition of the term stock is somewhat conditional on methodology, and different methods offer complementary perspectives on population structure. Accordingly, the “stock concept” evolved as methodological approaches evolved, from early morphological methods through the development of genetic techniques and the increased power to detect environmental signals in tissues. The compendium of methodological reviews (sections II to VI) was designed to serve as a resource for researchers interested in comparative studies in stock identification as well as a general introduction for all scientists and managers of natural resources. Methodological chapters are not necessarily comprehensive reviews but focus more on historical development, benchmark case studies, critique of current issues, and prescriptions for the most effective protocols for stock identification. Section VII, Stock Identification Data Analysis, offers insights into a variety of statistical procedures and provides guidance on their proper application. Considerations for proper sampling, data treatments, and interpretations are dis-

Stock Identification Methods—An Overview

5

cussed. The final section describes how stock identification information is used in resource management, illustrating the difficulties and limitations, and reviewing case studies in which stock identification played a central role. One theme that emerged throughout the development of this volume is the strength of interdisciplinary analyses. Over the history of stock identification, new methods were developed and promoted as better ways to approach stock identification, often leading to equivocal information from competing methodological camps. However, when results from each approach are viewed in the context of what precise aspect of stock structure they reveal (defined in this volume), a more holistic view with multiple perspectives is possible, providing more reliable information for resource management. As new methods continue to emerge, their results should be considered, along with those from traditional approaches, in improving our ability to study stock structure. Finally, despite its importance in the development of fishery advice and management, stock identification continues to be an afterthought. The fishery science community has a habit of building assessments from back to front, often giving only cursory treatment to stock identification, and in the name of being expeditious, population vital rates are estimated without regard to lingering questions about stock structure. We hope this volume not only will provide source material to improve the quality of stock identification research, but also will stimulate new research on stocks being assessed without the benefit of reliable stock identification.

ACKNOWLEDGMENTS We thank the ICES community, particularly David Griffith and Mette Bertelson, for their support throughout this endeavor. We are also grateful to Dave Cella, Kelly Sonnack, and Chuck Crumly for their assistance in the publication.

REFERENCES Begg, G., Friedland, K. D., and Pearce, J. B. 1999. Stock identification—its role in stock assessment and fisheries management. Fisheries Research 43: 1–8. Ihssen, P. E., Bodre, H. F., Casselman, J. M., McGlade, J. M., Payne, N. R., and Utter, F. 1981. Stock identification: materials and methods. Canadian Journal of Fisheries and Aquatic Sciences 38: 1838–1855. Kumpf, H. E., Vaught, R. N., Grimes, C. B., Johnston, A. G., and Nakamura, E. L. 1987. Proceedings of the Stock Identification Workshop. NOAA Tech. Mem. NMFS-SEFC-199. Pawson, M. G. and Jennings, S. 1996. A critique of methods for stock identification in marine capture fisheries. Fisheries Research 25: 203–217. Simon, R. C. and Larkin P. A. (eds.). 1972. The stock concept in Pacific salmon. H. R. MacMillan Lectures in Fisheries. University of British Columbia.

6

Steven X. Cadrin et al.

Smith, T. D. 1994. Scaling Fisheries: A Science Driven by Economics and Politics 1855–1955. Cambridge University Press, Cambridge, UK. Templeman, W. 1982. Stock discrimination in marine fishes. NAFO SCR Doc. 82/IX/79. Thorpe, J., Gall, G., Lannan, J., and Nash, C. 1995. Conservation of Fish and Shellfish Resources: Managing Diversity. Academic Press, San Diego, CA.

CHAPTER

2

Definition of Stocks: An Evolving Concept JOHN R. WALDMAN Hudson River Foundation for Science and Environmental Research, New York, New York Currently, Biology Department, Queens College, The City University of New York, New York, New York, USA

I. Introduction II. Defining the “Stock”: An Evolving Concept A. Early Viewpoints B. Late Twentieth-Century Definitions C. Stocks and Conservation Biology III. Some Fundamental Issues in Defining Stocks A. Genotypic vs. Alternative Approaches B. Negative Results C. Congruence IV. Conclusions References

I. INTRODUCTION Fisheries science is based on the notion of an idealized “unit stock,” a discrete entity with its own origin, demographics, and fate. Fish stock assessment is the science of estimating certain key population dynamics parameters to better manage the unit stock. If the rates of growth, natural mortality, reproduction, and present fishing mortality can be estimated, their effects may be combined in order to evaluate the effects of changes in the fishing mortality rate. An understanding of stock structure is necessary for designing appropriate management regulations in fisheries where multiple stocks are differentially exploited (Ricker, 1981). Stock identification is an essential partner to stock assessment—unit stocks cannot be assessed unless they are circumscribed, that is, their boundaries defined in relation to other units of the same species. At its core, stock identification is the process that seeks to identify coherent units of individuals that have complete-to-partial discreteness in space or time Stock Identification Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

7

8

John R. Waldman

from their congeners. These units have certain characteristics that render them fundamental to management: (1) they occupy their own physical life history circuit, including spawning grounds that are geographically or temporally unique; (2) they experience their own natural demographic influences, such as mortality suffered from a particular suite of predators; (3) their complete-to-partial isolation allows fine tuning of their morphological and genetic characteristics to their particular environmental circumstances; and (4) their abundances and life history characteristics respond to their own sets of unnatural influences, such as fisheries and contamination of their habitats. Before identifying these units, fisheries science must grapple with defining them, a task that remains confusing and problematic. Definitions of both parts of the term stock identification are muddled, that is, “stock” and “identification,” pattern and process. But of the two, process is far more easily specified. Concerning process, although the term stock identification is often used to represent an entire realm of investigation, in a narrower sense it may be taken to mean only the initial identification of units within a species, whereas stock discrimination means the process of classifying individuals or collections of individuals to those units. The term stock composition analysis is an extension of stock discrimination where the proportions of unit stocks to a mixed-stock fishery are estimated. It is common that stock identification and stock discrimination are used interchangeably. Stock composition analysis is also referred to as mixed-stock analysis and relative contribution analysis. Concerning pattern or structure in fishery resources, however, definitions abound. Royce (1972) believed there were “. . . a bewildering array of semantic problems because there is little agreement on the meaning of the words used to define groups in the hierarchy with the rank of subspecies and below. . . .” Reaching a consensus in this area remains immensely difficult. Older categories beneath subspecies that have largely fallen out of favor among fishery biologists include subpopulation, race, strain, breed, and variety. A more current hierarchy below subspecies is illustrated by Secor (this volume; Fig. 3-1) that includes metapopulation, population, contingent, year-class, school, brood, and individual. A fundamental problem in defining stocks (and other biological categories such as species) is assuming the optimal balance between precision and generality: too much precision and the definition will not be robust enough to serve all situations, too general and the definition will have little utility. This conundrum runs throughout the history of stock definition. Kutkuhn (1981) observed that “. . . the literature is replete with observations and opinions on the concept, character, and implications of something we commonly refer to as the (fish) ‘stock.’ But nowhere, insofar as I can determine, has any body of authority, as is often established to perform such functions, acclaimed a definition of it.” Nonetheless, many definitions have been proposed.

Definition of Stocks: An Evolving Concept

9

II. DEFINING THE “STOCK”: AN EVOLVING CONCEPT

A. EARLY VIEWPOINTS Secor (this volume, Chapter 3) reviews notions of unit stock and population thinking, noting how the unit stock arose as a means of practically defining vital rates and renewal rates pertinent to geographic areas where fisheries were undertaken. Dahl (1909) viewed stock as a source of fish, that is, a specific portion of a population that is influenced by an anthropogenic activity that affects population productivity. This definition is purely operational in which the portion of fish exposed to the fishery defines the stock. This category might be referred to as a “harvest stock.” The baseline for establishment of a harvest stock is not nature’s knife (i.e., isolation and the process of differentiation) and its natural subdivisions below that of species. Instead, a harvest stock is defined by human interaction with the species, cutting across and grouping whichever natural subdivisions (lineages) happen to encompass a particular fishery.

B. LATE TWENTIETH-CENTURY DEFINITIONS Despite its beginnings as an operationally defined fisheries term, the term stock began to drift from the practical toward more theoretical definitions that recognized the microevolutionary subdivisions occurring below the species level, regardless of whether fisheries are promulgated. A transitory definition was provided by Larkin (1972), who wrote that a stock is “. . . a population of organisms which, sharing a common gene pool, is sufficiently discrete to warrant consideration as a self-perpetuating system which can be managed.” Both a common gene pool and management are explicit in this definition. However, definition of the stock by the fishery alone continued in the Magnuson Fishery Conservation and Management Act of 1976 in which it meant a species, subspecies, geographic grouping, or other category of fish capable of management as a unit (Fox and Nammack, 1995). The decades-long conundrum over the definition of stock brought about an ambitious symposium held in 1980 in Ontario, Canada, to consider that subject. Indeed, the papers originating from the Stock Concept International Symposium (Canadian Journal of Fisheries and Aquatic Sciences, 1981, vol. 33, no. 12) provide a fine summary of the state of our perceptions of stock at that time. Several observations can be made. One is that the issue was far from settled at, or even after, the gathering—the resultant papers reflect a wide diversity of unreconciled opinion. Another was a movement from utilitarian definitions to ones that seek to outline naturally occurring units. A third was a sometimes expressed desire to base the definition on genetics, a response, in part, to the growing wealth

10

John R. Waldman

of genotypic knowledge gained through the application and maturation by 1980 of protein electrophoretic analysis. Among these papers, Booke (1981) tackled the issue head on, wishing ambitiously to propose a working definition of stock that would be useful for all fish species. He offered one that he recognized was highly general, that is, “a species group, or population, of fish that maintains and sustains itself over time in a definable area.” He then suggested more precisely that a genotypic stock is a population of fish maintaining and sustaining Castle–Hardy–Weinberg equilibrium. And that if his preferred genotypic stock characterization is not possible, a phenotypic stock “. . . has to be recognized as a group, or population of fish maintaining characteristics which are expressed in one or more ways depending on the type of environment or domicile.” Bailey and Smith (1981) echoed this genotypic and phenotypic dichotomy, defining stocks as local populations that maintain recognizable genetic differentiation by separation of their spawning place or time, but also stating that “. . . a (phenotypic) stock has to be recognized as a group, or population, of fish maintaining characteristics which are expressed in one or more ways depending on the type of environment or domicile.” However, their genotypic criterion contrasted with Booke’s in that it was based on differentiation among units, whereas Booke mentioned “group” and “population” but did not define them as differentiated (but he did uniquely invoke Castle–Hardy–Weinberg equilibrium, a withinpopulation measure). Another attempt was made by Casselman et al. (1981), who stated that a stock is “. . . a population of fish that behaves as a cohesive unit whose members exhibit common responses to environmental conditions within its geographic boundaries.” They believed this includes but is not restricted to population units that are reproductively isolated, which is less restrictive than many other definitions and which conceivably could embrace other concepts such as a regional stock or metapopulation. The definition of stock made at this symposium, which I believe was most robust, yet sufficiently specific to be useful, was offered by Ihssen et al. (1981), who proposed that a stock is “. . . an intraspecific group of randomly mating individuals with temporal or spatial integrity.” This definition places an upper boundary at the species level yet allows for a kind of operational definition anywhere below that boundary at whatever level temporal or spatial integrity is displayed; that is, by this definition various units of interest expressed by Royce (1972) as groups in the hierarchy with the rank of subspecies and below (such as metapopulation, population, or contingent) would qualify, depending on the pattern expressed and the degree of technical stringency applied to the problem. A newer and less restrictive definition was offered by Hilborn and Walters (1992) in which stocks are considered as arbitrary groups of fish large enough

Definition of Stocks: An Evolving Concept

11

to be essentially self-reproducing, with members of each group having similar life history characteristics.

C. STOCKS

AND

CONSERVATION BIOLOGY

Beyond its operational definitions linked to fisheries, stocks may be considered to be units below species that are naturally occurring and of interest to managers and scientists beyond reasons concerning harvest. With its emphasis on preserving biodiversity, the relatively new science of conservation biology is intimately involved with identifying these below-species units. Many of the technical approaches to resolving stocks for fisheries’ purposes are also applicable to the goals of conservation biology. However, new terms have emerged that border on, yet differ from, more traditional fisheries’ stock concepts. Primary among these today is the “evolutionarily significant unit” (ESU) of the biological species, a concept refined for Pacific salmon under the general demands of the U.S. Endangered Species Act to conserve genetic diversity within and between species (Waples, 1991, 1995). The ESU as defined by Waples (1991) is a population that (1) is substantially reproductively isolated from other conspecific reproductive units, and (2) represents an important component in the evolutionary legacy of the species. The first criterion is essentially the definition of a stock. But the second criterion attempts to answer a question with respect to a population’s evolutionary legacy, that is, if the population became extinct, would this represent a significant loss to the ecological-genetic diversity of the species? Thus, the ESU goes beyond the stock concept in that its designation includes a qualitative and often difficult judgment (King and Ludke, 1995) about each unit’s importance. Other definitions of ESU beyond Waples (1991) were reviewed by Fraser and Bernatchez (2001). They argued that no single approach works best in all situations, but that each has its strengths and weaknesses under different circumstances. Thus, they propose “adaptive evolutionary conservation” in which alternative ESU criteria are fit to situational circumstances.

III. SOME FUNDAMENTAL ISSUES IN DEFINING STOCKS

A. GENOTYPIC

VS.

ALTERNATIVE APPROACHES

Some workers have referred to the genotypic approach to defining stocks as if it is the avatar of stock identification (e.g., Booke, 1981). Genotypic approaches do offer many advantages to alternative techniques, including permanence across an individual’s life cycle, freedom from environmental modulation, often large

12

John R. Waldman

substrates of variation, and usually, nonfatal sampling (Wirgin and Waldman, 1994). But it is important not to be so swayed by these advantages as to dismiss the fact that any consistently detectable differences among stocks are both valid and useful; that is, a stock is a stock if a marker discriminates among them, even if these differences are partly or completely environmental in origin. Indeed, there are situations in which it is conceivable that nongenetic approaches might be preferable. One such situation is when there are simple, qualitative characteristics, such as observations of different timings of runs of an anadromous species in a river; another might be an obvious phenotypic marker available, such as a color difference. Another is when newly formed populations through restocking or natural colonization do not yet show genetic differences despite their discreteness. Yet another is where stock identification information can emerge from mark-recapture work conducted for stock assessment purposes. (However, it would help the stock identification field if more controlled “common garden” rearing experiments in which the environment was held constant were conducted in order to gain a better understanding of genetic and nongenetic control of stock differences.) Today’s recognition of the complementarity of genotypic and phenotypic approaches contrasts with the apparently short-lived view espoused by Marr (1957) who believed that “subpopulations” are characterized by genetic selfsustainment, whereas stocks are populations or portions of populations of which all members are characterized by similarities that are not heritable, but are induced by the environment. The scientist interested in identifying fish stocks has a large number of tools available in the kit. Genotypic approaches may be highly prominent among them, but they have not supplanted the remainder.

B. NEGATIVE RESULTS Kutkuhn (1981) summarized the scientific process of stock identification. First, one needs indication of the existence of stocks, from which a hypothesis is mounted (vs. the null hypothesis of a single stock). Second, a well-conceived survey must be undertaken. Finally, an efficient discrimination technique must be applied to the collections. Each of these stages may be nullified by flaws that result in a failure to falsify the null hypothesis, including defective study logic, faulty survey techniques, and questionable classification procedures (Kutkuhn, 1981). Notwithstanding the first two problems, it is possible in a study that a competent classification procedure was applied but that it lacked the sensitivity to detect evidence for the stocks at a statistically significant level; that is, a careful and earnest effort using one approach yields negative results (Type 1 error). Should analysis stop there and the absence of discrete stocks be accepted?

Definition of Stocks: An Evolving Concept

13

My answer is—only with caveats. Nonexistence cannot be proved and, in a formal sense, what has occurred is that the null hypothesis has not been falsified. It is entirely possible that a different stock identification approach would falsify the null hypothesis. Unfortunately, there is no hard-and-fast rule to know how far to proceed with additional analyses. Most often, resource limitations preclude any, or at least, much more analysis. But it is important to make a distinction in such cases where negative results have been obtained: The existence of stocks has not been disproved, only the null hypothesis of a single stock has not been falsified. Also, although failure to falsify the null hypothesis may be less compelling to the investigator, such negative results should nonetheless be published inasmuch as they still provide valuable information about the nature of the species studied and the sensitivities of the stock identification approaches employed.

C. CONGRUENCE Falsification of the null hypothesis of a single stock implies that multiple stocks exist, and much of what we know about the existence of particular stocks is derived from the successful application of single stock identification approaches. But why might one approach be successful in detecting multiple stocks, whereas another is unsuccessful? That is, why do different stock identification approaches have different sensitivities to a particular stock problem? One factor is the “depth” of the stock division being addressed. As anyone who has examined branching diagrams of relationships among fish stocks has observed, stocks show subdivisions with different levels of relatedness, sometimes within a metapopulation structure (Stephenson, 1999). For example, several anadromous fish stocks in rivers that drain into a common bay or sound may group as a regional stock, yet there may be tributary stocks within one of these single rivers. This depth is a product of space and time, that is, the greater the degree and length of isolation, the greater the depth of the stock division. But differentiation of stocks is estimated by examining a wide variety of phenotypic and genotypic features, and each of these features may undergo microevolution at their own rate (Waldman, 1999). Fish have literally been “taken apart” down to the level of DNA in the search for useful stock discrimination characters. Some of these features respond rapidly to differentiating forces (e.g., fin ray counts, nuclear DNA microsatellites), whereas others are more conservative (e.g., vertebral counts, the coding region of any nuclear DNA gene). Thus, a particular stock identification problem involves choosing among approaches that address features of the fish that compose these stocks, which are passing through differing microevolutionary trajectories—some of which may be applicable to the problem (offering a high signal-to-noise ratio) and others which may be less, or not at all applicable.

14

John R. Waldman

Application of additional stock discrimination techniques (a holistic approach) has several advantages to single approach studies (Begg and Waldman, 1999). One advantage is that one of these techniques may detect stock structure where others fail to do so. Another is that greater confidence is gained when contrasting approaches provide congruent results. Also, additional levels of stock structure may be observed with approaches that offer different sensitivities. Finally, multiple applications of discrete approaches to individual stock identification problems provide more empirical information on the relative merits of alternative approaches to this still developing field.

IV. CONCLUSIONS Since the advent of modern fisheries science, the concept of the unit stock has remained the same while the practice of identifying fish stocks has undergone significant change. The unit stock is still an idealized fundamental unit which, when correctly identified, allows estimation of important population parameters without the biases that result from incorporation of unknown components of additional unit stocks. But the stock searched for in the stock identification process may not always be a unit stock. Under an early, and presently minority, point of view, this does not matter; that is, a harvest stock is simply “what is caught” and so the fishery itself defines the stock. Secor (1999) noted that the increased technical ability to discriminate fish populations has led to synonomy between population and stock. He argues, however, that this classical definition still has validity in the sense that accessibility is implicit in stock definitions and that accessibility is mediated largely by within-population ecological attributes such as migratory modalities. How consideration of these within-population differences will affect future notions of stock is unclear, but it is likely that much new information will emerge as powerful new tools are employed, such as microelemental analysis of otoliths (e.g., Secor et al., 2001) and stable isotope analysis (e.g., Adams et al., 2003). However, the numerous modern definitions of stock reflect an evolution toward naturally defined divisions or lineages within species that has occurred for at least two reasons, one philosophical and one practical. The philosophical rationale is that lineage-based divisions correspond more closely with the notion of the idealized unit stock. Although defining such stocks involves necessary steps beyond letting the fishery itself delimit the stock, and these steps may be costly and technically challenging (sometimes producing unclear results), they nonetheless represent a search for fundamental biological units, not units of convenience. The practical reason is the century-long trend toward improved stock identification capabilities; that is, the tool kit has grown considerably larger and far more technically keen, as evidenced by this volume.

Definition of Stocks: An Evolving Concept

15

Earlier fisheries scientists had no choice but to consider harvest stocks inasmuch as stock discrimination techniques did not exist and so synonomy between stock and population was not even possible. Today a wealth of techniques can be applied to discriminate among stocks, and in many instances these results can be applied to estimate their proportions in mixed stocks. Thus, the possibly “mixed” harvest stock may be quantitatively decomposed to its constituent unit stocks. Begg and Waldman (1999) underscored the analogy between the surprisingly difficult problems in defining species (e.g., Mayden and Wood, 1995) and in defining stocks. Defining the more recently conceptualized ESU presents similar problems (Fraser and Bernatchez, 2001). All of these notions have shifted and diversified over time partly in response to changes in thinking about evolution (albeit, at different levels) and to technological advances. Yet, despite the challenges in defining them, on the operational level, all are usually discriminated without much debate. If a single stock definition is needed, I favor that of Ihssen et al. (1981), but others may be more appropriate in particular cases. Definitions of stock will likely continue to evolve as management requirements change and technologies advance.

ACKNOWLEDGMENTS Thanks to Steven Cadrin, Kevin Friedland, and David Secor for their insightful comments on the manuscript.

REFERENCES Adams, C., Fraser, D., McCarthy, I., Shields, S., Waldron, S., and Alexander, G. 2003. Stable isotope analysis demonstrates ecological segregation in a bimodal size polymorphism in Arctic charr from Loch Tay, Scotland. Journal of Fish Biology 62: 474–481. Bailey, R. M. and Smith, G. R. 1981. Origin and geography of the fish fauna of the Laurentian Great Lakes basin. Canadian Journal of Fisheries and Aquatic Sciences 38: 1539–1561. Begg, G. A. and Waldman, J. R. 1999. An holistic approach to fish stock identification. Fisheries Research 43: 35–44. Booke, H. E. 1981. The conundrum of the stock concept—are nature and nurture definable in fishery science? Canadian Journal of Fisheries and Aquatic Sciences 38: 1479–1480. Casselman, J. M., Collins, J. J., Crossman, E. J., Ihssen, P. E., and Spangler, G. R. 1981. Lake whitefish (Coregonus clupeaformis) in the Great Lakes region. Canadian Journal of Fisheries and Aquatic Sciences 38: 1772–1789. Dahl, K. 1909. The problem of sea fish hatching. Special Part B, No. 5. In J. Hjort (ed.). Rappurt sur les Travaux de Commission A, Dans La Période 1902–1907, Conseil Permanent International pour l’Exploration de La Mer. Rapports et Procès-Verbaux, Vol. X. Fox, W. W., Jr. and Nammack, M. F. 1995. Conservation guidelines on significant population units: responsibilities of the National Marine Fisheries Service. American Fisheries Society Symposium 17: 419-422.

16

John R. Waldman

Fraser, D. J. and Bernatchez, L. 2001. Adaptive evolutionary conservation: towards a unified concept for defining conservation units. Molecular Ecology 10: 2741–2752. Hilborn, R. and Walters, C. J. 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty. Chapman and Hall, New York. 570 pp. Ihssen, P. E., Booke, H. E., Casselman, J. M., McGlade, J. M., Payne, N. R., and Utter, F. M. 1981. Stock identification: materials and methods. Canadian Journal of Fisheries and Aquatic Sciences 38: 1838–1855. King, T. L. and Ludke, J. L. 1995. A national biological service perspective on defining unique units in population conservation. American Fisheries Society Symposium 17: 425–429. Kutkuhn, J. H. 1981. Stock definition as a necessary basis for cooperative management of Great Lakes fish resources. Canadian Journal of Fisheries and Aquatic Sciences 38: 1476–1478. Larkin, P. A. 1992. The stock concept and management of Pacific salmon. In R. C. Simon and P. A. Larkin (eds.), The Stock Concept in Pacific Salmon. H. R. MacMillan Lectures in Fisheries. University of British Columbia, Vancouver, BC, pp. 11–15. Marr, J. C. 1957. Contributions to the study of sub-populations of fishes. U.S. Fish and Wildlife Service Special Scientific Report—Fisheries 208: 1–6. Mayden, R. L. and Wood, R. M. 1995. Systematics, species concepts, and the evolutionary significant unit in biodiversity and conservation biology. American Fisheries Society Symposium 17: 58–113. Ricker, W. E. 1981. Changes in the average size and average age of Pacific salmon. Canadian Journal of Fisheries and Aquatic Sciences 38: 1636–1656. Royce, W. F. 1972. Introduction to the Fishery Sciences. Academic Press, New York. 351 pp. Secor, D. H. 1999. Specifying divergent migrations in the concept of stock: the contingent hypothesis. Fisheries Research 43: 13–34. Secor, D. H., Rooker, J. R., Zlokovitz, E., and Zdanowicz, V. S. 2001. Identification of riverine, estuarine, and coastal contingents of Hudson River striped bass based upon otolith elemental fingerprints. Marine Ecology Progress Series 211: 245–253. Stephenson, R. L. 1999. Stock complexity in fisheries management: a perspective of emerging issues related to population sub-units. Fisheries Research 43: 247–249. Waldman, J. R. 1999. The importance of comparative studies in stock analysis. Fisheries Research 43: 237–246. Waples, R. S. 1991. Pacific salmon, Oncorhynchus spp. and the definition of “species” under the endangered species act. Marine Fisheries Review 53: 11–22. Waples, R. S. 1995. Evolutionarily significant units and the conservation of biological diversity under the Endangered Species Act. American Fisheries Society Symposium 17: 8–27. Wirgin, I. I. and Waldman, J. R. 1994. What DNA can do for you. Fisheries 19(7): 16–27.

CHAPTER

3

Fish Migration and the Unit Stock: Three Formative Debates D. H. SECOR Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, Solomons, Maryland, USA

I. The Unit Stock and Population Thinking II. Debate 1. Early Cod Hatchery Enhancement: Local or Global Effects on Fisheries? III. Debate 2. The Parent Stream Theory: Directed Migrations or Local Wanderings? A. Natural Tags B. Marking Experiments C. Transplant Experiments D. The Year-Class Phenomenon (Again) E. Ocean Studies F. The Parent Stream Theory and Population Thinking IV. Debate 3. The Eel Problem: Who Contributes to Reproduction? A. Mediterranean vs. North Atlantic Origin of Eels B. North Atlantic Eels: Speciation or Population Structure? C. Failed Adults D. “Freshwater Eels” and Contingent Thinking E. More Eel Problems and Population Thinking V. Summary References

I. THE UNIT STOCK AND POPULATION THINKING Modern fisheries science emerged through the early efforts of the International Council for the Exploration of the Sea (ICES) to understand factors that caused Stock Identification Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

17

18

D. H. Secor

fluctuations in important coastal fisheries (Smith, 1994; Smed and Ramster, 2002). A major research goal early on for ICES scientists was to establish relationships among fish distribution, behavior, and environmental factors (Nakken, 2002). Late nineteenth-century investigations on Atlantic herring by Heincke (1898) supported a widely held view that many local races structured the distribution of herring and their availability to fisheries. Local fisheries might then have local effects. This so-called modern migration theory displaced an earlier view of panmixia in cod and other coastal fishes. In contrast to these earlier ideas, ICES scientists discovered that fish became accessible to fisheries according to population renewal processes structured over large but distinct geographic regions (Hjort, 1914). The unit stock arose as a means of practically defining vital rates and renewal rates pertinent to geographic regions where fisheries were undertaken. Early use of the term was by Dahl (1909), who defined stock as the source of fish. Later, Russell (1931), in his catch equation, defined stock as the exploitable portion of a population. Stock as an operational definition could be tailored for regions where vital rates and recruitment were deemed homogenous (Gulland, 1983; Cushing, 1995). Thus, while population thinking underlies the way we define and manage stocks, stocks themselves are not ecological entities per se; they are operationally defined by the geographic extent of anthropogenic effects and other practical considerations. Levels of biological structure relevant to stock issues can range from species to brood (Fig. 3-1). In some instances, lineage (species to population levels) may be critical in defining management units, as is the case when large reductions occur to abundance and threaten maintenance of the underlying gene pool. This often entails an evolutionary perspective that can extend millions of years and across ocean basins. In other cases, ecological time and spatial scales may be of greatest relevance. For instance, a manager may need to know how important a given habitat is as a source for recruits. Here, stocks are defined according to regions of production, and biological levels such as contingents, cohorts, and shoals may be important (e.g., Beck et al., 2001). Despite the operational definition of stocks, there is little doubt that population structure is fundamental to how we assess and manage living resources (Sinclair, 1988; Sinclair and Smith, 2002). Population thinking arose during the early twentieth century as scientists contended with Hjort’s discovery of population cycles common over large coastal regions (Solemdal and Sinclair, 1989). Issues remained as to which geographic scales were relevant to populations (scale and entity), which behaviors contributed to population structure (migration, life cycle closure, and philopatry), and the consequences of population structure (or alternatively, the consequence of deviations from life cycle closure). Here, I introduce three formative debates, which relate to pattern, process, and consequence. These include: (1) the effectiveness of cod hatcheries in fjords of southern

19

Fish Migration and the Unit Stock: Three Formative Debates Region of Coherence

Years of Coherence

Stock Entity

107 Species

106 Subspecies

105 Metapopulation

104 103 102 10

Population Contingent Year-class School Brood

Individual

FIGURE 3-1. Temporal and spatial domains, and levels of biological organization relevant to the unit stock. Here, the unit stock is operationally defined by the anthropogenic effects of interest (Gulland, 1983).

Norway; (2) evaluation of the parent stream theory for Pacific salmon; and (3) the eel problem: Who contributes to population renewal processes?

II. DEBATE 1. EARLY COD HATCHERY ENHANCEMENT: LOCAL OR GLOBAL EFFECTS ON FISHERIES? Johan Hjort’s pioneering observation of population response to periodically strong year-classes remains a principal paradigm that guides fisheries science and management. To make this revelation, Hjort and his team had to turn away from prevailing theory, which stipulated local races and local effects (Sinclair and Solemdal, 1988), and discover a new way of thinking about the renewal process. Several scientists and historians have argued that Hjort’s “population thinking” may have originated from practical considerations on the effectiveness of hatchery releases of larval cod into Norwegian fjords over a century ago (Solemdal et al., 1984; Sinclair and Solemdal, 1988; Smith, 1994; Schwach, 1998; Secor, 2002; Sinclair and Smith, 2002). Did these releases produce local effects in a fjord? Or were released cod swamped by natural juvenile production within the fjord or, perhaps, from outside the fjord?

20

D. H. Secor

By the mid-1890s the Flødevigen hatchery under the leadership of G. M. Dannevig was releasing millions of cod larvae into southern Norway fjords. Early development of egg fertilization and hatching led to the belief that “. . . cod fry artificially hatched have the power and energy to live, grow, and develop when set at liberty and left to care for themselves in their natural element, the sea” (Rognerud, 1887). From 1890 to 1906, 23.3 billion yolk-sac larvae were released into small fjords, sounds, and bays in southern Norway (Solemdal et al., 1984). Hjort questioned the effectiveness of these releases and a study was commissioned in 1903 by the Norwegian Parliament (Smith et al., 2002) that required the participation by both Dannevig and Hjort. Hjort assigned Norwegian scientist K. Dahl to work with Dannevig. During spring 1904 and 1905, millions of cod larvae were released into two fjords, and subsequent summertime juvenile abundances were estimated through beach seine sampling. These abundances were compared to summertime abundances in 1903, when no releases were made (Table 3-1). Dannevig reported increased juvenile abundances associated with the releases for 1904–1905 in comparison to 1903, the control year. Dahl, on the other hand, investigated other fjord systems that did not receive hatchery larvae in 1904 and 1905. In 1904, large numbers of juveniles were encountered in the fjords Dahl surveyed regardless of whether releases were made. But in 1905, the abundance of juveniles was relatively scarce in all fjords, again independent of whether larvae were released or not (Table 3-1). Dahl concluded (1909) that, “. . . the formation of the fish stock (the fish supply) in these fjords is not really much dependent on the spawning and hatching taking place in the fjord itself, it is more dependent on the quantities of fry brought by currents (the circulation of fry).” Thus, the same study resulted in conflicting conclusions. Parliament

TABLE 3-1. Number of Released Larvae (“Fry”) into Different Fjords as Part of G. M. Dannevig’s Cod Hatching Programa Søndeled Fjord

Year

Larvae released

1903 0 1904 33.5 ¥ 106 1905 33 ¥ 106

Helle Fjord

Sandnes Fjord

Støle Fjord

Cod per haul

Larvae released

Cod per haul

Larvae released

Cod per haul

Larvae released

6.3 33.7 11.4

0 0 10 ¥ 106

5.4 10.9 1.5

— 0 0

— 49 4.1

— 0 0

Cod per haul

Christiania Fjord Larvae released

Cod per haul

— — — 112 20 ¥ 106 10.8 2.7 20 ¥ 106 1.9

a The study on the effectiveness used “cod per haul” (number of juveniles collected using beach seines) to index juvenile abundance among years for months of July and August. Dannevig emphasized interannual comparisons in Søndeled and Helle Fjords, 1903–1905, using 1903 as a control year. K. Dahl emphasized between Fjord contrasts in 1904 and 1905 using Sandnes, Støle, and Christiania Fjords as controls. Data from Dahl, 1909 (p. 31).

Fish Migration and the Unit Stock: Three Formative Debates

21

assigned a committee to evaluate the opposing conclusions, which determined that the uncertainty in the findings dictated continued study. Hatchery releases of cod and juvenile monitoring continued into modern times, and the issue of the effectiveness of hatchery releases of cod into fjords remains a point of contention (e.g., Smith et al., 2002). The diverging views on the effectiveness of hatchery releases, which persist today (e.g., Secor et al., 2000, 2002), center on scale. Dahl maintained that fluctuations in juvenile cod abundances had to be thought about at a spatial scale larger than a fjord. Dannevig subscribed to the modern migration theory, which prescribed restricted movements inshore and offshore by numerous local races (Heincke, 1898; Smith, 1994). Dahl (1909) specified the theoretical underpinnings of Dannevig’s program: “. . . the importance of the size of the waters was reduced by accepting the doctrine that each area of the sea, even the smallest, possessed its own tribe of fish. These tribes were supposed to be highly local during the whole life of the individuals. They were easily injured by overfishing and had to be replaced by the aid of man.” Based on his studies of differing stages of Norwegian coastal cod, Dahl (1909) thought it “just to consider the fish stock of a considerable stretch of coast as belonging to the whole of the area.” How could relevant spatial domains be applied to issues of stock renewal? The answer came through migration studies. Because adult cod do not reside the year round in spawning or nursery habitats, ontogenetic and seasonal migration patterns were needed to link stage-specific distribution patterns. Through early tagging studies, Hjort (1909) confirmed seasonal migrations between Lofoten Island spawning grounds and far-off Barents Sea feeding habitats. For Atlantic herring, Hjort and his colleagues used “certificates of origin”—unique optical patterns of scale annuli—to chart the seasonal migrations to and from spawning grounds. Based on herring studies, Hjort argued that different stages or sizes of herring located in different regions were in fact members of the same population (Secor, 2002). Such studies, informed by the hatchery investigation, provided Hjort with the critical spatial domain needed to combine samples across relevant scales and observe decadal cycles in abundance of Norwegian herring (Hjort and Lea, 1914). Thus, Hjort used complex life cycles to reveal the spatial scale relevant to stock renewal processes. But how then were complex life cycles maintained? Resolution of this issue came with early studies of the parent stream theory in Pacific salmon.

III. DEBATE 2. THE PARENT STREAM THEORY: DIRECTED MIGRATIONS OR LOCAL WANDERINGS? Prior to the twentieth century, naturalists, commercial fishermen, and anglers long wondered: Where do spawning Pacific and Atlantic salmon originate? Does

22

D. H. Secor

a stream’s spawning run represent a random mixture of mature fish migrating in from the coast? Or, does each spawning run represent the return of a generation of salmon to their parent’s stream? The specific migration and spawning behaviors associated with salmon reproduction would seem to represent adaptations that only lineage could guarantee. Thus, the parent stream theory became dogma well in advance of scientific evidence. Two central issues required resolution: (1) Do salmon home to natal streams? (2) Where do spawning runs of maturing adults originate—from distant oceanic waters or from regional coastal waters? For Pacific salmon, scientific investigations and debate related to these issues were concentrated during the first half of the twentieth century starting with C. H. Gilbert’s important work on natural tags (circuli patterns in scales) and homing for sockeye salmon, and culminating with work on ocean migrations by the International North Pacific Fisheries Commission (Neave, 1964; Royce et al., 1968). Nevertheless, much research remains to be done on mechanisms of migration, which remain controversial (Hansen and Quinn, 1998). Is homing to natal streams obligatory or facultative in Pacific and Atlantic salmon? With 100 years of hindsight, this may seem like a ridiculous question, but as the famous ichthyologist Jordan (1887) pointed out, “It seems more probable than parent stream theory that the young salmon hatched in any river mostly remain in the ocean, within a radius of 20, 30, or 40 miles of its mouth.” This idea was in keeping with the modern migration theory, prevalent in Europe during the late nineteenth century, which viewed seasonal disappearances of diadromous and coastal fishes as restricted migrations to deeper near-shore coastal regions rather than the result of extensive ocean migrations. In this view, homing is not due to directed behaviors ensuring philopatry, but is due only to proximity.

A. NATURAL TAGS Gilbert provided early evidence for the parent stream theory in a series of reports on Fraser River sockeye salmon (1914–1919) with an early application of natural tags. Like his contemporary Hjort (1909, 1914), Gilbert developed a demographic approach based on annuli in scales to explain cycles of abundance. Further, like Hjort, he used scale circuli patterns during the first year of life as certificates of origin. Distinct frequencies of circuli that were associated with the early freshwater juvenile period occurred among tributaries and, on occasion, even within tributaries (Fig. 3-2; Gilbert, 1919). Comparisons between juveniles and adults captured at Fraser River spawning localities showed the same number of circuli corresponding to the freshwater juvenile period, leading Gilbert (1915) to conclude, “Examination of the scales had removed any possible doubt that the progeny of the Fraser River fish return to the Fraser at their maturity, and that this is true also of the fish of each of the large river basins.”

23

Number of First Growth Season Circuli

Fish Migration and the Unit Stock: Three Formative Debates

A

30

A D

B C

E

20

10

0

Birkenhead Harrison Hatchery Pitt Lake Hatchery FishCanyon Morris Creek Skookumchuck

Spawning Locality FIGURE 3-2. Box-whisker plots of circuli counts of Fraser River juvenile sockeye salmon, collected in 1918 (Table III; Gilbert, 1919). Not shown due to low sample size (n = 12) is the Hanceville locality. Sample sizes for other sites ranged from 40 to 153. Letters represent similar and discrete statistical groupings (ANOVA; Tukey post hoc test; a = 0.05).

Were these natural tags the result of environment or lineage? Gilbert argued for both causes. He believed that heredity fixed racial differences existed among tributaries, but also stated that “. . . during their life in freshwater salmon are subjected to obviously diverse external conditions. These are most marked perhaps, between salmon inhabiting different watersheds, but undoubtedly exist, if in less degree, between those that live for a year or more in different lakes belonging to the same river basin” (Gilbert, 1915). Although speculative, reconciliation of these two apparently diverging views may have come from Gilbert’s appreciation for trait variance within species and populations: “. . . the peculiarities of each race in the matter of habitat do not lie outside but within the total range of variation as found in other river basins. Nothing new to the species, then, is found in a rare phenomenon, within the range of variation exhibited elsewhere” (Gilbert, 1914). Thus, Gilbert may have recognized well in advance of population genetics (e.g., Fisher, 1930) that heredity leads to modalities in trait expression rather than invariant race-specific traits. To refute Jordan’s criticism of the parent stream theory, it was insufficient to only recognize trait differences between tributaries—this could be due to local

24

D. H. Secor

ambits rather than philopatry. Initial evidence for philopatry came from observations of low straying rates between adjacent spawning tributaries. For instance, from many thousands of sockeye salmon examined from Rivers Inlet Race, only 24 were observed to exhibit different scale patterns, which were attributed to the adjacent Fraser River race. Gilbert (1917) concluded, “. . . the spawning runs in these streams and tributaries enjoy practical isolation, each from every other. . . .” The question of hatcheries underlay the early work on Pacific salmon, as it did with Norwegian cod. As previously described, Dahl (1909) and Hjort (1909) used the issue of scale to argue against local benefits due to local releases of cod. In contrast, Gilbert’s early work suggested that hatcheries could have local benefits. Gilbert (1915) advised that for hatcheries to be effective, “every stream must receive its own quota of fry.” Using natural tags to evaluate philopatry entails a troubling conundrum. What if the salmon of two tributaries exhibit the same certificate of origin? Gilbert (1919) noted two possible causes: Racial segregation has not occurred, or segregation has occurred, but has not resulted in divergence in the natural tag. Gilbert and others (e.g., Neave, 1964; Harden Jones, 1968; see also Waldman, 1999) have used multiple phenotypic traits (e.g., growth, age at maturity, length of life in freshwater, color, quality of flesh) to reduce the chance that common certificates of origin for different races will be misinterpreted. Still, the parent stream theory entails lineage and natural tags can only provide circumstantial evidence. Later, Huntsman (1937a), a chief antagonist of the parent stream theory, would argue strongly against the use of natural tags in support of philopatry: “The characters that have been used to distinguish ‘races’ in species of marine fishes, such as herring and cod, are being demonstrated to result from the action of the environment on the individual during its lifetime, so that it seems doubtful whether there are heritable differences between the populations of different districts.”

B. MARKING EXPERIMENTS Tagging should provide advantages in investigating homing because origins (spawning tributaries and localities) can be identified without the reliance of distinguishing natural tags. A central problem in early marking experiments for Pacific and Atlantic salmon was low rates of returning adults in comparison to tagged and released juveniles. Return rates of adults from a marked sample of juveniles ranged from 0% to 10% in early studies. What then happened to the majority of tagged juveniles? Were these lost due to straying to other systems, tagging and release mortality, mortality at sea, fishing mortality, or were they

Fish Migration and the Unit Stock: Three Formative Debates

25

somehow otherwise “lost at sea,” as Huntsman and others believed? Interestingly, this debate remains important in investigations on homing mechanisms in salmon. Geolocation homing mechanisms entail high overall ocean mortality but efficient (>90%) homing by adult survivors (Quinn and Groot, 1984; Quinn and Dittman, 1990). Random walk models imply low ocean losses prior to spawning migrations, but less efficient (25 (black) larvae 10m-2. Distributions indicate mixing of larvae after approximately 8 weeks between the Georges Bank and Nantucket Shoals spawning groups.

The Use of Early Life Stages in Stock Identification Studies

99

herring larvae spawned in the St. Lawrence River during the spring were retained near spawning grounds, whereas larvae spawned during the fall were advected away from spawning grounds. Other studies have found that Atlantic herring larvae were advected away from spawning areas (e.g., Townsend et al., 1986; Chenoweth et al., 1989), bringing into question the retention of Atlantic herring larvae near spawning grounds. Based on nearly 20 years of ichthyoplankton data from the northeast United States continental shelf, Smith and Morse (1993) found evidence of discrete distributions of larvae 100 km) from tag-recapture data (Begg et al., 1997). 124

Life History Parameters

125

FIGURE 6-4. Fisheries-dependent annual commercial catch-per-unit-effort (CPUE) and mean catch of silver kob (Argyrosomus inodorus) for line fishers operating along the South African coastline. CPUE and catch data were used to indicate three modal groups in abundance that were indicative of separate stocks (Griffiths, 1997).

catch statistics to provide information on the association and geographic continuity of adjacent groups of fish (Casselman et al., 1981; Ihssen et al., 1981; Campbell and Mohn, 1983; Griffiths, 1996, 1997; Begg, 1998a). For example, Griffiths (1997) used CPUE data to indicate that there were three modal groups in abundance of silver kob (Argyrosomus inodorus) along the South African coastline that were indicative of separate stocks (Fig. 6-4). Moreover, covariability between stock abundances at geographically dispersed locations is a valuable source of information regarding the dynamic structure and physical forcing of spatially distributed populations (Botsford and Paulsen, 2000). Although there are obvious advantages with using fisheries-dependent data compared to expensive and often logistically unfeasible fisheries-independent research survey data, there are also potentially severe limitations with using such data that need to be considered. One of the main limitations with using fisheries-dependent data is the inherent potential for the samples to be biased or unrepresentative of the stock owing to the selective nature of the fishing gear, handling and discard practices, and/or market demands (Hilborn and Walters, 1992). In addition, stocks can be hyperstable, migrate on a temporal and spatial basis, exhibit density-dependent effects

126

Gavin A. Begg

such as changing distribution with abundance, and form age-, size-, or sexsegregated groups (Swain and Wade, 1993; Jennings et al., 2001). Distribution and abundance parameters derived from fisheries-dependent data are innately biased because these typically reflect only where high-density fishable aggregations exist. In contrast, fisheries-independent research surveys are designed to avoid these biases by sampling the approximate range of a species distribution. Abundance parameters derived from such data are generally estimated via a timeor area-specific CPUE measure, which is assumed to be proportional to stock size. Several methods have been established for collecting fisheries-independent research survey data to estimate distribution and abundance parameters, although the most common are based on stratified-random or fixed survey sampling designs (Azarovitz, 1981; Clark, 1981; Pálsson et al., 1989; Anonymous, 1992). The effects of these biases, however, may not be so pronounced or as important when the objective is to use distribution and abundance data for stock identification. Stock identification is rarely the main aim of distribution and abundance studies, and many research surveys and commercial fisheries collections are too irregular or geographically imprecise to provide good evidence of stock separation ( Jennings et al., 2001). However, fisheries-dependent data are particularly important in data-sparse fisheries where there is often little fiscal or logistic support for extensive fisheries-independent research surveys and in many situations may be the only data available. Distribution and abundance data, where available, should be the first data examined in any stock identification study to assist in refining more specific questions concerning spatial detail of stock structure. Following on from such initial investigations and collation of baseline data, similarly readily available data for estimation of other life history parameters such as age, growth, and reproduction should be examined to corroborate and refine preliminary interpretations of stock structure derived from distribution and abundance data.

B. AGE, GROWTH,

AND

MORTALITY

Age, growth, and mortality characteristics are the most frequently used life history parameters to identify putative fish stocks. Strong geographic differences in age or size composition, if not reflective of fishing gear differences and other factors (i.e., sampling biases), suggest independence of recruitment or other biological or fishery factors as a basis for assuming discrete stocks (Begg and Waldman, 1999). Like other life history parameters, age- and size-based parameters are strongly influenced by environmental factors, although differentiating the effects of these factors from exploitation is inherently difficult (Serchuk et al., 1994). Several derivatives of age and size can be used to describe the dynamics of a stock, which in turn can be used as a means for stock identification, including

Life History Parameters

127

age and size distributions (Boyar, 1968; Casselman et al., 1981; Hanchet, 1999). maximum age, length, and weight (or a percentile thereof) (Begg et al., 1999b; Fromentin and Fonteneau, 2001), modal size and age class (Hanchet, 1999); natural or instantaneous mortality (Begg et al., 1999b; Horn and Hurst, 1999; Williams et al., 2003), length–weight relationships ( Japp, 1990; Lowe et al., 1998), and commonly used growth curves and their associated parameters (Griffiths, 1996; DeVries and Grimes, 1997). Age- and size-based life history parameters have received the greatest attention in stock identification studies because they are also the vital parameters used in calculations of yield and productivity, and, as such, provide the basis for stock assessment and management (Casselman et al., 1981). Furthermore, the resultant yield dynamics of individual stocks may differ with respect to their biological productivity as a function of age- and size-based life history parameters and the relevant impacts of exploitation (Pawson and Jennings, 1996), thereby providing an additional basis for stock differentiation, while satisfying a fundamental criterion of fisheries management. Individual life history parameters of longevity, senescence, mortality, growth, and year-class strength can be described parametrically and provide a basis for stock differentiation, although their plasticity in response to short-term environmental variation through density-dependent control mechanisms and selective effects of fishing have been suggested to reduce their value (Dizon et al., 1992; Pawson and Jennings, 1996). In phenotypic-based studies, it is essential to consider any confounding variation or sampling biases that may be present owing to selective effects or differences in fishing gears and/or differences between samples in age group, year-class, or sex ratio, so as to not mistake stock differences for sample differences (Castonguay et al., 1991; Begg and Brown, 2000; Bolles and Begg, 2000). Parameter estimates will be biased if samples used in any comparison of stock dynamics are unrepresentative of the stock in question or if results cannot be corrected to account for sampling bias (Jennings et al., 2001). Failure to account for such extraneous influences may result in falsely attributing differences between stocks to a stock effect, whereas differences may in fact simply be reflective of sample variation (Begg and Waldman, 1999; Begg and Brown, 2000). Differential sex-specific growth is a common characteristic among fish stocks and is a typical factor that needs to be accounted for in any interstock comparisons (DeVries and Grimes, 1997; Begg and Sellin, 1998; Hanchet, 1999). Likewise, aggregating data across sampling years because of insufficient sample numbers may bias the resulting parameter estimates, although some may question the general applicability of the findings if sampling is restricted to a single year (Simard et al., 1992). Prior to analysis, therefore, samples should initially be stratified according to sampling year, age group, sex, and so forth, or standardized with respect to fish length or age, depending on the particular variable under scrutiny, to minimize such biases and potential errors. Appropriate statistical analyses can then be conducted to determine if there is a need to account for any

128

Gavin A. Begg

sampling biases before conclusions regarding stock status are made. In addition to these analytical and sampling errors or biases that need to be considered, there are also the inherent process errors associated with measurement and age interpretation. Although development and validation of reliable aging techniques is an evolving process designed to reduce these errors and permit more effective interpretation of age and growth information (Pawson and Jennings, 1996), I do not discuss this further, but direct readers to several excellent sources that review these issues in more detail (see Beamish and McFarlane, 1983; Summerfelt and Hall, 1987; Campana, 2001). Comparison of growth curves between putative fish stocks is the most commonly used approach in life history–based stock identification studies. A plethora of studies have examined differences in population or individual growth trajectories, at both the daily and annual level, as a basis for differentiating stocks (e.g., Japp, 1990; Simard et al., 1992; Griffiths, 1996; DeVries and Grimes, 1997; Begg and Sellin, 1998). Several growth models have been fitted to observed and backcalculated length-at-age data including Schnute, Richards, Gompertz, and logistic (Schnute, 1981; Haddon, 2001), although the von Bertalanffy (1938) growth model is the most commonly used in fisheries science since its first application by Beverton and Holt (1957): L t = L • [1 - e - K ( t -t 0 ) ] where

Lt L• K t0

= = = =

length at age t; mean asymptotic length; rate at which L• is approached; and age at which fish have a theoretical length of zero.

The von Bertalanffy growth model is often fitted to length-at-age data using nonlinear least squares methods and frequently due to fishing selectivity and size limits, when there are few data available for younger and older fish (Fig. 6-5). This typical lack of available data at the extremes of the distributions invariably results in the estimation of L• and t0 by means of extrapolation, where care must be taken in the interpretation of the resultant growth parameters and comparisons with other putative stocks (Haddon, 2001). Back-calculation methods have attempted to minimize these sampling biases by estimating lengths at ages of younger fish that are rarely observed in fishery-dependent samples (Campana, 2001). Nonetheless, the nonlinearity that depicts most growth curves has led to major difficulties in comparing these curves, which, in turn, has generated an expanse of literature detailing how best to address these difficulties (e.g., Kimura, 1980; Misra, 1980; Bernard, 1981). Likelihood ratio, t-, univariate (Chi)2-, and Hotelling’s T2-tests have all been used to compare von Bertalanffy–derived growth parameters among stocks, with the accuracy of each dependent on the functionality of the growth model, sample size, and the degree of heterogeneity in the

A 900

LCF (mm)

800 700 600 500

Northern Central Southern

400 300 0

1

2

3

4

6

5

7

8

9

10

Age class (years) B 490

Mackay

L• (mm)

480 470 460 450

Townsville

Storm Cay

440 0.34

0.39

0.44

0.49

0.54

C

75

Mean fork length (cm)

K (years-1)

60 45 Div. 4TVW Div. 4X Div. 5Y Div. 5Z

30 15

0

2

4

6 10 8 Age (years)

12

14

FIGURE 6-5. Comparison of age and growth data as a basis for differentiating putative fish stocks: (A) von Bertalanffy growth curves fitted to length to caudal fork (LCF) at age data for female school mackerel (Scomberomorus queenslandicus) sampled in three regions throughout Queensland, Australian waters (Begg and Sellin, 1998); (B) 95% confidence ellipsoids for the von Bertalanffy growth model parameters, K and L•, for a large coral reef fish (Lethrinus miniatus) sampled from three regions of the Great Barrier Reef, Australia (Williams et al., 2003); and (C) mean lengths at age among four haddock (Melanogrammus aeglefinus) stocks in the northwest Atlantic Ocean (Begg, 1999a). 129

130

Gavin A. Begg

error variances (Cerrato, 1990). In an empirical comparison of these tests, Cerrato (1990) found that the likelihood ratio test was the most accurate and advocated that it be the test of choice for growth curve comparisons. Kimura (1980) first introduced the likelihood ratio test as a means of comparing von Bertalanffy growth curves among populations and also demonstrated practical approaches for constructing confidence ellipsoids around parameter estimates that could be used to identify differences in stock dynamics (Fig. 6-5). Although a common occurrence, growth models fitted to fishery-dependent data can have many biases and the resultant growth parameters may not be representative of the actual stock in question due to size-selective fishing where larger, faster-growing individuals are differentially removed from the stock (Kimura, 1980; Haddon, 2001). Parameters of growth models that do not adequately fit observed data are also likely to produce erroneous results when used in subsequent analyses and should be used with caution (Griffiths, 1996). In addition, von Bertalanffy and other growth model parameters are correlated, making univariate tests inappropriate for comparing differences between like parameters from putative stocks (Bernard, 1981; DeVries and Grimes, 1997). An alternative approach to avoid these biases, and more importantly, the difficulties associated with comparing nonlinear growth curves and extrapolating parameter estimates beyond the range of the data is to statistically compare mean lengths at age among putative stocks across a common age range. Instead of applying growth models to observed length at age data, age-length keys can be derived where mean lengths at a given age or the proportional distribution of numbers at different sizes for given ages are estimated with an associated variance that can be compared in an analysis of variance or Generalized Linear Model (GLM) framework (Haddon, 2001) (Fig. 6-5). Gear selectivity problems, differences in sampling times, and the general lack of homogeneity in samples, however, may still confound results and need to be considered in any stock comparisons (Ihssen et al., 1981; Begg and Sellin, 1998). Consistent differences in age- and size-based life history parameters such as growth and mortality rates, therefore, have frequently been used to separate stocks (Begg and Waldman, 1999). The degree of asynchrony in population statistics of these parameters provides useful information on the disparity and phenotypic separation of adjacent stocks that is beneficial to fisheries management (Casselman et al., 1981). Significant differences in population statistics between fish stocks can be accepted as evidence that different environments, and hence, different territories are occupied throughout the life history of the fish (Ihssen et al., 1981), but cannot be used to indicate whether stocks of these fish are genetically discrete (Pawson and Jennings, 1996). Discreteness determined in this manner may not be complete and, although often assumed, reproductive isolation is not necessarily manifest for stocks so identified (Ihssen et al., 1981). More definitive life history parameters related to spawning, however, may provide

131

Life History Parameters

a greater foundation for the assumption of reproductive isolation and, hence, genotypic separation of fish stocks.

C. REPRODUCTION, SPAWNING,

AND

MATURITY

Reproductive life history parameters provide fundamental information to assist in understanding biological processes that may be responsible for maintaining the underlying stock structure of a species (Begg, 1998b). The underpinning of the biological definition of a fish stock is that they are self-reproducing or reproductively isolated units, with members of each putative stock exhibiting similar life history characteristics (Hjort, 1914; Ihssen et al., 1981; Hilborn and Walters, 1992). This definition depends directly on our knowledge of spawning behavior and other reproductive parameters that are necessary for the formation and maintenance of stock structure. Individual stocks can develop phenotypic and genotypic differences in these parameters over time due to reproductive isolation (Waldman et al., 1988), which arise from diverse environmental conditions, differential selection pressure, and evolutionary divergence through drift and local adaptation (Dizon et al., 1992; Adkinson, 1995; Waldman, 1999). Numerous reproductive life history parameters have been used to describe the dynamics of a fish stock and provide the basis for stock differentiation, including timing, duration, and location of spawning (Finucane et al., 1986; Hutchings et al., 1993; Begg, 1998b); egg and larval distributions (O’Boyle et al., 1984; Begg et al., 1999b; Bruce et al., 2001); median or mean age, length, and weight at maturity (Beacham, 1982; O’Brien, 1990; Griffiths, 1997; Trippel et al., 1997); egg weight, size, viability, and fecundity relationships (Bradford and Stephenson, 1992; Marteinsdottir et al., 2000a); proportion of recruit and repeat spawners (Rochet, 2000); and, more recently, maternal effects and reproductive potential (Trippel, 1999; Marteinsdottir and Begg, 2002). Knowledge of the temporal and spatial extent of spawning can provide information on intraspecific variation in life history parameters that can be used to discriminate separate stocks (Schaefer, 1987). Differences in timing and location of spawning provide a particularly valuable criterion because they can result in reproductive isolation among stocks by restricting gene flow to a level that effectively isolates stock units (Iles and Sinclair, 1982; Dizon et al., 1992; Bailey et al., 1999). Reproductive isolation among stocks is necessary for the formation and maintenance of stock integrity which can be derived from concurrent spawning of stocks in geographically dispersed locations (Horrall, 1981). Several genetically distinct stocks of herring (Clupea harengus) in the northwest Atlantic Ocean, for example, have been determined by the number of geographically stable spawning and larval retention areas, where the stocks spawn in relatively discrete geographic locations (Iles and Sinclair, 1982; Stephenson, 1991).

132

Gavin A. Begg

Likewise, separate stocks of king mackerel (Scomberomorus cavalla) in the United States have been postulated because of disjunct spawning in locations along the Atlantic seaboard and in the Gulf of Mexico (Finucane et al., 1986). Numerous other studies have also implied stock discreteness for a range of species because of differential spawning times and locations of adult spawning fish (e.g., Hutchings et al., 1993; Sinclair and Tremblay, 1984; Page and Frank, 1989). Differences in spawning and hatch-day distributions of eggs and larvae have also been used to demonstrate differential stock or spawning components (Begg and Marteinsdottir, 2000, 2002b; Marteinsdottir et al., 2000b; Bruce et al., 2001; Gaughan et al., 2001) (Fig. 6-6). Discrete larval distributions linked to particular geographic regions or hydrological features provide a mechanism for stock structure, imprinting and spawning site fidelity (O’Boyle et al., 1984; Stephenson, 1991; Begg, 1998a). Egg and larval surveys frequently provide information which assists with stock identification because stock integrity depends on spawning fish from different stocks being separated in space or time, even if they mix at other stages of their life history (Pawson and Jennings, 1996). Eggs identified from such surveys provide a direct or immediate response to spawning, while larvae provide an indication of movement to nursery grounds. Prediction of larval movements using oceanographic models can further indicate the extent to which progeny from different spawning stocks are dispersed and separated, and, in turn, assist the identification of stock-specific spawning locations which provide recruitment to specific nursery grounds (Pawson and Jennings, 1996). Collection and analysis of life history data on spawning adult fish and their progeny should be a priority of any stock identification study because of the direct relationship to reproductive isolation and stock discreteness. Individual fish should be sampled from putative stocks during their respective spawning season to maximize stock discreteness which may otherwise be obscured by spatial overlap and stock mixing during other times of the year (Casselman et al., 1981). Spawning (i.e., ripe and running) fish collected from assumed spawning locations would overcome the potential problem of stock mixing (Stephenson, 1991) and refine spawning periodicity. Likewise, given the imminent release of eggs once hydration occurs, the collection location of spawning females with hydrated eggs should closely approximate spawning locations (Hutchings et al., 1993). As discussed previously, spatial distribution plots and georeferenced statistical techniques could be applied quite readily to presence/absence ratios or abundance indices of mature or spawning individuals, eggs, and/or larvae to define stock boundaries. In addition, data collected on individual fish during the spawning season could be used to estimate age and length at maturity and other reproductive parameters that have proved useful as indicators of stock status (Fig. 67). Similar sampling and analytical issues, as discussed previously, will be encountered with the use of reproductive-based life history parameters and need to be examined accordingly depending on the parameter in question. For

A 140 300

1

5

120

400

Frequency

2

6

25

250

3

600

7

200

4

50

22 Mar

19 Feb

100

3 May

10 Apr

200

150 30 May

8

50

19 Jul

22 Mar

19 Feb

100

3 May

10 Apr

Spawning day (DOY)

150 30 May

200 19 Jul

Spawning day (DOY)

B 68°

3

4

0.87

0.83

2

Latitude N

8

5 0.77

0.97 *

66° 0.99 0.74

6

1

64°

0.63

0.99

7

62° 36°

32°

28°

24°

20°

16°

12°

8°

Longitude W

FIGURE 6-6. Spawning day distributions of pelagic juvenile (0-group) Atlantic cod (Gadus morhua) used to demonstrate differential stock components in Icelandic waters: A) spawning day distributions of 0-group cod sampled in eight offshore regions (1970–1998 data combined). Dashed vertical lines: mean spawning day and 99th and 95th percentiles for main spawning grounds (Region 1); and (B) proportion of 0-group cod in each region predicted to have originated from main spawning grounds. Proportions are based on number of cod spawned at a later DOY than that estimated for the 99th percentile. Results demonstrate that large numbers of the surviving juvenile population may originate from other stocks besides that of the main spawning stock in the south (Begg and Marteinsdottir, 2000b).

133

1.0 0.8 0.4

0.6

1989 South n=1058 a=-5.6699 b= 0.0837 L50=67.7

0.0

0.2

North n=3077 a=-8.2215 b= 0.1014 L50=81.1

0.8

40

60

80

100

120 140

1994 South n=1200 a=-6.3391 b= 0.0988 L50=64.2

0.4

0.6

20

0.2

North n=1959 a=-7.2835 b= 0.1046 L50=69.6

0.0

Proportion mature

1.0

0

1.0

0

20

40

60

80

100

120

140

0.8

1999

0.4

0.6

South n=1414 a=-7.0447 b= 0.1100 L50=64.0

0.0

0.2

North n=2684 a=-7.4881 b= 0.1018 L50=73.6

0

20

40

60

80

100

120 140

Total length (cm) FIGURE 6-7. Logistic regression models fitted to length at maturity data for Atlantic cod (Gadus morhua) collected in waters of northern and southern Iceland from spring (March) Marine Research Institute groundfish surveys (1989–1999). The models were used to estimate the length at 50% maturity (L50), which has proved useful as an indicator of stock status. n, sample size; a, intercept term; and b, regression coefficient (Begg and Marteinsdottir, 2002a). 134

Life History Parameters

135

example, estimates of age and length at maturity need to account for sampling biases, as discussed for growth and mortality, while analytical issues associated with georeferenced data will need to be considered for distribution and abundance studies, irrespective of the life history stage investigated. Reproductive life history parameters are extremely useful in discerning the underlying biological characteristics that shape stock structure. These parameters provide insight into the isolating mechanisms that are responsible for the maintenance of stock integrity and are elementary inputs for defining the productivity and discreteness of a fish stock. Inexplicably linked with reproductive life history parameters are those related to recruitment and critical early life history stages, which are postulated to be the principal determinants of year-class strength (Cushing, 1969; Campana et al., 1989; Mertz and Myers, 1994).

D. RECRUITMENT Relative indices of recruitment (i.e., the number of fish that have attained the age at which they are vulnerable to fishing) and abundance or biomass of early life history stages in putative fish stocks can provide information on year-class strength and stock resilience, as well as stock relatedness. Information on the origins of early life history stages is needed to understand stock structure and the mechanisms responsible for recruitment variability (Marteinsdottir et al., 2000a). Similar to other life history parameters, intraspecific geographic differences in recruitment provide an indirect basis for stock separation (Begg and Waldman, 1999). Recruitment and growth are the major contributors to the productivity and long-term sustainability of a stock (Haddon, 2001). The productivity of individual stocks and their relative contribution to a fishery shift with time due to stockspecific differences in recruitment and exploitation rates (Waldman and Fabrizio, 1994). Recruitment success is most likely dependent on favorable environmental and stock effects that influence the dispersal and survival of early life history stages (Begg and Marteinsdottir, 2002b). Recruitment to fish stocks is naturally highly variable, and the mechanisms responsible are often poorly known. However, differentiating environmental and stock effects on early life history stages and the relationship between spawning stock biomass and recruitment are important steps in understanding the factors that govern recruitment dynamics and stock structure (Ricker, 1954; Beverton and Holt, 1957; Myers et al., 1995). Similar to other life history parameters, asynchronous fluctuations in recruitment over time and among geographic regions provide circumstantial evidence for the existence of separate stocks (Waldman et al., 1988). Conversely, interrelationships may exist between stocks when the same year-classes demonstrate similar patterns in recruitment strength (Clark et al., 1982; Koslow et al., 1987;

136

Gavin A. Begg A

B

FIGURE 6-8. Time series of recruitment indices lagged back to the year of spawning for: (A) five Atlantic cod (Gadus morhua) and (B) four haddock (Melanogrammus aeglefinus) stocks in the northwest Atlantic Ocean. Asynchronous fluctuations in recruitment over time and among geographic regions provide circumstantial evidence for the existence of separate stocks (Koslow et al., 1987).

Thompson and Page, 1989) (Fig. 6-8). The occurrence of similar year-class strength in walleye pollock (Theragra chalcogramma) throughout different regions of the Bering Sea is one example where uniformity in recruitment patterns was cited as evidence of stock panmixia (Dawson, 1994). Several studies

Life History Parameters

137

have suggested that large-scale physical and biological forcing are partly responsible for synchrony in recruitment and year-class strength among other gadoid stocks in the northwest Atlantic Ocean (Koslow, 1984; Koslow et al., 1987), although other studies have indicated that local-scale processes are more influential (Cohen et al., 1991). This disparity highlights one of the major issues in fisheries science, that being the difficulty in deciphering the mechanisms responsible for year-class strength and recruitment variability. However, if recruitment is determined primarily by biological interactions, stronger correlations should be found in recruitment among stocks co-occurring within the same geographic region than among stocks with disjunct distributions (Koslow, 1984). Putative fish stocks have usually been identified via coarse observations that reveal a hiatus in some aspect of their life history such as large intraannual differences in recruitment and year-class strength (Waldman, 1999). Evidence of stock structure has been provided by spawning and recruitment patterns in relation to localized oceanographic conditions within each region where members of a stock reside (Begg, 1998a). Recruitment indices, as with other life history parameters, are useful indicators of phenotypic stock differences, but typically do not enable classification of individual fish to a specific stock owing to the wide variability that naturally occurs within these parameters between individual fish (Waldman et al., 1988). Life history parameters also vary temporally within stocks as well as spatially between stocks, bringing into question the long-term stability of these parameters as indicators of stock structure (Ihssen et al., 1981; Pawson and Jennings, 1996; Begg et al., 1999b). In addition, some parameters may be less invariant over time, further questioning their utility as long-term indicators of stock status, although fisheries management is innately short-term and as such, information derived from life history parameters may be more useful, depending on the management objective to be addressed.

III. TEMPORAL STABILITY Fish stocks are dynamic, interactive groups characterized by spatial boundaries and associated vital life history parameters that vary with time (Cowen et al., 1993). Numerous studies have demonstrated temporal variation in a range of life history parameters that have occurred in response to changing environmental conditions and/or exploitation patterns (e.g., Beacham, 1982; O’Brien, 1990; DeVries and Grimes, 1997; Trippel et al., 1997; Overholtz, 2002). Since the 1960s, when peak landings occurred for a suite of groundfish and pelagic fish stocks in the northwest Atlantic Ocean, for example, there have been consistent and definitive changes in their life history parameters. Generally throughout this period, stocks of Atlantic cod (Gadus morhua), haddock (Melanogrammus aeglefinus), silver hake (Merluccius bilinearis), and yellowtail flounder (Limanda

138

Gavin A. Begg

ferruginea) have grown to lower maximum lengths, reached lower ages and lengths at sexual maturity, and suffered higher mortality rates (e.g., O’Brien et al., 1993; Sinclair and Murawski, 1997; Begg et al., 1999b) (Fig. 6-9). This interdependence of different life history parameters is implicit in compensatory responses of exploited stocks to fishing pressure (Beverton, 1963; O’Brien et al., 1993), where these responses occur between parameters to most likely maintain evolutionary fitness (Jennings and Beverton, 1991). Atlantic herring (Clupea harengus) stocks, and others, have also experienced significant expansions and contractions in their distributions relative to changing abundance levels (Overholtz, 2002) (Fig. 6-10). Associated with these changes in life history parameters have been large-scale fluctuations in atmospheric circulation patterns (Drinkwater, 1996; Drinkwater and Mountain, 1997) and significant increases in fishing pressure (Sinclair and Murawski, 1997). However, differentiating the influence of environmental conditions from exploitation patterns is inherently difficult, particularly given the magnitude of fishery-induced changes that have occurred on these stocks (Serchuk et al., 1994). The existence of temporal variability in life history parameters does not negate their utility for the purpose of stock identification, although it does highlight the need to examine these parameters among stocks over consistent time frames. If only a particular time frame was analyzed among stocks, or worse still, dissimilar time frames, erroneous results concerning the stock structure of a species could be derived because of the plasticity of vital life history parameters to the effects of changing environmental conditions and fishing pressures (Begg et al., 1999b). The influence of temporal variability in life history parameters for stock identification has largely been ignored because, typically, the assumption is made that temporal variation within stocks is not confounded with geographic variation among stocks or that within-stock variation is insignificant relative to among-stock variation (Blouw et al., 1988; Stephenson, 1991). Failure to account for such temporal variation, however, as mentioned previously, may result in falsely attributing differences between stocks to a stock effect, whereas these differences may in fact be reflective of differences in sampling times (Begg and Waldman, 1999). The use of inconsistent or a single time frame by which to interpret life history parameters for stock identification can be misleading and stresses the need to investigate the temporal variation in these parameters (Begg et al., 1999b). Studies that fail to account for such within-stock variability are subject to error, which will depend on the magnitude of within-stock variation relative to among-stock variation, the proportion of life history parameters that demonstrate temporal variation, and the degree to which fish stocks vary synchronously for the parameters in question (Blouw et al., 1988). Temporal variation should be minimized in any attempt to identify stock structure by collecting or analyzing data when temporal effects are least pronounced (Ihssen et al., 1981). Synchronous

139

Life History Parameters

A

Females

1.28

CC GB

0.96

K (± s.e.)

0.96

K (± s.e.)

Males

1.28

CC GB

0.64

0.64 0.32

0.32

1970 1975 1980 1985 1990 1995

1970 1975 1980 1985 1990 1995

Year 5

Year 5

CC GB

4

A 50 (years)

A 50 (years)

4 3 2 1

3 2 1

1970 1975 1980 1985 1990 1995

1970 1975 1980 1985 1990 1995

Year

Year

40

40

CC GB

CC GB

30

L50 (cm)

30

L50 (cm)

CC GB

20 10

20 10

1970 1975 1980 1985 1990 1995

1970 1975 1980 1985 1990 1995

Year

Year

FIGURE 6-9. Temporal variation in life history parameters. (A) Yellowtail flounder (Limanda ferruginea) sex-specific mean von Bertalanffy growth rate coefficient (K), and age (A50) and length (L50) at 50% maturity for putative Cape Cod (CC) and Georges Bank (GB) stocks in the northwest Atlantic Ocean (1970–1997) (Begg et al., 1999b); and (B) Atlantic cod (Gadus morhua) sex-specific mean age and length at 50% maturity in waters of northern and southern Iceland (Marteinsdottir and Begg, 2002).

sampling across putative stocks is also recommended, as too is sampling during periods when stocks are likely to be homogeneous and optimally separated, such as during spawning times for adult fish (Casselman et al., 1981; Ihssen et al., 1981).

140

Gavin A. Begg

B

FIGURE 6-9. Continued

Stock identification should be an evolving process where, because of their plasticity, life history parameters are reexamined periodically in response to changing environmental and resource conditions. Importantly, the existence of temporal variation in life history parameters does not negate their use, particularly if members of a stock respond in a similar manner over time to environmental and anthropogenic perturbations, as such a response in itself can be viewed as a stock attribute (Casselman et al., 1981; Ihssen et al., 1981).

IV. SPATIAL STABILITY Maintenance of temporal and spatial integrity among fish stocks is important for population structuring, particularly where spawning is adapted to the physical dispersive properties of a geographic location (Heath, 1992). Local hydrological conditions can minimize the mixing of eggs and larvae between neighboring stocks, effectively maintaining genetic discreteness and reproductive isolation (Iles and Sinclair, 1982; Palumbi, 1994). Alternatively, mixing between stocks may occur during egg, larval, or juvenile stages, with subsequent resegregation

Life History Parameters

141

FIGURE 6-10. Temporal variation in the relative center of abundance of Atlantic herring (Clupea harengus) collected from Northeast Fisheries Science Center (NEFSC) spring bottom trawl surveys (1968–1998) (Overholtz, 2002).

later in life (Swain et al., 1980), leading to potential differences among stocks in a range of life history parameters. In such circumstances, the level of stock integrity largely depends on the degree of larval mixing and postsettlement movement (Bruce et al., 2001). Understanding these structuring mechanisms, both biological and physical, can yield insights on factors affecting the stability of individual fish stocks and their associated life history parameters. Mechanisms by which putative fish stocks and their respective vital life history parameters are maintained are undoubtedly a combination of the biological processes and the physical environment in which they reside. Reproduction plays a fundamental role in the dispersal or retention of progeny through their initial spatial and temporal placement (Hare and Cowen, 1993) and assists in defining the stock structure of a species, as genetic isolation must involve the spawning life history stage to restrict gene flow to the level that effectively isolates stock

142

Gavin A. Begg

units (Iles and Sinclair, 1982). Spatial distributions and spawning times of a fish stock may represent evolutionary adaptations to circulation patterns (Parrish et al., 1981; Sinclair, 1988), which assists in maintaining reproductive isolation and stock integrity. Physical oceanographic processes also influence the distribution of early life history stages on a variety of spatial scales that can delineate and maintain stock boundaries (Bruce et al., 2001). Spawning times and locations of demersal gadoid stocks are usually associated with well-defined circulation or hydrological features that enhance the retention of eggs and larvae (Hutchings et al., 1993; Page et al., 1999). Likewise, spawning locations of marine pelagic fish species and their stocks have been hypothesized to be dependent on the geographic extent of oceanographic larval retention areas (Iles and Sinclair, 1982; O’Boyle et al., 1984). Several studies have supported this theory, particularly for Atlantic herring (Clupea harengus) (Sinclair, 1988; Stephenson, 1991), but also for a number of other species (e.g., Johnson et al., 1994; Bruce et al., 2001; Gaughan et al., 2001). In contrast, coral reef fish species generally exist as metapopulations of sedentary adult stocks linked by pelagic larval dispersal (Sale, 1998), resulting in differences in life history parameters at a range of spatial scales dependent on their position within the respective reef connectivity matrix and the degree of selfseeding (Kritzer, 2002; Williams et al., 2003). Studies of biophysical processes and early life history dynamics are critical in understanding the mechanisms responsible for the maintenance of stock structure and for the determination of spatial scales over which potentially connected stocks operate (Gaughan et al., 2001). Maintenance of stock integrity requires that progeny recruit to their source population and that mixing between stocks is restricted (Bruce et al., 2001). However, despite some degree of stock mixing, which often occurs during early life history stages of marine species, persistent differences in life history parameters are generally maintained between putative fish stocks (e.g., Begg et al., 1999b). Several reasons may explain these persistent differences in the possibility of stock mixing, including the following: (1) mixing, is variable between years and depends on the specific spawning locations, circulation patterns, and survival of progeny during a given year; (2) mixing occurs during the larval stages, with segregation occurring at some later point in the life history; and, more pertinently, (3) some stocks are not genotypic groups of fish, but simply reflect differences in phenotypic life history parameters in response to environmental variation and fishing pressure (Begg et al., 1999b).

V. CONCLUSIONS Life history parameters provide fundamental biological information for fisheries management and have the dual functionality in being useful descriptors of puta-

Life History Parameters

143

tive fish stocks. Distribution, abundance, growth, mortality, reproduction, recruitment, and other life history parameters provide baseline information that typically assists with the initial recognition and delineation of geographic regions that are representative of individual stocks (Pawson and Jennings, 1996) and is an almost essential prerequisite for successful stock identification (Griffiths, 1997). The use of such parameters is an efficient and cost-effective means for stock identification, as these data are routinely collected for assessment and management purposes (Ihssen et al., 1981; Pawson and Jennings, 1996) and can often be derived from archived databases. Although the utility of these parameters for stock identification appears to decrease with stock complexity, their applicability increases with the number and diversity of parameters examined (Ihssen et al., 1981; Begg and Waldman, 1999). Life history parameters are useful indicators of phenotypic stock differences, but typically do not enable classification of individual fish to a specific stock because of the wide variability that naturally occurs within these parameters between individual fish (Waldman et al., 1988; Begg et al., 1999b). Phenotypic differences in life history parameters also do not provide direct evidence of genetic isolation between stocks, but can indicate the prolonged separation of postlarval fish subject to different environmental conditions and/or fishing pressures (Campana et al., 1995; Begg et al., 1999a). Moreover, phenotypic differences in life history parameters among putative stocks, irrespective of genetic differences, provide a firm basis for separate management units and should be modeled separately for stock assessment purposes because of inherently related productivity differences (Cadrin and Friedland, 1999). Failure to recognize such differences and the related stock structure of an exploited species, as is typical of most stock assessment methods which model the dynamics of closed populations and assume homogeneous life history parameters, can lead to overfishing and depletion of less productive stocks with unknown ecological consequences (Ricker, 1954; Stephenson, 1999). Anthropogenic, biophysical, and ecological processes affecting life history parameters operate at a range of temporal and spatial scales that govern the formation and maintenance of stock structure. Indeed, life history parameters have been used successfully for stock identification at a diversity of scales, although which parameters to use will ultimately depend on the management objective and relevant scale for assessment. Nonetheless, it is critical to estimate life history parameters across a range of scales to infer which are of greatest importance for assessment and management (Stephenson, 1991; Sale, 1998). Furthermore, because of the diversity of these scales and complexity of the processes operating on life history parameters (Kritzer, 2002; Williams et al., 2003), stock identification studies should examine a multitude of parameters to maximize the likelihood of correctly defining stock structures. Investigation of any single parameter will not necessarily reveal stock differences even when true stock

144

Gavin A. Begg

differences exist (in statistical hypothesis testing this is referred to as “Type 1 error”), while the strongest inferences on stock structure are drawn from a suite of complementary parameters that cover multiple aspects of the biology of a species (Begg and Waldman, 1999). Future studies should adopt a holistic approach to stock identification by integrating information in a multivariate framework that captures the salient aspects of a range of life history parameters (e.g., Begg and Waldman, 1999; Rochet, 2000; Fromentin and Fonteneau, 2001). Such an approach would optimize available information and provide a comprehensive understanding of a species life history, which is an essential requirement for successful stock identification (Pawson and Jennings, 1996). Life history parameters, therefore, should be the first data examined in any stock identification study, as these are typically available for exploited species in archived databases of most fisheries and management agencies or can be readily collected in the field and analyzed accordingly in a cost-effective manner. Information derived from these parameters can be used to describe stock boundaries at a range of spatial scales that may assist in directing future studies to refine stock structures using more sophisticated techniques. Temporal and spatial variability in life history parameters, and potential biases in sampling, should also be examined in an effort to understand the mechanisms responsible for structuring putative stocks and to ensure that conclusions reached are reflective of true stock differences and not sampling anomalies.

ACKNOWLEDGMENTS I would like to thank Steven Cadrin for the invitation to contribute this chapter and his continued perseverance and encouragement to ensure its completion, and Jon Hare for his collaboration on an earlier manuscript which formed the basis of this work.

REFERENCES Adkinson, M. 1995. Population differentiation in Pacific salmon: local adaptation, genetic drift, or the environment? Canadian Journal of Fisheries and Aquatic Sciences 52: 2762–2777. Anonymous. 1992. Report of the ICES workshop on the analysis of trawl survey data. ICES CM 1992/D: 6. Azarovitz, T. R. 1981. A brief historical review of the Woods Hole Laboratory trawl survey time series. In W. G. Doubleday and D. Rivard (eds.), Bottom Trawl Surveys. Canadian Special Publication of Fisheries and Aquatic Sciences 58: 62–67. Bailey, K. M., Quinn, T. J., II, Bentzen, P., and Grant, W. S. 1999. Population structure and dynamics of walleye pollock, Theragra chalcogramma. Advances in Marine Biology 37: 179–255. Beacham, T. D. 1982. Some aspects of growth, Canadian exploitation, and stock identification of Atlantic cod (Gadus morhua) on the Scotian Shelf and Georges Bank in the northwest Atlantic Ocean. Canadian Technical Report of Fisheries and Aquatic Sciences 1069: 43 pp.

Life History Parameters

145

Beamish, R. J. and McFarlane, G. A. 1983. The forgotten requirement for age validation in fisheries biology. Transactions of the American Fisheries Society 112: 735–743. Begg, G. A. 1998a. A review of stock identification of haddock, Melanogrammus aeglefinus, in the northwest Atlantic Ocean. Marine Fisheries Review 60: 1–15. Begg, G. A. 1998b. Reproductive biology of school mackerel (Scomberomorus queenslandicus) and spotted mackerel (S. munroi) in Queensland east-coast waters. Marine and Freshwater Research 49: 261–270. Begg, G. A. and Brown, R. W. 2000. Stock identification of haddock Melanogrammus aeglefinus on Georges Bank based on otolith shape analysis. Transactions of the American Fisheries Society 129: 935–945. Begg, G. A., Cameron, D. S., and Sawynok, W. 1997. Movements and stock structure of school mackerel (Scomberomorus queenslandicus) and spotted mackerel (S. munroi) in Australian east-coast waters. Marine and Freshwater Research 48: 295–301. Begg, G. A., Friedland, K. D., and Pearce, J. B. 1999a. Stock identification and its role in stock assessment and fisheries management: an overview. Fisheries Research 43: 1–8. Begg, G. A., Hare, J. A., and Sheehan, D. D. 1999b. The role of life history parameters as indicators of stock structure. Fisheries Research 43: 141–163. Begg, G. A. and Marteinsdottir, G. 2000. Spawning origins of pelagic juvenile cod Gadus morhua inferred from spatially explicit age distributions: potential influences on year-class strength and recruitment. Marine Ecology Progress Series 202: 193–217. Begg, G. A. and Marteinsdottir, G. 2002a. Environmental and stock effects on spatial distribution and abundance of mature cod Gadus morhua. Marine Ecology Progress Series 229: 245–262. Begg, G. A. and Marteinsdottir, G. 2002b. Environmental and stock effects on spawning origins and recruitment of cod Gadus morhua. Marine Ecology Progress Series 229: 263–277. Begg, G. A. and Sellin, M. J. 1998. Age and growth of school mackerel (Scomberomorus queenslandicus) and spotted mackerel (S. munroi) in Queensland east-coast waters with implications for stock structure. Marine and Freshwater Research 49: 109–120. Begg, G. A. and Waldman, J. R. 1999. An holistic approach to fish stock identification. Fisheries Research 43: 35–44. Bernard, D. R. 1981. Multivariate analysis as a means of comparing growth in fish. Canadian Journal of Fisheries and Aquatic Sciences 38: 233–236. Beverton, R. J. H. 1963. Maturation, growth and mortality of clupeid and engraulid stocks in relation to fishing. Rapports et Procés-Verbaux des Réunions, Conseil International pour l’Exploration de la Mer 154: 44–67. Beverton, R. J. H., and Holt, S. J. 1957. On the dynamics of exploited fish populations. Fisheries Investigation Series II London 19: 553 pp. Blouw, D. M., Saxon, S. D., and Chadwick, E. M. P. 1988. Temporal variation of meristic traits within an Atlantic salmon (Salmo salar) stock, and implications for stock identification. Canadian Journal of Fisheries and Aquatic Sciences 45: 1330–1339. Bolles, K. L. and Begg, G. A. 2000. Distinction between silver hake (Merluccius bilinearis) stocks in U.S. waters of the northwest Atlantic based on whole otolith morphometrics. Fishery Bulletin 98: 451–462. Boyar, H. C. 1968. Age, length, and gonadal stages of herring from Georges Bank and the Gulf of Maine. Northwest Atlantic Fisheries Research Bulletin 5: 49–61. Botsford, L. W. and Paulsen, C. M. 2000. Assessing covariability among populations in the presence of intraspecific correlation: Columbia River spring-summer chinook salmon (Oncorhynchus tshawytscha) stocks. Canadian Journal of Fisheries and Aquatic Sciences 57: 616–627. Bradford, R. G. and Stephenson, R. L. 1992. Egg weight, fecundity, and gonad weight variability among northwest Atlantic herring (Clupea harengus) populations. Canadian Journal of Fisheries and Aquatic Sciences 49: 2045–2054.

146

Gavin A. Begg

Bruce, B. D., Evans, K., Sutton, C. A., Young, J. W., and Furlani, D. M. 2001. Influence of mesoscale oceanographic processes on larval distribution and stock structure in jackass morwong (Nemadactylus macropterus: Cheilodactylidae). ICES Journal of Marine Science 58: 1072–1080. Cadrin, S. X. and Friedland, K. D. 1999. The utility of image processing techniques for morphometric analysis and stock identification. Fisheries Research 43: 129–139. Campana, S. E. 2001. Accuracy, precision and quality control in age determination, including a review of the use and abuse of age validation methods. Journal of Fish Biology 59: 197–242. Campana, S. E., Frank, K. T., Hurley, P. C. F., Koeller, P. A., Page, F. H., and Smith, P. C. 1989. Survival and abundance of young Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) as indicators of year class strength. Canadian Journal of Fisheries and Aquatic Sciences 46 (suppl. 1): 171–182. Campana, S. E., Gagné, J. A., and McLaren, J. W. 1995. Elemental fingerprinting of fish otoliths using ID-ICPMS. Marine Ecology Progress Series 122: 115–120. Campbell, A. and Mohn, R. K. 1983. Definition of American lobster stocks for the Canadian maritimes by analysis of fishery-landing trends. Transactions of the American Fisheries Society 112: 744–759. Casselman, J. M., Collins, J. J., Crossman, E. J., Ihssen, P. E., and Spangler, G. R. 1981. Lake whitefish (Coregonus clupeaformis) stocks of the Ontario waters of Lake Huron. Canadian Journal of Fisheries and Aquatic Sciences 38: 1772–1789. Castonguay, M., Simard, P., and Gagnon, P. 1991. Usefulness of Fourier analysis of otolith shape for Atlantic mackerel (Scomber scombrus) stock discrimination. Canadian Journal of Fisheries and Aquatic Sciences 48: 296–302. Cerrato, R. M. 1990. Interpretable statistical tests for growth comparisons using parameters in the von Bertalanffy equation. Canadian Journal of Fisheries and Aquatic Sciences 47: 1416– 1426. Clark, S. 1981. Use of trawl survey data in assessments. In W. G. Doubleday and D. Rivard (eds.), Bottom Trawl Surveys. Canadian Special Publication of Fisheries and Aquatic Sciences 58: 82–92. Clark, S. H., Overholtz, W. H., and Hennemuth, R. C. 1982. Review and assessment of the Georges Bank and Gulf of Maine haddock fishery. Journal of the Northwest Atlantic Fishery Science 3: 1–27. Cohen, E. B., Mountain, D. G., and O’Boyle, R. 1991. Local-scale versus large-scale factors affecting recruitment. Canadian Journal of Fisheries and Aquatic Sciences 48: 1003–1006. Cowen, R. K., Hare, J. A., and Fahay, M. P. 1993. Beyond hydrography: can physical processes explain larval fish assemblages within the Middle Atlantic Bight? Bulletin of Marine Science 53: 567–587. Cushing, D. H. 1969. The regularity of the spawning season of some fishes. Journal Conseil International pour l’Exploration de la Mer 33: 81–97. Dawson, P. 1994. The stock structure of Bering Sea walleye pollock (Theragra chalcogramma). Master of Science thesis. University of Washington, Seattle, WA. 220 pp. DeVries, D. A. and Grimes, C. B. 1997. Spatial and temporal variation in age and growth of king mackerel, Scomberomorus cavalla, 1977–1992. Fishery Bulletin 95: 694–708. Dizon, A. E., Lockyer, C., Perrin, W. F., Demaster, D. P., and Sisson, J. 1992. Rethinking the stock concept: a phylogeographic approach. Conservation Biology 6: 24–36. Drinkwater, K. F. 1996. Atmospheric and oceanic variability in the northwest Atlantic during the 1980s and early 1990s. Journal of the Northwest Atlantic Fishery Science 18: 77–97. Drinkwater, K. F. and Mountain, D. G. 1997. Climate and oceanography. In J. Boreman, B. S. Nakashima, J. A. Wilson, and R. L. Kendall (eds.), Northwest Atlantic Groundfish: Perspectives on a Fishery Collapse. American Fisheries Society, Maryland, pp. 3–25. Eastwood, P. D., Meaden, G. J., and Grioche, A. 2001. Modelling spatial variations in spawning habitat suitability for the sole Solea solea using regression quantiles and GIS procedures. Marine Ecology Progress Series 224: 251–266.

Life History Parameters

147

Finucane, J. H., Collins, L. A., Brusher, H. A., and Saloman, C. H. 1986. Reproductive biology of king mackerel, Scomberomorus cavalla, from the southeastern United States. Fishery Bulletin 84: 841–850. Fletcher, W. J. and Sumner, N. R. 1999. Spatial distribution of sardine (Sardinops sagax) eggs and larvae: an application of geostatistics and resampling to survey data. Canadian Journal of Fisheries and Aquatic Sciences 56: 907–914. Fromentin, J.-M. and Fonteneau, A. 2001. Fishing effects and life history traits: a case study comparing tropical versus temperate tunas. Fisheries Research 53: 133–150. Gaughan, D. J., White, K. V., and Fletcher, W. J. 2001. The links between functionally distinct adult assemblages of Sardinops sagax: larval advection across management boundaries. ICES Journal of Marine Science 58: 597–606. Griffiths, M. H. 1996. Age and growth of South African silver kob Argyrosomus inodorus (Sciaenidae), with evidence for separate stocks. South African Journal of Marine Science 17: 37–48. Griffiths, M. H. 1997. The life history and stock separation of silver kob, Argyrosomus inodorus, in South African waters. Fishery Bulletin 95: 47–67. Grimes, C. B., Johnson, A. G., and Fable, W. A., Jr., eds. 1987. Delineation of king mackerel (Scomberomorus cavalla) stocks along the U.S. east coast and in the Gulf of Mexico. In H. E. Kumpf, R. N. Vaught, C. B. Grimes, A. G. Johnson, and E. L. Nakamura (eds.), Proceedings of the Stock Identification Workshop 5–7 November 1985, Panama City Beach, Florida. NOAA Technical Memorandum NMFS-SEFC-199. United States Government Printing Office, Panama City, pp. 186–187. Haddon, M. 2001. Modelling and Quantitative Methods in Fisheries. Chapman and Hall, Boca Raton, FL. 406 pp. Hanchet, S. 1999. Stock structure of southern blue whiting (Micromesistius australis) in New Zealand waters. New Zealand Journal of Marine and Freshwater Research 33: 599–609. Hare, J. A. 2004. The use of early life history stages in stock identification studies. In S. X. Cadrin, K. D. Friedland, and J. R. Waldman (eds.), Stock Identification Methods. Academic Press, Burlington, MA, pp. 85–113. Hare, J. A. and Cowen, R. K. 1993. Ecological and evolutionary implications of the larval transport and reproductive strategy of bluefish Pomatomus saltatrix. Marine Ecology Progress Series 98: 1–16. Heath, M. R. 1992. Field investigations of the early life stages of marine fish. Advances in Marine Biology 28: 1–174. Hilborn, R. and Walters, C. J. 1992. Quantitative Fisheries Stock Assessment. Choice, Dynamics and Uncertainty. Chapman & Hall, London, UK. 570 pp. Hjort, J. 1914. Fluctuations in the great fisheries of northern Europe viewed in the light of biological research. Rapports et Procés-Verbaux des Réunions, Conseil International pour l’Exploration de la Mer 20: 1–228. Horn, P. L. and Hurst, R. J. 1999. Age and stock structure of gemfish (Rexea solandri) in New Zealand waters. Marine and Freshwater Research 50: 103–115. Horrall, R. M. 1981. Behavioural stock isolating mechanisms in Great Lake fishes with special reference to homing and imprinting. Canadian Journal of Fisheries and Aquatic Sciences 38: 1481–1496. Hutchings, J. A., Myers, R. A., and Lilly, G. R. 1993. Geographic variation in the spawning of Atlantic cod, Gadus morhua, in the northwest Atlantic. Canadian Journal of Fisheries and Aquatic Sciences 50: 2457–2467. Ihssen, P. E., Booke, H. E., Casselman, J. M., McGlade, J. M., Payne, N. R., and Utter, F. M. 1981. Stock identification: materials and methods. Canadian Journal of Fisheries and Aquatic Sciences 38: 1838–1855.

148

Gavin A. Begg

Iles, T. D. and Sinclair, M. 1982. Atlantic herring: stock discreteness and abundance. Science 215: 627–633. Japp, D. W. 1990. A new study on age and growth of kingklip Genypterus capensis off the south and west coasts of South Africa, with comments on its use for stock identification. South African Journal of Marine Science 9: 223–237. Jennings, S. and Beverton, R. J. H. 1991. Intraspecific variation in life history tactics of Atlantic herring (Clupea harengus L.) stocks. ICES Journal of Marine Science 48: 117–125. Jennings, S., Kaiser, M. J., and Reynolds, J. D. 2001. Marine Fisheries Ecology. Blackwell Science, Oxford, UK. 417 pp. Johnson, A. G., Fable, W. A., Jr., Grimes, C. B., and Trent, L. 1994. Evidence for distinct stocks of king mackerel, Scomberomorus cavalla, in the Gulf of Mexico. Fishery Bulletin 92: 91–101. Kimura, D. K. 1980. Likelihood methods for the von Bertalanffy growth curve. Fishery Bulletin 77: 765–776. Koslow, J. A. 1984. Recruitment patterns in northwest Atlantic fish stocks. Canadian Journal of Fisheries and Aquatic Sciences 41: 1722–1729. Koslow, J. A., Thompson, K. R., and Silvert, W. 1987. Recruitment of northwest Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) stocks: influence of stock size and climate. Canadian Journal of Fisheries and Aquatic Sciences 44: 26–39. Kritzer, J. P. 2002. Variation in the population biology of stripey bass Lutjanus carponotatus within and between two island groups on the Great Barrier Reef. Marine Ecology Progress Series 243: 191–207. Kutkuhn, J. H. 1981. Stock definition as a necessary basis for cooperative management of Great Lakes fish resources. Canadian Journal of Fisheries and Aquatic Sciences 38: 1476–1478. Lowe, S. A., Van Doornik, D. M., and Winans, G. A. 1998. Geographic variation in genetic and growth patterns of atka mackerel, Pleurogrammus monopterygius (Hexagrammidae), in the Aleutian archipelago. Fishery Bulletin 96: 502–515. Maravelias, C. D. and Reid, D. G. 1997. Identifying the effects of oceanographic features and zooplankton on prespawning herring abundance using generalized additive models. Marine Ecology Progress Series 147: 1–9. Marteinsdottir, G. and Begg, G. A. 2002. Essential relationships incorporating the influence of age, size and condition on variables required for estimation of reproductive potential in Atlantic cod Gadus morhua stocks. Marine Ecology Progress Series 235: 235–256. Marteinsdottir, G., Gudmundsdottir, A., Thorsteinsson, V., and Stefánsson, G. 2000a. Spatial variation in abundance, size composition and viable egg production of spawning cod (Gadus morhua L.) in Icelandic waters. ICES Journal of Marine Science 57: 824–830. Marteinsdottir, G., Gunnarsson, B., and Suthers, I. M. 2000b. Spatial variation in hatch date distributions and origin of pelagic juvenile cod in Icelandic waters. ICES Journal of Marine Science 57: 1182–1195. Mertz, G. and Myers, R. A. 1994. Match/mismatch predictions of spawning duration versus recruitment variability. Fisheries Oceanography 3: 236–245. Misra, R. K. 1980. Statistical comparisons of several growth curves of the von Bertalanffy type. Canadian Journal of Fisheries and Aquatic Sciences 37: 920–926. Morse, W. W., Fahay, M. P., and Smith, W. G. 1987. MARMAP surveys of the continental shelf from Cape Hatteras, North Carolina, to Cape Sable, Nove Scotia (1977–1984). Atlas no. 2. Annual distribution patterns of fish larvae. NOAA Technical Memorandum NMFS-S/NEC, 47: 215 pp. Myers, R. A., Barrowman, N. J., Hutchings, J. A., and Rosenberg, A. A. 1995. Population dynamics of exploited fish stocks at low population levels. Science 269: 1106–1108. O’Boyle, R. N., Sinclair, M., Conover, R. J., Mann, K. H., and Kohler, A. C. 1984. Temporal and spatial distribution of ichthyoplankton communities of the Scotian Shelf in relation to biological,

Life History Parameters

149

hydrological, and physiographic features. Rapports et Procés-Verbaux des Réunions, Conseil International pour l’Exploration de la Mer 183: 27–40. O’Brien, L. 1990. Effects of fluctuations in stock abundance upon life history parameters of Atlantic cod, Gadus morhua, for the 1970–1987 year classes from Georges Bank and the Gulf of Maine. Master of Science thesis. University of Washington. Seattle, WA. 95 pp. O’Brien, L., Burnett, J., and Mayo, R. K. 1993. Maturation of nineteen species of finfish off the northeast coast of the United States, 1985–1990. NOAA Technical Report National Marine Fisheries Service 113: 66 pp. Overholtz, W. J. 2002. The Gulf of Maine–Georges Bank Atlantic herring (Clupea harengus): spatial pattern analysis of the collapse and recovery of a large marine fish complex. Fisheries Research 57: 237–254. Page, F. H. and Frank, K. T. 1989. Spawning time and egg stage duration in northwest Atlantic haddock (Melanogrammus aeglefinus) stocks with emphasis on Georges and Browns Bank. Canadian Journal of Fisheries and Aquatic Sciences 46 (suppl. 1): 68–81. Page, F. H., Sinclair, M., Naimie, C. E., Loder, J. W., Losier, R. J., Berrien, P. L., and Lough, R. G. 1999. Cod and haddock spawning on Georges Bank in relation to water residence times. Fisheries Oceanography 8: 212–226. Pálsson, O. K., Jonsson, E., Schopka, S. A., Stefánsson, G., and Steinarsson, B. A. 1989. Icelandic groundfish survey data used to improve precision in stock assessments. Journal of Northwest Atlantic Fishery Science 9: 53–72. Palumbi, S. R. 1994. Genetic divergence, reproductive isolation, and marine speciation. Annual Review of Ecology and Systematics 25: 547–572. Parrish, R. H., Nelson, C. S., and Bakiun, A. 1981. Transport mechanisms and reproductive success of fishes in the California current. Biological Oceanography 1: 175–203. Pawson, M. G. and Jennings, S. 1996. A critique of methods for stock identification in marine capture fisheries. Fisheries Research 25: 203–217. Petitgas, P. 1993. Geostatistics for fish stock assessments: a review and an acoustic application. ICES Journal of Marine Science 50: 285–298. Ricker, W. E. 1954. Stock and recruitment. Journal of the Fisheries Research Board of Canada 11: 559–623. Rochet, M.-J. 2000. May life history traits be used as indices of population viability? Journal of Sea Research 44: 145–157. Rulifson, R. A. and Dadswell, M. J. 1995. Life history and population characteristics of striped bass in Atlantic Canada. Transactions of the American Fisheries Society 124: 477–507. Sale, P. F. 1998. Appropriate spatial scales for studies of reef-fish ecology. Australian Journal of Ecology 23: 202–208. Schaefer, K. M. 1987. Reproductive biology of black skipjack, Euthynnus lineatus, an eastern Pacific tuna. Inter-American Tropical Tuna Commission Bulletin 19: 260 pp. Schnute, J. 1981. A versatile growth model with statistically stable parameters. Canadian Journal of Fisheries and Aquatic Sciences 38: 1128–1140. Serchuk, F. M., Grosslein, M. D., Lough, R. G., Mountain, D. G., and O’Brien, L. 1994. Fishery and environmental factors affecting trends and fluctuations in the Georges Bank and Gulf of Maine Atlantic cod stocks: an overview. ICES Marine Science Symposium 198: 77– 109. Simard, P., Castonguay, M., D’Armours, D., and Magnan, P. 1992. Growth comparison between juvenile Atlantic mackerel (Scomber scombrus) from the two spawning groups of the northwest Atlantic. Canadian Journal of Fisheries and Aquatic Sciences 49: 2242–2248. Sinclair, A. F. and Murawski, S. A. 1997. Why have groundfish stocks declined? In J. Boreman, B. S. Nakashima, J. A. Wilson, and R. L. Kendall (eds.), Northwest Atlantic Groundfish: Perspectives on a Fishery Collapse. American Fisheries Society, Bethesda, MD, pp. 71–93.

150

Gavin A. Begg

Sinclair, M. 1988. Marine Populations: An Essay on Population Regulation and Speciation. University of Washington Press, Seattle, WA. 252 pp. Sinclair, M. and Tremblay, M. J. 1984. Timing of spawning of Atlantic herring (Clupea harengus) populations and the match-mismatch theory. Canadian Journal of Fisheries and Aquatic Sciences 41: 1055–1065. Smith, P. J., Jamieson, A., and Birley, A. J. 1990. Electrophoretic studies and the stock concept in marine teleosts. Journal du Conseil International pour l’Exploration de la Mer 47: 231–245. Stephenson, R. L. 1991. Stock discreteness in Atlantic herring: a review of arguments for and against. In V. Wespestad, J. Collie, and E. Collie (eds.), Proceedings of the International Herring Symposium, Anchorage, Alaska, October 23–25, 1990 (9th Lowell Wakefield Fisheries Symposium). University of Alaska, Fairbanks, pp. 659–666. Stephenson, R. L. 1999. Stock complexity in fisheries management: a perspective of emerging issues related to population sub-units. Fisheries Research 43: 247–249. Stoner, A. W., Manderson, J. P., and Pessutti, J. P. 2001. Spatially explicit analysis of estuarine habitat for juvenile winter flounder: combining generalized additive models and geographic information systems. Marine Ecology Progress Series 213: 253–271. Summerfelt, R. C. and Hall, G. E. (eds.). 1987. Age and Growth of Fish. Iowa State University Press, Ames, IA. 544 pp. Swain, A., Parrish, B. B., and Horsted, S. A. 1980. Tagging of salmon smolts in European rivers with special references to recaptures off West Greenland in 1972 and earlier years. Rapports et ProcésVerbaux des Réunions, Conseil International pour l’Exploration de la Mer 176: 93–113. Swain, D. P. and Wade, E. J. 1993. Density-dependent geographic distribution of Atlantic cod (Gadus morhua) in the southern Gulf of St. Lawrence. Canadian Journal of Fisheries and Aquatic Sciences 50: 725–733. Swartzman, G., Huang, C., and Kaluzny, S. 1992. Spatial analysis of Bering Sea groundfish survey data using generalized additive models. Canadian Journal of Fisheries and Aquatic Sciences 49: 943–962. Thompson, K. R. and Page, F. H. 1989. Detecting synchrony of recruitment using short, autocorrelated time series. Canadian Journal of Fisheries and Aquatic Sciences 46: 1831–1838. Trippel, E. A. 1999. Estimation of stock reproductive potential: history and challenges for Canadian Atlantic gadoid stock assessments. Journal of the Northwest Atlantic Fishery Science 25: 61–81. Trippel, E. A., Morgan, M. J., Fréchet, A., Rollet, C., Sinclair, A., Annand, C., Beanlands, D., and Brown, L. 1997. Changes in age and length at sexual maturity of northwest Atlantic cod, haddock and pollock stocks, 1972–1995. Canadian Technical Report of Fisheries and Aquatic Sciences 2157: 120 pp. Utter, F. M. 1991. Biochemical genetics and fishery management: an historical perspective. Journal of Fish Biology 39(A): 1–20. Von Bertalanffy, L. 1938. A quantitative theory of organic growth. Human Biology 10: 181–213. Waldman, J. R. 1999. The importance of comparative studies in stock analysis. Fisheries Research 43: 237–246. Waldman, J. R. and Fabrizio, M. C. 1994. Problems of stock definition in estimating relative contributions of Atlantic striped bass to the coastal fishery. Transactions of the American Fisheries Society 123: 766–778. Waldman, J. R., Grossfield, J., and Wirgin, I. 1988. Review of stock discrimination techniques for striped bass. North American Journal of Fisheries Management 8: 410–425. Williams, A. J., Davies, C. R., Mapstone, B. D., and Russ, G. R. 2003. Scales of spatial variation in demography of a large coral-reef fish—an exception to the typical model? Fishery Bulletin 101: 673–683.

PART

Natural Marks— Morphological Analyses

III

This Page Intentionally Left Blank

CHAPTER

7

Morphometric Landmarks STEVEN X. CADRIN National Marine Fisheries Service,1 Woods Hole, Massachusetts, USA

I. Introduction II. Methodological Protocols A. Sampling B. Choice of Characters C. Statistical Analysis III. Interpretation of Morphometric Differences A. Salmon Case Studies IV. Discussion References

I. INTRODUCTION Patterns of morphometric variation in fishes indicate differences in growth and maturation rates because body form is a product of ontogeny. Morphometric landmark methods comprise one of the two major categories of morphometric analysis, the other being outline methods (see this volume, Chapter 8). The distinction between the two is that landmark methods analyze data derived from discrete morphometric points, linear distances between points, and geometric relationships among points, whereas outline methods deal with perimeter shapes. Beginning with Huxley and Teissier’s pioneering work on bivariate allometry of crustaceans and finfish in the 1920s (reviewed by Huxley, 1932 and Teissier, 1960), stock identification studies have played a central role in the development of traditional landmark methods (see Cadrin, 2000 for a brief history). Royce (1957) reviewed methods of multivariate morphometrics for studying subpopulations of fishes more than a decade before general texts on morphometrics were published (e.g., Blackith and Reyment, 1971; Pimentel, 1979), and many early applications of multivariate morphometrics were for stock identification. For 1 Much of this work was completed while under the employment of the Massachusetts Division of Marine Fisheries.

Stock Identification Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

153

154

Steven X. Cadrin 70

7000 morphometrics fisheries

6000

50

5000

40

4000

30

3000

20

2000

10

1000

0

Fisheries Abstracts per Year

Morphometric Abstracts per Year

60

0 1970-1974

1975-1979

1980-1984

1985-1989

1990-1994

1995-2000

FIGURE 7-1. Frequency of published case studies on morphometric stock identification by 5-yr period, with fishery references for comparison (data source: Cambridge Scientific Abstracts, Aquatic Sciences and Fisheries, Biological Sciences and Living Resources, http://www.csa2.com).

example, Saila and Flowers’ (1969) morphometric discrimination of American lobster stocks is considered a benchmark study in the field (see reviews by Gould and Johnston, 1972; Thorpe, 1976). The proliferation of morphometric applications for stock identification during the 1970s and 1980s is illustrated by the case studies reviewed by Lee (1971), Winans (1987), and Saila and Martin (1987), as well as the subsequent increase in published case studies (Fig. 7-1). The development of digital imaging systems and advances in analytical methods revolutionized the study of morphometric variation, and have increased the power of morphometric analysis for stock identification (Cadrin and Friedland, 1999). However, the application of advanced geometric methods as applied to stock identification lags behind applications to other biological fields such as taxonomy and biomedical research (Cadrin, 2000). In the context of interdisciplinary stock identification, or the use of information from various approaches, morphometric analysis provides information on phenotypic stocks, groups of individuals with similar growth, mortality, and reproductive rates (Booke, 1981). The phenotypic stock definition is less conservative than the genetic stock definition because it allows for some mixing among stocks, but partial isolation is enough that geographic differences persist. Despite dependence on the environment, ontogenetic rates influence many population attributes (e.g., reproduction, fecundity, longevity, size structure) that are intimately related to population dynamics (intrinsic rate of increase, carrying capacity, productivity, resilience, and so on; Cole, 1954) and determine how each stock responds to exploitation (Garrod and Horwood, 1984). Therefore, for the

Morphometric Landmarks

155

purpose of fishery stock assessment, groups with different growth or reproductive dynamics should be modeled and managed separately, regardless of genetic homogeneity. The objective of this chapter is to review common protocols for sampling, analyzing, and interpreting variation associated with morphometric landmarks for stock identification applications.

II. METHODOLOGICAL PROTOCOLS

A. SAMPLING Analysis of morphometric data has become more powerful for stock identification through more rigorous sampling designs and more efficient data acquisition. Optimal sampling designs vary as a function of precise objectives (Cadrin, 2000). For species that have little information about stock structure, morphometric analyses may be exploratory in nature. The optimal sampling design for exploratory studies involves obtaining representative samples from the entire geographic range as well as all seasons to investigate patterns of variation and potential for mixing. A second tier of inquiry is for species with information on the location of spawning grounds and fidelity to them, for which morphometric analysis can be used to test hypotheses about putative stocks. The optimal design for stock discrimination is sufficient sample sizes from locations at the center of spawning grounds, during spawning seasons, when mixing among putative stocks is minimal. The most refined tier of investigation is either delineation stocks (for species with little mixing) or stock composition analysis (for species that mix seasonally). Ideally, sampling for stock delineation or composition analysis has comprehensive representation, geographically and seasonally, to define discrete boundaries of morphometric variation, clines in morphometry, or the proportion of each morphometric variant in mixed samples. Optimal sample sizes are a function of the degree of morphometric variation within groups and the magnitude of difference among stocks that is desired to detect. Reliable estimates of bivariate correlation require at least 50 observations (Tabachnick and Fidell, 1989), multivariate analyses require more samples (n) than variables (p), and the stability of multivariate ordination is related to the ratio n : p. Saila and Martin (1987) advocated an ad hoc rule that sample sizes should be three times the number of variables. One complicating factor in sampling morphometric features for stock discrimination is that morphometry changes during the spawning process. Ripe gonads can contribute up to 40% of female body mass, greatly altering body proportions. Analysis of exploratory sampling conducted before, during, and after spawning can describe the degree of morphometric change, identify the time or stage that should be sampled to minimize the confusion between geographic

156

Steven X. Cadrin

variation and spawning-induced variation, and define which morphometric characters are influenced by spawning (e.g., Armstrong and Cadrin, 2001). If geographic variation is based on different spawning stages, subsequent stock delineations or composition analyses will be biased toward classifying spawning fish to the area that had the most spawners in the discrimination samples. A similar complication may be associated with feeding. Ideally, comparisons should be made among specimens with comparable stomach fullness and spawning stage. In addition to stomach and gonad observations, other individual attributes such as age, gender, and color may be associated with morphometric variation, and thus should be recorded for each specimen. Location information such as depth, salinity, temperature, and sea floor substrate should also be recorded. Such data serve as useful covariates and possibly causal factors for exploring patterns of morphometric variation. Although traditional measurement systems such as calipers and measuring boards are commonly used in morphometric studies, digital imaging with calibration provides superior data format, accuracy, design flexibility, and potential for substantially increasing sample size. Coordinates of digital images can be calibrated if specimens are placed on a plane with a grid of known distances for a standard view (usually lateral or dorsal). Images can be archived from digital camera, digital video, or frame-grabbing software for videotape. Images can be calibrated to unit distances and corrected for tilt as a function of known distance in each corner of the image (Fig. 7-2). One major advantage of deriving morphometric data from digital images is the ability to store the image and the potential for reprocessing each individual to confirm anomalous measurements or drive alternative character sets. Storage of images allows detailed inspection of extreme variants or outliers, as well as more flexible character selection (Cadrin and Friedland, 1999). Imaging software also allows enhancement of images to accentuate subtle features. Another advantage of image processing is that data are stored as coordinates, facilitating geometric methods of analysis.

B. CHOICE

OF

CHARACTERS

Similar to other phenotypic approaches, there are an infinite number of morphometric dimensions that can be measured to study patterns of variation. We can study general morphometry (i.e., shape of the entire organism) or shape of individual features (e.g., scales, otoliths, vertebrate, chelae). Perhaps the best criterion for choosing morphometric features is their use in interpretation. Morphometric stock identification can be designed to focus on features that exhibit changes associated with development or maturation because geographic variation in ontogenetic rates is the basis of effective phenotypic stock identification (i.e., identifying groups with different biological rates). Information from life history

157

Morphometric Landmarks

studies can guide a researcher’s choice of morphometric features. Such a priori choice of characters that are sensitive to ontogenetic processes may lead to clearer interpretation of intrapopulation patterns of morphometric variation. For example, differences in locomotion may be associated with general body form and fin size of fishes (e.g., Riddell and Leggett, 1981; Winans, 1984; Taylor and McPhail, 1985a,b; Swain and Holtby, 1989; Taylor, 1991), variation in mouth shape may be correlated to differences in diet (e.g., Skulason et al., 1989, 1996; Albertson and Kocher 2001), and differences in size of male secondary sex characters can indicate differences in size at maturity (Holtby et al., 1993; Cadrin, 1995, 2000). Landmarks should be homologous, representing the same developmental feature among specimens, and should be easily located (Winans, 1987; Bookstein, 1990). The most effective landmarks are those defined by the intersection of different tissues, such as insertion points of fins and anal pores. Extreme points, such as the posterior edge of the caudal fin or tip of the snout or a spine, are also effective, but may not be strictly homologous. Using the same yellowtail flounder example, landmarks range from homologous and clearly defined, to nonhomologous and arbitrary (Fig. 7-2). Some landmarks are homologous, such

5

2

10

1

6

7

3

12

4

11 8 9

FIGURE 7-2. A set of 12 morphometric landmarks for yellowtail flounder (numbered) and 12 calibration points.

158

Steven X. Cadrin

as the center of eyes (landmarks 2 and 3) and fin insertion points (5, 6, 8, 10, 11); some are nearly so, like the tips of bones (1 and 4) and fin tips (6, 7, 12). Landmark 5 (insertion of dorsal fin in line with landmarks 6 and 8) is particularly nonhomologous. However, there are no homologous features along the dorsal fin because the number of dorsal fin rays varies among individuals. Note that 12 landmarks were also located on the measured grid to calibrate the image. Linear morphometric distances can be measured directly with calipers or a ruler or calculated from digital landmark coordinates. For example, 90 distances can be calculated between the 10 nonarticulating landmarks (i.e., those that do not move position relative to other landmarks) illustrated in Figure 7-2, but many distances would measure the same general feature. Bookstein et al. (1985) refer to this set of distances as the “globally redundant network.” However, redundancy should be minimized because minimum sample sizes required for multivariate analysis increase as a function of the number of variables, and the likelihood of spurious significant differences increases with the number of characters used in analysis (Misra and Easton, 1999). Strauss and Bookstein (1982) developed the box-truss network in which trapezoidal cells with two crossing diagonals are formed between four adjacent landmarks. A box-truss network of 22 distances (plus two distances for pelvic and pectoral fin lengths) is illustrated in Figure 73. Note that two of the landmarks (7 and 9) are the tips of articulating fins, and the only meaningful distances derived from them are to their associated insertion points (6 and 8, respectively). An even less redundant set of linear distances is the triangle-truss network (Bookstein et al., 1985) in which triangular cells are formed between three adjacent landmarks with no crossing lines [e.g., a set of 10 triangle-truss networks plus the two paired fin lengths were used for analysis of the same yellowtail flounder landmarks by Cadrin and Silva (2004)]. The choice and number of morphometric distances for traditional multivariate analysis is a trade-off between a comprehensive measure of shape and sample size limitations. As the number of variables increases, the required number of specimens also increases by at least a factor of three (Saila and Martin, 1987). One subjective criterion for the selection of distances and networks is the visual resemblance of the network to the specimen from which it was derived. For example, the network in Figure 7-3 resembles the specimen somewhat, especially in the head region, but body depth and posterior tail shape is not represented as accurately. As described for landmark 5, few homologous landmarks can be found along the dorsal or anal fins, and measuring yellowtail flounder shape is a compromise between homology and comprehensive depiction. In addition to linear distances between landmarks, geometric deformations from one set of landmarks to another can also be considered as morphometric characters. Traditional morphometric analyses use linear distances as correlated variables for multivariate analyses (e.g., principal components analysis, cluster analysis, discriminant function analysis). However, the geometry of linear dis-

Morphometric Landmarks

159

FIGURE 7-3. A box-truss network of linear distances among morphometric landmarks for yellowtail flounder.

tances (i.e., the orientation and proximity of line segments) is ignored by traditional analyses. Geometric methods were developed to quantify shape variation as a distribution of deformations from an average shape (Rohlf and Marcus, 1993). For example, sexual dimorphism of yellowtail flounder can be viewed as a deformation from an average male to an average female (Fig. 7-4). Deformations can be measured using thin-plate spline analysis (Bookstein, 1991; Rohlf, 1998), and the resulting partial warps, composite measures of shape variation, can be used as morphometric characters for multivariate analysis. Note that landmarks on articulated structures, such as fins and jaws, cannot be considered in thin-plate spline analysis because geometric positions relative to other landmarks are not fixed, but methods have been developed to consider the geometry of articulating landmarks (Adams, 1999). Although traditional morphometric analyses are common for stock identification (Fig. 7-1), few studies have used geometric characters, such as partial warps, to investigate geographic variation in morphometry (Corti and Crosetti, 1996; Walker, 1996, 1997; Sheehan et al., 2004; Cadrin and Silva, 2004). Perhaps geometric methods have not been widely

160

Steven X. Cadrin

Female

Male

FIGURE 7-4. Sexual dimorphism of yellowtail flounder, viewed as a thin-plate spline deformation. For comparison to traditional morphometric analysis, linear distances that loaded strongly positive in discriminant function analysis are indicated with solid bold lines, and those that loaded negatively are indicated with dashed bold lines.

applied because biological interpretation of partial warps is difficult (Rohlf, 1998). Therefore, Rohlf and Marcus (1993) suggest that partial warps be used to locate dimensions that vary among groups for selection of characters for traditional analysis of linear distances. For example, if the depth of the head is the principal deformation between two putative stocks, linear head depth should be included in a traditional multivariate analysis.

C. STATISTICAL ANALYSIS Morphometric characters are continuous variables with meaningful correlations and are therefore appropriate for conventional multivariate analysis (Blackith and Reyment, 1971; Pimentel, 1979; Reyment et al., 1984; Marcus, 1990; Klingenberg, 1996). As with all proper statistical analysis, routine descriptive diagnostics should be conducted to identify statistical outliers, assess normality (or lognormality), and inspect linearity (or log-linearity) of correlations. Principal components analysis (PCA) is a valuable diagnostic and exploratory tool. On the basis of multidimensional growth, correlation among log-transformed distances and resulting principal components can be interpreted in terms of isometric size variation and allometric shape variation (Teissier, 1960; Jolicoeur, 1963; Reyment, 1990; Klingenberg, 1996). If all characters are positively correlated and load nearly equally on the first principal component (i.e., are all simi-

Morphometric Landmarks

161

larly correlated with the PC1 score), PC1 can be interpreted as isometric size and scales the relative size of specimens. Differences in size distribution among putative stocks may result from growth or mortality differences among areas and need to be considered in discriminations so that classification is based on shape differences rather than size differences. For example, a recent study that discriminated sturgeon species was found to be invalid because it incorrectly classified individuals to species based on size (Rinçon, 2000). Several methods of multivariate size correction have been developed, but Burnaby’s (1966) method, which involves the removal of within-group multivariate size, appears to be the most appropriate (Rohlf and Bookstein, 1987; Klingenberg, 1996). The second principal component accounts for the maximum amount of variation remaining after isometric size variance is removed by PC1, and therefore measures shape variation. Within-group PCA is an effective method for detecting statistical outliers from processing errors or abnormal morphometric development. Morphometric measurements should be repeated for all specimens that are considered to be statistical outliers according to their shape component scores (e.g., PC2 scores) to eliminate processing errors (thus emphasizing the advantages of archiving images). If reprocessed measurements confirm that the outlier specimens have a significantly different shape, the nature of the difference should be determined. For example, if a specimen is somehow mutilated, it can be removed from the analysis without biasing results. However, if the specimen represents an extreme of regular natural variation, alternative data transformations should be considered to normalize the distribution and retain the observation to represent natural variation. Pooled-group PCA is a powerful exploratory tool for examining patterns of morphometric variation and choosing character sets that may efficiently discriminate groups. Individual attributes and location information collected during sampling can be used as categories for labeling observations in PC score plots to illustrate group membership. Attributes that cluster together can guide subsequent analytical designs. For example, if observations cluster by sex, the significance of sexual dimorphism should be tested, and sex should be considered as a covariate for detecting geographic differences or stock discriminations should be separate for each sex. Group differences can be easily interpreted from PC loadings. Characters that load strongly positive or strongly negative on PC2 have large influence and reveal shape contrasts. In the simplistic example of “boxfish” illustrated in Figure 7-5, PC1 accounts for size (i.e., both body length and body depth load positively on PC1) with large boxfish having high PC1 scores. In the boxfish example, PC2 contrasts length and width (i.e., body depth loads strongly positive and length strongly negative), and PC2 scores distinguish long-narrow boxfish from short-wide ones (Fig. 7-5). Cluster analysis is also useful for exploring patterns of shape variation in size-adjusted data, but interpreting group differences is more difficult than from PCA results.

162

Steven X. Cadrin

3

2

PC2 Score (shape)

1

0

-1

-2

-3 -3

-2

-1

0

1

2

3

PC1 Score (size)

FIGURE 7-5. Schematic principal components analysis of a sample of “boxfish,” illustrating the interpretation of PC1 as an index of size and PC2 as an index of shape.

Significance of morphometric differences among putative stocks is commonly tested using multivariate analysis of variance or discriminant function analysis. However, multivariate tests with a large number of morphometric characters and many observations are extremely sensitive, and statistical significance may be spurious (Misra and Easton, 1999). A more meaningful criterion for detecting differences is the ability of a discriminant function to classify extrinsic specimens to the correct stock with greater accuracy than random classification (Solow, 1990). Interpretation of discriminant function results is more difficult than PCA. Pooled within-groups correlations between variables and discriminant scores can be used to interpret canonical variates, similar to the way PCA loadings are interpreted. Plotting truss networks or thin-plate spline deformations as canonical

163

Morphometric Landmarks 5

G ra nd B an k

4

A

S t.P ierre B an k N ortheas t U .S .

3

CV 2 S core

2 1 0 -1 -2 -3 -4 -5

-4

-3

-2

-1

0

1

2

3

4

5

CV 1 S core

5

B

4 3

CV2 Score

2 1 0 -1 -2 -3 -4 -5

-4

-3

-2

-1

0

1

2

3

4

5

CV1 Score

FIGURE 7-6. Geographic variation of female yellowtail flounder as illustrated by canonical variates scores of size-adjusted morphometric data (A), and thin-plate splines for four extreme examples. (B) dashed lines indicate features that load negatively; solid lines indicate features that load positively.

variate scores can also help to interpret discriminant functions (Sheehan et al., 2004; Cadrin and Silva, 2004). For example, plotting the partial warps for yellowtail flounder with extreme CV1 scores shows that Newfoundland females have relatively longer snouts and shorter bodies than those off the northeastern United States (Fig. 7-6).

164

Steven X. Cadrin

III. INTERPRETATION OF MORPHOMETRIC DIFFERENCES Although image analysis techniques and geometric analyses enhance descriptions of morphometric variation, results remain equivocal with respect to genetic or environmental bases of morphometric differences. However, if morphometry is locally adaptive, morphometric analyses can suggest the existence of discrete genetic stocks if supplemented with functional tests and rearing experiments. “Common garden experiments” can be used to partition morphometric variance into environmental and genetic variation. Adaptation involves natural selection of characters that improve survival and reproduction of individuals. Local environments vary, selecting for different characters in different areas. Differences in selected characters are maintained through reproductive isolation among groups. If morphometric variation among stocks is adaptive to different environments, it is more likely to indicate genetic stocks. However, adaptive interpretations require explicit functional hypotheses and tests (Homberger 1988), and common environment rearing experiments are needed to determine heritability (Swain and Foote, 1999, Chapter 4, this volume).

A. SALMON CASE STUDIES Several studies of morphometric variation among salmon stocks illustrate how hypotheses about the functional utility of phenotypic variation can be tested. Such case studies complement morphometric stock identification and serve as models for the advancement of morphometric analysis. Anadromous salmon inhabit a wide range of aquatic environments, from relatively calm spawning beds, to river rapids, to pelagic waters of the open ocean. Accordingly, they exhibit a range of swimming behavior including prolonged swimming (“subcarangiform periodic axial undulation”) and burst acceleration (“transient axial undulation,” Webb and Blake, 1985). In Webb’s (1984) continuum of functional morphology for swimming form and function, salmon are intermediates between generalists and speed specialists. As will be demonstrated, species do not occupy a single point on the morphology continuum but demonstrate a variety of behavior (i.e., a range of intraspecific points in the continuum), with some individuals more specialized for speed and others more specialized for acceleration. 1. Atlantic Salmon Riddell and Leggett (1981) found that body morphology of Atlantic salmon varied between two tributaries of the Miramichi River, New Brunswick. Parr from Rocky Brook had more fusiform bodies (i.e., less robust and longer head length) and larger pelvic and pectoral fins than those from the Sabbies River. They hypothe-

Morphometric Landmarks

165

sized that morphometric differences were adaptive because salmon from Rocky Brook had higher energetic costs from overwintering in the stream as compared to those from the Sabbies River, which leave the river in spring. General body shape was measured with “fineness ratio” (body length: maximum body diameter), so that a greater ratio indicates a more fusiform body. The difference in fineness ratio between tributaries was only 5%, but was speculated to result in substantial differences in energetic costs associated with feeding and maintaining position. Salmon parr maintain position on river beds by extending their pelvic and pectoral fins, which act as hydrofoils, creating greater water pressure over the fins and forcing the body downward against the substrate. The larger paired fin size of Rocky Brook salmon is capable of generating more negative lift because lift is proportional to surface area of fins. Riddell and Leggett (1981) tested their hypothesis by comparing observed morphometrics to flow-predicted morphology of Atlantic salmon from two other tributaries in the Miramichi River system as well as two tributaries of the Big Salmon River, based on their moderate and fast flows. As expected, specimens from the Big Salmon River were significantly more streamlined and had significantly larger fins than those from tributaries with more moderate flow. The accuracy of morphometric predictions from flow information strongly suggests that morphometric differences are adaptive, especially because they were tested on two very different river systems (the Miramichi flows into the Gulf of Saint Lawrence and the Big Salmon River flows into the Bay of Fundy). Riddell et al. (1981) conducted breeding experiments to determine if the different morphologies from different tributaries had a genetic basis. They sampled eggs from each tributary and reared them over two years in controlled environments. They found that fish from Rocky Brook retained more fusiform bodies and larger fins than those from the Sabbies River. Therefore, they concluded that morphometric characters were heritable and differences among populations were adaptive. Similar morphometric patterns were also found in other comparisons among Atlantic salmon groups. Atlantic salmon from the Machias River also had more fusiform bodies and deeper caudal peduncles than those from nearby rivers with lower flow and elevations (“machias” is a native American word meaning “bad little falls”), and morphometric differences were maintained in common environment experiments (Sheehan et al., 2004). 2. Coho Salmon Taylor and McPhail (1985a) compared morphometrics of juvenile coho salmon from interior portions of the Fraser River to those from coastal streams and found that interior fish were more streamlined, having longer heads, shallower bodies, and narrower peduncles. They hypothesized that the more fusiform bodies of

166

Steven X. Cadrin

interior fish were adapted for prolonged swimming performance, and the more robust bodies of coastal fish were adapted for high burst performance. They collected eggs from each stream and cultured them in controlled environments in rearing troughs to find that morphometric differences were retained in the laboratory. Taylor and McPhail (1985b) used two separate tank experiments to test their functional hypotheses. Burst performance was tested in a tank equipped with a high-speed video camera and electrical stimulus, and comparisons were made between similarly sized fish from coastal and interior brood stock. Prolonged swimming performance was tested in an oval tank with a fixed-velocity tube and downstream electrical field; swimming was timed from the onset of flow to time to impingement on a downstream screen to measure time to fatigue. Results showed that coastal juveniles (with robust bodies) attained greater burst velocities, and interior juveniles (with streamlined bodies) had greater swimming stamina at several flow rates. These patterns confirm the general morphologies associated with fast-start bursts and prolonged fast swimming (Webb, 1984; Webb and Blake, 1985): the large girth of robust fish adds inertia and minimizes the energy lost to lateral recoil produced from burst acceleration (i.e., C-start transient oscillation), and narrow peduncles of streamlined fish reduce surface area in the most posterior portion of the body where drag is greatest during prolonged, periodic oscillation. Taylor and McPhail (1985b) then used reared fish in the same tank experiments that were conducted on wild fish (described above) and found the same patterns: Coastal fish had greater burst velocities, and interior fish had greater swimming stamina. Therefore, they once again concluded that the morphometric differences were heritable and adapted to local environments. Streamlined bodies appear to be adaptive for long migrations to inland streams, and robust bodies appear to be adaptive for avoiding predators, which are much more abundant in coastal streams than interior streams. Hale (1999) also found that C-start burst performance is maximal for small coho salmon, which are the most vulnerable to predation. In a similar case study, Swain and Holtby (1989) found that juvenile coho salmon reared in lakes had more posteriorly placed pectoral fins, more streamlined bodies, and smaller dorsal and anal fins than those reared in streams, and differences persisted in common environment experiments. They concluded that differences were adaptive, providing greater prolonged swimming performance, citing the functional conclusions of Taylor and McPhail (1985b). 3. Other Salmon Most salmon species have generally similar life histories, similar morphology, and presumably similar locally selective forces. For example, Hale (1999) found

Morphometric Landmarks

167

similar swimming kinematics for juvenile coho salmon, Chinook salmon, and brown trout. Riddell and Leggett (1981) reported that the patterns of body form and fin size with respect to flow rate were also found in brown trout. Winans (1984) speculated that morphometric differences observed in chinook salmon may be adaptive to migration differences. Taylor (1991) found that pink and chum salmon demonstrate similar patterns to those previously described for Atlantic and coho salmon, that is, salmon from higher flow environments were more fusiform. Beacham (1985) found that pink salmon from large rivers in British Columbia and Puget Sound generally had larger heads, peduncles, and fins than those from small rivers and speculated that differences were adapted to greater water velocity in large rivers. Beacham et al. (1988b) expanded the sampling to more rivers and analyzed allozymes to confirm the morphometric differences between small and large rivers. They concluded that morphology was locally adaptive. In a similar series of field studies, Beacham (1984) found that chum salmon from large rivers in British Columbia also had longer fins, larger heads, and thicker peduncles than those from small rivers. He concluded that morphometric differences were adapted to differences in water flow because larger fins provide greater lift and wider peduncles provide greater propulsion for migrating long distances. Furthermore, he proposed that such adaptive morphology explains homing of salmonids. Beacham and Murray (1987) sampled chum salmon from more rivers in British Columbia and confirmed the morphometric differences among fish from large and small rivers. They also repeated the speculation that migrating long distances required more muscular peduncles. The speculations about robust bodies being more adapted to long migrations from case studies on pink and chum salmon are contrary to the findings of Taylor and McPhail (1985b), who found that more streamlined bodies provide greater swimming endurance. The distinction between the two case studies is that the functional hypotheses for Atlantic and coho salmon were tested, whereas the hypotheses for pink and chum salmon were not (Beacham, 1984, 1985; Beacham and Murray, 1987; Beacham et al., 1988b). Interestingly, Beacham et al. (1988a) compared morphometry of chum salmon from British Columbia and from the Yukon, an extremely large river system, and found that Yukon fish were more streamlined than British Columbia fish. Although Beacham et al. (1988b) speculated that wide peduncles of pink salmon were adapted to greater distances of upstream migration, the same authors (Beacham et al., 1988a) speculated that the narrow peduncles observed for Yukon fish were adapted for long-distance migrations. Clearly, adaptive hypotheses can easily be proposed for any morphometric pattern. Although adaptive explanations are attractive, they do not support morphometric stock identification unless they are rigorously tested. Taylor (1991) reviewed the issue of local adaptation in salmon stocks and discussed implications of these case studies for many aspects of fisheries science.

168

Steven X. Cadrin

For conservation biology, functional interpretations facilitate the identification of all genetic components to maintain minimum viable populations. For fishery management, self-sustaining resources can be delineated for setting separate catch limits, seasons, and so on for each stock. For population enhancement, managers can ensure that streams are stocked with spawners that are from the same genetic stock, or at least they can stock one that has morphometric attributes necessary to survive and reproduce in the target environment. For population restoration (i.e., reestablishing salmon runs in extirpated rivers) morphology can be used for “population matching” so that a parental stock is chosen that is most fit for the physical conditions of the extirpated river. The case studies just reviewed illustrate how practical aspects of functional morphology (e.g., working hypotheses and tests) and rearing experiments complement morphometric stock identification. Functional hypotheses and associated tests lend interpretability to morphometric variation with respect to local adaptation and temporally stable differences and thereby increase the value of morphometric analysis for stock identification in an interdisciplinary approach. Such integration of functional morphology, quantitative genetics, and morphometric analysis is surely applicable to many other fishery resources. For example, any populations that are adapted to different flow regimes should exhibit similar morphometric differences. Also, many other functional hypotheses such as variation in feeding morphometry or secondary sex characters may enhance morphometric stock identification. For example, local groups of arctic charr (Salvelinus alpinus) that have different feeding habits have different mouth shapes (Skulason et al., 1989), and different morphs are genetically different (Skulason et al., 1996). Morphometric research is more biologically meaningful if coupled with functional hypotheses regarding the adaptive significance of differences in body shape. Such synthesis is needed to further develop the advancement of morphometric research. However, the more formalized methodology described by Homberger (1988), involving the construction of structural and functional models, is necessary to ensure accurate interpretations. As stated by Taylor and McPhail (1985b), most case studies of morphometric stock identification merely report differences among groups, with no functional interpretation. Such case studies have limited value for determining separate stocks because differences may not persist over time or may not be biologically significant. Many other morphometric case studies pose functional hypotheses or speculate on the adaptive value of observed differences, but do not test their hypotheses (e.g., Beacham, 1984, 1985; Beacham and Murray, 1987; Beacham et al., 1988a,b). Unfortunately, applications of functional morphology for morphometric stock identification (as illustrated by the case studies on Atlantic and coho salmon) are rare. The field validation of functional predictions (e.g., Riddell and Leggett, 1981) and laboratory experiments (e.g., Taylor and McPhail, 1985b) for salmon stocks should serve as models for

Morphometric Landmarks

169

the integration of functional morphology and morphometric stock identification. Thus, the powerful advances in morphometric analysis can have greater success in achieving the goals of stock identification.

IV. DISCUSSION As a potential indicator of phenotypic stocks, analysis of morphometric landmarks is a valuable tool that complements other stock identification methods. The identification, discrimination, and delineation of phenotypic stocks are essential for population modeling, which generally assumes homogeneous ontogenetic rates within a stock. Recent reviews agree that the most comprehensive and effective strategy for stock identification is to integrate results from disparate methods and disciplines to form conclusions about population structure that are consistent with the various approaches (Hohn, 1997; Coyle, 1998; Begg and Waldman, 1999). Carvalho and Hauser (1994) concluded that simultaneous collection of molecular genetic data and phenotypic information is crucial for stock structure analysis. Although the use of morphometric landmark characters to identify phenotypic stocks is more than a century old, imaging technology and analytical techniques have increased the power of morphometric analysis for stock discrimination and stock composition analysis. The development of affordable digital cameras and storage capacity offers the potential for collecting many images of specimens during routine fishery and research sampling. The technological advances and subsequent methodological advances that occurred in the past decade are expected to continue, enhancing the discriminatory and interpretive power of landmark morphometrics for stock identification.

ACKNOWLEDGMENTS I thank Kevin Friedland, Robert Edgar, Michael Armstrong, and Timothy Sheehan for our collaborations, and I appreciate their influences on my perspectives toward morphometric analysis. Cheryl Wilga was instructive in my understanding of functional morphology. Steve Murawski provided helpful comments on the draft manuscript. I also thank Kevin Friedland for the invitation to contribute to the work of the ICES Stock Identification Methods Working Group.

REFERENCES Adams, D. C. 1999. Methods for shape analysis of landmark data from articulated structures. Evol. Ecol. Res. 1: 959–970. Albertson, R. C. and Kocher, T. D. 2001. Assessing morphological differences in an adaptive trait: a landmark-based morphometric approach. J. Exp. Zool. 289: 385–403.

170

Steven X. Cadrin

Armstrong, M. P. and Cadrin, S. X. 2001. Morphometric patterns within and among spawning aggregations of Atlantic herring (Clupea harengus) off the northeast United States. In F. Funk, J. Blackburn, D. Hay, A. J. Paul, R. Stephenson, R. Toressen, and D. Witherell (eds.), Herring 2000. Alaska Sea Grant Report AK-SG-2000-01, pp. 575–590. Beacham, T. D. 1984. Age and morphology of chum salmon in southern British Columbia. Trans. Am. Fish. Soc. 113: 727–736. Beacham, T. D. 1985. Meristic and morphometric variation in pink salmon (Oncorhynchus gorbuscha) in southern British Columbia and Puget Sound. Can. J. Zool. 63: 366–372. Beacham, T. D. and Murray, C. B. 1987. Adaptive variation in body size, age, morphology, egg size, and developmental biology of chum salmon (Oncorhynchus keta) in British Columbia. Can. J. Fish. Aquat. Sci. 44: 244–261. Beacham, T. D., Murray, C. B., and Withler, R. E. 1988a. Age, morphology, developmental biology, and biochemical genetic variation of Yukon river fall chum salmon, Oncorhynchus keta, and comparisons with British Columbia populations. Fish. Bull. 86: 663–674. Beacham, T. D., Withler, R. E., Murray, C. B., and Barner, A. L. 1988b. Variation in body size, morphology, egg size, and biochemical genetics of pink salmon in British Columbia. Trans. Am. Fish. Soc. 117: 109–126. Begg, G. A. and Waldman, J. R. 1999. An holistic approach to fish stock identification. Fish. Res. 43: 35–44. Blackith, R. E. and Reyment, R. A. 1971. Multivariate Morphometrics. Academic Press, London, UK. 71 pp. Booke, H. E. 1981. The conundrum of the stock concept—are nature and nurture definable in fishery science? Can. J. Fish. Aquat. Sci. 38: 1479–1480. Bookstein, F. L. 1990. Introduction to methods for landmark data. In F. J. Rohlf and F. L. Bookstein (eds.), Proceedings of the Michigan Morphometrics Workshop. University of Michigan Museum of Zoology Special Publication 2, pp. 215–226. Bookstein, F. L. 1991. Morphometric Tools for Landmark Data. Cambridge University Press, Cambridge, UK. 435 pp. Bookstein, F. L., Chernoff, B., Elder, R. L., Humphries, J. M., Smith, G. R., and Strauss, R. E. 1985. Morphometrics in evolutionary biology, the geometry of size and shape change with examples from fishes. Acad. Natl. Sci. Philadelphia Spec. Pub. 15, 277 pp. Burnaby, T. P. 1966. Growth-invariant discriminant functions and generalized distances. Biometrics 22: 96–110. Cadrin, S. X. 1995. Discrimination of American lobster stocks off southern New England based on secondary sex character allometry. Can J. Fish. Aquat. Sci. 52(12): 2712–2723. Cadrin, S. X. 2000. Advances in morphometric analysis of fish stock structure. Rev. Fish Biol. Fish. 10: 91–112. Cadrin, S. X. and Friedland, K. D. 1999. The utility of image processing techniques for morphometric analysis and stock identification. Fish. Res. 43: 129–139. Cadrin, S. X. and Silva, V. M. 2004. Morphometric variation of yellowtail flounder. J. Northwest Atl. Fish. Sci. (in review). Carvalho, G. R. and Hauser, L. 1994. Molecular genetics and the stock concept in fisheries. Rev. Fish. Bio. Fish. 4: 326–350. Corti, M. and Crosetti, D. 1996. Geographic variation in the grey mullet Mugil cephalus (Pisces: Mugilidae): a geometric morphometric analysis using partial warp scores. J. Fish Biol. 48: 255–269. Cole, L. C. 1954. The population consequences of life history phenomena. Quart. Rev. Biol. 29: 103–137. Coyle, T. 1998. Stock identification and fisheries management: the importance of using several methods in a stock identification study. In D. A. Hancock (ed.), Taking Stock: Defining and Managing Shared Resources. Australian Society for Fishery Biology, Sydney, pp. 173–182.

Morphometric Landmarks

171

Garrod, J. D. and Horwood, J. W. 1984. Reproductive strategies and the response to exploitation. In G. W. Potts and R. J. Wootton (eds.), Fish Reproduction. Academic Press, New York, pp. 367–384. Gould, S. J. and Johnston, R. F. 1972. Geographic variation. Ann. Rev. Ecol. Syst. 3: 457–498. Hale, M. 1999. Locomotor mechanics during early life history: effects of size and ontogeny on faststart performance of salmonid fishes. J. Exp. Biol. 202: 1465–1479. Hohn, A. A. 1997. Design for a multiple-method approach to determine stock structure of bottlenose dolphins in the mid-Atlantic. NOAA Tech. Mem. NMFS-SEFSC 401. Holtby, L. B., Swain, D. P., and Allan, G. M. 1993. Mirror-elicited agonistic behaviour and body morphology as predictors of dominance status in juvenile coho salmon (Oncorhynchus kisutch). Can. J. Fish. Aquat. Sci. 50: 676–684. Homberger, D. G. 1988. Models and tests in morphology: the significance of description and integration. Am. Zool. 28: 217–229. Huxley, J. S. 1932. Problems of Relative Growth. Dial Press, New York. 276 pp. Jolicoeur, P. J. 1963. The multivariate generalization of the allometric equation. Biometrics 19: 497–499. Klingenberg, C. P. 1996. Multivariate allometry. In L. F. Marcus, M. Corti, A. Loy, G. J. P. Naylor, and D. E. Slice (eds.), Advances in Morphometrics. NATO ASI Series A: Life Sci. 284: 23–49. Lee, P. J. 1971. Multivariate analysis for the fisheries biology. Fish. Res. Bd. Can. Tech. Rep. No. 244: 1–182. Marcus, L. F. 1990. Traditional morphometrics. In F. J. Rohlf and F. L. Bookstein (eds.), Proceedings of the Michigan Morphometrics Workshop. University of Michigan Museum of Zoology Special Publication. 2, pp. 77–122. Misra, R. K. and Easton, M. D. L. 1999. A note on the number of morphometric characters used in fish stock delineation studies employing a MANOVA. Fish. Res. 42: 191–194. Pimentel, R. A. 1979. Morphometrics, the Multivariate Analysis of Biological Data. Kendall Hunt, Dubuque, Iowa. 276 pp. Reyment, R. 1990. Reification of classical multivariate analyses in morphometry. In F. J. Rohlf and F. L. Bookstein (eds.), Proceedings of the Michigan Morphometrics Workshop. University of Michigan Museum of Zoology Special Publication. 2, pp. 123–144. Reyment, R., Blackith, R. E., and Campbell, N. A. 1984. Multivariate Morphometrics, 2nd ed. Academic Press, London, UK. 232 pp. Riddell, B. E. and Leggett, W. C. 1981. Evidence of an adaptive basis for geographic variation in body morphology and time of downstream migration of juvenile Atlantic salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 38, 308–320. Riddell, B. E., Leggett, W. C., and Sanders, R. R. 1981. Evidence of an adaptive polygenic variation between two populations of Atlantic salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 38, 321– 333. Rinçon, P. A. 2000. Big fish, small fish: still the same species. Lack of morphometric evidence of the existence of two sturgeon species in Guadalquivir River. Mar. Biol. 136: 715–723. Rohlf, F. L. 1998. On applications of geometric morphometrics to studies on ontogeny and phylogeny. Syst. Biol. 47: 147–158. Rohlf, F. J. and Bookstein, F. L. 1987. A comment on shearing as a method for “size correction.” Syst. Zool. 36: 356–367. Rohlf, F. L. and Marcus, L. F. 1993. A revolution in morphometrics. Trends Ecol. Evol. 8: 129–132. Royce, W. F. 1957. Statistical comparison of morphological data. In J. C. Marr (ed.), Contributions to the Study of Subpopulations of Fishes. U.S. Fish and Wildlife Serv. Spec. Sci. Rep. Fisheries 208, pp. 7–28. Saila, S. B. and Flowers, J. M. 1969. Geographic morphometric variation in American lobster. Syst. Zool. 18, 330–338.

172

Steven X. Cadrin

Saila, S. B. and Martin, B. K. 1987. A brief review and guide to some multivariate methods for stock identification. In H. E. Kumpf, R. N. Vaught, C. B. Grimes, A. G. Johnson, and E. L. Nakamura (eds.), Proceedings of the Stock Identification Workshop. NOAA Tech. Mem. NMFS-SEFC 199, pp. 149–175. Sheehan, T. F., Kocik, J. F., Atkinson, E., Cadrin, S. X., Legault, C., Atkinson, E., and Bengston, D. 2004. Marine growth and morphometrics for three populations of Atlantic salmon from eastern Maine, USA. Trans. Am. Fish. Soc. (in review). Skulason, S., Noakes, D. L., and Snorranson, S. S. 1989. Ontogeny of trophic morphology in four sympatric morphs of Arctic charr Salvelinus alpinus in Thingvallavatn, Iceland. Biol. J. Linn. Soc. 38: 281–301. Skulason, S., Snorranson, S. S., Noakes, D. L. G., and Ferguson, M. M. 1996. Genetic variation of life history variations among sympatric morphs of Arctic charr Salvelinus alpinus. Can. J. Fish. Aquat. Sci. 53: 1807–1813. Solow, A. 1990. A randomization test for misclassification probability in discriminant analysis. Ecology 71: 2379–2382. Strauss, R. E., and Bookstein, F. L. 1982. The truss: body form reconstructions in morphometrics. Syst. Zool. 31: 113–135. Swain, D. P. and Foote, C. J. 1999. Stocks and chameleons: the use of phenotypic variation in stock identification. Fish. Res. 43: 113–128. Swain, D. P. and Holtby, L. B. 1989. Differences in morphology and behavior between juvenile coho salmon (Oncorhynchus kisutch) rearing in a lake or in its tributary stream. Can. J. Fish. Aquat. Sci. 46: 1406–1414. Tabachnick, B. G. and Fidell, L. S. 1989. Using Multivariate Statistics. Harper Row & Collins, New York. 746 pp. Taylor, E. B. 1991. A review of local adaptation in Salmonidae, with particular reference to Pacific and Atlantic salmon. Aquaculture 98: 185–207. Taylor, E. B. and McPhail, J. D. 1985a. Variation in body morphology among British Columbia populations of coho salmon, Oncorhynchus kisutch. Can. J. Fish. Aquat. Sci. 42: 2020–2028. Taylor, E. B. and McPhail, J. D. 1985b. Variation in burst and prolonged swimming performance among British Columbia populations of coho salmon, Oncorhynchus kisutch. Can. J. Fish. Aquat. Sci. 42, 2029–2033. Teissier, G. 1960. Relative growth. In T. H. Waterman (ed.), The Physiology of Crustacea. Academic Press, New York, pp. 537–560. Thorpe, R. S. 1976. Biometric analysis of geographical variation and racial affinities, Biol, Rev. 51: 407–452. Thorpe, R. S. 1988. Multiple group principal components analysis and population differentiation. J. Zool. Lond. 216: 37–40. Walker, J. A. 1996. Principal components of body shape variation within an endemic radiation of threespine stickleback. In L. F. Marcus, M. Corti, A. Loy, G. J. P. Naylor, and D. E. Slice (eds.), Advances in Morphometrics. NATO ASI Series A: Life Sciences 284, pp 321–334. Walker, J. A. 1997. Ecological morphology of lacustrine threespine stickleback Gasterosteus aculeatus L. body shape. Biol. J. Linn. Soc. 61: 3–50. Webb, P. W. 1984. Form and function in fish swimming. Sci. Am. 251: 72–82. Webb, P. W. and Blake, R. W. 1985. Swimming. In M. Hildebrand, D. M. Bramble, K. F. Liem, and D. B. Wake (eds.), Functional Vertebrate Morphology. Harvard University Press, Cambridge, MA, pp. 110–128. Winans, G. A. 1984. Multivariate morphometric variability in Pacific salmon: technical demonstration. Can. J. Fish. Aquat. Sci. 41: 1150–1159. Winans, G. A. 1987. Using morphometric and meristic characters for identifying stocks of fish. In H. E. Kumpf, R. N. Vaught, C. B. Grimes, A. G. Johnson, and E. L. Nakamura (eds.), Proceedings of the Stock Identification Workshop. NOAA Tech. Mem. NMFS-SEFC 199, pp. 135–146.

CHAPTER

8

Morphometric Outlines STEVEN X. CADRIN* AND KEVIN D. FRIEDLAND† *National Marine Fisheries Service, Woods Hole, Massachusetts, USA, †UMass/NOAA Cooperative Marine Education and Research Program, University of Massachusetts, Amherst, Massachusetts, USA

I. Introduction II. Methods A. Image Processing B. Statistical Model Fitting C. Multivariate Analysis III. Interpretation IV. Case Studies in Stock Identification V. Discussion References

I. INTRODUCTION The study of shape variation has advanced from measuring simple linear distances to deriving geometric variables. The shift from traditional morphometrics to more complex geometric functions was facilitated by the development of image processing tools (Cadrin and Friedland, 1999). Recently developed geometric approaches to morphometric analysis are generally categorized as either “landmark methods” (this volume, Chapter 7) or “outline methods” (Bookstein et al., 1985; Rohlf and Bookstein, 1990; Rohlf and Marcus, 1993; Marcus et al., 1996). Geometric outline methods quantify boundary shapes so that patterns of shape variation within and among groups can be evaluated. Patterns of variation in the shape of fish scales and otoliths as well as mollusc valves have been used to identify phenotypic stocks. Prior to the development of outline methods, researchers noted that otolith shape varied among stocks. For example, Messieh (1972) described two different “types” of otoliths between spring and autumn spawning herring. However, efficient classification of fish to the correct stock requires quantification of shape and its variation. The most common outline method involves fitting a Fourier series to the point coordinates along the perimeter of a morphometric feature (Jarvis et al., 1978). Fourier Stock Identification Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

173

174

Steven X. Cadrin and Kevin D. Friedland

coefficients are commonly used as multivariate observations for discriminant analysis, and several studies have successfully used Fourier transforms of scale or otolith shape for group discrimination of finfish stocks.

II. METHODS The general approach of geometric outline methods is (A) capturing the outline of a structure, (B) fitting a geometric model to concisely describe the outline, and (C) multivariate analysis of model parameter estimates to detect patterns of variance, discriminate groups, and classify individuals to groups.

A. IMAGE PROCESSING The development of hardware and software for image analysis enhanced the capability of archiving outline shapes. Digital images of hard structures (e.g., scales, otoliths, valves) are usually recorded with video cameras, and more recently digital cameras, mounted on microscopes. Image processing tools are used to enhance contrast of the image, transforming gray-scale images to blackand-white silhouettes of the structure. Image analysis software includes search algorithms to trace the outline, deriving Cartesian (x,y) coordinates of outline shape. The spatially calibrated boundary shape is now easily sampled for morphometric measurements such as area and perimeter, providing useful shape indices and the outline itself, which is usually analyzed to produce derived discriminating variables. Although most stock identification studies have utilized scales and otoliths, other hard parts (e.g., vertebrae) can also exhibit shape variation and are potential stock discriminators. Campana and Casselman (1993) found that among the types of otoliths, saggita shape variables performed best for discriminating cod stocks. Little variation has been found between right and left sagitta within individual fish (Bird et al., 1986; Castonguay et al., 1991; Campana and Casselman, 1993).

B. STATISTICAL MODEL FITTING The goal of mathematically modeling an outline is to describe the form accurately (i.e., achieving a good statistical fit) with the fewest number of model parameters. Efficient description of shape is critical for stock identification because each parameter is treated as a variable for conventional multivariate analysis, and the number of samples required to detect differences increases as a function of the number of variables (Saila and Martin, 1987).

Morphometric Outlines

175

Homology, the developmental similarity of a structure, is one aspect of morphology that should be considered for describing outline shape (Rohlf and Bookstein, 1990). Most geometric outline methods require that boundary traces begin at a standard, homologous point on the outline. For example, Begg and Brown (2000) used the distal edge of otolith rostrum to start the outline trace of haddock otoliths. Some researchers also use a homologous point near the center of the outline (e.g., the focus of the scale or otolith) to derive radius functions. However, Campana and Casselman (1993) found no noticeable advantage in discriminating cod stocks by using the focus (a homologous feature) over the geometric centroid (a geometrically derived feature that may not be homologous). Most outline models require the outline to be transformed to a series of radial distances from a central point to locations on its perimeter. Each radius of the series is spaced by an equal step angle of rotation from the previous radius (Fig. 8-1). The unrolled series of radii is then treated as a truncated time series to be fit by the statistical model of choice. The shape can also be expressed as tangent angle functions rather than radius functions. Several commonly used methods for fitting outlines will be described. However, only Fourier analysis has been routinely used for stock identification. For a more detailed explanation of statistically fitting outlines, see Rohlf (1990).

FIGURE 8-1. Derivation of a radius function from outline coordinates.

176

Steven X. Cadrin and Kevin D. Friedland

1. Polynomials One empirical approach to representing any curvilinear function is to fit a polynomial equation with enough terms to adequately describe the shape: Y = b 0 + b1 X + b 2 X 2 + . . . + b n X n A first-degree polynomial (Y = b0 + b1X) is a linear equation, second degree is quadratic (Y = b0 + b1X + b2X2), etc. The model is fit to the data using least squares, with stepwise addition of successively greater degrees from a linear equation or stepwise elimination of degrees from a multinomial equation. Polynomial splines are also used to describe outlines. These include linear splines (discrete points connected with line segments), quadratic splines (points connected with tangents), or cubic splines (points connected with first and second derivatives of the curve). 2. Fourier Analysis Outline shapes are more commonly described by trigonometric functions of angles around a central point. Periodic functions generally use polar coordinates (r, q), rather than Cartesian coordinates, where x = r cos q, y = r sin q. Fourier functions involve periods (complete cycles), amplitudes (maximum radii), and harmonics (orthogonal components of waveform): k

q = a 0 + Â [a i cos (iq) + b i sin(iq)] i= 1

where q is the angle (in radians, ranging from 0 to 2p), and a and b are the Fourier coefficients of the ith harmonic. The greater number of harmonics included in the model, the closer the fit to the original shape. Lower-order harmonics are associated with general circularity and elongatedness, whereas higher-order harmonics are associated with details of the shape (Fig. 8-2). Images must be oriented similarly to compare coefficients among different shapes (i.e., the angle of the starting radius, at the homologous point, must be equal among specimens). Alternatively, the equation can be rearranged to account for amplitude and phase-lags: k

q = A 0 + Â [ A i cos (iq - f i )] i= 1

where A represents amplitude and f represents phase-lag (Rayner, 1971). The solution sets are commonly calculated by an implementation of the algorithm of Wallace and Wintz (1980). An example of Fourier analysis is provided by

177

Morphometric Outlines

125

Magnitude

100

75

50

25

0

0

5

10

15

20

Harmonic FIGURE 8-2. Estimated amplitudes of each Fourier harmonic for the otolith shape illustrated in Figure 8-1.

Campana and Casselman (1993), who explained 97% to 99% of shape variance in cod otoliths using the first ten harmonics and 99.9% of variance using the first 20 harmonics (Fig. 8-3). 3. Elliptical Fourier Analysis An alternative method to describe outline shape is elliptical Fourier analysis, which involves the decomposition of first differences of x,y coordinates k

x t = a 0 + Â [a i cos ( it k ) + b i sin(it k -1 )] i= 1 k

y t = c 0 + Â [c i cos ( it k ) + d i sin(it k -1 )] i= 1

where t is a distance, ranging from 0 to 2p radians. Other outline methods include median axis analysis (Straney, 1990) and eigenshape analysis (Lohmann and Schweitzer, 1990), but neither has been used for fish stock identification.

178

Steven X. Cadrin and Kevin D. Friedland

FIGURE 8-3. Fourier reconstruction of a cod otolith. The number represents the number of harmonics used to derive the shape. The actual digitized shape is presented last [from Campana and Casselman (1993), with permission].

C. MULTIVARIATE ANALYSIS A rich selection of variables is produced from the outline shapes, including derived variables and morphometric variables from physical measurements. The variables are suited to parametric methods such as discriminant analysis as well as nonparametric classification techniques such as decision trees. Two of the morphometric variables derived from physical measurements are rectangularity, defined as the otolith area divided by the area of its minimum enclosing rectangle, and circularity, defined as the perimeter squared divided by the area. The derived variables take the form of polynomial coefficients, Fourier amplitudes, phase angles, or combinations of data types. As with all multivariate analyses, standard statistical diagnostics should be explored to test assumptions and detect outliers. Because it is possible to derive a large number of shape parameters, cross-validation of classifications from discriminant analysis (i.e., classification of extrinsic specimens with known group membership) is essential for evaluating model performance (Campana and Casselman, 1993). There are limited returns in model performance from using many characters (i.e., higherorder harmonics or polynomial terms); though they tend to increase intrinsic classification accuracy, they decrease extrinsic accuracy. An alternative to conventional multivariate analysis of Fourier coefficients is contingency tables of amplitude frequency distributions using chi-square tests (Bird et al., 1986).

Morphometric Outlines

179

Similar to traditional morphometric analysis, size adjustment is a consideration for stock discrimination because classification should be based on shape of the structure, not its size. For example, a size effect confounded stock discrimination of red snapper (Smith, 1992). Two common approaches to removing size from outline data are removing a size covariate or standardizing to a mean radius. Campana and Casselman (1993) evaluated both methods. They removed the common within-group slope of otolith length on all variables, which should remove all otolith/fish size effects. They also standardized data to the mean radius, which still had some relationships with size. However, they found no obvious differences in classification accuracy between the two methods for removing size variance from the data. Another method of removing size is normalizing Fourier amplitudes by setting the zero harmonic component to zero and dividing the amplitude coefficients by the value for the first component, respectively (Wallace and Wintz, 1980). Other covariates that should be considered for stock identification are age and sex. Significant age effects have been found in otoliths of several species (Bird et al., 1986; Campana and Casselman, 1993). Age effects on otolith shape confounded discrimination of Atlantic mackerel from the Northeast and Northwest Atlantic (Castonguay et al., 1991). A significant sex effect was found in cod (Campana and Casselman, 1993), but not in herring (Bird et al., 1986).

III. INTERPRETATION Similar to other morphometric approaches, patterns of outline shape can be used to infer phenotypic stocks. However, otolith or scale shapes are less subject to short-term variability than body shape caused by changes in feeding or spawning condition. Furthermore, otolith shape has been correlated with individual growth rate (Campana and Casselman, 1993). For example, Atlantic mackerel groups with different growth rates had the largest differences in otolith shape (Castonguay et al., 1991). Biological interpretations of Fourier coefficients have been criticized because coefficients may not be strictly homologous among specimens (Bookstein et al., 1982; Bookstein, 1990). However, others maintain that Fourier harmonics can be oriented by homologous landmarks, and coefficients for lower-order harmonics are interpretable (e.g., the second harmonic coefficient is a measure of “elongatedness”; Ehrlich et al., 1983). Biological significance should not be interpreted directly from eigenshape functions either and can only be suggested indirectly by correlations between eigenshapes and ancillary information on group membership (Lohmann and Schweitzer, 1990).

180

Steven X. Cadrin and Kevin D. Friedland

IV. CASE STUDIES IN STOCK IDENTIFICATION A brief review of some case studies in stock identification that use outline morphometrics illustrates the rapid advancement of both data collection and analytical systems as well as general strengths and weaknesses of the approach. Among the first applications of outline morphometrics for stock identification, Jarvis et al. (1978) discriminated walleye from two locations in Lake Erie according to scale shape, adopting Fourier analysis, a method previously used to identify fossil ostracodes according to their marginal shape (Kaesler and Waters, 1972). Scales were projected onto a digitizer and manually traced to derive digital coordinates of scale outlines. The first 10 harmonics of scale outlines accounted for 95% of variance in radial lengths. The discriminant function correctly classified 80% of specimens to Seneca Shoals or Cattaraugus Falls. With the first 20 harmonics, intrinsic classification accuracy increased to 100%. Riley and Carline (1982) extended the method to discriminate five putative stocks of walleye, but classification accuracy using 20 harmonics was low (57% of intrinsic samples and 25% of extrinsic samples). When stocks were grouped to western Lake Erie and Lake St. Clair, accuracy increased (82% intrinsic and 65% extrinsic), but they concluded that differences in scale shape were not sufficient to discriminate stocks. Ferson et al. (1985) used the first 10 harmonics from elliptical Fourier analysis of common mussel shells to discriminate between two electrophoretically different populations. The right valve of each mussel specimen was digitally scanned, and the resulting gray-scale images were transformed to a silhouette of black and white pixels. Outlines were derived using a line-tracing algorithm to search the transition from white to black pixels. Intrinsic classification accuracy was 97%, and cross-validated accuracy was 74%. Scale outline features were used to discriminate groups of striped bass (Ross and Pickard, 1990; Richards and Esteves, 1997a,b). Images of scales were captured using a frame-grabber system for video that was recorded from a camera mounted on a microscope. Outlines were derived from image processing and a boundary-tracking algorithm. Ross and Pickard (1990) used the first 8 harmonics from Fourier analysis and classified striped bass to wild or hatchery origins with 75% accuracy. Richards and Esteves (1997a and 1997b) found significant differences in the first 12 Fourier harmonics among two wild stocks of striped bass. Both studies (Ross and Pickard, 1990; Richards and Esteves, 1997a,b) then used Fourier harmonics in combination with other scale pattern features to classify stocks. A multidisciplinary analysis of stock identification techniques for striped bass compared classification accuracies from various information, including mitochondrial DNA characters, immunoassay data, meristics, landmark morphometrics, and scale outline features (Waldman et al., 1997). Their results indicate that morphometric features (including Fourier amplitudes from scale outlines) produced the most accurate classifications.

Morphometric Outlines

181

More recently, otolith shape was used to challenge the current assumptions used to manage the king mackerel fishery (DeVries et al., 2002). Otolith outlines were archived with an image analysis system, and a discriminant function was derived from the standardized amplitudes of the first 20 Fourier harmonics as well as perimeter and area. Extrinsic classification accuracy was 71% to 78%. The discriminant function was used for stock composition analysis to determine the origin of fish caught in the 1996–1997 winter fishery. Although the management plan assumed that all fish in the winter fishery were from the Gulf of Mexico stock, the discriminant function indicated that 99.8% of fish sampled from the winter fishery were from the Atlantic stock.

V. DISCUSSION The extent to which otolith shape differences are genetically or environmentally induced is not clear. Otolith morphology is commonly used as a species-level character in fish systematics; thus, the pattern of phenotypic expression is considered to be similar to that of other calcified structures (Lagler et al., 1977). However, recent work has demonstrated that otolith morphology can vary in response to differences in growth regimes for a range of species (Reznick et al., 1989; Secor and Dean, 1989; Smith, 1992). A limited comparison made by Friedland and Reddin (1994) suggests that the greater influence on otolith shape may be genetic. In comparisons to other stock identification methods, scale and otolith outline shape generally performed well for discriminating stocks (Casselman et al., 1981; Waldman et al., 1997). However, size and age effects commonly confound stock discrimination and require appropriate statistical treatment and interpretation (Castonguay et al., 1991; Smith, 1992). Therefore, outline morphometrics is a promising approach for stock identification, but interpreting patterns of variance can be difficult.

ACKNOWLEDGMENTS We thank Robert Edgar and Richard Strauss for their instruction on statistical methods for fitting outlines. We are grateful to Steve Campana for permission to use Figure 8-3, and to Fred Serchuk for reviewing the manuscript.

REFERENCES Begg, G. A. and Brown, R. W. 2000. Stock identification of haddock Melanogrammus aeglefinus on Georges Bank based on otolith shape analysis. Trans. Am. Fish. Soc. 129: 935–945.

182

Steven X. Cadrin and Kevin D. Friedland

Bird, J. L., Eppler, D. T., and Checkley, D. M., Jr., 1986. Comparisons of herring otoliths using Fourier series shape analysis. Can. J. Fish. Aquat. Sci. 43: 1228–1234. Bookstein, F. L. 1990. Introduction to methods for landmark data. In F. J. Rohlf and F. L. Bookstein (eds.), Proceedings of the Michigan Morphometrics Workshop. University of Michigan Museum of Zoology Special Publication 2, pp. 215–226. Bookstein, F. L., Strauss, R. E., Humphries, J. M., Chernoff, B., Elder, R. L., and Smith, G. R. 1982. A comment on the use of Fourier methods in systematics. Syst. Zool. 31: 85–92. Bookstein, F. L., Chernoff, B., Elder, R. L., Humphries, J. M., Smith, G. R., and Strauss, R. E. 1985. Morphometrics in Evolutionary Biology, the Geometry of Size and Shape Change with Examples from Fishes. Academy of Natural Sciences, Philadelphia, Special Publication 15. 277 pp. Cadrin, S. X. and Friedland, K. D. 1999. The utility of image processing techniques for morphometric analysis and stock identification. Fish. Res. 43: 129–139. Campana, S. E. and Casselman, J. M. 1993. Stock discrimination using otolith shape analysis. Can. J. Fish. Aquat. Sci. 50: 162–1083. Casselman, J. M., Collins, J. J., Crossman, E. J., Ihssen, P. E., and Spangler, G. R. 1981. Lake whitefish (Coregonus clupeaformis) stocks of the Ontario water of Lake Huron. Can. J. Fish. Aquat. Sci. 38: 1772–1789. Castonguay, M., Simard, P., and Gagnon, P. 1991. Usefulness of Fourier analysis of otolith shape for Atlantic mackerel (Scomber scombrus) stock discrimination. Can. J. Fish. Aquat. Sci. 48: 296–302. DeVries, D. A., Grimes, C. B., and Prager, M. H. 2002. Using otolith shape analysis to distinguish eastern Gulf of Mexico and Atlantic Ocean stocks of king mackerel. Fish. Res. 57: 51–62. Ehrlich, R., Baxter Pharr, R., Jr., and Healy-Williams, N. 1983. Comments on the validity of Fourier descriptors in systematics: a reply to Bookstein et al. Syst. Zool. 32, 202–206. Ferson, S., Rohlf F. J., and Koehn, R. K. 1985. Measuring shape variation of two-dimensional outlines. Syst. Zoo. 34: 59–68. Friedland, K. D. and Reddin, D. G. 1994. The use of otolith morphology in stock discriminations of Atlantic salmon (Salmo salar L.). Can. J. Fish. Aquat. Sci. 51: 91–98. Jarvis, R. S., Klodowski, H. F., and Sheldon, S. P. 1978. New method of quantifying scale shape and an application to stock identification in Walleye (Stizostedion vitreum vitreum). Trans. Am. Fish. Soc. 107: 528–434. Kaesler, R. L. and Waters, J. A. 1972. Fourier analysis of the ostracode margin. Geol. Soc. Am. Bull. 83: 1169–1178. Lagler, K. F., Bardach, J. E., Miller, R. R., and Passino, D. R. M. 1977. Ichthyology. John Wiley and Sons, New York. Lohmann, G. P. and Schweitzer, P. N. 1990. On eigenshape analysis. In F. J. Rohlf and F. L. Bookstein (eds.), Proceedings of the Michigan Morphometrics Workshop. University of Michigan Museum of Zoology Special Publication 2, pp. 147–166. Marcus, L. F., Corti, M., Loy, A., Naylor, G. J. P., and Slice, D. E. 1996. Advances in Morphometrics. NATO ASI Series A: Life Sciences 284. 587 pp. Messieh, S. N. 1972. Use of otoliths in identifying herring stocks in southern Gulf of St. Lawrence and adjacent waters. J. Fish. Res. Board Can. 29: 1113–1118. Rayner, J. N. 1971. An Introduction to Spectral Analysis. Pion, London, UK. 174 pp. Reznick, D., Linbeck, E., and Bryga, H. 1989. Slower growth results in larger otoliths: an experimental test with guppies (Poecilia reticulata). Can. J. Fish. Aquat. Sci. 46: 108–112. Richards, R. A. and Esteves, C. 1997a. Stock-specific variation in scale morphology of Atlantic striped bass. Trans. Am. Fish. Soc. 126: 908–918. Richards, R. A. and Esteves, C. 1997b. Use of scale morphology for discriminating wild stocks of Atlantic striped bass. Trans. Am. Fish. Soc. 126: 919–925. Riley, L. M. and Carline, R. F. 1982. Evaluation of scale shape for the identification of walleye stocks. Western Lake Erie. Trans. Am. Fish. Soc. 111: 736–741.

Morphometric Outlines

183

Rohlf, F. J. 1990. Fitting curves to outlines. In F. J. Rohlf and F. L. Bookstein (eds.), Proceedings of the Michigan Morphometrics. University of Michigan Museum of Zoology Special Publication 2, pp. 167–177. Rohlf, F. J. and Bookstein, F. L. (eds.). 1990. Proceedings of the Michigan Morphometrics Workshop. University of Michigan Museum of Zoology Special Publication 2. Rohlf, F. L. and Marcus, L. F. 1993. A revolution in morphometrics. Trends in Ecology and Evolution 8: 129–132. Ross, W. R. and Pickard A. 1990. Use of scale pattern and shape as discriminators of wild and hatchery striped bass stocks in California. In N. C. Parker, A. E. Giorgi, R. C. Heidinger, D. B. Jester, E. D. Prince, and G. A. Winans (eds.), Fish-Marking Techniques. American Fisheries Society Symposium 7. American Fisheries Society, Bethesda, MD, pp. 71–77. Saila, S. B. and Martin, B. K. 1987. A brief review and guide to some multivariate methods for stock identification. In H. E. Kumpf, R. N. Vaught, C. B. Grimes, A. G. Johnson, and E. L. Nakamura (eds.), Proceedings of the Stock Identification Workshop. NOAA Tech. Mem. NMFS-SEFC 199, pp. 149–175. Secor, D. H., and Dean, J. M. 1989. Somatic growth effects on the otolith—fish size relationship in young pond-reared striped bass (Morone saxatilis). Can. J. Fish. Aquat. Sci. 46: 113–121. Smith, M. K. 1992. Regional differences in otolith morphology of the deep slope red snapper (Etelis carbunculus). Can. J. Fish. Aquat. Sci. 49: 795–804. Straney, D. O. 1990. Median axis methods in morphometrics. In F. J. Rohlf and F. L. Bookstein (eds.), Proceedings of the Michigan Morphometrics Workshop. University of Michigan Museum of Zoology Special Publication 2, pp. 179–200. Waldman, J. R., Richards, R. A., Schill, W. B., Wirgin, I., and Fabrizio, M. C. 1997. An empirical comparison of stock identification techniques applied to striped bass. Trans. Am. Fish. Soc. 126: 369–385. Wallace, T. P. and Wintz, P. A. 1980. An efficient three dimensional aircraft recognition algorithm using normalized Fourier descriptors. Comp. Graph. Image Processing 13: 99–126.

This Page Intentionally Left Blank

CHAPTER

9

Analyses of Calcified Structures-Texture and Spacing Patterns KEVIN D. FRIEDLAND* AND STEVEN X. CADRIN† *NOAA Cooperative Marine Education and Research Program, University of Massachusetts, Amherst, Massachusetts, USA, †National Marine Fisheries Service, Woods Hole, Massachusetts, USA

I. Introduction II. Methodology A. Spacing and Texture from Optical Density Profiles B. Selection of Characters III. Selected Case Studies A. Salmon B. Striped Bass C. Aquaculture Releases and Escapees IV. Discussion References

I. INTRODUCTION Analysis of texture and spacing patterns found in hard body parts is a wellestablished technique of stock separation. Harden Jones (1968) described the usefulness of scale and otolith patterns for determining origin of fish and the method’s historical development. Hjort’s (1914) investigation of herring scale patterns was among the earliest identifications of distinct intraspecific groups of fishes. The approach developed utilizing traditional laboratory techniques (Clutter and Whitesel, 1956; Henry, 1961; Mosher, 1963; Anas and Murai, 1969; Lear and Sandeman, 1980; Antere and Ikonen, 1983; Reddin et al., 1988; Lund and Hansen, 1991). However, newer imaging methods have greatly enhanced the approach and increased its power for stock discrimination (Cook, 1982; Barlow and Gregg, 1991; Schwartzberg and Fryer, 1989; Ross and Pickard, 1990; Friedland et al., 1994; Cadrin and Friedland, 1999). Stock Identification Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

185

186

Kevin D. Friedland and Steven X. Cadrin

The interpretation of texture patterns is partly dependent on the correlation between the growth of the animal and the calcified structure to be analyzed. For example, fish scale growth and the rate of circuli deposition are related (Doyle et al., 1987; Barber and Walker, 1988; Fisher and Pearcy, 1990). The variation of these features throughout the species’ range, as a product of both genetic and environmental influences, allows discrimination of groups with different growth patterns. Therefore, texture patterns of calcified structures relate to phenotypic stocks (Booke, 1981), which need to be defined to model population dynamics. Spacing patterns can be measured with conventional measurement techniques or with enhanced technologies such as image processing. Simple features, such as the distance from one life history transition zone to another, can be recognized visually and measured manually. For example, the distance from an otolith focus to a check marking the transition from juvenile to adult life stages could serve as a stock separation character. Spacing patterns may also be a more complex set of measurements requiring the use of image processing techniques to achieve measurement accuracy and to process a large volume of data. For example, the spacing or distance from a landmark to a large number of structures can be extracted. When this approach is applied to fish scales or otoliths, the location of upward of 50 circuli may be measured, thus generating 49 spacing variables per individual. Image processing allows quick and precise measurement of many distances. For example, Cook and Guthrie (1987) found that 64,000 luminescence measures could be measured on a sockeye salmon scale in 6 sec, and with updated equipment, image handling is now even more powerful. The texture of calcified body parts can be measured with physical probes or by the analysis of optical density profiles. The idea of developing a two- or threedimensional map of a scale or otolith using a microstylus has been considered and appears technically feasible. However, it does not appear that the approach has been attempted in stock identification research. On the other hand, using optical density profiles to represent texture has been applied to stock identification. The method was first proposed by Major et al. (1972) and simply involves the use of transmitted light from a specimen as a representation of surface features. For example, the circuli on a fish scale appear dark because they are surface features, which are optically denser than intercirculi spaces. The periodicity of circuli can be analyzed, and spacing can be calculated from modes of luminescence. Analysis of calcified structures can effectively reveal patterns in circuli spacing that correspond to the specimen’s growth history. Therefore, phenotypic stocks that have significantly different growth rates can be discriminated using texture analysis.

Analyses of Calcified Structures-Texture and Spacing Patterns

187

II. METHODOLOGY

A. SPACING

AND

TEXTURE

FROM

OPTICAL DENSITY PROFILES

Specimens should be prepared and examined with an image processor equipped with an optics subsystem appropriate for the specimen. For small specimens, this may entail the use of a compound microscope, whereas for larger specimens, the use of a dissecting microscope may be sufficient. A transect from the focus to a homologous point on the margin is identified for data extraction (Fig. 9-1). The data path is selected to represent a growth phase or life history feature comparable to other specimens. From this line, a luminescence profile of transmitted light is extracted which can be processed further. Manual methods or automated algorithms can be used to identify features along the data path (e.g., the location of circuli or annuli relative to the focus). The raw measurements can be saved for use as spacing indices or landmark distances. Alternatively, the luminescence profile can be treated as a frequency domain series. A Fourier transform of the luminescence pattern can be calculated, and various statistics from the transform may serve as classification model variables. For example, textural features of a scale can be expressed as the magnitudes of the Fourier transform. Other frequency domain statistics may also be used.

B. SELECTION

OF

CHARACTERS

Imaging techniques offer the investigator the ability to produce vast quantities of data. A number of investigators have begun to explore ways of reducing the number of variables generated by these sorts of analyses. One such approach has been to employ variable averaging or expressing spacing or circuli distance measures as means of pairs or quadruplets of adjacent variables. In studies using circuli spacing data, Barlow and Gregg (1991) reported that model efficiency was similar or only slightly higher for averaged data. In a similar study, Friedland et al. (1994) reported similar or slightly lower model classification efficiency. The appeal of averaged data lies in the anticipated robustness of models to the potential problem that information content may be dispersed over a number of adjacent variables. However, there is no clear advantage to treating the data in this manner, and there may be a loss of information content when averaging is performed. Friedland et al. (1994) suggested that there may be important information in individual imaging derived variables (spacing or magnitude values) and that preprocessing of variables, like averaging, should be applied on a case-bycase basis only where it improves classification efficiency. One potential source of bias from image processing techniques to extracting circuli spacing data was identified by Friedland et al. (1994). When automated

188

Kevin D. Friedland and Steven X. Cadrin

A

B

FIGURE 9-1. Plot of the luminescence profile from a sockeye salmon scale (A) and (B) the same profile with the circuli lactations marked based on local negative minima of luminescence. From Cook and Guthrie (1987).

procedures are used to mark a circulus and images are visually inspected for manual correction, unmarked circuli are infrequent. However, circuli are often marked more than once, and the double mark could escape manual correction. This has been observed to occur with circuli that are very wide or are of complex

Analyses of Calcified Structures-Texture and Spacing Patterns

189

morphology. This error will tend to add to the total number of circuli for a specimen and decrease the circuli spacing for the adjacent circuli pairs. The direction of the bias would be to reduce circuli spacing for the specimen and miscode subsequent circuli so that comparisons with other specimens would mislead interpretations about growth differences. Texture and spacing data are continuous variables with strong correlations and are usually analyzed using conventional multivariate morphometrics [e.g., principal components analysis, cluster analysis, and discriminant analysis (Tabachnick and Fidell, 1989)]. Accordingly, exploratory diagnostics should be performed to identify outliers and test multivariate assumptions such as multivariate normality, equal variance among groups, and equal covariance among groups.

III. SELECTED CASE STUDIES

A. SALMON Several researchers have used growth patterns on scales and otoliths to discriminate groups of sockeye salmon, and the series of studies demonstrated the methodological progression from manual measurements to image analysis. In the early 1900s, Charles Gilbert used the frequency of circuli in the first year to classify sockeye from the Fraser River to spawning location (reviewed by Secor in Chapter 3, this volume). Gilbert’s methods were refined and applied widely over the next century. High-seas samples of sockeye salmon were classified to their river of origin with 72% to 95% accuracy based on the number of scale circuli in seasonal growth zones and the width of growth zones (Cook and Lord, 1977). Manual scale measurements also were used to discriminate sockeye salmon to region of origin (Bristol Bay, Gulf of Alaska, or Kamchatka Peninsula), with approximately 70% accuracy (Cook, 1982). Image processing was used by Cook (1987) to discriminate 14 races of sockeye salmon in the Fraser River, but classification accuracy varied by year, from 35% to 100%. Waltemyer et al. (1996) also found that scale pattern variables (number of circuli, width of seasonal growth zones, and circuli distances; derived from the projection of images onto a digitizing pad) varied by year and had significant overlap among sockeye stocks in the Upper Cook Inlet. Otolith banding patterns formed during incubation (derived from Fourier analysis of luminescence profiles) were used by Finn et al. (1997) to discriminate sockeye from the wild and from hatcheries, with 83% accuracy. Quinn et al. (1999) also compared otolith microstructure to estimate straying rates of sockeye from spawning sites within a river system. Much of the work being done on Pacific salmonids remains in the gray literature, where it is being directly applied to management issues.

190

Kevin D. Friedland and Steven X. Cadrin

A similar series of studies demonstrate the utility of scale spacing patterns for discriminating groups of Atlantic salmon (reviewed in detail by Reddin and Friedland, 1999). Lear and Sandeman (1980) were the first to use scale characters to discriminate the continent of origin of Atlantic salmon in the West Greenland fishery. A refined discriminant analysis based on the number of circuli in the first marine summer and winter growth zones was developed by Reddin (1986) to classify salmon to North American or European origins. Reddin et al. (1988) used the discriminant function to estimate the portion of salmon caught off West Greenland that were spawned in North America. Reddin et al. (1990) expanded the discriminant function to include electrophoretic data with circuli numbers. Circuli spacing, expressed as the first 50 Fourier coefficients of luminescence profiles, classified farmed and wild salmon with 90% accuracy (Friedland et al., 1994). Salmon researchers also have derived great utility in applying these stock identification techniques to identify the contrasts in ecological variation in stock structure. Circuli spacing was used to compare post-smolt growth because circuli spacings are wider for 1-seawinter than 2-seawinter salmon of the same cohort (Friedland and Haas, 1996). Differences in post-smolt growth among spawning populations were detected using intercirculi distances, with salmon from the Penobscot River having wider spacing (i.e., faster growth) than those from the Connecticut River (Friedland et al., 1996). Annual variability in circuli spacing led to inferences about the residence time in estuarine and coastal nursery habitats (Friedland et al., 1999). Circuli spacing patterns have been used to investigate recruitment synchrony among Atlantic salmon stocks (Friedland and Reddin, 2000). Scale circuli patterns currently are being used to discriminate Atlantic salmon from geographic regions of the Narraguagus river system (Haas-Castro et al., 2004). Similar patterns of growth and circuli patterns have been used to discriminate interspecific groups of other salmonid species. For example, Fisher and Pearcy (1990) used scale circuli spacing to compare growth rates among groups of coho salmon, and Marcogliese and Casselman (1998) discriminated rainbow trout from the wild and from hatcheries, with 90% accuracy.

B. STRIPED BASS Scale circuli spacing has been used to discriminate groups of striped bass. Ross and Pickard (1990) classified Pacific striped bass to wild or hatchery origin with approximately 90% accuracy and to hatchery with 66% to 88% accuracy using luminescence profiles with a weighted moving average filter. Methods for using width and spacing of the first 10 scale circuli were developed by Richards and Esteves (1997a) to discriminate groups of Atlantic striped bass (Fig. 9-2), and

Analyses of Calcified Structures-Texture and Spacing Patterns

191

FIGURE 9-2. Transect of a striped bass scale used to measure circulus patterns. From Richards and Esteves (1997), with permission.

classify specimens to three wild stocks (Hudson River, Chesapeake Bay, and Roanoke River), with 57% to 84% accuracy (Richards and Esteves, 1997b). These example case studies demonstrate that circuli patterns can be used to discriminate groups with different growth patterns.

C. AQUACULTURE RELEASES

AND

ESCAPEES

With the proliferation of finfish aquaculture, managers are faced with the contrasting issues of assessing intentional releases of aquaculture fish in stock enhancement programs and the unintentional escape of cultured fish that potentially pose a threat to wild stocks. The stock identification of hatchery products can be simple if the fish are marked prior to release, but many species are cultured at a size that precludes effective marking. Growers will not assume the cost of tagging without mandated tagging requirements. Analysis of scale features analyses, among other characters, has proved to be effective to meet this data requirement. Silva and Bumguardner (1998) attempted to develop a classification function to identify the hatchery contribution of red drum to Texas fisheries. These researchers relied on circuli spacing patterns of juvenile stages to identify adults. Similar approaches were used to assess releases of barramundi in Australia (Barlow and Gregg, 1991) and silver perch, also in Australia (Willett, 1993). Marcogliese and Casselman (1998) discriminated rainbow trout from the wild and from hatcheries with 90% accuracy. Friedland et al. (1994) looked at the issue of identifying Atlantic salmon fish farm escapees using scale characteristics. Circuli spacing and texture, expressed as the first 50 Fourier coefficients of

192

Kevin D. Friedland and Steven X. Cadrin

Farm Ranch Wild

Magnitude

2500

2000

1500

1000

0

10

20

30

40

50

Harmonic FIGURE 9-3. Magnitude of the first 50 harmonics differentiating three groups of Atlantic salmon: farm escapees, ranched fish, and wild fish. From Friedland et al. (1994).

luminescence profiles, classified farmed and wild salmon with 90% accuracy (Fig. 9-3).

IV. DISCUSSION Analysis of texture and spacing patterns offers a promising method for stock identification. A major strength of the approach is that characters have meaningful interpretations. Differences in circuli distance and spacing patterns indicates groups that grow differently, and relative position of major checks indicate the relative timing of important life history events (e.g., the movement from riverine to marine environments). Detecting significant differences in these characters is essential for phenotypic stock identification. One advantage of deriving spacing and texture data from image processing is that it is quantitative and objective in that measurements are automated and without the problems associated with manual scale readings (Lund et al., 1989). Douglas, Minckley, and Tyus (1989) suggested that qualitative characters are

Analyses of Calcified Structures-Texture and Spacing Patterns

193

excellent features upon which to base group separations. They presented extensive data that demonstrated even untrained observers show a high degree of feature interpretation. However, the automated approach does offer improvements since it addresses sources of procedural inaccuracy, such as those associated with reader fatigue, and removes any doubt of reader subjectivity from potentially sensitive management data. Manual scale reading and image processing techniques use essentially the same features of the scale or otolith to form information databases to classify the samples to origin. With image processing, the classification algorithm can be defined explicitly for review, and there is a complete quantitative audit trail for each decision. Spacing and texture data are undoubtedly influenced by the environment; therefore, it is essential to test the robustness of the variables based on these data for annual or long-term sources of variability. For example, annual variations in climate and food resources are known to affect circuli deposition in Atlantic salmon (Reddin et al., 1988). This has necessitated setting up new parameters for classification models with that species (Reddin et al., 1990). It is probably wise with any scale-based discrimination procedure to maintain reference collections so that classification models can be updated. Annual or longer-term sources of variability will be irrelevant if the proper reference samples are collected and applied. Precision with image processing techniques is very high, which is in contrast to approaches dependent on a scale reader. It is well known that fatigue, pattern of prior observations, and long term familiarity can affect the precision of scale readers (Lund et al., 1989). Feature extraction with the image processor is identical regardless of stage of the analysis, and it is also insensitive to problems created by changes in project personnel. Scales, otoliths, vertebrae, statoliths, and other hard parts are routinely collected from many species for age determinations. A large number of samples have generally been collected to represent the population or the catch, and sampling has been maintained for many years. Therefore, a wealth of archived hard parts is available for many populations, allowing the analysis of spacing patterns and exploration of growth differences and stock structure. Despite the continued promise associated with this family of techniques, they have not enjoyed widespread testing and application as predicted when hardware and software capabilities to extract texture and spacing features were first introduced. Because negative findings do not usually make their way into the literature, it is difficult to assess whether poor results or low usage of the methods account for their poor dissemination. A systematic evaluation of the discriminating power of this class of features would be useful.

194

Kevin D. Friedland and Steven X. Cadrin

REFERENCES Anas, R. E. and Myrai, S. 1969. Use of scale characteristics and a discriminant function for classifying sockeye salmon Oncorhychus nerka by continent of origin. Int. Pac. Salmon Fish. Com. Bull. 26. Antere, I. and Ikonen, E. 1983. A method of distinguishing wild salmon from those originating from fish farms on the basis of scale structure. ICES CM 1983/M: 26. 6 pp. Barber, W. E. and Walker, R. J. 1988. Circuli spacing and annulus formation: is there more than meets the eye? The case for sockeye salmon, Oncorhynchus nerka. J. Fish Biology 32: 237–245. Barlow, C. G. and Gregg, B. A. 1991. Use of circuli spacing on scales to discriminate hatchery and wild barramundi, Lates calcarifer (Bloch). Aquaculture Fish. Management 22: 491–498. Brooke, H. E. 1981. The conundrum of the stock concept—are nature and nurture definable in fishery science? Canadian Journal of Fisheries and Aquatic Sciences 38: 1479–1480. Cadrin, S. X. and Friedland, K. D. 1999. The utility of image processing techniques for morphometric analysis and stock identification. Fisheries Research 43: 129–139. Clutter, R. and Whitesel, L. 1956. Collection and interpretation of sockeye salmon scales. Int. Pac. Salmon Fish. Com. Bull. 9. Cook, R. C. 1982. Stock identification if sockeye salmon (Oncorhychus nerka) with scale pattern recognition. Can. J. Fish. Aquat. Sci. 39: 611–617. Cook, R. C. 1987. Optical pattern recognition for stock identification: past, present and future. NOAA Tech. Mem. NMFS-SEFC-199: 182–183. Cook, R. C. and Guthrie, I. 1987. In-season stock identification of sockeye salmon (Oncorhynchus nerka) using scale pattern recognition. In H. D. Smith, L. Margolis, and C. C. Wood (eds.), Sockeye Salmon (Oncorhynchus nerka) Population Biology and Future Management. Can. Spec. Publ. Fish. Aquat. Sci., no. 96, pp. 327–334. Cook. R. C. and Lord, G. E. 1977. Stock identification of sockeye salmon (Oncorhychus nerka) with scale pattern recognition. Fish. Bull. 76: 415–423. Douglas M. E., Minckley, W. L., and Tyus, H. M. 1989. Qualitative characters, identification of Colorado River chubs (Cyprinidae: Genus Gila) and the “art of seeing well.” Copeia 89: 653–662. Doyle, R. W., Talbot, A. J., and Nicholas, R. R. 1987. Statistical interrelation of length, growth, and scale circulus spacing: appraisal of growth rate estimator for fish. Can. J. Fish. Aquat. Sci. 44: 1520–1528. Finn, J. E., Burger, C. V., and Holland-Bartels, L. 1997. Discrimination among populations of sockeye salmon frye with Fourier analysis of otolith banding patterns formed during incubation. Trans. Am. Fish. Soc. 126: 559–578. Fisher, J. P. and Pearcy, W. G. 1990. Spacing of scale circuli versus growth rate in young coho salmon. Fish. Bull. 88: 637–643. Friedland, K. D., Dutil, J.-D., and Sadusky, T. 1999. Growth patterns in postsmolts and the nature of the marine juvenile nursery for Atlantic Salmon, Salmo salar. Fish. Bull. 97: 472–481. Friedland, K. D., Esteves, C., Hansen, L. P., and Lund, R. A. 1994. Discrimination of Norwegian farm, ranch, and wild origin Atlantic salmon by image processing. Fish. Management Ecol. 1: 117–128. Friedland, K. D. and Haas, R. E. 1996. Marine post-smolt growth and age at maturity of Atlantic salmon. J. Fish Biol. 48: 1–15. Friedland, K. D., Haas, R. E., and Sheehan, T. F. 1996. Post-smolt growth, maturation, and survival of two stocks of Atlantic salmon. Fish. Bull. 94: 654–663. Friedland, K. D. and Reddin, D. G. 2000. Growth patterns of Labrador Sea Atlantic salmon postsmolts and the temporal scale of recruitment synchrony for North American salmon stocks. Can. J. Fish. Aquat. Sci. 57: 1181–1189. Harden Jones, F. R. 1968. Fish Migration. St. Martin’s, New York. 325 pp. Hass-Castro, R. E., Sheehan, T. F., and Cadrin, S. X. 2004. Scale pattern analysis discriminates Atlantic salmon by river-reach rearing. Can. J. Fish. Aquat. Sci. (in press).

Analyses of Calcified Structures-Texture and Spacing Patterns

195

Henry, K. A. 1961. Racial identification of Fraser River sockeye salmon by means of scales and its application to salmon management. Int. Pac. Salmon Fish. Com. Bull. 12. Hjort, J. 1914. Fluctuations in the great fisheries of northern Europe. Rapp. P.-v. Reun. Cons. Inst Explor. Mer 20. Lear, W. H. and Sandeman, E. J. 1980. Use of scale characteristics and discriminant functions for identifying continental origin of Atlantic salmon. Rapp. P.-v. Reun. Cons. int. Explor. Mer. 176: 68–75. Lund, R. A. and Hansen, L. P. 1991. Identification of wild and reared Atlantic salmon, Salmo salar L., using scale characters. Aquaculture Fish. Management 22: 499–508. Lund, R. A., Hansen, L. P., and Järvi, T. 1989. Identification of reared and wild salmon by external morphology, size of fins and scale characteristics. NINA Forskningsrapport 1: 1–54. Major, R. L., Mosher, K. H., and Manson, J. E. 1972. Identification of stocks of Pacific salmon by means of scale features. In R. C. Simon and P. A. Larkin (eds.), The Stock Concept in Pacific Salmon, H. R. MacMillan Lectures in Fisheries, University of British Columbia, Vancouver, BC, pp. 209–223. Marcogliese, L. A. and Casselman, J. M. 1998. Scale methods for discriminating between Great Lakes stocks of wild and hatchery rainbow trout, with a measure of natural recruitment in Lake Ontario. North Am. J. Fish. Management 18: 253–268. Mosher, K. H. 1963. Racial analysis of red salmon by means of scales. Int. Pac. Salmon Fish. Com. Bull. 11. Quinn, T. P., Volk, E. C., and Hendry, A. P. 1999. Natural otolith microstructure patterns reveal precise homing to natal incubation sites by sockeye salmon (Oncorhynchus nerka). Can. J. Zool. 77: 766–775. Reddin, D. G. 1986. Discrimination between Atlantic salmon (Salmo salar L.) of North American and European origin. J. Cons. Int. Explor. Mer 43: 50–58. Reddin, D. G. and Friedland, K. D. 1999. A history of identification of continent of origin of Atlantic salmon (Salmo salar L.) at West Greenland, 1969–97, Fisheries Research 43: 221–235. Reddin, D. G., Stansbury, D. E., and Short, P. B. 1988. Continent of origin of Atlantic salmon (Salmo salar L.) at West Greenland. J. Cons. Int. Explor. Mer 44: 180–188. Reddin, D. G., Verspoor, E., and Downton, P. R. 1990. An integrated phenotypic and genotypic approach to stock discrimination of Atlantic salmon. J. Cons. Int. Explor. Mer 47: 83–88. Richards, R. A. and Esteves, C. 1997a. Stock-specific variation in scale morphology of Atlantic striped bass. Trans. Am. Fish. Soc. 126: 908–918. Richards, R. A. and Esteves, C. 1997b. Use of scale morphology for discriminating wild stocks of Atlantic striped bass. Trans. Am. Fish. Soc. 126: 919–925. Ross, W. R. and Pickard, A. 1990. Use of scale pattern and shape as discriminators of wild and hatchery striped bass stocks in California. In N. C. Parker, A. E. Giorgi, R. C. Heidinger, D. B. Jester, E. D. Prince, and G. A. Winans (eds.), Fish-Marking Techniques. American Fisheries Society Symposium 7. American Fisheries Society, Bethesda, MD, pp. 71–77. Schwartzberg, M. and Fryer, J. 1989. Experiments in identifying hatchery and naturally spawning stocks of Columbia Basin spring chinook salmon using scale pattern analysis. Technical Report 89-3, Columbia River Inter-Tribal Fish Commission. 25 pp. Silva, P. S. and Bumguardner, B. W. 1998. Use of scale circuli pattern analysis to differentiate between hatchery and wild red drum. Trans. Am. Fish. Soc. 60: 38–43. Tabachnick, B. G. and Fidell, L. S. 1989. Using Multivariate Statistics. Harper Row & Collins. 746 pp. Waltemyer, D. L., Bue, B. G., and Tarbox, K. E. 1996. Evaluation of scale pattern analysis for Upper Cook Inlet sockeye salmon stocks. Alaska Fish. Res. Bull. 3: 69–80. Willett, D. J. 1993. Discrimination between hatchery stocks of silver perch, Bidyanus bidyanus (Mitchell), using scale growth pattern. Aquaculture Fish. Management 24: 347–354.

This Page Intentionally Left Blank

CHAPTER

10

Meristics JOHN R. WALDMAN Hudson River Foundation for Science and Environmental Research, New York, New York, USA Currently, Biology Department, Queens College, The City University of New York, New York, New York, USA

I. II. III. IV.

Introduction Use of Meristics for Stock Identification A Case History: Striped Bass Conclusions References

I. INTRODUCTION Meristic characters are enumerable morphological features of fishes. Strictly, meristic features are those that corresponded evolutionarily with body segmentation; however, today, the term is used more broadly (Strauss and Bond, 1990). The most commonly enumerated features have been external (Fig. 10-1), including fin spines and fin rays, gill rakers, and scales (along several possible vectors). Internal meristic features that have been analyzed include pterygiophores, vertebrae, branchiostegal rays, and pyloric caeca. Values attained for meristic features are the products of interactions between genetics and environment (Marr, 1957; Swain and Foote, 1999). Environmental factors influencing meristic counts include temperature, salinity, oxygen, pH, food availability, and growth rate, among others (Tåning, 1952; Barlow, 1961; Lindsey, 1988). The final counts of most meristic features in fishes are set prior to transformation from the larval to juvenile stages; for some meristic features, the sensitive period governing the actual number of elements attained may be fixed prior to or after hatching, depending on the taxon (Barlow, 1961). On a global level, a phenomenon (termed Jordan’s rule) has often been observed in which the number of vertebrae tends to be higher in fish populations from colder waters. Other meristic features also seem to adhere to this pattern (Lindsey, 1988). An important aspect of meristic analysis is that the data obtained are discrete, not continuous, as results from analysis of body dimensions, that is, morphometrics. Stock Identification Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

197

198

John R. Waldman

FIGURE 10-1. (a) Common external meristic characters, and (b) additional meristic (and morphometric) characters. From Cailliet et al. (1986).

There is a long history of stock identification of fishes through meristic analysis; most fish species that occur as multiple stocks and that have been the subject of fishery management also have received at least some meristic analysis. Examples of stock-related meristic studies conducted on fish by family include clupeidae (Rounsefell, 1930; Carscadden and Leggett, 1975; Schweigert, 1990), coregonidae (Lindsey, 1981; Henault and Fortin, 1989), engraulidae (McHugh, 1951), gadidae (Clark and Vladykov, 1960; Templeman, 1981), myxinidae

Meristics

199

(Martini et al., 1998), pleuronectidae (Lux, 1963; Bowering and Misra, 1982), sciaenidae (Perlmutter et al., 1956), sebastinae (Ni, 1982), serranidae (Shepherd, 1991), salmonidae (Winter et al., 1980; MacCrimmon and Claytor, 1985), scombridae (Schaefer, 1991), and trachichthyidae (Haddon and Willis, 1995).

II. USE OF MERISTICS FOR STOCK IDENTIFICATION Standardization of technique is imperative in meristic analysis so that both repeatability and comparison among studies are possible. Conventions exist for enumerating features that may take ambiguous forms, such as branched fin rays and pyloric caeca, and for which gill arch should be used in making gill raker counts. Hubbs and Lagler (1958) provide widely accepted criteria for making meristic counts. Care should be taken to ensure that counts are accurate. Good lighting is essential; fin spines and rays are often most readily viewed when backlit. Scales may be more easily counted if first blotted to remove moisture, which reveals their margins. One approach to assure accuracy is to incorporate some level of quality control. In a study of coho salmon, Oncorhynchus kisutch, Hjort and Schreck (1982) recounted the meristic characters of two fish from each sample. If an error was found, meristic characters of all fish from that sample were recounted. Many meristic features, such as fin rays and gill rakers, are elaborated in serial fashion. Fin rays normally reach their full complement by the early juvenile stage, but gill raker counts may increase for a much longer period, for example, for alosids (Smith, 1985). Thus, when comparing meristic counts among stocks composed of different aged or sized fishes, it is important to consider whether a full complement of these features has been achieved. Meristic analysis also may be used for larval fishes; however, characters examined of postlarval individuals may not yet be formed or fully elaborated in larvae. In these instances, it may be useful to examine internal features that are correlated with external features. For example, fin spines and fin rays form only after their internal bases, that is, pterygiophores are formed. These may be viewed through clearing and staining (Dingerkus and Uhler, 1977) or radiography (Tucker and Larouche, 1984). Another meristic-based character that has not received attention at the stock level is the interdigital relationship between vertebral spines and pterygiophores or supraneurals. However, Waldman and Andreyko (1993) found evidence of stock differences among both striped bass, Morone saxatilis, and white perch, Morone americana, across much of their respective ranges and also among tributaries of Chesapeake Bay. Conventions exist for coding these features (Olney et al., 1983). Prior to stock discrimination analysis, it is necessary to determine whether statistically significant differences exist among reference stocks. Histograms and Dice

200

John R. Waldman

FIGURE 10-2. Graphic analysis of gill-raker counts from Spanish mackerel (see Table 10-1). Upper graph is of Dice diagrams showing sample range (horizontal line), mean (vertical line), one standard deviation on either side of mean (open bar), and t standard errors (hatched bar) on either side of mean. Thus, nonoverlapping hatched bars indicate that there is only a 5% chance or less of the two means being samples of the same population. Lower graph is a frequency histogram (hatched vertical bars) of gill-raker counts describing a sample from one population. Areas under the curve estimate the probabilities of counting the included numbers of rakers. From Cailliet et al. (1986).

diagrams (Fig. 10-2) may be useful in visualizing the raw data which usually are displayed in frequency tables (Table 10-1). Simple, descriptive, univariate statistical analysis may be conducted so as to forecast which characters will be effective in stock identification and to provide another error check by scanning for anomalous data. For example, in their analysis of meristic variation among world hatchery stocks of rainbow trout, Oncorhynchus gairdneri, MacGregor and

201

Meristics

TABLE 10-1. Frequency Tabulation of Gill-Raker Counts Making Up Three Collections from Presumably Different Biological Populations in Two Species of Spanish Mackerel (Scomberomorus)a Total gill rakers on first arch Species and geographic population Scomberomorus sierra (E. Pacific) Mexico Central and South America S. maculatus (W. Atlantic)

11

2

12

13

14

15

16

17

Sample size

4

10 1 16

10 5 6

8 14 2

1 12 1

2

33 34 34

7

a

Adapted from Cailliet et al. (1986).

MacCrimmon (1977) calculated the mean, standard error, range, mean intrastock coefficient of variation (CV), and overall CV for each meristic character. Traditionally, stock differences for individual characters have been tested either with analysis-of-variance (t-tests for paired comparisons) or c2 tests. In the latter case, it is common that numerous rare categories are encountered, and until recently, these rare categories needed to be grouped so as not to violate the statistical assumptions of c2. However, such grouping diminishes the information content contained in the rare phenotypes. An alternative approach based on c2 is now available that was developed for genetic frequency data (Roff and Bentzen, 1989). This Monte Carlo-based approach allows for virtually unlimited numbers of rare categories; software is available as part of the REAP package (McElroy et al., 1992). Waldman and Andreyko (1993) applied this approach successfully to an analysis of osteological interdigitation patterns (as many as 32 phenotypes per character) among stocks of striped bass and white perch. Although establishment of the existence of statistically significant differences is essential toward the use of meristic data, Royce (1953) concluded that even with samples from closely related stocks, significant statistical differences could always be found by increasing the size of the sample or by considering enough characters. Royce (1957) believed that statistically significant differences among samples is a necessary but trivial preliminary in stock studies, and that once a predetermined level of significance is reached, larger samples and further sampling merely reiterates the conclusion. Consequently, additional interest should be focused on the quantity and direction of the differences because these differences largely will determine the efficacy of stock composition analysis of mixed stocks. Royce (1957) provided a number of ways in which overlap in character values among stocks can be assessed. For multivariate analyses, multivariate statistical significance is important, but the utility of the multivariate model is best assessed in terms of its classification

202

John R. Waldman

performance with cross-validation techniques, including jackknifing and bootstrapping methods (Lachenbruch, 1975; James and McCulloch, 1990). Its utility may further be evaluated by removing the effects of correct classification by chance by use of the kappa statistic (Titus et al., 1984). Within the univariate context, characters need not necessarily remain as counts of single meristic features. Prior to when routine multivariate analysis became possible with computers, Raney and de Sylva (1953) obtained improved univariate separation among striped bass stocks through creation of a compound character, which they labeled their “character index.” The index was the sum of the counts of the soft rays of the second dorsal and anal fins, and the left and right pectoral fin rays. This compound character was later used as one of five morphological variables in a stock composition study (discriminant analysis) of mid-Atlantic striped bass (Berggren and Lieberman, 1978). Although compound characters represent an interesting historical innovation, they usually have peculiar statistical properties and are rarely used anymore. Meristic data are, by definition, discontinuously distributed, and such distributions have different statistical properties than continuously distributed data. As such, fewer complications and violations of normality are likely to arise if statistical methods appropriate to discontinuously distributed data are used (e.g., c2 or logistic regression) rather than their continuous counterparts (ANOVA or discriminant analysis). Most multivariate stock composition analyses of meristic data have been conducted using discriminant analysis, which may yield satisfactory, but not necessarily optimal results. Alternative multivariate stock composition approaches that are receiving increased attention are maximum-likelihood analysis and logistic regression. In analyses of the same data, Van Winkle et al. (1988) found little difference between discriminant analysis and maximum-likelihood analysis in their estimated and true error rates in a relative contribution study of striped bass stocks. In contrast, Prager and Fabrizio (1990) analyzed a set of meristic data obtained from American shad, Alosa sapidissima, using both discriminant analysis and logistic regression; they found that logistic regression provided both lower allocation and classification errors. With meristic data, measurement means and variances are often positively correlated (Winans, 1987). This effect may be reduced by transforming the data. Sokal and Rohlf (1981) recommended transforming meristic data to square roots. Meristic data also may be transformed to better fit the requirements of a particular statistical model; such tailoring may result in better resolution among stocks. However, the necessity of data transformation is less clear for multivariate analyses. Van Winkle et al. (1988) chose not to transform raw meristic and morphometric data because they could not determine statistically whether transformations provided a better or poorer fit to a multivariate normal distribution; such a distribution is assumed for both the discriminant function and maximumlikelihood methods they used.

Meristics

203

In multivariate analyses, meristic data may be used with other discrete or continuous character data, and these other data may be morphological or nonmorphological in origin. For example, Grove et al. (1976) combined protein electrophoresis data with both meristic and morphometric data in a discriminant function analysis to discriminate among three mid-Atlantic striped bass stocks. Fournier et al. (1984) presented a robust maximum-likelihood stock composition analysis model useful for discrete data, continuous data, or any combination of the two. Stock identification using meristics (and other features) also may be complicated by stocks that are made up of substocks (Stephenson, 1999). In a simulation study involving stock composition estimates of the Hudson River and Chesapeake Bay striped bass stocks to a mixed fishery, Waldman and Fabrizio (1994) used meristic and morphometric data of striped bass from 4 of the 11 or so tributary-specific populations that make up the Chesapeake Bay striped bass stock. Although the inclusion of one, two, three, or four substocks in the discriminant analysis did not greatly affect the overall correct-classification rates, the specific combination of substocks significantly affected the relative contribution estimates derived from the mixed stock sample.

III. A CASE HISTORY: STRIPED BASS Striped bass provide an excellent example in which meristic analysis helped provide fundamental information on their stock structure. As recently as the mid-1930s (Vladykov and Wallace, 1938), there was a lack of understanding of whether striped bass were a migratory species and if those that occurred along the mid-Atlantic were composed of multiple stocks. To answer these questions, E. C. Raney of Cornell University and his students and colleagues began systematic analyses of the meristic features of striped bass over their entire range. Raney and de Sylva (1953) compared fin spine and fin ray counts between striped bass from the Chesapeake Bay and the Hudson River; fin ray counts showed substantial differences between fish from these locations. Raney and Woolcott (1955) found differences in counts of lateral line scales and fin rays among striped bass from rivers of the southeastern United States. Lewis (1957) used gill raker counts and Raney (1957) used lateral line and fin ray counts to distinguish three Chesapeake Bay subpopulations. Murawski (1958) compared lateral line scale counts of striped bass from Canada to Florida and found evidence of distinct populations throughout this range. He also identified three Chesapeake Bay subpopulations, consistent with the findings of Lewis (1957) and Raney (1957). Barkuloo (1970) analyzed lateral line scale counts of striped bass from the St. John’s River, a Florida tributary to the Atlantic Ocean, with those of other Atlantic and Gulf coast stocks and concluded that the St. John’s population is endemic.

204

John R. Waldman

Throughout these studies, the authors found year-class effects, that is, environmental influences on meristic counts that strongly affected the degree of overlap in character values among stocks. However, the stock divisions indicated by these studies largely have been confirmed by subsequent studies that used more sensitive biochemical approaches (Waldman et al., 1988).

IV. CONCLUSIONS Meristic analysis remains a technically simple, inexpensive alternative for the stock identification of fishes. Drawbacks include that in almost all instances, specimens must be sacrificed—it is difficult (although not impossible) to make accurate counts of meristic features on living fish. Also, variation among year-classes as a consequence of environmental effects must be considered. However, there may be instances in which ecophenotypic variation of meristic traits is more effective toward stock identification than are genetic approaches. One instance is where genetic differences are limited because some or all populations to be analyzed are very recent in origin due to natural recolonization or stocking. Another instance is where there is sufficient gene flow among populations to preclude or erode genetic differentiation.

REFERENCES Barkuloo, J. M. 1970. Taxonomic status and reproduction of striped bass (Morone saxatilis) in Florida. U.S. Bureau of Sportfisheries and Wildlife Technical Paper 44. Barlow, G. W. 1961. Causes and significance of morphological variation in fishes. Systematic Zoology 10: 105–117. Berggren, T. J. and Lieberman, J. T. 1978. Relative contribution of Hudson, Chesapeake, and Roanoke striped bass, Morone saxatilis, stocks to the Atlantic coast fishery. U.S. National Marine Fisheries Service Fishery Bulletin 76: 335–345. Bowering, W. R. and Misra, R. K. 1982. Comparisons of witch flounder (Glyptocephalus cynoglossus) stocks of the Newfoundland-Labrador area, based upon a new multivariate analysis method for meristic characters. Canadian Journal of Fisheries and Aquatic Sciences 39: 564–570. Cailliet, G. M., Love, M. S., and Ebeling, A. W. 1986. Fishes: A Field and Laboratory Manual on Their Structure, Identification, and Natural History. Wadsworth, Belmont, California. Carscadden, J. E. and Leggett, W. C. 1975. Meristic differences in spawning populations of American shad, Alosa sapidissima: evidence for homing to tributaries in the St. John River, New Brunswick. Journal of the Fisheries Research Board of Canada 32: 653–660. Clark, J. R. and Vladykov, V. D. 1960. Definition of haddock stocks of the northwestern Atlantic. U.S. Fish and Wildlife Service Fishery Bulletin 60: 283–296. Dingerkus, G. and Uhler, L. 1977. Enzyme clearing of alcian blue stained small vertebrates for demonstration of cartilage. Stain Technology 52: 229–232. Fournier, D. A., Beacham, T. D., Riddell, B. E., and Busack, C. A. 1984. Estimating stock composition in mixed stock fisheries using morphometric, meristic, and electrophoretic characteristics. Canadian Journal of Fisheries and Aquatic Sciences 41: 400–408.

Meristics

205

Grove, T. L., Berggren, T. J., and Powers, D. A. 1976. The use of innate tags to segregate spawning stocks of striped bass, Morone saxatilis. Estuarine Processes 1: 166–176. Haddon, M. and Willis, T. J. 1995. Morphometric and meristic comparison of orange roughy (Hoplostethus atlanticus: Trachichthyidae) from the Puysegur Bank and Lord Howe Rise, New Zealand, and its implications for stock structure. Marine Biology 123: 19–27. Henault, M. and Fortin, R. 1989. Comparison of morphometric and meristic characters among springand fall-spawning ecotypes of cisco (Coregonus artedii) in southern Quebec, Canada. Canadian Journal of Fisheries and Aquatic Sciences 46: 166–173. Hjort, R. C. and Schreck, C. B. 1982. Phenotypic differences among stocks of hatchery and wild coho salmon, Oncorhynchus kisutch, in Oregon, Washington, and California. U.S. National Marine Fisheries Service Fishery Bulletin 80: 105–119. Hubbs, C. L. and Lagler, K. L. 1958. Fishes of the Great Lakes Region, 2nd Ed. Cranbrook Institute of Science Bulletin 26: 1–213. James, F. C. and McCulloch, C. E. 1990. Multivariate analysis in ecology and systematics: panacea or Pandora’s box? Annual Review of Ecology and Systematics 21: 129–166. Lachenbruch, P. A. 1975. Discriminant Analysis. Hafner Press, New York. Lewis, R. M. 1957. Comparative studies of populations of the striped bass. U.S. Fish and Wildlife Service Special Scientific Report—Fisheries 204. Lindsey, C. C. 1981. Stocks are chameleons: plasticity in gill rakers of coregonid fishes. Canadian Journal of Fisheries and Aquatic Sciences 38: 1497–1506. Lindsey, C. C. 1988. Factors controlling meristic variation. Fish Physiology 11B: 197–274. Lux, F. E. 1963. Identification of New England yellowtail flounder groups. U.S. Fish and Wildlife Service Fishery Bulletin 63: 1–10. MacCrimmon, H. R. and Claytor, R. R. 1985. Meristic and morphometric identity of Baltic stocks of Atlantic salmon. Canadian Journal of Zoology 63: 2032–2037. MacGregor, R. B. and MacCrimmon, H. R. 1977. Meristic variation among world hatchery stocks of rainbow trout, Salmo gairdneri Richardson. Environmental Biology of Fishes 1: 127–143. Marr, J. C. 1957. The problem of defining and recognizing subpopulations of fishes. U.S. Fish and Wildlife Service Special Scientific Report—Fisheries 208: 1–6. Martini, F. H., Lesser, M. P., and Heiser, J. B. 1998. A population profile for hagfish, Myxine glutinosa, in the Gulf of Maine. Part 2: Morphological variation in populations of Myxine in the North Atlantic Ocean. U.S. National Marine Fisheries Service Fishery Bulletin 96: 516–524. McElroy, D., Moran, P., Bermingham, E., and Kornfield, I. 1992. REAP: an integrated environment for the manipulation and phylogenetic analysis of restriction data. The Journal of Heredity 83: 157–158. McHugh, J. L. 1951. Meristic variations and populations of northern anchovy (Engraulis mordax mordax). Bulletin of the Scripps Institute of Oceanography 6: 123–160. Murawski, W. S. 1958. Comparative study of populations of the striped bass, Roccus saxatilis (Walbaum), based on lateral-line scale counts. Master’s thesis. Cornell University, Ithaca, New York. Ni, I. H. 1982. Meristic variation in beaked redfishes, Sebastes mentella and S. faciatus, in the Northwest Atlantic. Canadian Journal of Fisheries and Aquatic Sciences 39: 1664–1685. Olney, J. E., Grant, G. C., Schultz, F. E., Cooper, C. L., and Hageman, J. 1983. Pterygiophoreinterdigitation patterns in larvae of four Morone species. Transactions of the American Fisheries Society 112: 525–531. Perlmutter, A., Miller, S. W., and Poole, J. C. 1956. The weakfish (Cynoscion regalis) in New York waters. New York Fish and Game Journal 3: 1–43. Prager, M. H. and Fabrizio, M. C. 1990. Comparison of logistic regression and discriminant analyses for stock identification of anadromous fish, with application to striped bass (Morone saxatilis) and American shad (Alosa sapidissima). Canadian Journal of Fisheries and Aquatic Sciences 47: 1570–1577.

206

John R. Waldman

Raney, E. C. 1957. Subpopulations of the striped bass, Roccus saxatilis (Walbaum) in tributaries of Chesapeake Bay. U.S. Fish and Wildlife Service Special Scientific Report—Fisheries 208: 85–107. Raney, E. C. and de Sylva, D. P. 1953. Racial investigations of the striped bass, Roccus saxatilis (Walbaum). Journal of Wildlife Management 17: 495–509. Raney, E. C. and Woolcott, W. S. 1955. Races of the striped bass, Roccus saxatilis (Walbaum) in the southeastern United States. Journal of Wildlife Management 19: 444–450. Roff, D. A. and Bentzen, P. 1989. The statistical analysis of mitochondrial DNA polymorphisms: c2 and the problem of small samples. Molecular Biology and Evolution 6: 539–545. Rounsefell, G. A. 1930. Contribution to the biology of the Pacific herring, Clupea pallasii, and the condition of the fishery in Alaska. Bulletin of the U.S. Bureau of Fisheries 45: 227–320. Royce, W. F. 1953. Preliminary report on a comparison of the stocks of yellowfin tuna. Proceedings of the Indo-Pacific Fisheries Council 4, Section 2: 130–145. Royce, W. F. 1957. Statistical comparison of morphological data. U.S. Fish and Wildlife Service Special Scientific Report—Fisheries 208: 7–28. Schaefer, K. M. 1991. Geographic variation in morphometric characters and gill-raker counts of yellowfin tuna Thunnus albacares from the Pacific Ocean. U.S. National Marine Fisheries Service Fishery Bulletin 89: 289–297. Schweigert, J. F. 1990. Comparison of morphometric and meristic data against truss networks for describing Pacific herring stocks. American Fisheries Society Symposium 7: 47–62. Shepherd, G. 1991. Meristic and morphometric variation in black sea bass north of Cape Hatteras, North Carolina. North American Journal of Fisheries Management 11: 139–148. Smith, C. L. 1985. The inland fishes of New York State. New York State Department of Environmental Conservation, Albany. Sokal, R. R. and Rohlf, F. J. 1981. Biometry. W. H. Freeman, San Francisco. Stephenson, R. L. 1999. Stock complexity in fisheries management: a perspective of emerging issues related to population sub-units. Fisheries Research 43: 247–249. Strauss, R. E. and Bond, C. E. 1990. Taxonomic methods: morphology. In C. B. Schreck and P. B. Moyle (eds.), Methods for Fish Biology, American Fisheries Society, Bethesda, pp. 109–140. Swain, D. P. and Foote, C. J. 1999. Stocks and chameleons: the use of phenotypic variation in stock identification. Fisheries Research 43: 113–128. Tåning, A. V. 1952. Experimental study of meristic characters in fishes. Biological Reviews 27: 169–193. Templeman, W. T. 1981. Vertebral numbers in Atlantic cod, Gadus morhua, of the Newfoundland and adjacent areas, 1947–1971, and their use in delineating cod stocks. Journal of Northwest Atlantic Fishery Science 2: 21–45. Titus, K., Mosher, J. A., and Williams, B. K. 1984. Chance-corrected classification for use in discriminant analysis: ecological applications. The American Midland Naturalist 111: 1–7. Tucker, J. W. and Larouche, J. L. 1984. Radiographic techniques in studies of young fishes. American Society of Ichthyologists and Herpetologists Special Publication 1: 37–39. Van Winkle, W., Kumar, K. D., and Vaughan, D. S. 1988. Relative contributions of Hudson River and Chesapeake Bay striped bass stocks to the Atlantic coastal population. American Fisheries Society Monograph 4: 255–266. Vladykov, V. D. and Wallace, D. H. 1938. Is the striped bass (Roccus lineatus) of Chesapeake Bay a migratory fish? Transactions of the American Fisheries Society 67: 67–86. Waldman, J. R. and Andreyko, H. 1993. Variation in patterns of interdigitation among supraneurals, pterygiophores, and vertebral elements diagnostic for striped bass and white perch. Copeia 1993: 1097–1113. Waldman, J. R. and Fabrizio, M. C. 1994. Problems of stock definition in estimating relative contributions of Atlantic striped bass to the coastal fishery. Transactions of the American Fisheries Society 123: 766–778.

Meristics

207

Waldman, J. R., Grossfield, J., and Wirgin, I. 1988. Review of stock discrimination techniques for striped bass. North American Journal of Fisheries Management 8: 410–425. Winans, G. A. 1987. Using morphometric and meristic characters for identifying stocks of fish. NOAA (National Oceanic and Atmospheric Administration) Technical Memorandum NMFS (National Marine Fisheries Service) SEFC-199: 25–62. Winter, G. W., Schreck, C. B., and McIntyre, J. D. 1980. Meristic comparison of four stocks of steelhead trout (Salmo gairdneri). Copeia 1980: 160–162.

This Page Intentionally Left Blank

PART

Natural Marks— Environmental Signals

IV

This Page Intentionally Left Blank

CHAPTER

11

Parasites as Biological Tags K. MACKENZIE* AND P. ABAUNZA† *School of Biological Sciences, Department of Zoology, The University of Aberdeen, Aberdeen, Scotland, United Kingdom, †Instituto Español de Oceanografía, 39080 Santander, Spain

I. II. III. IV. V. VI. VII. VIII. IX. X.

Introduction General Principles Advantages and Limitations of Parasite Tagging Selection of Parasites for Use as Tags Methodology Collection of Hosts and Parasites Fixation and Preservation of Parasites Identification of Parasites Parasite Genetics Interpretation of Results References

I. INTRODUCTION The first publication describing the use of a naturally occurring parasite as a biological tag in a population study of marine fish dates back over 60 years (Herrington et al., 1939). Many original papers on the subject have been published since then, plus a number of reviews (Sindermann, 1961, 1983; Kabata, 1963; MacKenzie, 1983, 1987a, 2002; Lester, 1990; Moser, 1991; Williams et al., 1992; Arthur, 1997; MacKenzie and Abaunza, 1998). In this chapter, we describe procedures and methods for applying this method to stock identification of marine fish.

II. GENERAL PRINCIPLES The basic principle underlying the use of parasites as tags in fish population studies is that fish can become infected with a parasite only when they come within the endemic area of that parasite. The endemic area is that geographic Stock Identification Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

211

212

K. MacKenzie and P. Abaunza

region in which conditions are suitable for the transmission of the parasite. If infected fish are found outside the endemic area, we can infer that these fish had been within that area at some time in their past history. Information on the maximum life span of the parasite in that particular host allows us to estimate the period of time since the fish left the parasite’s endemic area. The more parasites with different endemic areas that can be used, the more information that can be obtained about the past movements of fish populations, and hence stock structure.

III. ADVANTAGES AND LIMITATIONS OF PARASITE TAGGING The most efficient approach to stock identification is the multidisciplinary one in which the results of different tagging methods and techniques are compared and used to complement one another (e.g., see Campbell et al., 2002). However, each method has its own strengths and weaknesses, and the use of parasites as tags is recognized as having certain advantages over other methods such as artificial tagging (Williams et al., 1992) and genetic studies (MacKenzie, 2002). • Parasite tags are more appropriate for studies of small, delicate species of

•

• • •

fish, such as small clupeoids, deepwater species, and crustaceans, for which artificial tags can either be used with difficulty or not at all. Each specimen sampled represents a valid observation, whereas with artificial tags each individual must be sampled, tagged, and recaptured to obtain a valid observation. They are less expensive to use because samples can be obtained from routine sampling programs. The use of biological tags eliminates doubts concerning the possible abnormal behavior of artificially tagged hosts. Parasites can often be used to identify subpopulations of fish distinguished by behavioral differences, but between which there is still a considerable amount of gene flow (“ecological stocks”; see MacKenzie, 2002).

We should also be aware of the following limitations of biological tagging, some of which were first discussed by Sindermann (1983): • Lack of adequate information on the complex ecology and biology of

aquatic parasites can limit their efficient use as tags. However, as research adds to our knowledge of the biology and ecology of marine parasites, their use as tags is becoming more efficient. • The identification of many parasite species is uncertain and subject to disagreement among taxonomists. The recent application of molecular

Parasites as Biological Tags

213

biology techniques to parasite taxonomy has resulted in the identification of two or more “sibling species” in parasites which had previously been regarded as comprising a single species (Nascetti et al., 1986, 1993; Paggi et al., 1991; Renaud et al., 1983). Conversely, the same techniques have also shown that parasites which had previously been regarded as separate species may in fact be conspecific (Raibaut et al., 1986). • It is usually desirable to know the age of host individuals, but in some species of fish and invertebrates the techniques of age determination have not been validated.

IV. SELECTION OF PARASITES FOR USE AS TAGS The ideal tag parasite may be described as having the following features, according to the selection criteria suggested by Kabata (1963), Sindermann (1983), MacKenzie (1983, 1987a), and Williams et al. (1992). Parasites fulfilling all of these criteria are rarely encountered, so compromises usually have to be made. • It should have significantly different levels of infection in the subject host

in different parts of the study area. Infection data can be analyzed according to prevalence, intensity and abundance of infection, as defined by Bush et al. (1997). • It should persist in the subject host for a long period of time, the minimum time depending on the nature of the study. For stock identification and recruitment studies, only parasites with life spans of more than one year should be used, whereas for studies of seasonal migrations, species with life spans of less than one year are acceptable. • Parasites with single-host life cycles, such as monogenetic trematodes and most parasitic crustaceans, are the simplest to use. Those with complex life cycles, such as digenetic trematodes, tapeworms, nematodes, and acanthocephalans, involving two or more stages in different hosts, are more difficult to use because more information is required on the biotic and abiotic factors which influence the transmission of the parasite between hosts. Given this information they can, however, be used just as effectively. Køie (1983), in fact, suggested that digenetic trematodes have advantages as tags over other taxonomic groups of parasites because they tend to be highly specific to the primary host, which is usually a mollusc. The endemic area of a digenean is therefore largely determined by the geographic distribution of its mollusc host. • The level of infection should remain relatively constant from year to year. The effects of annual variations, however, can be nullified by following infection levels in single year-classes of the subject host over several years.

214

K. MacKenzie and P. Abaunza

• The parasite should be easily detected and identified. Examination of the

host should involve the minimum of dissection; otherwise time can become a limiting factor. • Parasites that are serious pathogens, particularly those that affect host behavior, should be avoided. Many different taxonomic groups of parasites have been used as tags for freshwater, anadromous, and marine fish and marine invertebrates, as described by Williams et al. (1992). Protozoans, myxosporeans, larval and adult helminthes, and parasitic crustaceans have all been used as tags for commercially important marine fish such as the Atlantic herring, Clupea harengus, and the Atlantic cod, Gadus morhua (see MacKenzie, 1987b; Hemmingsen and MacKenzie, 2001). The parasites most commonly used as tags are larval anisakid nematodes, probably because they are among the most common and widespread parasites of teleost fish (MacKenzie, 1987a; Sindermann, 1990; Quinteiro, 1990).

V. METHODOLOGY Two different approaches to the use of parasites as biological tags can be recognized. • A small number of parasite species are selected according to the criteria

previously outlined and a large number of host individuals are examined specifically for these species. Examples are the studies of Kabata (1963), Margolis (1963), Chenoweth et al. (1986) and MacKenzie (1990). The more information on the parasite fauna of the host that is available prior to the start of a biological tag study, the more efficient the selection of parasites is likely to be. For this reason, it is useful to begin with a preliminary survey of the parasite fauna of the host in the proposed study area, as was done by Arthur and Arai (1980a,b), Gaevskaya and Kovaleva (1985), McGladdery and Burt (1985), Bower and Margolis (1991), and MacKenzie and Longshaw (1995). This type of approach is most applicable to studies of host species which are readily available for examination in large numbers. • Entire parasite assemblages can be analyzed using sophisticated statistical techniques. Examples are the studies of Lester et al. (1986, 1988, 2001), George-Nascimento and Arancibia (1992), Arthur and Albert (1993), Speare (1994, 1995), Sewell and Lester (1995), Boje et al. (1997), and George-Nascimento (2000). This type of approach can be applied to any host species but is particularly applicable to those which are large and valuable and are not readily available for examination in large numbers. For both approaches, summary statistics of levels of infection in terms of prevalence, mean intensity, and/or abundance should be expressed by the mean value

Parasites as Biological Tags

215

plus and minus the standard deviation, and the range. In some cases, the median intensity will also be an appropriate descriptor. The biological interpretations of mean, median, and other statistical parameters should be clearly described and understood to avoid misleading interpretations concerning the true nature of the infection (Rózsa et al., 2000). For example, in a highly overdispersed parasite distribution, the median intensity could provide more information about the level of infection than the mean. Various methods have been used for testing differences between samples for statistical significance, with the Chi-squared test, Fisher’s exact test, and G-tests for differences between prevalence values being the most frequently used. When parasite distributions are aggregated, with highly skewed data and/or severe kurtosis, then nonparametric methods are more appropriate (Potvin and Roff, 1993; Stewart-Oaten, 1995). In such cases, account should be taken of what the test actually does (usually compares other characteristics of the distribution instead of means), and one should be confident about the assumption of the equality of variances in the original data set (Stewart-Oaten, 1995). The nonparametric Kruskal–Wallis and Mann–Whitney tests have been used frequently to test for significance between prevalence and abundance values (see Brattey and Ni, 1992; Khan and Tuck, 1995). The parametric tests have broad validity for moderate nonnormal data (Stewart-Oaten, 1995). In this sense, t-tests and various ANOVA-type tests have been applied successfully to compare means of parasite infection levels (Bishop and Margolis, 1955; McClelland et al., 1990; Boily and Marcogliese, 1995). In all these cases, the data were logarithmically transformed to fulfil the assumptions of the parametric methods. Randomization methods (Manly, 1997) represent a solution to the problems of small sample size or unbalanced data (Potvin and Roff, 1993). Bush et al. (1997) strongly recommended that authors publish an appropriate statistical measure of how good the estimate is, such as confidence limits or standard errors. For skewed distributions, and particularly if the sample is small, bootstrap confidence intervals are a good solution. Rózsa et al. (2000) showed how to construct confidence intervals for the mean and the median as well as a distribution-free comparison of mean intensities. Among the most frequently used methods applicable to the parasite assemblage approach is the use of a discriminant analysis. This operates on data sets for which prespecified well-defined groups exist and describes the maximum differences among those groups (McGarigal et al., 2000). Examples of the application of this and related techniques can be found in Arthur and Albert (1993) and George-Nascimento (2000) (nonparametric discriminant analyses), and Lester et al. (1988) and Sewell and Lester (1995) (canonical multivariate analysis). Ordination and classification techniques (i.e., detrended correspondence analysis, cluster analysis) were also used by George-Nascimento and Arancibia (1992), Speare (1995), and George-Nascimento (2000). A multivariate maximumlikelihood model was applied by Bailey et al. (1988) for stock composition

216

K. MacKenzie and P. Abaunza

analysis. These methods are described and summarized in Elliot (1979), Everitt and Dunn (1992), Johnson and Wichern (1992), Sokal and Rohlf (1995), Manly (1997), and McGarigal et al. (2000). Observed frequency distributions of parasites can provide information about parasite population dynamics and so help to explain different levels of infection in different host age groups (Anderson, 1978; Anderson and May, 1978; Pacala and Dobson, 1988; Brattey and Ni, 1992). Basically, the most informative way to quantify the occurrence of parasites is to describe the frequency distributions (Rózsa et al., 2000). To study the distributions or the dispersion patterns of parasites, GLMs (General Linear Models) should be used (Wilson et al., 1996). The best way to obtain a more precise adjustment of the mathematical model (with regard to parasite distributions, etc.) is by applying maximum-likelihood methods in the estimation of parameters (Brattey and Ni, 1992; Williams and Dye, 1994). Special care must be taken with analyses of data on long-lived parasites such as larval anisakid nematodes in fish. When intensity of infection is shown to be age-dependent, all analyses carried out to determine possible differences in the geographic distributions of different groups within a host population must be performed on similar host age groups (Abaunza and Villamor, 1994; Abaunza et al., 1995). Analyses of length rather than age are less accurate because of the effects of different environmental conditions on fish growth. In general, it is good practice, prior to any comparison of infection levels between areas, to analyze the effect of host biological factors such as sex, age, or length on the observed prevalences or intensities.

VI. COLLECTION OF HOSTS AND PARASITES Host samples should preferably be examined fresh. If this is not possible they should be deep-frozen or preserved in 10% buffered formal saline as soon as possible after capture. Frozen specimens should be packaged individually or in small groups so that large numbers do not have to be defrosted at the same time. In the laboratory each host specimen should first be measured, weighed, sexed, and, where possible, aged. In the initial stages of a study, before selection of the most appropriate tag parasites, the entire protistan and metazoan parasite fauna of the host should be recorded, so a complete autopsy of each host is necessary. The autopsy requires examination of host tissues under a dissecting microscope at X10–25 followed by examination of smears at magnifications up to X2000. The external surfaces should be examined first; then the host should be dissected according to a clearly defined procedure. The alimentary tract should be divided into sections, for example, stomach, pyloric caeca, anterior intestine, mid-intestine, and rectum, each of which should be opened longitudinally before examination. Numbers of metazoan parasites of each species should be noted.

Parasites as Biological Tags

217

Special precautions should be taken when collecting the following types of parasite. • Myxosporeans. These can make excellent tags, but care should be taken to

note the presence of not only spores, but also the less easily observed vegetative stages. • Adult cestodes and acanthocephalans. The scoleces of cestodes and the proboscides of acanthocephalans have important diagnostic features. When these parasites are found firmly attached to host tissues, they should be removed together with a piece of host tissue around the site of attachment to ensure that the entire worm remains intact. • Larval nematodes. Many of these occur scattered throughout the musculature and other soft tissues of fish. They can be detected by “candling” fillets over a light box or by digesting them in a pepsin digest solution (Stern et al., 1958; Smith and Wootten, 1975). • Digenean metacercariae and cestode plerocercoids. These larval or juvenile stages are often encysted and must be removed from their cysts for identification. This can be done either by dissection with needles or by digesting the cyst with a pepsin digest solution (Smith and Halton, 1983). The nature of the proboscid armament is an important diagnostic feature for trypanorhynch cestodes, but in plerocercoids the proboscides are usually inverted and must be everted by placing the plerocercoid in fresh water under coverslip pressure. This can only be done with fresh material; it is not usually possible to evert the proboscides in frozen specimens.

VII. FIXATION AND PRESERVATION OF PARASITES A reference collection of permanently mounted specimens of all parasite species recovered should be made. Good general guides to the methods involved are provided by Pritchard and Kruse (1982) and Ash and Orihel (1987), while the following are recommended for particular taxonomic groups. • Protozoa and Myxozoa: Canning and Lom (1986); Lom and Dykova (1992). • Helminths: Gibson (1984); Hendrix (1994). • Crustacea: Kabata (1970); McLaughlin et al. (1982).

VIII. IDENTIFICATION OF PARASITES Parasites are best examined when fresh and preferably live, but this is not always possible. Nomarski interference contrast microscopy is recommended for protozoans and small fresh helminths. Phase contrast is recommended for examining

218

K. MacKenzie and P. Abaunza

sporozoan protozoans and small crustaceans. Scanning electron microscopy is useful for confirming the identity of protozoans and small helminths. Most of the published keys to identification of parasite species are to be found in specialist papers scattered throughout the parasitological literature. The following taxonomic textbooks, apart from being useful in their own right, include references to most of these specialist publications. • Protozoa: Canning and Lom (1986); Lom and Dykova (1992). • Helminths: Golvan (1969), Schmidt (1986); Anderson (1992); Khalil

et al. (1994); Williams and Jones (1994); Gibson et al. (2002). • Crustacea: Kabata (1970, 1979).

IX. PARASITE GENETICS Genetic analyses of parasite populations may also provide a tool for host stock identification. Beverley-Burton (1978) was the first to use genetic techniques in this way, in the form of a study of the frequencies of different acid phosphatase alleles in larvae of the nematode Anisakis simplex. Subsequent studies have shown that several of the ascaridoid nematode genera infecting fish and marine mammals consist of two or more morphologically similar but genetically distinct sibling species (Nascetti et al., 1986, 1993; Paggi et al., 1991; Mattiucci et al., 1997). Some recent papers have incorporated this information in the analyses of levels of infection (Brattey and Bishop, 1992; Brattey and Ni, 1992; Boily and Marcogliese, 1995). Although no further genetic studies have been carried out on parasites for fish stock identification, they have been used to confirm the species composition of parasite faunas to ensure uniformity in data analyses.

X. INTERPRETATION OF RESULTS After selection of the tag parasites and collection and analysis of the data, the final important stage in a biological tag study is the interpretation of the results in terms of host population biology. Initially the results of such a study may be open to more than one possible interpretation. For example, a decrease in the level of infection in a particular area could be explained by (1) loss of parasites from the same host population; (2) selective mortality of heavily infected hosts; (3) immigration of another host population with a lower level of infection; (4) emigration of the most heavily infected element of the original population; or (5) a combination of two or more of the above. All of these possibilities must be considered in the light of the available information on the biology of both host and parasite until all but one have been eliminated. The guidelines previously given are designed to assist with this process of elimination of the outset. As we acquire

219

Parasites as Biological Tags TABLE 11-1.

Life Cycles of Some Common Parasites Infecting Fish

Parasite species Derogenes varicus (Digenea) Cryptocotyle lingua (Digenea) Renicola sp. (Digenea) Prosorhynchoides gracilescens (Digenea) Lacistorhynchus tenuis (Cestoda) Diphyllobothrium spp. (Cestoda) Anisakis simplex (Nematoda) Hysterothylacium aduncum (Nematoda) Echinorhynchus gadi (Acanthocephala) Corynosoma spp. (Acanthocephala) Lernaeocera branchialis Parasitic Copepod)

First intermediate or primary host(s) Natica spp. (gastropod molluscs) Littorina spp. (gastropod molluscs) Turritella spp. (gastropod molluscs) Abra spp. (bivalve molluscs) Copepods

Second intermediate host(s)

Definitive or final host(s)

Crustaceans and chaetognaths

Teleost fish: many spp.

Køie (1979)

Teleost fish

Piscivorous birds

Stunkard (1929)

Small teleost fish

Piscivorous birds

Gadoid fish

Wright (1956); MacKenzie (1985) Matthews (1974)

Key references

Copepods

Pelagic teleost fish Teleost fish

Euphausiids

Teleost fish

Crustaceans

Small teleost fish

Angler fish, Lophius piscatorius Elasmobranch fish Mudry and Dailey (1971) Piscivorous birds Vik (1974) and mammals Cetaceans Smith (1983); Køie et al. (1995) Large piscivorous, Køie (1993) teleost fish

Gammarid crustaceans Amphipods

None

Gadoid fish

Teleost fish

Seals

Valtonen et al. (1983) Valtonen (1983)

Flatfish, lumpfish

None

Gadid fish

Kabata (1960)

more information on the biology of marine parasites, the interpretation of infection data becomes more accurate. This is particularly true of parasite life cycles, where great advances in knowledge have been made in recent years. Even if the life cycle of a particular species of tag parasite is not fully known, it will usually be broadly similar to those of related species for which more information is available. Examples of parasite life cycles involving fish as hosts are shown in Table 11-1, and the entire procedure for using parasites as biological tags is illustrated in Figure 11-1.

220 FIGURE 11-1. Summary of procedures and methods for using parasites as biological tags for stock discrimination of marine fish.

Parasites as Biological Tags

221

ACKNOWLEDGMENT The contribution of one of us (P. Abaunza) was prepared under the projects IEO 105 and XUGA 30103A93.

REFERENCES Abaunza, P. and Villamor, B. 1994. Further considerations into the usefulness of parasites as biological tags for marine fish stock discrimination, with special reference on use of Anisakis simplex (L3). Working Document presented to the 1994 ICES Study Group on Stock Identification Protocols for Finfish and Shellfish Stocks. Abaunza, P., Villamor, B., and Pérez, J. R. 1995. Infestation by larvae of Anisakis simplex (Nematoda: Ascaridata) in horse mackerel, Trachurus trachurus, and Atlantic mackerel, Scomber scombrus, in ICES Divisions VIIIb, VIIIc and IXa (N–NW of Spain). Scientia Marina 59: 223–233. Anderson, R. C. 1992. Nematode Parasites of Vertebrates. Their Development and Transmission. CAB International, Oxford, UK. 600 pp. Anderson, R. M. 1978. The regulation of host population growth by parasitic species. Parasitology 76: 119–158. Anderson, R. M. and May, R. M. 1978. Regulation and stability of host-parasite population interactions: I. Regulatory processes. Journal of Animal Ecology 47: 219–247. Arthur, J. R. 1997. Recent advances in the use of parasites as biological tags for marine fish. In T. W. Flegel and I. H. MacRae (eds.), Diseases in Asian Mariculture III. Fish Health Section, Asian Fisheries Society, Manila, pp. 141–154. Arthur, J. R. and Albert, E. 1993. Use of parasites for separating stocks of Greenland halibut (Reinhardtius hippoglossoides) in the Canadian northwest Atlantic. Canadian Journal of Fisheries and Aquatic Sciences 50: 2175–2181. Arthur, J. R. and Arai, H. P. 1980a. Studies on the parasites of Pacific herring (Clupea harengus pallasi Valenciennes): survey results. Canadian Journal of Zoology 58: 64–70. Arthur, J. R. and Arai, H. P. 1980b. Studies on the parasites of Pacific herring (Clupea harengus pallasi Valenciennes): a preliminary evaluation of parasites as indicators of geographical origin for spawning herring. Canadian Journal of Zoology 58: 521–527. Ash, L. A. and Orihel, T. C. 1987. Parasites: A Guide to Laboratory Procedures and Identification. ASCP Press. American Society of Clinical Pathologists, Chicago. 328 pp. Bailey, R. E., Margolis, L., and Groot, C. 1988. Estimating stock composition of migrating juvenile Fraser River (British Columbia) sockeye salmon, Oncorhynchus nerka, using parasites as natural tags. Canadian Journal of Fisheries and Aquatic Sciences 45: 586–591. Beverley-Burton, M. 1978. Population genetics of Anisakis simplex (Nematoda: Ascaridoidea) in Atlantic salmon (Salmo salar) and their use as biological indicators of host stocks. Environmental Biology of Fishes 3: 369–378. Bishop, Y. and Margolis, L. 1955. A statistical examination of Anisakis larvae (Nematoda) in herring (Clupea harengus) of the British Columbia coast. Journal of the Fisheries Research Board of Canada 12: 571–592. Boily, F. and Marcogliese, D. J. 1995. Geographical variations in abundance of larval anisakine nematodes in Atlantic cod (Gadus morhua) and American plaice (Hipoglossoides platessoides) from the Gulf of St. Lawrence. Canadian Journal of Fisheries and Aquatic Sciences 52(suppl. 1): 105– 115.

222

K. MacKenzie and P. Abaunza

Boje, J., Riget, F., and Køie, M. 1997. Helminth parasites as biological tags in population studies of Greenland halibut [Reinhardtius hippoglossoides (Walbaum)], in the northwest Atlantic. ICES Journal of Marine Science 54: 886–895. Bower, S. M. and Margolis, L. 1991. Potential use of helminth parasites in stock identification of flying squid, Ommastrephes bartrami, in North Pacific waters. Canadian Journal of Zoology 69: 1124–1126. Brattey, J. and Ni, I-H. 1992. Ascaridoid nematodes from the stomach of harp seals, Phoca groenlandica, from Newfoundland and Labrador. Canadian Journal of Fisheries and Aquatic Sciences 49: 956–966. Brattey, J. and Bishop, C. A. 1992. Larval Anisakis simplex (Nematoda: Ascaridoidea) infection in the musculature of Atlantic cod, Gadus morhua, from Newfoundland and Labrador. Canadian Journal of Fisheries and Aquatic Sciences 49: 2635–2647. Bush, A. O., Lafferty, K. D., Lotz, J. M., and Shostak, A. W. 1997. Parasitology meets ecology on its own terms: Margolis et al. revisited. Journal of Parasitology 83: 575–583. Campbell, N., MacKenzie, K., Mattiucci, S., Ramos, P., Pereira, A., and Abaunza, P. 2002. Parasites as biological tags in a population study of horse mackerel Trachurus trachurus. In Proceedings of the 10th International Congress of Parasitology—ICOPA X: Symposia, Workshops and Contributed Papers. Vancouver (Canada), August 4–9, 2002, pp. 217–222. Monduzzi Editore, International Proceedings Division, Bologna, Italy. Canning, E. U. and Lom, J. 1986. The Microsporidia of Vertebrates. Academic Press, London. 304 pp. Chenoweth, J. F., McGladdery, S. E., Sindermann, C. J., Sawyer, T. K., and Bierm, J. W. 1986. An investigation into the usefulness of parasites as tags for herring (Clupea harengus) stocks in the Western North Atlantic, with emphasis on use of the larval nematode Anisakis simplex. Journal of Northwest Atlantic Fisheries Science 7: 25–33. Elliot, J. M. 1979. Some Methods for the Analysis of Samples of Benthic Invertebrates, 2nd ed. Freshwater Biological Association Scientific Publication No. 25. 160 pp. Everitt, B. S. and Dunn, G. 1992. Applied Multivariate Data Analysis. Oxford University Press, New York. 304 pp. Gaevskaya, A. V. and Kovaleva, A. A. 1985. Parasitofauna of Trachurus picturatus and ecological and geographical features of its formation (in Russian). Ekologiya Morya 20: 80–84. George-Nascimento, M. 2000. Geographical variations in the jack mackerel Trachurus symmetricus murphyi populations in the southeastern Pacific Ocean as evidenced from the associated parasite communities. Journal of Parasitology 86: 929–932. George-Nascimento, M. and Arancibia, H. 1992. Stocks ecológicos del jurel (Trachurus symmetricus murphyi Nichols) en tres zonas de pesca frente a Chile, detectados mediante comparación de su fauna parasitaria y morfometría. Revista Chilena de Historia Natural 65: 453–470. Gibson, D. I. 1984. Technology as applied to museums collections. Systematic Parasitology 6: 241–255. Gibson, D. I., Jones, A., and Bray, R. A. (eds.). 2002. Keys to the Trematoda. Vol. 1. CABI and The Natural History Museum, London. 521 pp. Golvan, Y. J. 1969. Systématique des Acanthocéphales (Acanthocephala Rudolphi 1801). Premiére partie. L’ordre des Palaeacantocephala Meyer 1931. Premier fascicule: la super-familie des Echinorhynchoidea (Cobbold 1876) Golvan et Houin 1963. Mémoires du Muséum national d’histoire naturelle, Série A, 57, fasc. Unique. 373 pp. Hemmingsen, W. and MacKenzie, K. 2001. The parasite fauna of the Atlantic cod, Gadus morhua L. Advances in Marine Biology 40: 1–80. Hendrix, S. S. 1994. Marine flora and fauna of the eastern United States. Platyhelminthes: Monogenea. NOAA Technical Report NMFS 121. 106 pp.

Parasites as Biological Tags

223

Herrington, W. C., Bearse, H. M., and Firth, F. E. 1939. Observations on the life history, occurrence and distribution of the redfish parasite Sphyrion lumpi. United States Bureau of Fisheries Special Report, No. 5: 1–18. Johnson, R. A. and Wichern, D. W. 1992. Applied Multivariate Statistical Analysis, 3rd ed. PrenticeHall International, New Jersey. 642 pp. Kabata, Z. 1960. On the specificity of Lernaeocera (Copepoda parasitica). Annals and Magazine of Natural History, Series 13(3): 133–139. Kabata, Z. 1963. Parasites as biological tags. ICNAF Special Publication No. 4: 31–37. Kabata, Z. 1970. Diseases of Fishes, Book 1: Crustacea as Enemies of Fishes. TFH Publications, Jersey City, NJ. Kabata, Z. 1979. Parasitic Copepoda of British Fishes. The Ray Society, London, UK. 468 pp. Khalil, L. F., Jones, A., and Bray, R. A. (eds.). 1994. Keys to the Cestode Parasites of Vertebrates. CAB International, Oxford, UK. 768 pp. Khan, R. A. and Tuck, C. 1995. Parasites as biological indicators of stocks of Atlantic cod (Gadus morhua) off Newfoundland, Canada. Canadian Journal of Fisheries and Aquatic Sciences 52(suppl. 1): 195–201. Køie, M. 1979. On the morphology and life-history of Derogenes varicus (Müller, 1784) Looss, 1901 (Trematoda, Hemiuridae). Zeitschrift für Parasitenkunde 59: 67–78. Køie, M. 1983. Digenetic trematodes from Limanda limanda (L.) (Osteichthyes, Pleuronectidae) from Danish and adjacent waters, with special reference to their life histories. Ophelia 22: 201– 228. Køie, M. 1993. Aspects of the life cycle and morphology of Hysterothylacium aduncum (Rudolphi, 1802) (Nematoda, Ascaridoidea, Anisakidae). Canadian Journal of Zoology 71: 1289–1296. Køie, M., Berland, B., and Burt, M. D. B. 1995. Development to third-stage larvae occurs in the eggs of Anisakis simplex and Pseudoterranova decipiens (Nematoda, Ascaridoidea, Anisakidae). Canadian Journal of Fisheries and Aquatic Sciences 52(suppl. 1): 134–139. Lester, R. J. G. 1990. Reappraisal of the use of parasites for fish stock identification. Australian Journal of Marine and Freshwater Research 41: 855–864. Lester, R. J. G., Barnes, A., and Habib, G. 1986. Parasites of skipjack tuna, Katsuwonus pelamis: fishery implications. Fishery Bulletin 83: 343–356. Lester, R. J. G., Sewell, K. B., Barnes, A., and Evans, K. 1988. Stock discrimination of orange roughy, Hoplostethus atlanticus, by parasite analysis. Marine Biology 99: 137–144. Lester, R. G. J., Thompson, C., Moss, H., and Barker, S. C. 2001. Movement and stock structure of narrow-banded Spanish mackerel as indicated by parasites. Journal of Fish Biology 59: 833–843. Lom, J. and Dykova, I. 1992. Protozoan Parasites of Fishes. Developments in Aquaculture and Fisheries Science (26). Elsevier, Amsterdam. 315 pp. MacKenzie, K. 1983. Parasites as biological tags in fish population studies. Advances in Applied Biology 7: 251–331. MacKenzie, K. 1985. The use of parasites as biological tags in population studies of herring (Clupea harengus L.) in the North Sea and to the north and west of Scotland. Journal du Conseil International pour l’exploration de la Mer 42: 33–64. MacKenzie, K. 1987a. Parasites as indicators of host populations. International Journal for Parasitology 17: 345–352. MacKenzie, K. 1987b. Relationships between the herring, Clupea harengus L., and its parasites. Advances in Marine Biology 24: 263–319. MacKenzie, K. 1990. Cestode parasites as biological tags for mackerel (Scomber scombrus L.) in the Northeast Atlantic. Journal du Conseil International pour l’Exploration de la Mer 46: 155–166. MacKenzie, K. 2002. Parasites as biological tags in population studies of marine organisms. Parasitology 124: S153–S163.

224

K. MacKenzie and P. Abaunza

MacKenzie, K. and Abaunza, P. 1998. Parasites as biological tags for stock discrimination of marine fish: a guide to procedures and methods. Fisheries Research 38: 45–56. MacKenzie, K. and Longshaw, M. 1995. Parasites of the hakes Merluccius australis and M. hubbsi in the waters around the Falkland Islands, southern Chile, and Argentina, with an assessment of their potential value as biological tags. Canadian Journal of Fisheries and Aquatic Sciences 52(suppl 1): 213–224. Manly, B. F. J. 1997. Randomization, Bootstrap and Monte Carlo Methods in Biology. 2nd ed. Chapman & Hall, London. 399 pp. Margolis, L. 1963. Parasites as indicators of the geographical origin of sockeye salmon, Oncorhynchus nerka (Walbaum), occurring in the North Pacific Ocean and adjacent seas. Bulletin of the International North Pacific Fisheries Commission 11: 101–156. Matthews, R. A. 1974. The life cycle of Bucephaloides gracilescens (Rudolphi, 1819) Hopkins, 1954 (Digenea: Gasterostomata). Parasitology 68: 1–12. Mattiucci, S., Nascetti, G., Cianchi, R., Paggi, L., Arduino, P., Margolis, L., Brattey, S., Webb, S., D’Amelio, S., Orecchia, P., and Bullini, L. 1997. Genetic and ecological data on the Anisakis simplex complex, with evidence for a new species (Nematoda, Ascaridoidea, Anisakidae). Journal of Parasitology 83: 401–416. McClelland, G., Misra, R. K., and Martell, D. J. 1990. Larval anisakine nematodes in various fish species from Sable Island Bank and its vicinity. In W. D. Bowen (ed.), Population Biology of Sealworm (Pseudoterranova decipiens) in Relation to Its Intermediate and Seal Hosts. Canadian Bulletin of Fisheries and Aquatic Sciences 222: 83–118. McGarigal, K., Cushman, S., and Stafford, S. 2000. Multivariate Statistics for Wildlife and Ecology Research. Springer, New York. 283 pp. McGladdery, S. E. and Burt, M. D. B. 1985. Potential of parasites for use as biological indicators of migration, feeding and spawning behaviour of northwestern Atlantic herring (Clupea harengus). Canadian Journal of Fisheries and Aquatic Sciences 42: 1957–1968. McLaughlin, P. A., Taylor, G. T., and Tracey, M. L. 1982. Systematic methods in research. In L. G. Abele (ed.), The Biology of Crustacea, 1. Systematics, the Fossil Record and Biogeography. Kluwer, Dordrecht, Belgium, pp. 29–63. Moser, M. 1991. Parasites as biological tags. Parasitology Today 7: 182–185. Mudry, D. R. and Dailey, M. D. 1971. Postembryonic development of certain tetraphyllidean and trypanorhynchan cestodes with a possible alternative life cycle for the order Trypanorhyncha. Canadian Journal of Zoology 49: 1249–1253. Nascetti, G., Paggi, L., Orecchia, P., Smith, J. W., Mattiucci, S., and Bullini, L. 1986. Electrophoretic studies on the Anisakis simplex complex (Ascaridida: Anisakidae) from the Mediterranean and North-East Atlantic. International Journal for Parasitology 16: 633–640. Nascetti, G., Cianchi, R., Mattiucci, S., D’Amelio, S., Orcchia, P., Paggi, L., Brattey, J., Berland, B., Smith, J. W., and Bullini, L. 1993. Three sibling species within Contracaecum osculatum (Nematoda, Ascaridida, Ascaridoidea) from the Atlantic arctic-boreal region: reproductive isolation and host preferences. International Journal for Parasitology 23: 105–120. Pacala, S. W. and Dobson, A. P. 1988. The relation between the number of parasites/host and host age: population dynamic causes and maximum likelihood estimation. Parasitology 96: 197–210. Paggi, L., Nascetti, G., Cianchi, R., Orecchia, P., Mattiucci, S., D’Amelio, S., Berland, B., Brattey, J., Smith, J. W., and Bullini, L. 1991. Genetic evidence for three sealworm species within Pseudoterranova decipiens (Nematoda: Ascaridida: Ascaridoidea) in the North Atlantic, and Norwegian and Barents Seas. International Journal for Parasitology 21: 195–212. Potvin, C. and Roff, D. A. 1993. Distribution-free and robust statistical methods: viable alternatives to parametric statistics? Ecology 74: 1617–1628. Pritchard, M. A. and Kruse, G. O. W. 1982. The Collection and Preservation of Animal Parasites. University of Nebraska Press, Lincoln. 141 pp.

Parasites as Biological Tags

225

Quinteiro, P. 1990. Helmintos parásitos de peces de las costas gallegas. Tesis Doctoral, Universidad de Santiago de Compostela. 243 pp. Raibaut, A., Berrebi, P., and Rousset, V. 1986. Utilisation de la génétique enzymatique pour comparer les copépodes parasites du genre Lernaeenicus (Pennellidae), selon la zone d’implantation dans l’hôte, Sardina pilchardus (Walbaum, 1792). Compte rendu hebdomadaire des séances de l’Académie des sciences, 303, Série III, No. 2: 127–129. Renaud, F., Gabrion, C., and Pasteur, N. 1983. Le complexe Bothriocephalus scorpii (Mueller, 1776): différenciation par électrophorèse enzymatique des espèces parasites du turbot (Psetta maxima) et de la barbue (Scophthalmus rhombus). Compte rendu hebdomadaire des séances de l’Académie des sciences, 296, Série III, No. 2: 127–129. Rózsa, L., Reiczigel, J., and Majoros, G. 2000. Quantifying parasites in samples of hosts. Journal of Parasitology 86: 228–232. Schmidt, G. D. 1986. Handbook of Tapeworm Identification. CRC Press. Boca Raton, FL. 675 pp. Sewell, K. B. and Lester, R. J. G. 1995. Stock composition and movement of gemfish, Rexea solandri, as indicated by parasites. Canadian Journal of Fisheries and Aquatic Sciences 52(suppl. 1): 225–232. Sindermann, C. J. 1961. Parasite tags for marine fish. Journal of Wildlife Management 25: 41–47. Sindermann, C. J. 1983. Parasites as natural tags for marine fish: a review. NAFO Scientific Council Studies 6: 63–71. Sindermann, C. J. 1990. Principal Diseases of Marine Fish and Shellfish, 1, 2nd ed. Academic Press, San Diego. 521 pp. Smith, J. D. and Halton, D. W. 1983. The Physiology of Trematodes, 2nd ed. Cambridge University Press, Cambridge, UK. 446 pp. Smith, J. W. 1983. Anisakis simplex (Rudolphi, 1809, det. Krabbe, 1878) (Nematoda: Ascaroidea): morphology and morphometry of larvae from euphausiids and fish, and a review of the life-history and ecology. Journal of Helminthology 57: 205–224. Smith, J. W. and Wootten, R. 1975. Experimental studies on the migration of Anisakis sp. larvae (Nematoda: Ascaridida) into the flesh of herring, Clupea harengus L. International Journal for Parasitology 5: 133–136. Sokal, R. R. and Rohlf, F. J. 1995. Biometry, 3rd ed. W.H. Freeman, New York. 887 pp. Speare, P. 1994. Relationships among black marlin, Makaira indica, in eastern Australian coastal waters, inferred from parasites. Australian Journal of Marine and Freshwater Research 45: 535–550. Speare, P. 1995. Parasites as biological tags for sailfish Istiophorus platypterus from east coast Australian waters. Marine Ecology Progress Series 118: 43–50. Stern, J. A., Chakravarti, D., Uzmann, J. R., and Hesselholt, M. N. 1958. Rapid counting of Nematoda in salmon by peptic digestion. United States Fish and Wildlife Service, Special Scientific Report—Fisheries No. 255. 5 pp. Stewart-Oaten, A. 1995. Rules and judgements in statistics: three examples. Ecology 76: 2001–2009. Stunkard, H. W. 1929. The life cycle of Cryptocotyle lingua (Creplin), with notes on the physiology of the cercaria. Journal of Morphology 50: 143–183. Valtonen, E. 1983. Relationships between Corynosoma semerme and C. strumosum (Acanthocephala) and their paratenic fish hosts in the Bothnian Bay, Baltic Sea. Acta Universitatis Ouluensis, Series A, Scientiae Rerum Naturalium No. 155, Biologica No. 21: 1–32. Valtonen, E. T., van Maren, M. J., and Timola, O. 1983. A note on the intermediate hosts of Echinorhynchus gadi Zoega, in Müller (Acanthocephala) in the Baltic Sea. Aquila Series in Zoology 22: 93–97. Vik, R. 1964. The genus Diphyllobothrium. An example of the interdependence of systematics and experimental biology. Experimental Parasitology 15: 361–380.

226

K. MacKenzie and P. Abaunza

Williams, B. G. and Dye, C. 1994. Maximum likelihood for parasitologists. Parasitology Today 10: 489–493. Williams, H. H. and Jones, A. 1994. Parasitic Worms of Fish. Taylor & Francis, London, UK. 593 pp. Williams, H. H., MacKenzie, K., and McCarthy, A. M. 1992. Parasites as biological indicators of the population biology, migrations, diet and phylogenetics of fish. Reviews in Fish Biology and Fisheries 2: 144–176. Wilson, K., Grenfell, B. T., and Shaw, D. J. 1996. Analysis of aggregated parasite distributions: a comparison of methods. Functional Ecology 10: 592–601. Wright, C. A. 1956. Studies on the life history and ecology of the trematode genus Renicola Cohn, 1904. Proceedings of the Zoological Society of London 126: 1–49.

CHAPTER

12

Otolith Elemental Composition as a Natural Marker of Fish Stocks STEVEN E. CAMPANA Marine Fish Division, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada

I. Introduction II. Sampling and Assays A. Sample Preparation and Quality Control B. Statistical Analysis III. Case Studies A. Population Mixing of Atlantic Cod B. Natal Homing of Weakfish C. Estuarine Contribution of Juvenile Snapper to the Adult Fishery D. Determination of River of Origin of Atlantic Salmon IV. Conclusion References

I. INTRODUCTION To the extent that groups of fish inhabit different environments, the otolith elemental composition often serves as a natural marker or tag of those groups. Two key properties of the otolith underlie the use of the otolith elemental composition as a natural marker: (1) unlike bone, the otolith is metabolically inert; therefore, newly deposited material is neither resorbed nor reworked after deposition (Campana and Neilson, 1985); and (2) trace element uptake onto the growing otolith reflects the physical and chemical environment (Fowler et al., 1995; Gallahar and Kingsford, 1996), albeit with significant physiological regulation (Kalish, 1989; Farrell and Campana, 1996). Isotopic ratios of elements such as strontium (Kennedy et al., 1997) and oxygen (Thorrold et al., 1997a) are similarly influenced by environmental availability and temperature. Such Stock Identification Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

227

228

Steven E. Campana

environmental responses, recorded permanently in the otolith, imply that the otolith concentration of selected elements and isotopes (the “elemental fingerprint”) can be used as a biological tag to discriminate among groups of fish which have spent at least part of their lives in different environments (Fig. 12-1). As a 40

BA

30

20

10

0 200

300

400

500

600

700

SR 3

Discriminant Function 2

40

BA

30

20

10

0 0.0

.5

1.0

1.5

2.0

2.5

3.0

MN

1

-1

-3 -6

-4

-2

0

2

4

6

8

Discriminant Function 1

700

600

SR

500

400

300 200 0.0

.5

1.0

1.5

2.0

2.5

3.0

MN

FIGURE 12-1. Example of the preparation of a multivariate otolith elemental fingerprint for shad (Alosa sapidissima). The concentrations of three elements (Ba, Mn, and Sr) were measured in otoliths from about 60 shad collected in each of three river systems. When one element is plotted against another (left panels), there are varying degrees of differences among the three rivers (each river is represented by a different symbol). The differences in elemental composition among rivers become more evident when the individual elements are pooled into a multivariate fingerprint (right panel). Full details of the shad study are available in Thorrold et al. (1998).

Otolith Elemental Composition as a Natural Marker of Fish Stocks

229

result, the elemental fingerprint appears to be an excellent biological tracer of groups of fish, both in freshwater (Kalish, 1990; Northcote et al., 1992; Bronte et al., 1996; Kennedy et al., 1997, 2000, 2002; Limburg, 1998; Thorrold et al., 1998a) and saltwater environments (Edmonds et al., 1989, 1991, 1992, 1995; Gunn et al., 1992; Sie and Thresher, 1992; Campana et al., 1994, 1995, 1999; Campana and Gagné, 1995; Thresher et al., 1994; Proctor et al., 1995; Severin et al., 1995; Dove et al., 1996; Gillanders and Kingsford, 1996; Milton et al., 1997; Thorrold et al., 1997b, 1998b, 2001; Begg et al., 1998; Dufour et al., 1998; Newman et al., 2000; Volk et al., 2000; Gillanders, 2001, 2002; Secor et al., 2001). The presence of geographic variations in water temperature and chemistry, both of which can result in different otolith composition, suggests that otolith elemental fingerprints should discriminate well among fish that have grown up in different environments. However, it is probably inappropriate to refer to the use of elemental fingerprints as stock discriminators, since genetic differences are not implied and spatial heterogeneity in the stock environment can result in different fingerprints for different stock components (Campana et al., 2000; Thorrold et al., 1998a). Perhaps more importantly, ontogenetic effects and agerelated differences in exposure history can result in very different fingerprints for fish of different size classes from the same population (Edmonds et al., 1989; Hoff and Fuiman, 1993; Campana et al., 1995, 2000; Begg et al., 1998; Begg and Weidman, 2001). Since the elemental fingerprint reflects the exposure of the individual fish to both the environment and its own physiology, it would be expected to differ among any groups of fish which have experienced different histories, whether or not the groups come from the same population. Logically, the presence of different fingerprints could not be used to infer the length of time that the groups of fish remained separate, since even occasional residency in a different environment would have the potential to introduce a detectable difference in the elemental composition. By corollary, the absence of differences would not necessarily imply that the groups of fish are of common origin. As a result, it is fair to categorize otolith elemental fingerprints as powerful discriminators of groups when differences exist, but of negligible value when differences cannot be detected. Where differences are detected, additional information would be required to determine if the groups actually corresponded to stocks or populations. Nevertheless, the presence of different fingerprints among groups of fish of similar age necessarily implies different environmental histories. To the extent that populations or stocks of fish inhabit different environments, otolith elemental composition can then serve as an indicator of stock identity. Use of the fingerprint as a long-term stock discriminator may be justified in instances where environmental differences among stock areas are larger than those within areas or across year-classes, and where the effect of size-related effects on the fingerprint have been statistically removed. The assumption of long-term stability in the fingerprint is probably met in some, but not all, stocks.

230

Steven E. Campana

In principle, the otolith elemental composition can be used to identify and track any groups of fish of different environmental history. In practice, there are three practical limitations to their use. The first limitation is that many of the most easily measured elements are under strict physiological regulation, and thus unsuitable for use as environmental indicators. This list includes the major elements calcium, oxygen, and carbon (which make up the calcium carbonate matrix), as well as the minor (>100 ppm) elements Na, K, S, P, and Cl, although it excludes Sr (Thresher et al., 1994; Proctor et al., 1995; Schwarcz et al., 1998). Nevertheless, even physiologically regulated elements can prove useful as biological tracers of a group of fish, as long as the otolith concentrations of those elements vary significantly among groups. The second limitation concerns the analysis of the less abundant trace (

>

<



> >

0.81 0.17 13.9 0.87 0.55 0.32 6.0 12.2 3.6 0.30 2.56 0.48 0.24 1.8 0.25 3.0 0.08 0.39 9.9 0.8 0.18 0.14 0.12 0.42 0.99 32 2.2 1.08 3.3

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.06 0.01 0.4 0.06 0.08 0.03 0.3 0.4 0.2 0.02 0.09 0.02 0.03 0.1 0.02 0.2 0.01 0.02 0.7 0.1 0.01 0.01 0.01 0.03 0.04 1 0.2 0.05 0.2

a

The cases in which the amounts of fatty acids in brain tissue were significantly different (p < 0.01) between the two stocks are marked with inequality signs.

is, areas F1, F4, and F5 in Figure 13-8, and one in the Bill Bailey Bank/Wyville Thomson Ridge area to the southwest of the islands, that is, areas F2 and F3 in Figure 13-8. These latter areas are separated from the Faroe Plateau by the more than 800 m deep Faroe Bank Channel. Redfish from the Norwegian Sea belong to the same stock as the Faroe Plateau redfish, while redfish from the waters southeast and southwest of Iceland, IC1 and IR1, are related to the other Faroe Island stock (Fig. 13-9). Redfish from the northwestern part of the Icelandic

15:0

PC 2 15%

14:0

18:2n6 18:3n3

c1 c2

22:5n6 24:1n9 22:1n9

18:1n9

PC 1 52% FIGURE 13-7. PC plot of cod from Faroe Plateau (squares) and from Faroe Bank (circles), based on the eight most discriminating fatty acids and two cholesterol derivatives, c1 and c2, in heart tissue. Two parallel samples of 15 cod from each stock are shown. The borderline between the two stocks is shown.

70

N1 IC2

IC1 IR1

F2 F3

60

N2

F5

F1 F4

IR2

50 -50

-40

-30

-20

-10

0

10

20

FIGURE 13-8. Eleven locations for catches of redfish during the summer and fall of 1999.

261

262

O. Grahl-Nielsen

FIGURE 13-9. Plot of the average redfish from 11 locations shown in Figure 13-7, based on fatty acids in heart tissue, in three dimensions, that is, PC1, accounting for 48% of the total variance, from left to right, PC2, accounting for 14% of the total variance, from front to back, and PC3, accounting for 10% of the total variance, vertical.

plateau, IC2, on the other hand, form a distinct stock. Sebastes mentella, from depths between 650 and 800 m in the Irminger Sea, IR2, also form a distinct stock. The same sample of redfish was independently subjected to electrophoretic analysis of the enzyme MEP-1 in another laboratory (Institute of Marine and Fisheries Biology, University of Bergen). This analysis revealed the same stock structure of the redfish from the waters around the Faroe Islands as was found by the fatty acid profiles (T. Johansen, personal communication). The electrophoretic method also confirmed the finding by the fatty acid method that redfish from the Norwegian coast belonged to the same stock as redfish from the Faroe Island Plateau, and that redfish from the areas to southeast and southwest of Iceland belonged to the same stock as redfish from the Bill Bailey Bank/Wyville Thomson Ridge area to the southwest of the Faroe Islands. The electrophoretic method did not, however, distinguish redfish from the northwestern part of the Icelandic plateau or redfish from depths between 650 and 800 m in the Irminger Sea as separate stocks, as was done by the fatty acid method. Rather, they were found to belong to the stock extending from southwest of the Faroe Islands to areas to the south of Iceland. This suggests that

263

Fatty Acid Profiles as Natural Marks for Stock Identification

PC 2 8%

E B G G FF F B FG EA B A E A AG C D D B D C C D C E

PC 1 88% FIGURE 13-10. PC plot of two parallel samples from the jawbone from each of six harp seals from the western population (squares) and from each of four harp seals from the eastern population (circles), based on four fatty acids: 14 : 0, 18 : 1n7, 22 : 5n3, and 22 : 6n3, with highest discrimination power between the two populations. Four parallel samples from each of seven test seals, A to G, are projected into the plot, but without any influence on PCs.

the fatty acid method has a higher resolving power than the electrophoretic method.

I. IDENTIFICATION

OF

HARP SEALS

ON

FORAGING MIGRATIONS

In the winter of 1986–1987, a large invasion of harp seals took place along the Norwegian coast down to the southernmost part, that is, the Skagerrak coast. Approximately 300,000 animals were estimated to have approached the coast, and 60,000 of these were trapped and drowned in gill nets. It was not known if the seals originated from the western population, with breeding areas in the Greenland Sea by Jan Mayen, or from the eastern population, breeding in the White Sea. Reference seals from the two populations were obtained from their respective breeding areas. To be identified were six seals which had drowned in gill nets on the Norwegian coast at Sunnmøre, approximately 63°30¢N. In addition, one seal from the Barents Sea was considered as a test specimen.

264

O. Grahl-Nielsen

TABLE 13-5. Relative Amounts, as Percentage of Sum ± SD, of Fatty Acids in Samples of Jawbone of Harp Sealsa Reference seals West (N = 6) 14:0 14:1n5 16.0 16:1n9 16:1n7 18:0 18:1n9 18:1n7 18:1n5 18:2n6 18:3n3 18:4n3 20:1n9 20:4n6 20:5n3 22:5n3 22:6n3

4.5 1.2 9.1 0.8 14.4 1.2 29 6.6 0.6 2.3 0.8 1.8 13 0.7 4.8 2.5 6.2

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.3 0.1 0.8 0.1 1.5 0.3 1 0.6 0.1 0.2 0.1 0.3 2 0.2 0.9 0.3 0.9

> >
< > >

East (N = 4)

A

B

C

D

E

F

G

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

4.4 0.6 14.3 0.9 11.1 3.1 30.4 8.2 0.7 2.2 0.6 0.9 14.6 1.0 2.6 1.4 3.0

4.5 0.5 18.2 1.1 13.6 3.6 29.8 9.9 0.7 1.8 0.5 0.7 9.5 1.0 1.9 0.9 1.8

5.7 0.5 20.5 1.0 6.6 6.3 28.5 6.9 0.6 1.9 0.4 0.4 16.1 1.3 1.3 0.6 1.7

4.9 1.6 12.4 1.0 15.4 4.1 32.5 7.8 0.8 1.7 0.6 0.7 12.7 1.0 1.2 0.6 1.0

5.8 0.9 14.6 1.0 12.2 2.8 28.3 9.1 0.7 1.9 0.8 1.3 11.4 0.9 2.8 1.8 3.9

7.1 0.7 18.6 1.2 11.4 3.2 28.6 9.8 0.9 1.8 0.6 0.9 10.0 0.8 1.7 0.8 1.9

4.9 1.3 13.1 1.0 16.1 2.5 28.8 9.3 0.9 1.7 0.5 1.2 11.8 0.8 2.7 1.1 2.3

3.6 0.9 13 0.7 15 5 29 8 0.6 2.1 0.5 1.2 9 3 5 1.4 2.3

0.8 0.3 3 0.2 3 3 1 1 0.1 0.3 0.2 0.4 4 2 1 0.2 0.6

Test seals

a Test seal A was from the Barents Sea and the other six were from the Norwegian coast, the average of four replicate analyses in each case. The cases in which the amounts of fatty acids in the jawbone were significantly (p < 0.05) different between the western and eastern seals are marked with inequality signs.

A small piece of bone was removed from the rear part of the lower jaw. Flesh and blood were carefully removed. The bone sample was rinsed in water, dried, and crushed in a mortar. Parallel samples of 50 mg were subjected to chemometric analysis. The fatty acid profiles were different in seals from the two populations (Table 13-5). Principal component analysis of the reference seals from the two populations was carried out on the basis of the four most discriminating fatty acids. The seven test seals were matched against the reference seals, and all were found to belong to the eastern population (Fig. 13-9) (Grahl-Nielsen et al., 1993).

IV. DISCUSSION The cases presented are all from the author’s laboratory. Other investigations have demonstrated differences in tissue fatty acids on species and stock level. For example, Armstrong et al. (1994) distinguished five species of fish from temper-

Fatty Acid Profiles as Natural Marks for Stock Identification

265

ate Australian waters by PCA of fatty acid composition in the fillet. Within some of the species, seasonal and geographic differences in the fatty acid profile were detected. Castell et al. (1995), using multivariate discriminant analysis, distinguished lobster (Homarus americanus) eggs from three Nova Scotia wild stocks. The fatty acid profiles of muscle lipids differed between cultured and wild sturgeon, Acipenser oxyrinchus desotoi (Chen et al., 1995). Silversand et al. (1996) used the fatty acids 18 : 2n6 and 20 : 1n9 in lipids from ovulated eggs to distinguish between wild and cultured turbot, Scophthalmus maximus. Pickova et al. (1997) distinguished Skagerrak and Baltic cod in terms of egg lipid fatty acid composition. Seaborn et al. (2000) used profiles of muscle fatty acids to differentiate wild striped bass and its hybrids from cultured hybrid striped bass. It turns out that the applications of fatty acid profiles for identification purposes fall into two categories, based on different assumptions: One is that diet influences the composition of fatty acids in triacylglycerides in storage lipids in, for example, muscle tissue. In this case, differences in diet is the key identification factor, which may be used on a short timescale. The other assumption is that the composition of fatty acids in membrane phospholipids is genetically controlled and stable over time. The phospholipid fatty acids may therefore be used as a natural mark over a longer timescale. A wide range of investigations have shown that the composition of fatty acids in fish tissue is influenced by the composition of fatty acids in the diet. Still, many of these investigations have been conducted with diets of anomalous compositions, often based on vegetable oils (Cowley et al., 1983; Bell et al., 1985; Leray and Pelletier, 1985; Lie et al., 1986; Anderson and Arthington, 1989). In addition, many of the investigations were carried out on juvenile stages of fish which are more easily affected than fish in their mature stages (Navarro et al., 1995; Muje et al., 1989). However, even in cases where natural diets have been used on mature fish, an influence on the fatty acid composition has been demonstrated. Kirsch et al. (1998) fed cod squid, Illex illecebrosus, for 6 weeks followed by mackerel for 8 weeks. The fatty acid composition of the total body lipids first changed in the direction of the pattern found in squid, and thereafter in the direction of the pattern in mackerel. The change in pattern in the body lipids occurred during the first three to five weeks after change in diet. However, despite changes in the fatty acid pattern, the pattern in the cod could be readily distinguished from that in the diets. The richer a tissue is in triacylglycerides, the closer is the resemblance of its fatty acid composition with that of the diet (Viga and Grahl-Nielsen, 1990). Diet-based, short-term identification should therefore be based on intestinal fat or brown or white muscle tissue. Since these tissues make up the bulk of the bodies, whole, ground-up fish may also be suitable for this type of identification.

266

O. Grahl-Nielsen

The majority of published investigations on identifications are based on dietinduced composition of fatty acids in tissues rich in triacylglycerides, that is, muscle tissue in cultured and wild sturgeon (Chen et al., 1995); total body lipids in Canadian fish and invertebrates (Budge et al., 2002); egg lipids in lobsters from Nova Scotia (Castell et al., 1995); muscle tissue in various Australian fishes (Armstrong et al., 1994); and muscle lipids in cultured and wild striped bass (Seaborn et al., 2000). The drawback with diet-induced identification is that observed differences in tissue fatty acid profiles from different specimens may not necessarily be due to differences in stock. This is because many fish species are opportunistic feeders. Selection of diet may therefore not be fenotypic. Still, the method may be advantageously used in cases where the purpose is to distinguish between groups of fish with clear-cut differences in diet, for example, in distinguishing between farmed and wild specimens. Contrary to the labile profile of fatty acids in triacylglycerides, the profile of fatty acids in phospholipids may serve as a natural mark for identification of stocks. The condition is that the fatty acid profile of these lipids is typical for a stock and stable over time. Our investigation of the Faroe stocks of cod (Joensen et al., 2000) showed that the between-stock difference in fatty acid composition in heart tissue, in which the lipids were made up of between 80% and 90% phospholipids, was caused by inborn factors. The stability of the fatty acid profile then needs to be questioned. Compared with the composition of the fatty acids in triacylglycerides, the composition of fatty acids in phospholipids is generally expected to be less sensitive to diet. Pickova et al. (1997) demonstrated that fatty acid composition of total phospholipids in cod eggs was population-specific and diet-independent. They used egg phospholipid fatty acids to distinguish between Skagerrak and Baltic cod. Bandarra et al. (1997) have shown that the fatty acid composition of sardine phospholipids is hardly affected by seasonal change in dietary planktonic lipids. In rainbow trout, Oncorhynchus mykiss, dietary fatty acids are selectively incorporated into muscle phospholipids to obtain narrowly defined physiological levels (Greene and Selivonchick, 1990). In fact, this was also the case for triacylglycerols. Owen et al. (1972) detected relatively little change in the fatty acids of phospholipids of liver and extrahepatic tissue in plaice, Pleuronectes platessa, subjected to different dietary regimes. However, even if dietary influence on fatty acid composition of phospholipids, and thus phospholipid-rich tissue, such as heart, might be negligible, other environmental factors may cause changes. We need to sample fish from different populations at intervals, preferably during an entire year, to see how stable the fatty acid profile is under shifting dietary regimens and during changes in other environmental factors, such as temperature. Changing physiological conditions of the fish also need to be considered.

Fatty Acid Profiles as Natural Marks for Stock Identification

267

Stock identification methods based on natural marks, such as morphological and genetic analyses as presented in this volume, are carried out as frequency distributions, and a large number of specimens are needed. The examples discussed in the present chapter demonstrate the superiority of the fatty acid profile method in that it is powerful enough for identification of individual specimens. It may be applied in research and management of stocks of various fish species, in surveillance of commercial catches, and in detecting fish farm escapees.

REFERENCES Ackman, R. G. 1980. Fish lipids. In J. J. Connell (ed.), Advances in Fish Science and Technology. Fishing New Books, Farnham, UK, pp. 86–103. Anderson, A. J. and Arthington, A. H. 1989. Effect of dietary lipid on the fatty acid composition of silver perch (Leiopotherapon bidyanus) lipids. Comparative Biochemistry and Physiology 93B: 715–720. Armstrong, S. G., Wyllie, S. G., and Leach, D. N. 1994. Effects of season and location of catch on fatty acid composition of some australian fish species. Food Chemistry 51: 295–305. Bandarra, N. M., Batista, I., Nunes, M. L., Empis, J. M., and Christie, W. W. 1997. Seasonal changes in lipid composition of sardine (Sardina pilchardus). Journal of Food Science 62: 40–42. Bell, M. V., Henderson, R. J., and Sargent, J. R. 1985. Changes in the fatty acid composition of phospholipids from turbot (Scophthalmus maximus) in relation to dietary polyunsaturated fatty acid deficiencies. Comparative Biochemistry and Physiology 81B: 193–198. Budge, S. M., Iverson, S. J., Bowen, W. D., and Ackman, R. G. 2002. Among- and within-species variability in fatty acid signatures of marine fish and invertebrates on the Scotian Shelf, Georges Bank, and southern Gulf of St. Lawrence. Canadian Journal of Fisheries and Aquatic Sciences 59: 886–898. Castell, J. D., Boston, L. D., Miller, R. J., and Kenchington, T. 1995. The potential identification of the geographic origin of lobster eggs from various wild stocks based on fatty acid composition. Canadian Journal of Fisheries and Aquatic Sciences 52: 1135–1140. Chen, I.-C., Chapman, F. A., Wei, C.-I., Portier, K. M., and O’Keefe, S. F. 1995. Differentiation of cultured and wild sturgeon (Acipenser oxyrinchus desotoi) based on fatty acid composition. Journal of Food Science 60: 631–635. Cowley, C. B., Wee, K. L., and Tacon, A. G. J., 1983. Effect of fatty acid intake on growth and fatty acid composition of liver and muscle of snakehead. Bulletin of the Japanese Society of Scientific Fisheries 49: 1573–1577. Esbensen, K., Schønkopf, S., and Midtgaard, T. 1994. Multivariate analysis in practice. In Camo. Computer-Aided Modelling AS. Trondheim, Norway, pp. 157–212. Grahl-Nielsen, O. and Barnung, T. N. 1985. Variations in the fatty acid profile of marine animals caused by environmental and developmental changes. Mar. Environ. Res. 17: 218–221. Grahl-Nielsen, O., Mjaavatten, O., and Tvedt, E. 1993. Distinguishing between different populations of harp seal (Phoca groenlandica) by chemometry of the fatty acid profiles in jaw bone. Canadian Journal of Fisheries and Aquatic Sciences 50: 1400–1404. Grahl-Nielsen, O. and Mjaavatten, O. 1992. Discrimination of striped bass stocks: a new method based on chemometry of fatty acid profile in heart tissue. Transactions of the American Fisheries Society 121: 307–314. Grahl-Nielsen, O. and Ulvund, K. A. 1990. Distinguishing populations of herring by chemometry of fatty acids. American Fisheries Society Symposium, 7: 566–571.

268

O. Grahl-Nielsen

Greene, D. H. S. 1990. Lipid metabolism in fish. In M. S. Stansby (ed.), Fish Oils in Nutrition. Van Nostrand Reinhold, New York, pp 226–246. Greene, D. H. S. and Selivonchick, D. P. 1990. Effects of dietary vegetable, animal and marine lipids on muscle lipid and hematology of rainbow trout (Oncorhynchus mykiss). Aquaculture 89: 165–182. Hornnes, H. K. 1993. Fettsyrer i sild. M.Sc. Thesis, Department of Chemistry, University of Bergen. Joensen, H., Steingrund, P., Fjallstein, I., and Grahl-Nielsen, O. 2000. Discrimination between two reared stocks of cod (Gadus morhua) from the Faroe Islands by chemometry of the fatty acid composition in the heart tissue. Marine Biology 136: 573–580. Joensen, H. and Grahl-Nielsen, O. 2004. Stock structure of Sebastes mentalla in the North Atlantic revealed by chemometry of the fatty acid profile in hear tissue. ICES Journal of Marine Science 61: 113–126. Kirsch, P. E., Iverson, S. J., Bowen, W. D., Kerr, S. R., and Ackman, R. G. 1998. Dietary effects on the fatty acid signature of whole atlantic cod (Gadus morhua). Canadian Journal of Fisheries and Aquatic Sciences 55: 1378–1386. Kvalheim, O. M. and Kvarstang, T. V. 1987. A general-purpose program for multivariate data analysis. Chemometr. Intell. Lab. Syst. 2: 235–237. Leray, C. and Pelletier, X. 1985. Fatty acid composition of trout phospholipids: effect of (n-3) essential fatty acid deficiency. Aquaculture 50: 51–59. Lie, Ø., Lied, E., and Lambertsen, G. 1986. Liver retention of fat and of fatty acids in cod (Gadus morhua) fed different oils. Aquaculture 59: 187–196. Meier, S. 1997. Variasjon i fettsyresammensetningen i sild, Clupea harengus L. M.Sc. Thesis, Department of Chemistry, University of Bergen. Morris, R. J. and Culkin, F. 1989. Fish. In R. G. Ackman (ed.), Marine Biogenic Lipids, Fats and Oils, Vol. 2. CRC Press, Boca Raton, FL, pp. 145–178. Muje, P., Ågren, J. J., Lindqvist, O. V., and Hänninen, O., 1989. Fatty acid composition of vendace (Coregonus albula L.) muscle and its plankton feed. Comparative Biochemistry and Physiology 92B: 75–79. Navarrro, J. C., McEvoy, L. A., Amat, F., and Sargent, J. R. 1995. Effects of diet on fatty acid composition of body zones in larvae of the sea bass Dicentrarchus labrax: a chemometric study. Mar. Biol. 124: 177–183. Owen, J. M., Adron, J. W., Sargent, J. R., and Cowey, C. B. 1972. Studies on the nutrition of marine flatfish. The effect of dietary fatty acids on the tissue fatty acids of the plaice Pleuronectes platessa. Marine Biology 13: 160–166. Pickova, J., Dutta, P. C., Larsson, P.-O., and Kiessling, A. 1997. Early embryonic cleavage pattern, hatching success, and egg-lipid fatty acid composition: comparison between two cod (Gadus morhua) stocks. Canadian Journal of Fisheries and Aquatic Sciences 54: 2410– 2416. Roseth, I. E. 1994. Fettsyrer i laks. M.Sc. Thesis, Department of Chemistry, University of Bergen. Sargent, J., Henderson, R. J., and Tocher, D. R. 1989. The lipids. In J. E. Halver (ed.), Fish Nutrition. Academic Press, San Diego, pp. 153–218. Seaborn, G. T., Jahncke, M. L., and Smith, T. I. J. 2000. Differentiation between cultured hybrid striped bass and wild striped bass and hybrid bass using fatty acid profiles. North American Journal of Fisheries Management 20: 618–626. Silversand, C., Norberg, B., and Haux, C. 1996. Fatty acid composition of ovulated eggs from wild and cultured turbot (Scophthalmus maximus) in relation to yolk and oil globule lipids. Marine Biology 125: 269–278. Viga, A. and Grahl-Nielsen, O. 1990. Genotypic and phenotypic fatty acid composition in the tissues of salmon, Salmo salar. Comparative Biochemistry and Physiology 96B: 721–727.

Fatty Acid Profiles as Natural Marks for Stock Identification

269

Vogt, N. B., Moksness, E., Sporstøl, S. P., Knutsen, H., Nordenson, S., and Kolset, K. 1986. SIMCA principal component analysis of fatty acid patterns in Day-1 and Day-8 cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) eggs. Marine Biology 92: 173–182. Wold, S. and Sjøstrøm, M. 1977. SIMCA: a method for analyzing chemical data in terms of similarity and analogy. In B. R. Kowalski (ed.), Chemometrics: Theory and Applications. Symp. Ser. Am. Chem. Soc. 52: 243–282. Wold, S. 1978. Cross validatory estimation of the number of components in factor and principal models. Technometrics 20: 397–406. Økland, F., Lund, R. A., and Hansen, L. P. 1991. Rømt oppdrettslaks i vassdrag 1989 og 1990; tidspunkt for oppvandring i elver, og betydningen av oppdrettsnæringens omfang. NINA Oppdragsmelding 082: 1–16.

This Page Intentionally Left Blank

PART

Natural Marks— Genetic Analyses

V

This Page Intentionally Left Blank

CHAPTER

14

Chromosome Morphology RUTH B. PHILLIPS Washington State University, Vancouver, Washington, USA

I. Intraspecific Variation in Fish and Shellfish Chromosome Number and Morphology A. Variation in Chromosome Number B. Variation in Chromosome Structure C. Variation in Chromosome Banding Patterns II. Standard Methods for Detecting Chromosome Variation A. Tissue Sampling and Treatment to Obtain Dividing Cells B. Colchicine Treatment, Hypotonic Treatment, and Fixation C. Slide Preparation D. Staining E. Chromosome Banding Techniques F. Photography and Analysis III. Molecular Cytogenetic Methods A. Fluorescence In Situ Hybridization (FISH) B. Application of FISH to Fish and Shellfish Chromosomes IV. Discussion and Conclusions References

I. INTRASPECIFIC VARIATION IN FISH AND SHELLFISH CHROMOSOME NUMBER AND MORPHOLOGY Variation in chromosome number and morphology has the potential to be used to identify fish and shellfish stocks, although chromosome markers have been applied to stock identification in only a few cases. Their use has been limited because the karyotypes of relatively few species have been determined and the Stock Identification Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

273

274

Ruth B. Phillips

methods require examination of dividing cells. Compilations of chromosome data on fish species can be found in Gold et al. (1980), Ojima (1980), Sola et al. (1981), and Klinkhardt et al. (1995). Reviews of specific groups include Galetti et al. (2000) (marine fishes), Rab and Collares-Pereira (1995) (European cyprinid fishes), and Phillips and Rab (2001) (salmonid fishes). References on shellfish include Nakamura (1985) and Thiriot-Quievreux and Ayraud (1982).

A. VARIATION

IN

CHROMOSOME NUMBER

1. Polyploidy Chromosome number variation occurs in natural populations in the form of spontaneous polyploids, especially triploids. Polyploid species such as sturgeons with even numbers of chromosome sets (4n, 8n, 16n) (Birstein et al., 1993) are fertile, but odd-numbered ones such as triploids (3n) usually cannot form viable gametes. However, unisexual triploid fishes that reproduce parthenogenetically occur in several groups of live-bearing fish of the family Poecillidae from the southern United States and Mexico. Among cyprinids, there is a diploid–triploid complex in the genus Rivulus in the southeastern United States and in the genus Rutilus on the Iberian peninsula (Collares-Pereira, 1985). The triploids usually have a higher temperature tolerance and survive better in harsh environments. Naturally occurring triploids are also found in shellfish. For example, both triploids and diploids are found in different species of Korean Corbicula (Park et al., 2001). Triploidy has also been artificially induced in many fish and shellfish species to obtain sterile animals, and some of these have been stocked. Triploid shellfish are useful for aquaculture because of their sterility, superior growth, and improved meat quality (Guo and Standish, 1994; Yang et al., 2001). Tetraploids are also valuable for 100% producing triploids through mating with diploids (Guo et al., 1996; Guo and Allen, 1997). In fishes, triploids usually are sterile, but effects on growth have been variable (reviewed in Tillmann, 2001; Felip et al., 2001; Cotter et al., 2002; Lilysestrom et al., 1999; and Sheenhan et al., 1999). Polyploids have extra haploid sets of chromosomes and can be detected by the increase in DNA content per cell with flow cytometry as well as cytogenetically (reviewed in Thorgaard and Allen, 1987). If a species has only one chromosome pair with nucleolar organizer regions (NORs), then polyploid cells can be identified by the number of nucleolar organizers found per cell (Phillips et al., 1986) (Fig. 14-1). 2. Robertsonian Translocations The most common type of chromosome number variation that has been identified in fishes results from Robertsonian translocations. In this type of chromo-

Chromosome Morphology

275

A FIGURE 14-1. Silver-stained cells from rainbow trout (Oncorhynchus mykiss) embryos of three different ploidy levels: (A) haploid cells, (B) diploid cells, and (C) triploid cells. From Phillips et al. (1986).

some rearrangement, two uniarmed chromosomes fuse to form a biarmed chromosome, or a biarmed chromosome undergoes fission to form two uniarmed chromosomes. There is no change in the amount of genetic material, but there is a change in chromosome number. Robertsonian translocations have been reported for many fish species (reviewed in Gold, 1979; Sola et al., 1981; Thorgaard and Allen, 1987; Hartley, 1987; Phillips and Ihssen, 1990; and Phillips and Rab, 2001), and intraspecific variation in chromosome number is especially common in salmonid fishes. In contrast, there are no known examples of intraspecific variation in chromosome number in shellfish (Guo, personal communication, Rutgers University).

B

C FIGURE 14-1. Continued

276

277

Chromosome Morphology

Different populations of rainbow trout vary in chromosome number between 2n = 58 and 2n = 64 (Thorgaard, 1976; 1983; Hartley and Horne, 1982) (Fig. 14-2). Hatchery stocks also differ in chromosome numbers, so in some cases stocked vs. wild populations can be distinguished on this basis. There are two evolutionarily distinct chromosome lineages in rainbow trout native to western Washington. Populations between and including the Elwha River have 2n = 60 chromosomes, while populations on the central Washington coast have 2n = 58 chromosomes (Ostberg and Thorgaard, 1999). The difference between the two groups is a Robertsonian translocation and a pericentric inversion. Another example of a Robertsonian polymorphism occurs in pink salmon on the northwest coast of North America and the eastern coast of Russia (Gorshkov and Gorshkova, 1981; Phillips and Kapuscinski, 1987). The even year-class has 2n = 52, but in many locations the most common chromosome number among odd-year fish is 2n = 53, with some 2n = 52 and 2n = 54 being found (Phillips and Kapuscinski, 1988). In southeastern Alaska and northern British Columbia, a second chromosome rearrangement (an inversion) has occurred, with fish heterozygous for this inversion (2n = 53) being the most numerous. 3. Tandem Translocations Tandem fusions can cause a change in chromosome number. In this type of translocation, there is a “head to tail” fusion of two uniarmed chromosomes and deactivation of one of the centromeres to produce a large uniarmed chromosome. Heterozygotes for such fusions usually produce inviable gametes. Tandem translocations have been important in the production of the highly derived karyotype found in Atlantic salmon (Hartley and Horne, 1984a,b; Ueda and Kobayashi, 1990). The North American (and European) Atlantic salmon populations have different chromosome numbers (2n = 54 and 2n = 58, respectively), and inspection of the karyotypes reveals that they differ by several independent tandem and fusion translocations (Roberts, 1970) (Fig. 14-3). European Atlantic salmon have been introduced into North America and some North American hatchery stocks are heterozygous for these rearrangements (Phillips, unpublished).

B. VARIATION

IN

CHROMOSOME STRUCTURE

1. Inversions Inversions have also been documented in a number of fishes. including the goodeid Ilyodon fucidens (Turner et al., 1985), Atlantic salmon (Roberts, 1970; R. B. Phillips and S. E. Hartley, unpublished), and chum salmon, Oncorhynchus keta (Kulikova, 1971). In this type of rearrangement, two breaks occur in a

A

B FIGURE 14-2. Karyotypes from rainbow trout (Oncorhynchus mykiss) with different chromosome numbers. (A) Donaldson strain with 2n = 60 and (B) Goldendale strain with 2n = 61. The arrow shows the chromosome pair consisting of one metacentric and two acrocentrics produced by a chromosome fission. Chromosomes were stained with DAPI and the images reversed so that the DAPI bright bands at the centromeres appear dark. From Phillips (unpublished).

278

A

B FIGURE 14-3. Karyotypes from (A) European (2n = 58) and (B) North American (2n = 54) Atlantic salmon. Chromosomes were stained with DAPI and the images reversed so that the DAPI bright bands appear dark. An interstitial DAPI band can be seen near the end of the long arm of the largest metacentric pair in the European karyotype and two interstitial bands in the largest acrocentric chromosome pair in both karyotypes. These bands apparently mark the sites of tandem fusions. The karyotypes differ by several rearrangements including both tandem fusions and centric fusions.

279

280

Ruth B. Phillips

chromosome, and the piece is inverted before fusion. Pericentric inversions that involve the centromere result in a different chromosome arm ratio, so are easier to detect. As described in the previous section, Robertsonian translocations have been followed by inversions to produce derived karyotypes that are specific to certain populations of pink salmon. 2. Duplications and Deletions Small duplications or deletions can cause changes in the size of the short arms in subtelocentrics that are detectable without special staining methods. This type of variation appears to be very common in many fish species (reviewed by Sola et al., 1981). If the duplications involve repetitive DNA, they may be detected using special staining methods (see later). Differences in the amounts of ribosomal DNA (either 18S or 5S) may also cause a difference in the size of the short arm. These differences in copy number can be detected using various methods, as explained later. 3. Reciprocal Translocations Reciprocal translocations do not produce a change in chromosome number. In this type of translocation, portions of the chromosome are exchanged between two pairs. Robertsonian translocations and tandem fusions are easy to detect because they change the diploid chromosome number, but detection of reciprocal translocations requires markers for each chromosome arm or paint probes, neither of which is available for most fishes at the present time.

C. VARIATION

IN

CHROMOSOME BANDING PATTERNS

Several banding techniques have been used to reveal intraspecific polymorphisms in animal chromosomes. Variation in the amount, location, and sequence of constitutive heterochromatin (the highly repetitive DNA found near centromeres and telomeres) can be detected by C banding and various fluorescent banding techniques. Each species has several subclasses of these sequences that vary in the base sequence of the repeating unit (100–600 bp). C banding stains repetitive DNA, regardless of sequence; DAPI and Quinacrine stain primarily AT-rich repetitive DNAs; and CMA3 (chromomycin A3) stains GC-rich repetitive DNAs (reviewed in Comings, 1978; Sumner, 1982). In a number of species, these repetitive DNAs have been cloned and characterized (reviewed in Phillips and Reed, 1996; Phillips, 2001), so specific families of sequences can be detected using molecular cytogenetic methods. In addition to the highly repetitive DNAs, special staining methods can be used to detect the chromosomal location of the ribosomal DNA (rDNA) cistrons, which code for the 5.8S, 18S, and 28S ribosomal RNAs

Chromosome Morphology

281

and are found in multiple copies at the nucleolar organizer regions in eukaryotes, as described later. 1. Nucleolar Organizer Regions Variation in the number and size of the nucleolar organizer regions (NORs) can be detected staining with silver (Howell and Black, 1980) or CMA3, which stains NORs regardless of transcriptional activity in many fish species (Amemiya and Gold, 1986; Phillips and Ihssen, 1985b). There are a few notable exceptions including zebrafish in which NORs are not stained by CMA3 (Gornung et al., 1997). In this species, CMA3 stains only a GC-rich repetitive DNA sequence that is found adjacent to the centromeres on 40% of the chromosomes (He et al., 1992; Phillips, unpublished). Currently, the most accurate technique for identifying the NORs is in situ hybridization with a ribosomal RNA probe. Stock variation in the number and size of NORs has been found in many fish species. Examples include lake trout (Phillips et al., 1989), Arctic char (Phillips et al., 1988), brown trout (Pendas et al., 1993; Castro et al., 1994), various cyprinids (reviewed in Gold, 1984 and Gold and Amemiya, 1986), and poecilids (Sola et al., 1990, 1992). Figure 14-4 shows variation in Arctic char. Variation in the number of NORs per cell can also be documented in interphase cells as shown in Figure 14-1 in which it was used as a method for identification of triploidy. [Accurate assessment of polyploidy from NOR staining is only feasible in species that normally have only 1 pair of NORs per cell and must be done on cells from the same age fish, Phillips et al. (1986)]. Differences between hatchery and wild populations in Atlantic salmon have been detected using variation in NORs (Woznicki and Jankun, 1994a,b). Often closely related species with otherwise similar or identical karyotypes will have different chromosomal locations of NORs. For example, the NOR is located on the second largest chromosome pair in the eastern oyster, Crassostrea virginia, but on the smallest chromosome pair in C. giga, the Pacific oyster (Xu et al., 2001). 2. Heterochromatin Additions and Deletions (C Bands) Variation in the total amount of repetitive DNA found near the centromeres and telomeres is very common in fish species and can be detected using C banding. Intraspecific variation in the size and location of C bands has been observed in natural populations of several fishes including the iwana, Salvelinus leucomaenis (Ueda and Ojima, 1983a), Arctic char (Hartley, 1989), lake trout (Phillips and Ihssen, 1989), and various whitefishes (Jankun et al., 1995) and can serve as population markers (Fig. 14-5). In a number of cases, variation in the amount of heterochromatin on certain chromosome pairs has been used to distinguish hatchery stocks from wild stocks [e.g., Atlantic salmon (Woznicki and Jankun, 1996)].

282

Ruth B. Phillips

FIGURE 14-4. Partial CMA3-stained karyotypes showing locations of NORs in individuals from three different stocks of Arctic char (Salvelinus alpinus). Top row: Northwest Territories; middle row: Labrador; bottom row: Scotland. From Phillips et al. (1988).

3. Variation in Sequence of Repetitive DNAs: Fluorochrome Bands In certain species, some of the heterochromatin blocks stain positively with various fluorochromes, and polymorphisms in the number and location of these bands have been shown to be inherited (Phillips and Ihssen, 1986). Stockspecific variation in the number of these bands has been found for several fish species, including lake trout (Phillips and Ihssen, 1989), Arctic char (Pleyte et al., 1988), and iwana (Ueda and Ojima, 1983b). Minor changes in the amount and sequence of the AT-rich repetitive sequences in closely related fish species may result in bright staining of the heterochromatin

Chromosome Morphology

283

FIGURE 14-5. C-banded karyotype from a male lake trout (Salvelinus namaycush). Note the large C band on the short arm of the X which is missing on the Y chromosome (Phillips and Ihssen, 1985a; Reed and Phillips, 1995).

with quinacrine in one species, but not in another species. For example, most of the large blocks of telomeric heterochromatin on the metacentric chromosomes of lake trout stains with quinacrine, but similar sites in brook trout generally do not. Thus, the parental origin of most of the metacentric chromosomes in the hybrid splake can be identified with Q banding. Repetitive DNAs have been isolated and sequenced from a number of fish species (reviewed in Phillips and Reed, 1996). For example, the sequence of the AT-rich telomeric heterochromatin in lake trout has been isolated and can be detected using in situ hybridization (Reed and Phillips, 1995a). A very similar sequence is also found at the telomeres of some of the chromosomes in other salmonid species, including Arctic char (Hartley and Davidson, 1994; Reed and Phillips, 1997) and rainbow trout (Reed et al., 1997) (see section III, A). Recently, the vertebrate telomeric sequence (TTAGGG)n was localized to telomeres in four bivalve molluscs (Wang and Guo, 2001).

II. STANDARD METHODS FOR DETECTING CHROMOSOME VARIATION Chromosome methods for fish have been reviewed recently (Gold, et al., 1990; Thorgaard and Disney, 1990; Report of the First International Workshop on Fish Cytogenetic Techniques, 1992; Phillips and Reed, 2003). Methods for shellfish are given in Thiriot-Quievreux and Ayraud (1982) and Leitao et al. (1999). In order to examine chromosomes, dividing tissue must be obtained. There are

284

Ruth B. Phillips

invasive methods that require sacrifice of the animal and noninvasive methods that do not. The most widely used methods in the first category include preparations from anterior kidney of juvenile and adult fish, gills of shellfish, and embryos of developing fish and shellfish. Methods in the second category for finfish include blood culture (Hartley and Horne, 1985) and regenerating tissue from finclips. For very small fishes and shellfish, cell lines can be started either from embryos, fins, or gills. Kidney and embryo preparations are best when made after incubation of the cells or tissues in colchicine, a chemical that delays the formation of the spindle. For some species the number of dividing cells (mitotic index) is low and better preparations are obtained if fish are stimulated by injection of the mitogen phytohemaglutinin or cobalt chloride a few days prior to sacrifice. Increased rates of mitosis in embryos may be stimulated by raising the incubation temperature (heat shock).

A. TISSUE SAMPLING DIVIDING CELLS

AND

TREATMENT

TO

OBTAIN

Since it is important to obtain dividing cells in order to examine the chromosomes, cells with high mitotic activity must be examined. This means tissue sampling is limited to tissues with a high mitotic index, or cell division must be induced during in vitro culturing. Tissues with high mitotic index include embryos, anterior kidney (analogous to bone marrow in mammals), and regenerating tissues. In vitro stimulation is usually applied to lymphocyte cultures or tissue cultures using mitogens such as bacterial lipopolysaccharides or phytohemaglutinin.

B. COLCHICINE TREATMENT, HYPOTONIC TREATMENT, AND FIXATION Mitotically active cells must be blocked with colchicine in order to produce condensed chromosomes, exposed to a hypotonic solution to swell the cells to enhance spreading, and fixed, usually in Carnoy’s fixative (3:1, methanol:acetic acid). The suspension of fixed cells is dropped onto slides and dried as described below.

C. SLIDE PREPARATION The method of slide preparation depends on whether solid tissue or cells have been fixed. The method of Kligerman and Bloom (1977) is used for solid tissue, while the standard splash technique is used for fixed cells. Slides made by the

Chromosome Morphology

285

splash technique are usually superior for banding purposes. To obtain good spreading of chromosomes it is important for the slides to dry slowly. We usually allow slides to dry on a slide warmer at 30°–40°C with adequate humidity. (To ensure adequate humidity, use the cover on the slide warmer and place a couple of wet paper towels under the cover with the slides.)

D. STAINING Although chromosome number can be determined without specific banding techniques, detection of many of the chromosome polymorphisms requires special staining methods (see section II, F). For normal staining, a solution of 10% Giemsa made up in a phosphate buffer at pH 6.8–7.0 is applied for 15 min.

E. CHROMOSOME BANDING TECHNIQUES 1. C Banding C bands represent constitutive heterochromatin, which is visualized by selective removal of the less compacted DNA with alkali reagents such as barium hydroxide and/or treatment with salt solutions followed by staining with Giemsa. A commonly used protocol is that of Sumner (1972). 2. Fluorochrome Banding: DAPI and Q Bands These bands can be revealed by staining of chromosome slides with a fluorochrome solution and examination with the fluorescence microscope (see Phillips and Hartley, 1988). Mounting in an antifade solution is important to avoid bleaching of slides. 3. NOR Banding Nucleolar organizer regions (NORs) are the sites of the ribosomal RNA genes (rDNA). Many of these sites are actively transcribed during the cell cycle so that they appear decondensed and may be visualized as secondary constrictions or gaps in the chromosome, if they are located at interstitial locations. The silver staining technique of Howell and Black (1980) detects active NORs because ribosomal proteins are associated with these regions and these are stained with silver. In most fishes, the rDNA is GC-rich and NORs can be visualized regardless of activity in most species using CMA3 and mithromycin. An alternative (DPI, denaturation/propidium iodide staining) based on the thermal characteristics of rDNA has recently been described for the staining of NORs in fishes (Rab et al., 1996). Bands produced with this technique directly correspond with those pro-

286

Ruth B. Phillips

duced by CMA3 staining. However, in some species, both of these techniques stain sites of GC-rich heterochromatin instead of NORs. Thus, for unambiguous identification of NORs it is necessary to use in situ hybridization with probes specific to ribosomal DNA (rDNA). The rDNA probes will also identify small NORs which can be missed by the other methods (Reed and Phillips, 1995b). 4. Replication Banding Replication bands can be obtained in fishes by adding BudR during the last 8 hr of cell culture (Fujiwara et al., 2001) or by injecting fish directly with it several hours before sacrifice (Report of the First International Workshop on Fish Cytogenetic Techniques, 1992). 5. Restriction Enzyme (RE) Banding Restriction enzymes have been used to reveal bands on fish chromosomes (Lloyd and Thorgaard, 1988). A solution containing the enzyme is applied to the slide for several hours and then washed off. Apparently these enzymes can digest tandemly repetitive DNA on chromosomes if the repeats contain a restriction site for the particular enzyme being used. After digestion, the C band which contains repetitive DNA cut by the enzyme will appear faintly stained as compared to C bands containing other repetitive DNAs that lack the proper sites. Thus, different restriction enzymes can be used to identify subclasses of heterochromatin (C bands). C bands with the same RE patterns may have completely different sequences, although they must have at least one restriction site in common. 6. Fluorescence In Situ Hybridization (FISH) A more direct approach to classifying and localizing repetitive DNAs is to isolate, clone, and sequence them. Individual clones can then be labeled as probes to identify the location of these repeats in the genome with FISH (Fig. 14-6) (see section III).

F. PHOTOGRAPHY

AND

ANALYSIS

The chromosomes are examined under the microscope and karyotypes are prepared. The conventional method of preparing karyotypes is to photograph the cells, make prints, and cut out and arrange the chromosomes. An alternative method is to capture digital images directly from the microscope or from negatives for computer-based image analysis and karyotyping. Chromosomes can be analyzed using software packages such as Adobe Photoshop, NIH image, or

Chromosome Morphology

287

FIGURE 14-6. Metaphase of zebrafish chromosomes from the AB strain hybridized with a clone to a specific centromeric DNA (L22) labeled with Spectrum Orange (probe shown in white). This sequence is found at the centromeres of most, but not all chromosome pairs. The size of the bands is also variable.

software especially prepared for karyotyping, such as Cytovision (Applied Imaging, Inc.).

III. MOLECULAR CYTOGENETIC METHODS

A. FLUORESCENCE IN SITU HYBRIDIZATION (FISH) DNA sequences can be localized in metaphase and interphase cells using the technique of fluorescence in situ hybridization (FISH). This method has revolutionized human cytogenetics in the past decade (reviewed in Lichter, 1997). Specific chromosome regions, entire chromosomes, or genomes of one species in interspecific hybrids can be “painted” using the appropriate probes. Chromosome paint probes are available for individual human chromosomes and

288

Ruth B. Phillips

chromosome arms, and probes are being produced for individual chromosome bands. Abnormalities in chromosome number and structure can be identified by examination of interphase cells with these probes. For example, if DNA from chromosome 21 is labeled with an orange fluorochrome, individuals with trisomy 21 will have three orange spots in each interphase nucleus. Fetuses are routinely sexed using a red centromere probe for the X chromosome and a green probe to the repetitive DNA on the Y chromosome. Human paint probes have been used to identify homologous blocks of genes in many other mammals including carnivores, artiodactyls, and primates (reviewed in Chowdhary et al., 1998). The FISH technique involves several steps: probe preparation and denaturation, slide preparation and denaturation, hybridization of probes on the slide, posthybridization washes, detection, and visualization of label. Probes are prepared by labeling specific DNA sequences with biotin, dioxygenin, or, more recently, a nucleotide directly conjugated to a specific fluorochrome. Until very recently, most FISH experiments used one or two probes at a time because direct labeling with different colored fluorochromes was not sensitive enough for single copy genes. In order to localize two genes at a time, one would be labeled with biotin and the other with dioxygenin. The direct-labeled fluorochromes were used only for localizing repetitive DNAs. However, it is now possible to localize single copy genes using direct labeling. This is the result of introduction of digital cameras which can detect lower light levels, marketing of a number of new fluorochromes with restricted nonoverlapping emission spectra, and the availability of large insert clones from BAC and PAC libraries. The order of genes on a chromosome arm can be determined by in situ hybridization with probes to these BACs labeled with different colors (reviewed in Lichter, 1997). A new karyotyping method based on multicolor fluorescence in situ hybridization (M-FISH) has been introduced for human chromosomes (Speicher et al., 1996). Chromosome-specific paint probes have been constructed using DNA from flow-sorted chromosomes of each pair. These are labeled with a different combination of fluorochromes so each chromosome pair can be visualized as a different color. The M-FISH protocol involves acquiring digital images separately with a CCD camera and combining them with software that generates a composite image in which each chromosome is pseudocolored based on its fluorochrome composition. Large clones containing single genes obtained from libraries constructed with a variety of vectors, including yeast (YACs), bacterial artificial chromosomes (BACs), and P1 phage (PACs), are easily visualized on chromosomes with FISH. With M-FISH many probes can be localized in a single experiment. Thus, gene order on individual chromosome arms can be determined, or these can be combined to produce paint probes. The M-FISH technique could be useful for gene localization experiments utilizing in situ hybridization with fish chromosomes because probes to specific centromeres for chromosome identification could be added to the ones for the genes being mapped.

289

Chromosome Morphology

B. APPLICATION OF FISH TO FISH SHELLFISH CHROMOSOMES

AND

The application of fluorescence FISH to fish genetics has been reviewed recently (Phillips and Reed, 1996; Phillips, 2001). Different repetitive and moderately repetitive DNAs including ribosomal RNAs and histones (Pendes et al., 1994) have been localized to centromeres, telomeres, and sex chromosomes of several fishes, and paint probes have been produced using PCR-based microdissection of chromosome regions. Centromeric sequences have been localized in the Pacific oyster (Wang et al., 2001). Single copy genes have been localized in zebrafish and rainbow trout using PAC or BAC clones containing specific genes as probes (Phillips, unpublished) (Fig. 14-7). In pufferfish, the BAC clones are being used to help produce a framework genetic map (Catherine Ozouf-Costaz, personal communication). Rainbow trout probes have been shown to work on other salmonids including chinook salmon, lake trout, and Atlantic salmon. Such probes could be used to make a quick genetic map for other related species and to identify the chromosome arms involved in Robertsonian fusions. Currently, genome projects are under way for fish species, including zebrafish, rainbow trout, Atlantic salmon, medaka, tilapia, catfish, and pufferfish. The large insert libraries prepared for these projects will produce reagents that could be used for paint probes for these species and other related fish species. With appropriate probes it should be possible to identify Robertsonian translocations in interphase cells if the two acrocentric chromosomes involved in the fusion are labeled with different colors. Intraspecific chromosome variation has been documented in rainbow trout, cutthroat trout, and Atlantic salmon, so in the future these probes might be used for stock identification.

IV. DISCUSSION AND CONCLUSIONS Chromosome markers usually do not require as much development time as molecular markers, but they have the disadvantage of requiring living tissue. Once developed for a particular group, molecular methods are more efficient because multiple loci can be scored on a single gel. In addition, although DNA variation of some type is present in virtually every species, intraspecific chromosome variation may not be present. In fact it appears that marine fishes have much more stable karyotypes than freshwater fishes (Singh et al., 1997), so chromosome number variation is rare in them. Although chromosome number variation has been observed in shellfish, no intraspecific variation has been documented. Usually NOR staining will reveal intra-specific variation in almost any species, but it may not be stock-specific. Most species have a unique karyotype, so cytogenetic methods can be very valuable for analysis of hybrid zones between closely related species or

290

Ruth B. Phillips

FIGURE 14-7. Karyotype of rainbow trout chromosomes from the Donaldson strain hybridized with a BAC clone containing the Fgf6 gene labeled with Spectrum Orange (probe shown in white).

subspecies. For fish species with intraspecific chromosome number variation, hatchery fish may have a different chromosome number than wild fish, so in that case it would be possible to estimate the percentage of hatchery vs. wild fish and identify any hybrids between the two using cytogenetic methods. Such variation is especially common in salmonid fishes, and often involves intraspecific translocation polymorphisms. In human genetics, molecular markers diagnostic for specific translocations involved in specific tumors and birth defects have been developed. These types of assays could make identification of chromosome variation more feasible for population studies in the future.

REFERENCES Amemiya, C. T. and Gold, J. R. 1986. Chromomycin A3 stains nucleolar organizer regions of fish chromosomes. Copeia 1986(1): 226–231. Birnstein, V. J., Poletaev, A. I., and Goncharov, B. F. 1993. The DNA content in Eurasian sturgeon species determined by flow cytometry. Cytometry 14: 377–383. Castro, J., Rodriguez, S., Arias, J., Sanchez, L., and Martinez, P. 1994. A population analysis of Robertsonian and AgNOR polymorphisms in brown trout (Salmo trutta). Theoretical Applied Genetics.

Chromosome Morphology

291

Chowdhary, B. P., Taudsepp, T., Fronicke, L., and Scherthan, H. 1998. Emerging patterns of comparative genome organization in some mammalian species as revealed by Zoo–FISH. Genome Research 8: 577–589. Collares-Pereira, M. J. 1985. The “Rutilus alburnoides (Steindachner, 1866) Complex” (Pisces, Cyprindae). II. First data on the karyology of a well-established diploid-triploid group. Arquivos do Museu Bocage. Series A III: 69–89. Comings, D. E. 1978. Mechanisms of chromosome banding and implications for chromosome structure. Ann. Rev. Tenet. 12: 25–46. Cotter, D., O’ Donovan, V., Drumm, A., Roche, N., Nigel, L. E., and Wilkins, N. P. 2002. Comparison of freshwater and marine performances of all female diploid and triploid Atlantic salmon (Salmo salar L.). Aquacul. Res. 33: 43–53. Felip, A., Piferrer, F., Znuy, S., and Carillo, M. 2001. Comparative growth performance of diploid and triploid European sea bass over the first four spawning seasons. J. Fish Biol. 58: 76–88. Fujiwara, A., Nishida-Umehara, C., Sakamoto, T., Okamoto, N., Nakayma, I., and Abe, S. 2001. Improved fish lymphocyte culture for chromosome preparation. Genetica 111: 77–89. Galetti Jr., P. M., Aguilar, C. T., and Molina, W. F. 2000. An overview of marine fish cytogenetics. Hydrobiologia 420: 55–62. Gold, J. R. 1979. Fish cytogenetics. In Fish Physiology, Vol. 8, 1979, pp. 353–404. Gold, J. R. 1984. Silver staining and heteromorphism of chromosomal nucleolus organizer regions in north American cyprinid fishes. Copeia 1984: 133–139. Gold, J. R. and Amemyia, C. T. 1986. Cytogenetic studies in North American minnows (Cyprinidae). XII. Patterns of chromosomal nucleolus organizer region variation among 14 species. Canadian Journal of Zoology 64: 1869–1877. Gold, J. R., Karel, W. J., and Strand, M. R. 1980. Chromosome formulae of North American fishes. Progressive Fish Culturist 42: 10–23. Gold, J. R., Li, Y. C., Shipley, N. S., and Powers, P. K. 1990. Improved methods for working with fish chromosomes with a review of metaphase chromosome banding. Journal of Fish Biology 37: 563–575. Gornung, E., Gabrielli, I., Cataudella, S., and Sola, L. 1997. CMA3-banding pattern and fluorescence in situ hybridization with 18S rRNA genes in zebrafish chromosomes. Chromosome Research 5: 40–46. Gorshkov, S. A. and Gorshkova, G. V. 1981. Chromosome polymorphism of the pink salmon Onocrhynchus gorbuscha (Walb). Tsitologiya 23: 954–960. Guo, X. and Allen, S. K., Jr. 1994. Reproductive potential and genetics of the triploid Pacific oyster, Crassostrea gigas (Thunberg). Biol. Bull. (Woods Hole). 187: 309–318. Guo, X., DeBrosse, G., and Allen, S. K., Jr. 1996. All-triploid Pacific oysters (Crassostrea gigas Thunberg) produced by mating tetraploids and diploids. Aquaculture 142: 149–161. Guo, X. and Allen, S. K., Jr. 1997. Sex and meiosis in autotetraploid Pacific oyster, Crassostrea gigas (Thunberg). Genome 40: 397–405. Hartley, S. E. 1987. The chromosomes of salmonid fishes. Biological Reviews (Cambridge) 62: 197–214. Hartley, S. E. 1989. Chromosome and constitutive heterochromatin distribution in Arctic charr, Salvelinus alpinus (L.) (Pisces: Salmonidae). Genetica 79: 161–166. Hartley, S. E. and Davidson, W. S. 1994. Characterization and distribution of genomic repeat sequences from Arctic char (Salvelinus alpinus). In A. R. Beaumont (ed.), Genetics and Evolution of Aquatic Organisms. Chapman & Hall, London, UK, pp. 271–280. Hartley, S. E. and Horne, M. T. 1982. Chromosome polymorphism in the rainbow trout (Salmo gairdneri) Richardson. Chromosoma 87: 461–468. Hartley, S. E. and Horne, M. T. 1984a. Chromosome relationships in the genus Salmo. Chromosoma 90: 229–237.

292

Ruth B. Phillips

Hartley, S. E. and Horne, M. T. 1984b. Chromosome polymorphism and constitutive heterochromatin in Atlantic salmon, Salmo salar. Chromosoma 89: 377–380. Hartley, S. E. and Horne, M. T. 1985. Cytogenetic techniques in fish genetics. J. Fish Biol. 26: 575–582. He, L., Zhu, Z., Faras, A. J., Guise, K. S. Hackett, P. B., and Kapuscinski, A. R. 1992. Characterization of AluI repeats of zebrafish (Brachydanio rerio). Molecular Marine Biology and Biotechnology 1: 125–135. Howell, W. M. and Black, D. A. 1980. Controlled silver-staining of nucleolus organizer regions with a protective colloidal developer: a 1-step method. Experientia 36: 1014–1015. Jankun, M., Rab, P., Vuorinen, J., and Luczynski, M. (1995). Chromosomal polymorphism in Coregonus lavaretus populations from two locations in Finland and Poland. Arch. Hydrobiol. Spec. Issues Adv. Limnol., 46: 1–11. Kligerman, A. D. and Bloom, S. E. 1977. Rapid chromosome preparations from solid tissues of fishes. J. Fish. Res. Board Can. 34: 266–269. Klinkhardt, M., Tesche, M., and Greven, H. J. 1995. Database of Fish Chromosomes. Westarp Wissenschaften, Magdeburg. 237 pp. Kulikova, N. I. 1971. Intraspecific variability of karyotypes of the chum salmon (Oncorhynchus keta) (Walb.). Journal of Icthyology 11: 977–983. Lichter, P. 1997. Multicolor FISHing: what’s the catch? Trends in Genetics 13: 475–478. Lilysestrom, C. G., Wolters, W. R., Bury, D., Rezk, M., and Dunham, R. A. 1999. Growth, carcass traits, and oxygen tolerance of diploid and triploid catfish hybrids. N. Am. J. Aqua. 61: 293– 303. Lloyd, M. A. and Thorgaard, G. H. 1988. Restriction endonuclease banding of rainbow trout chromosomes. Chromosoma 96: 171–177. Nakamura, H. 1985. A review of molluscan cytogenetic information based on CISMOCH—Computerized index system for molluscan chromosomes. Bivalvia, Polyplacophora and Cephalopoda. Venus Jpn. J. Malacol. 44: 193–225. Ojima, Y. 1980. Chromosomes in evolution of eukaryotic groups. Fish Cytogenetics, Vol. 1, Ch. 2. Ostberg, C. O. and Thorgaard, G. H. 1999. Geographic distribution of chromosome and microsatellite DNA polymorphisms in Oncorhynchus mykiss native to western Washington. Copeia, 1998). Park, G. M., Yong, T. S., Im-Kyung, I. L, and Chung, E. Y. Karyotypes of three species of Corbicula (Bivalvia: Veneroida) in Korea. J. Shellfish Res. 19: 979–982. Pendas, A. M., Moran, P., and Garcia-Vasquez, E. 1993. Multichromosomal location of ribosomal genes and heterochromatin association in brown trout. Chromosome Res. 1: 63–67. Pendas, A. M., Moran, P., and Garcia-Vasquez, E. 1994. Organization and chromosomal localization of the major histone cluster in brown trout, Atlantic salmon and rainbow trout. Chromosoma 103: 147–152. Phillips, R. B. 2001. Application of fluorescence in situ hybridization to fish genetics and genomics. Marine Biotechnology 3: S145–S152. Phillips, R. B. and Hartley, S. E. 1988. Fluorescent banding patterns of the chromosomes of the genus Salmo. Genome 30: 193–197. Phillips, R. B. and Ihssen, P. E. 1985a. Identification of sex chromosomes in lake trout (Salvelinus namaycush). Cytogenetics and Cell Genetics 39(1): 14–18. Phillips, R. B. and Ihssen, P. E. 1985b. Chromosome banding in salmonid fishes: nucleolar organizers in Salmo and Salvelinus. Can. J. Genet. Cytol. 27: 433–440. Phillips, R. B. and Ihssen, P. E. 1986. Inheritance of Q band chromosomal polymorphisms in lake trout (Salvelinus namaycush). J. Hered. 77: 93–97. Phillips, R. B. and Ihssen, P. E. (1989). Population differences in chromosome banding polymorphisms in lake trout (Salvelinus namaycush). Transactions of the American Fisheries Society 118: 64–73.

Chromosome Morphology

293

Phillips, R. B. and Ihssen, P. E. 1990. Genetic marking of fish using variation in chromosomes and nuclear DNA. In N. C. Parker, A. E. Giogri, R. C. Heidinger, D. B. Jerter Jr., E. D. Prince, and G. A. Winans (eds.), Fish Marking Techniques. American Fisheries Symposium 7: 499–513. Phillips, R. B. and Kapuscinski, A. R. 1987. A Robertsonian polymorphism in pink salmon (Oncorhynchus gorbuscha) involving the NOR region. Cytogenetics and Cell Genetics 44: 148–152. Phillips, R. B. and Kapuscinski, A. R. l988. High frequency of translocation heterozygotes in odd year populations of pink salmon (Oncorhynchus gorbuscha). Cytogenetics and Cell Genetics 48: 178–182. Phillips, R. B. and Rab, P. 2001. Chromosome evolution in the Salmonidae (Pisces): an update. Biological Reviews. 76: 1–25. Phillips, R. B. and Reed, K. M. 1996. Application of fluorescence in situ hybridization (FISH) to fish genetics. Aquaculture 140: 197–216. Phillips, R. B. and Reed, K. M. 2003. Chromosome variation. In E. M. Hallerman (ed.), Genetic Principles and Practices for Fisheries Scientists. American Fisheries Society, Ch. 3. Bethesda, MD, pp. 37–58. Phillips, R. B., Pleyte, K. A., and Hartley, S. E. 1988. Stock-specific differences in the number and chromosome positions of the nucleolar organizer regions in Arctic char (Salvelinus alpinus). Cytogenet. Cell Genet. 48: 9–12. Phillips, R. B., Pleyte, K. A., and Ihssen, P. E. 1989. Patterns of chromosomal nucleolar variation in fishes of the genus Salvelinus. Copeia 1980(1): 47–53. Phillips, R. B., Zajicek, K. D., Ihssen, P. E., and Johnson, O. 1986. Application of silver staining to the identification of triploid fish cells. Aquaculture 54: 313–319. Pleyte, K. A., Phillips, R. B., and Hartley, S. E. l989. Q band chromosomal polymorphisms in Arctic char (Salvelinus alpinus). Genome 32: 129–133. Rab, P. and Collares-Pereira, M. J. 1995. Chromosomes of European cyprinid fishes (Cyprinidae, Cypriniformes): a review. Folia-Zoologica 44: 193–214. Rab, P., Reed, K. M., Ponce De Leon, A., and Phillips, R. 1996. Denaturation/propodium iodide staining: a new method for detecting nucleolar organizer regions (NORs) in fish chromosomes. Biotechnic. Histochem. 71: 157–162. Reed, K. M. and Phillips, R. B. 1995a. Molecular characterization and cytogenetic analysis of highly repeated DNAs of lake trout, Salvelinus namaycush. Chromosoma 104: 242–251. Reed, K. M. and Phillips, R. B. 1995b. Molecular cytogenetic analysis of the double CMA3 chromosome in lake trout, Salvelinus namaycush. Cytogenetics and Cell Genetics 70: 104–107. Reed, K. M. and Phillips, R. B. 1997. Polymorphism of the nucleolus organizer region (NOR) on the putative sex chromosomes of Arctic char (Salvelinus alpinus) is not sex related. Chromosome Research 5: 221–227. Reed, K. M., Dorschner, M. O., and Phillips, R. B. 1997. Characteristics of two salmonid repetitive DNA families in rainbow trout (Oncorhynchus mykiss). Cytogenetics and Cell Genetics 79: 184–187. Report of the First International Workshop on Fish Cytogenetic Techniques, Concarneau, France, September, 1992. (Available from C. Ozouf-Costaz, Laboratorie d’Ichtyologie, MNHN 43 rue Cuvier, 75231, Paris Cedex 05, France. Roberts, F. L. 1970. Atlantic salmon (Salmo salar) chromosomes and speciation. Transactions of the American Fisheries Society 99: 105–111. Sheenhan, R. J., Shasteen, S. P., Suresh, A. V., Kapuscinski, A. R., and Seeb J. E. 1999. Better growth in all female diploid and triploid rainbow trout. Trans. Am. Fish. Soc. 128: 491–498. Singh, L. B., Nagpure, N. S., Singh, S. P., and Pandey, O. P. 1997. Hydrobiologia 420: 55–62. Sola, L., Cataudella, S., and Capanna, E. 1981. New developments in vertebrate cytotaxonomy. III. Karyology of bony fishes: a review. Genetica 54: 285–328.

294

Ruth B. Phillips

Sola, L., Iaselli, V., Rossi, A. R., Rasch, E. M., and Monaco, P. J. 1990. Cytogenetics of bisexual/ unisexual species of Poecilia. I. C-bands, Ag-NOR polymorphisms and sex chromosomes in three populations of Poecilia latipinna. Cytogenet. Cell Genet. 53: 148–154. Sola, L., Rossi, A. R., Iaselli, V., Rasch, E. M., and Monaco P. J. 1992. Cytogenetics of bisexual/ unisexual species of Poecilia. II. Analysis of heterochromatin and nucleolar organizer regions in Poecilia mexicana mexicana by C-banding and DAPI, quinacrine, chromomycin A3 and silver staining. Cytogenet. Cell. Genet. 60: 229–235. Speicher, M. R., Ballard, G. S., and Ward, D. C. 1996. Karyotyping human chromosomes by combinatorial multi-fluor FISH. Nat. Genet. 12: 368–375. Sumner, A. T. 1972. A simple technique for demonstrating centromeric heterochromatin. Exp. Cell Res. 75: 304–306. Sumner, A. T. 1982. The nature and mechanisms of chromosome banding. Cancer Genetics and Cytogenetics 6: 59–87. Thiriot-Quievreux, C. and Ayraud, N. 1982. Les caryotypes de quelques epseces de Bivalves et de Gasteropodes marins. Mar. Biol. 70: 165–172. Thorgaard, G. H. 1976. Robertsonian polymorphism and constitutive heterochromatin distribution in the chromosomes of the rainbow trout. Cytogenet. Cell Genet. 17: 174–1190. Thorgaard, G. H. 1983. Chromosomal differences among rainbow trout populations. Copeia 1983: 650–662. Thorgaard, G. H. and Allen, S. K. 1987. Chromosome manipulation and markers in fishery management. In N. Ryman and F. Utter (eds.), Population Genetics and Fishery Management. Ch. 13: 319–331. University of Washington Press, Seattle, WA, and London. Thorgaard, G. H. and Disney, J. E. 1990. Chromosome preparation and analysis. Methods in Fish Biology, Ch. 6. American Fisheries Society, Bethesda, MD. Tillmann, B. 2001. Use of sterile triploid Atlantic salmon (Salmo salar L.) for aquaculture in New Brunswick, Canada. ICES-J. Mar. Sci. 58: 525–529. Turner, B. J., Grudzen, T. A., Adkinsson, K. P., and Worrell, R. A. 1985. Extensive chromosomal divergence within a single river basin in the goodeid fish, Ilyodon furcidens. Evolution. 39: 122–134. Ueda, T. and Kobayashi, J. 1990. Karyotype differentiation of Atlantic salmon, Salmo salar, especially the sequential karyotype change. La. Kromosomo II-58: 1967–1972. Ueda, T. and Ojima, Y. 1983a. Karyotypes with C banding patterns of two species in the genus Salvelinus of the family Salmonidae. Proc. Jap. Acad. 59: Ser B: 343–346. Ueda, T. and Ojima, Y. 1983b. Geographic and chromosomal polymorphisms in the iwana (Salvelinus leucomaenis). Proc. Jpn. Acad. 59(B)8: 259–262. Wang, Y. and Guo, X. 2001. Chromosomal mapping of the vertebrate telomeric sequence TTAGGG)n in four bivalve molluscs by fluorescence in situ hybridization. J. Shellfish Res. 20: 1187–1190. Wang, Y., Xu, Z., and Guo, X. 2001. A centromeric satellite sequence in the Pacific oyster (Crassostrea gigas Thunberg) identified by fluorescence in situ hybridization. Mar. Biotech. 3: 486–492. Woznicki, P. and Jankun, M. 1994a. Chromosome polymorphism of Atlantic salmon (Salmo salar) from the Dzwina River, Baltic Sea Basin: arm length and NOR location variation of the eighth chromosome. Can. J. Zool. 72: 364–367. Woznicki, P. and Jankun, M. 1994b. New cytotype of a highly polymorphic NOR bearing chromosome pair in Atlantic salmon Salmo salar (L.) Cytobios 79: 59–62. Xu, Z., Guo, X., Gaffney, P. M., and Pierce, J. 2001. Chromosomal location of the major ribosomal RNA genes in Crassostrea virginica and Crassostrea gigas. Veliger 44: 79–83. Yang, H. P., Li, L., and Guo, X. M. 2001. Preliminary study on inducing polyploidy in Japanese scallop (Patinopecten yessoensis) by cytochalasin B. Acta Zoologica Sinica 47: 459–464.

CHAPTER

15

Genetic Analysis: Allozymes M.-L. KOLJONEN* AND R. WILMOT† *Finnish Game and Fisheries Research Institute, Helsinki, Finland † United States Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Juneau, Alaska, USA

I. II. III. IV. V.

Introduction Electrophoresis Allele Frequencies Mixed-Stock Analysis Advantages and Limitations of the Allozyme Data in MSA VI. Factors Affecting the Reliability of Allozyme-Based MSA VII. Examples of Application of Allozymes to MixedStock Analysis References

I. INTRODUCTION Allozymes are the electrophoretic expression of alternative gene forms (alleles) of functionally similar enzymes produced by a gene or gene locus. The combination of two alleles at a particular gene locus determines the genotype of an individual. By determining the genotypic composition of an individual or a population over several loci, we can establish a multilocus genetic mark that may distinguish that individual or population from others (Pella and Milner, 1987). Population and stock are here defined as a group of interbreeding individuals that are sufficiently isolated from other groups of individuals of the same species for some level of genetic differentiation to have occurred. Numerous inheritance studies on fish have established that the observed allozyme variation follows simple known rules of Mendelian inheritance according to which two alleles of each gene locus are inherited from both parents (May et al., 1980; Kornfield et al., 1981). Because genes are inherited, the genetic structure of populations is relatively stable over generations and can Stock Identification Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

295

296

M.-L. Koljonen and R. Wilmot

be determined by standard population genetics methods. If the genetic differentiation is large enough between populations or stocks, analytical methodologies will permit determination of the stock proportions in mixed-stock fisheries (Pella and Milner, 1987). Mixed-stock analysis (MSA) using allozyme variation was first developed for Pacific salmon (Oncorhynchus sp.) (Grant et al., 1980, Fournier et al., 1984, Milner et al., 1985). The greatest number of MSA studies have indeed been conducted on Pacific salmon (see Shaklee and Phelps, 1990; Shaklee et al., 1990a; Utter, 1991; Begg et al., 1999), but it has also been used with lake trout (Salvelinus namaycush) (Perkins and Krueger, 1995, Marsden et al., 1989), brown trout (Salmo trutta) (Krueger and May, 1987), Atlantic salmon (Salmo salar) (Koljonen, 1995; Koljonen and Pella, 1997), and Dolly Varden (Salvelinus malma) (Krueger et al., 1999);

II. ELECTROPHORESIS Allozyme variation is studied by enzyme electrophoresis (Shaw and Prasad, 1970; Harris and Hopkinson, 1976; Siciliano and Shaw, 1976; Aebersold et al., 1987), a method based on the separation of electrically charged protein molecules, usually enzymes, in an electric field. Mutation in a protein-coding gene locus causes a change in the DNA nucleotide order, which then changes the amino acid composition of the enzyme protein. Of the 20 potential amino acids that make up protein molecules, five carry an electrical charge—three a positive and two a negative one. Therefore, different protein molecules have different net electrical charges. In practice, about one-third of DNA nucleotide changes are noticeable as charge changes at the enzyme level. Each individual may have several gene loci producing slightly different forms (called isozymes) of the same functional enzyme. These different loci may be active in the same tissue or they may be expressed only in certain tissues. For example, the five loci of lactate dehydrogenase (LDH) enzyme are tissuespecific in salmonid fishes, usually such that LDH-1* and LDH-2* are mainly active in muscle tissue, LDH-3* in the heart, LDH-4* in the liver, and LDH-5* in the eyes (Shaklee et al., 1973). In Pacific salmon, samples of heart, liver, muscle, and eye are usually required for the acquisition of data on the full range of usable loci. As enzymes begin to degrade immediately after the death of organisms, fresh or frozen tissue is used for electrophoresis. The tissues are macerated in a buffer solution and introduced into a gel of starch, agarose, cellulose acetate, or other medium with absorbent wicks (Fig. 15-1). Each wick represents a single tissue of an individual fish, and usually 25 to 40 wicks are used per gel. An electric

297

Genetic Analysis: Allozymes 1 2

Frequency of allele A = 12/20 = 0.6 Frequency of allele B = 8/20 = 0.4

3 4

5 7

6 8

9

(+)

10

1 1

2

3

4

5

6

7

8

9 10

(-)

A

AA AB AA AA AB AB AB BB AB AB

D

Tissue extract

Specific staining procedures 1

B

(+) Power Supply 2

1 2 3 4 5 6 7 8 9 10 3 4 5 6 7 8 9 10

(-)

C FIGURE 15-1. Standard steps for obtaining allele frequency data from electrophoresis. (A) Crude protein is extracted from tissue. (B) Extract from each fish is introduced individually to gel by filter paper inserts. (C) Different allozymes move different distances in an electric field. (D) Allozymes are made visible with specific stains, the genotypes (AA, AB, BB) are interpreted, and the allele frequencies are calculated. Modified from Utter et al. (1987). Reprinted by permission from Washington Sea Grant Program, University of Washington.

field is applied over the gel, normally for 4 to 8 hr, and the enzyme molecules migrate through the gel at a rate related to their electrical charges. The shape and weight of the molecules may affect the migration rate to some extent. The gel is then sliced horizontally into thin slabs (usually 3–5). Each slab is individually stained for a different enzyme, and an enzyme-specific banding pattern appears that can be read and interpreted for each individual. For interpretation of the banding patterns, the molecular structure of the enzyme and the locus structure should be known (Utter et al., 1974, 1987). A standardized system for interpretation and locus nomenclature has been developed by Shaklee et al. (1990b). The end product of electrophoresis is the multilocus genotype of each individual organism. The simplest banding patterns occur for monomeric protein molecules, which are composed of single subunits. If a locus for monomeric proteins has two

298

M.-L. Koljonen and R. Wilmot

codominant alleles, they are usually designated by A and B. In diploid individuals, in which one allele is inherited from each parent, three potential genotypes are possible—AA, AB, and BB. The homozygote individuals with either AA or BB genotypes each produce a single band because of their different migration rates through the gel. The heterozygote individual with the AB genotype displays two bands since it produces both A and B proteins (Fig. 15-1). More than two alleles may occur at the same locus in the population, but only two of the alleles can exist at any locus in any diploid individual. If three alleles occur in the population, one individual could potentially have an AA, AB, BB, AC, BC, or CC genotype at one locus. The number of alleles at variable gene loci in fish populations is usually 2 to 3 in allozyme data, but it can be as high as 11 in pink (Oncorhynchus gorbuscha) and chum salmon (Oncorhynchus keta). In invertebrates, 6 to 8 alleles are more common, although there are often 8 to 15. In DNA microsatellite data on fishes, the number of alleles easily exceeds 30. The interpretation of banding patterns becomes much more complex when we are dealing with a multimeric enzyme protein molecule composed of two or more protein subunits. An enzyme consisting of two subunits is called a dimer. If a heterozygote individual (AB) with two different alleles produces two types of protein subunit, three types of dimeric protein molecule (AA, BB, BB) could be formed, and thus a three-banded gel pattern will be displayed for all heterozygote individuals. The higher the number of loci, subunits, and alleles, the more complex and difficult to interpret the banding patterns may become. A good overview of the interpretation of complex banding patterns has been given by Utter et al. (1987). Once several individual loci have been analyzed separately, the multilocus genotype for these loci can be formed for each individual. For example, a 7-locus genotype for one individual gets the form A1A2B1B1C2C2D1D3E1E2F2F3G1G1, where a letter designates the locus and the subindex the particular allele. Separate loci can usually be assumed to be independent variables, and allozymes are thought to be selectively relatively neutral. The use of multilocus genotypes causes the potential number of genotype combinations to increase very rapidly. The number of possible multilocus genotypes for loci with two alleles increases as 3L when several loci (L) are analyzed. For five loci with two alleles, the number of different potential genotypes is 243. When the number of alleles and loci is high, the number of possible genotypes will exceed the population size. The power of different identification methods is indeed based on the differences in multilocus genotype distributions among populations.

Genetic Analysis: Allozymes

299

III. ALLELE FREQUENCIES The proportions of each allele at each locus—the allele frequencies—are used to describe and determine the population structure (Fig. 15-1). A population is assumed to be sufficiently reproductively isolated from other populations of the same species for some degree of genetic divergence to have taken place. It is extremely rare to find fixed or diagnostic allele differences between stocks of the same species, that is, only an A allele in one stock and only a B allele in another. Such differences usually denote separate species. Absolute classification of individuals into different populations of the same species is therefore rarely possible with allozyme data. According to genetic theory, a known relationship called Hardy–Weinberg equilibrium should exist between allele frequencies and genotype frequencies in a diploid, random mating population where random union of gametes is assumed and the alleles are inherited according to Mendelian rules. In addition, migration, mutation, selection, or random changes due to very small population size (genetic drift) must not have a marked effect on the genotype frequencies of the population. The theory predicts that if a population is in Hardy–Weinberg equilibrium, the proportions of the genotypes can be determined from the allele frequencies. For a two-allelic locus where p is the frequency of allele A and q is the frequency of allele B (p + q = 1), the expected genotype frequencies can be computed with the formula (p + q)2. This gives the three terms: p2 for the proportion of genotype AA, 2pq for genotype AB, and q2 for genotype BB. For multiallelic loci where pi is the frequency of allele Ai the formula for expected genotype frequencies can be extended to the form (p1 + p2 + . . . + pm)2. This gives m(m + 1)/2 terms corresponding to the frequencies of both possible homozygotes, AiAi, and heterozygotes, AiAj. When independence of loci is assumed, the distributions for multilocus genotypes are products of the single-locus distributions. Significant deviations from expected genotype distributions could mean that some of the assumptions are not true. The Hardy–Weinberg equilibrium test can then be run to determine whether marked mixing or substructure occurs in the populations studied. Mixing of genetically differentiated populations results in an excess of heterozygotes in the next generation. Subpopulation structure could cause a deficiency in the proportion of heterozygote individuals in relation to the expected numbers and is called the Wahlund effect. The most important feature of the Hardy–Weinberg theorem is that it enables the genotype distributions at a locus in a population to be expressed entirely in terms of allele frequencies. Assuming independence of loci and Hardy–Weinberg equilibrium, it is straightforward to compute the multilocus genotype distributions, including all the potential multilocus genotypes occurring in the populations from the allele frequencies at that locus. These multilocus genotype

300

M.-L. Koljonen and R. Wilmot

distributions are then used to evaluate probabilities of sources for mixture individuals in stock identification methods. This advantage of using Hardy– Weinberg equilibrium can also be applied to other Mendelian inherited data such as DNA microsatellite data, but not to maternally inherited mitochondrial DNA data. Allele frequencies are also used to calculate numerous other statistics measuring the amount of genetic differentiation and diversity among populations, that is, genetic distances (Nei, 1987) and mean heterozygosities (Wright, 1969). Methods such as the gene diversity analysis of Chakraborty (1980) and the standardized allele frequency variance among populations, the FST analysis of Wright (1965) or Weir and Cockerham (1984), can be used to measure the significance of population differentiation. FST can also be used to assess the probability of successful identification of populations in mixed stock fisheries. Simulations with the true baseline data and test analyses are recommended for estimating the potential resolution power of the data set before application to true situations.

IV. MIXED-STOCK ANALYSIS Successful MSA depends on the degree of genetic differentiation among stocks. Calculation of the above population genetic statistics will usually provide a good indicator of whether the degree of differentiation among stocks is sufficient for MSA. Traditional methods of stock identification based on maximum-likelihood statistics are not used to identify the stock origin of individual fish. They can, however, provide fairly reliable estimates of the proportions of stocks contributing to a mixture. The estimation methods include the EM algorithm (Millar, 1987), GIRLSEM (Masuda et al., 1991) and SPAM (Debevec et al., 2000). MSA assumes that allele frequency data are available for all possible major stocks contributing to the mixture (the baseline) and that the frequencies are in Hardy–Weinberg equilibrium. Maximum likelihood estimation (MLE) is based on the idea that, given the multilocus genotypic distributions in c baseline stocks (pi, i = 1, 2, . . . , c, c

 pi = 1 ),

and the distribution observed in the catch mixture sample, the

i= 1

proportions of baseline stocks composing the stock mixture are best estimated as those for which the observed mixture genotype distribution is most probable. The probability of sampling a particular multilocus genotype h from any mixture c

is l h =

 pi g hi, where g

hi

is the frequency of that genotype in the baseline stock

i= 1

i. The probability of the sample, or the likelihood function that is maximized with respect to the pi’s, is obtained as the product of the individual probabilities

301

Genetic Analysis: Allozymes

of the N observed genotypes: L = ’ lmh h , where mh is the observed number of h

fish of the multilocus genotype h in the catch sample. The baseline multilocus genotype frequencies used in the estimation process are calculated from the observed baseline allele frequencies by assuming Hardy–Weinberg genotypic distribution and independence among loci (Pella and Milner, 1987). A new method of MSA based on Bayesian statistics has been developed by Pella and Masuda (2001). According to the Bayesian method, an informative prior for genetic characters of the separate stocks in a mixture is derived from baseline samples. In addition, a neutral, low information prior is used for the stock proportions in the mixture. A Gibbs sampler—the data augmentation algorithm—is used to alternatively generate samples from the posterior distributions of genetic parameters of the baseline stocks and for the stock proportions in the mixture. The posterior distribution incorporates the information about genetic characters in the baseline stocks, including relatedness of stocks, with that in the stock mixture sample to better estimate genotypic composition of the separate stocks. The posterior distribution of stock proportions is then used as an estimator of stock composition. Given the inherent bias of MLE to underestimate predominant components, the Bayesian mode is a sensible estimator with no logical counterpart in the MLE method other than the MLE point estimate. The program also provides posterior probabilities of the source population for each individual in the mixture. These probabilities can be used to assign the individuals to a particular stock. Other programs currently available can also be used for plane individual assignment, for example, GENECLASS (Cornuet et al., 1999), WHICHRUN (Banks and Eichert, 2000) and STRUCTURE (Pritchard et al., 2000). GENECLASS and WHICHRUN assign individuals to a population without regard to the mixture composition other than assuming equal proportions of stocks in the mixture.

V. ADVANTAGES AND LIMITATIONS OF THE ALLOZYME DATA IN MSA Allozyme data have several advantages over other types of genetic data. The cost of analysis is low and a large number of samples can be analyzed in a relatively short time as compared with DNA methods. Laboratory analysis is simple and does not require complicated techniques. Genetic tags are inherited traits and thus the environment does not change their expression. Genotypes are discrete characteristics; interpretation is therefore relatively unambiguous and differences can be quantified. In most cases, loci can also be assumed to be independent (not correlated) variables, which simplifies mathematical treatment. Genetic differences are relatively stable over time and from generation to generation (Waples, 1990).

302

M.-L. Koljonen and R. Wilmot

The advantages of genetic tags over external tags are the following: no costs associated with actual tagging, no loss of tags, and no bias due to possible effects of the external tags. Moreover, all fish are tagged for life, which enables studies to be conducted on fishes that cannot be tagged by other methods, for example, wild fish in remote areas or newly hatched fish in releasing programs (Mathias et al., 1992). In genetic stock identification, the time and place of sampling can be chosen more freely and precisely than with external tagging as they are not dependent on preceding tag and release programs. Further, genetic stock identification does not depend on fishermen for the return of external tags. The most obvious limitation to the wider use of allozyme analysis in stock identification is that sufficient reproductive isolation must occur among contributing stocks for temporally stable genetic differences in allele frequencies to arise. Statistically significant differences in allele frequencies often occur, but quantitatively they may be too small for sufficient stock separation. For species formed of several clearly different reproduction units, such as salmonids, allozyme analysis will usually provide a useful tool. Even a relatively small amount of gene flow may dilute the differences, especially in the loci presumed to be selectively rather neutral. Where allozyme information alone is not sufficient for accurate stock identification, additional information may be necessary, for example, parasite infection rates and/or scale pattern characteristics (Rutherford et al., 1988; Wood et al., 1989; Wood et al., 1988; Wilmot et al., 1999; Pella et al., 1998) and smolt age distributions (Koljonen and Pella, 1997). For cases in which identification of individual stocks has failed, groups of genetically similar stocks have been estimated accurately enough for management purposes (Seeb and Crane, 1999). Intentional changes in allele frequencies caused by controlled matings in hatchery production can create an identifiable population. These changes need not be dramatic, but a relatively small increase in the frequency of a rare allele may improve separation of the stock of interest (Seeb et al., 1986, 1990).

VI. FACTORS AFFECTING THE RELIABILITY OF ALLOZYME-BASED MSA Special interest should be focused on estimation reliability. The method based on maximum likelihood will always produce an estimate, but the usefulness of these estimates depends on the criteria set for their accuracy and precision. In MLE, errors in composition estimates may derive from the mixture sample or the baseline sample, or both. When the sample sizes are large, these two errors are additive in theory (Pella and Robertson 1979; Millar, 1987). The bias is greatest when genetically similar stocks differ greatly in abundance (Millar, 1987).

Genetic Analysis: Allozymes

303

The variation resulting from mixture sampling depends on the size of the sample and the stock composition of the sample. According to Wood et al. (1987), the critical sample size is about 40 fish per stock for ordinary allozyme data; below that, the reliability of the estimates is greatly reduced. In general, it is known that with small mixture samples the contributions of dominant stocks are underestimated and those of minor stocks overestimated. Baseline sampling tends to be more problematic than mixture sampling. The amount of variation and bias in the estimates of stock proportions may be affected by several factors: coverage of the baseline data, conformity to Hardy–Weinberg expectations, temporal variation in allele frequencies, sample sizes, number of loci, genetic differentiation among stocks, and strategies for pooling the individual stock proportion estimates. An important issue is whether all the contributing stocks are represented in the baseline data (Smouse et al., 1990). Errors in baseline sampling can also arise if individual stocks are composed of several breeding units, either natural or hatchery ones, or if variation in the allele frequencies of different year-classes is marked. This may cause deviations from the Hardy–Weinberg distributions in the stocks and may also mean that the allele frequencies observed are not representative of the stock as a whole. Such problems are most likely to occur in small natural stocks or in hatchery stocks with changing brood fish and breeding ranges. Waples (1990) analyzed the implications of temporal allele frequency changes for MSA and observed that, in general, temporal changes should be taken into account as potential sources of error. However, any effects of changes caused by genetic drift can be compensated for to a marked extent by collecting baseline data over several years. The importance of repeated sampling depends on the life history of the species concerned and on the degree of overlapping in the year-classes. The Bayes program captures information about allele frequencies from mixture samples. Therefore, if annual mixture sampling from fisheries capturing the same source populations were analyzed sequentially by date of sampling, or even pooled for the season, the Bayesian method could be used to detect and estimate long-term changes in allele frequencies among contributing stocks. This would eliminate or reduce the baseline sampling needs. According to Wood et al. (1987), a critical value for the baseline sample size was about 40 fish per stock. The number of fish needed per stock, however, also depends on the level of genetic differentiation between the stocks. Both the number of loci and the level of differentiation between the stocks affect the discriminatory power of the baseline data. The number of loci that have been analyzed in studies of Pacific salmonids has been relatively high (e.g. Shaklee et al., 1990a, 14–22 loci; Brodziak et al., 1992, 15 loci). In the event of small separation, performance of the analysis can be improved by increasing the number of

304

M.-L. Koljonen and R. Wilmot

loci or the size of the mixture sample. In some cases, however, only pooled estimates of stock proportions are obtained.

VII. EXAMPLES OF APPLICATION OF ALLOZYMES TO MSA Genetic results have been used for Pacific salmonids both in season and postseason to determine fishery openings and closures with a view to providing harvest benefits or meeting conservation needs, to address catch allocation and equity issues among user groups and between countries, to provide data for in-season run-size updates, and to investigate migration patterns and timing (Shaklee et al., 1999). The Columbia River Chinook salmon (Oncorhynchus tshawytscha) gill-net fishery has been regulated on the basis of allozyme information. Mixed-stock analysis is based on 27-stock, 22-loci allozyme baseline, and MLE (Shaklee 1991; Shaklee et al., 1999). The Chinook fishery in the lower river was limited to ensure that adequate numbers of upper river stocks would return to their spawning grounds. The harvest rate in the lower river was set at 4.1% of the total upriver run. Since 1990, the results of in-season allozyme analyses together with daily counts of the total fish harvest have been the primary data used to manage the fishery. Estimates of the cumulative impacts on upper river stocks are determined, and when the harvest rate approaches the maximum acceptable impact level (4.1%), based on preseason run-size predictions, the fishery is closed. The pink salmon fishery (Oncorhynchus gorbuscha) in the Fraser River provides another example of the systematic use of allozyme analysis in fisheries management (Shaklee et al., 1999). In 1987, the Pacific Salmon Commission began to use genetic stock mixture analysis to identify Fraser River pink salmon in catches from Alaska southward. In-season tissue samples were generally analyzed within 3 days, thus providing fishery managers with current information on Fraser River pink salmon contributions to important fisheries and also with a tool for the implementation of management measures to meet in-season catch and escapement requirements. Postseason estimates of actual Fraser River pink salmon catches by country and within user groups in the United States were compared with the allocated catches, and any shortfalls or overages in allocation by country or user group were to be remedied in future catch paybacks. More examples of the application of allozyme variation to MSA of Pacific salmon are given in Table 15-1.

TABLE 15-1.

Examples of Mixed-Stock Analysis Studies on Pacific Salmon

Species Chum salmon (O. keta)

Chinook salmon (O. tshawytscha)

Pink salmon (O. gorbuscha)

305

Sockeye salmon (O. nerka)

Region

No of loci/ populations

Eastern Bering Sea

20/77

Aleutian Islands

20/77

North Pacific Ocean

20/77

Central Bering Sea

16/273

Yukon River (U.S./Canada) Fraser River (Canada)

19/31

Vancouver Island (Canada) Yukon River (U.S./Canada) Columbia River (U.S.) North Pacific Ocean

22/21 10/14 20/77

Washington State Coast Fraser River (Canada)

14/23

Central Bering Sea

19/95

North Pacific Ocean

14/165

Southcentral Alaska

6/13

Results Determined region of origin of chum salmon by catch in Pollock fishery Determined region of origin of chum salmon by catch in commercial sockeye salmon fishery Region of origin of chum salmon harvest by illegal high seas drift netters Region of origin of chum salmon seized from Russian trawler in U.S. waters Determined catch of Canadian origin chum salmon in U.S. fishery at mouth of Yukon River Estimated abundance of Fraser River chum salmon in test fishery in Johnstone Strait Estimated origin of chum salmon in mixed-stock fishery near Vancouver Island Determined catch of Canadian origin Chinook salmon in U.S. fishery at mouth of Yukon River Determined in-season origins of Chinook salmon in mixed-stock fishery at mouth of Columbia River Region of origin of Chinook salmon harvest by illegal high seas drift netters Determined stock composition of Chinook salmon caught in coastal troll fishery Determined in-season origins of pink salmon harvested in U.S. and Canadian mixed-stock fisheries Region of origin of pink salmon seized from Russian trawler in U.S. waters Region of origin of sockeye salmon harvest by illegal high seas drift netters Determined origin of sockeye salmon in mixed-stock fishery in Cook Inlet, Alaska

Reference Wilmot et al., 2000 Crane and Seeb, 2000 Wilmot et al., 1999 Wilmot et al., 2000 Kondzela et al., 2002 Wilmot et al., 1992 Beacham et al., 1985 Fournier et al., 1984 Wilmot et al., 1992 Shaklee et al., 1999 Wilmot et al., 1999 Wilmot et al., 2000 Milner et al., 1983 Shaklee et al., 1999 Kondzela et al., 2002 Wilmot et al., 2000 Grant et al., 1980

306

M.-L. Koljonen and R. Wilmot

REFERENCES Aebersold, P. B., Winans, G. A., Teel, D. J., Milner, G. B., and Utter, F. M. 1987. Manual for starch gel electrophoresis: A method for the detection of genetic variation. NOAA Technical Report NMFS 61, Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service. 19 pp. Banks, M. A. and Eichert, W. 2000. WHICHRUN (Version 3.2): a computer program for population assignment of individuals based on multilocus genotype data. J. Hered. 91: 87–89. Beacham, T. D., Withler, R. E., and Gould, A. P. 1985. Biochemical genetic stock identification of chum salmon (Oncorhynchus keta) in southern British Columbia. Can. J. Fish. Aquat. Sci. 42: 437–448. Begg, G. A., Friedland, K. D., and Pearce, J. B. 1999. Stock identification—its role in stock assessment and fisheries management: a selection of papers presented at a symposium of the 128th annual meeting of American Fisheries Society in Hartford, Connecticut, 23–27 August 1998. Fish. Res. 43 (1–3), 249 pp. Brodziak, J., Bentley, B., Bartley, D., Gall, G. A. E., Gomulkiewicz, R., and Mangel, M. 1992. Test of genetic stock identification using coded wire tagged fish. Can. J. Fish. Aquat. Sci. 49: 1507–1517. Chakraborty, R. 1980. Gene-diversity analysis in nested subdivided populations. Genetics 96: 721–726. Cornuet, J.-M., Piry, S., Luikart, G., Estoup, A., and Solignac, M. 1999. New methods employing multilocus genotypes to select or exclude populations as origins of individuals. Genetics 153: 1989–2000. Crane, P. A. and Seeb, L. W. 2000. Genetic analysis of chum salmon harvested in the South Peninsula, post June fishery, 1996–1997. Alaska Department of Fish and Game, Anchorage, Alaska. Regional Information Report No. 5J00–05. Debevec, E. M., Gates, R. B., Masuda, M., Pella, J., Reynolds, J., and Seeb, L. W. 2000. SPAM (Version 3.2): statistics program for analyzing mixtures. J. Hered. 91: 509–510. Fournier, D. A., Beacham, T. D., Riddell, B. E., and Busack, C. A. 1984. Estimating stock composition in mixed stock fisheries using morphometric, meristic, and electrophoretic characteristics. Can. J. Fish. Aquat. Sci. 41: 400–408. Grant, W. S., Milner, G. B., Krasnowski, P., and Utter, F. M. 1980. Use of biochemical genetic variants for identification of sockeye salmon (Oncorhynchus nerka) stocks in Cook Inlet, Alaska. Can. J. Fish. Aquat. Sci. 37: 1236–1247. Harris, H. and Hopkinson, D. A. 1976. Handbook of Enzyme Electrophoresis in Human Genetics. American Elsevier, New York. Koljonen, M.-L. 1995. Distinguishing between local and migrating Atlantic salmon (Salmo salar L.) stocks by genetic stock composition analysis. Can. J. Fish. Aquat. Sci. 52: 665–674. Koljonen, M.-L. and Pella, J. J. 1997. The advantage of using smolt age with allozymes for assessing wild stock contributions to Atlantic salmon catches in the Baltic Sea. ICES J. Mar. Sci. 54: 1015–1030. Kondzela, C. M., Hawkins, S. C., Guthrie III, M., and Wilmot, R. L. 2002. Origins of salmon seized from the F/V Petropavlovsk. (NPAFC Doc. 598) Auke Bay Fisheries Laboratory, Alaska Fisheries Science Center, NMFS, NOAA, 11305 Glacier Highway, Juneau, AK 99801–8626. 10 pp. Kornfield, I., Beland, K. F., Moring, J. R., and Kircheis, F. W. 1981. Genetic similarity among endemic Arctic char (Salvelinus alpinus) and implications for their management. Can. J. Fish. Aquat. Sci. 38: 32–39. Krueger, C. C. and May, B. 1987. Stock identification of naturalized brown trout in Lake Superior tributaries: differentiation based on allozyme data. Trans. Am. Fish. Soc. 116: 785–794. Krueger, C. C., Wilmot, R. L., and Everett, R. J. 1999. Stock origins of Dolly Varden collected from Beaufort Sea coastal sites of Arctic Alaska and Canada. Trans. Am. Fish. Soc. 128: 49–57.

Genetic Analysis: Allozymes

307

Marsden, J. E., Krueger, C. C., and May, B. 1989. Identification of parental origins of naturally produced lake trout in Lake Ontario: application of mixed-stock analysis to a second generation. N. Am. J. Fish. Manag. 9: 257–268. Masuda, M., Nelson, S., and Pella, J. 1991. The computer programs for computing conditional maximum likelihood estimates of stock composition from discrete characters. USA-DOC-NOAANMFS, Auke Bay Laboratory, Alaska Fisheries Science Center, Juneau, AK. Mathias, J. A., Franzin, W. G., Craig, J. F., Babaluk, J. A., and Flannagan, J. F. 1992. Evaluation of stocking walleye fry to enhance a commercial fishery in a large, Canadian prairie lake. N. Am. J. Fish. Manag. 12: 299–306. May, B., Stoneking, M., and Wright Jr., J. E. 1980. Joint segregation of biochemical loci in Salmonidae: II. Linkage associations from a hybridized salvelinus genome (S. namaycush x S. fontinalis). Genetics 95: 707–726. Millar, R. B. 1987. Maximum likelihood estimation of mixed stock fishery composition. Can. J. Fish. Aquat. Sci. 44: 583–590. Milner, G. B., Teel, D. J., and Utter, F. M. 1983. Genetic stock identification study. National Oceanic and Atmospheric Administration, Northwest and Alaska Fisheries Center, Seattle, WA. 33 pp. (and Appendix). Milner, G. B., Teel, D. J., Utter, F. M., and Winans, G. A. 1985. A genetic method of stock identification in mixed populations of Pacific salmon, Oncorhynchus spp. Mar. Fish. Rev. 47: 1–8. Nei, M. 1987. Molecular Evolutionary Genetics. Columbia University Press, New York. Pella, J. J. and Masuda, M. 2001. Bayesian methods for analysis of stock mixtures from genetic characters. Fish. Bull. 99: 151–167. Pella, J. J. and Milner, G. B. 1987. Use of genetic marks in stock composition analysis. In N. Ryman and F. Utter (eds.), Population Genetics and Fishery Management. Washington Sea Grant Program, University of Washington Press, Seattle, pp. 247–276. Pella, J. J. and Robertson, T. L. 1979. Assessment of composition of stock mixtures. Fish. Bull. 77: 387–398. Pella, J., Masuda, M., Guthrie, C., Kondzela, C., Gharrett, A., Moles, A., and Winans, G. 1998. Stock composition of some sockeye salmon, Oncorhynchus nerka, catches in southeast Alaska, based on incidence of allozyme variants, freshwater ages, and a brain-tissue parasite. NOAA Tech. Rpt NMFS 132. Auke Bay Laboratory, Alaska Fisheries Science Center, Juneau, AK. 23 pp. Perkins, D. L. and Krueger, C. C. 1995. Dynamics of reproduction by hatchery-origin lake trout (Salvelinus namaycush) at Stony Island Reef, Lake Ontario. J. Great Lakes Res. 21 (suppl. 1): 400–417. Pritchard J. K., Stephens, M., and Donnelly, P. 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945–959. Rutherford, D. T., Wood, C. C., Jantz, A. L., and Southgate, D. R. 1994. Biological characteristics of Nass River sockeye salmon (Oncorhynchus nerka) and their utility for stock composition analysis of test fishery samples. Can. Tech. Rep. Fish. Aquat. Sci. 1988. 72 pp. Seeb, J. E., Seeb, L. W., and Utter, F. M. 1986. Use of genetic marks to assess stock dynamics and management programs for chum salmon. Trans. Am. Fish. Soc. 115: 448–454. Seeb, L. W., Seeb, J. E., Allen, R. L., and Hershberger, W. K. 1990. Evaluation of adults returns of genetically marked chum salmon, with suggested future applications. Am. Fish. Soc. Symp. 7: 418–425. Seeb, L. W. and Crane, P. A. 1999. Allozymes and mitochondrial DNA discriminate Asian and North American populations of chum salmon in mixed-stock fisheries along the south coast of the Alaska Peninsula. Trans. Am. Fish. Soc. 128: 88–103. Shaklee, J. B. 1991. Simulations and other analysis of the 1991 Columbia River spring chinook GSI baseline. WDF Tech. Rpt. 115. Washington Department of Fisheries, Olympia, WA. 40 pp.

308

M.-L. Koljonen and R. Wilmot

Shaklee, J. B. and Phelps, S. R. 1990. Operation of a large scale, multiagency genetic stock identification program. Am. Fish. Soc. Symp. 7: 817–830. Shaklee, J. B., Kepes, K. L., and Whitt, G. S. 1973. Specialized lactate dehydrogenase isozymes: the molecular and genetic basis for the unique eye and liver LDHs of teleost fishes. J. Exp. Zool. 185: 217–240. Shaklee, J. B., Busack, C., Marchall, A., Miller, M., and Phelps, S. R. 1990a. The electrophoretic analysis of mixed-stock fisheries of Pacific salmon. In Z. I. Ogita and C. L. Markert (eds.), Isozymes: Structure, Function, and Use in Biology and Medicine. Progress in Clinical and Biological Research, Vol. 344. Wiley-Liss, Inc. New York, pp. 235–265. Shaklee, J., Allendorf, F. W., Morizot, D. C., and Whitt, G. S. 1990b. Gene nomenclature for proteincoding loci in fish. Trans. Am. Fish. Soc. 119: 2–15. Shaklee, J. B., Beacham, T. D., Seeb, L., and White, B. A. 1999. Managing fisheries using genetic data: case studies from four species of Pacific salmon. Fish. Res. 43: 45–78. Shaw, C. R. and Prasad, R. 1970. Starch gel electrophoresis of enzymes—a compilation of recipes. Bioch. Gen. 4: 297–320. Siciliano, M. R. and Shaw, C. R. 1976. Separation and visualization of enzyme gels. In I. Smith (ed.), Cromatographic and Electrophoretic Techniques. Zone Electrophoresis. Interscience, Wiley, New York, Vol. 2, pp. 217–238. Smouse, P. E., Waples, R. S., and Tworek, J. A. 1990. A genetic mixture analysis for use with incomplete source population data. Can. J. Fish. Aquat. Sci. 47: 620–634. Utter, F. M. 1991. Biochemical genetics and fishery management: an historical perspective. J. Fish Biol. 39: 1–20. Utter, F. M., Hodgins, H. O., and Allendorf, F. W. 1974. Biochemical genetic studies on fishes: potentials and limitations. In D. Maklins (ed.), Biochemical and Biophysical Perspective in Marine Biology. Academic Press, San Francisco, Vol. 1, pp. 213–237. Utter, F., Aebersold, P., and Winans, G. 1987. Interpreting genetic variation detected by electrophoresis. In N. Ryman and F. Utter (eds,), Population Genetics and Fishery Management. Washington Sea Grant Program, University of Washington Press, Seattle, pp. 21–45. Waples, R. S. 1990. Temporal changes of allele frequency in Pacific salmon: implications for mixedstock fishery analysis. Can. J. Fish. Aquat. Sci. 47: 968–976. Weir, B. C. and Cockerham, C. C. 1984. Estimating F-statistics for the analysis of population structure. Evolution 38: 1350–1370. Wilmot, R. L., Everett, R. J., Spearman, W. J., and Baccus, R. 1992. Genetic stock identification of Yukon River chum and chinook salmon—1987 to 1990. Prog Rep. U.S. Fish and Wildlife Service, Anchorage, AK. 132 pp. Wilmot, R. L., Kondzela, C. M., Guthrie, C. M., and Masuda, M. M. 1998. Genetic stock identification of chum salmon harvested incidentally in the 1994 and 1995 Bering Sea trawl fishery. N. Pac. Anadr. Fish Comm. Bull. 1: 285–299. Wilmot, R. L., Kondzela, C. M., Guthrie III, C. M., Moles, A., Martinson, E., and Helle, J. H. 1999. Origins of sockeye and chum salmon seized from the Chinese vessel Ying Fa. (NPAFC Doc.) Auke Bay Fisheries Laboratory, Alaska Fisheries Science Center, NMFS, NOAA, 11305 Glacier Highway, Juneau, AK 99801–8626. 20 pp. Wilmot, R. L., Kondzela, C. M., Guthrie III, C. M., Moles, A., Pella, J. J., and Masuda, M. 2000. Origins of salmon seized from the F/V Arctic Wind. (NPAFC Doc.) Auke Bay Fisheries Laboratory, Alaska Fisheries Science Center, NMFS, NOAA, 11305 Glacier Highway, Juneau, AK 99801–8626. 18 pp. Wood, C. C., McKinnel, S, Mulligan, T. J., and Fournier, D. A. 1987. Stock identification with the maximum-likelihood mixture model: sensitivity analysis and application to complex problems. Can. J. Fish. Aquat. Sci. 44: 866–881.

Genetic Analysis: Allozymes

309

Wood, C. C., Oliver, G. T., and Rutherford, D. T. 1988. Comparison of several biological markers used in stock identification of sockeye salmon (Oncorhynchus nerka) in northern British Columbia and southeast Alaska. Can. Tech. Rep. Fish. Aquat. Sci. 1624. 49 pp. Wood, C. C., Rutherford, D. T., and McKinnell, S. 1989. Identification of sockeye salmon (Oncorhynchus nerka) stocks in mixed-stock fisheries in British Columbia and southeast Alaska using biological markers. Can. J. Fish. Aquat. Sci. 46: 2108–2120. Wright, S. 1965. The interpretation of population structure by F-statistics with special regard to system of mating. Evolution 19: 395–420. Wright, S. 1969. Evolution and the Genetic of Populations, Vol. 2: The Theory of Gene Frequencies. University of Chicago Press, Chicago.

This Page Intentionally Left Blank

CHAPTER

16

Mitochondrial DNA ANTONIOS MAGOULAS Hellenic Centre for Marine Research, Institute of Marine Biology and Genetics, Heraklion, Crete, Greece

I. Introduction A. General Features of Mitochondrial DNA B. mtDNA as a Tool for Intraspecific Analyses II. Methods for the Analysis of mtDNA A. Restriction Analysis B. Polymerase Chain Reaction (PCR) Analysis III. Review of mtDNA Studies of Fish Populations IV. Critique References

I. INTRODUCTION

A. GENERAL FEATURES

OF

MITOCHONDRIAL DNA

1. Molecular Cåharacteristics Mitochondrial DNA (mtDNA) is a small, double-stranded circular DNA molecule contained in multiple copies in the mitochondria, which are cytoplasmic organelles found in all eukaryotic cells. There may be up to several thousand copies per cell, depending on the cell type. In higher animals, mtDNA typically is around 16,000 base pairs (bp) long, although there is some length variation. The largest mtDNA molecule found in higher animals is that of scallops (Placopecten magellanicus), which is more than 39,000 bp long (Snyder et al., 1987). Variability in the size can be found not only between species, but also intraspecifically (see heteroplasmy, section I, A, 3). The animal mitochondrial genome contains 13 genes coding for proteins, two genes coding for ribosomal RNAs (12S and 16S rRNA), 22 genes coding for transfer RNAs (tRNAs), and one noncoding control region (also called D-loop in vertebrates). The coding genes code for enzyme subunits involved in electron Stock Identification Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

311

312

Antonios Magoulas

transport and oxidative phosphorylation. The ribosomal RNAs and the transfer RNAs participate in protein translation on mitochondrial ribosomes. The control region is about 1,000 bp long and contains the origin of mtDNA replication. Mitochondrial gene order differs slightly among vertebrates, and the piscine gene order does not differ from the vertebrate consensus order (Meyer, 1993). Usually the differences found are due to tRNA gene translocations. Mitochondrial DNA has been characterized as an “extreme example of genetic economy” (Attardi, 1985) because there are no introns within the coding genes, no repetitive DNA, and essentially no spacer sequences between genes. 2. Evolution of mtDNA Despite the conservation of the mitochondrial gene content and order over long evolutionary time, the nucleotide sequence of mtDNA evolves rapidly. The divergence of mtDNAs of taxa that shared a common ancestor is believed to be 2% per million years and to remain linear for 8 to 10 million years (Moritz et al., 1987). Some portions of the control region evolve exceptionally rapidly and are very useful for high-resolution analysis of population structure. The slowly evolving protein coding genes like CO I, II, III, and cytochrome b are more suitable for comparisons at the interspecific or higher level. 3. Transmission Genetics Generally, somatic and germ cells of an individual animal contain a single type of mtDNA, a state known as homoplasmy. However, there is a number of reported cases of heteroplasmy, that is, of presence of more than one type of mtDNA in an individual. In most cases of heteroplasmy, the two mtDNA variants differ in their size (length), typically due to tandem duplications in some portion of the molecule, largely in the control region. But there also have been reports of siteheteroplasmy in which the two variants differ in their sequence due to point mutations. Generally, the existence of heteroplasmy does not constitute a serious complication in population genetic analysis. Animal mtDNA is usually considered to be nonrecombining, but even if recombination does occur as evidence has been reported recently (see Rokas et al., 2003 and references therein), usually no novel genotypes will be generated because the two recombining molecules will most probably be the same due to homoplasmy. Moreover, it is almost exclusively maternally inherited. Despite reports of several cases of paternal mtDNA leakage to the progeny (Rokas et al., 2003), it seems that the predominantly maternal inheritance of mtDNA, in combination with the fact that recombination usually does not produce new detectable variants of mtDNA, creates a linear evolutionary history of maternal transmission (matriarchal phylogeny) of mtDNA genotypes.

313

Mitochondrial DNA

B. mtDNA

AS A

TOOL

FOR INTRASPECIFIC

ANALYSES

Mitochondrial DNA has become a very effective molecule to use for the assessment of intraspecific genetic variation and genealogy. The basic idea behind using mtDNA for stock structure analysis is that if samples of individuals, taken from different areas or from the same area at different times, belong to the same stock, they will contain the same types of mtDNA molecules (mitotypes hereinafter) in similar frequencies. On the contrary, if substantial differences in the mitotype distribution between the samples are encountered, there is strong evidence that these samples belong to different stocks characterized by a certain degree of genetic isolation. Traditionally, the most commonly used method for population-level analysis and stock identification was the restriction analysis of mtDNA, that is, analysis by means of the digestion enzymes called restriction endonucleases. This type of analysis assesses the mtDNA sequence variability by sampling small regions, randomly distributed over the molecule, which correspond to the recognition sites of the enzymes. The reliability and resolution of the method depends on the number of restriction enzymes used (the more the enzymes, the higher the reliability and resolution). The invention of polymerase chain reaction (PCR) in the late 1980s allowed for the in vitro amplification of portions of the mtDNA molecule, even from minute amounts of total DNA, provided that two or more conserved regions had been identified and sequenced near the boundaries of the portion to be assayed (see Mullis et al., 1986; Saiki et al., 1988). The amplified region can subsequently be studied either by restriction analysis or, more importantly, by sequencing. The introduction of PCR amplification revolutionized studies of intraspecific variation and finds an ever-increasing application. However, one should bear in mind that there is a trade-off between the detailed information that PCR provides for a portion of the mtDNA genome and the less detailed, but more inclusive information provided by restriction analysis across the entire mtDNA genome. Here both methods, restriction analysis and PCR-based analysis, will be presented briefly.

II. METHODS FOR THE ANALYSIS OF mtDNA

A. RESTRICTION ANALYSIS 1. General Description of the Method Restriction endonucleases (RE) are enzymes that recognize specific short sequences, usually 4 to 6 bp long, on the DNA molecule and cleave the DNA in

314

Antonios Magoulas

a constant position within the recognition sequence. The process of cutting the DNA with the RE is called restriction or digestion. For a circular molecule, such as mtDNA, it is obvious that after digestion with a given RE, it will be cut in as many fragments as there are recognition sites for the enzyme. The sizes of the fragments are determined by the position of these sites on the molecule. The number and size of fragments produced after digestion can be detected after separation of the fragments by gel electrophoresis and appropriate visualization of the fragments. Thus, for each mtDNA molecule (or, equivalently, individual since, as mentioned, each individual usually bears one type of mtDNA) a restriction or digestion profile is produced. The comparison of restrictionfragment profiles of several individuals is considered to be representative of the nucleotide differences of their whole mtDNA sequences. This is the main assumption behind restriction analysis, which is also known as RFLP (restriction fragment length polymorphism) analysis. To exemplify this, consider the case shown in Figure 16-1. Let x be an ancestral molecule that bears three recognition sequences of the EcoRI RE. Note that there is also another position, which is only one base removed from being an EcoRI recognition sequence. After digestion with this enzyme three linear fragments of different size will be produced. If these fragments are separated and visualized on a gel, the three fragments of profile A will be produced. This profile is considered as representing mitotype (or haplotype) A. But as mtDNA passes from generation to generation, it replicates repeatedly and each replication has a certain (small) probability of error, or mutation. So, it is possible that a molecule y, bearing a single base substitution in one of the recognition sequences, will arise and be fixed in some individual in the future. This recognition site will not be recognized and cleaved by the enzyme (an event known as site loss), and profile B of molecule y will contain only two bands. Of those two bands, the larger corresponds to the same fragment as the largest band of profile A (we say that this is a shared band), and the smaller represents the fragment that resulted from the unification of the two fragments of molecule x that were separated by the site that was lost. Thus the profiles/mitotypes A and B have one shared fragment (band) and three unshared fragments. On the other hand, it is possible that in another evolutionary lineage the site that was one base away from being a recognition sequence in molecule x may undergo the “right” mutation (C Æ G) to turn into an EcoRI recognition sequence in molecule z (a site gain). The resulting profile/mitotype C will have two shared bands with A and no shared band with B. In this way, even with this kind of crude analysis, the comparison of the digestion profiles would provide evidence that mitotype A is more closely related to each of mitotypes B and C than those two mitotypes are related to each other. Thus, a first phylogenetic network of the mitotypes can be constructed which gives to mitotype A an intermediate position between B and C.

GAA TT A

Site loss

y GAATTC TC T A A G

x

G A A T T C AC TA T C

z

Site gain

C TT AA

B

G

A

C Large size

C

A

B

Small size

FIGURE 16-1. Evolutionary changes of restriction sites and the resulting profiles after electrophoresis of the digests. 315

316

Antonios Magoulas

The accuracy and reliability of the method increases if several (usually from 5 to 20) REs are used. The mitotypes are then determined by taking into account the digestion profiles produced by all the REs used and are designated usually by a multiletter code in which each letter is the profile of each separate RE used, with a specified order. With some extra effort (which is considerably time consuming) the position of the restriction sites on the molecule can be determined and thus a restriction map can be produced for each mitotype. The site changes interconnecting the mitotypes can then be determined by comparison of their restriction maps. Once the site changes have been determined, the nucleotide divergence between the mitotypes can be estimated on the basis of the number of their shared/not shared sites (Nei, 1987). If information on the site changes is available, a parsimonious phylogenetic network, that is, a network that minimizes the evolutionary changes (site gains or losses) through which the mitotypes are connected, can be constructed. 2. Raw Data Acquisition The procedures for the acquisition of raw data are schematically presented in Figure 16-2. The first step is the extraction of DNA, usually from tissues such as ovary, liver, brain, and muscle. Depending on the kind of tissue available and on what kind of assays are to be conducted subsequently, mtDNA can be isolated either in highly purified condition (enriched mtDNA extraction) or in crude form (together with nuclear DNA, total DNA extraction). The extraction of enriched mtDNA is much more tedious and time consuming than total DNA extraction and requires fresh tissue. On the other hand, the subsequent analysis of enriched mtDNA is much easier than that of total DNA, the analysis of which involves the laborious method of Southern blot analysis and relies heavily on the availability of extra pure mtDNA for use as a probe for the hybridization step. It should be noted that the development of the PCR-based methods in combination with recent technological advances (e.g., automated high throughput genetic analyzers, which use fluorescent dyes) tend to restrict the application of methods like those described in this section. However, they are briefly explained here, also for historical reasons. a. Method Based on Enriched mtDNA Extraction and Direct Visualization of the Profiles mtDNA isolation. There are various protocols for the isolation of enriched mtDNA. One of the most widely applied is that of Lansman et al. (1981), which uses a CsCl density gradient centrifugation to separate the closed-circular mtDNA from the lineal nuclear DNA. The purified mtDNA, in addition to being used for

Mitochondrial DNA

317

FIGURE 16-2. Outline of restriction analysis of mtDNA. Methods for data collection.

individual scoring as described in the following paragraphs, can also be used as a probe in the Southern blot analysis of total DNA extracts (see later). A collection of several protocols for isolating enriched mtDNA can be found in White and Densmore (1992). Restriction digestion and electrophoresis. Several hundred REs are commercially available. The digestion conditions are usually specified by the manufacturer. After digestion, the produced fragments are separated by molecular weight by gel electrophoresis. At neutral pH, DNA is negatively charged and so, if placed in an electric field, migrates toward the anode. The digests are loaded into media (agarose or polyacrylamide) that form a dense matrix, through the pores of which

318

Antonios Magoulas

smaller fragments migrate faster than larger fragments (for details on gel electrophoresis see Sambrook et al., 1989). The method of visualization of the digestion profiles on the gels is dictated by the purity and amount of mtDNA available. If there is enough DNA the fragments can be identified by direct staining with ethidium bromide. The minimum amount of DNA in a band detectable by this method is 2 ng (Dowling et al., 1996). If there is a limited amount of DNA, visualization is achieved by radioactively labeling the restriction fragments prior to electrophoresis and subsequent exposure of the gel to X-ray film. Development of the film (autoradiography) reveals the digestion profile (see Sambrook et al., 1989). Usually for the determination of the size of the restriction fragments a molecular weight marker, that is, a pool of DNA fragments of known size, is run together with the digest under assay onto the gel. The sizes of the restriction fragments are determined by comparison of their migration on the gel with that of the marker fragments or “size standards.” b. Method Based on Total DNA Extraction and Southern Blotting Total DNA isolation. Fresh or frozen tissue is homogenized and all the cellular membranes lysed by means of a detergent, such as SDS (sodium dodecyle sulfate). Usually proteinase k is added concurrently to disintegrate the proteins. DNA is then separated from impurities by phenol/chloroform extractions and precipitated with ethanol. Several protocols for total DNA extraction are used nowadays (e.g., see Sambrook et al., 1989). Restriction digestion and electrophoretic separation. Total DNA is restricted and electrophoresed much the same way as in the case of enriched mtDNA. Again, double digestions should be performed if a site map is to be produced. Transfer and hybridization. After electrophoresis, the DNA on the gel is denatured by dipping the gel in a high pH solution, and then transferred (blotted) to nitrocellulose or nylon membranes. During the subsequent hybridization step, the membrane is incubated, under specified conditions, with a solution containing a radioactively or otherwise labeled probe. The probe is pure mtDNA from the same or a closely related species, which was obtained by cloning the molecule (or a portion of it), or by PCR amplification, or by enriched mtDNA extraction. In the latter case, the mtDNA can be extracted from pooled tissues coming from several individuals if the animals under assay are small. The probe under the appropriate stringency will anneal only to its homologous strands on the membrane, that is, only to the mtDNA strands. If a radioactively labeled probe has been used, autoradiography will reveal the position of the DNA fragments on the original gels; if a nonradioactive tag (such as digoxigenin) has been used, the detection of the fragments is usually performed with an immune-enzymatic assay, according to the instructions of the manufacturers.

Mitochondrial DNA

319

3. Data Processing As shown in Figure 16-3, once the raw data have been collected, the first thing to be done is to assign to each animal a composite pattern or mitotype based on the restriction pattern that it demonstrated for each RE used. There are two main general directions in which further analysis of data may proceed. The first involves the geographic patterning of the mitotypes. The second takes also into account the evolutionary relationships between the mitotypes themselves (phylogeographic approach). These types of analysis can be combined to help obtain a better understanding of present population structure and of the past or present forces that may have shaped it.

FIGURE 16-3. Outline of restriction analysis of mtDNA. Processing of data.

320

Antonios Magoulas

a. Analysis of Geographic Partitioning of Mitotypes The first type of analysis compares the mitotype frequencies in the different samples and estimates the diversity within and between the samples. mtDNA is thought to be one of the best genetic markers for the study of population structure, gene flow, hybridization zones, and other questions at the population level. For stock assessment applications, the mitotype frequencies of two or more samples are compared. If there are statistically significant differences between them, the samples are considered to belong to different stocks that exhibit a certain degree of genetic isolation. Statistical tests usually employed are the X2 test and the G-test, which has the advantage of hierarchical partitioning of heterogeneity within the samples and among samples or among groups of samples. Other conventional parameters of population genetics analysis can also be estimated. The index of mitotype diversity (h) corresponds to the gene diversity index of Nei (1987, eq. 8.5) and gives the probability that two different animals drawn at random from the population will have the same mitotype. Nei’s genetic distances (Nei, 1972) between pairs of populations can be estimated from their mitotype frequencies. The populations can then be clustered based on a distance matrix using any of the conventional clustering methods (UPGMA, Neighbour Joining, Fitch-Margoliash, etc.; see Swofford and Olsen, 1996, for details). There are several programs for population genetic analysis that can be used for assessing genetic variation and heterogeneity, such as GENEPOP (Raymond and Rousset, 1995) and Arlequin (Snyder et al., 2000). b. Phylogeographic Analysis In many cases, it is important to examine the evolutionary relationships and divergence among mitotypes because they may provide valuable information on population structure, demography, and history. This has led to the introduction of a new scientific discipline, phylogeography, which is the study of principles and processes governing the geographic distribution of genealogical lineages (Avise, 1994; Avise, 2000). To mention an example of the matters dealt with by the phylogeographic approach, the existence in a population of two groups of mitotypes that are separated from each other by high divergence (genetic gap), yet characterized by genetic similarity within each group, most likely indicates that the two groups evolved in populations that were isolated in the past. There are two main approaches for the assessment of phylogenetic relationships: parsimony and analysis based on distance matrices. For a detailed description of the methods used for the construction of phylogenetic trees, see Swofford and Olsen (1996).

Mitochondrial DNA

321

B. POLYMERASE CHAIN REACTION (PCR) ANALYSIS 1. General Description of the Method PCR uses the thermostable enzyme Taq polymerase to replicate a stretch of the DNA molecule, starting from minute amounts of DNA samples, even in crude form, provided that the appropriate primers are available. The primers are synthetic single-stranded oligonucleotides (around 20 bp in length) that are complementary to the regions flanking the target segment. During each cycle of PCR the number of copies of the target segment is doubled. Advantages of PCR have rendered it the most popular tool for sequence analysis today: it is a very rapid method permitting the amplification of DNAs from several hundreds of individuals within a day or so; it is much faster and cheaper than conventional cloning techniques; it is a robust method that can utilize tiny amounts of tissue; and, the tissue can be frozen, dried, or preserved in ethanol or formalin. The crucial step of the PCR method is the availability of the appropriate primers. The primers can be designed only if the sequences flanking the target sequence in the species under consideration are known, so it would seem at first glance that a sequencing step would be necessary before any PCR-based assay could be started. Fortunately, the conservatism of the mitochondrial gene order and of certain portions of the molecule provide the opportunity to circumvent this problem. All we need is to find evolutionary conserved sequences bracketing evolutionary variable regions. For example, the tRNA and rRNA genes contain sequences that are sufficiently conserved to design primers, which can be used in a wide spectrum of animal species. There are already several such universal primers available, which were defined by comparison of the mtDNA sequence of divergent species, such as human, frog, and fly (Kocher et al., 1989). Another important step in PCR-based analysis is to choose the appropriate region of mtDNA to be amplified. For population structure analysis the control region is usually preferable because it is the most variable part of the mitochondrial genome and is expected to exhibit substantial polymorphism intraspecifically. However, it has been shown that even conservative protein-coding genes like those for cytochrome b tend to show intraspecific variation and can be used to identify fish stocks (Meyer, 1993, and references therein). 2. Raw Data Acquisition PCR amplification is achieved usually by using crude preparations of total DNA as a source of template DNA. However, even simple homogenates of the tissues can sometimes be used efficiently. Once double-stranded PCR products have been produced from a number of specimens, there are two main alternatives for further analysis: Either the products will be analyzed using restriction analysis or

322

Antonios Magoulas

the products may be sequenced to provide the raw data for analysis. The first analysis is much easier and cheaper, but the second provides the highest possible resolution. a. Restriction Analysis of PCR Products This analysis is performed much the same way described in the section on restriction analysis. Usually, in a preliminary stage several REs (preferably 4-cutters) are tested for the purpose of choosing the enzymes that can detect different mitotypes. This identification is relatively difficult because the amplified DNA will usually be less than 2,000 bp in length, around eight times smaller than the entire mtDNA, which is analyzed through traditional restriction analysis. Nevertheless, data obtained from PCR amplification of different portions of the mtDNA molecule can be combined for restriction analysis. Once such enzymes have been identified, they can be used for the analysis of a large number of animals. After digestion and gel electrophoresis, the restriction profiles can be detected by direct ethidium bromide staining because the PCR product contains a large amount of mtDNA and minute amounts of impurities. b. Sequencing of the PCR Product The most commonly used method for DNA sequencing is that of the dideoxy chain-terminating technique, but there are several alternatives for sequencing a PCR product. Information about these techniques can be found in Sambrook et al. (1989). 3. Data Processing The data from the restriction analysis of the PCR products can be used to address population-level questions such as the existence of polymorphism and the geographic distribution of the mitotypes in the samples. Their usefulness for phylogenetic inferences depends on their size because it is possible that a very small (1%) within the world’s human populations, or one variant site per every 300 bases. SNP sites constitute about 90% of DNA sequence variation in human populations (International HapMap Consortium, 2003). Comparative estimates of overall levels of SNPs in fish genomes will soon be available when the results of several genome-wide sequencing projects are made available. Thus, there are almost certainly sufficient levels of diversity at single-copy loci to serve as sensitive targets for stock identification studies. Comparative sequence data from these fish genome projects will allow for easier design of PCR primers at individual coding-gene loci. Multiplexing of PCRs from several single-copy loci and automated methods, such as Pyrosequencing, for screening multiple SNP loci simultaneously for allelic variants at previously defined informative polymorphic loci will allow for rapid multiloci surveys of many individuals. The few studies in fish on single-copy coding nDNA loci used DNA hybridization techniques that by today’s standards are labor intensive and demanding in terms of DNA quality. Wirgin et al. (1992) used five single-copy proto-oncogene (genes that are highly conserved in animal taxa and are important in cancer development) probes such as K-ras and c-abl to distinguish among all four North American species of Morone (striped bass, white bass, white perch, and yellow bass). They found fixed differences among the species at nine genetic loci, ensuring a high degree of certainty (97%) in distinguishing among parental species, F1 hybrids, and later generation hybrids. This type of nDNA also has been used to identify differences among salmonid stocks. Although single-copy coding genes reside at a single genetic locus, internal regions may be repeated within a locus, thus providing potentially informative

340

Isaac Wirgin and John R. Waldman

polymorphisms. Nuclear ribosomal RNA genes (rDNA) contain three coding regions that evolve slowly: 5.8S, 18S, and most of 28S rDNA. However, the internal and external transcribed spacer regions (ITS-1, ITS-2, 5¢ ETS) as well as portions of 28S rDNA evolve more rapidly, and the intergenic spacer region (IGS) evolves most rapidly (Phillips and Pleyte, 1991; Phillips et al., 1992). To date, fixed differences in the 5¢ ETS were found among allopatric populations of Arctic charr, Salvelinus alpinus (Zhuo, 1991). Also, a polymorphism was found in the spacer region of rRNA genes that discriminates Atlantic salmon, Salmo salar, from North America and Europe (Cutler et al., 1991). Individualspecific variation in the IGS was found in five populations of lake trout, Salvelinus namaycush, with most of the variation occurring within, rather than between, populations (Zhuo et al., 1995). As will be described in detail later, populationdiagnostic polymorphisms at the pantophysin (PanI) locus have been described in Atlantic cod from the northeastern and northwestern Atlantic (Pogson and Fevoldsen, 2003) and have provided exciting results both in terms of stock identification and in understanding those evolutionary processes that shape their population structure.

IV. SINGLE-COPY NONCODING NUCLEAR DNA The vast majority of the nuclear genome is noncoding and, thus, may evolve rapidly and provide a fertile substrate for stock identification studies. For example, most of the nuclear genome is within potentially rapidly evolving intergenic regions. However, a major disadvantage to an approach based on singlecopy, noncoding nDNA is the need to identify polymorphic loci and develop and test species-specific probes. This labor-intensive process, as originally described for lesser snow goose, Anser caerulescens caerulescens by Quinn and White (1987), included development of a genomic DNA library from the species of interest in plasmid or viral vectors, selection of recombinant clones representative of nonrepetitive nDNA, and empirical testing of each cloned DNA in conjunction with various restriction enzymes to determine which combinations revealed informative polymorphisms. An almost infinite number of clones can be generated, each most probably representative of different nDNA sequences. Unfortunately, there is no a priori way of determining which clone-restriction enzyme combinations will be informative, and so, each must be tested on individuals of the stocks being investigated. However, once informative clonerestriction enzyme combinations are identified, diagnostic clones can be sequenced, PCR primers designed, and the assay made PCR-based as described for striped bass, Morone saxatilis, in Wirgin et al. (1997). Alternatively, nucleotide sequence data from fish genome projects can be used to design PCR primers for noncoding nDNA regions. Also, most of the PCR products generated from RAPDs

Use of Nuclear DNA in Stock Identification

341

or AFLPs analyses are from intergenic regions and can be sequenced to design PCR primers. Because of its technical complexity, this approach saw only limited use in stock identification of fishes. Wirgin and Maceda (1991) screened 20 clones developed from striped bass. Seven of these clones revealed polymorphisms across collections representative of most of the species’ distribution, a level of variation that was low but which is consistent with the limited genetic diversity found in striped bass through analyses of isozymes, mtDNA, and microsatellite DNA (Waldman et al., 1988; Wirgin et al., 1991). Among these samples, informative polymorphisms were found at a broad scale, for example, between fish from the Atlantic and Gulf of Mexico coasts, and at a finer scale, for example, the Hudson River and Chesapeake Bay (Waldman et al., 1996). Karl and Avise (1992) used a modification of the above approach to screen for restriction fragment length polymorphisms in single-copy nDNA sequences of American oysters, Crassotrea virginica. Pairs of 24 to 25 PCR primers were designed to amplify polymorphic intervening sequences, and the products were digested with restriction enzymes, separated electrophoretically, and visualized by ethidium bromide staining. This approach revealed a sharp genetic discontinuity between Atlantic and Gulf of Mexico oyster populations; this finding was consistent with results from an earlier mtDNA study on the same populations. Pogson and colleagues successfully used a slight modification of this approach to explore for population structure in Atlantic cod, Gadus morhua using a panel of 17 single-copy probe-enzyme combinations (Pogson et al., 1995). Details of this study will be described in a later section.

V. REPETITIVE NUCLEAR DNA Analysis of repetitive nDNA sequences has become the major approach for fish stock identification purposes and is especially useful in those instances where alternative approaches have revealed depauperate levels of genetic variation. Short, minisatellite (~10–40 bp) and very short, microsatellite (1–6 bp) DNA sequence motifs, sometimes referred to as VNTRs (variable number of tandem repeats), often are found as tandem repeats throughout the nuclear genome. Although functional or selective constraints on the evolution of repetitive nDNA are probably limited, a degree of concerted evolution of these repeats apparently exists among some taxa so that hierarchical groupings at the stock level based on the analysis of repetitive nDNA may emerge (Dowling et al., 1990). Additionally, the number of tandem repeats in the promoter, introns, or other flanking regions of genes may be functionally important in regulating expression of coding genes.

342

Isaac Wirgin and John R. Waldman

A. MINISATELLITES Minisatellites were the original repetitive nDNA motifs analyzed in stock identification studies, and their use revolutionized the field of molecular evolution because of the high levels of genetic diversity they exhibited. In fact, rates of mutations at many minisatellite loci in humans exceed those at microsatellite loci (Armour et al., 2001). DNA fingerprinting is a process in which the allelic products of many minisatellite loci are visualized simultaneously. The term DNA fingerprinting was coined because profiles of variation at a panel of minisatellite loci could be used with high certainty to uniquely identify individuals within human populations. Minisatellites are less common than microsatellites in genomes, probably present at hundreds or thousands of different loci per genome (Armour et al., 2001). Unlike microsatellites, which are distributed throughout the entire genome at regular 10 kbp intervals, minisatellites are frequently clustered on chromosomes. DNA fingerprinting examines allelic diversity at minisatellite loci which contain much longer repeat units (>10 bp) than those at microsatellite loci. Many hypervariable minisatellite loci show some variation in the precise sequence of repeat units. An allele at a typical hypervariable minisatellite locus is not perfectly repeated, but instead is a mixture of two or more repeat units (Armour et al., 2001). Minisatellite mutations probably result from a combination of processes including unequal recombination at meiosis, gene conversion, and slippage at replication forks (Yauk, 1998). Early studies of repetitive nDNA in fishes were dependent on the efficacy of heterologous DNA probes isolated and characterized in higher vertebrates to recognize repetitive sequences in fish DNA. Minisatellite probes such as Per, M13, and Jeffreys’ core probes 33.6 and 33.15 were evaluated and found to hybridize to most fish DNA and to reveal highly polymorphic patterns (Castelli et al., 1990). That is because the target minisatellite tandem repeat sequences are fairly long and conserved, and that universal DNA probes that were readily available were able to detect multilocus DNA fingerprints in a variety of disparate taxa, including fishes. Because of the large number of loci visualized on DNA fingerprinting gels, there is uncertainty regarding assignment of minisatellite DNA bands to individual loci. Thus, it is inadvisable to use the Hardy–Weinberg model in analyzing allelic frequencies of these data. Similar to RAPDs and AFLPs, the extent of band sharing between individuals is calculated and then used as a measure of dissimilarity among individuals and between populations. Additionally, DNA fingerprinting is not a PCR-based approach and so demands on tissue condition are much more stringent than for PCR-based approaches. However, if one family of minisatellite DNA fragments proves particularly informative in DNA fingerprinting, the locus that encodes these bands can be cloned, flanking sequences characterized, primers designed, and the assay for that one minisatellite locus can be

Use of Nuclear DNA in Stock Identification

343

made PCR-based and even multiplexed with microsatellite loci or other minisatellite loci. In DNA fingerprinting, relatively large amounts of highly intact DNAs are isolated and fragmented by digestion with one or more restriction enzymes. The digested DNA fragments are electrophoretically separated in agarose gels, transferred to hybridization membranes, and fixed on the membranes. The DNA fragments on the membranes are visualized by hybridization with labeled DNA fingerprinting probes and visualized by exposure to X-ray film. The amount of DNA fragment sharing among individuals is then determined within a population, and these data are then used to calculate interpopulation genetic diversity. Heath et al. (1995) evaluated the ability of 12 different oligonucleotides containing minisatellite core sequences to hybridize to total DNA isolated from chinook salmon, Oncorhynchus tshawytscha. Of the 12 minisatellite probes, 7 produced highly variable fingerprint-like banding patterns, but only 3 of these revealed clear, distinct DNA bands. Wirgin et al. (1991) analyzed minisatellite nDNA in striped bass with the heterologous M-13 and Per DNA fingerprinting probes and found single DNA fragments that were diagnostic at the population level among selected Atlantic and Gulf of Mexico stocks. Prodohl et al. (1992) used multilocus DNA fingerprint analysis to determine genetic variability within and among brown trout, Salmo trutta, populations in Ireland. Using Jeffreys’ et al. (1985) human minisatellite 33.6 probe, Prodohl et al. (1992) found significant differences in overall levels of genetic heterozygosity within these populations, and band sharing among populations. Single-locus hypervariable minisatellite probes have been developed in several laboratories for use in salmonid species, including Atlantic salmon (Taggart and Ferguson, 1990; Bentzen et al., 1993). Applicability of one of the Atlantic salmon probes (3.15.34) to chinook salmon was demonstrated (Stevens et al., 1993) and polymorphisms were found among a limited number of hatchery families. Taylor et al. (1994) examined minisatellite variation among 42 populations of chum salmon, Oncorhynchus keta, over most of their range in the North Pacific. Their single locus probe (Ssa I) hybridized to at least two linked minisatellite loci; evidence was found of three regional chum salmon groupings that were consistent with probable patterns of postglacial dispersal from three refugia. Three minisatellite probes, Ssa I (Bentzen et al., 1993) and 3.15.34 (Taggart and Ferguson, 1990) isolated from Atlantic salmon, and OtPBS1 isolated from chinook salmon, were used to discriminate among 10 sockeye salmon, Oncorhynchus nerka, populations in Russia, western Alaska, and British Columbia–Washington (Beacham et al., 1995). Significant differences in allelic frequencies were observed with all three probes at regional, among-river, and within-river levels. Although it was not possible to classify individual fish to specific populations with a high degree of confidence, accurate and precise estimates of the relative contributions of regional stocks to simulated mixed stocks were

344

Isaac Wirgin and John R. Waldman

obtained. Furthermore, contributions to a population of two subpopulations within a drainage system were estimated accurately with this approach. While analysis of minisatellite loci revealed high levels of variation in nDNA, the uncertainty in assignment of bands to individual loci, the inability to make the assay PCR-based, and the need for high-quality DNA have resulted in the apparent abandonment of this approach in stock identification.

B. MICROSATELLITES 1. What Are Microsatellites? Microsatellite loci contain tandemly repeated motifs of 1 to 6 bases that are found in all prokaryote and eukaryote genomes investigated to date. That means that a simple DNA motif, for example, AC, is arranged head-to-tail and in perfect repeats usually without interruption by any other motif or base (Fig. 17-2). Microsatellites that are used in stock identification studies typically contain di(AC)n, tri-(ACC)n, or tetranucleotide (GATA)n repeats. There may be from 5 to 100 tandem copies of a repeat motif at a single microsatellite locus. Microsatellite loci are abundant in all eukaryote genomes and it has been estimated that there are from 103 to 105 microsatellite loci dispersed at 7- to 10-100 kilobase pair (kb) intervals in the eukaryotic genome (Wright and Bentzen, 1994). Fish genomes may contain more microsatellite loci than most other invertebrate and vertebrate taxa (Zane et al., 2002). Mapping studies suggest more or less even distributions of microsatellites throughout genomes, although they are somewhat rarer within coding sequences. 2. Why Use Microsatellites? Microsatellite analysis has become the tool of choice in studies of molecular evolution, genetic stock identification, and genetic mixed stock analysis. This is primarily due to the very high levels of genetic variation that are often detected at individual microsatellite loci, the large number of loci that can be screened, and their relative ease of analysis, particularly with automated DNA sequencers. Microsatellite loci present higher levels of genetic diversity than in most, if not all, other types of DNA typically analyzed, which may be particularly true for fish. For example, an average of 13.7 alleles at AC dinucleotide repeat loci was found across 27 fish species compared to 8.6 alleles in mammals and 7.7 alleles in reptiles (Neff and Gross, 2001). Estimates of microsatellite mutation rates vary considerably among taxa and varieties of microsatellites (Zhang and Hewitt, 2003). From pedigree analyses, microsatellite mutation rates were 10-3 to 10-4 in mice (Dallas, 1992), 1 ¥ 10-3 to 5.6 ¥ 10-4 in humans (Weber and Wang, 1993),

Use of Nuclear DNA in Stock Identification

345

Panel A 1) CTAGCTACTGGAACACACACACACACACTGACTAGGATCGA 2) CTAGCTACTGGAACCACCACCACCACCTGACTAGGATCGA 3) CTAGCTACTGGAACCTACCTACCTACCTACCTACCTTGACTAGGATCGA Panel B 1) CTAGCTACTGGAACACACACACACACACTGACTAGGATCGA 2) CTAGCTACTGGAACACACACACACACACACTGACTAGGATCGA 3) CTAGCTACTGGAACACACACACACACACACACTGACTAGGATCGA Panel C

M 1 2 3 4

FIGURE 17-2. (A). Three different types of microsatellite repeats: (1) dinucleotide, (2) trinucleotide, and (3) tetranucleotide. Repeat units are underlined, and single-copy flanking sequences from which PCR primers are designed are indicated in bold. (B). Variation among individual fish in number of dinucleotide AC repeats at a single microsatellite locus. Individual 1 has 8 AC repeats, individual 2 has 9 AC repeats, and individual 3 has 10 AC repeats. Single-copy flanking sequences are indicated in bold. (C). Depiction of gel showing polymorphic microsatellite alleles in the three individuals in (B) and a fourth individual who is a heterozygote for alleles with 8 and 10 AC repeats. Lane M contains a molecular weight ladder; lane 1 contains allele from a homozygote for 8 AC repeats; lane 2, a homozygote for 9 AC repeats; lane 3, a homozygote for 10 AC repeats; and lane 4, a heterozygote for alleles with 8 and 10 AC repeats.

346

Isaac Wirgin and John R. Waldman

and 1.5 ¥ 10-4 in zebrafish (Shimoda et al., 1999) events per locus per generation. This compares to rates of SNP mutations that in most taxa are on the order of 10-8 to 10-10 per locus per generation (Hancock, 2001). Also, because allele variants at microsatellite loci are codominant and are inherited in Mendelian fashion, traditional approaches, such as Hardy–Weinberg, can be used in initial data analysis. This is not the case with dominant/recessive RAPDs in which band sharing is typically used to quantify genetic relatedness of samples or for haploid mtDNA markers. In stock identification studies, the extent to which genotypes conform to Hardy–Weinberg equilibrium confers information about population structure, presence of mixed stocks, and those processes (admixtures, migration, selective pressure) that may have contributed to deviations from expected Hardy–Weinberg genotype frequencies. Multilocus microsatellite surveys are powerful tools in determining population structure and evolutionary history of target species. 3. How and Why Do Microsatellites Vary Among Individuals? Panels of microsatellite loci have been variable, and sometime hypervariable, in all taxa investigated to date. The number of copies of repeats at a microsatellite locus often differs among individuals within a population and serves as the basis for microsatellite allelic variation. The origin of such high levels of variability is probably due to frequent slippage events during DNA replication (Schlotterer and Tauritz, 1992). The functional significance of variation at microsatellite loci is largely unknown; consequently, microsatellites have usually been considered to be neutral genetic markers. However, evidence has accumulated that microsatellites serve functional roles as coding or regulatory elements (Kashi and Soller, 2001). The conserved presence among species of particular microsatellite motifs in the regulatory regions of specific genes, binding of proteins to these microsatellite sequences, and alterations in gene expression when the microsatellites are artificially removed provides strong support that they are sometimes important in regulation of gene transcription. Additionally, instability in the number of tandem repeats at individual microsatellite loci in humans, particularly trinucleotide repeats (Eisen, 2001), has been shown to be associated with progression of various types of human diseases, including some cancers (Rubinsztein, 2001). In some diseases in humans and possibly other animals, the number of repeats at a locus increases during disease progression. 4. Are Levels of Polymorphisms at Microsatellite Loci Predictable? Within a taxon, microsatellite loci differ greatly in their molecular characteristics, and it is not clear what effects these have on their levels or patterns of variation. Characteristics in which individual microsatellite loci vary include the following:

Use of Nuclear DNA in Stock Identification

347

the number of bases in the core tandem repeat unit (2, 3, or 4), the number of tandem repeats at an individual locus (5 to 100), the actual DNA sequence of the core nucleotide repeat motif, and the DNA sequence in single-copy regions that flank the microsatellite loci. Tandem repeats at a locus may be perfect (CTCTCT), imperfect (CTCTACT), or compound (CTCTGAGA), characteristics that also may affect their levels of variation. Recombination rate, transcription rate, age and sex, and efficiency of mismatch DNA repair probably all contribute to the differing rates and patterns of variation among individual microsatellite loci. Although it is impossible to predict which loci will exhibit high or low levels of allelic diversity a priori based on sequence, studies in yeast suggest that longer, perfect repeats provide higher levels of allelic diversity than loci with shorter, imperfect repeats (Wierdel et al., 1997; Petes et al., 1997), but that has not been empirically verified for fishes. 5. How to Conduct Microsatellite Studies Di-, tri-, and tetranucleotide repeat loci are the microsatellites that are almost always used in population studies. The shorter dinucleotide repeats are much more numerous in vertebrate genomes than the longer tri- and tetranucleotide repeats and, thus, are somewhat easier to isolate (Tooth et al., 2000). However, allelic variants at dinucleotide repeat loci are more difficult to resolve in gels because of frequent “stuttering” in PCR amplification (O’Reilly and Wright, 1995) and because of the similar size of the variant alleles (minimally 2 bases). Thus, in the long run, the isolation of loci containing the longer 3 and 4-base repeat units probably warrants the extra effort. Typically, at least 4 to 10 loci microsatellite loci are screened in population studies. Because most loci are unlinked and inherited independently, the greater the number of loci screened, the greater the likelihood of selecting loci that reveal significant allelic frequency differences among populations. Additionally, analysis of a larger number of loci may provide a more accurate picture of the evolutionary history of the populations. Also, when the extent of population differentiation using microsatellites is weak, the greater the number of loci analyzed, the more statistical power is gained in individual-based population assignment tests (Paetkau et al., 1995; Bernatchez and Duchesne, 2000) that are now often used to quantify the extent of genetic differentiation among populations. Similarly, the number of informative loci used is important in assignment power in mixed-stock analyses. Analysis of microsatellite polymorphisms is a PCR-based approach in which oligonucleotide primers are designed based on unique single-copy sequences flanking the microsatellite repeats. PCR primer pairs are selected such that PCR products are of small molecular size (usually 1000 bp) in tarakihi and king tarakihi; and the presence/absence of small fragments (~500 bp) in lanes 1–4, and one small fragment in lanes 5–8. Data from Smith et al. (1996).

be produced by excessive PCR cycles; Bell and DeMarini (1991) have shown that increasing the number of PCR cycles above 30 can lead to nonspecific DNA products. However in preliminary amplifications with frozen fish tissue samples, I found that fewer than 40 cycles produced faint fragment patterns for many primers, necessitating 40 cycles as a standard technique. The addition of nonionic detergent to the PCR reaction can improve the yield of weak products (Patwary et al., 1994). Sometimes a smear of DNA stain is observed in lanes in the gel, which may indicate an excessive concentration of sample template DNA in the amplification reaction (Bell and DeMarini, 1991; Devos and Gale, 1992). Most RAPD polymorphisms are scored by the presence or absence of an amplification product (see Fig. 18-1). Standard genetic calculations are not applicable to RAPD data because most fragments are dominant. Heterozygosity cannot be calculated (other than by assuming samples to be in Hardy–Weinberg equilibrium) because homozygous individuals carrying two copies of the dominant allele cannot be distinguished from heterozygous individuals carrying one copy of the allele, and both genotypes appear as a single fragment. Occasionally, polymorphisms appear as the brightness of the fragment, but such polymorphisms are subjective to score in the absence of breeding studies (Hunt and Page, 1992; Rabouam et al., 1999). In the honey bee Apis mellifera codominant fragmentlength polymorphisms have been reported (Hunt and Page, 1992). An index of similarity (or differences) can be calculated as the number of shared (or unique)

378

P. J. Smith

fragments between pairs of individuals divided by the number of fragments scored for both individuals (e.g., Gilbert et al., 1990; Jeffreys and Morton, 1987; Lynch, 1990; Wetton et al., 1987). The similarity index (S) calculated after Wetton et al. (1987), is S = 2N ab / N a + N b where Nab is the number of fragments shared by both individuals a and b, and Na and Nb are the number of fragments in individuals a and b, respectively. The average percentage similarity is the average of all S values multiplied by 100 (Gilbert et al., 1990). Dendrograms of genetic similarity can be constructed by cluster analysis (e.g., Nei, 1987). Lynch and Milligan (1994) have developed a general theory for the analysis of population structure with dominant markers and provided formulae for the estimation of conventional population-genetic measures such as allele frequencies and gene diversity for RAPD data sets. The scored characters are the fraction of individuals with (1 - x) and without (x) the fragment. Assuming that there are two alleles per locus and that the population is in Hardy–Weinberg equilibrium, then x = q2 and x1/2 is the null allele frequency. However, there are some restrictions, and Lynch and Milligan (1994) suggest that analyses are limited to loci for which the frequency of the null phenotype is 5m) habitats where radio telemetry cannot be used (discussed later). Tagged fish can often be detected at distances up to 3km, but the signal can only be detected in water (i.e., no aircraft or land-based reception). Vegetation, sharp water density changes (e.g., thermoclines), entrained air (turbulence), boat engines, and high suspended material concentrations can degrade signal transmission. Acoustic signals can also be reflected off rock, water density gradients, and underwater structures, although experienced field investigators can often learn to identify and circumvent these complications. Individual fish are generally identified by the transmission frequency of the tag they carry. However, when many tagged fish are aggregated, it is difficult or impossible to isolate each signal. In these situations, ultrasonic transmitters can be constructed to send signals that

Electronic Tags

439

vary in pulse rate (pulses per second), pulse interval (varied times between pulses), pulse duration (length of pulse time, can be combinations of variable duration), and combinations of these signal attributes. Pulse attributes can also be used to transmit sensor information on water depth (pressure sensor) and temperature being experienced by the tagged fish. Ultrasonic tags with detection ranges of from 1 to 3km typically have durations of 10 to 50 months, and these tags can be used with fish that are at least 0.4 to 1.2 kg (Fig. 21-1) in weight. Overall, ultrasonic telemetry works well in many confined marine environments because the tag transmits signals well in saltwater but the detection range is limited. Radio transmitters emit radio frequency signals from wire antennas through water and into the atmosphere. The signals are received with a tuned antenna of varied sizes and designs, including aircraft-mounted antenna. A radio signal receiver is used to detect and amplify the signals for listening with headphones or recording by a computer. Radio telemetry is preferred in low-conductivity freshwaters, shallow (