Advances in Agronomy, Volume 100

V O LU M E O N E ADVANCES H U N D R E D IN AGRONOMY ADVANCES IN AGRONOMY Advisory Board PAUL M. BERTSCH RONALD...

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V O LU M E

O N E

ADVANCES

H U N D R E D

IN

AGRONOMY

ADVANCES IN AGRONOMY Advisory Board

PAUL M. BERTSCH

RONALD L. PHILLIPS

University of Kentucky

University of Minnesota

KATE M. SCOW

LARRY P. WILDING

University of California, Davis

Texas A&M University

Emeritus Advisory Board Members

JOHN S. BOYER

KENNETH J. FREY

University of Delaware

Iowa State University

EUGENE J. KAMPRATH

MARTIN ALEXANDER

North Carolina State University

Cornell University

Prepared in cooperation with the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Book and Multimedia Publishing Committee DAVID D. BALTENSPERGER, CHAIR LISA K. AL-AMOODI

SALLY D. LOGSDON

KENNETH A. BARBARICK

CRAIG A. ROBERTS

WARREN A. DICK

MARY C. SAVIN

HARI B. KRISHNAN

APRIL L. ULERY

V O LU M E

O N E

ADVANCES

H U N D R E D

IN

AGRONOMY EDITED BY

DONALD L. SPARKS Department of Plant and Soil Sciences University of Delaware Newark, Delaware

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

Academic Press is an imprint of Elsevier 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 32 Jamestown Road, London, NW1 7BY, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands First edition 2008 Copyright # 2008 Elsevier Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made ISBN-13: 978-0-12-374361-9 ISSN: 0065-2113 (series) For information on all Academic Press publications visit our website at elsevierdirect.com Printed and bound in USA 08 09 10 10 9 8 7 6 5 4 3 2 1

CONTENTS

Contributors Preface

1. Dr. Norman E. Borlaug: Twentieth Century Lessons for the Twenty-First Century World

vii ix

1

Kenneth M. Quinn 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Introduction Iowa Roots Minnesota and a Focus on Plant Pathology and Wheat Confronting Poverty in Mexico India, Pakistan, and the Green Revolution The Impact in Asia The Nobel Peace Prize Bringing the Green Revolution to Africa The World Food Prize Inspiring the Leaders of Tomorrow A Lasting Global Legacy Extraordinary Recognition for a Humble Man

2. Contaminants as Tracers for Studying Dynamics of Soil Formation: Mining an Ocean of Opportunities

2 3 4 5 6 7 8 9 9 10 12 13

15

Jonatan Klaminder and Kyungsoo Yoo 1. Introduction 2. Outlining the Quest for New Tracers of Soil Formation 3. Atmospherically Derived Lead and SCPs: Tracers of What? 4. Constraining Mass Fluxes Involved in Geochemical Evolution of Soils 5. Lead and SCP as a Tracer of Organic Matter Dynamics 6. Conclusions Acknowledgment References

3. Epigenetics: The Second Genetic Code

16 20 21 34 45 47 50 50

59

Nathan M. Springer and Shawn M. Kaeppler 1. Introduction 2. Molecular Mechanisms of Epigenetic Inheritance

60 60 v

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Contents

3. Epigenetic Phenomena in Plants 4. Epigenetic Inheritance and Crop Improvement References

4. Microbial Distribution in Soils: Physics and Scaling

65 71 73

81

I. M. Young, J. W. Crawford, N. Nunan, W. Otten, and A. Spiers 1. Soil as a Habitat 2. What Characteristics of Structure Matter and Why? 3. Spatial and Temporal Distribution of Microbes 4. Habitat–Biofilm Interactions 5. Habitat–Microbe Interactions 6. Future References

5. Nanoscale Particles and Processes: A New Dimension in Soil Science

82 84 90 99 105 112 112

123

Patricia A. Maurice and Michael F. Hochella 1. Nanoscience as a Key Element of Soil Science 2. Nanoparticles and Nanominerals 3. Nanoparticles in Soils 4. Some Nano-scale Techniques for Soils Applications 5. The Unique World of the Nanoparticle 6. Why Nanoparticles Behave Differently 7. Nanoparticle Stability: Size and More than Size Matter 8. Nanoparticle Mobility in Soils and Sediments 9. Nanoparticle Effects on Pollutant Transport and Bioavailability 10. Nanoparticle Toxicity in Soil Environments 11. The Special Role of the Soil Sciences in Environmental Nanoscience Acknowledgments References

6. Combining Biomarker with Stable Isotope Analyses for Assessing the Transformation and Turnover Soil Organic Matter

124 125 127 131 135 139 140 144 146 147 148 149 149

155

W. Amelung, S. Brodowski, A. Sandhage-Hofmann, and R. Bol 1. Introduction 2. Major Biomarkers 3. Using Carbon Isotopes in SOM Studies 4. Biomarker Specific Stable Isotope Analyses 5. Conclusions and Perspectives Acknowledgments References Index

156 159 180 188 220 222 226 251

CONTRIBUTORS

Numbers in Parenthesis indicate the pages on which authors contributors begin

W. Amelung (155) Institute of Crop Science and Resource Conservation (INRES), Soil Science and Soil Ecology, University of Bonn, Nussallee 13, D-53115 Bonn, Germany R. Bol (155) Soils and Water Team, North Wyke Research, Okehampton, Devon, EX20 2SB, United Kingdom S. Brodowski (155) Institute of Crop Science and Resource Conservation (INRES), Soil Science and Soil Ecology, University of Bonn, Nussallee 13, D-53115 Bonn, Germany J. W. Crawford (81) Faculty of Agriculture, Food and Natural Resources, University of Sydney, NSW 2006, Australia Michael F. Hochella (123) Department of Geosciences, Virginia Tech, Blacksburg, Virginia 24061-0420, USA Shawn M. Kaeppler (59) Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706, USA Jonatan Klaminder (15) Department of Plant and Soil Sciences, University of Delaware, 531 S. College Avenue, Newark, Delaware 19716-2170, USA Patricia A. Maurice (123) Department of Civil Engineering and Geological Sciences, University of Notre Dame, Notre Dame, Indiana 46556, USA N. Nunan (81) CNRS, Laboratoire BioEMCo, UMR7618, Baˆtiment EGER, Aile B, Campus AgroParisTech, F-78850 THIVERVAL-GRIGNON, France W. Otten (81) SIMBIOS Centre, University of Abertay Dundee, DD1 1HG, Scotland

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Contributors

Kenneth M. Quinn (1) World Food Prize Foundation 1700 Ruan Center, 666 Grand Avenue, Des Moines IA 50309, USA. Kenneth M. Quinn has been President of the World Food Prize Foundation since 2000. From 1996 to 1999, he served as US Ambassador to the Kingdom of Cambodia. He holds a Ph.D. degree in International Relations from the University of Maryland A. Sandhage-Hofmann (155) Institute of Crop Science and Resource Conservation (INRES), Soil Science and Soil Ecology, University of Bonn, Nussallee 13, D-53115 Bonn, Germany A. Spiers (81) SIMBIOS Centre, University of Abertay Dundee, DD1 1HG, Scotland Nathan M. Springer (59) Department of Plant Biology, University of Minnesota, Saint Paul, Minnesota 55108, USA Kyungsoo Yoo (15) Department of Plant and Soil Sciences, University of Delaware, 531 S. College Avenue, Newark, Delaware 19716-2170, USA I. M. Young (81) School of Environmental and Rural Sciences, University of New England Armidale NSW 2351, Australia

PREFACE

This centennial volume of Advances in Agronomy marks a seminal milestone in the history of this venerable serial review. Volume 1 first appeared in 1949, and over the past 49 years, more than 35,000 pages of cutting-edge and heavily cited reviews have been contributed by 1506 authors from throughout the world on various topics in the crop and soil sciences. These reviews have been a continuing source of information to scientists, engineers, other professionals and students. Advances in Agronomy has consistently been ranked in the top echelon of serial reviews in the agricultural sciences. A.G. Norman, a soil microbiologist who served as president of ASA and later as Vice President for Research at the University of Michigan, was the first editor and held the position from 1949–1968, during which 20 volumes appeared. In Volume 1, Editor Norman stated that the objective of the serial was to ‘‘provide an objective survey of progress in agronomic research and practice’’. The first several volumes were dominated by authors from North America, and particularly the United States. The second editor, Nyle Brady, professor at Cornell University, Director General of the International Rice Research Institute in Manila, and Advisor at the U.S. Agency for International Development, served as editor from 1969–1991, during which 25 volumes were published, consisting of 195 review articles authored by 339 scientists from 24 countries. During Dr. Brady’s tenure as editor of Advances in Agronomy the serial took on more of an international flavor with topics ranging from classical areas in the crop and soil sciences to the more contemporary areas of molecular biology and environmental science and technology. In 1991, I had the honor of being named editor of Advances in Agronomy. Since that time we have published 54 volumes that have included first-rate contributions by authors worldwide, not only from the fields of crop and soil science but also from allied fields including plant molecular biology, plant pathology, microbiology, geology and geochemistry, chemistry, physics, marine science, and health sciences. Topics have been wide ranging, and have addressed areas that have global impact and significance. Volume 100 continues the rich tradition of excellence of Advances in Agronomy by including six state-of-the-art reviews on contemporary and significant topics. To celebrate the past as well as the future, authors were requested to discuss previous accomplishments, the current state-of-the-art of their topic, and most importantly, to look into the crystal ball and address ix

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Preface

what we don’t know. Chapter 1 is a wonderful retrospective on the life of one of our greatest agronomists, Nobel laureate Norman Borlaug. Chapter 2 presents a novel approach of using contaminants as tracers to study the dynamics of soil formation. Chapter 3 covers a frontier area in molecular biology, epigenetics. Chapter 4 is a fascinating treatise on microbial distribution in soils and aspects of scaling and physics. Chapter 5 provides a stateof-the-art review on nanoscale processes and particles and their significance in soil science. Chapter 6 is a timely review on the use of biomarkers and stable isotope analyses to assess the transformation and turnover of soil organic matter. I appreciate the authors’ insightful and outstanding reviews. They help assure that the excellence of Advances in Agronomy will continue for the next 100 volumes. Since assuming the editorship of Advances in Agronomy in 1991 I have been indeed fortunate to work with outstanding editors at Academic Press and Elsevier. These include: Phyllis Moses, my first editor, Charles Crumly, Judith Taylor, Christine Minihane, and Kristi Gomez. I am indeed grateful for their tremendous support and friendship. I have also had the privilege of working with a distinguished group of advisory board members that have included: Kenneth Frey, Martin Alexander, Eugene Kamprath, John Boyer, Ron Phillips, Larry Wilding, Kate Scow, and Paul Bertsch. Their suggestions of review topics and authors have been invaluable to the continued success and reputation of Advances in Agronomy. I also appreciate the affiliation and input of the American Society of Agronomy membership, in particular the Monographs Committee (now ASA Book Committee). This group has been involved with Advances in Agronomy since its inception. I have also been aided by excellent support staff at the University of Delaware including Jerry Hendricks, Muriel Toomey, Fran Mullen, and Kathy Fleischut. I am grateful for all their wonderful assistance. I would not have been able to carry out the duties as editor of Advances in Agronomy, along with all my other responsibilities in teaching, research, and administration, without the unfailing support and encouragement of my wife, Joy. To her I express my most sincere gratitude. DONALD L. SPARKS Newark, Delaware

C H A P T E R

O N E

Dr. Norman E. Borlaug: Twentieth Century Lessons for the Twenty-First Century World Kenneth M. Quinn1

Contents 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Introduction Iowa Roots Minnesota and a Focus on Plant Pathology and Wheat Confronting Poverty in Mexico India, Pakistan, and the Green Revolution The Impact in Asia The Nobel Peace Prize Bringing the Green Revolution to Africa The World Food Prize Inspiring the Leaders of Tomorrow A Lasting Global Legacy Extraordinary Recognition for a Humble Man

2 3 4 5 6 7 8 9 9 10 12 13

Abstract In all history, only five persons have received the Nobel Peace Prize, the Presidential Medal of Freedom and the Congressional Gold Medal. Norman Borlaug is one. The others are Mother Teresa, Nelson Mandela, Elie Wiesel, and Dr. Martin Luther King. The Congressional Gold Medal was the capstone of an extraordinary journey that began on an Iowa farm in 1914 and that took Borlaug to the University of Minnesota, hardscrabble farm fields of Mexico, famine threatened areas of India and Pakistan, poverty-stricken villages of Africa, the faculty of Texas A&M University, the White House to accept the National Medal of Science, and eventually back to Iowa to establish the World Food Prize. 1 World Food Prize Foundation 1700 Ruan Center, 666 Grand Avenue, Des Moines IA 50309, USA. Kenneth M. Quinn has been President of the World Food Prize Foundation since 2000. From 1996 to 1999, he served as US Ambassador to the Kingdom of Cambodia. He holds a Ph.D. degree in International Relations from the University of Maryland

Advances in Agronomy, Volume 100 ISSN 0065-2113, DOI: 10.1016/S0065-2113(08)00601-9

#

2008 Kenneth Quinn published by Elsevier All rights reserved.

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Named by TIME Magazine as one of the 100 most influential minds of the twentieth century, Borlaug labored in Mexico for 13 years developing a rust-resistant wheat variety that dramatically increased yields. In the immediate aftermath of World War II, the specter of mass starvation haunted the Indian subcontinent. But, India and Pakistan were pulled back from enormous human tragedy by the pioneers who ushered in the Green Revolution. Norman Borlaug was presented the Nobel Peace Prize in 1970 for leading this effort. One of Norman Borlaug’s most lasting contributions may be the creation of the World Food Prize to further inspire such breakthrough achievements. It would be only fitting if Borlaug’s twentieth century accomplishments would be the vehicle that brought peace and reconciliation to a deeply troubled twenty-first century world.

1. Introduction On July 17, 2007, I was privileged to be present as President George W. Bush and the bipartisan leadership of the Congress gathered in the Rotunda of the U.S. Capitol to award the Congressional Gold Medal, America’s highest civilian honor, to Dr. Norman E. Borlaug. Speaker after speaker praised Norman Borlaug as the ‘‘Father of the Green Revolution,’’ whose approach to increasing global food production had resulted in the saving of as many as one billion people worldwide from famine, starvation, and death. In all of the American history, only five persons have ever received the Nobel Peace Prize, the Presidential Medal of Freedom, and the Congressional Gold Medal. Norman Borlaug is one of them. The other four are Mother Teresa, Nelson Mandela, Elie Wiesel, and Dr. Martin Luther King. The Congressional Gold Medal was the capstone of an extraordinary personal journey that began on an Iowa farm on March 25, 1914. It would take Norman Borlaug to the University of Minnesota, hardscrabble farm fields of rural Mexico, famine threatened areas of India and Pakistan, Norway to receive the Nobel Peace Prize, poverty-stricken villages of Africa, the faculty of Texas A&M University, the White House to accept the National Medal of Science, and eventually back to Iowa to establish the World Food Prize. It was in a remote village in the Mekong Delta in 1969 that I first encountered the agricultural miracle that Norman Borlaug had unleashed. His combination of new high-yielding seeds, fertilizer, and irrigation had been applied to rice. The result was that it tripled production and dramatically improved lives in a remarkably short period of time. It truly was a Green Revolution.

Dr. Norman E. Borlaug

3

Thirty years later, I finally met this great but humble man when I assumed leadership of the World Food Prize, the award he created to be the ‘‘Nobel Prize for Food and Agriculture.’’ During the next decade, we spent hours and hours together, during which Norm recounted to me many stories from his life which I have endeavored to faithfully inscribe in the following paragraphs. In preparing this tribute to Norm, I have endeavored to trace his career, his sacrifices and his achievements in increasing global food production. It is dedicated to him, his family, and all those who assisted and supported him in his six-decade long agricultural and humanitarian odyssey that culminated with his being credited with having ‘‘saved more lives than anyone who has ever lived.’’

2. Iowa Roots Named by TIME Magazine as one of the 100 most influential minds of the twentieth century, Norman Borlaug is a quintessential American success story. Norm, as he is known to all who work with him, was born in 1914 to Norwegian-American parents outside Cresco in northeastern Iowa. His boyhood was spent on a Norman Rockwell-esque farm, where he had indelibly etched on his psyche the value of hard work, first inculcated by his family and later by his teachers and mentors. His formal education began in a one-room schoolhouse. It was there that a young Norm Borlaug first learned the lesson that confronting the harsh realities of prairie farm life could bring disparate people together and impel them to cooperate. Each morning, Borlaug recalls, the Lutheran Norwegian children from Cresco and the Czech (Bohemian) Catholics from Spillville would stand and sing ‘‘The Iowa Corn Song,’’ celebrating their new identity and the bond they now shared as Iowans. Borlaug and his classmates discovered in that small Iowa school that they had much in common, just as their parents found that working together to ensure sufficient food for all was more important than any ethnic, religious, or linguistic differences that might initially divide them. It was an insight that would remain with Borlaug throughout his life and permeate his work. Norm developed a dogged tenacity from participating in his high school wrestling program— another quality that would play a crucial role in some of his greatest achievements. His coach taught him never to give up, no matter how formidable his adversary. This attitude propelled Borlaug to the top of the inter-collegiate wrestling world and would later earn him induction in the NCAA Wrestling Hall of Fame. Still another lesson Norm Borlaug absorbed was the critical importance of rural roads to spreading the word about the latest agricultural innovations

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and helping farmers get crops to market. Iowa was dramatically affected by the Great Depression, with foreclosures on family farms bringing displaced families close to insurrection. The network of farm-to-market roads being built all over the state not only facilitated agricultural production, but also the transport of children to school and access to medical care. Those roads uplifted an entire generation of rural Iowans in a way almost nothing else could. Life improved and the prospect of political unrest receded. All of these factors came together in a way that steered Norman Borlaug to seek a university education, the first person in his family to do so. His initial thought was that he would study at Iowa State Teacher’s College and prepare for a career as a high school science teacher. This was a particularly arduous undertaking in the heart of the Great Depression. After graduating from high school, Norm labored for 50 cents a day as a hired farm hand to save enough to pay for a year of college.

3. Minnesota and a Focus on Plant Pathology and Wheat Eventually, Norm had earned enough money to pay for college and made his way north to the University of Minnesota, where he would major in agricultural science, become an accomplished wrestler, meet his wife Margaret and earn a Ph.D. in plant pathology. In Minneapolis, Borlaug worked in a coffee shop, served meals in a sorority house and parked cars. He earned 10 cents an hour doing clerical work in a Depression era relief program on campus, and credits the Roosevelt administration with enabling him to get his degree. He was deeply affected by the urban misery and hopelessness he encountered in the Twin Cities where he saw people sleeping in the streets. His commiseration with such poverty-stricken people would remain with him for the rest of the life. Thanks to his major in forestry, Norm obtained a summer job as a ranger with the U.S. Forest Service stationed first along the Salmon River in a remote part of Idaho and later in western Massachusetts. He came to embrace the solitude of the forest and cared deeply about the plants and wildlife that were sustained in this habitat. His expectation was that upon graduation, he would become a full-time employee of the Forest Service. However, fate intervened to redirect his life and to impact human history. As Norm tells the story, just a few weeks before graduation, he received a letter from his supervisor in the Forest Service informing him that a tight budget situation meant that he could not start his new full-time forest ranger position for another 6 months. A disappointed Borlaug, forced to delay his arrival, decided to take some additional courses on the Minnesota campus. One day, he saw a notice on a bulletin board for a lecture by Dr. Elvin Stakman, the head of the university’s plant pathology program. Borlaug decided to attend.


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Norm was riveted by Stakman’s lecture on rust, the parasitic fungus that attacked a wide variety of plants, especially wheat. As Lennard Bickel wrote in his 1974 biography of Borlaug, Facing Starvation, ‘‘. . .that night. . .Stakman was a magnetic and compelling teacher. His style, his sincerity, and the intensity of his delivery made his words ring in Borlaug’s ears.’’ Stakman ended his discourse with a moving charge that it was science which would ‘‘. . .go further than has ever been possible to eradicate the miseries of hunger and starvation from this earth.’’ Norman Borlaug was hooked. Following the lecture, he said he rushed after Stakman seeking admission to the Ph.D. program in plant pathology and giving up the possibility of a career in the Forest Service. It was a decision that would change his life, and save one billion people.

4. Confronting Poverty in Mexico Graduating in the middle of World War II, Dr. Borlaug went to work for the DuPont Corporation. But he was soon approached about joining a fledgling research project being initiated by the Rockefeller Foundation in rural Mexico. After completing his obligatory wartime service at DuPont, he accepted the offer. There, he first saw the plight of poverty-stricken farmers barely able to sustain themselves due to repeated poor harvests. Once again, Borlaug found a wide chasm to be bridged. There was an instinctive hesitation on the part of most subsistence farmers to adopt untried new technologies, and an understandable reluctance to trust the word of an American college boy who did not even speak their language. Borlaug admitted to being extremely discouraged in this initial venture into the developing world. But his commitment to learn Spanish, a healthy dose of the determination he learned in high school sports and his willingness to get his hands dirty working in the fields eventually enabled him to connect with some farmers who tried his new approach to wheat production. As Professor R. Douglas Hurt observed: ‘‘In 1944, when Borlaug arrived in Mexico, its farmers raised less than half of the wheat necessary to meet the demands of the population. Rust perennially ruined or diminished the harvest. . .. Borlaug labored for 13 years before he and his team of agricultural scientists developed a disease resistant wheat. . .(but) still problems remained.’’

While the new wheat variety he had developed increased yields and resisted rust, it did not have stems strong enough to hold the now heavy heads of grain. Plants would topple over in the wind and rain. Dr. Borlaug then turned to Japanese dwarf strains, which he crossbred with the varieties being raised in the hot, dry climate of northern Mexico. To accelerate his research and

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the development of new varieties, using ‘‘shuttle breeding,’’ Borlaug and his team would rush seeds to southern Mexico where it was possible to carry out a second growing season each year. The results were as astonishing as they had been difficult to attain. Aided by the use of fertilizer and irrigation, Borlaug’s new wheat varieties enabled Mexico to achieve self sufficiency in 1956. His belief in scientific research and a hands-on connection to the farmers paid off in what was considered an agricultural miracle. Borlaug’s accomplishments on wheat plants were perhaps unexpected given his upbringing in the cornfields of Iowa. This anomaly was noted by Vice President Henry A. Wallace, himself a native Iowan, on a visit to Mexico in the 1940s. Wallace jokingly asked Borlaug, ‘‘What’s a good Iowa boy like you doing working on wheat?’’

5. India, Pakistan, and the Green Revolution Inspired by his breakthroughs in Mexico, in 1960 the UN Food and Agriculture Organization and the Rockefeller Foundation asked Borlaug to turn his attention to the Middle East and South Asia. In the immediate aftermath of World War II, famine and the prospect of mass starvation haunted the Indian sub continent and other parts of the globe. The great Bengali famine in the late 1940s seemed an ominous harbinger of starvation that would extract a devastating toll, increasing exponentially the more than 160 million people worldwide who had already died of famine or starvation during the previous 100 years. But much of the developing world was pulled back from the precipice of enormous human tragedy by the scientific pioneers who ushered in the Green Revolution. Leading them was Norman Borlaug, and the young agricultural scientists he had trained at what would become the International Maize and Wheat Improvement Center (CIMMYT) located outside Mexico City. Having overcome great resistance by farmers in Mexico, Borlaug and his compatriots now faced the seemingly impossible task of convincing the leaders of both India and Pakistan—two countries bitterly divided—to embrace an entirely new approach to agriculture. Borlaug recalled that going in to speak to these two most powerful political leaders required summoning the same amount of courage as when he stepped on the wrestling mat. But he went forward and presented the options available to the political leaders of both countries. With the support of Malik Khuda Bakhsh Bucha (then Minister of Agriculture in Pakistan) and C. Subramaniam (then Minister of Agriculture and Food of India) and young scientists like Dr. M.S. Swaminathan, both countries made the courageous decision to adopt Borlaug’s breakthrough technology. It arrived just in time to prevent a human catastrophe.


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By increasing crop yields in Pakistan and India fourfold, those traditionally food deficient countries became self sufficient in an amazingly short time, saving hundreds of millions of lives. Borlaug’s achievements in wheat spread throughout the Middle East and North Africa with similar life-saving results. Beginning in the early 1960s, his approach to wheat breeding was introduced in Egypt, Tunisia, Syria, Iran, Libya, Jordan, Lebanon, Turkey, Iraq, Afghanistan, Algeria, and Saudi Arabia, in many cases through those young scientists who had studied with him at CIMMYT. Borlaug’s lasting impact was brought home to me 40 years later during a visit to Egypt when I called on the Minister of Agriculture. When I mentioned Norman Borlaug’s name, the Minister immediately stopped the meeting and sent several aides rushing from the room. They returned a few minutes later with displays of robust wheat plants which the Minister proudly showed me. ‘‘We know Norman Borlaug very well,’’ the Minister declared, going on to point out how Borlaug’s innovations had helped transform agriculture in his country and throughout the region, to the benefit of millions upon millions of the citizens of all these countries.

6. The Impact in Asia Borlaug’s successes in wheat were quickly replicated in other grains, most notably rice, by scientists such as M.S. Swaminathan in India, and Robert Chandler, Henry ‘‘Hank’’ Beachell and Gurdev Khush at the International Rice Research Institute in the Philippines. Together, with countless others, they helped avert famine and starvation in much of the developing world in the second half of the twentieth century. I was a young development worker in the Mekong Delta in 1968 when this new ‘‘miracle rice’’ from the Philippines arrived. Its impact was as stunning as it was immediate, but it was remarkably affected and limited by the condition of the roads and bridges that linked the eight villages in my district. The four villages that were accessible by a newly improved road experienced dramatic improvements, both in terms of nutrition and the well being of the people. The new IR-8 rice spread rapidly as peasant farmers with small plots were suddenly able to experience both increased yields and double or even triple crops. This in turn led to tangible improvements in the quality of life: child mortality dropped; malnutrition abated; and children, especially girls, stayed in school longer. At the same time, there was a rapid corresponding decrease in the level of armed conflict and military hostilities. It was as though the combination of new roads and new rice seeds caused the roots of violent extremism to wither and disappear in a way that military action alone could not.

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By contrast, the four other villages, with no bridges and no road access, remained mired in poverty: the new ‘‘miracle seeds’’ were not put to use, children remained stunted, and warfare and political dissidence continued there unabated. This experience seemed to confirm one of the central lessons of Norman Borlaug’s boyhood—the ability of agricultural innovation when combined with rural roads to dramatically change social conditions.

7. The Nobel Peace Prize Dr. Norman Borlaug was presented the Nobel Peace Prize in 1970 for his accomplishments in India and Pakistan and for his role as ‘‘Father of the Green Revolution.’’ It is indicative of the kind of person he is that on October 20, 1970, when the phone call came to advise him of his selection as the Laureate, Borlaug was in a remote farm field in Mexico. His wife Margaret had to drive for over an hour to tell him the news and ask him to return home to respond to the call and the accompanying press requests for interviews. Lennard Bickel, in Facing Starvation, describes Norm’s reaction: ‘‘He told Margaret that he did not see how he could possibly come to speak on the phone since he and his assistants still had much more work to do. He then went back to recording data on his test plots. It was there that the TV camera crews from Mexico City found him two hours later.’’ In a sense, when Borlaug received the Peace Prize on December 10, 1970, his life had come full circle. Here he was, the son of immigrants who had left Norway due to extreme food shortages, now, back in their country of origin to receive one of the world’s highest honors for his role in increasing the global food supply. As he stood in the great hall of the University of Oslo, Borlaug was lauded as an man who fought ‘‘not only weeds and rust fungus but just as much the deadly procrastination of the bureaucrats and red tape that thwart quick action. . ..More than any other person of this age, he has helped to provide bread for a hungry world.’’ Borlaug remains the only agricultural scientist ever to receive the Nobel Peace Prize, and one of its least known recipients. It is ironic that his name is so little recognized, as he has probably saved more lives than all of the more celebrated honorees put together. In his laureate address, Borlaug stressed that the agricultural breakthrough achievements for which he was being honored were only providing a brief window of time during which the world must confront the specter of a burgeoning population that would have to be fed. As a result, Dr. Borlaug’s efforts did not cease or even slow after this recognition by the Nobel Foundation.


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8. Bringing the Green Revolution to Africa While many individuals might consider retiring after receiving such high recognition, Dr. Borlaug worked even harder in the struggle against world hunger and malnutrition in the four decades following his selection as the Peace Prize Laureate. Even in his tenth decade of life, Dr. Borlaug kept a heavy travel schedule, pressing forward with projects in Africa, passionately advocating the primacy of science and technology in improving global food security, devoting significant time and energy to education and promoting biotechnology as a way to preserve the environment. Starting in 1986, Dr. Borlaug headed the Sasakawa Africa Association, whose programs aimed at defeating malnutrition and poverty in Africa. His activities centered on bringing science-based crop production methods to the small farms of sub Saharan Africa. Proven agricultural technology, he believed, was the key to overcoming widespread food shortages that condemned millions of people in Africa to lives of hardship and hunger. Part of the Sasakawa Global 2000 endeavor, the Sasakawa Africa projects he designed were initiated in a dozen African countries. Perhaps the most significant achievement of this effort has been the successful development of highly nutritious corn, known as Quality Protein Maize, which offers great promise in preventing acute malnutrition among children in Ghana, Mozambique, and other African countries, as well as in Mexico. Perfected by longtime Borlaug prote´ge´s at the Maize and Wheat Improvement Center in Mexico, Dr. Borlaug and the Sasakawa Africa Association helped spread this life-saving food into villages with immediate effect, enhancing the lives of thousands and thousands of children. Norm, no doubt, takes solace in the fact that the international community has begun to provide a sharper focus to the needs of Africa through the Millennium Development Goals and AGRA, the association to bring the Green Revolution to Africa, headed by former UN Secretary General Kofi Annan. But whatever satisfaction he derives from seeing his leadership fulfilled is tempered by the re-emergence of a virulent new strain of rust disease that threatens to undo many of the achievements of the past decades.

9. The World Food Prize One of Norman Borlaug’s most lasting contributions may be the creation of the World Food Prize. Norm often said he believed he was nominated for the Nobel Peace Prize because there was no Nobel Prize for agriculture or for efforts to counter poverty and hunger. Dr. Borlaug felt

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Kenneth M. Quinn

there should be such an award, so shortly after receiving the Peace Prize, he approached the Nobel Committee urging the creation of a new Nobel Prize for Agriculture. But it was not possible. Not even Borlaugian grit and determination could change Alfred Nobel’s will. Undeterred, Norm set out to create just such an honor. In 1986, with the assistance of Carleton Smith and the support of the General Foods Corporation, he established a new award to recognize exceptional achievements in increasing the quality, quantity, and availability of food—The World Food Prize. The $250,000 Prize was eventually endowed by philanthropist and businessman John Ruan, himself with origins in a small Iowa town like Borlaug. Ruan ‘‘rescued’’ The Prize when General Foods withdrew its sponsorship in 1989. Ruan and Borlaug, both born in 1914, became fast friends and partners in transplanting ‘‘the foremost international award’’ in the struggle against hunger to the plains of their home state. They moved the Prize to Des Moines and established a foundation with a bipartisan Council of Advisors that included former Philippine President Corazon Aquino, former U.S. Presidents Jimmy Carter and George Bush, and Olusegun Obasanjo who would become the President of Nigeria. Borlaug serves as head of the Selection Committee that each year chooses the World Food Prize Laureates, while John Ruan served as Chairman of The World Food Prize Foundation until 2003, when his son, John Ruan III, replaced him. Since its creation, the World Food Prize has honored individuals who have made significant breakthrough achievements in reducing hunger and countering malnutrition. Recipients of The Prize include experts and scientists born in or representing: Bangladesh, Brazil, Cuba, China, Denmark, India, Mexico, Sierra Leone, Switzerland, United Kingdom, United Nations, and the United States. In addition to the Laureate Award Ceremony, which takes place in the magnificent Iowa State Capitol, Borlaug and Ruan created an International Symposium and Youth Institute to foster a dialogue on world hunger and related issues. The symposium, recently renamed ‘‘The Borlaug Dialogue,’’ attracts over 700 people from more than 60 countries to Des Moines each October for what organizers hope will become ‘‘the most significant observance of World Food Day anywhere around the globe.’’

10. Inspiring the Leaders of Tomorrow Athlete, Scientist, Humanitarian, Educator—all are appellations that capture an essential aspect of Norman Borlaug’s life. World leaders would call him Pioneer, Genius, Laureate, and Hero. But he remains humble and


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true to his origins despite all of these laurels. Ever the teacher, his commitment to educating and inspiring young students has been an overriding passion of his life. I recall being in the rotunda of the Iowa State Capitol on the day his native state was establishing October 16, World Food Day, as an official Day of Recognition in his honor (only the second time in the 161-year history of Iowa this had been done). As the governor was about to begin the ceremony to proclaim Norman Borlaug Day, we discovered that Norm was missing. I eventually found him down a hallway with a group of fourth graders, explaining to them one of the exhibits about Iowa history. Dr. Borlaug has been committed to youth activities and education throughout his career. While pursuing agricultural breakthroughs in Mexico, he served as Scoutmaster for his son’s Boy Scout Troop and as coach of the first Mexican Little League baseball team (with a young Bill Richardson, now Governor of New Mexico, as a player). Even well into his nineties, he continued to devote himself to passing on to the next generation his passion for science and education as the means to uplift people mired in poverty. Between attending conferences and giving lectures around the world, he continues to teach at Texas A&M University, where he has held a post as a Distinguished Professor in the Department of Soil and Crop Sciences for almost 25 years. To promote interest in global food security, Norm teamed with John Ruan to create The World Food Prize Youth Institute, which is held in conjunction with ‘‘The Borlaug Dialogue’’ each October in Des Moines. There, high school students interact with Dr. Borlaug, World Food Prize Laureates and other experts to discuss the potential solutions to world hunger and the roles they, the leaders of tomorrow, might play in making them a reality. In 1994, its first year, the Youth Institute had only 13 schools represented. A decade later, close to 100 students and 100 teachers were attending each year. Under Dr. Borlaug’s direction, the Youth Institute added the Borlaug– Ruan International Internship program, which each summer sends a dozen exceptional high school students on eight week internships to international agricultural research centers in Bangladesh, Brazil, China, Egypt, Ethiopia, India, Kenya, Malaysia, Mexico, Peru, Philippines, and Taiwan. Norm wanted them to have that same type of life altering experience that he had when he heard Elvin Stakman speak on that cold Minnesota night in 1937. When speaking to young people in the early years of the twenty-first century, Borlaug often paraphrases Thomas Jefferson as rhetorically asking whether: ‘‘Ease and security, were these the drugs that abated the eternal challenge of the minds of men? . . .Did nations, like men, become lethargic when well fed and bodily comfortable?’’ It is clear that Borlaug worries that this may be the case, particularly now that almost all young Americans are physically removed from farming, and

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Kenneth M. Quinn

the connection between our food supply and agriculture production is no longer so clearly understood. But, no doubt, Norm takes heart when some of the students returning from their Borlaug–Ruan International Internships tell him that coming face to face with third world poverty was a ‘‘life changing experience,’’ perhaps not unlike Borlaug’s own epiphany as he listened to Stakman almost 70 years ago.

11. A Lasting Global Legacy Exhibiting the virtues he learned growing up, Norm Borlaug has traveled the world to promote greater attention to, and investment in, education, agricultural research, and rural infrastructure (like roads and railroads). Norm believed all these are essential if we are to have the next ‘‘Green Revolution,’’ the one which will lift the remaining one billion people out of the misery of malnutrition and end pandemic poverty. In his speeches he advocates biotechnology and the crucial role he sees for it in feeding and enhancing the nutrition of those still in tenuous food security situations, particularly in Africa. His dream is that one day a scientist will discover the gene in the rice plant that prevents it from developing rust disease, and transplant it into wheat and other crops devastated by this scourge. Genetically modified crops are controversial, but, never one to back away from a confrontation, Dr. Borlaug argues that we must rely on science and research to answer the questions about whether such foods pose any environmental risks. He laments the declining trend in support for public agricultural research, such as at CIMMYT, where the crucial discoveries that led to the first Green Revolution took place. In June 2002, he and all the living World Food Prize Laureates issued a statement at the World Food Summit in Rome calling for a reversal of this trend. In the spring of 2008, despite his failing health, he flew to Washington to meet with members of Congress to urge U.S. funding for the World Bank CGIAR research centers. At the conclusion of his speeches, something of the old forester would come to the fore. Dr. Borlaug would point out that with the earth’s population increasing exponentially, all these new people can be fed in only one of two ways. Either we significantly increase yields on the land now in production, or we plow under the remaining rainforests and other habitats for wild animals in order to have more land to farm. Biotechnology, he stressed, will help preserve the ecosystem while also reducing hunger and malnutrition by providing these increased yields. As he once told a group of Iowa high school students, he may have saved more trees as a plant pathologist than he ever would have as a forest ranger.


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12. Extraordinary Recognition for a Humble Man Dr. Borlaug’s position as one of history’s greatest humanitarians was cemented with the presentation of the Congressional Gold Medal, America’s highest civilian honor, in 2007. The Congressional Gold Medal capped several significant awards and recognition Dr. Borlaug has received in the twilight of his life. Others included the U.S. National Medal of Science in 2004 and India’s Padma Vibhushan, that country’s second highest civilian honor and an award that is rarely given to foreigners, in 2006. Never one to seek recognition, Dr. Borlaug nevertheless became one of the most honored individuals in modern history. At the presentation of the Congressional Gold Medal, President Bush pointed to Borlaug as a testament to the idea that ‘‘one human being can change the world.’’ ‘‘Ours is a land of hope and promise and compassion,’’ said President Bush, ‘‘and we see that compassion and promise in the man we honor today, a farm boy, educated in a one-room schoolhouse, who left the golden fields of Iowa to become known as ‘the man who fed the world.’’’ ‘‘The name Norman Borlaug may not be known in many households on Earth, but his life’s work has reached almost every kitchen table on Earth,’’ said Senate Majority Leader Harry Reid. Speaker of the House Nancy Pelosi recalled President John F. Kennedy’s 1963 declaration that ‘‘the war against hunger is truly mankind’s war of liberation. No person, before or since, has done more to answer the call to help liberate the world from hunger,’’ said Pelosi. ‘‘As such, Dr. Borlaug is one of the greatest liberators the world has ever known.’’ In his acceptance remarks, proving that he was not satisfied to rest on his laurels, Dr. Borlaug stressed the importance of continuing the fight against hunger. He concluded by saying: ‘‘We need better and more technology, for hunger and poverty and misery are very fertile soils into which to plant all kinds of ‘isms,’ including terrorism.’’

Norman Borlaug’s message is just as relevant for those who seek to counter terrorism and bring a lasting peace in the Middle East and South Asia. It just may be that a ‘‘New Green Revolution’’ represents one of the most potent forces available to this generation to dissipate the sources of terrorism which breed and are sustained in the poorest parts of the world. As the person who has probably also saved more lives in the Islamic world than anyone who has ever lived, it would be only fitting if Norman Borlaug’s twentieth century message of using seeds and roads to reach across political, ethnic and religious chasms to uplift hungry, suffering people, would be the vehicle that brought peace and reconciliation to a deeply troubled and divided twenty-first century world.

C H A P T E R

T W O

Contaminants as Tracers for Studying Dynamics of Soil Formation: Mining an Ocean of Opportunities Jonatan Klaminder and Kyungsoo Yoo Contents 16 20 21 22 23 24 29

1. Introduction 2. Outlining the Quest for New Tracers of Soil Formation 3. Atmospherically Derived Lead and SCPs: Tracers of What? 3.1. Biogeochemical properties of lead in soils 3.2. Biogeochemical properties of SCPs in soils 3.3. Tracking the tracers in soil matrix 3.4. Contaminants inputs to soils over time 4. Constraining Mass Fluxes Involved in Geochemical Evolution of Soils 4.1. Mass fluxes across the boundaries of a soil 4.2. Vertical mass fluxes within a soil pedon 5. Lead and SCP as a Tracer of Organic Matter Dynamics 6. Conclusions Acknowledgment References

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Abstract There is an ever increasing need to make pedology a process-oriented and quantitative scientific discipline. Key tools in this effort are tracers that help quantify the direction and rates of various pedogenic processes over timescales beyond the reach of field studies. Though not commonly acknowledged, several atmospheric pollutants could be ideal tracers for studying soil forming processes and their rates. This review, among many potential pollutants, focuses on lead (Pb) and spheroidal carbonaceous particles (SCPs) entering the soil through atmospheric inputs. These contaminants enter soils at their

Department of Plant and Soil Sciences, University of Delaware, 531 S. College Avenue, Newark, Delaware 19716-2170, USA Advances in Agronomy, Volume 100 ISSN 0065-2113, DOI: 10.1016/S0065-2113(08)00602-0

#

2008 Elsevier Inc. All rights reserved.

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Jonatan Klaminder and Kyungsoo Yoo

surface, where mass fluxes through accumulation, erosion, mixing, and translocation are most vigorous. The rationale for expecting these atmospheric contaminants to be suitable as tracers are; (1) the atmospherically derived fractions can be distinguished within the complex soil matrix, (2) the boundary condition for their inputs to the soil can be well constrained in time and space, and (3) their biogeochemical properties make them suitable proxies for the movement of various solid components in soils. By adapting the pollutants to Simonson’s conceptual view of soil formation within a mathematical framework we demonstrate how the vertical distribution of the atmospheric contaminants could be used to infer the mass-fluxes responsible for biogeochemical evolution of soils. Although there could be problems in applying the methods outlined in this paper for weakly contaminated soils with a strong geogenic lead source, the potential for using the methods for soils formed in industrialized urban areas is high.

1. Introduction In his classical book on soil formation in 1941, Hans Jenny stressed the need to systematically study soil properties and relevant soil processes as a function of well characterized environmental factors and thereby outlined the path that many soil scientists have followed since ( Jenny, 1943). The factorial theory, however, does not necessarily offer mechanistic insights into the biogeochemical or geomorphic processes responsible for soil formation. Later, in Simonson’s largely conceptual model (Simonson, 1959; Simonson and Mahaney, 1978), soil forming processes became synonymous with mass fluxes across spatial boundaries of soils and operationally defined material pools within soils. These mass fluxes include addition, translocation, removal, and transformation (Fig. 1). The challenge in implementing Simonson’s model is to determine the direction and magnitude of various mass fluxes and link them to the observed geochemical and morphological properties of soils. In practice, given the often slow pace of soil formation compared to the human time scales, it is difficult to monitor these mass fluxes. Instead, we look into tracers, the behaviors of which are well characterized in soils. Thus these tracers allow us to quantify slow pedogenic processes on timescales greater than those possible using field based monitoring. Few scientists would oppose the statement that isotopic tracers have greatly improved our knowledge regarding pedogenic processes that are difficult to grasp with short-term field monitoring. For example, strontium isotopes (87Sr and 86Sr) were critical in determining the contribution of aeolian dusts in shaping soil geochemistry and helped pry open the black box of mineralogical transformation and loss via chemical weathering (e.g., Miller et al., 1993). With the advent of the modern measurements of in situ

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Contaminants as Tracers of Soil Formation

Translocation

Soil erosion

Parent material

Leaching

Soil production

Soil

Atmospheric input

Figure 1 A schematic showing Simonson’s conceptual view of soil forming processes. The box represents a three dimensional volume of a soil pedon between the ground surface and parent material with the surface area of roughly 1 m2. Gray arrows indicate the mass fluxes into the soil, whereas the darker arrows indicate mass fluxes out of the soil. Mass fluxes within the soil are considered translocation. Among the mass fluxes proposed by Simonson, transformation (e.g., mineralogical changes via weathering) is not included in this diagram.

produced cosmogenic radionuclides such as 10Be and 26Al with Accelerator Mass Spectrometers (AMS), we now can determine the soil production rate from the underlying bedrock (Heimsath et al., 1997) and soil and rock erosion rates at the time scales of thousands to millions of years (Bierman, 2004). At shorter time scales, bomb peak 137Cs has been widely used as a metric to determine accelerated soil redistribution within agricultural watersheds (e.g., Zapata, 2003). When one turns attention to the organic side of the advances, 14C has been established as a standard clock for the rates of carbon cycling in soils (Trumbore, 1993), and the stable isotopes of carbon and nitrogen of soil organic matter are becoming routinely determined to trace the pathways and fates of these two essential elements within the global biogeochemical cycle (e.g., Dawson et al., 2002). Despite the increasing number of available tracers, it is unlikely we will exhaust the need for more tracers. Soils are fundamentally complex systems. The temporal extent that each mass flux operates in soils varies greatly; from

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minutes and hours of storm driven solute flux to millennial scale movement of colluvial soils. Likewise, vertical and lateral length scales of the mass fluxes can easily vary over several orders of magnitude. Furthermore, many groups of individual mass fluxes are tightly coupled, and deciphering their interactions and feedbacks forms one of the challenges before soil scientists. Solving such complex problems inevitably requires diverse tools or tracers. In this review, we suggest a suit of atmospheric pollutants as another group of tracers for studying soil processes. A large number of papers have been published involving topics related to soils and metal contaminants such as Pb, Cu, and Cd (Fig. 2). Although there are multiple reasons for a high production of papers involving metal contaminants and soil, the net outcome is still the same—we have accumulated a sizeable body of knowledge about the behavior and fate of metals in soils and many soil scientists are equipped to work with metal contaminants. Despite the rapidly increasing understanding of the behaviors of pollutants within soils, however, few studies have turned the question around and asked how the knowledge of metal contaminants could be used to infer rates of soil forming processes. This review proposes to take advantage of the known behavior and fate of common atmospheric contaminants in inferring the directions and sizes

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Articles in ISI web of science (no.)

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Search phrases Contaminants Soil mercury Hg Soil lead Pb Soil cadmium Cd Soil copper Cu Tracers Soil cesium Cs Soil berrylium Be Soil strontium Sr Soil carbon C-14

150 100 50 0 1992

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Figure 2 Number of articles found in the ISI-Web of science database that relate to different metals and soil. Text and symbols in the left corner show search phrases used to identify articles covering each metal.


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of the mass fluxes responsible for shaping the biogeochemical compositions of soils and their topographic and vertical variations. We demonstrate our idea using lead (Pb) and spheroidal carbonaceous particles (sometimes referred to as soot spherules) as example contaminants, even though we further stress the potential use of other atmospheric contaminants such as mercury (Hg), copper (Cu) and cadmium (Cd). Lead is a metal that has been highly enriched above natural levels in the atmosphere due to emissions from processing of sulphide ores and combustion of fossil fuel and car exhaust, while spheroidal carbonaceous particles (SCPs) are particles at a micrometer scale derived from incomplete high-temperature combustion of fossil fuel (Fig. 3). The rationale for using Pb and SCPs as ‘‘master’’ contaminants in this review is that they, in our opinion, currently offer the greatest potential as tracers because; (1) their biogeochemical properties make them suitable as proxies of movements of solid particles and colloids in soils (Section 3.2), (2) they could be fairly easily distinguished from in situ geogenic materials within soils (Section 3.3), and (3) their pollution history is fairly well known and can be reconstructed at local scale over long time periods using natural archives such as lake sediments, peat bogs and ice-cores (Section 3.4). The main characters of the commonly used tracers are discussed, and these criteria are used for evaluating the potential utility of atmospheric derived lead and SCPs as tracer.

Figure 3 Scanning electron micrograph of a spheroidal carbonaceous particle. The diameter is approximately 30 mm.

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2. Outlining the Quest for New Tracers of Soil Formation We begin by determining main criteria of a good tracer. This is most easily done by identifying common characters of current tracers. The strontium isotope technique is often applied to studying the significance of atmospheric dusts in soil formation, where the 87Sr/86Sr ratio of the atmospheric input is different from that of the parent material. In such conditions, a binary mixing model, in conjunction with Sr isotopes, could be used to calculate the relative contributions from atmospheric vs. geogenic sources in vegetation, bulk soil and in pedogenic minerals or to determine the provenance of atmospheric dust entering the soil (Capo et al., 1998). In combination with estimates from soil water fluxes or mass-balance models, these mixing calculations could provide insights to current and long-term weathering rates of soil minerals or rates of subsurface carbonate accumulations (see Stewart et al., 1998 for a thorough review). The absolute age of a soil (equivalent of the age of its geomorphic surface), which is crucial for calculating various aspects of long-term soil formation rates or soil erosion rates, is commonly determined using cosmogenic radionuclides (CRN). For example, 10Be with the half life of 1.51 Myrs is produced in the atmosphere due to the collision of cosmic rays with nitrogen and oxygen atoms and then arrives onto ground surface by dry and wet deposition where it is strongly adsorbed onto clay surface (Pavich et al., 1986; Valettesilver et al., 1986). Beryllium-10 is also produced within mineral quartz when cosmic neutron hits oxygen atom in the crystal lattice (Bierman, 2004). Consequently, the 10Be inventory in soils and soil minerals is expected to increase with the increasing length of time that the soil resides on the geomorphic surface and could be used to determine the age of the soil. Another cosmogenic isotope commonly used is 14C that is formed in the atmosphere and then incorporated into the pool of organic matter, which allows the dating and thus cycling rates of organic carbon in soils (Trumbore, 1993). Carbon-14 has been also used for dating pedogenic carbonates (Wang et al., 1996). The 14C dating methods utilizes the well known half-life of the 14C atom and the historic 14C levels of the atmosphere, which have been reconstructed using tree rings and varved lake sediments (Kitagawa and Van der Plicht, 1998; Stuiver and Reimer, 1993), to calculate the date in which the radionuclide was assimilated by plants given its current 14C content. This technique can provide ages as well as providing turnover rates of the organic matter in soils (Baisden et al., 2002). Thermonuclear-bomb-testing in the 1950s induced rise of natural 14C. This ‘‘bomb pulse 14C’’ is well constrained in time such that the peak has been used to calculate decadal turnover rates of soil organic matter (Baisden et al., 2002) or to infer vertical soil mixing


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rates (Obrien, 1984). Furthermore, stable isotopic ratios of 13C and 12C have been combined with end-number mixing models to track down the pathways of carbon originated from, for example, different plants (Dawson et al., 2002). The explosion of scientific understanding of the carbon cycle during the past decades can, in large part, be attributed to the fortunate fact that the element has both radioactive and stable isotopes. The rates of inputs into the soil of the tracers we discussed so far are controlled by natural sources (except the bomb peak, 14C). However, there are also anthropogenic tracers operating within a centennial time-scale. Like the bomb peak 14C, the elevated atmospheric levels of 137Cs between 1950s to 1960s due to the bomb testing programs, together with the atmospheric pulse released during the Chernobyl nuclear accident in 1986, have been used to infer soil erosion and accumulation rates (Walling and Quine, 1991). In short, widely used tracers, including strontium isotopes, cosmogenic isotopes and 137Cs, for studying soil mass fluxes satisfy the following three characteristics: (1) the fate of the tracer in the soil could be directly linked to the fate of specific soil components, (2) the tracer could be clearly identified within the soil matrix and separated from geogenic sources, and (3) the input or within-soil production of the tracer could be reconstructed and constrained over time. As will be argued in the following sections, these three general characteristics are shared by Pb and its stable and radioactive isotopes as well as SCPs (and likely other industrial atmospheric contaminants as well). The biogeochemical properties of the common metal contaminants have been extensively studied. A branch of this research has notably facilitated their use as proxies for mass-movements of solid masses in soils such as minerals and organic matter (see Sections 3.1 and 3.2). In many cases, atmospheric inputs of lead and SCPs into soils have been so substantial that atmospherically derived metals can be separated from geogenic sources through mass-balance models or isotope mixing models (see Section 3.3). Finally, the fallout history of metal contaminants to the soil surface has been reconstructed and shown to be synchronous over large regions (Section 3.4). The higher publication rate on the pollutants in soils than on the commonly used tracers for soil studies implies that we may know more about the behavior of metal contaminants than the common tracer elements such as Sr and Cs (Fig. 2).

3. Atmospherically Derived Lead and SCPs: Tracers of What? It is essential for a tracer that tracks mass movements of soils to have a strong affinity for the major solid surfaces found within the soil matrix, that is, clay minerals, organic matter and (hydr)oxides. For example, 137Cs can

22


be used as a tracer of soil erosion or mixing, because Cs is strongly sorbed to clay minerals (Poinssot et al., 1999) and the loss of 137Cs could be argued to be proportional to the mass loss of the soil (Quine et al., 1997). To be able to functionally relate the vertical distribution of atmospherically derived Pb or SCPs to mass fluxes in soils, it is of crucial importance to know their biogeochemical properties, which are discussed in the following two subsections.

3.1. Biogeochemical properties of lead in soils The chemistry of Pb in soils is dominated by a strong sorption by various soil surfaces, where Pb forms strong inner-sphere complexes with clay minerals (Strawn and Sparks, 1999), organic matter (Strawn and Sparks, 2000) and oxides (Trivedi et al., 2003). In addition, Pb forms precipitates with a moderate to low solubility in the soil, where Pb hydroxides and phosphates dominate in noncalcareous soils and carbonates dominates in calcareous soils (Bradl, 2004; Evans, 1989). Consequently, Pb is strongly retained in the soil. In fact, Pb is generally more strongly retained in the soils than Cs—the currently more widely used tracer of soil erosion. Lead has also been shown to be a fairly immobile element in comparison to Cs in both agricultural (He and Walling, 1997) and forested soils (Klaminder et al., 2006b). Furthermore, atmospheric inputs of Pb have been shown to be retained in buried paleosol layers at a millennia time-scale (Kylander et al., 2008), suggesting that Pb forms long-term stable and insoluble complexes in soils. Plant cycling can significantly reduce the usefulness of a tracer tracking the mass movements of soil solids resulting from erosion or translocation of minerals and colloids because plants may take up elements strongly sorbed to minerals and organic matter and redistribute them within the soil matrix. For some tracers like Sr and Cs, the uptake by plants can be significant (Capo et al., 1998; Tamponnet et al., 2008). Plant uptake lowers the potential for these tracers to be used in the study of soil erosion and translocation processes in environments where these mass fluxes proceed at slower or similar rates in comparison to fluxes due to plant-derived cycling. This, however, is not the case for Pb. Even though Pb is not an essential element for plants, it enters plants by root uptake from the soil and through interception of atmospheric Pb by the stomata cells (Lindberg et al., 1982) and bark (Watmough and Hutchinson, 2003). Studies using Pb isotopes have indicated that about half of the Pb found in the biomass of forest plants is derived from direct interception from the atmosphere and whereas 40% and 10% of the remaining Pb is derived from root uptake in the organic horizon and the mineral soil, respectively (Klaminder et al., 2005). Furthermore, mass-balance calculations combined with mixing models based on the stable Pb isotopic composition of the plants and the soil, suggest that the uptake rate of Pb from the mineral soil by forest plants is


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small (1.7 billion years) granitic and gneissic bedrock. The 206Pb/207Pb ratio of the atmospheric Pb varies in time and space depending mainly on the mixture of pollution sources. For example, aerosols in Canada, Asia, Europe, South Africa, and Australia have a 206Pb/207Pb ratio typically in-between 1.09 and 1.17, while the range in South America and the USA is typically between 1.07–1.2 and 1.16–1.23, respectively (Bollhofer and Rosman, 2002). However, the 206Pb/207Pb does not only vary geographically, but also over time. For example, the increased usage of radiogenic Pb from the Missouri Pb ore (206Pb/207Pb > 1.35), caused the 206Pb/207Pb ratio of the atmosphere in the USA to rise from a value around 1.16 prior to 1968 to ratios up to 1.23 about a decade later (Shirahata et al., 1980). Another example is the 206Pb/207Pb ratio of the European atmosphere, which decreased from values around 1.16–1.17 in the beginning of the twentieth century to values around 1.12–1.14 in the 1970–1980s when the proportional fallout of alkyl-lead (206Pb/207Pb ratio 1.08) was the highest and then, increased again during the following two decades to a current value typically around 1.16 (Farmer et al., 2002). Even though the 206Pb/207Pb ratios of atmospheric Pb has varied between regions, paleoecological reconstructions based on analysis of herbarium collections of forest mosses (Farmer et al., 2002), peat bogs (Bra¨nnvall et al., 1997), lake sediments (Bra¨nnvall et al., 1999) or ice-cores (Rosman et al., 1997) indicate that the temporal trends in this ratio has been similar over large regions. The input of atmospheric Pb with a generally lower 206Pb/207Pb ratio than the geogenic Pb results in soil profiles in which the 206Pb/207Pb ratio increases gradually from values similar or close to atmospheric Pb in the organic horizon to the values typical for the soil parent material in the C-horizon (Fig. 4A). The concentration of atmospherically derived Pb (Pbatm) in the soil Pb is commonly calculated using a binary mixing model:

Pbatm ¼

Pb=207 Pbsample 206 Pb=207 Pbgeogenic Pbsample 206 Pb=207 Pb 206 Pb=207 Pb atm geogenic

206

ð1Þ

where 206Pb/207Pbsample is the 206Pb/207Pb ratio of a given sample, 206Pb/207Pb geogenic is the ratio of Pb found in the parent material and 206Pb/207Pb atm is the ratio of pollution Pb, and Pbsample is the total concentration of Pb in the sample. In Fig. 4A, atmospheric derived Pb has been calculated by applying Eq. (1), assuming mixing between an atmospheric source having a 206Pb/207Pb around 1.14 and a geogenic source similar to that of the basal

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A 0 Depth (cm)

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100 200 300 400 −20 0 20 40 60 Zr mg kg−1 Excess Pb mg kg−1

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Depth (cm)

20 40 60 80 100 1.2 1.4 1.6 1.8 2.0 2.2 −20 0 20 40 60 206Pb/207 Pb Atmospheric Pb mg kg−1

Figure 4 (A) Excess Pb concentrations and (B) geogenic Pb concentrations calculated using Zr as conservative element and using the 206Pb/207Pb ratio for the same podzolic soil profiles. The Pb and Zr concentration used in calculation of Pbexcess concentrations are derived from XRF-analysis (unpublished data) while the 206Pb/207Pb data are from Klaminder et al. (2006a) and were measured using ICP-MS (digestion HNO3 þ HClO4, 1:10). Error bars of the atmospheric derived Pb reflect the uncertainty range caused by variations in the 206Pb/207Pb ratio of the geogenic fraction found at depth in the soil (open boxes).

C-horizon of profiles from the area having a ratio ranging from 1.4 to 2.1. Mixing calculations like this one have been extensively used within environmental science to quantify and trace soil Pb contamination (Bra¨nnvall et al., 2001a; Erel et al., 1997; Kaste et al., 2003; Klaminder et al., 2006a; Semlali et al., 2001). These data, however, have been rarely collected for the purpose of understanding the mass fluxes in soils. It is also worth mentioning that none of the estimated inventories of atmospheric derived Pb contaminants has been corrected for stoniness, that is, the volume of the soil that is taken up by stones, making the reported inventories of atmospheric derived Pb in the mineral soil likely overestimates. In addition to Pb isotopes, atmospheric Pb could be identified as an excess concentration (Pbexcess) by normalizing the total concentrations to immobile elements such as Zr, Ti, and Nb (Brimhall and Dietrich, 1987).

27


Pbexcess ¼ Pbconc Zrsample

Pbparent Zrparent

ð2Þ

where Zrsample is the concentration of the immobile element in the sample and the Pbparent and Zrparent is the concentration of Pb and the immobile element in the parent material, respectively. Most studies indicate excess pools of Pb in the upper parts of the soil (Cortizas et al., 2003; Jersak et al., 1997; Watmough et al., 2004) and that most Pb mobilized during weathering is retained within the B-horizons (Klaminder et al., 2006a; Wang et al., 1995). Therefore, the calculated excess pool should approximate atmospheric inputs. In Fig. 4B, the Pb excess concentrations [Eq. (2)] is compared with atmospherically derived Pb estimated using the 206Pb/207Pb ratio [Eq. (1)]. The calculated vertical trends and inventories of each Pb-fraction are about the same (Fig. 4A and B), stressing that both methods could be used for estimating the atmospherically derived Pb fractions in soils. Stable lead isotope analysis of soil extractions show that atmospherically derived Pb tends to accumulate in phases less resistant to chemical extractions than geogenic Pb (Bacon et al., 2004; Erel et al., 1997; Klaminder et al., 2005; Kylander et al., 2008; Teutsch et al., 2001) and the possibility to use chemical extractions to separate atmospheric derived Pb from the bulk soil is a topic of recent research. Weak acid extractions have been used to trace atmospheric Pb from geogenic Pb in forest soils (Miller and Friedland, 1994). Since this pioneering study, Pb isotope analysis of soil extractions have shown that the geogenic fraction could still be significant in weak acid extractions and atmospherically derived Pb could be incorporated into chemically resistant fractions (Bacon et al., 2004; Erel et al., 1997; Klaminder et al., 2005; Kylander et al., 2008; Teutsch et al., 2001). Furthermore, even the most studied and standardized metal extraction schemes struggle with targeting actual functional soil pools (Bacon and Davidson, 2008). Therefore, it seems difficult to use Pb concentration measurements of chemical extractions to quantitatively separate atmospherically derived Pb from the soil matrix without including measurements of Pb isotopes that allow the possibility to calculate the size of the atmospheric fraction [Eq. (1)]. In addition to the multiple stable isotopes, lead also has a short-lived radiogenic isotope, 210Pb (t1/2 ¼ 22.3 year), which is derived from decay of radon gas. Even though 210Pb is not an atmospheric contaminant, this radionuclide is discussed in this review given its current use as a tracer of soil erosion (Walling and He, 1999) and its chemical equivalence to Pb derived from pollution sources. The 210Pb originating from atmospheric fallout (often called unsupported fraction) within the soil matrix can be determined by subtracting the 210Pb derived from in situ decay of

28


(t1/2 ¼ 1620 year)-this activity is called supported fraction-from the total activity, which could be done in parallel to 210Pb measurements using g-ray spectroscopy (Appleby and Oldfield, 1992). Lead-210 could be also determined by measuring the decay rate of the daughter isotope, 210Po using a-particle spectroscopy (Benoit and Hemond, 1988), which has a much lower detection limit than g-ray spectroscopy but where the measurements of 226Ra is more complicated and less commonly done. However, the geogenic 210Pb fraction is often minute in comparison to the atmospheric 210Pb fraction in the upper parts (1000 years) processes and is limited to special case where the soils’ thickness and geochemistry can be considered time-invariant along an eroding hillslope transect. Accelerated soil erosion and redistribution in upland agricultural systems, however, is becoming an important global issue partly because of the ongoing debates on potential carbon sequestration during the anthropogenic erosion and sedimentation of the soils (Stallard, 1998; Van Oost et al., 2007). To go beyond the steady state, we would need erosion tracers that operate at different time scales, for example the contrasting time scales of Pb and SCPs versus in situ 10Be. In addition, expanding the scope of this avenue of research to address transient soil evolution will require the application of numerical models which can capture historic variations in atmospheric Pb and SCPs fallout (e.g., Section 3.4).

4.2. Vertical mass fluxes within a soil pedon 4.2.1. Mass balance model of a soil layer Following the mass fluxes in and out of a soil system (Fig. 8), this section looks into the utility of atmospherically derived Pb and SCPs in determining vertical mass fluxes within a soil system (Fig. 9). Among the vertical mass

Solute flux Qw, j, n−1

Qw, j, n

(n-1)th soil layer Velocity of Soil Mixing Colloidal Concentration of an element j: Cj, n−1 Thickness : Δ h flux Bulk density: rn−1 Qx, j, n−1 Vn−1, n nth soil layer

Qx, j, n

Concentration of an element j: Cj,n Thickness : Δ h Bulk density: rn

Vn, n + 1

(n-1)th soil layer Concentration of an element j: Cj, n + 1 Thickness : Δh Bulk density: rn + 1

Figure 8 A schematic of a soil layer with vertical mass fluxes. Radioactive decay of elements in Eq. (6) is not included in this mass balance diagram.

40


Cumulative depth (kg m2)

A

Local pollution 0 1 2 3 4 5 6 7

ca 1999

ca 1970

Cumulative depth (kg m2)

Oe-layer

ca 1970 ca 1970 ca 1930

0

B

Oi-layer

Oa-layer

10000 1.15 1.20 1.25 5000 206Pb/207Pb Pb (mg kg−1)

150 000 300 000 SCPs (no m−2)

1 2 Hg (mg g−1)

Regional pollution 0 1 2 3 4 5 6 7

Oi-layer Oe-layer ca 1970

Oa-layer

20

40

Pb (mg kg−1)

60

1.15

1.20

206Pb/207Pb

1.25

5000

10000

SCPs (no m−2)

0.5 Hg (mg g−1)

1.0

Figure 9 The content of lead, mercury, spheroidal carbonaceous particles (SCP), and the 206Pb/207Pb ratio trend in a vertically subsectioned organic layer sampled (A) 3 km from a large smelter in northern Sweden and (B) sampled outside the Pb fallout range of the smelter (distance 100 km). Data from Klaminder et al. (2008b). Expected atmospheric inputs are the same as the reconstructed fallout in Fig. 6 and arrows indicate dates inferred from wiggle-matching the fallout trend with the vertical distribution contaminants in the O-horizon stratigraphy. Subhorizons are indicated in the right panel.

translocation processes, soil mixing and translocation of clay and organic colloids appear to be most amenable to the application of Pb and SCPs (see Section 3.2). For a level landscape where mass fluxes are predominantly vertical, we may ignore the colluvial mass fluxes and the lateral components of solute fluxes. Even at eroding or depositional locations, vertical mass fluxes may be far greater than lateral fluxes such that the colluvial and lateral solute fluxes may be ignored in constructing mass balance models for individual layers in the soils. Below is a mass balance for a soil layer experiencing vertically directed movement of materials via bulk soil mixing, colloidal translocation, and solute transport (Fig. 8). This model is essentially identical to many of convection–diffusion models that have been used to describe soil depth profiles of radio isotopes such as 14C and 137Cs (Baisden et al., 2002; Bunzl, 2002; Do¨rr and Mu¨nnich, 1989).

41


@ðrn Cj;n Dhn Þ @t = Vn1;n ½rs;n1 Cs;j;n1 rs;n Cs;j;n þVn;nþ1 ½rs;nþ1 Cs;j;nþ1 rs;n Cs;j;n þ½Qx;j;n1 Qx;j;n þ½Qw;j;n1 Qw;j;n lj ðrn Cs;j;n Dhn Þ

)ðbulk soil mixingÞ )ðcolloidal translocationÞ )ðsolute transportÞ )ðradioactive decayÞ ð6Þ

where the subscript n represents the nth soil layer with the number increasing from the surface to deeper layers, Dh is the thickness of the layer [L], V is the velocity of bulk soil moved [LT1] via physical movements, Qx,j is the vertical mass flux of j via organic or mineral colloidal transports [ML2T1], and Qw,j is the vertical mass flux of j via solute transport [ML2T1]. Examples of Qx,j are elements transported by clay translocation processes or as organic complexes during the podzolization processes, whereas the Qw,j could be exemplified by the flux of elements dissolved in soil water in their ionic forms or as complexes formed in the atmosphere. 4.2.2. Constraining vertical mixing While a soil is often viewed as a sum of static materials that are acted upon by hydrological and biological processes, minerals and organic matter are in constant motion by soil dwelling organisms’ burrowing activities, tree throw, freeze-thaw cycles, and the action of swelling and shrinking of clay minerals (Gabet et al., 2003; Hole, 1981). Mixing of organic matter and minerals has been considered as a key characteristic of A-horizon. While A-horizons’ organic carbon storage is typically equated as a balance of net primary productivity and decomposition, the turnover time of organic matter generally increases with increasing soil depth ( Jenkinson and Coleman, 2008), which makes the mechanism of vertically mixing an important factor of carbon cycle. Furthermore, many studies have demonstrated that the biological availability of organic matter is greatly reduced when it is complexed by mineral surfaces (Baldock and Skjemstad, 2000; Kennedy et al., 2002). Such complexation, however, requires physical contact between organic matter and minerals, which is dependent on mixing processes. The rates of vertical soil mixing, however, have been only marginally discussed in pedological texts. The notable theories on the role of soil

42


bioturbation in dynamic landscape denudation introduced by Johnson et al. (2005), for example, have rarely been subjected to quantitative tests by pedologists equipped with modern geochemical or geophysical techniques. Instead soil mixing rates have been occasionally determined by environmental scientists who are primarily interested in transports of relatively insoluble organic contaminants, heavy metals, or radioactive wastes. By combining the observed depth profiles of 210Pbatm and/or 137Cs with various types of diffusion transport models, the rates of vertical mixing have been estimated (Bunzl, 2002; Do¨rr, 1995b; Schimmack and Schultz, 2006). In soils where leaching of organically complexed Pb is significantly smaller than the Pb flux via mixing, the colloidal and solute flux terms in Eq. (6) can be neglected as an approximation. We caution, however, that colloidal translocation of organo-metal complexes, which has been traditionally considered significant only for Spodosols, was recently found to play a critical role in the carbon cycle in grassland Molisols (Masiello et al., 2004), implying that the removal of colloidal translocation of Pb for simplification may be valid only in limited circumstances. Removing the colloidal and solute translocation terms (Qx,j and Qw,j) from Eq. (6), the mass of soiled moved through mixing can be expressed as:

@ðrn Cj;n Dhn Þ ¼ Vn1;n ½rs;n1 Cs; j;n1 rs;n Cs;j;n @t þ Vn;nþ1 ½rs;nþ1 Cs; j;nþ1 rs;n Cs; j;n

ð7Þ

lj ðrn Cs; j;n Dhn Þ where the time derivative of total 210Pbatm contents in a modeled soil layer (the left side of the equality) can be assumed to be negligible, the equation, when combined with the mass balance of the adjacent layer and the depth increment measurements of 210Pbatm activities and soil bulk densities, can be numerically solved for the vertical speed, V, of bulk soil materials. While soil bioturbation in general has been only descriptively related to soil morphology in pedology, geomorphologists have recently reexamined soil bioturbation as one of the key processes responsible for sediment transport (Gabet et al., 2003; Yoo et al., 2005). Heimsath et al. (2002) quantified the rates of vertical movements of mineral grains as they migrate in the downslope direction on a South-western Australian hillslope by combining optically stimulated luminescence (OSL) measurements of single grain quartz and numerical modeling. This work represented a significant advance over previous studies because the rates of movement were determined for individual mineral grains and because both vertical and lateral velocities of the mineral grains were determined. Over a wide range of


43

vegetation types, Kaste et al. (2007) determined soil mixing rates using a suit of short lived isotopes (210Pb, 137Cs, 7Be, and 241Am); their results suggest that soil mixing rates tend to be highest in grassland ecosystems with high population density of burrowing mammals. These results are probably the first quantitative support for the traditional interpretation that thick A horizons in grasslands is partly due to bioturbation. Recent results from environmental scientists and geomorphologists all show that the rates of vertical bulk soil movement by bioturbation cannot be overlooked in understanding the evolution of geochemical soil profiles. For example, Heimsath et al. (2002), in the Eucalyptus forest hillslope in the south eastern Australia, reported the vertical mineral grain velocities ranging from 7 to 26 cm/1000 years, which illustrate the significant impact of bioturbation on soil profile development over centennial to millennia time scales. Though no rates of bioturbation were provided, Phillips (2007) also made a strong argument that texture contrasted Ultisols in South Eastern Costal Plains in the US cannot be explained without considering bioturbation. The product of soil mixing rates with the bulk elemental concentrations will allow estimates of elemental fluxes via bioturbation. To better constrain soil mixing rates, in addition to 210Pb, we propose to use the concentration and stable isotope ratios of Pb and the abundance of SPCs. For example, one may compare the vertical soil profiles of 206Pb/207Pb ratio (Fig. 9) with the reconstructed history of 206Pb/207Pb ratio of the atmospheric input at the site (Figs. 5 and 7). A combination of stable and short-lived isotopes of Pb would result in better accuracy of soil mixing rates. Additionally, SCPs have a great potential because they have 150-year long pollution history and can likely be incorporated only into the mineral soil by mixing; they are insoluble and too large to be transported by soil water flux. When dealing with stable isotopes and SCPs, the radio decay term is further removed from the Eq. (7).

@ðrn Cj;n Dhn Þ ¼ Vn1;n ½rs;n1 Cs; j;n1 rs;n Cs; j;n @t þ Vn;nþ1 ½rs;nþ1 Cs; j;nþ1 rs;n Cs; j;n

ð8Þ

Both Eqs.(7) and (8), with steady state assumptions, would equally serve the purpose of determining soil mixing rates in the soil zones receiving inputs of atmospheric contaminants with a low solubility. Mixing rates obtained using steady state approximations can be used to numerically simulate the progressive downward movement of atmospherically deposited dusts or organic matter into soil matrix. Such simulations can be compared to the field observed migration of contaminants peaks (as in Fig. 9).

44


The calibration of these models may ultimately lead to mechanistic understanding of how vertical soil profiles of relatively insoluble elements and minerals and texture develop.

4.2.3. Constraining vertical clay translocation within soils Compared to the soil mixing, clay translocation has traditionally attracted more attention among the pedology community. The concept of clay translocation is embedded in the widely used nomenclature of eluvial and illuvial horizons. Often the degree of soil formation or weathering has been semiquantitatively equated with the amount of accumulated clay minerals in illuvial soil horizons and the development of clay-rich Bt horizons, and clay film in B horizons has been considered as an evidence of clay translocation (Birkeland, 1999). Surprisingly, however, besides the reports of accelerated clay translocation in irrigated sodium enriched soils (Presley et al., 2004), quantitative measurements of the rates of clay translocation in natural soils are rare. A half century long soil lysimeter experiment suggested that the clay translocation and subsequent development of Bt horizons may occur at decadal time scales rather than thousand to million years (Graham and Wood, 1991). The authors of this paper also showed that soil mixing driven by earthworms may delete the signature of the clay translocation, which implies that bioturbation might proceed at a rate that is faster than the clay translocation. In the Coastal Plains in North Carolina, it was argued that texture contrast Ultisols can be explained by cooccurring processes of bioturbation and clay translocation (Phillips, 2007). Therefore, it appears reasonable to consider that the rates of clay translocation and bioturbation are comparable at the site. This limited but enlightening information on the time scale of clay translocation indicates that environmental metal pollutants have the potential to be successful tools for dating clay translocation, given the strong adsorption characteristics of some of the atmospheric metal contaminants (Fig. 4). To date, however, we are not aware of such studies. The maximum depth of a metal pollutant adsorbed onto clay, when combined with its local pollution history, may be used to estimate the time-averaged speed that the clay proceed to the depth from the surface. A historic shift in the 206Pb/207Pb ratio of the atmospheric Pb, coupled with past deposition trends, have been used to infer ages of Pb migration plumes in the soil and to calculate migration rates of the atmospheric Pb (Erel, 1998)—rates that should approximate clay transport rates in organic poor soils. While this method may be limited to locations where relatively high concentrations of metals are found, globally present 210Pb can be used for the same purpose. Ultimately, techniques to use multiple metal species and their stable and radioactive isotopes could be developed to study how and how fast


45

simultaneous actions of soil mixing and clay translocation lead to diverse morphological and geochemical soil profiles. Thus, Pb and SCPs (possibly other atmospherically derived pollutants) may significantly contribute to enhancing our understanding of soil profile development. With measurements of soil mixing rates and clay translocation rates over diverse ecosystem types, strong patterns between the vertical mass translocation rates in soils and Jenny’s soil forming factors may eventually emerge.

5. Lead and SCP as a Tracer of Organic Matter Dynamics The strong affinity between the Pb atom and organic matter (see Section 3.1) makes this element a suitable proxy for mass-movement of organo-metal complexes. In soils not significantly affected by lateral mass fluxes, Pb could serve as tracer of the podzolization process where organometal complexes are transported downwards from the organic and eluvial horizons to the underlying illuvial horizon. This assumption is supported by several studies showing that the loss of Pb from the organic horizons cooccurs with the loss of organic matter (Bergkvist, 2001; Tyler, 1981; Wang and Benoit, 1996). The potential use of Pb as a proxy for organic matter leaching looks significant given the growing consensus that the organic matter complexed with metals such as Al, Fe, and Ca turns over slowly and thus controls the long-term storage of organic carbon in soils from many soil orders (Masiello et al., 2004). Steinnes and Friedland (2005), for example, have highlighted the use of the distribution of atmospheric derived Pb in soils for studying the podzolization. They noted in this study, which included a synthesis of data regarding the distribution of atmospherically derived Pb in soils from the North America and Scandinavia, that Scandinavian Spodosols have a higher proportion of the total pool of atmospherically derived Pb in the mineral soil underlying the organic horizons. Intuitively, this is due to the longer pollution history in Europe than in North America (3500 vs 150 years). However, this example not only illustrates the potential to use stable lead isotopes to quantify the still poorly quantified podzolization rate—the rate in which organo-metal complexes are formed, transported and accumulated—but also demonstrate how the time-scale covered by atmospheric Pb as a tracer can be adjusted by choosing study sites with different pollution histories. The rate in which Pb and thus organo-metal complexes are lost from O horizons have been estimated using various methods including: (1) repeated sampling at the same site over a decadal time-scale to monitor changes in Pb inventories over time (Kaste et al., 2006; Miller and Friedland, 1994);

46


(2) analysis of archived soil samples (Semlali et al., 2004); (3) inferring Pb migration rates from the vertical distribution of 210Pb in the soil [e.g., Eq. (7); Do¨rr, 1995a); (4) modeling the downward migration of atmospheric Pb contaminants using the vertical distribution of a stable Pb isotopes [e.g., Eq. (8); Erel, 1998). As argued above, the rate of Pb loss determined by these different approaches should provide insights regarding the turnover of organic matter; a high rate of Pb loss would imply rapid depletion of organic matter. The loss rate of Pb from the O-horizon is affected by factors known to be related to the turnover of organic matter such as soil age (Fig. 9A), temperature (Fig. 9B) and the thickness of the O-horizon (Fig. 9C). How these variable loss rates of Pb relate to actual fluxes of organic matter, that is, leaching of organo-metal complexes, respiration and mixing, remains to be resolved by future studies. Here, SCP appears to be a suitable tracer of the organic matter pool not affected by soil– water leaching that could be used in combination with Pb to separate between mass-fluxes generated by leaching and physical mixing. The age of decomposed plant remains found at a depth in the O-horizon provides important information about rates in which litter deposited at the surface of the soil are accumulated. In this horizon, the bulk soil consists of a mixture of recently fixated carbon ‘‘pumped’’ into the soil by plant roots and older carbon remaining in residues of plant litter. Therefore, 14C-dating of the bulk soil will only indicate the mean residence time of the organic matter, that is, the average time spent in the analyzed soil compartment among carbon atoms rather than the time since the litter was deposited at the soil surface. Lead has the capacity to be used as a tracer of the age of the litter found within organic matrixes, that is, the time since the litter constituted the surface of the soil and thus, received atmospheric inputs of Pb with a particular strength and 206Pb/207Pb ratio. In cold or wet environments where O horizon thickens with little bioturbation, the age of the organic matter could be inferred by matching fluctuations in the vertical trends in Pb concentrations and the 206Pb/207Pb ratio of the soil to similar fluctuations found in well-dated environmental archives (ice-cores, varved lakes) or documented emission statistics. Hence, analogous to dating methods based on linking activity peaks of 137Cs and 14C in peat and lake sediments to known pollution events like the nuclear bomb testing program in the 1960s and the nuclear reactor accident in Chernobyl 1986 (Appleby, 2008). The method can be demonstrated by matching the dated fallout trends in Fig. 6A and B with the vertical profiles of O-horizon that has been exposed to the same local pollution source (Fig. 10A) and the same regional fallout of Pb contaminants (Fig. 10B) as the dated ombrotrophic peat core. Influence from the peak pollutants fallout around the 1970s is seen as a subsurface peak in Pb and SCPs (and also for Hg) concentrations in the organic horizon close to the strong local pollution source (Fig. 10A). Also periods

47


Annual Pb loss (%)

A

B

C

0.6 0.1 0.01 1E-3 10

100 1000 10000 Soil age

2

4 6 Mean annual temperature ⬚C

8

0 2 4 6 8 10 12 14 Organic horizon (cm)

Figure 10 (A, B) Loss rates of lead in the organic layer as a function of (A) soil age (Klaminder et al., 2006b); (B) mean annual temperature and soil orders ( Johnson et al., 1995; Klaminder et al., 2006b; Miller and Friedland, 1994; Watmough et al., 2004); and (C) the thickness of the organic layer (Kaste et al., 2006; Klaminder et al., 2006b).

characterized by low and high 206Pb/207Pb ratios, that is, corresponding to the 1930s and around the 2000s (Fig. 6A), is seen in the O-horizon stratigraphy (Fig. 10A). In the vertical O-horizon profile exposed to the weaker regional fallout of contamination, there is no subsurface peak in Pb and SCPs concentrations (Fig. 10B), but the vertical 206Pb/207Pb ratio trend mimics the temporal variations (Fig. 6B), suggesting that the chronology of the atmospheric fallout is still preserved within its stratigraphy. The absence of a subsurface peak in this profile is important since it demonstrates that the fallout needs to be substantial to result in a subsurface enrichment of the contaminants that are not deemed by decomposition and leaching processes. Matching of fluctuations between the cumulative percentage of the total pool of Pb or SCP found in the soil (¼total fallout) and a similar curve established for a well-dated archive, could in some cases be preferable to using vertical concentration trends. The former method provides a more robust method to infer ages with depth in soil that are independent on vertical variations in densities and has been successfully applied to date lake sediments (Renberg and Wik, 1984; Rose and Appleby, 2005). At the current state of knowledge, calculated Pb turnover rates and Pb inferred litter ages can only be interpreted as a relative measurement of organic matter turnover and accumulation. The relationship between Pb and organic matter turnover needs to be further assessed. For example, recent findings indicate that a significant fraction of Pb within organic horizons might accumulate with iron hydroxides (Schroth et al., 2008), making the turnover of Pb not solely a function of the fate of the organic matter. This means that Pb turnover rates only provides a relative measurements of organic matter turnover until this methods has been validated against field measurements (DOC and CO2 losses) and other tracers (14C).

48


6. Conclusions Linking the geochemical evolution of soils to the directions and rates of various mass-fluxes is the main challenge in pedology. In order to transform pedology to a process-oriented quantitative discipline beyond soil taxonomy, we need diverse set of tracers that can target wide range of mass fluxes involved in forming soils over variable time and spatial scales. Each of the currently popular tracers has strengths and weaknesses; we cannot expect one tracer to have a universal application. Use of multiple tracers is preferable. Lead and SCPs are two tracers whose properties make them candidates to play important roles in future multitracer studies. Atmospheric Pb and SCPs have been entering the soil surface at an accelerated pace due to human contamination of the atmosphere. After deposition, these tracers become incorporated into the soil and subsequently move along with slightly different phases in the soil. Lead will bind strongly to the clay minerals, hydroxides and organic matter, and the fate of these new host surfaces will control its postdeposition distribution. Given the extensive sorption to mineral surfaces and organic matter, Pb may serve as a tracer for mineral fluxes as wells as solid and mobile organic compounds. In contrast, SCPs are a more precise tracer for tracking physical movement of particles. In hypothetical soils not affected by erosion, mixing and leaching, the vertical distribution of deposited airborne Pb and SCPs, should follow the temporal pattern of their known fallout history. Any divergence from this expected trend could be used to quantify rates of these processes. Hence, there is a great potential in using Pb contaminants and SCPs as tracers of soil genesis. The atmospheric deposition history of Pb and SCPs can be reconstructed at the local scale. By doing so, the pedologist will increase the precision regarding the fallout chronology. In addition, a local reconstruction of the deposition history can possibly result in an estimate of the isotopic composition of the fallout thus decreasing the uncertainty involved in the mixing model [Eq. (1)] necessary for separating the atmospheric derived Pb from the geogenic source. Furthermore, by locating study sites along local and regional pollution gradients, it is possible to adjust the fallout conditions to improve the boundary conditions of the modeling, that is, search for a site where the 206Pb/207Pb of the atmospheric Pb is strongly separated from that of the parent material or where the deposition rate of Pb and SCP has been high to facilitate tracing. There is also a possibility to use sites at different continents to vary the time-constraints of the tracer. A wide array of applications is possible once atmospheric derived contaminants such as Pb and SCPs are considered as tracers. Lead and SCPs are only two atmospheric contaminants out of a whole array of anthropogenic elements and compounds that have been entering the soil over variable


49

time-scales. For example, copper has a similar long atmospheric pollution history as Pb (Hong et al., 1996), while atmospheric Hg and Cd has an input to soils that largely mirrors that of the SCPs (Hong et al., 1997; MartıńezCortizas et al., 1999). The method outlined in this paper for Pb and SCP, might as well apply to these other metal contaminants given that they, in similarity to Pb, also have a high affinity for soil surfaces (Evans, 1989). However, at more remote sites where geogenic sources and plant cycling could complicate separation of atmospheric deposited contaminants, their usage as tracers are likely limited. Significant variations in the isotopic composition of mercury (196Hg, 199Hg, 200Hg, 201Hg, 202Hg, and 204Hg) and copper (48Cu and 47Cu), occur in the environment indicating that these isotopes might qualify as tracers, but no general consensus has been reached regarding their utility for tracing natural and anthropogenic sources in similarities to Pb. Their current application for studying soil processes appears, therefore, limited, but an increased understanding regarding their fractionations in the soil might change this in the near future. Many of the persistent organic pollutants (POPs) offer another interesting potential. These compounds, with well characterized chemical properties and a structure rarely found naturally in soils, has been incorporated at well constrained points in time. There appears to be scope for applying POPs to quantitatively study the behavior of organic matter in soils, especially in studying podzolization processes. For example, POPs may be used as tracers to examine how organic compounds with different hydrophobic properties, molecular weights and sizes are relocated in soils. Our suggested approach outlined in this paper is contrasting the common practice within soil science. Instead of using the science of soils to enhance our understanding of the fates of pollutants, we propose to use the pollutants as tracers to deepen our understanding of pedogenic processes. In the end, however, this effort will be mutually beneficial for both fields because we need to involve the processes that form the soil and govern its long-term stability to be able to predict the fates of contaminants in the environment. For example, soil erosion might generate significant fluxes of Hg even at a large scale as the Arctic Ocean where the contamination of this metal is of high concern (Outridge et al., 2008). It was realized in the late 1960s that humans had the capacity to pollute on a global scale. One person who made significant contributions to highlighting the global reach of Pb contamination was Clair Patterson, a brilliant scientist that successfully merged physics, geology, human history and paleoecology under the roof of applied environmental science. The tool that he used to link these various scientific disciplines was stable Pb isotopes, which he used to both derive the first reliable estimate on the age of earth and to demonstrate the extensive influence of anthropogenic Pb in time and space. Determining the isotopic composition of soil minerals and constraining the historic inputs to the environment of anthropogenic Pb played an

50


important role in his studies. It is now a good time to harvest the knowledge that Clair Patterson and those who expanded upon his work have gathered regarding the fate of Pb and other contaminants in the soil, and to use this knowledge as tools to further extract knowledge about soil forming processes—tools that were not available when the founders of pedology, Jenny and Dokuchaev, envisioned paths to understand soil formation.

ACKNOWLEDGMENT We thank the Swedish research council and University of Delaware College of Agriculture and Natural Resources (Seed grant) for financial support and Simon Marius Mudd for his presubmission review of this manuscript.

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Epigenetics: The Second Genetic Code Nathan M. Springer* and Shawn M. Kaeppler† Contents 1. Introduction 2. Molecular Mechanisms of Epigenetic Inheritance 2.1. DNA methylation 2.2. Histone modifications 2.3. Chromatin structure 2.4. Role of RNA in heritable silencing 2.5. Interactions among DNA methylation, histone modifications, and chromatin structure 3. Epigenetic Phenomena in Plants 3.1. Phenotypic examples of epigenetic inheritance 3.2. Genomic and molecular genetic examples of epigenetic variation 4. Epigenetic Inheritance and Crop Improvement 4.1. Epigenetics in quantitative inheritance and selection response 4.2. Epialleles and gene discovery References

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Abstract Plant breeders utilize directed selection and transgenics to produce novel cultivars of diploid and polyploid species. DNA sequence is clearly important in these processes, but growing evidence implicates epigenetics as an important factor in controlling genetic variation and gene/transgene expression. In this article, we focus on epigenetic variation defined as mitotically and meiotically heritable but reversible states of gene expression that are not conditioned by differences in DNA sequence. We summarize mechanisms underlying epigenetic states of expression, and discuss implications of epigenetics in cultivar development.

* {

Department of Plant Biology, University of Minnesota, Saint Paul, Minnesota 55108, USA Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706, USA


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Nathan M. Springer and Shawn M. Kaeppler

1. Introduction Plant breeders have made tremendous progress towards altering plant phenotypes to provide more productive crops. The majority of these gains have been made through selection of variation that is present within a species. While it is clear that phenotypic selection has resulted in improved plant characteristics, the molecular basis of the selected variation remains largely unknown. Traditionally, it was assumed that the majority of heritable variation was due to genetic sequence differences. However, a growing body of evidence suggests that intraspecific expression differences among genotypes can also be caused by epigenetic variation. To some researchers, epigenetics is used as a catchall term to describe any variation that does not seem to follow Mendelian inheritance patterns. In this article, we use the term epigenetics to specifically describe meiotically or mitotically heritable differences in gene expression that are not caused by sequence differences. We will begin by describing some of the molecular changes that are associated with epigenetic variation and then proceed to discussing some of the well characterized examples of epigenetic variation. Finally, we will discuss the potential impact of epigenetic inheritance in crop improvement.

2. Molecular Mechanisms of Epigenetic Inheritance Epigenetics involves changes in heritable phenotypes without changes in DNA sequence. With the exception of protein-based inheritance such as prions, epigenetic differences are the result of altered gene expression levels. Research on the molecular mechanisms of epigenetic inheritance has identified several complementary pathways for the stable regulation of gene expression without sequence changes. The basis of epigenetic inheritance is the manner in which DNA is packaged and modified. DNA in plant cells contains four standard bases, cytosine, guanine, thymine, and adenine, but can also contain the modified base 5-methylcytosine. Most often, presence of 5-methylcytosine is associated with repressed gene expression. DNA in the cell is packaged by various sets of proteins. The first level of packaging involves wrapping DNA around cores of histone octamers containing two each of the proteins: Histone 2A (H2A), Histone 2B (H2B), Histone 3 (H3), and Histone 4 (H4). The histone proteins in the octamer can contain various modifications. The most relevant modifications for epigenetic expression are acetylation and methylation, which occur on the tails of H3 and H4. The tails of these histones extend outside of the DNA/protein core, and the modifications affect how those cores are packaged into a higher order structure.

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Various proteins are involved in methylation of the DNA, modification of the histones, recognition of chromatin state, and in energy-dependent remodeling of one chromatin state to another. Following is a brief summary of some of the most important players in this process, chosen largely because one or more members have proven effects on gene expression in plants.

2.1. DNA methylation DNA methylation is found in the genomes of many plant and animal species. In eukaryotic genomes, DNA methylation refers primarily to 5-methylcytosine although some evidence for methylated adenines has been reported. The methyl moiety is added to cytosine residues present in double stranded DNA by a group of enzymes referred to as DNA methyltransferases. The majority of DNA methylation is found in CpG dinucleotide (plants and animals) and CpHpG trinucleotide (plants only) sequence contexts. Plant genomes encode several different DNA methyltransferase enzymes that fall into three different functional categories (reviewed by Chan et al., 2005). The Domains Rearranged Methyltransferases (DRM) encode de novo methyltransferases. These enzymes are capable of adding methyl groups to DNA that is unmethylated. The other two categories, DNA methyltransferases (MET) and chromomethylases (CMT), encode maintenance methyltransferases. Following DNA replication, the parent strand retains 5-methylcytosine but all cytosines within the daughter strand are unmethylated. This hemimethylated substrate is the target of the maintenance methyltransferases. Hemimethylated CpGs are the target of the MET class of enzymes while hemimethylated CpHpGs are methylated by CMTs. The targeting of a DRM protein to a specific genomic location will result in methylation of all cytosines within the region. If the targeting signal is no longer present then only the CpG and CpHpG methylation will be maintained by maintenance activities. These are likely oversimplifications of the activities and preferences for these enzymes as there appears to be some redundancy and locus-specific activities for these classes (reviewed by Chan et al., 2005). In general, DNA methylation is associated with transcriptional silencing of a locus. DNA methylation can result in silencing by directly interfering with the binding of transcriptional activators or by recruiting proteins that bind to methylated DNA and recruit transcriptional repressors (Bird and Wolffe, 1999; Klose and Bird, 2006). DNA methylation is often associated with centromeres and repetitive elements (Zhang et al., 2006). Recently, the application of microarrays and high-throughput sequencing approaches have provided a view of genome-wide DNA methylation patterns in Arabidopsis and the relationship of DNA methylation and gene expression (Cokus et al., 2008; Lister et al., 2008; Zhang et al., 2006; Zilberman et al., 2007). DNA methylation is quite high in transposon sequences and loss of CpG

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methylation often results in transcriptional activation of these sequences. Two different types of genic methylation were noted (Lister et al., 2008; Zhang et al., 2006; Zilberman et al., 2007). A significant proportion of genes (33%) exhibit methylation within the coding region and a much smaller proportion of genes (5%) exhibit promoter methylation. However, a relatively small number of genes (500) exhibit altered expression when DNA methylation is reduced (Zhang et al., 2006). The majority of genes which are sensitive to DNA methylation exhibit promoter methylation. Interestingly, most of the genes controlled by CpG methylation are pseudogenes located within pericentromeric heterochromatin while the genes regulated by CpHpG methylation are spread throughout euchromatic portions of the genome (Zhang et al., 2006). There is also evidence for altered expression of antisense and nc RNAs in plants with reduced DNA methylation levels suggesting that DNA methylation may be required to reduce transcriptional ‘‘noise.’’

2.2. Histone modifications The histone proteins exhibit remarkable sequence conservation in eukaryotes within the globular head domain that interacts with other histones and DNA as well as within the ‘‘tail’’ domain that protrudes from the central octomer– DNA complex. Recent research has identified a number of posttranslational modifications that occur to the histone tails including acetylation, methylation, SUMOlation, ubiquitination, and others (reviewed by Kouzarides, 2007; Pfluger and Wagner, 2007). These histone modifications can provide a variety of functions including transcriptional activation, transcriptional repression, efficient assembly into chromatin, and DNA replication. The theory of a histone code, in which each modification indicates a specific meaning and the combinations of modifications result in interpretations of chromatin state, was proposed ( Jenuwein and Allis, 2001). However, most current research suggests that there are actually a limited number of chromatin states and that the many of the modifications can act in a redundant manner (Kouzarides, 2007; Peterson and Laniel, 2004). Histone modifications can have both direct and indirect effects upon transcription. The presence of modifications, such as acetylation, may affect the ability of adjacent nucleosomes to interact. Histone modifications can also provide binding sites for other proteins which in turn may act as corepressors or coactivators (Jenuwein and Allis, 2001; Kouzarides, 2007; Peterson and Laniel, 2004). The epigenetic information of histone modifications is generally thought to be less stable than that of DNA methylation. The mechanisms for maintenance of DNA methylation patterns following replication are well understood. However, the mechanisms for maintaining histone modification patterns following dispersive replication of chromatin are unclear. In addition, most histone modifications are


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reversible and the equilibrium for a particular locus is controlled by the access of the modifiers and demodifiers. Recent studies have provided a genomic view of the distribution of certain histone modifications. Cytogenetic studies have documented the chromosomal distribution for several histone modifications (Baroux et al., 2007; Fuchs et al., 2006; Houben et al., 2003; Jackson et al., 2004; Jasencakova et al., 2003; Soppe et al., 2002). A higher level of resolution has been provided by studies that combine chromatin immunoprecipitation and microarray hybridization (Bernatavichute et al., 2008; Gendrel et al., 2005; Turck et al., 2007; Zhang et al., 2007). Trimethylation of lysine 27 of histone H3 (H3K27me3) is present at 18% of genes and is enriched at transcription factors and developmental regulators that are not expressed in the tissues being studied (Zhang et al., 2007). H3K27me3 was often enriched near the promoter of genes. Interestingly, another histone modification that is associated with silencing, H3K9me3, does not tend to colocalize with H3K27me3 (Turck et al., 2007). In general, H3K9me3 is more prevalent at constitutive heterochromatin such as transposons while H3K27me3 is found at loci with more dynamic tissue-specific regulation. The mutually exclusive presence of these modifications suggests two different types of silencing that can be conditioned by histone methylation.

2.3. Chromatin structure Alterations to chromatin structure are also important in epigenetic regulation. Chromatin structure alterations can include chromatin structure changes caused by histone variants (reviewed by Henikoff and Ahmad, 2005; Williams and Tyler, 2007) or the physical remodeling of chromatin structure. Histone variants are critical for the epigenetic definition of centromeres in plants (Dalal et al., 2007; Dawe and Henikoff, 2006; Zhang et al., 2008). In addition, histone variants are also involved in epigenetic processes such as vernalization and plant immunity (Deal et al., 2007; March-Diaz et al., 2008). Relatively little is known about the genomic distribution of histone variants in plant cells. Studies on animal genomes have revealed that the H3.3 variant histone is deposited in a replication independent manner at regions with active transcription (Mito et al., 2005). A recent study found differences in H3.3 distributions within the developing embryo and endosperm of Arabidopsis (Ingouff et al., 2007). Chromatin structure can be altered by a family of enzymes that utilize ATP to physically alter chromatin (reviewed by Jerzmanowski, 2007; Kwon and Wagner, 2007). Arabidopsis encodes a large family of ATP-dependant chromatin remodeling enzymes ( Jerzmanowski, 2007; Verbsky and Richards, 2001). Several of these genes have been identified in genetic screens for epigenetic or developmental regulators ( Jerzmanowski, 2007; Kwon and Wagner, 2007). Very little is known about how these chromatin

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remodeling enzymes are targeted to specific genomic regions. However, it is clear that these chromatin remodeling activities are critical for epigenetic regulation. In most cases, the chromatin remodeling activities appear to be important for gene regulation in response to environmental cues, not long-term changes in expression (Kwon and Wagner, 2007). A model that links histone modifications, histone variants and chromatin structure to nucleasome stability was recently proposed by Henikoff (2008).

2.4. Role of RNA in heritable silencing Posttranscriptional gene silencing (PTGS) or RNAi is a well-characterized process by which RNA is transcribed, but then degraded before translation. While PTGS is important in processes including development, transgene silencing, and plant responses to the environment, it does not fit the definition of epigenetics that we have used in this review, and will therefore not be addressed. However, recent results provide evidence that RNA can also play a critical role in establishing and maintaining heritable chromatin states. Evidence for the role of RNA in establishing and maintaining heritable chromatin states come from several types of studies. Research utilizing mutants in RNAi genes found that plants mutant for genes in this pathway displayed altered heritable DNA methylation patterns (Chan et al., 2004, 2006; Lippman et al., 2003; Tran et al., 2005; Zilberman et al., 2003). The mop1 mutant in maize is an example of the role of RNA in multiple types of heritable silencing. The mop1 gene encodes an RNA-dependent RNA polymerase 2-like protein that is involved in paramutation (Alleman et al., 2006), heritable transgene silencing (McGinnis et al., 2006), and transposon silencing (Lisch et al., 2002). While the complete mechanism of RNA-directed heritable silencing has not been completely elucidated, several important players in this pathway have been identified. Plants contain a unique family of RNA polymerase, PolIV. These polymerases contain component proteins of the NRPD1 and NRPD2 classes, and play an important role in RNA directed DNA methylation via the production of 24 nt siRNAs (Pikaard et al., 2008). Production of these siRNAs involves RNA-dependent RNA polymerase 2 type proteins which produce dsRNA that is processed by RISC complexes containing Argonaute 4 or Argonaute 6 (Vaucheret, 2008) homologs. Also involved in the process is SGS3, a protein of currently unknown function. An excellent summary of the various roles of RNA pathways in transcriptional and posttranscriptional silencing is provided by Shiba and Takayama (2007).


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2.5. Interactions among DNA methylation, histone modifications, and chromatin structure The general descriptions of the molecular mechanisms of epigenetic inheritance provided above suggest discrete pathways for epigenetic regulation. However, it is clear that there is significant cross-talk and redundancy between these molecular mechanisms. The first plant mutant with reduced DNA methylation levels was Arabidopsis ddm1 (Vongs et al., 1993). The DDM1 gene actually encodes an ATP-dependent chromatin remodeling protein that is required for proper DNA methylation patterns ( Jeddeloh et al., 1999). A similar relationship between DNA methylation and chromatin remodeling has been noted in mammals (Dennis et al., 2001). This suggests that chromatin remodeling activities are required for proper DNA methylation patterns. There is also evidence for a strong relationship between histone methylation and DNA methylation in plants. Mutations in the H3K9 histone methyltransferase KRYPTONITE affect CpHpG methylation ( Jackson et al., 2002; Malagnac et al., 2002). There is evidence that alterations in DNA methylation patterns affects the histone methylation patterns (Gendrel et al., 2002; Johnson et al., 2002; Lawrence et al., 2004; Lindroth et al., 2004; Soppe et al., 2002; Tariq et al., 2003). The CMT DNA methyltransferase exhibit preferential binding to chromatin containing both H3K9 and H3K27 methylation (Lindroth et al., 2004). Several of the plant H3K9 methyltransferases contain an SRA domain which exhibit methylDNA specific binding activities ( Johnson et al., 2007; Woo et al., 2007, 2008). These molecular properties of the DNA and histone methyltransferases lead to complementary reinforcement of CpHpG DNA methylation and H3K9 histone methylation. Proper histone acetylation patterns are also required for maintenance of DNA methylation (Aufsatz et al., 2002; Probst et al., 2004). Mutations in chromatin remodeling genes can also affect histone modification patterns (Gendrel et al., 2002; Kanno et al., 2004, 2005; Lippman et al., 2003). The epigenetic mechanisms for gene regulation exhibit a high level of redundancy and interdependence. It is likely that these relationships strengthen the heritability and help to preserve this epigenetic information following DNA replication and cell division.

3. Epigenetic Phenomena in Plants As discussed above there are a number of different molecular mechanisms for storing epigenetic information. Similarly, epigenetic inheritance exhibits a variety of different types of inheritance. In some cases, an epigenetic difference can be quite stable and can appear to follow Mendelian segregation patterns. In other cases epigenetic states can be programmed by

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the parent of origin or exposure to other alleles. We will begin by discussing several examples of epigenetic inheritance in plants. This discussion will be limited to examples of epigenetic inheritance that can be meiotically transmitted. There are examples of epigenetic gene regulation during development, such as vernalization (Schmitz and Amasino, 2007), but these do not affect phenotypes in the off-spring. A survey of studies on epigenetic variation within plant species will also be presented.

3.1. Phenotypic examples of epigenetic inheritance 3.1.1. Imprinting Imprinting is a form of epigenetic regulation in which the maternal and paternal alleles exhibit differential expression following fertilization. At an imprinted locus, there are two alleles with identical, or nearly identical, sequences in the same nucleus, yet these two alleles exhibit differential expression. To date, all examples of imprinting in plants occur in endosperm tissue (Huh et al., 2007). The endosperm exhibits unique epigenetic states relative to other plant tissues (Baroux et al., 2007; Lauria et al., 2004). Most imprinted genes exhibit expression only from the maternal allele but several examples with paternal expression have also been identified (reviewed by Huh et al., 2007). Imprinting can result in phenotypic differences between reciprocal hybrids. For example, the phenotype conditioned by some r1 locus haplotypes differs depending upon maternal or paternal transmission. The maternally transmitted haplotype provides solid kernel coloration while the paternally transmitted allele conditions a mottled pattern (Kermicle, 1970, 1978; Kermicle and Alleman, 1990). Interestingly, while some r1 haplotypes are subject to imprinting, the majority do not exhibit any evidence of imprinting (Kermicle and Alleman, 1990). The number of plant genes that exhibit complete, or binary, imprinting is relatively small. Many of the imprinted plants genes including Medea, Fis2, ZmFie1, and Mez1, have sequence homology to proteins in the Polycomb repressive complex2 (PRC2) (Kohler and Makarevich, 2006). There is evidence that the imprinting mechanism involves DNA methylation and histone modifications (reviewed by Huh et al., 2008). In plants, the mechanism for imprinting at the Arabidopsis Medea (MEA) locus is understood in the greatest detail. MEA encodes a SET domain protein that can perform methylation of H3K27 (Baroux et al., 2006; Gehring et al., 2006; Grossniklaus et al., 1998; Jullien et al., 2006). DNA methylation establishes a default silenced state at the MEA locus and maintenance of this silent state requires MET1 (Xiao et al., 2003). A DNA glycosylase enzyme, DEMETER (DME ) is expressed in the central cell and removes DNA methylation from the maternal allele of MEA (Choi et al., 2002; Gehring et al., 2006; Kinoshita et al., 2004). Upon fertilization the maternal allele is


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unmethylated and active while the paternal allele is methylated. During endosperm growth and development the MEA protein expressed from the maternal allele is required for maintenance of silencing of the paternal allele (Baroux et al., 2006; Gehring et al., 2006; Jullien et al., 2006). The maternally produced MEA protein is recruited to the promoter of the paternal MEA allele and catalyzes H3K27me3. There is evidence for differential DNA methylation of the maternal and paternal alleles of FWA (Kinoshita et al., 2004), Fis2 ( Jullien et al., 2006), ZmFie1 (Gutierrez-Marcos et al., 2006; Hermon et al., 2007), and Mez1 (Haun et al., 2007). Three imprinted maize genes, Mez1, ZmFie1, and Nrp1 also exhibit evidence for H3K27me3 enrichment at the promoter of the silenced paternal allele and H3 acetylation and H4 acetylation enrichment within the coding region of the maternal allele (Haun and Springer, 2008). 3.1.2. Paramutation Paramutation is a form of epigenetic inheritance that involves the communication of two alleles. Paramutable alleles can be heritably altered by being exposed to a paramutagenic allele in a heterozygote (reviewed by Chandler, 2007; Chandler and Stam, 2004; Hollick et al., 1997). Paramutation has been well-characterized at the r1, b1, p1, and pl1 loci in maize (Brink, 1956; Coe, 1966; Hollick et al., 1995; Sidorenko and Peterson, 2001). The b1 locus of maize provides the best characterized example of paramutation. The b gene encodes a transcription factor that regulates the production of anthcyanin. The B-I allele provides dark pigmentation of several vegetative tissues while the B’ allele provides very light pigmentation of these tissues. The B-I allele is dominant over loss-of-function b alleles (Coe, 1966; Patterson et al., 1993, 1995). However, plants that are heterozygous for B-I/B’ exhibit light pigmentation similar to B’/B’ homozygotes. Selfpollination of B-I/B’ heterozygotes produces only off-spring with lightpigmentation. The paramutable B-I allele is affected by the paramutagenic B’ allele in the heterozygote such that the B-I allele is transformed into a B’ allele. The transition of B-I to B’ can happen spontaneously at low rates but is 100% when exposed to B’ in a heterozygote. This transition of B-I to B’ does not involve any sequence changes at the B locus. A series of elegant genetic experiments have identified a series of direct repeats 100 kb 50 of the B gene that are required for paramutation (Stam et al., 2002a,b). Characterization of mutants that are impaired in paramutation reveals that RNAi and chromatin remodeling are important components of paramutation (Alleman et al., 2006; Dorweiler et al., 2000; Hale et al., 2007). There is no evidence that cytosine DNA methylation is required for paramutation at the B locus. However, altered DNA methylation patterns have been associated with paramutation at the R and P1 loci (Sidorenko and Peterson, 2001; Walker et al., 1998). There is genetic evidence that a common mechanism

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might underlie paramutation at these distinct loci (Dorweiler et al., 2000; Hollick and Chandler, 2001; Hollick et al., 2005). There is some evidence for paramutation-like interactions between transgenes in other plant and animal species (reviewed by Chandler and Stam, 2004). It is unclear whether paramutation is limited to a small number of loci or might be acting at many genomic locations. 3.1.3. Naturally occurring epialleles Stable epigenetic alleles have also been identified by studies on intraspecific variation. In the process of analyzing floral development mutants, Jacobsen and Meyerowitz (1997) identified several alleles of the SUP locus. These alleles were mapped to the SUP locus and were confirmed by complementation tests. However, no sequence differences were identified at the SUP locus. These clark kent alleles exhibit higher methylation levels than the wild-type SUP allele and reduced expression. While the clark kent alleles generally exhibit stable inheritance, 1–3% of progeny reverted to wild-type phenotype and these revertents had reduced, wild-type, methylation levels ( Jacobsen and Meyerowitz, 1997). The epigenetic silencing of SUP requires CpHpG DNA methylation (Lindroth et al., 2001), histone methylation ( Jackson et al., 2002) and RNAi components (Zilberman et al., 2003). The PAI gene family of Arabidopsis also exhibits epigenetic variation (Bender and Fink, 1995). The four copies of PAI present in the WS ecotype are methylated and transcriptionally silenced while the three PAI genes in Col are unmethylated and expressed (Bender and Fink, 1995). This silencing in WS is triggered by the inverted repeat arrangement of the PAI1 and PAI4 genes (Melquist and Bender, 2004; Melquist et al., 1999) and requires CpHpG methylation (Bartee et al., 2001) and histone methylation (Ebbs et al., 2006; Malagnac et al., 2002). There is also evidence that a disease resistance gene cluster exhibits naturally occurring epigenetic variation (Yi and Richards, 2007). There are several examples of naturally occurring epigenetic variants in other plant species as well. Linneus described a variant of Linaria vulgaris that had altered floral morphology (Cubas et al., 1999). This natural variant has been stably maintained for over 250 years and exhibits relatively stable genetic transmission. Molecular characterization revealed that the altered floral morphology is due to increased cytosine methylation at the Lcyc gene (Cubas et al., 1999). There is a mutation that affects tomato fruit development and ripening that is caused by epigenetic changes at the Cnr locus (Manning et al., 2006). There are naturally occurring alleles of several maize genes affecting seed or plant pigmentation levels, including Pl1 (Della Vedova et al., 2005; Hoekenga et al., 2000); P1 (Chopra et al., 2003; Sekhon et al., 2007; Sidorenko and Peterson, 2001) and R (Kermicle et al., 1995; Ronchi et al., 1995; Walker and Panavas, 2001), that exhibit epigenetic regulation.


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3.1.4. Polyploid formation Epigenetic regulation may also play an important role in the success of polyploids (Hegarty and Hiscock, 2008; Liu and Wendel, 2003). Polyploidy involves an alteration in gene dosage (autopolyploidy) or a fusion of two complete genomes (allopolyploidy). In both instances, a situation is created in which the majority of genes are redundant and novel functions or expression patterns can be sampled. There are numerous examples of altered epigenetic states in recently formed polyploids. Newly formed polyploids often show substantial genomic instability, however the exact type of changes observed varies in different species. There is evidence for sequence elimination in wheat and Tragopogon polyploids (Feldman et al., 1997; Shaked et al., 2001; Tate et al., 2006). Chromosomal translocations and transposon insertions are common in Brassica polyploids (Song et al., 1995) while mainly gene expression changes are observed in Arabidopsis and cotton (Adams, 2007; Adams et al., 2003; Lee and Chen, 2001; Wang et al., 2004). Many polyploids also exhibit altered DNA methylation patterns upon hybridization of the two paternal genomes (reviewed by Liu and Wendel, 2003). The alteration of DNA methylation patterns following polyploidization suggests that epigenetic changes may be common in newly formed allopolyploids. Indeed, a number of studies have provided evidence that epigenetic changes cause altered gene expression in newly formed polyploids (Comai, 2000; Comai et al., 2000; Kashkush et al., 2002; Lee and Chen, 2001; Madlung et al., 2002). Madlung and Comai (2004) proposed that the formation of a polyploidy causes a high level of genomic stress which results in relaxation of epigenetic silencing and expression of normally suppressed sequences. As the epigenetic systems are reestablished, novel epigenetic states are formed in the polyploids relative to the parental genomes. This system allows for novel expression states to be sampled and selected in polyploids. 3.1.5. Nucleolar dominance Ribosomal RNA genes in plants are highly repeated and occur in long contiguous stretches. Interestingly, only a portion of these genes are normally expressed in any cell, and the silenced portion is in a contiguous stretch indicating a mechanism to simultaneously silence megabase segments of DNA. Furthermore, when multiple clusters of ribosomal genes are introduced into an organism by hybridization, the phenomena of nucleolar dominance is observed. Nucleolar dominance is achieved during an interaction of gene clusters on different chromosomes in which an entire cluster on one chromosome is silenced and the other remains active. The mechanism underlying this process is still being characterized, but the current state of knowledge is provided in Preuss and Pikaard (2007).

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3.2. Genomic and molecular genetic examples of epigenetic variation In addition to the previously described examples of epigenetic inheritance, there are several studies that address the prevalence of epigenetic variation within plant species. In general, it is quite difficult to attribute variation to epigenetic mechanisms. However, by studying the mechanisms of epigenetic inheritance, such as DNA methylation, it is possible to identify epigenetic variation. It is likely that these studies represent only the tip of the iceberg and that the application of genomic technologies will provide a more detailed understanding of epigenetic variation. 3.2.1. Natural variation for methylation of repetitive elements One approach towards the identification of epigenetic variation within a species is to simply monitor DNA methylation levels in populations. Eric Richards and colleagues have used Southern blot analysis of DNA methylation at repetitive sequences to monitor variation in Arabidopsis (Riddle and Richards, 2002, 2005; Woo et al., 2007). The methylation of rDNA ranged from 20% in some Arabidopsis ecotypes to over 90% in others (Riddle and Richards, 2002). There was a correlation between the methylation level of ribosomal DNA and the copy number for rDNA. A QTL analysis of rDNA methylation identified two major QTL located at the genomic locations of the rDNA that explain 50% of the variation in DNA methylation levels. It is likely that much of the methylation variation contributed by these QTL was due to inheritance of parental DNA methylation patterns at these loci (Riddle and Richards, 2002). In addition, several trans-acting QTL were identified on chromosomes 1, 3, and 5 (Riddle and Richards, 2002). A larger screen of DNA methylation levels in Arabidopsis ecotypes revealed that the Bor-4 ecotype exhibits low levels of DNA methylation at the 180-bp repeats but not at other loci (Woo et al., 2007). This reduced methylation segregated as a simple Mendelian trait and map-based cloning identified the VIM1 gene (Woo et al., 2007). There is a large deletion within the Bor-4 allele of VIM1 (Woo et al., 2007). The intraspecific variation for VIM1 function therefore leads to intraspecific variation for centromeric methylation in Arabidopsis. There is no evidence for functional significance for the variation of DNA methylation levels at centromeres or rDNA in Arabidopsis. 3.2.2. Natural variation for genic epigenetic variation Several approaches have been used to identify genes that exhibit variation for DNA methylation levels within a population. Rangwala et al. (2006) used microarray profiling to identify natural epigenetic variants in Arabidopsis. One noncoding transcript that may be derived from a retrotransposon was characterized in detail. This Sadhu element (At2g10410) is methylated


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in most of the accessions tested, but in Col and N13 this sequence is highly expressed and hypomethylated (Rangwala et al., 2006). Multiple members of the Sadhu family of retroelements exhibit natural epigenetic variation (Rangwala et al., 2006). Interestingly, different Sadhu elements are controlled through different epigenetic mechanisms (Rangwala et al., 2007) and these members of this family do not exhibit coordinate regulation (Rangwala et al., 2006). Vaughn et al. (2007) used chromosome 4 tiling microarrays to characterize the DNA methylation patterns in two Arabidopsis accessions. This approach identified numerous examples of variable DNA methylation in Ler and Col ecotypes. A limited analysis of the stability of DNA methylation patterns found that some polymorphisms were quite stable while others exhibit high reversion frequencies in F2 families (Vaughn et al., 2007). A survey of sixteen loci in 96 different ecotypes revealed high levels of methylation polymorphism within Arabidopsis. Using a different approach Zhang et al. (2008) identified high levels of DNA methylation among Arabidopsis ecotypes. Methylation polymorphisms were more common near gene ends than within the coding region and were correlated with expression differences in some examples (Zhang et al., 2008). Another recent study identified widespread epigenetic natural variation for RNAi targets in Arabidopsis (Zhai et al., 2008). There is also evidence for natural epigenetic variation in maize. Makarevitch et al. (2007) used microarray profiling to identify the targets of CpHpG methylation in the maize inbreds B73 and Mo17. Over 100 genes sensitive to CpHpG methylation were identified by comparing expression isogenic wild-type inbred and zmet2-m1 mutant lines. The majority of the genes are sensitive to CpHpG methylation only in one of the two inbred lines. In most cases, these genes exhibit different expression levels in wild-type B73 and Mo17 and this variation maps to the gene itself and is controlled by DNA methylation (Makarevitch et al., 2007). A survey of the methylation and expression levels for several of these genes provided evidence for stable natural epigenetic variation in eight different inbred lines (Makarevitch et al., 2007).

4. Epigenetic Inheritance and Crop Improvement Heritable transcriptional gene silencing is common in transgenic experiments. Transgene silencing reduces the efficiency of the transformation process and increases the work necessary to identify good events. Epigenetics also has a role in somaclonal variation (Kaeppler et al., 2000). Somaclonal variation generally is detrimental to the processes of transformation and clonal propagation, but in some instances can produce useful phenotypes.

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One vastly underexplored area of epigenetics is the potential role of heritable states of expression as a mechanism of quantitative variation, and as a rapid-response reservoir of variation that can contribute to selection response. In the following section, we will discuss the potential role of epigenetic inheritance in the process of plant breeding.

4.1. Epigenetics in quantitative inheritance and selection response Recent research provides evidence for the following interesting attributes of epigenetic inheritance. 1. Naturally occurring epialleles have been documented in numerous species and are relatively stable, sometimes over many generations. 2. Epialleles have reversion frequency 2–3 orders of magnitude greater than changes in the primary sequence. In the case of silenced alleles, reversion is to the active state in contrast to reversion of base sequence to a prior state which is very rare. 3. Stability and formation of epigenetic states can be influenced by the environment. 4. Epigenetic states of expression are not simply on or off, but stable intermediate levels of expression can be established. 5. Introduction of trans-acting alleles in ‘‘chromatin genes’’ can cause transient (one generation) or permanent alteration in state. For example, an epiallele could be stably maintained over many generations, but could revert to activity after a single generation interaction in a loss-offunction methyltransferase mutant. 6. Hybridization/heterozygosity is required for establishment of some epigenetic states such as paramutation, so epiallelic variation is enhanced in outcrosses and may increase in proportion to diversity. The molecular basis of allelic variation for quantitative traits in plants is just starting to be characterized, so it is difficult to provide examples for which quantitative models based on sequence information would be insufficient to explain the variation present. In fact, the stability of epialleles would make them appear no different than any sequence variant within the temporal context of most plant breeding experiments. However, long-term selection experiments indicate an amazing ability of plants to respond to selection in short periods of time. The Illinois longterm selection program is one unique example of selection in plants (Dudley, 2007). An interesting attribute of this program was the strong response to reverse and switchback selection. Especially intriguing is the ability of populations selected in the low direction (e.g., for low oil) to respond to selection for increases in the biochemical components. The reverse selection in the low oil population was initiated at a cycle when it


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appeared that selection response had dramatically reduced and that there was little genetic variation remaining. While there are various sequence-based genetic mechanisms that could cause this result, it is intriguing to speculate that epiallelic variation may maintain a reservoir of variation that might be selected upon in a situation such as this. In natural populations, such a reservoir of variation could allow population responses to rapid changes in environment for traits with seemingly little phenotypic variation.

4.2. Epialleles and gene discovery Substantial effort is currently being devoted in plants to characterizing the sequence variation that underlies quantitative phenotypic variation. This is accomplished by associating sequence-based haplotypes with phenotypic performance to determine causal phenotypes. Epigenetic variation that contributes to phenotypic variation would complicate this effort. Epigenetic molecular variation would not be detected by normal sequence-based approaches to haplotype characterization. Methylation mapping, using a technique such as bisulfite sequencing, or chromatin mapping would be required to characterize the epigenetic states of target genes. Alternatively, epialleles might be predicted by detection of transcription states that cannot be explained by sequence haplotype variation. A further complication of epigenetics in this process is that epialleles may occur in multiple lineages in a comparatively short time frame. Whereas sequence variants occur in a logical progression with a founder sequence giving rise to a series of accumulating variants over the course of times, epialleles may appear and disappear at any point within this process, and will likely occur independently of most sequence polymorphisms. Therefore, epialleles may at best cause noise in the process of searching for causal sequence polymorphisms, and in extreme cases may be more important for specific genes in determining phenotype than any sequence polymorphism. Growing documentation of the presence of stable epialleles in numerous species suggests that epigenetic variation needs to be considered in the process of associating molecular variation with phenotype. Advancing technology may allow chromatin patterns to be included with sequence as another layer of information that can be included in the analysis complex traits in plants.

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C H A P T E R

F O U R

Microbial Distribution in Soils: Physics and Scaling I. M. Young,* J. W. Crawford,† N. Nunan,‡ W. Otten,§ and A. Spiers§

Contents 1. Soil as a Habitat 2. What Characteristics of Structure Matter and Why? 2.1. Moisture characteristic 2.2. 3D stucture–water interactions 2.3. Water-film thickness 2.4. Surface area 3. Spatial and Temporal Distribution of Microbes 3.1. Fungi in soil 3.2. Visualisation and quantification of fungal hyphae in soil 3.3. Relevance of spatial temporal dynamics to ecosystem function 4. Habitat–Biofilm Interactions 4.1. Bacterial movement 4.2. The biofilm environment 4.3. Celluose as a legacy in soil 4.4. Surfactants as a legacy in soil 5. Habitat–Microbe Interactions 5.1. Regulatory feedbacks in soil–microbe interactions 5.2. The soil–microbe complex as a complex adaptive system 5.3. Functional consequences 6. Future References

* { {

}

82 84 85 85 88 89 90 95 95 97 99 100 101 104 105 105 106 108 111 112 113

School of Environmental and Rural Sciences, University of New England, Armidale NSW 2351, Australia Faculty of Agriculture, Food and Natural Resources, University of Sydney, NSW 2006, Australia CNRS, Laboratoire BioEMCo,UMR7618, Baˆtiment EGER, Aile B, Campus AgroParisTech, F-78850 THIVERVAL-GRIGNON, France SIMBIOS Centre, University of Abertay Dundee, DD1 1HG, Scotland


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Abstract In a handful of fertile soil there are billions of microorganisms and yet, even with a conservative estimate, the surface area covered by these organisms is considerably less than 1%. What does this tell us about the function of the physical structure in which soil organisms reside and function, collecting, and separating micropopulations from each other and from resources? It would seem that most of the soil is akin to desert regions with little life been supported on its terrains, yet with vast communities of individuals, from an amazing array of species, supported in small-scale habitats, connected or disconnected by saturated or unsaturated pore space over relatively short time-scales. The biodiversity of these communities remains impressive yet overall functionally illusive, bar some considerations of inbuilt redundancy. What is far more impressive is the range of habitats on offer to populations with short-term evolutionary time frames. The availability of spatially and temporally diverse habitats probably gives rise to the biodiversity that we see in soil. It is not too far fetched to state that the majority of habitats on Earth (and indeed extraterrestrial) are revealed in that handful of soil. The key question is what is the functional consequence of such habitat heterogeneity? To answer this it is clear that we need to bring together a new discipline that combines the biology and physics of the soil ecosystem. This biophysical approach, combined, where required, with important mineral-microbe knowledge is needed to help us understand the mechanisms by which soils remain productive, and to identify the tipping-points at which there may be no return to sustainability. This review aims to highlight the importance of addressing the soil ecosystem as a dynamic heterogeneous system focusing on microbiota–habitat interactions.

1. Soil as a Habitat The defining features of materials that provide good habitats are constant: adequate supplies of food, shelter, and water; refuge from predators; and access to mates. Habitats in above ground ecosystems do not approach the complexity of soil habitats. The spatial-temporal variation of the physical, chemical, and biological structure of soil habitats is awesome even over relatively small spatial and temporal resolutions. However, the complexity of soil ecosystems is also matched by the difficulty in adequately quantifying the functional traits of the habitats linked through to biotic activity. Soils are opaque, fragile constructs that offer limited opportunity for nondestructive analysis. Indeed, it has only been within the last 20 years that a usable molecular tool box has become available to partially quantify the soil microbial populations, albeit destructively. Similarly, until recently, quantification of soil structure has been limited to relatively simple

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qualitative assessments through measuring arbitrarily units of 2D Ped structures, or infield assessments categorizing soil structure into quasigeometric units (e.g., subangular blocky). Whilst these approaches have served to highlight the difficulty of assessing structures, and in significant ways the spatial heterogeneity of these structures, they have offered little advance in our understanding of how the inherent heterogeneity of the structure of soils can be linked to their functions. Standard stability tests of soil structure are relatively useless in assessing the soil’s real stabilities as they are akin to asking questions about the operational efficiency of a skyscraper from the debris of that building sieved through a 2-mm mesh. Similar issues can be raised over the use of pore size distribution datasets as they fail to quantify pore connectivities, never mind the real pore geometries that control permeability. A detailed review of methodologies and modeling approaches that are used to quantify soil structure is provided by Young et al. (1989). Uniquely, soils are characterized by significant spatio-temporal heterogeneities that few, if any, other porous media exhibit. The classic work of FitzPatrick (1993) and Foster and Rovira (1976) using high quality thin sections illustrate the impressive complexity of the biophysical framework and mineralogy of many soils and environments. Adderley et al. (2002) present a good summary of quantification techniques used for thin sections. Figure 1 shows the typical spatial heterogeneity of soil architecture and mineralogy in soils. Across macro- and microscales, the variation and patchiness of pore structures is demonstrated. The mineralogical diversity emphasizes the potential chemical variation, leading to, in functional terms,

Pores

Figure 1 Thin section showing highly heterogeneous pore space and mineralogy. Image width is 4 mm. Image provided by Dr. Sacha Mooney, Nottingham University.

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diverse responses to a wide range of processes: sorption, flow, microbial adhesion, and plant development (Lehmann et al. 2008). There is a growing awareness that not only are such heterogeneities crucial to understand in terms of accurately predicting a wide range of soil processes, but also, information at the small scale (<mm), where microorganisms reside and interact with their environment, is a key area to investigate. At this scale, microbes are active in relatively thin films of solutes within a biofilm, adhering to pore surfaces.

2. What Characteristics of Structure Matter and Why? Soil is a porous medium with a hierarchy of pore dimensions and a large internal surface composed of a wide variety of materials (organic and inorganic). The volume, distribution, and movement of solutes depends on the characteristics of the solutes active surfaces and how solutes interact with the soil surfaces. In short, surface tension and contact angles of the solute and the capillary forces and hydrophilic nature of the soil. The moisture characteristic (MC) of soil arises from an integration of all these properties to provide a unique fingerprint of how water interact with a specific soil.

Water-filled pore space and water-film thickness decrease

Water content

Air entry: drainage begins and matrix becomes unsaturated

CO2 moves about 1cm in 1 day when soil is saturated

Point of inflection: smaller pores drain-air connectivity increases and hydraulic connectivity decreases

Large pores drain

CO2 moves about 1 cm in 1 hour when hydraulic connectivity is low

Matric potential

Figure 2 Schematic of moisture characteristic of soil: moisture-matric potential relation is represented by the black curve.


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2.1. Moisture characteristic The MC is the function that describes the relationship between matric potential and water content (Brady, 1974), and is a direct consequence of the physical geometry of the habitat (Crawford et al., 1995) and the physicochemistry of the composition of the habitat and the draining solute (Read et al., 2003). The MC schematic in Fig. 2 shows a succession of hydric states typical of many soils. Soil is perhaps unique as a porous material that has a sufficient hierarchy of pore sizes that permit solutes to be held over a wide range of matric potentials, thus allowing saturated and desaturated volumes of pores to coexist in close proximity. Additionally, the complex construction of soils with mineral and organic components provides hydrophobic and hydrophilic materials that have properties that are polar opposites of each other in relation to their interactions with water (Feeney et al., 2006b). In terms of habitats for microorganisms these characteristics are crucial. Many organisms (e.g., bacteria, protozoa, bacterial-feeding nematodes, etc.) are essentially aquatic, living in thin water-films adhered on pore surfaces, controlled by matric potential. Along with others, such as fungi, they rely on the happy coincidence of available water and connected air-filled pore spaces that permit their development and connectivity to the wider soil ecosystem. Thus, the ability of soil to allow water to penetrate into it, and hold water, is the key characteristic of all soil ecosystems and highlights the MC as probably the most important relationship in Ecological Studies. The hierarchical habitat of soil permits a gradual transition from a, saturated, aquatic system towards a fully, unsaturated, aerated system where, if water does exist, it is tightly adsorbed onto mineral surfaces. This transition has been highlighted by Ghilarov (1959), Vannier (1987), and Coleman et al. (2004) who point to soil systems as ideal transitional mediums for the evolution of many groups of terrestrial invertebrates. In essence, soil is both an aquatic and a terrestrial ecosystem. It is our contention that much research into soil microbiology fails to account for the MC and where water is mentioned it is shown as a simple absolute moisture value or concepts such as Field Capacity or Water Holding Capacity. Where structure is mentioned typically a surrogate measure of pore size is used, which is wholly inappropriate as it is the pore shape that is the key characteristic (Or et al., 2007).

2.2. 3D stucture–water interactions Recent work by Carminati et al. (2008) reveals the intimate associations between pore geometry and water. Using synchrotron X-ray tomography Carminati et al. (2008) explored the soil–water interfaces between isolated, yet heterogeneous, aggregates within a desorbing and adsorbing soil, controlled by matric tensions. The monochromatic, relatively low energy,

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X-rays used permitted good segmentation of water, solid, and pore space from the 3D volumes (Fig. 3). At the micron scale Fig. 3A relates to the wet end of the MC where air has entered and larger pores have started to drain. The water menisci are clearly evident, hydraulically connecting each aggregate. At this point some large pores which drain through smaller pores will remain saturated until the matric head equivalent to the pore radii of the smaller pores is reached. As the matric head increases (Fig. 3A–D) water empties out of all but the smallest pores and the hydraulic contact between aggregates significantly decreases. Figure 3D highlights the smaller pore retaining water and the hydraulic bridge between aggregates. In reality the volume of water within and between aggregates may be higher as the X-ray tomography system had a lower resolution of 5.92 mm. Any features below this spatial scale would not be identified. This impressive work, for the first time, allows us to observe and quantify the water–soil interface near the appropriate scale of bacterial populations, and across scale relevant to the root–soil interface in 3D. This technology and application potentially represents an unprecedented step forward in our understanding of the soil–plant–microbial interactions. In a further study Culligan et al. (2004), using similar technology, examine the importance of hysteresis (i.e., whether soil is wetted or dried to a specific matric potential) in the volume and distribution of water in soil in 3D. They demonstrate the importance of understanding the mechanisms controlling the distribution of water in soil. The mechanisms of hysteresis in

A

[h = -0.025 m] B

[h = -0.15 m] C

[h = -0.43 m] D

[h = -1.08 m]

Figure 3 2D tomographic segmented slices over time (A–D), showing desorbing water within and between two heterogeneous aggregates (voxel resolution 5.92 mm). Black, grey, and white relate to solid, water, and pore, respectively. Numbers in brackets represent matric head—the more negative the drier the soil. Arrows highlight isolated water pockets within aggregates. Circle highlights hydraulic contact between aggregates. Adapted from Carminati et al. (2008).


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soil are given in Marshall and Holmes (1992). Figure 4 shows the impact of hysteresis on the air–water interface and highlights the impact on potential diffusive and hydraulic pathways. Here we can see clearly that on the wetting curve there is a potential increase in diffusive pathways for gas and fungal hyphae spread. It is clear that, for the same structure, at the same matric potential, the pathways are radically different, leading to potentially significantly different microbial functionality. In 3D such pathways would increase. Whitmore and Heinen (1999) provide one of the few attempts to account for the impact of hysteresis in microbial activity, providing estimates of its impact on mineralization. Another interesting feature of Culligens’ work (Culligan et al., 2004) is that, in the saturated state, significant pore volumes remain air-filled, irrespective of the initial wetting state (Fig. 4A and D). This may highlight specific pore volumes that remain untouched by water until a physical

A

B

D

C

Figure 4 2D tomographic slice of saturated soil (A) initially dried to a specific matric potential (B), then wetted to that same potential (C) and finally to saturation (D). The bright circle highlights the main change in the distribution of water, and the impact of such change is highlighted by increase in proposed diffusive pathways (dotted yellow lines). Adapted from Culligan et al. (2004).

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perturbation exposes the surfaces. The lack of water ingress into these volumes may be due to hydrophobic substances coating the pore surfaces, acting as a barrier to water influx: perhaps old, lignified organic matter remnants acting as protected organic matter.

2.3. Water-film thickness As seen from Fig. 2 the MC defines the hydraulic and gaseous connectivity of soil ecosystems. A key process is the alteration of water-film thicknesses held by capillary forces at the pore surfaces. It is notoriously difficult to get good experimental data on the thickness of water films in soil. In an excellent review, Or et al. (2007) examine this, and related topics, in relation to bacterial habitats and activity. Tokunaga et al. (2003) measure water-film thickness on gravel surfaces at different matric potentials. What is clear is that even under relatively wet conditions (high matric potential) thickness values are in the order of 2–10 mm, which is bridging the size of single bacterium and small bacterial colonies. Figure 5 summarizes results from Tokunaga et al. (2003) and draws a link the work of Wallace (1958), who studied the influence of water-film Distance travelled in 1 hr (mm)

Typical water film thickness (micrometers)

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0

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10 Transverse section 20 (mm) of nematode

8 Water film

6

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0 0

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3 4 Matric potential (-kPa)

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Figure 5 Typical water-film thickness versus matric potential (adapted from Tokunaga et al., 2003), and associated relation with potential nematode movement (adapted from Wallace, 1958).


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thickness on nematode movement. Wallace (1958) in a seminal piece of work showed that for a nematode with a cross-sectional area of 20 mm, the optimal water-film thickness for movement was approximately 4 mm. This permitted the nematodes to remain motile, maximized forward movement and minimized sideward movement. This work has been extended by Anderson et al. (1997) and Young et al. (2002) connecting the hydraulic and gaseous connectivity through to the chemotaxis processes ongoing in soil that permit microorganisms to search and locate food sources. The ‘‘resource seeking’’ strategies of protozoa, fungi, nematodes, and bacteria and other microbes have clearly evolved to cope with the heterogeneous maze imposed by the soil microarchitecture. In real heterogeneous soils the correlations found by Tokunaga et al. (2003) in absolute terms, will be rare. Due to variabilities in pore connectedness, sizes, and roughness, at any given matric potential, we would expect not only lager water-films, but also the presence of relatively large, yet isolated pockets of water, at low potentials. This is evident in the work of Culligen et al. (2004).

2.4. Surface area How connected a habitat is plays an important role in most soil microbially centered processes. The accessibility of surface area to microbes has obvious consequences to a host of issues: pesticide degradation; remediation of pollutants; carbon degradation and sequestration. The question is what proportion of the surface area is covered by microorganisms? Taking 1 cm3 of soil the total surface area, at a conservative estimate, will be roughly 20 m2. Assuming that we have 10,000 protozoa, 107 bacteria and 5 km of fungi, the total surface area that is covered is in the order of 106%. This figure of course dependent on clay type. However, no matter what reasonable combination of inputs may be, it is highly unlikely that, for many soil ecosystems, more than 1% of the total surface area is available. Given the fact that most the microbial inputs rely on the presence of aquatic environments in soils and that much of the surface area is physically inaccessible, the percentage of surface area covered by active microbes is significantly less. Developing on the theme of surface area and microbial activity, a recent paper by Tarlera et al. (2008) attempts to quantify the link between bacterial diversity and surface area. They have shown that, in certain coarse textured immature soils, low surface areas have a direct impact on community composition. As soils age (5000–77,000 years) the percentage of surface area that could be occupied by prokaryotic cells dropped from 100% to 1%. No such correlation was found for fine textured soils. Whilst these results await confirmation across a wider range of soil ecosystems, and a better understanding of what available surface area is needs to be sought, they do offer new insight into microhabitats and microbial diversity.

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The spatial location of carbon substrate allied to the location of water and microbes is also a key determinate of the amount and type of microbial activity. Often organic matter provides a rich and large surface area on which many microbes reside. Additionally, models assume that there is a physically protected portion of organic matter isolated from microbial and chemical degradation (e.g., Balesdenta et al., 2000). Often the concept of soil macroaggregates (>200 mm) and microaggregates (50–200 mm) is invoked in an attempt to understand the importance of physical protection. These relatively arbitrary upper and lower limits ignore the importance of understanding the 3D nature of soil and the fact that, typically, it does not exist as isolated aggregates (or indeed pores) but as a highly complex interconnected system.

3. Spatial and Temporal Distribution of Microbes The importance of spatial variability in soil microbial ecology has long been recognized and the spatial distribution of microbes and microbial activity has been described at scales ranging from individual bacteria in communities to the landscape scale (Franklin and Mills, 2003; Franklin et al., 2002; Jones and Griffiths, 1964; Morris, 1999; Morris and Boerner, 1999; Nunan et al., 2001, 2002; Parkin and Shelton, 1992; Robertson et al., 1997; Vieuble´-Gonod et al., 2006). However, much of the work has been descriptive in nature and spatial structure has not been fully exploited for understanding soil function nor fully recognized as a fundamental property of soils. The spatial structure of soil microbial populations is the result of various environmental controls operating at different scales and of intrinsic population processes such as dispersal, reproduction, mortality, or competition, that occur primarily at microbial scales (Ettema and Wardle, 2002). Because microbial communities respond simultaneously to a range of variables that display different spatial patterns, the spatial patterns of microbial communities are likely to be highly complex. This has been borne out by empirical evidence. Oline and Grant (2001) have shown that the spatial pattern of soil microbial biomass was more complex than the spatial patterns of other soil properties, as measured by the fractal dimension. It has been established that subsets of microbial communities show different spatial patterns, suggesting that they respond differently to structuring agents or that they respond to different structuring agents (Ritz et al., 2004; Saetre and Ba˚a˚th, 2000). This was achieved using principal components analysis to reduce the multivariate community data (PLFA or CLPP) to a smaller set of derived variables, each of which can be related to different patterns within the community structure (Legendre and Legendre, 1998). The derived variables (principal


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components) can be considered to represent specific subsets of the microbial communities. A geostatistical analysis of the variables showed that subsets of the communities and their capacity to use different substrates were spatially structured over a wide range of patch sizes (Ritz et al., 2004; Saetre and Ba˚a˚th, 2000). Microbial communities, or subsets thereof, also display nested scales of spatial structure, indicating that community development can respond to several drivers at once (Franklin and Mills, 2003; Nunan et al., 2002). This results then, in an overall picture that is highly complex and difficult to unravel. Spatial structure can be used to help identify the external or intrinsic drivers of population development and activity (Ettema and Wardle, 2002). However, in order to understand how external and intrinsic drivers shape microbial population activity, structure, and distribution it is important to account for the scales at which the drivers operate. Many studies have examined the role of environmental variables that display large or medium scale gradients such as disturbance history (Robertson et al., 1993), moisture content (Morris and Boerner, 1999), organic matter content (Parkin, 1987; Vieuble´-Gonod et al., 2003), distribution of plants (Klironomos et al., 1999; Saetre and Ba˚a˚th, 2000) or preferential flow paths (Bundt et al., 2001; Gaston and Locke, 2002) on microbial populations and activity at landscape, field, plot, or core scales. Whilst there is no doubt that all these factors influence microbial population development and activity, they are not related to aspects of microbial populations, such as the high levels of diversity sustained at small scales and the noncompetitive diversity patterns found in soil microbial communities (Treves et al., 2003; Zhou et al., 2002), that underlie fundamental properties of soil (the robustness and resilience of soil biological function for example). Here clearly, microscale ( Ω0.5 E500 < E100 < E0.5 n500 < n100 < n0.5 E0.5 + ΔGrxn

E100 E500 n500

−

n100

Increasing size of nucleus

Homogeneous 500 mg/L

n0.5 Ω500

Ω100

Homogeneous 100 mg/L

Ω0.5

Heterogeneous 0.5 mg/L

Figure 11 How saturation state affects nucleation of the Pb-phosphate mineral pyromorphite in the presence of apatite. O = saturation state, E = activation energy at different saturation states, n = crystal size at different saturation states. From Lower et al. (1998). With permission.

This discussion suggests that nanoparticles should be highly unstable; yet, we shall see in subsequent discussion that many nanoparticles can remain stable for long periods of time and that particle size and stability can be related in a complex manner at the nanoscale. Next, let us consider one of the key equations in hydrology and sedimentology, Stokes Law, which describes how particle settling velocity (ns) related primarily to gravity increases with the particle size (dp) and density (rp) for small particles ( TiO2 > SiO2. Toxicity in the presence of light was likely due to photocatalyzed formation of ROS. Dark toxicity was also observed, suggesting that additional mechanism(s) could also be involved. Also in this study, nanoparticles of different sizes aggregated into similar-sized structures, so that size per se of individual particles within the nanoscale did not appear to play a role in toxicity. Lin and Xing (2007, 2008) observed phytotoxicity of ZnO nanoparticles to ryegrass (Lolium perenne). The toxicity could not be explained by ZnO dissolution releasing aqueous Zn. Light microscopy of root sections showed that the ZnO particles adsorbed into root tissues and cells and damaged the root tissues. Only very limited translocation from root to shoot was observed. Zn nanoparticles inhibited ryegrass germination and ZnO nanoparticles inhibited corn germination (Lin and Xing, 2007). An additional important question for research is whether nanoparticles taken up into plants can then prove toxic upon consumption by animals and humans. Currently, not much is known about the biodegradation of nanomaterials (EPA, 2005), and whether biodegradation products may themselves be toxic.

11. The Special Role of the Soil Sciences in Environmental Nanoscience Throughout most of this chapter, we have focused primarily on how new advances in nanoscience and technology are affecting the soil sciences, from providing new analytical and theoretical tools to releasing fabricated particles that may create unlooked-for challenges to ecosystems and agriculture. But, let us not forget the ongoing and potential contributions of the soil sciences to the broader nanoscience community. Soil science has always dealt with the nanoscale, and soil scientists have understood for generations that small particles show unique behaviors. This recognition predated by many decades with the use of terms such as nanoparticle, nanomineral, nanotechnology, and nanotoxicity. Some key questions that soil scientists may make major contributions to in coming years include: 1. What are the unique properties of water at the nanoscale?

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2. What are the controls on and effects of nanoparticle aggregation relative to a host of physical, biological, and chemical processes? 3. How do we approach the complex interactions of nanoparticles with each other and with such complicated and heterogeneous environments as the rhizosome and the hyporheic zone? 4. What are the controls on nanoparticle toxicity to humans and ecosystems? and 5. How can we best apply approaches from a broad array of disciplines to the science and technology of nanoparticles and nanoscale processes? Because soil scientists have enormous expertise in applying interdisciplinary and multiscale approaches to complex environmental problems, the soil science community is well poised to take a leadership role in addressing the opportunities and challenges of nanoparticles in the environment.

ACKNOWLEDGMENTS P. Maurice thanks the NSF-funded (grant EAR02–21966) Environmental Molecular Science Institute at the University of Notre Dame. M. Hochella gratefully acknowledges NSF grant DGE-0504196, DOE grant DE-FG02–06ER15786, and funding from the Institute for Critical Technology and Applied Science at Virginia Tech.

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Combining Biomarker with Stable Isotope Analyses for Assessing the Transformation and Turnover of Soil Organic Matter W. Amelung,*,1 S. Brodowski,* A. Sandhage-Hofmann,* and R. Bol† Contents 1. Introduction 1.1. Rationale 1.2. Objective 2. Major Biomarkers 2.1. Biomarkers for plant-derived C 2.2. Biomarkers of multiple origin (plants, microbes, animals) 2.3. Biomarkers for living microbial biomass 2.4. Biomarkers for dead microbial biomass 2.5. Black carbon (BC) 3. Using Carbon Isotopes in SOM Studies 3.1. Stable isotopes and their measurement units 3.2. Analytical techniques 3.3. Isotope fractionation and tracing 3.4. Artificial labeling techniques 3.5. Natural labeling techniques 4. Biomarker Specific Stable Isotope Analyses 4.1. Incubation studies 4.2. Field studies 4.3. Ageing phenomena 4.4. Fate of individual SOM compounds: A comparative synthesis

* { 1

156 157 159 159 169 173 175 176 180 180 180 181 183 184 185 188 189 201 216 217

Institute of Crop Science and Resource Conservation (INRES), Soil Science and Soil Ecology, University of Bonn, Nussallee 13, D-53115 Bonn, Germany Soils and Water Team, North Wyke Research, Okehampton, Devon, EX20 2SB, United Kingdom Corresponding author: e-mail: [email protected]


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Abstract Soil organic matter (SOM) consists of a vast range of biomolecules, but their individual contribution to the biogeochemical cycling of nutrients and CO2 release has eluded researchers. Here, we review the current knowledge on combining biomarker with stable isotope analyses for identifying the mechanisms and rates of SOM genesis and transformation. After an overview of the major biomarkers that are used for identifying decomposer communities and the origin of SOM far beyond microbial life cycles, we reexplain the principles and potentials of applying artificial and natural stable isotope labeling techniques in soil research. Major focus is finally laid on the quantitative evaluation of the published compound-specific stable isotope data of soils to characterize the niches and activity of soil microorganisms as well as their role in controlling the short-to long-term fate of SOM. Our literature research suggested that fungi appear to feed mainly on fresh plant material, whereas gram-positive bacteria also consume both fresh and older SOM. The newly synthesized structures have apparent mean residence time (MRT) of 1–80 years, while refractory plantderived biomarkers may even dissipate faster. In no case did we find evidences for inert soil C. However, MRT was not constant but increased with increasing time after C3/C4 vegetation change. It is concluded that calculated MRTs from C3/C4 vegetation changes are currently underestimated, because,there is also a the formation of stable C4-derived C pools that did not reach steady-state equilibrium within few decades.

1. Introduction Soil organic matter (SOM) comprises a vast range of different organic structures with a mean residence time (MRT) in soils ranging from days to millennia (e.g., Amelung, 2003; Bol et al., 1996; Buyanovski et al., 1994; Derrien et al., 2007; Flessa et al., 2008; Gleixner et al., 2002; Jenkinson, 1990). This review describes how the combination of biomarker analyses with stable isotope assessment helps reconstructing the pathways and timescales of SOM transformation. A biomarker is an organic compound with a defined structure indicative of its producer. It may represent a larger group of molecules in living or dead organism cells. Hence, there are biomarkers that characterize living soil biomass and thus the structural diversity of the living soil microbial community. Other biomarkers rather point to the organic residues of plants, microorganisms, animals, or anthropogenic sources in soil. In any case, biomarker analyses help to elucidate the mechanisms of SOM genesis and transformation, but in no case do the mere

Combining Biomarker with Stable Isotope Analyses

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biomarker concentrations provide a clue to the rates of SOM dynamics. To estimate the rates, we must be able to distinguish whether a given SOM structure has simply been preserved in soil or whether it has been resynthesized. We can distinguish between these processes, when the date of biomarker synthesis is known and labeled by a specific isotopic composition. If a biomarker is solely preserved, the isotopic composition is maintained. If it has been readded from other C and N sources, its isotopic composition is likely to be changed. Compound-specific tracing of d13C and d15N abundances thus provides a novel and powerful tool for a better quantitative understanding of the mechanisms and rates of SOM genesis and transformation.

1.1. Rationale SOM is the major component in the biogeochemical cycling of nutrients and its quantity and transformation both control primary productivity. A minimum SOM content is required to prevent erosion and further losses of soil quality. In the European Community, compulsory financial support for the farmers is rejected, if certain crop rotations, or a positive humus balance or a minimum SOM content is not sustained (1–1.5% SOM for less or more than 130 g clay kg1, respectively). Higher soil organic carbon (SOC) contents are advised to maintain soil structural stability (Carter, 2002; Greenland et al., 1975; Six et al., 2000), and to mitigate global warming (Lal, 2001). The potential of soils to sequester C, however, is limited (Hassink, 1997; Six et al., 2002; West and Six, 2007), because close to saturation additional C is stored in labile C pools only (Gulde et al., 2008). Maximum SOM contents may thus be hard to maintain under ongoing agricultural use. Furthermore, they increase the risk that excess nitrate from mineralized SOM is released into the ground and surface waters after harvest. To be able to establish optimum, site-typical SOM contents (see also related work of Pullman et al., 2000; Schmitt and Wessolek, 2005; Verheijen et al., 2005), a quantitative insight into the rates of SOM formation and decomposition is required. The rates of SOM genesis and transformation cannot be depicted from litter decomposition experiments but must be determined on-site, in order to account for soil-specific effects on the decomposition paths of the different organic molecules. Lignin, for instance, is selectively enriched during litter decomposition (e.g., Ko¨gel et al., 1988; Meentemeyer, 1978; Ziegler and Zech, 1991); however, when soil minerals are present, lignin dissipates in preference to saccharides, despite higher chemical lability of the latter. Microbial resynthesis and different stabilization reactions may account for this (Amelung et al., 1997; Eusterhues et al., 2005; Marschner et al., 2008) and all these rates must be known to truly understand the mechanisms of SOM transformation and genesis.

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Mathematical models have been developed for predicting the evolution of bulk SOM contents (Balesdent and Mariotti, 1996, Falloon and Smith 2000, Jenkinson, 1990, Parton et al., 1987), also under different climate and management regimes (e.g., Ludwig et al., 2005; Smith et al., 1997). Many of these models have in common that SOM is conceptualized into pools with different turnover rates. Recently, initialization of the RothC model has been achieved on the basis of measurable, physically isolated SOM fractions (Skjemstadt et al., 2004; Zimmermann et al., 2007). However, these pools are still operationally defined and the mean first-order loss rates certainly are not transferable to all SOM constituents of the respective pool. Hence, it is currently hardly possible to exactly predict the fate of SOM by such models. Four mechanisms have basically been identified to control the fate of organic compounds in soil: (1) selective preservation of recalcitrant molecules during SOM decomposition, (2) biological recycling of C and N moieties through microbial biomass, (3) physical inaccessibility of SOM in micro- and nanopores of oxides, of SOM intercalated into three-layer clay minerals, occluded within aggregates or merely protected through encapsulation and hydrophobic surroundings, and (4) chemical interactions of SOM with phenols, amides, metal ions, and minerals (e.g., Amelung et al., 1997; Christensen, 1996; Gleixner et al., 1999; Krull et al., 2003; Ladd et al., 1993; Schmidt-Rohr et al., 2004; Sollins et al., 1996; von Lu¨tzow et al., 2006). The time-scale of these processes exceeds, by far, the life cycle of most soil organisms. However, the assessment of the specific turnover times of the different SOM constituents helps to estimate the proportions of these mechanisms. The analysis of SOM composition provides a first clue to the role of (1) selective preservation mechanisms, yet the mere presence of a structure in soil does not inform about its residence time. Biomarker analyses has the advantage that it indicates the origin of SOM, thus helping to elucidate the (2) mechanisms of SOM recycling and transformation and to point to the involved members of the soil microbial community (an overview of the major biomarkers used for such purposes is given in Section 2). To quantify these SOM transformation rates, and to assess how they are influenced by (3) physical inaccessibility, and (4) chemical interactions, we finally need to know the input rates and residence times of the biomarkers in soil. This is achieved by combining biomarker analyses with isotope tracing. Section 3 outlines the tools of using stable 13C isotopes for this purpose and highlights thus the basic principles of using isotopes in SOM research. In general, incubation studies are used with a radioactive (14C, in principle also possible for 3H, 33P, 35S), or stable isotope labeling (13C, 15N, 2H, in principle also feasible for 34S, 18O) to understand the mechanisms of short-term SOM dynamics (days to months) and priming effects (e.g., Bol et al., 2002, 2003a; Hamer and Marschner, 2002; Hamer et al., 2004; Kuzyakov et al., 2000). For tracing medium-term SOM dynamics (years to decades), we may either use historical labeling experiments (e.g., Gerzabek et al., 2004), follow the


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fate of bomb-14C through SOM pools and fractions (Bol et al., 1996, 1999b; Torn et al., 1997) or take advantage of natural 13C isotope discrimination processes during photosynthesis, when vegetation changes (Balesdent and Mariotti, 1996; Boutton et al., 1998). As C3 plants discriminate more strongly against the heavier 13C isotope than C4 plants do, changes in d13C natural abundances in soil upon C3/C4 plant input changes inform on the portions of the remaining former material, that is, on its turnover rates (e.g., Balesdent et al., 1987; Veldkamp, 1994; Vitorello et al., 1989). Increasingly, these approaches are now being combined with biomarker analyses in soil for assessing their residence time (reviewed in detail in Section 4) or for palaeoclimate reconstruction (not considered here). Only for the long-term run (centuries to millennia) these methods fail, and true compound-specific radiocarbon ages need to be assessed (e.g., Bol et al., 1996, 2005; Huang et al., 1999).

1.2. Objective The objective of this paper is to give an overview on the scientific potentials of using biomarkers and stable isotopes in SOM research, and to evaluate exhaustively the state of the art by combining these two approaches for characterizing SOM. Major focus is on 13C studies to elucidate the compound-specific turnover of individual SOC moieties in agricultural soils. Stable isotope probing (SIP) of selected genes is only briefly mentioned in Sections 3 and 4, as many other reviews (e.g., Neufeld et al. 2007) deal with recent research developments in this field. Compound-specific isotopic analyses for the source appointment of pollutants (see e.g., Wilcke et al., 2002; Glaser et al., 2005) or for palaeoclimate reconstuctions (see e.g., Glaser, 2005), which may additionally rely on d18O and compound-specific d2H measurements, are not reviewed in this paper.

2. Major Biomarkers Whereas sophisticated analytical techniques, such as nuclear magnetic resonance spectroscopy and pyrolysis-field ionization mass spectrometry (Ko¨gel-Knabner, 1997; Leinweber and Schulten, 1999), allow for a screening of bulk SOM composition, only biomarkers give a clue to the sources of SOM (plants, fungi, bacteria, animals, fire, or anthropogenic origin; Section 1). The more specific the biomarker structure, the better is the source assignment, but the larger frequently the risk that this source is not representative for the majority of organisms or molecules involved in SOM genesis. Table 1 provides an overview of biomarkers that have been of major relevance for soil science, so far.

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Table 1

Major biomarkers and their source assignment in soil science (in alphabetical order of major compound class) Biomarker

Major source (remark)

Reference in texta

C25–C35, odd Predominantly >C20 Predominantly C20

Plants

Glycerides Acylglycerides C16, C18 monoglycerides Glycerol dialkyl glycerol tetraether lipids (GDGTs) Isoprenoid GDGTs Crenarchaeol Branched GDGTs Lipopolysaccharides a, b

Feng and Simpson (2007); Otto and Simpson (2005) Kolattukudy (2001) Gon˜i and Hedges (1990); Nierop and Verstraten (2004) Feng and Simpson (2007); Otto and Simpson (2005) Feng and Simpson (2007); Otto and Simpson (2005) Wiesenberg and Schwark (2006)

All organisms

Feng and Simpson (2007)

Archaea Crenarchaeota Bacteria

Schouten et al. (2007) Sinninghe Damste´ et al. (2002) Schouten et al. (2007)

Gram-negative bacteria

Evershed et al. (2006); Zelles (1999) Evershed et al. (2006); Zelles (1999) Evershed et al. (2006); Zelles (1999) Harwood and Russel (1984); Zelles (1999)

Fungi

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Dicarboxylic

Cutin, suberin

Ester- and ether-linked plasmalogens

Anaerobic bacteria

(continued)

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Table 1

(continued)

Structural class


Reference in texta

Living microbial biomass

Zelles (1999)

Straight chain

Procaryotes and eukaryotes

>C20

Eukaryotes, mosses, higher plants

Branched chain

Gram-positive bacteria

iso/anteiso (e.g., i15:0, i17:0, a15:0) 10Me 10Me17:0, 10Me18:0 10Me16:0; i17:1

Gram-positive bacteria

Evershed et al. (2006); Zelles (1999) Evershed et al. (2006); Zelles (1999) Evershed et al. (2006); Zelles (1999) Evershed et al. (2006)

Cyclopropyl

Gram-negative bacteria

Biomarker

Phospholipid fatty acids Ester-linked, saturated

Gram-negative bactera Actinomycetes Sulfate reducers

Anaerobic Gram-positive bacteria Ester-linked, mono-unsaturated 16:1o5 o7 o7

Arbuscular mycorrhizal fungi Gram-negative aerobes Obligate anaerobes

Kroppenstedt (1992) O’Leary and Wilkinson (1988) Evershed et al. (2006); Kroppenstedt (1992) Ratledge and Wilkinson (1988) Ratledge and Wilkinson (1988) Olsson et al. (2005) Evershed et al. (2006); Zelles (1999) Evershed et al. (2006); Zelles (1999)

o9 o8, for example, 16:1o8c, 18:1o8c

Gram-positive bacteria: widespread Methanotrophic bacteria Eukaryotes

20:4o6 20:5o3; 18:3o3

Cyanobacteria Protozoa Algae

18:2o6

Fungi

Ester-linked, hydroxylated a

Eukaryotes

Evershed et al. (2006); Zelles (1999) Evershed et al. (2006); Zelles (1999) Evershed et al. (2006); Zelles (1999) Zelles (1999)

Fungal hyphae

Wells et al. (1996)

Plants

Bianchi (1995)

Gram-negative bacteria Actinomycetales

o

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Non-ester-linked, unsubstituted Non-ester-linked, hydroxylated a, for example, a24:0; a26:0 Cyclic lipids/isoprenoids Steroids b-Sitosterol, stigmasterol, campesterol, sitosterone, stigmastan-3b-ol, stigmastan-3-one

Evershed et al. (2006); Zelles (1999) Evershed et al. (2006) Ratledge and Wilkinson (1988) Zelles (1999) Cavigelli et al. (1985) Boschker and Middelburg (2002) Boschker and Middelburg (2002)

Fungi

(continued)

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Table 1

(continued)

Structural class

Biomarker


Reference in texta

Ergosterol Cholesterol Coprostanol

Fungi Animals, fungi, plants Manure (major sterol in human faeces) Manure (5b-campestanol and 5b-stigmastanol higher in ruminants than nonruminants) Manure

Clemmensen et al. (2006) Voet and Voet (1995) Bull et al. (2002); Leeming et al. (1984) Evershed et al. (1997); Voet and Voet (1995)

5b-Stanols

Colestanol Estrogens Androgens Gestagens

Animals, human (natural: estrone, 17b-estradiol, estriol) Animals, human (natural: testosterone) Animals, human

Glucocorticoids (e.g., cortisol) Mineralocorticoids

Raven and Johnson (1999)

Bile acids Group of C24, C27, and C28 steroidal acids Deoxycholic acid Lithocholic acid

Evershed et al. (1997); Voet and Voet (1995) Shore and Shemesh (2003); Yin et al. (2002) Shore and Shemesh (2003); Yin et al. (2002) Shore and Shemesh (2003); Yin et al. (2002) Raven and Johnson (1999)

Only secondary bile acids are excreted Herbivore and omnivore, except pigs Omnivore

Bull et al. (2002); Elhmmali et al. (2000) Bull et al. (2002); Elhmmali et al. (2000) Bull et al. (2002); Elhmmali et al. (2000)

Terpenoids Diterpenoids Triterpenoids Biohopanoids

Geohopanoids

Aromatic structuresc Phenols

Vannilyl (V) Syringyl (S)

Hyocholic acid

Pigs

Elhmmali et al. (2000); Bull et al. (2002)

Abietane, primarane, isopimarane Oleanene, ursane, lupine, friedelanes Diplopterol, diploptene

Conifers

Otto and Simpson (2005)

Angiosperms

Otto and Simpson (2005)

Bacteria, ferns, lichens

Bacteriohopanepolyols (e.g., bacteriohopane tetrol, aminoba cteriohopanetriol) Hopanoic acids, hopanols, C30 hopenes, hopanoidal aldehydes, and ketones

Bacteria

Shunthirasingham and Simpson (2006) Shunthirasingham and Simpson (2006)

Sum V + S + Ci

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Vanillin, acetovanillone, vanillic acid Syringaldehyde, acetosyringalde hyde, syringic acid

Hopanoid degradation

Innes et al. (1997)

Lignin (only reactive side-chains of lignin isolated) Lignin decomposition (acids-to aldehydes ratio; VSC ratio in gymno- and angiosperms differ; S decompose faster than V) Lignin (gymnosperm wood)

Ertel and Hedges (1984); Gon˜i et al. (1998)

Lignin (gymnosperm and angiosperm wood)

Gon˜i et al. (1998) Gon˜i et al. (1998)

(continued)

Table 1

(continued)

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Structural class

Biomarker


Reference in texta

Cinnamyl (Ci)

p-Coumaryl, ferulic acid

Lignin (also part of needles and grasses) Lignin and suberin associated waxes Sedges Tannins Gymnosperms (monocotyles: CT only) (Dicotyles: either CT and HT or CT or HT) Black carbon (conversion factor unknown)

Gon˜i et al. (1998)

Ferulic acid C19–C25

5-n-Alkylresorcinol Polyphenols Condensed (CT)

Proanthocyanidins

Hydrolyzable (HT)

Gallotannins, ellagitannins

Benzene polycarboxylic acids Tricarboxylated (B3CAs) Tetracarboxylated (B4CAs) Pentacarboxylated (B5CA) Hexacarboxylated (B6CA) Carbohydrates Acidic sugars

Sum of B3–B6CA inherent C

Neutral sugars Disaccharide

Hemimellitic, trimellitic, trimesic acids Pyromellitic, mello-phanic, prehnitic acids Benzenepenta-carboxylic acid Mellitic acid

Otto and Simpson (2005) Avsejis et al. (2002) Bate-Smith (1977); Haslam (1988) Kraus et al. (2003) Brodowski et al. (2005); Glaser (2005)

Biological sources under dispute

Portions increase with aromatic condensation

Galacturonic acid, glucuronic acid

Extracellular bacterial gums, mucilage

Amelung et al. (1999b); Cheshire (1979)

Trehalose

Fungi, bacteria, insects

Feng and Simpson (2007); Wingler (2002)

Hexoses Pentoses Organic N structures Amino acids Nonprotein amino acids

Enantiomers

Galactose, mannose, fucose, rhamnose Arabinose, xylose

Mainly microorganisms

Oades (1984)

Mainly plants

Oades (1984)

ß-Alanine

Organic matter decomposition (produced from aspartic acid)

g-Aminobutyric acid

Organic matter decomposition (produced from glutamic acid)

D-Alanine

Peptidoglycane

Cowie and Hedges (1994); Dauwe and Middelburg (1998) Cowie and Hedges (1994); Dauwe and Middelburg (1998) Amelung (2003); Schleifer and Kandler (1972) Amelung (2003); Schleifer and Kandler (1972) Amelung (2003) Preger et al. (2007); Purin and Rillig (2007)

D-Glutamic

acid

D-Lysine

Glomalin related soil proteins (GRSP)

Glomalin

Amino sugars

Glucosamine

Peptidoglycane Protein ageing Arbuscular mycorrhizal fungi (AMF) (+ other heat-stable proteins coextracted) Fungal chitin (excess to muramic acid attributed to fungi) Bacterial peptidoglycane (in 1:1 portions to muramic acid) Teichoic acids of bacteria Arthropods (negligible contribution to total glucosamine contents in soil)

Amelung (2001); Chantigny et al. (1997); Parsons (1981) Amelung (2001) Amelung (2001) Amelung (2001); Chantigny et al. (1997); Parsons (1981)

167 (continued)

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(continued)

Structural class

Biomarker


Reference in texta

Muramic acid

Bacterial peptidoglycane

Mannosamin

Not in actinomycetes (some bacteria, fungal unknown) Bacterial capsular and extracellular polysaccharides (GluN/GalN ratio indicates shift of bacterial to fungalresidues) Bacterial cell walls

Amelung (2001); Parsons (1981) Amelung (2001)

Galactosamine

Attached to bacterial lipopolysaccharides and teichoic acids Small amounts in some fungi, for example, myxomycetes

Ko¨gel and Bochter (1985); Sharon (1965)

Amelung (2001); Parsons (1981) Ladd and Jackson (1982); Parsons (1981) Herrera (1992); Sharon (1965)

Nucleotides Adenosintri-phosphate (ATP), -di- (ADP) and -monophosphate (AMP) a b c

Living microbial biomass (calculation of energy status)

Atkinson and Walton (1967); Dyckmanns et al. (2003)

The references given in this column relate to those referred to in the main text of our review, it however, is possible that for any given biomarker another earlier references exist. For n-alkanes or n-carboxylic acids, for example, there are many different ratios used in the literature to differentiate between different sources such as C3 or C4 vegetation but not further discussed here. PAHs, PCBs, etc. are not discussed in this review.


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2.1. Biomarkers for plant-derived C Once plant litter is incorporated into soil, it loses its anatomical characteristics during degradation. Hence, the morphology is no longer of any value for inferring the origin. Biomarker analyses may help to reconstruct plant type and even the type of plant tissue it originated from and whether or not the organic material was derived from above- or below-ground plant material. In any case, the preserved structure must be relatively stable in soils to detect it after a certain period of time. Useful biomarkers for plant derived-C are lignins and tannins as well as aliphatic compounds. 2.1.1. Lignins and tannins Lignin is a well-characterized plant-derived constituent in soil. Originally it occurs as a ligno–cellulose complex in vascular plants (Hedges, 1992; Otto and Simpson, 2006). Lignins are, similarly to tannins, not commonly used as energy source for soil microorganisms in soil and may be selectively preserved when litter decays, thus controlling its loss rates. In addition, they are not synthesized by aquatic plants. Especially the content of lignin structures have thus gained particular attention to (1) elucidate the aromatic origin of SOM and its leachates (e.g., Guggenberger and Zech, 1994; Guggenberger et al., 1998; Ko¨gel, 1986), to (2) predict litter decomposition rates from lignin contents (e.g., Meentemeyer, 1978; Parton et al., 1987), and to (3) trace terrestrial C sources in marine and limnic environments (Hedges et al., 1997; Laskov et al., 2002). No analytical method is currently available that allows for the determination of the absolute lignin content in soil, as intact lignin is insoluble. Pyrolysis field-ionization mass spectrometry can help to identify both lignin monomers and dimers in soil (e.g., Schulten and Leinweber, 1993); yet this method requires a specific set of sophisticated scientific instruments, and may be sensitive to catalytic oxidizing minerals. Another method is the release of phenols from reactive sites of the lignin macromolecule by alkaline CuO oxidation. The sum of vanillyl (V: vanillin, acetovanillone, vanillic acid), syringyl (S: syringaldehyde, acetosyringaldehyde, syringig acid), and cinnamyl (Ci): p-coumaryl, ferulic acid) phenolic CuO oxidation products (VSC) serves as relative measure of the total lignin concentration in plants, sediments (Hedges and Mann, 1979; Otto et al., 2005), and in soils (Ko¨gel, 1986). As angiosperm and gymnosperm woods and grasses comprise different abundances of V, S, and C units, plant source assignment may be achieved by calculating S/V and C/V ratios (Gon˜i et al., 1998; Hedges and Ertel, 1982; Hedges and Mann, 1979). A lignin phenol vegetation index, LPVI, was introduced by Tareq et al. (2004) with distinct values for gymnosperm and angiosperm woods and needles. However, the CuO method has also been used to clarify the pathways of lignin degradation in terrestrial forest ecosystems. During litter decomposition, the mass ratios of acids to aldehydes of the vanillyl (ac/al)V and of syringyl structural

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units (ac/al)S increase with an increasing degree of lignin oxidation (Amelung et al., 1999b; Lobe et al., 2002; Meentemeyer, 1978), whereas selective loss of syringyl units are reflected by decreasing S/V ratios (e.g., Ertel and Hedges, 1984; Ko¨gel, 1986; Zech et al., 1996). Among the CuO oxidation products also several aromatic compounds from proteins and/or phenolic plant molecules such as tannins are observed (Gon˜i et al., 2000; Otto and Simpson, 2006). Ferulic acid is also extracted from suberin associated waxes (Otto and Simpson, 2005). A new, robust off-line pyrolysis, and compound-specific stable carbon isotope analysis of lignin is now also available and the method did show excellent potential to determine the fate of lignin moieties derived from cattle dung residues in grassland soil (Dungait et al., 2008). Tannins make up a significant proportion of terrestrial biomass C being the fourth most abundant component of vascular plant tissue after cellulose, hemicellulose, and lignin (Hernes and Hedges, 2000). In leaves, bark, and needles the tannin concentration may reach 40 wt% and hence, can even exceed the proportion of lignin present (Benner et al., 1990; Kuiters, 1990; Matthews et al., 1997). A diverse group of woody and some herbaceous plant species contain tannins for herbivore defense, drought resistance, and freezing-tolerance (Cates and Rhoades, 1977; Chalker-Scott and Krahmer, 1989). In soil, they may inhibit microorganisms (Kraus et al., 2003). Tannins exhibit intermediate stability in the environment compared to other major biochemical compounds (Hernes et al., 2001; Tiarks et al., 1992). Tannins as water-soluble polyphenolic compounds have the ability to precipitate proteins (Bate-Smith and Swain, 1962), which might be the primary effect of tannins on biogeochemical cycling (Kraus et al., 2003). Two major classes of higher plant tannins are referred to as condensed (CT; proanthocyanidins) and hydrolyzable tannins (HT; gallotannins and ellagitannins; Kraus et al., 2003), both with large structural diversity (Okuda et al., 1995; Porter, 1992). While gymnosperms and monocots contain only CT, dicots can produce either CT or HT or a mixture of both tannin types (Bate-Smith, 1977; Haslam, 1988). Moreover, tannin production is thought to vary by plant species depending on genotype, phenology, environmental conditions, and season (Kraus et al., 2003). However, the analysis of tannins is difficult, and sophisticated detection techniques such as MALDI-TOF MS (Behrens et al., 2003) have not yet been combined with stable isotope assessment. 2.1.2. Aliphatic compounds According to Nierop (1998) the plant-derived aliphatic compounds can be divided into three classes: (1) (extractable) lipids, (2) the biopolyesters cutin and suberin, and (3) nonhydrolyzable biopolymers, such as cutan and suberan (Tegelaar et al., 1989).


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The most easily detectable components of the extractable lipid (1) fraction are n-alkanes. They are typical for epicuticular waxes produced by vascular plants, which are in general complex mixtures of different aliphatic compounds (Baker 1982; Bianchi 1995; Kolattukudy and Espelie 1989; Otto and Simpson, 2005; Tulloch 1976). However, fossil fuels (e.g., Bi et al., 2005; the markers for fossil fuels are not considered in this paper), bacteria and insects (Schnitzer et al., 1986), and reduction or decarboxylation of other aliphatic precursors such as n-alkenes, n-alcohols, or n-fatty acids (Lichtfouse and Eglinton, 1995; Lichtfouse et al., 1998) contribute to n-alkanes in soil. Hence, exact source assignment is difficult. The n-alkane chain-length distribution is not distinct but overlapping for plants with C3, C4, and CAM photosynthetic pathway (e.g., Bi et al., 2005) and is also dependent on the plant part (e.g., Wiesenberg et al., 2004). However, principal component analysis and other statistical analyses of leaf wax alkane patterns have successfully been used as chemotaxonomic markers (Maffei, 1996), and broad source assignment has been achieved on the basis of C numbers. The n-alkanes typically occur in the n-C25 to n-C35 range with characteristic odd-over-even (Collister et al., 1994; Eglinton and Hamilton, 1967; Feng and Simpson, 2007) and n-alkanols in the n-C20 to n-C34 range with even-over-odd carbon number predominance (Baker, 1982; Bianchi, 1995). Ratios like the carbon preference index (CPI, originally named oddto even-carbon-number ratio by Bray and Evans, 1961) or the terrestrial: aquatic ratio (TAR; see, e.g., recent review of Glaser, 2005) may help to differentiate between higher land plants and oil or between terrestrial plants and algae, respectively. These and other ratios (also used after carboxylic acid analyses) provide some valuable information for stable isotope data interpretation but are not further discussed here as these are out of our main focus. In general, cuticle waxes of terrestrial plants contain predominantly longchain n-alkanes (>C20; Collister et al., 1994), while short-chain n-alkanes ( 40%) of n-C24 carboxylic acid. C3 plants contain larger proportions of nC22 and n-C26 acids with variable abundances in stem, leaves, and roots during the growing season. The authors suggest a carboxylic acid ratio

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[CAR = n-C24/(n-C22 + n-C26)], which allows for differentiation of C4 (> 0.67) and C3 crops (< 0.67) and thus may help to apportion the contribution of C3 and C4 grasses to soils for carbon turnover determination (Wiesenberg, 2004). The ester-bound biopolymers cutin and suberin (2) are generally well preserved in soils (Bull et al., 2000; Nierop and Verstraten, 2004; Nierop et al., 2003). The cuticle of all aerial parts (leaves, fruits, flowers, seeds, etc.) of higher plants contains cutin, waxes, and sometimes, cutan. Suberin is an important component of all protective and wound-healing layers of all other plant parts, including bark, woody stems, and underground parts (Nierop et al., 2004). Cutin is mainly composed of n-alkanoic acids and C16 and C18 o-hydroxyalkanoic acids (Kolattukudy, 2001), while suberin is made of an aliphatic polyester and a polyphenolic domain (Bernards and Razem, 2001), and o-hydroxyalkanoic acids with chain lengths C20. Additionally, a,oalkanedioic acids with predominant chain lengths of C16–C24 are only present in suberinised, but not in cutinized tissue (Kolattukudy, 2001). Additionally, the presence of o-hydroxyalkanoic acids with chain lengths of C16 is characteristic for the suberin of Pinaceae (Matzke and Riederer, 1991; Nierop, 2001). Hence, o-hydroxyalkanoic acids and the ratio of the di- and trihydroxyalkanoic acids (cutin) and a,o-alkanedioic acids (suberin) may be used to differentiate between above- and below-ground plantderived debris in soil (Nierop and Verstraten, 2004). The (3) plant-derived cutan and suberan are thought to be the most resistant aliphatic fractions and, thus, to account for the relative enrichment of aliphatic compounds during SOM degradation (Augris et al., 1998; de Leeuw and Largeau, 1993; Tegelaar et al., 1989). They are not hydrolyzable, but may contribute to the production of alkanes, alkenes, and methyketones in pyrolysis GC/MS measurements (Buurman et al., 2007; Nierop, 1998). Other specific plant biomarkers are triterpenoids of the oleanane, ursane, and lupane type, which are specific for angiosperms (Baker, 1982; Bianchi, 1995; Otto and Simoneit, 2001; Tulloch, 1976). Friedelin, a-amyrenone, b-amyrenone, and lupenone are frequent constituents of tree barks, or could have been formed by oxidation of the corresponding 3-alcohols, which are ubiquitous in green plants (Corbet et al., 1980). In contrast, diterpenoid acids of the abietane, pimarane, and isopimarane classes occur in conifers (Hegnauer, 1992; Karrer et al., 1977; Otto and Wilde 2001). Hopanoids also belong to the class of triterpenoids but are used as biomarkers for bacterial residues and discussed later. Yet, terpenoids have rarely been used for elucidating the fate of SOM (Otto and Simpson, 2005). Also some phenolic ones, such as 5-n-alkylresorcinols, are hardly used as biomarkers in agricultural research. Avsejis et al. (2002) reported that 5-n-alkylresorcinols with alkyl chain lengths varying from C19 to C25 are specific to


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bog-forming sedges and thus useful for palaeoclimate reconstruction in peat, whereas other 5-n-alkylresorcinols have also been found in some bacteria and algae (Cirigottis et al., 1974; Garcıá et al., 1997; Kozubek et al., 1996; Tyman, 1991; Zarnowski et al., 2000). The acyclic isoprenoids norpristane, pristane, and phytane are commonly derived from the phytol side chain of chlorophyll (Rontani and Volkman, 2003). The ratios of the acyclic isoprenoids pristane and phytane to the n-alkanes n-C17 and n-C18 (i.e., pristane/n-C17 and phytane/n-C18) are frequently used in petroleum and environmental geochemistry for estimating and monitoring biodegradation patterns (McIntyre et al., 2007) but have had little relevance for elucidating the fate of natural SOM.

2.2. Biomarkers of multiple origin (plants, microbes, animals) 2.2.1. Carbohydrates When plant compounds serve as an energy and carbon source for the soil microbial community they may be rapidly converted. Especially, the plantderived carbohydrates are rapidly degraded and new microbe-derived carbohydrates are formed. As a result of such recycling of carbohydrate C, the apparent turnover time of carbohydrate structures may appear to be high (e.g., Amelung et al., 1997; Gleixner et al., 2002). To reveal the true dynamics, we have to identify the origin of carbohydrate C in soil. Noncellulosic polysaccharides can be removed by different agents (Amelung et al., 1996; Cheshire, 1979; Ziegler and Zech, 1991) before cellulosic polysaccharides are analyzed. Fresh plant tissue comprises up to 40% cellulose-C (Molloy and Speir, 1977), while SOM only comprises less than 6% of this fraction, reflecting rapid cellulose degradation during SOM genesis (Amelung et al., 1997). Plant-derived hemicelluloses degrade even faster; yet the content of noncellulosic polysaccharides in soil exceeds that of the cellulosic ones by a factor of eight because they are microbially resynthesized (Amelung et al., 1997; Ziegler and Zech, 1991). A better source assignment has been achieved by analyzing the monomers. Plant-derived sugars comprise specific pentoses (e.g., arabinose, xylose), whereas soil microorganisms primarily produce the hexoses galactose, mannose, fucose, and rhamnose (Cheshire, 1979; Murayama, 1984; Oades, 1984). According to Oades (1984), the (gal + man)/(ara + xyl) ratio is < 0.5 for plants and > 2 for microorganisms. Also trehalose, a reserve disaccharide and stress protectant, occurs in a wide range of organisms, such as fungi, bacteria, and insects, but is only rarely found in plants (Feng and Simpson, 2007; Wingler, 2002). Uronic acids are common in extracellular bacterial gums (Cheshire, 1979) and have thus been suggested to reflect an enrichment of microbial products nearby soil mineral surfaces (Amelung et al., 1999c).

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2.2.2. Steroids and bile acids In contrast to carbohydrates, phytosterols (¼phytohormones) like campesterol, stigmasterol, stigmastanol, b-sitosterol, and sitosterone occur only in low concentrations in vascular plants (Baker, 1982; Bianchi, 1995; Harwood and Russell, 1984). Yet, steroid biomarkers have the advantage that they have specific degradation products. In the intestinal tracts of most higher mammals, both cholesterol (an important lipid of the plasma membrane of eukaryotes; Voet and Voet, 1995) and the higher molecular weight congeners campesterol, sitosterol, and stigmasterol are reduced to 5b-stanols (Bull et al., 2002). Hence, cholesterol and 5b-stanoles are characteristic biomarkers for feces and animal manure (Evershed et al., 1997; Voet and Voet, 1995). Cholesterol is further converted to coprostanol, the major sterol in human feces (Bull et al., 2002; Leeming et al., 1984; Ren et al., 1996). Furthermore, 5b-campestanol and 5b-stigmastanol have a higher relative abundance in the excreta of ruminant organisms such as cows and sheep. Little evidence of sterol reduction has been found in faeces of other animals such as dogs (cholesterol is predominant) and birds (high concentrations of both cholesterol and sitosterol), probably due to the absence of specific bifidobacteria (Leeming et al., 1996). Analyses of stanols may thus help to elucidate the relative input of different types of fecal material (human vs. herbivore) to SOM, especially in aerobic systems with significant coprostanol degradation (Grimalt et al., 1990; Evershed and Bethell, 1996; Leeming et al., 1996; Simpson et al., 1998). Additionally, animals and humans produce bile acids which are essential for fat digestion and cholesterol-level maintenance (Voet and Voet, 1995). They are a group of C24, C27, and C28 steroidal acids with a carboxylic acid group at the C23 position and a hydroxyl group on the A-ring and eventually some additional functional groups (Bull et al., 2002). Primary bile acids that form from cholesterol in the liver undergo several transformations to be converted to secondary bile acids such as lithocholic acid and deoxycholic acids (Bull et al., 2002; Hirano et al., 1981). A small proportion of those secondary bile acids is excreted. While in the feces of ruminant animals (bovines) mainly deoxycholic acid is found, the feces of omnivores (humans, canines) also contain significant amounts of lithocholic acid. Pigs do not produce deoxycholic acid but hyocholic acid being the distinguishing feature for human and canine (do not produce 5 b-stanols) contamination. Hence, these bile acids are suitable markers for sewage source assignment (Elhmmali et al., 2000) also in archeological studies (Evershed and Bethell, 1996). In summary, while 5b-stanols and related sterols allow for distinguishing between omnivores and ruminants, the bile acids additionally allow to differentiate between human and porcine derived feces (Bull et al., 2002).


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2.3. Biomarkers for living microbial biomass 2.3.1. Ergosterol Ergosterol is the specific steroid remaining that is not produced by humans, animals, or plants but is specific for living fungi (Harwood and Russell, 1984; Ruzicka et al., 2000; Weete, 1976). To estimate the fungal biomass, a conversion factor of 3 mg ergosterol mg1 fungal biomass has been suggested (Clemmensen et al., 2006, Salmanowicz and Nylund, 1988). Yet, total ergosterol contents do not differentiate between different fungal taxa. 2.3.2. Phospholipid fatty acids (PLFA) Phospholipids are essential components of all living cell membranes. After cell death, they are rapidly decomposed (White et al., 1979b), and they are not found in storage products (Kates, 1964). Hence, PLFA concentrations and distributions have the potential to mirror even rapid changes of the soil microbial community, with the exception of the archae bacteria as they do not contain fatty acids in their phospholipid membranes (Evershed et al., 2006; Zelles, 1999). The analysis of PLFA extends the former assessment of mere methyl-ester-linked fatty acid profiles (EL-FAMES; e.g., Viljoen et al., 1986). To figure out specific microbial community structures, both FAME [ester-linked, (EL-FAME) and/or phospholipid-linked (PL-FAME)] analyses frequently go along with multivariate statistical approaches (Haack et al., 1994; Hamman et al., 2007; Pankhurst et al., 2001). The total amount of PLFAs indicates the microbial biomass concentration (Balkwill et al., 1988; Zelles et al., 1994) and PLFA profiles reflect the fingerprint of microbial communities (Bossio and Scow, 1998), which can thus be characterized without selective culturing techniques (Amann et al., 1995; Findlay, 1996). Yet, information on individual taxa is rarely gained unless there is an unique lipid for a given microbial strain (Vestal and White, 1989; see also reviews of Evershed et al., 2006; Zelles, 1999, and references therein). In general, classes of PLFA are thus used as biomarkers for different taxonomic or functional groups of soil microorganisms (Zelles, 1999). The straight-chain fatty acids are present in most organisms (prokaryotes and eukaryotes; Evershed et al., 2006). Some of them, especially the longchain ester-linked saturated fatty acids (>C20) and polyunsaturated ones are mainly produced by eukaryotes, mosses, cyanobacteria, and higher plants (Evershed et al., 2006; Ratledge and Wilbkinson, 1988; Zelles, 1999; Table 1). The hyphal forms of fungi are a source of long-chain nonesterlinked OH-substituted fatty acids (Wells et al., 1996). The PLFA 18:2o6 has been widely used to estimate the proportions of fungi in soil microbial biomass (Clemmensen et al., 2006; Ho¨gberg, 2006; Olsson et al., 2003; Zelles, 1999). The neutral lipid fatty acid 16:1o5 is likely even specific for

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arbuscular mycorrhizal fungi (Olsson et al., 2003, 2005), and the PLFA 20:1o9 has been recommended for identifying and quantifying the external hyphae of Gigaspora rosea (Sakamoto et al., 2004). Many more PLFA indicate soil bacteria, for example, i15:0, a15:0, 15:0, i16:0, 16:1o7, i17:0, a17:0, cy-17:0, i18:0, 18:1o7, and cy-19:0 (Frostega˚rd and Ba˚a˚th, 1996; Zelles, 1999, nomenclature correspondingly). Also b-hydroxy, cyclopropane, and branched-chain fatty acids are only produced by bacteria and are not found in other microorganisms (Lechevalier, 1989). Gram-positive bacteria are indicated by branchedchain fatty acids (Haack et al., 1994) and the ester-linked monosaturated 16:1o9, whereas gram-negative ones can be tracked back by specific iso/ anteiso forms, monosaturated and cyclopropyl fatty acids (few of the latter though have also been detected in a few anaerobic strains of gram-positive bacteria (Ratledge and Wilkinson, 1988)), and methyl branching on the tenth C atom (also typical for actinomycetes (O’Leary and Wilkinson, 1988), and sulphate reducers (Evershed et al., 2006; Kroppenstedt, 1992)). Specific markers also exist for methanotrophic bacteria (e.g., 18:1o8c; Table 1). Also sphingolipids, ornithine lipids, plasmalogens, and other aminolipids contain nonester-linked PLFAs, being mainly characteristic to anaerobic bacteria (Harwood and Russel, 1984; Zelles, 1999). The unusual sulfonolipids capnoids may serve as a biomarker for gliding bacteria of the genera Cytophaga and Flexibacter, but these lipids do not persist in soil in an extractable form after cell death (Drijber and McGill, 1994). There are other chemical markers for characterizing total living microbial biomass, such as the content of adenylates (adenosine tri-, di-, and monophosphates; e.g., Bai et al., 1989; Dyckmans et al., 2003; Raubuch et al., 2002), the pattern of which has been recommended for assessing the energetic status of soil microorganisms (Brookes et al., 1983), and thus their vulnerability to changes in environmental conditions (e.g., Ciardi et al., 1993; Formowitz et al., 2007). Similarly, quinone profiles have been used to characterize the biomass and taxonomic diversity of the soil microbial community (Katayama et al., 2001, 2002; Saitou et al. 1999), but these methods have yet been of little potential for associated stable isotope analyses.

2.4. Biomarkers for dead microbial biomass In many cases, microbial products do not immediately disappear after cell death, but reside in soil for a limited period of time. The present biomarkers, then, no longer indicate living microbial biomass, but may still be used to track back the residue of different fungi and bacteria. The analytical results are robust against sample drying and changing weather conditions at different sampling days, because they integrate over a longer period of time. Mostly lipids and N-containing compounds may serve as such markers for


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bacterial or bacterial plus fungal residues, respectively. Frequently, they have been used without simultaneous characterization of living microbial diversity, but when this was done, the general patterns correlated (Appuhn et al., 2006; Glaser et al., 2004; Kandeler et al., 2000). 2.4.1. Hopanoids and tetraether lipids Hopanoids are amphiphilic pentacyclic triterpenoids that are synthesized as essential membrane lipids by eubacteria (e.g., Farrimond et al., 2003; Rohmer et al., 1984) and have a similar function as the cholesterol in higher organisms (e.g., Talbot et al., 2007). They are used as biomarkers for bacterial biomass contribution in soils and sediments (Innes et al., 1997; Shunthirasingham and Simpson, 2006; Winkler et al., 2001) and include diplopterol and diploptene—the biosynthetic precursors of bacteriohopanepolyols (BHPs) (Rohmer et al., 1992; Talbot et al., 2003a,b; Thiel et al., 2003). While diplopterol and diploptene may also be present in some ferns and lichens, the C30 hopane skeleton linked at C30 to a C5 n-alkyl polysubstituted chain gives a characteristic C35 bacteriohopane derivative (Crossman et al., 2001; Shunthirasingham and Simpson, 2006). Still more information is needed to clarify whether specific hopanoids may also serve as markers for specific bacteria. The biohopanoids undergo a wide range of degradation processes that result in the formation of geohopanoids such as hopanoic acids, hopanols, C30 hopenes, and hopanoidal aldehydes and ketones making them useful for palaeoenvironmental studies (Farrimond et al., 2003; Shunthirasingham and Simpson, 2006). None of the bacterial biomarkers mentioned so far was able to track archae. Leininger et al. (2006), for instance, recently stated that crenachaeota are the most abundant ammonia-oxidizing organisms in soil ecosystems. A novel clue to these organisms is provided from the analyses of glycerol dialkyl glycerol tetraethers (GDGT), which are core membrane lipids (Gattinger et al., 2003; Schouten et al., 2007). The isoprenoid GDGTs are characteristic for Archaea, with crenarchaeol being specific to the nonthermophilic Crenarchaeota (Leininger et al., 2006; Sinninghe Damste´ et al., 2002). The structures of the methanogenic archae are notably different from the specific bacterial isoalkane-tetraethers. The branched GDGTs of possibly anaerobic soil bacteria exhibited different cyclisation ratios and methylation indices, dependent on pH and temperature (Weijers et al., 2006, 2007). These differences helped developing a sea surface temperature (SST) proxy (Schouten et al., 2002) and to estimate the fluvial inputs of terrestrial organic matter into marine environment on the basis of a branched versus isoprenoid tetraether (BIT) index (Hopmans et al., 2004), but applications to elucidating SOM dynamics are still scarce. In principle, there is a long history to cleave the GDGTs and to determine d13C natural abundances in the acyclic and cyclic bisphytanes, which may reveal a discrimination of more than 90 delta units in extreme environments; however,

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these tools have up to date hardly been considered in agricultural research of terrestrial soil environments (Schwark, 2007, personal communication). 2.4.2. Amino sugars and soil proteins As plants do not contain significant amounts of amino sugars, the analysis of amino sugars in soil provides a clue to investigate the fate of C and N within residues of bacteria and fungi (for review see Amelung, 2001; Parsons, 1981). The concentration of amino sugars in the living biomass is small compared to the total microbial nekromass occurring in soil (Chantigny et al., 1997; Guggenberger et al., 1999; Nannipieri et al., 1979). Up to 26 amino sugars have been recognized in microorganisms (Sharon, 1965), four of them have been quantified in soil. These are glucosamine, muramic acid, galactosamine, and mannosamine. Fungal cell walls are the major source of glucosamine in soils (Appuhn and Joergensen, 2006), invertebrates contribute only in negligible amounts (Amelung, 2001; Parsons, 1981). Also bacteria contain glucosamine in their peptidoglycan cell wall, where it is linked in 1:1 proportions to N-acetylmuramic acid and partly also found in teichoic acids of the gram-positive bacteria. Hence, only the glucosamine that occurs in excess to muramic acid may be attributed to fungal sources (Amelung, 2001; Chantigny et al., 1997; Guggenberger et al., 1999). Muramic acid uniquely originates from bacterial peptidoglycan, most common in gram-positive organisms (Amelung, 2001; McCarthy et al., 1998). Changing portions of gram-positive to gram-negative bacteria thus theoretically limit the exact source assignment to bacteria and fungi on the basis of glucosamine-to-muramic acid ratios (Amelung et al., 1999d; Wichern et al., 2006). Appuhn and Joergensen (2006) suggested average conversion factors of 9 to convert glucosamine to fungal C and 45 to get a measure of bacterial C from the muramic acid content. The other bacterial amino sugar is galactosamine, frequently occurring in capsular and extracellular polysaccharides, but also as a part of the cell walls, especially of actinomycetes (Parsons, 1981; Sharon, 1965). It may be attached to lipopolysaccharides (Parsons, 1981) or teichoic acids (Ladd and Jackson, 1982), but only small amounts are produced by some taxonomic classes of fungi, such as trichonomycetes and myxomycetes (Herrera, 1992; Sharon, 1965). Increasing ratios of glucosamine to galactosamine have been employed to indicate a shift from bacterial- to fungal-derived amino sugarN in soils (Ko¨gel and Bochter, 1985; Sowden, 1959), and may generally support fungal/bacterial source assignment when correlating with changes in glucosamine/muramic acid ratios (e.g., Amelung et al., 2002). The analyses of glucosamine fail to differentiate between saprotrophs and biotrophic mycorrhizal fungi. Mycorrhizal fungi live in a symbiotic relationship with the majority of terrestrial plants (Smith and Read, 1997). They produce extraradical hyphae ( Johnson et al., 2002; Miller and Kling, 2000) supporting the plants’ nutrient acquisition (Olsson et al., 1997) and


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promoting soil aggregation (Rillig and Mummey, 2006; Tisdall and Oades, 1982). A marker for arbuscular mycorrhizal fungal development is glomalin (Gadkar and Rillig 2006; Johnson et al., 2004; Wright and Upadhyaya, 1996). It is extracted from soil by applying several cycles of autoclaving and quantified using a Bradford assay and immunoreactivity using the monoclonal antibody MAb32B11 (Rillig, 2004; Wright and Upadhyaya, 1996). The enantiomers of amino acids have been used as markers for both bacterial residues and cell ageing (e.g., Amelung, 2003; Bada, 1985; McCarthy et al., 1998). Especially D-alanine and D-glutamic acid indicate bacterial cell wall N, because they are incorporated into the peptidoglycan to protect it from cell-own proteases (Schleifer and Kandler, 1972; Voet and Voet, 1995). The bacterial identity of D-alanine in soil has been confirmed by compound-specific d13C analyses (Pelz et al., 1998). However, most of the D-amino acids may be formed during protein ageing (Bada, 1984, 1985). Life on earth almost exclusively uses laevorotatory, or left-handed amino acids (L-enantiomers), rather than D-enantiomers. The D-amino acids may be produced from their respective L-enantiomers through biotic or abiotic racemization (see the book of Jolle´s (1998) and articles therein). In functioning cells, the latter D-amino acids are usually excreted or decomposed by ´ s, 1998). Dead and nonworking cells, however, D-amino acid oxidases ( Jolle lack this enzymatic system. L-Amino acids continue to racemize slowly and abiotically, and the D-amino acids may accumulate (Bada, 1984) until equilibrium (50% of each enantiomer having one chiral center) is reached (Christen and Vo¨gtle, 1992). When the racemization rate constant is known, the age of a respective protein may theoretically be inferred from its D-amino acid content. However, estimating the absolute age of proteins from racemization rates and the D/L ratio of amino acids will only be possible when microbes do not alter the protein complex. Hence, absolute dating using amino acid-racemization assessment in sediments (Harada et al., 1996; Schroeder and Bada, 1976; Wehmiller and Hare, 1971) and palaeosoils (Mahaney and Rutter, 1989) must be considered with care, as long as effects of microorganisms on the enantiomeric composition of proteins cannot be fully excluded (Kimber et al., 1990). Nevertheless, most of the bacterial Damino acids in antibiotics, multienzymatic complexes, or antimicrobial peptides occur in free or water-soluble forms. When free D-amino acids are preextracted (e.g., using cold 1N HCl; Kvenvolden and Peterson, 1970), the true aging of amino acids may be revealed. Especially D-lysine is a promising age marker: it is totally devoid of any nutritional value (Friedman, 1999) and its formation in soil was independent from microbial activity and SOM turnover (Amelung, 2003). During organic matter decomposition, nonprotein amino acids such as b-alanine and g-aminobutyric acid are enzymatically formed from protein amino precursors, such as glutamic acid and aspartic acid (e.g., Cowie and Hedges, 1994; Whelan, 1977). Hence, the ratios aspartic acid to b-alanine

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and glutamic acid to g-aminobutyric acid are widely applicable for decompositional organic matter state assessment in water columns and sediments, though hardly tested for soils (e.g., Cowie and Hedges 1994; Dauwe and Middelburg, 1998; Haake et al., 1993; Whelan, 1977). Some limitations might be the variability of the source organisms’ protein composition, and the fact that the degradation of aspartic and glutamic acid does not always result in the production of b-alanine and g-aminobutyric acid (Cowie and Hedges, 1994).

2.5. Black carbon (BC) Apart from biological processes, also fires may significantly affect the properties and fate of SOM. Among the combustion markers (mainly polycyclic aromatic hydrocarbons (PAH)), however, only BC is quantitatively relevant for the C cycle. It is the remnant of an incomplete fossil fuel and biomass combustion (Goldberg, 1985), and may add up to 45% of total SOC (Schmidt et al., 1999). Different methods suggested for the quantification of BC in soils and sediments do not always give concise results (Hammes et al., 2007). However, it is possible to isolate soot-BC by chemo-thermal oxidation at 375 C (Gustafsson et al., 2001) or benzene polycarboxylic acids (BPCA) as specific markers for charred BC after HNO3 digestion (Brodowski et al., 2005). While BC assessment with former method does not rely on conversion factors, the latter method has the advantage that both the degree of BC condensation and partly also a source assignment (fossil fuel combustion vs. vegetation burning) can be inferred from the BPCA pattern (Brodowski et al., 2007).

3. Using Carbon Isotopes in SOM Studies 3.1. Stable isotopes and their measurement units Carbon has three naturally occurring isotopes (12C, 13C, and 14C). Two of these, 12C and 13C, are stable C isotopes, whereas 14C is radioactive. Their natural abundances are ca. 98.89% for 12C, 1.11% for 13C (Boutton, 1996), and finally < 1010% for 14C (Goh, 1991) of the total carbon present in the environment. There are various ways of expressing the amounts of the naturally rarer 13C isotope as compared to the lighter, relatively more common 12C isotope (Barrie and Prosser, 1996; Boutton, 1991). Generally, the d13C notation is used when the 13C concentration is at or near natural abundance levels and 13C at % when samples are highly enriched in 13C content as a result of labeling with a compound, artificially enriched in 13C. The d13C


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(%) is calculated from measured carbon isotope ratios of sample and standard as:

d13 Cð%Þ ¼ ½Rsample ¼ Rstandard =Rstandard 1000

ð1Þ

where d13C is the parts per thousand, or per mil (%) difference between the content of the sample and the standard, and R is the mass 45/44 ratio of the sample or standard. Hence, Rsample and Rstandard correspond to the molar ratio (13C/12C) of the sample and standard. By international convention d13C values are always expressed relative to the reference standard Vienna-PeeDee Belemnite (VPDB), with Rstandard being equal to 0.00112372. For example, a sample with a value of 27% has a 13C/12C ratio that is 27 part per thousand (or 2.7%) lower than the VPDB standard. The 13C atom% expresses the percentage of C atoms that are 13C atoms as follows: 13C

13

Cðatom%Þ ¼ 100 ð13 C=ð13 Cþ12 CÞÞ

ð2Þ

with 13C and 12C being the numbers of the respective atoms in the sample; the number of 14C present is negligible for this calculation. A useful index is the atom% excess, which is the enrichment level of a sample following the administration of 13C tracer in excess of the 13C background or baseline level prior to the administration of the tracer, expressed as:

atom%excess ¼ ð13 Catom%sample ¼13 atom%baseline Þ

ð3Þ

3.2. Analytical techniques There are several isotope ratio mass spectrometry (IRMS) methods for bulk (e.g., EA-IRMS, LA-IRMS, liquid digestion-IRMS, SIP, and SIMS; abbreviations cf. glossary; Fig. 1) and for compound specific stable isotope analysis (CSIA) (e.g., GC-C-IRMS; Py-GC-C-IRMS, LC-C-IRMS, and TG-DSC-QMS-IRMS) (Fig. 2) (for technical details see Barrie and Prosser, 1996; De Groot, 2004; Lopez-Capel et al., 2005a; Wieser and Brand, 1999). The use of CSIA techniques allows for the isotope analysis of individual substances occurring at trace levels in very complex mixtures. Applications of CSIA methods for environmental, ecological, forensics, and geochemistry research have been described in Bernard et al., 2007; Bull et al., 2000; Glaser, 2005; Gleixner, 2005; Herrmann et al., 2007; Lichtfouse, 2000; Lopez-Capel et al., 2005a,b; Medeiros and Simoneit, 2007; Philip, 2007; Staddon, 2004. Most of CSIA methods combine an initial chromatographic

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CO2

Autosampler

EA-IRMS He Standard gas

C

R

CO2N2

N2

P

Open split Elemental analyzer

Mass spectrometer

Figure 1 Elemental analyzer (EA) coupled to a isotope ratio mass spectrometer (IRMS); redrawn after Glaser (2005).

GC-C-IRMS Gas Chromatography

Isotope ratio Mass Spectrometry Combustion CuO 850⬚ C

CO2

Water trap −100⬚ C

m/z = h44, 45, 46

Relative Intensity

m/z = 44 ion current

−22‰

−36‰

−15‰

Time

Figure 2 Gas chromatograph coupled to a combustion furnace and isotope ratio mass spectrometer (redrawn after Glaser, 2005; Lichtfouse, 2000).


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or thermal separation step with a combustion unit to enable the individual combustion of the separated compounds. Their isotope 13C content is subsequently measured in the isotope ratio mass spectrometer (Fig. 2). Nonvolatile compounds have to be derivatized prior to GC separation, usually with a C containing agent. The final step is thus a correction of the individual d13C values of the biomarkers for those of the C added during the derivatization step. For this purpose, pure standards are analyzed twice, by EA-IRMS and after derivatization by GC-C-IRMS, the difference in d13C values being attributable to the 13C natural abundance of the added C (e.g., Docherty et al., 2001; Glaser and Amelung, 2002). The latter results from both, the original d13C value of the derivatization reagent and kinetic fractionation effects during the derivatization reaction. To reduce error propagation, new derivatization methods are being developed that reduce the amounts of C added and keep kinetic isotopic fractionation to a minimum (e.g., Gross and Glaser, 2004). However, uncertainties remain whether the soil matrix alters derivatization yields and isotopic fractionation processes relative to the pure standards. Such problems could potentially be avoided by separating polar compounds using liquid chromatography. Recently, also routine LC systems coupled to an IRMS via a catalytic oxidation reactor are commercially available (Krummen et al., 2004). In more complicated IRMS techniques, such as Curie-point Py-GC/ MS-C-IRMS, both isotopic content and the mass spectrum of the pyrolysis products are simultaneously measured and identified (Gleixner et al., 1999). Limitations of these methods mainly refer to incomplete pyrolysis yields and a less accurate source assignment of many pyrolysis products; to account for kinetic isotope fractionation during the pyrolysis process, it is necessary to analyze the samples from above-mentioned reference sites.

3.3. Isotope fractionation and tracing Wada et al. (1995) described the changes in relative abundance of natural isotopes, such carbon, which have occurred in the earth’s biosphere over the last 3.8 billion years as a large scale (but unreplicated) tracer experiment in space and time. The isotope changes having been continuously recorded at all scales (e.g., from microbes to whole ecosystems) and time aspects (e.g., minute to millennium) in the biosphere. Hence, samples can be taken from this ‘‘isotopically ordered world’’ at any stage for research purposes using natural abundance isotope techniques. In most biological systems, heavier isotopes are discriminated (sometimes referred to as fractionated) compared to their lighter counterparts because of kinetic and thermodynamic processes. Therefore, for example, the CO2 emitted in soil respiration contains relatively more 12C and less 13C than the soil it originated from (Bol et al., 2003a). The summation of biological, chemical, and physical fractionation processes in soil is that

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natural 13C/12C isotope ratios (d13C) uniquely record and integrate information relating to: 1. Types of sources and processes that formed the SOM and its constituent parts (Amelung et al., 1999a; Bol et al., 1999a,b, 2000, 2005; Dungait et al., 2005; Glaser et al., 2001; Huang et al., 1999; Kuzyakov and Bol, 2006; Simpson et al., 1999). 2. Rate of SOM transformation (Bardgett et al., 2007; Bol et al., 1996, 1999b, 2003b; 2004; Huang et al., 1996; Kramer and Gleixner 2006; Krull et al., 2007). 3. Environmental, landscape and land management conditions prevailing at the time SOM and its constituents were formed (Balesdent et al., 1990; Bol et al., 2003c; Boutton et al., 1998; Croft and Pye, 2003; Huang et al., 1996; Krull et al., 2005; Lichtfouse, 2000; Lobe et al., 2005). Assessing the d13C values of SOM or its constituents allows for more sensitively detecting alterations of conditions (see points 1–3) than using bulk carbon or bulk biomarker measurements (Bol et al., 2005; Briones and Bol 2003; Granger et al., 2007; Kalbitz et al., 2004). Additionally, it is possible to manipulate the natural d13C range by both artificial labeling experiments and natural isotope labeling (e.g., induced by cropping a C4 plant on a C3 soil), which is of great benefit to trace the mechanisms of SOM transformation and turnover (Bol et al., 2004a; Huang et al., 2000; Petersen et al., 2004). This can be achieved using either natural (Section 3.4) or artificial (3.5) labeling techniques.

3.4. Artificial labeling techniques Artificial labeling techniques can be roughly divided into three methods based on the levels of actual 13C enrichment achieved in the plant–soil system: natural abundance (Heim and Schmidt, 2007; Ineson et al. 1996; Klumpp et al., 2007; Thornton et al., 2004), near natural abundance (50– 500%) (Evershed et al., 2006; Leake et al., 2006; Ostle et al., 2000), or (highly) enriched 500% (Bromand et al., 2001; Bull et al., 2000; Treonis et al., 2004; Zak and Kling, 2006). An example of artificial natural abundance labeling techniques is the so-called free air CO2 enrichment (FACE) method. This is done in field-scale based experiments that artificially increase atmospheric CO2 to 100–240 ppm above the current ambient values (ca. 380 ppm) without directly altering any of the other environmental conditions. The additional CO2 supplied from artificial sources is generally different in the d13C value (generally 25 to 70%) compared to that present in air (ca. 8%) and by this changing the overall (atmospheric + artificial) CO2 d13C value. Plants present in FACE experiments thus become 13C labeled when compared to plants growing under ambient conditions. The calculations are similar to those used for natural


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isotope labeling outlined below (Section 3.5). The fractional input (F *) of C from the new 13C natural source into the existing soil C pool (or constituents) can be estimated using a linear mixing model as follows

F ¼ ðdfinal dinitial Þ=ðdsource dinitial Þ

ð4Þ

With F * being the proportion of new C present in the soil for all artificial labeling approaches, dsource being the d13C or atom% 13C of the source C applied to the soil, and dfinal and dinitial are the initial and final d13C or atom% 13C of the soil C pool at the beginning and end of the experimental period. By using this formula, any changes in bulk soil C and its constituents due the enhanced atmospheric CO2 content has been estimated (e.g., Bock et al., 2007; Giesemann, 2005; Glaser et al., 2006; Heim and Schmidt, 2007; Huang et al., 2000; Ineson et al., 1996), and further strengthened by subsequent modeling of the obtained data (Niklaus and Falloon, 2006). High costs of CO2 supplied from cylinders or gas tanks to enhance atmospheric CO2 limit long-term FACE experiments to the size of small field plots. Near natural or high enrichment labeled approaches generally use artificially labeled plant materials (Bromand et al., 2001; Leake et al., 2006; Ostle et al., 2000) or commercially available 13C labeled substrates (Bull et al., 2000; Evershed et al., 2006; Zak and Kling, 2006) to trace C flows. Due to cost implications, to produce labeled plant material or to buy expensive labeled 13C compounds, most of these approaches are laboratory based (e.g., Bull et al., 2000; Evershed et al., 2006) or rely on small field experiments (e.g., Leake et al., 2006; Zak and Kling, 2006). Experiments using artificial labeling methods are generally conducted for short periods of time (i.e., weeks to months), hence they generally only completely label those components of the soil with 13C, which have a relatively high turnover rate (e.g., soil microbial community and water soluble carbon).

3.5. Natural labeling techniques The natural abundance 13C labeling tracer approach in soil studies is based on the physiological differences during the photosynthetic fixation of CO2 between C3 and C4 plants (see glossary), which lead to plants with distinct d13C values. Plants with a C3 photosynthetic pathway have d13C values ranging from ca. 32 to 22% (mean 27%), whereas those with a C4 pathway range from 17 to 9% (mean 13%) (Boutton et al., 1998). Because both photosynthetic pathways are physiologically and ecologically distinct, any changes in the relative C3 to C4 abundance imply alteration of ecosystem structure and functioning. This is in natural systems generally induced by changes of climate, but in agricultural systems it generally signifies an alteration in the type of cropping or land management

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(Balesdent and Mariotti, 1996; Boutton, 1996). The natural (d13C) isotopic difference (ca. 14%) between C3 and C4 plants allows new carbon derived from one pathway (e.g., C3) to be traced in the SOM, which was derived from plants from the other pathway (e.g., C4; schematically shown in Fig. 3) (Arrouays et al., 1995; Balesdent and Mariotti, 1996; Balesdent et al., 1990; Bol et al., 2000, 2004b,c; Bull et al., 1999; Dungait et al., 2005; Gleixner et al., 2002; Jenkinson et al., 1999; Krull et al., 2005, 2007; Lobe et al., 2005). In Fig. 3, A and B represent the different photosynthetic pathway types (Balesdent and Mariotti, 1996). At time t0, SOM has an isotopic composition dA0, which is close to that of the original vegetation. This SOM from vegetation A progressively decays and is partially replaced by SOM derived from the new vegetation B. At a given time t, the total carbon content can be expressed as C = CA + CB. The isotope composition d AB of the soil C under mixed vegetation is then given by:

dAB ðCA þ CB Þ ¼ dAB ðCÞ ¼ dA CA þ dB CB

ð5Þ

With CA, CB being equal to the C contents from the old (A) and new (B) vegetation, respectively, and dB, dA being the d13C values of vegetation A and B, respectively. With CA = CCB Eq. (5) may be rewritten as

dAB ¼ dB CB =C þ dA ðC CB Þ=C ¼ dB CB =C þ dA ð1 CB =CÞ ð6Þ

Soil C content

δvegA

δvegB

δB δAo δA

to

Time

CB

CA

t

Figure 3 Schematic representation of the replacement of soil carbon derived from previous vegetation A by the new vegetation B (redrawn after Balesdent and Mariotti, 1996).


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Hence, we may derive the contribution of plant B to the total C content from:

F ¼ CB =C ¼ ðdAB dA Þ=ðdB dA Þ

ð7Þ

expressed as the fraction of new carbon in the soil (F). Because dA and dB can not be measured directly in the mixed cropping system, dB is estimated by the d13C value of the new vegetation (dVEG B), replacing also the dA values with d13C of a control site which still has the original vegetation (dVEG A), and soil d13CA value by that of the control soil (dREF A), respectively (Balesdent and Mariotti, 1996). Finally the new portions of vegetation B are estimated from:

F ¼ ðdAB dREFA Þ=ðdVEGB dVEGA Þ

ð8Þ

Under steady state, F may be a direct expression of the turnover of the original SOM. The temporal trend in the fraction of new soil carbon, F(t), can be obtained from samples of the same site taken at different dates after crop conversion or from a chronosequence of sites that changed to the new vegetation at different dates. The dynamics of decay of the original carbon would then be equivalent to C(1F ), whereas the kinetic of new C accumulation is equivalent to C F. Usually, however, soil C is not linearly replaced by the new vegetation input but it degrades exponentially. In general it is assumed that SOM decomposition follows first order kinetics. Under steady-state conditions, that is, total C content does not change the (1F ) values may then be directly translated into turnover times. For CA = (CA + CB) exp(kt), the MRT is defined as the inverse of the decay constant k, that is:

MRT ¼ 1=k ¼ t=ðlnð1 F ÞÞ

ð9Þ

In this paper, MRT is used synonymous to ‘‘turnover time.’’ For example, if 10 years after the vegetation changed, 30% of the CA is lost, MRT corresponds to 28 years. It should be noted, however, that MRT differs from the half-life of the compounds, which is the time at which the original concentration has reached 50% of its original value (CA = 0.5C). The time to reach this point is calculated as ln 2/k, that is, MRT as defined in isotope tracer studies exceeds the dissipation half-life of compounds by a factor of 1.44 (= 1/ln 2). Care must be taken in using the above formulas (4–6) to estimate bulk soil C change in C3/C4 vegetation (or vice versa) switch studies (Balesdent and Mariotti, 1996; Boutton, 1996). For example, it is known that d13C values of C3 and, to a lesser extent, C4 plants vary between different plant

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species (O’Leary, 1988), and are influenced by environmental conditions (CO2 concentration in the air, soil moisture etc.) under which these plants are grown (Boutton, 1996; O’Leary, 1995). Environmental conditions also change depending on the position in the landscape, hence plant composition and d13C values differ within the landscape (Balesdent and Mariotti, 1996; Mentot and Burns, 2001). Biochemical composition slightly varies between plants (Dungait et al., 2005; Wiesenberg and Schwark, 2006), and d13C values of different biochemical fractions can differ by up to 10–14% (Boutton, 1996; Glaser, 2005). Furthermore, during decomposition the d13C values of remaining ‘‘plant’’ C3 or C4 material may (Wedin et al., 1995) or may not change (Huang et al., 1997; Lichtfouse, 1998), depending on the decomposer organism, length of decomposition; decomposition pathway, and soil conditions. Also, plant d13C values were probably different in the past, that is, concentrations and d13C values of atmospheric CO2 have changed over the last few hundreds years, due to fossil fuel combustion, with atmospheric d13C values having decreased by ca. 1.5%. Such processes may thus be reflected in the d13C changes with depth in some soil profiles (Balesdent and Mariotti, 1996). However, the difference between C3 and C4 plants (ca. 14%) remains 5–15 times larger than all environmental and biological effects on the d13C values of these plants (Boutton 1996). The turnover times (or related MRT) of soil constituents estimated using d13C natural abundance tracer techniques can be compared to those obtained by radiocarbon (14C) dating (Boutton et al., 1998; Krull et al., 2005, 2007). The estimated turnover times of d13C natural abundance tracer techniques are based on organic matter changes which occurred after the vegetation shift over the decadal to century scale, whereas 14C dating evaluates the residence time of C in all pools (even the very slow or inert compartment with turnover times greater than hundreds to thousands of years). Therefore, the turnover times using the former method are generally lower than those obtained by 14C dating. On the other hand, recent inputs of C from fossil energy sources add C which is free of 14C (e.g., Brodowski et al., 2007; Rethemeyer et al., 2004). If this C is incorporated into the global soil C cycle, all bulk turnover times assessed via radiocarbon dating might be too long.

4. Biomarker Specific Stable Isotope Analyses Compound-specific stable isotope analyses allow both tracking back the biological source of a molecule and its MRT. Depending on the type of biomarker used, decomposer communities, food chains as well as mechanisms and rates of SOM genesis and transformation can be identified.


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As outlined in Section 3.4, artificial labeling induces the highest isotopic contents in the markers, and is thus ideal for characterizing living microbial community structure and short-term substrate usage, but costs limit such studies to short-term plot and laboratory experiments. In contrast, the investigation of long-term SOM dynamics and palaeoclimatic proxies (not considered here) usually relies on the elucidation of MRTs from natural isotopic shifts after C3/C4 vegetation or climate changes, respectively.

4.1. Incubation studies When specific mechanisms of SOM turnover and microbial performance are to be revealed, incubation studies are frequently performed under optimum or at least artificial environmental conditions (e.g., temperature and moisture, homogenized soil, etc.). Hence, they are usually not designed to simulate or replace field conditions, and frequently both laboratory and field studies are needed to explain the observed genesis and transformation of SOM. From a vast number of biomarkers (see Section 2), with broad applications in marine, brackish systems and further applications in different ecological scientific disciplines (see e.g., reviews of Platzner, 1998; Zhang, 2002), until now only very few biomarkers have been isotopically investigated in incubation studies in agricultural soils (see Table 2 for a brief main overview of the most recent studies), referring with few exceptions mainly to alkanes, carbohydrates, and PLFAs. 4.1.1. Aliphatic compounds n-Alkanes are assumed to be usually the first compound class consumed by bacteria in crude oils (Peters and Moldowan, 1991; Sun et al., 2005). When added to soils, Hough et al. (2006) observed that the d13C values of five n-alkanes were altered within 256 days of incubating soil with oil; however, the overall shifts were not substantial. This absence of isotopic fractionation of hydrocarbons during aerobic bacterial biodegradation provides evidence that these n-alkanes (C15, C20, C25, C30, and C40) and their precursors sustained their origin and were not microbially resynthesized. The finding was supported one year later by a 23-year litter bag experiment in More House Nature Reserve, which did also not observe significant compoundspecific 13C discrimination during extensive n-alkane degradation (Huang et al.; 1997). The stable carbon isotopic composition of n-alkanes is thus used for source identification of these compounds in environmental studies; the same applies to biogenic PAH (e.g., Mazeas et al., 2002; Wilcke et al., 2002, not further considered here). Lichtfouse et al., (1995, 1997) further elucidated the fate and transformation of n-alkanes in SOM. From both natural and artificial labeling experiments the authors reported that long-chain n-alkanes, n-alcohols, and n-alkanoic acids had similar d13C values, that is, they derived from

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Table 2 Major compound-specific isotope analyses in biomarkers for studying the mechanisms of soil organic matter transformation in soil incubation studies

Biomarker

Environment

References (choice)

Labeled substrate

Incubation time

Remarks

Aliphatic structures n-Alkanes, Soil microcosms norpristane

Oil

256 days, 30 C

Less temporal shifts in d13C values; useful as source correlation parameters

Hough et al. (2006)

Carbohydrates Neutral sugars

13

1 year, 26 C

Two decomposition phases of sugars (rapid, slow); Less effect of substrate quality—more effect of soil type on sugar production

Derrien et al. (2007)

13

3 weeks, 20 C

Bull et al. (2000)

Temperate forest soil: Humic Podzol

13

14 days

Different upland soils; Leptosol;

13

4 weeks, 25 C

MB related to type II; a novel bacteria br17:0 Type II MB and novel i17:0; at higher methane concentrations no population change Different MB in different soils

Different soils

Phospholipid fatty acids Methanotrophic Temperate bacteria (MB) forest soil

C-Glucose, 13 C glycine, 13 C-cellulose, 13 C-wheat straw

CH4, 1.8 ppmv, 100ppmv CH4, 10 ppmv, 10000 ppmv

CH4, 100–400 ppmv

Crossman et al. (2005)

Knief et al. (2003)

Luvisol; Cambisol; Podzol Gleysol

13

Entisol

13

2–11 weeks, 20 C

Landfill cover soil clay capped; sand capped; Different soil depth

13

2 weeks

CH4, 30 ppmv, 100 ppmv CH4, 2 ppmv

CH4, 10 ppmv, 10000 ppmv

Landfill cover soils; peat soil Arctic tundra Rice soil; forest soil: cambisol

Sediment cores

13

CH4, 0.01– 0.03% 13 CH4, 1000 ppmv, 10000 ppmv, NH4Cl, KNO3 13 CH4, 13acetate

17 days, 0 C 25 days, 25 C

7 days, 2 h, 22 C

mostly type II; pH 6.0 additional type I MB Different MB at higher 13CH4 concentration MB related to type II: Steady state of growth after 9 weeks MB types varied with soil depth and soil type: High O2, low 13CH4 type I; low O2; high 13 CH4 type II Moderate soils type II MB; acidic soils: type I and II Type I MB in soils at low temperatures Fertilizer addition stimulated consumption of methane Type I MB dominated

Knief et al. (2006) Maxfield et al. (2006) Crossman et al. (2004)

Ceebron et al. (2007) Zimmermann (2007) Mohanty et al. (2006) Boschker et al. (1998)

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(continued)

Table 2

(continued)

192 Biomarker

Environment

Labeled substrate

Incubation time

Yolo silt loam

13

119 h

Biotraps

13

32 days

Four different soils with different levels of PAH contamination Podzol; rhizodeposition

13

4 weeks, room temperature

13

7 days, 15 C

C-Toluene, 13 C-glucose

C-Toluene, 13 C-benzene

CPhenanthrene

C-Glucose, 13 C-glycine,

Remarks

methane oxidation at freshwater site. Acetate most consumed by sulphate reducing bacteria A small subset of PLFAs were assigned for toluene degradation Different microbial communities responsible for degradation of different compounds Similar PLFA profiles in highly contaminated, but different soils In rhizosphere a wide range of microorganisms

References (choice)

Hanson et al. (1999)

Geyer et al. (2005)

Johnsen et al. (2002) Paterson et al. (2007)

13

C-furamic acid

193

Temperate forest; Alfic Haplothord; elevated CO2 and O3 concentration

13

10 h, 19 C

Tropical soil

Natural labeling after C3/C4 vegetation change

103 days

Mesic Ultic Haploxeralf

13

65 days, 20 C

Mesic typic Fragiudult

13

0–48 h

C-Cellobiose, 13 C-Nacetylglucosamine

C-xylose, 13 C-starch, 13 C-vanillin, 13 C-pine needles

C-glucose

utilized glucose, furamic acid; only utilized glycine Microbial community metabolized more 13 C cellobiose under elevated CO2. Substrate type influenced metabolism At high temperature a shift of microbial communities was observed (degradation of more old C) Microbial communities of oak and grassland soils differed not in decomposition of labile C-substrates, but differed by degrading more recalcitrant communities Initial incorporation of glucose from

Philips et al. (2002)

Waldrop and Firestone (2004b)

Waldrop and Firestone (2004a)

Ziegler et al. (2005) (continued)

Table 2

(continued)

194 Biomarker

Environment

Tundra soils

Aquultic Argixeroll

Mesic Aquultic Argixeroll

Labeled substrate

Incubation time

C-vanillin, 13Ccellobiose, 13 C-Nacetylglucosamine 13 C enriched straw and roots of ryegrass and clover

5 days, 2.8 C

13

80 days, 25 C

13

C-labeled straw of ryegrass

9 months in the field

Remarks

gram + bacteria, with time actinomycetes became more important. Factor time must be considered Anaerobic conditions affect the microbial composition A subset of soil biomass responsible for assimilating residue derived C. 13 C among PLFA varied between sampling times Microbial communities of bulk soil and detritusphere were different; fungal biomarker was highly labeled during decomposition

References (choice)

Zak and Kling (2006)

Williams et al. (2006)

Mc Mahon et al. (2005)

Amino sugars Galactosamine, glucosamine, muramic acid

Agricultural soil

13

32 days

Galactosamine, glucosamine, muramic acid

Udoll

13

28 days

Galactosamine, glucosamine

Haplic Phaozem

13

61 days, 20 C

C-glucose

C-glucose

CO2 fumigation

Turnover of muramic acid faster than glucosamine and galactosamine Glucose skeleton not broken for glucosamine and muramic acid synthesis Small but detectable enrichments of amino sugars with 13 C after fumigation

Glaser and Gross (2004)

He et al. (2005)

Miltner et al. (2005)

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similar (C3-plant-) sources, if not even from fossil ones. In contrast, C14, C16, and C18 n-alkanoic acids were significantly enriched in 13C, thus pointing to a distinct microbial formation. 4.1.2. Carbohydrates Beside aliphatics, SOM also contains neutral carbohydrates in significant amounts (Amelung et al., 2006; Karroum et al., 2004). This appears to be in contrast to their high lability, and suggests that rapidly degraded carbohydrates were recycled and newly formed. Incubation studies estimating the biotransformation of different monosaccharides, however, are scarce. Derrien et al. (2007) incubated a subset of soils amended with 13C labeled glucose for one year. Additionally, 13C-glycine, 13C-cellulose, and 13Cwheat straw served as C and energy source in the incubated Cambisol. However, these structures were as rapidly converted as the labeled glucose, hinting at an immediate utilization of added C sources by soil microorganisms (Derrien et al., 2007). The pattern of sugars recovered after incubation was similar for different soils, and in general, the microorganisms thus utilized but also recycled predominantly glucose (68% of microbial sugars). Microbially synthesized hexoses, thereby, reached maximum concentrations in soil within a week (Derrien et al., 2007). The soil type exerted a small but evident impact on glucose conversion rates, and MRT increased in the order glucose (0.9 d) < cellulose (3.8 d) < labile metabolites (16 d) stabilized carbohydrates (MRT 1 y; Derrien et al., 2007). Hence, the incubation study indicated an existence of two pools of carbohydrate metabolisms: a labile carbohydrate fraction being rapidly converted accompanied by biosynthesis of hexoses (labile carbohydrate pool, MRT < 2 weeks), and a stabilized carbohydrate fraction that did not change its content at a significant scale during the 1-year incubation time (Derrien et al., 2007). 4.1.3. Incubation studies with tracing of living biomass by 13C analyses in phospholipids fatty acids (PLFAs) The ability to detect effects of changing environments on microbial performances has advanced soil ecological research in the last decade (Boschker et al., 1998; Evershed et al., 2006). Because of the uncultivable nature of many soil microbial populations, new techniques have emerged to study the populations without the need of laboratory cultures (Evershed et al., 2006; Maxfield et al., 2006). Gene probe-based methods are used to get information on phylogenetic and functional diversity or to characterize specific groups of microorganisms (Ceebron et al., 2007; Knief et al., 2003; Neufeld et al., 2007). The alternatives are incubation studies with labeled 13C in combination with CSIA of individual PLFAs (Hanson et al., 1999). Such studies help to identify the decomposer community of a given isotopelabeled food source, to elucidate the response of microbial community


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composition to changing environmental conditions and to characterize the decomposition pathways of plant (or microbial) residues. The identification of the decomposer communities on the basis of CSIA of different PLFAs has been particularly successful for methane-oxidizing bacteria (MB), which act as a sink for atmospheric and soil-born methane. Boschker et al. (1998) first demonstrated a rapid consumption of 13C-acetate by Desulfotomaculum acetoxidans, but not by the Desulfobacter species that had formerly been thought to be the main methane consumers in brackish environments. More recently, such labeling approaches have also been applied to forested and agricultural soils. The cultivable MB are classified into types I (more C16 fatty acids), II (more C18 fatty acids) and a type X, which shares some of the characteristics of type I (Bull et al., 2000; Crossman et al., 2005). In temperate climates, for instance, the 13C-labeled PLFAs of forest soils hint at type II of methanotrophic bacteria. Bull et al. (2000) incubated sieved soil (2 mm) from different soil layers (mineral, buried organic horizon) of a Sitka spruce stand at 20 C for 3 weeks, labeled with atmospheric (1.8 ppm) and elevated (100 ppm) 13C-methane concentrations. They found that beside the PLFAs of type II MB, some novel methanotrophic bacteria produced br17:0 PLFAs. Crossman et al. (2005) found in 13CH4-incubated humic layers of a Pinus sylvestris podzol that higher concentrations of methane increased the rate of methane oxidation by the same population of MB (Table 2). The 13C-labelled PLFA ageing related to type II methanotrophs, but differed from these species by the production of the i17:0 fatty acid. Knief et al. (2003) recovered the 13C labels in branched 17:0 PLFAs when incubating diverse upland soils under atmospheric CH4 or under elevated CH4 mixing rates (100–400 ppm), the isotope labels in i17:0 only comprised a tiny percentage. Only at pH 6.0 a novel group of sequences related to type I MB, suggesting that different MB were active. In three gleysols, higher methane concentrations (500 ppmv relative to 30 ppmv) also resulted in 13C labeling of PLFAs beside type II (16:0 and 18:1o7c; Table 2). Maxfield et al. (2006) developed a flow chamber with long-term atmospheric methane conditions (2 ppm 13CH4). An incubation period of 11 weeks showed an incorporation of 13C label into selected PLFAs between 10 and 50% (16:1o5 50%; 18:o7c 38.4%; i17:0 15.1%; 18:1o5c 9.8%; Table 2). Nevertheless, this 13C incorporation was slow compared with studies that relied on higher concentrations of methane (Crossman et al., 2005). Again, type II methanotrophs dominated, and the community reached steady state after 9 weeks. Yet, no significant proportions of 13Clabel were detected in other PLFAs, suggesting that there was no efficient recycling of the 13C labeled biomass in nonmethanothrophic bacteria in these Entisols. The response of microorganisms at variable environmental conditions can be easily studied, after the diagnostic labeled PLFA signals have been

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identified. Studies with methanotrophs indicated, for example, that uptake of labeled 13CH4 varied with depth. Incubating clayey and sandy soil samples from two landfill caps with 10 ppm 13CH4 or 10.000 ppm 13CH4 for four weeks revealed that type I methanotrophs (more C16 fatty acids) were more active in the surface layer with high oxygen and low methane concentrations, whereas type II methanotrophs (more C18 fatty acids) dominated in deeper soil layers with low oxygen and higher methane concentrations. The results were corroborated by DNA-SIP (Ceebron et al., 2007). Hence, more acidic soil conditions allowed the establishment of type I and type II methanotrophs, whereas more moderate soil conditions led to a dominance of type II methanotrophs, that is, compound-specific 13C-PLFA analyses may help identifying the ecological habitats and niches of soil bacteria. The arctic tundra showed dominant PLFA biomarkers which differed significantly from those in warmer soils of temperate climates (Table 2; Zimmermann, 2007). After incubation (17d) with 0.01–0.03% 13CH4, PLFAs diagnostic for type I MB were dominant and continued to be so in incubation studies at 0 C (possibly a psychophile community) but not at 22 C (Zimmermann, 2007). Beside temperature and pH, also soil nutrients such as ammonium affected specific growth rates and structure of methanotrophic communities (Crossman et al., 2006; Mohanty et al., 2006; Table 2). Fertilizer additions in general stimulated the uptake of 13C-labelled methane by type I MB, whereas type II methanotrophs were inhibited. In principle, such studies can be extended to any other combination of nutrient (CH4 or other source) uptake at variable environmental conditions. Beside methane, several studies focused on pollutants as substrates for microbial communities. The results showed that only a very specific, small subset of the soil microbial community has been involved in the degradation of, for example, 13C-labeled toluene, benzene, or phenanthrene (Hanson et al., 1999; Mauclaire et al., 2003; Pelz et al., 2001). Although toluene and benzene show only small structural differences, different microbial groups were involved in their biodegradation (Geyer et al., 2005). According to Pelz et al. (2001), the subset of 13C enriched PLFAs detected during degradation of 13C toluene corresponded to the PLFA pattern of MB in soils. Johnson et al. (2002) pointed out that similar degrading communities were involved in the turnover of PAHs of two different soil types (Table 2), dominated by Sphingomonas and ARJ45-like bacteria, the numerically dominant cultivable soil phenanthrene degraders. Compared to degradation of pollutants, the release of organic compounds from plant roots and their utilization by soil microbial communities is of much greater relevance for SOM genesis and transformation. It is estimated that plants excrete 10 to >40% of assimilated C through their roots, the different root exudates possibly select for specific beneficial groups of microorganisms (Bergsma-Vlami et al., 2005; Mac Donald et al., 2004;


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Ngyen, 2003; Shaw and Burns, 2003; Singh et al., 2004). Consequently, the soil–plant interface (rhizosphere) has been termed an ‘‘oasis in the desert’’ from the microbial point of view (Bertin et al., 2003). Paterson et al. (2007) was able to determine the specific microbial utilization of root-exudates and whole rhizodeposition, using 13C labeled substrate additions to incubated rhizosphere and bulk soil (podzol, C bands as model roots added). Glucose and fumaric acid were utilized by a wide range of microbial populations (13C enrichment in 25 and 26 PLFAs), whereas only nine PLFAs showed the 13C-label from glycine degradation, mainly assigned to gram-negative bacteria. These results corresponded to accompanying rhizosphere study with 13CO2 labeled Lolium perenne. Similar to studies with methanotrophs, the effects of changing environmental conditions on microbial performance in arable soils can be rapidly elucidated once the major PLFAs responding to 13C labeling have been identified. Philips et al. (2002), for instance, were able to show that increases in plant litter production under elevated CO2 may be counterbalanced by more rapid degradation rates of litter in temperate forest soils (Alfic Haplothords). After short-term incubations with cellobiose and N-acetylglucosamine (Table 2), elevated 13C atomic excess was primarily found for PLFAs related to a few gram-positive bacteria (a15:0, i15:0; i16:9), gram-negative ones (16:1o7c, 18:1o7c), nonspecific ones (16:0; 18:0) and fungi (18:2o6; 18:1o9c). Cellobiose amended soils promoted fungal metabolisms, whereas soils amended with N-acetylglucosamine resulted in an increase of the activity of both bacteria and fungi. Waldrop and Firestone (2004b) altered temperature, N-availability, for instance, to study their effects on the decomposition of old carbon (C3) and new carbon (C4) pools. For this purpose, they incubated soil for 103 days from a pineapple plantation that had previously been under tropical forest for 103 days. The microbial community degraded older C at warmer conditions (20–25 C) more efficiently than at cooler temperatures. In contrast, the microbial community structure was unaffected under N treatments. Subsequent short-term 13C labeling experiments in the terrestrial environment showed that (1) different microorganisms were adapted to different plant types (oak, grass), that (2) degradation of simple substrates (starch, xylose, and vanillin) was achieved by similar microbial groups (Waldrop and Firestone, 2004a), whereas that of more complex litter (pine, buried roots, and ryegrass) was not (Waldrop and Firestone, 2004a; Williams et al., 2006; see also Schadt et al., 2003), and that (3) fungi seem to be major sinks for newly added C, based upon the incorporation of 13C labels into 18:2o6,9 (McMahon et al., 2005; Waldrop and Firestone, 2004a; Williams et al., 2006). Hardly any isotopic labels were recovered during litter degradation in the PLFAs 16:1o5, and 10Me17:0 (0–5% of added 13C; Williams et al., 2006), which characterize AMF and actinomycetes (Table 1).

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The isotope labeling may also provide insight into SOM dynamics of more extreme environments, where on-site monitoring of C changes may be difficult to organize and long-lasting. For tundra soils, for instance, it could be shown that even at low temperatures of 2.8 C, field utilization of 13C labeled substrates was rapid (< 5 d), and increased in the order cellobiose < N-acetylglucosamine < vanillin. The 13C labels were mainly detected in bacterial 18:1o7t and, again, fungal 18:1o9c (Zak and Kling, 2006). Fungi appear, thus, to be more important for SOM cycling in tundra ecosystems than their low biomass initially indicated. They were most abundant and active in the tussock tundra, while anaerobic conditions in the birch-willow and wet sedge tundra restricted fungal abundance and activity. Under such circumstances, the PLFA pattern was dominated by bacteria. As indicated, most incubation studies are limited to short periods. When incubation time increases, isotopic 13C labels may hike through the different PLFAs, thus indicating that different members of the soil microbial community participated in SOM dynamics at different periods of time (Fernandez and Cadisch, 2003; Williams et al., 2006; Ziegler et al., 2005). Ziegler et al. (2005) showed that when amending soil with 13C labeled glucose for 0–48 h, gram-positive bacteria were responsible for initial incorporation of the isotopic label; after 48 h also actinomycetes showed this 13C atomic excess. The time-resolved 13C tracing in individual PLFAs thus provide a powerful clue to elucidate the role of different members of the soil microbial community for C and nutrient cycling at various environmental conditions and thus also in differently managed agricultural soils. Only recently, longer-term incubation experiments have been designed to investigate the recycling of 13C labels beyond the life-time of the first microbial generations in aerobic soil environments (e.g., Maxfield et al., 2006; review of Staddon, 2004). The assessment of microbial food webs, however, is still difficult because of unknown specific growth rates of the different microorganisms, unknown isotopic fractionation by the different metabolic pathways, different substrate selection from complex labeled substrates, as well as by unknown activity cycles of the living microorganisms and unknown turnover rates of their dead cells. 4.1.4. Dead microbial biomass For understanding the turnover of dead microbial biomass, we have to trace the isotopic labels also in microbial residues. This is possible, in principle, for hopanoids and GDGTs (see glossary) after specific chemical degradation (e. g., Crossman et al., 2001; Leininger et al., 2006; Schwark, 2007, personal communication); however, these biomarkers have so far received only very limited attention in soil science. However, first tools exist for CSIA in bacterial D-amino acids (Glaser and Amelung, 2002; Pelz et al., 2005; Zhang et al., 2007) and amino sugars (Glaser and Gross, 2004; He et al., 2005).


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Amino sugars in soils comprise about 1–5% of total SOM (Glaser, 2005). As they survive their producers, only a minority of amino sugars is present in living cells, and amino sugars have been shown to be useful biomarkers for indicating mainly the presence of fungal and bacterial residues in soil (Amelung, 2001). Miltner et al. (2005) incubated a Haplic Phaeozem in an atmosphere enriched with 13CO2. The authors showed that the 13CO2 was directly sequestered by soil microorganisms; glucosamine and galactosamine even exhibited a small enrichment of 13C by 6–7% relative to amino acids and polar lipid fatty acids. Glaser and Gross (2004) incubated agricultural soil samples for 32 d with 13C enriched glucose. Intriguingly and in contrast to most PLFAs analyses (see above), the isotope label was more enriched in bacterial-derived muramic acid than in fungal glucosamine when the experiments ended. The recycling of amino sugars was fairly slow, with muramic acid being turned over faster than glucosamine and galactosamine. Also, He et al. (2005) observed that muramic acid was much more efficiently labeled during a 4-week incubation study with U-13Cglucose (30% isotope atomic excess) relative to glucosamine and galactosamine (12% and 5% atom percentage excess, respectively). He et al. (2005), however, did not use GC-c-IRMS for stable isotope tracing but simple chemical ionization GC/MS for quantifying the high isotopic labels. In doing so the authors also claimed to have identified the major synthesis mechanisms. For glucosamine and muramic acid synthesis, for example, it appeared that the bulk glucose molecule was not broken (He et al., 2005). In the future, deeper insights into the mechanisms of substrate utilization and SOM genesis may be expected from multiple isotope labeling studies, such as from 13C/15N or 13C/14C double labeling experiments at different positions of the molecule.

4.2. Field studies When there are C3/C4 vegetation changes or vice versa, the d13C stable isotope signature of SOM is naturally labeled in situ. The new C added to soil derives primarily from decomposing above- and belowground litter and root exudates; though it may partly be already assimilated by soil microorganisms. The FACE studies allow for a 13C labeling of intact plant stands without further changes in crop type and management. Additionally, 13C isotope labeling in the field has been achieved by applying isotope labeled fertilizers, such as by applying a C4 dung to a C3 soil (see Table 3 for a brief overview on CSIA in agricultural sciences), whereas the potential of compound-specific biomarker analyses from 15N and 34S fertilization experiments has received yet only limited attention.

Table 3

Major compound-specific stable isotope analyses in biomarkers for investigating soil organic matter transformation and genesis in field studies

Biomarker

Soil

Time (years)

MRT (years)

C3/C4 maize

9

12–27

Natural

C3/C4 maize

9

7–17

Natural natural FACE1

C3/C4 maize C3/C4 Misc., 13 CO2

23 9 10

9–38

Labeling

Vegetation

Plant derived biopolymers Lignin-derived Eutric Cambisol phenols

Natural

Eutric Cambisol

Typic Hapludalf Haplic Luvisol Eutric Cambisol

Model

5–26

0.5 (pool 1) 20 (pool 2)

Dystric Cambisol

Natural

C3/C4 maize

23

Phenol 21 Toluene 24

Remarks

References (choice)

Lignin dynamic was faster than total OC (90 years) Turnover of lignin is monomer specific. Vanillyl units turnover slower than syringyl and cinnamyl phenols; turnover is nonlinear Lignin turnover is faster than turnover of bulk SOC (20–26 years; 51 years, respectively) Two pools of transformation 1. fresh lignin with short MRT 2. protected pool Phenol, toluene derived from lignin turned over faster than soil proteins (54 years) and polysaccharides (56 years)

Dignac et al. (2005) Bahri et al. (2006)

Heim and Schmidt (2007)

Rasse et al. (2006)

Gleixner et al. (2002)

Aliphatic structures C27 n-alkane C39 n-alkane n-Alkanes

n-Alkanes —free —bound C29 n-alkane

Eutrochrept

Natural

Eutrochrept Hapludulf

Natural

Typic hapludalf

Natural

Hapludalf

Natural

C31 n-alkane

n-Alkanes, (n-carboxylacids)

n-Alkanes, (n-carboxylacids

C3/C4 maize C3/C4 maize

0.4–4.3

C3/C4 maize

23

C3/C4 wheat– maize

23

4–23

4–12 18–150 30–32

19–41 30–58 C29 8–24 C31 9–20

Dystric cambisol

Natural

Haplic Phaeozem Stagnic Luvisol

Natural

Eutric cambisol

FACE 13 CO2

Natural

C3/C4 grain maize C3/C4 silage maize C3/C4 grain maize

23

35 (21)

39

60 (49)

23

35 (21)

10

30–50 (12–20)

Exceptional high MRT for C29 Turnover of molecular components differ from bulk compartments Faster turnover of free than of bound n-alkanes n-Alkanes are incorporated in soil via large particle size fractions, turnover depends on particle size Cropping techniques influence turnover times of n-alkanes and ncarboxylacids Plant-derived nalkanes have slower turnover times than bulk soil. Carboxylacids turn over faster than bulk soil

Lichtfouse et al. (1994) Lichtfouse et al. (1997)

Lichtfouse (1998) Cayet and Lichtfouse (2001)

Wiesenberg et al. (2006)

Wiesenberg et al. (2005)

(continued)

Table 3

(continued) Time (years)

MRT (years)

C3/C4 forest– maize

22

C27 76–118 C29 88–173 C31 146–173

Natural

C3/C4 forest– maize

35

350

Eutric Cambisol

FACE

13

10

33–57 (13– 19)

Carbohydrates Neutral sugars

Dystric gleysol

13

C slurry

Grassland

2 h–4 weeks

Ara 30 h Xyl 91 days

Neutral sugars

Eutric cambisol

FACE elevated 13 CO2

Lolium perenne

7

Ara 10–25 Xyl 6–22 Fuc 4–14 Rha 12–33 Gal 4–29 Man 8–24 Glu 4–15

Biomarker

Soil

Labeling

Vegetation

C27 n-alkane C29 n-alkane C31 n-alkane

Spodosol

Natural

n-Alkane

Veracrisol

n-Alkane, (n-carboxylacids)

CO2

Remarks

References (choice)

Maize carbon in soils shows after 22 years a minor contribution of 12–25% Small inputs (8%) of maize derived carbon in PSF 0– 10 mm Carboxylacids turned over faster than bulk SOC. n-Alkanes turned over slowest

Queńeá et al. (2006)

2-Phase slurry incorporation: 20% of slurry derived carbohydrates within 2 h in the surface layer; second phase within 2 weeks Short turnover times of carbohydrates under elevated CO2; MRT depends on PSF

Poirier et al. (2006)

Wiesenberg et al. (2008a)

Sauheitl et al. (2005)

Bock et al. (2007)

Different turnover times of glucose and xylose in different PSF 65% of root-derived carbon was neutral sugars (most polymerized glucose)


54

Turnover of proteins slower than turnover of phenols

Gleixner et al. (2002)

90

75–112

Burke et al. (2003)

50

66–125

65 25

39–69 74–114

Soil type influenced ratios of gram + to grambacteria; land use affected portions of fungi, protozoa, actinomycetes 13 C after labelling were incorporated fast. PLFA in rhizosphere varied during growing season Incorporation of 13 C in PLFA was small; 85–95%

Neutral sugars

Eutric cambisol

Natural

C3/C4 wheat– maize

23

Neutral sugars

Eutric cambisol


Wheat seedlings

2 weeks

Dystric cambisol

Natural

C3/C4 maize

23

Natural

C3/C4 Forestsugarcane

Glu 5–99 Xyl 7–91

Proteins

Phospholipid fatty acids Typic distrandepts Red latosol Andic humitropepts Oxiaquic dystrochept

13

CO2 pulse lab

Rice plants

6h

Typic hapludult

13

C urea

Trifolium

4 days, 14days


Lu et al. (2004)

Petersen et al. (2004) (continued)

Table 3

(continued)

Biomarker

Soil

Gleysol

Natural

Haplic luvisol

Natural

Haplic phaeozem Haplic phaeozem Gram-31–61

Amino sugars Glucosamine, Galactosamine, muramic acid, Mannosamine

Labeling

Eutric cambisol

Natural


Vegetation

C4/C3 Misc– wheat C3/C4 wheat– maize C3/C4 rye– maize C3/C4 rye– maize

Lolium perenne

Time (years)

MRT (years)

8 23

10–15

41

27–51

41

Gram + 35– 90

7

3–30

Remarks

were found in common fatty acids Wide ranges of PLFA during season Different groups of soil microorganisms prefer different C sources C4 labeled (young) carbon is the microbial carbon source in topsoils; in the subsoil older SOM is more used as C source Short turnover of amino sugars under elevated CO2. Amino sugars do not belong to the stable SOM pool. Amino sugars stabilized in order sand < silt < clay

References (choice)

Pelz et al. (2005) Kramer and Gleixner (2006)

Kramer and Gleixner (2008)

Glaser et al. (2006)


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4.2.1. Turnover of plant-derived biopolymers in soil As indicated in Section 1, selective preservation of refractory plant components controls litter decomposition rates (e.g., Meentemeyer, 1978; Parton et al., 1987). For mineral soils this process is in dispute (e.g., Amelung et al., 1997; Marschner et al., 2008) and was also questioned by first n-alkane natural 13C-isotope tracer studies (Lichtfouse et al., 1995). The authors reported that long-chain n-alkanes were usually enriched in 13C rather than depleted, supporting the hypothesis of an in situ formation of SOM, but not supporting the hypothesis of selective preservation processes being significant during SOM genesis (Lichtfouse et al., 1995). There are no studies on the compound-specific turnover of tannins (Table 3). However, the compound-specific 13C tracing in lignin compounds allowed for the very first time to quantify the disappearance rates of lignin in soil. Gleixner et al. (2002) applied pyrolysis GC/MS-c-IRMS to agricultural samples of the Boigneville plots (0–30 cm) that were 23 years under maize cropping after former wheat production. The method failed to detect specific lignin biomarkers. However, phenol and toluene that might have originated from C3 lignin indicated a turnover time of 21 and 24 years, respectively. This was much faster than assessed for soil proteins (49 19 years) and polysaccharides (56 36 years). Only styrene (MRT 46 years) and 4-Me-phenols (MRT 101 years), having lignin but also other structures as precursors in pyrolysis measurements, exhibited similar to larger residence times. 4.2.1.1. Lignin-derived phenols Alkaline CuO oxidation releases individual phenol monomers from intact lignin molecules (see Section 2), thus allowing for a specific assessment of their MRT. All studies that used this method so far (Table 3) confirmed earlier work that lignin decomposition is monomer specific, increasing in the order vanillyl (V) > syringyl (S) > Cinnamyl (Ci) units (Bahri et al., 2006; Dignac et al., 2005; Heim and Schmidt 2007). The monomer-specific MRT ranged from 7 to 33 years (18.2 6.9 years on average, calculated from the above-mentioned works). This MRT was faster than that of bulk SOM, suggesting that either compounds other than lignin were preserved for longer time-scales, or that the CuO method only detected reactive parts of the lignin, leaving altered, stabilized, and nonextractable parts behind (Table 1), or both. Yet, incorporation of new lignin in soil was nonlinear and tended to be slower after 6 y of cropping (Bahri et al., 2006). Hence, calculation of compoundspecific turnover rates is not exact using the simple isotope mixing model (see Section 3). Likely, even for individual SOM constituents at least two pools of transformation in soil must be distinguished. In this case, it is the fresh lignin that rapidly dissipates (MRT 0.5 years; Rasse et al., 2006; see also corroborating results from incubation experiments with 14C labeled

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lignin; Stott et al., 1983), while the protected lignin decomposes slowly (Amelung and Zech, 1996; Glaser, 2005; Rasse et al., 2006). According to the modeling of Rasse et al. (2006), only 8% of the introduced lignin reached the protected pool, from where it dissipated at a rate of 0.05 years1, corresponding to a MRT of 20 years. The lignin added to soil may weakly bind to minerals when intact or still part of the plant root. Hence, especially physical protection mechanisms might account for its preservation in the medium-term (e.g., Amelung and Zech, 1996; Six et al., 2000), but also adsorption of dissolved, chemically oxidized lignin to the clay fraction might sequester in the medium term run (years to very few decades; Guggenberger and Kaiser, 2003; Guggenberger et al., 1998). If true also for Biogneville sites, physical protection and adsorption mechanisms may apparently reduce decomposition rates of plant fragments by a factor of 40 (Rasse et al., 2006). 4.2.1.2. n-Alkanes and n-alkanoic acids Despite the vast range of aliphatic compounds used as biomarkers, only few studies addressed the turnover of plant-derived lipids in soil using compound-specific d13C analyses. Hence, also specific residence times of cutin and suberin in soil are largely unknown. The few isotope studies available referred almost exclusively to n-alkanes and n-alkanoic acids (Table 3). In addition, d13C– d2H characterization of sterols improved their source assignment in sediments (e.g., Chikaraishi and Naraoka, 2005), but the specific turnover rates of steroid and bile acids in soil remained uncertain. Bull et al. (2000) observed that sitosterol and triacylgylcerols dissipated rapidly, whereas 5ßstigmastanol appears to preserve the signal of manuring for > 100 years. Also branched alkyl moieties may persist in soil (e.g., Ko¨gel-Knabner et al., 1992), but again, we are not aware of any isotope tracer studies assessing residence times of plant-derived triterpenoids, microbial hopanoids, or other cyclic lipids or tetraether lipids in agricultural soil, even though the methodological basis is available (see above and Section 2). Assigning the exact origin of n-alkanes to higher plants may be complicated when reduction of alkenes and fatty alcohols, decarboxylation of bacterial fatty acids or degradation of aliphatic biopolymers also contribute to n-alkane formation (Lichtfouse 1998). A higher MRT for n-alkanes than for n-alkanoic acids (e.g., Wiesenberg et al., 2004, 2008a) may therefore not only be caused by a higher intrinsic stability but could also indicate a reformation of the n-alkane structures from their precursors. Lichtfouse et al. (1994) found a MRT for C27-alkanes of 4 years soon after cropping. However, under these conditions no steady-state is reached. The high MRT assigned to arable C29-alkanes (Lichtfouse et al., 1994; Fig. 4) have not been confirmed by later studies and, thus, appear to be an exception. In general, the MRT found for n-alkanes ranged from 8 to 60 years at arable sites (Fig. 4; e.g., Cayet and Lichtfouse, 2001; Lichtfouse et al., 1994, 1997,

209


MRT-C27 MRT-C29 MRT-long-chain n-alkanes MRT-short-chain alkanoic acids MRT-long-chain alkanoic acids MRT-C31

100

Mean residence time, MRT (years)

90

MRT-C33

Exceptional high

80 70 FACE

60 50 40 30 20 10 0 0

10

20

30

40

Time after crop labelling (years)

Figure 4 MRT of aliphatic biomarkers as related to time after free-air CO2 enrichment (FACE) experiment started or after C3–C4 crop conversions (data from Cayet and Lichtfouse, 2001; Lichtfouse, 1998; Lichtfouse et al., 1994, 1997; Wiesenberg et al., 2004, 2008a). The dashed lines indicate a subjective range of data point development.

1998; Marschner et al., 2007; Wiesenberg et al., 2004). Hence, also for plant waxes there is no evidence that they are selectively preserved in significant amounts during SOM genesis and transformation. The turnover rate of individual lipids depends on their bioaccessibility: ester-bound and mineral attached alkanes were converted less rapidly than free ones and those of the sand fractions, respectively (Cayet and Lichtfouse, 2001; Lichtfouse, 1998). An increasing binding to minerals during the process of SOM transformation and genesis will thus prolong the residence time of these compounds in soil. As outlined in Fig. 4, the MRT of the aliphatic fractions tends to increase with increasing time after C3/C4 vegetation change, suggesting that after rapid replacement of the labile alkane pool, protected alkane fractions may hinder rapid losses. Both, physical protection within aggregates and chemical preservation at or within clay minerals contribute to this result (Golchin et al., 1994; Schnitzer et al., 1988; Schulten et al., 1996).

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Compared to arable fields, the turnover time of alkanoic acid at grassland sites exposed to a 13CO2 atmosphere (FACE experiments) exceeded that in arable soils by a factor of 2: Nevertheless, the comparison between sites may be biased by smaller sampling depths (10 vs. 30 cm), and hence higher microbial activity in the grassland (Wiesenberg et al., 2008a). Subsequent decarboxylation of nonlabeled alkanoic acids thus resulted in apparently higher MRT of their products, the n-alkanes (Fig. 4). The MRT did not exceed 60 years, and was thus in the range of that for bulk SOC (Marschner et al., 2007). Only after conversion of forests to maize cropping, the MRT of n-alkanes reached 160 years in the bulk soil (Queńeá et al., 2006) to ca. 350 years in the mineral fraction (Poirier et al., 2006). Two reasons have been suggested to account for the long MRT observed in the acid forest soils. On the one hand, the maize has lower lipid contents, that is, replacing a relatively large pool of wood-derived lipids in SOM resulted in apparent low turnover rates (Queńeá et al., 2006; Wiesenberg, 2008, personal communication). On the other hand, fungal production of long-chain alkanes, for example, from larger biopolymers with the isotopic signature of the wood itself, may blur true turnover times (Queńeá et al., 2006). It, therefore, seems necessary to further fractionate the aliphatic fraction for a better understanding of the processes involved, and to consider the fate and turnover of microbial products for a detailed understanding of the mechanism involved. Nevertheless, the high MRT of lipids under acid conditions coincides with results of Bol et al. (1996) who found very old radiocarbon dated lipids in acidic water-logged Gleysols of the British uplands. 4.2.2. Formation and turnover rates of microbial-derived biopolymers in soil 4.2.2.1. PLFA When biomarkers, such as PLFAs, decay as soon as the organisms die, their concentration can be used as a marker for living microbial biomass, whereas their isotope assimilation informs on the activity of the respective microorganisms (e.g., Boschker and Middelburg, 2002; see also Section 2.2). Isotope labeling may be artificially achieved with gases (e.g., 13CO2, 13CH4), solutes (e.g., 13C-acetate, 13C urea, dissolved 13C glucose) or solids (e.g., burial 13C labelled plant tissues, 13C labeled algae or microbial cells) or occur naturally, for example, after vegetation change (see Section 4.1.3). SIP in microbial genes (not reviewed here) and 13CPLFA analyses have been the methods of choice for tracking back isotope assimilation into living biomass; we are not aware of any study that succeeded compound-specific isotope tracing in ergosterol or other markers for living cells. Usually all added substrates are taken up within days (e.g., Petersen et al., 2004; see also 4.13). Lu et al. (2004) reported that immediately after 13CO2 pulse labeling of rice plants, the isotopic signal was recovered in the PLFAs


211

of rhizosphere microorganisms, suggesting a direct coupling of photosynthetic production and microbial growth. Even symbiotic organisms such as extraradical arbuscular mycorrhiza were immediately provided by the new, labeled C source (Olsson et al., 2005). During vegetation development, the degree of isotopic labeling changed for the different PLFAs, suggesting that other bacterial populations evolved during rhizosphere development or that the microbial C had already been recycled by the soil microbial community or both (e.g., Butler et al., 2003; Lu et al., 2004; Pelz et al., 2005). However, frequently only few of the total PLFAs detected responded to such rapid labeling (e.g., Boschker and Middelburg 2002; Pelz et al., 2005; Petersen et al., 2004; see also Section 4.1.3); hence, the majority of soil microorganisms feeds on older SOM. If the soil microbial community assimilates old C, then the detected PLFA also appear old, although the organism is still alive. Calculating MRT of PLFAs from long-term natural isotope labeling studies do therefore not highlight their true turnover rates but provide insight into the age of assimilated C. Within a chronosequence after C3/C4 vegetation changes the fate of ‘‘old’’ and the incorporation of ‘‘new’’ SOM have been traced back by shifts in natural soil d13C abundances (e.g., Balesdent and Mariotti, 1996; Lobe et al., 2005; Trouve et al., 1994, and many more, see also Section 1.2 and 3). Nevertheless, it is difficult to accurately identify the C3/C4 carbon source assimilated by the living microbial community because of internal isotope fractionation during PLFA synthesis. It may account for isotopic shifts between 0 and 17 delta units within individual PLFAs, and it is usually larger for anaerobic C than for aerobic assimilation (Abraham et al., 1998, Salata, 1999). Consequently, data acquired for C source assignment and apparent MRT of living microbial cells on the basis of individual PLFA analyses must be considered with care unless adjacent controls are analyzed (e.g., a long-term C3 control site in addition to the C3/C4 plot that shows vegetation change, or a C3 control dung when studying the fate of a C4 dung, for instance; Bol et al., 1999b, 2000, 2003a,c; Derrien et al., 2006; Kramer and Gleixner, 2006; see also Section 3). Burke et al. (2003) suggested that only for the sum of all PLFAs, such isotope discrimination effects may partly level out in aerobic soils. As cropping period continues, SOM derived from former (e.g., C3-) vegetation dissipates slowly, and protected SOM pools may control the fate of the original structures in the long-term (see also Fig. 4). Compound-specific d13C analyses of PLFAs in the C4 cropped soils relative to their former C3 management give insight into how microbial communities are adapted to the changes in SOM origin. In Fig. 5, we summarized the current data available on this topic and recalculated MRTs from published data where feasible. Summarizing the respective literature and Fig. 5 reveal:

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W. Amelung et al.

MRT of assimilated C in PLFA (years)

181,2 (15:1) Gleysol and andisol Ferralsol Luvisol and phaeozem

120 100

r² = 0.98

80 60 r² = 0.60

40 r² = 1.00

20 0

0

20 40 60 80 Time after crop conversion (years)

100

Figure 5 MRT of C in soil recovered in living biomass based on compound-specific stable isotope analyses of phospholipids fatty acids (data taken or calculated from Burke et al., 2003; Kramer and Gleixner, 2006, 2008; Pelz et al., 2005; Gleysol data also included for curve fitting of the Luvisol/Phaozem plot). The high MRT of 181.2 years of the PLFAs 15:0 was an outlier and attributed to the assimilation of lignite or other fossil C sourses at site Halle, Germany.

1. With proceeding time after vegetation change to C4 and lower proportions of C3-derived C, increasing ‘‘older’’ proportions of the original C3-derived C are recovered in the PLFAs until after about 20–40 years saturation is reached. 2. Even when C3/C4 vegetation changes date back for decades, soil microorganisms continued to be able to utilize C from the former vegetation, that is, this C is likely recycled through microbial biomass beside being solely refractory and protected from decay. 3. The degree to which ‘‘old’’ C may still be utilized by the soil microbial community depended on soil type. The portions of the former, ‘‘old,’’ C3-derived C with higher MRT in the PLFAs increased in the order Ferralsol < Luvisol, Phaozeom < Andisol. 4. There are consistent differences in the C utilization by different groups of the soil microbial community: decreasing amounts of fresh SOM (total MRT low) might be recovered in the order fungi gramnegative bacteria gram-positive bacteria (Kramer and Gleixner, 2006; Fig. 5), which would suggest that especially gram-positive bacteria and not the fungi control the fate of old SOM (see also Section 4.1.3), however, more data especially for fungal PLFA are needed to support this conclusion. The exact reasons why the utilization of C was so different in the different soils have not yet been systemically investigated. Nevertheless,


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slow microbial conversion of SOM in Andisols is consistent with laboratory studies reporting that Al oxides were much more efficient than Fe oxides in retaining SOM and in suppressing microbial C utilization (e.g., Amelung et al., 2001; Miltner and Zech, 1998). The differences found between the two loess-type soils and the Ferralsol remain unexplained, since the major properties of both soils contribute to SOM stabilization, for example, via formation of aggregates or efficient C sequestration in Fe oxides (e.g., Eusterhues et al., 2005; Ladd et al., 1993; Six et al., 2000). Kemmitt et al. (2008) proposed that abiotic soil properties control the first step of SOM release and thus the mineralization and performance of soil microorganisms, since the SOM must be desorbed from mineral surfaces or liberated from aggregates prior to be mineralization. In this sense, these soil inherent factors also controlled the uptake pattern of recent and old soil C by soil microorganisms, reflected in the different age of their phospolipids. As soil depth increased, the MRT of PLFA-C increased to 80 (range 28) years for the gram-positive bacteria and to 125 (range 37 years) for the gram-negative ones in a Phaeozem subsoil (Kramer and Gleixner, 2008; data not included in Fig. 5). It should be noted that this Phaeozem near Halle, Germany, is contaminated with fossil C from lignite mining and fossil fuel combustion (Brodowsk et al., 2007; Rethemeyer et al., 2004). This contamination resulted in apparent high MRT for bulk SOC (ca. 300 years if not corrected for these inputs; Flessa et al., 2008; Kramer and Gleixner, 2006). Intriguingly, this contamination was not reflected in the majority of the PLFAs, despite the fact that microbes are able to feed on lignite and black C (Hamer et al., 2004; Rethemeyer et al., 2004; Rumpel et al., 2004). Only the PLFA 15:1 significantly lacked the C4-derived C (Fig. 5), confirming that not the whole microbial community but only some of their members utilize foreign C. 4.2.2.2. Neutral and amino sugars While PLFAs degrade as soon as the organism dies, several other microbial products in soil survive their producers and provide, thus, a link to assess the turnover and recycling rates of microbial metabolites and dead microbial cells structures in soil. Such microbial recycling may be particularly relevant for carbohydrates. More than one third of bulk SOM frequently consists of carbohydrates. Depicting turnover rates from compound-specific d13C abundance measurements is easier for carbohydrates than for PLFAs and alkanes because the variation of d13C natural abundances within individual carbohydrates is much lower (< 5% in monosaccharides of the bulk soil or between different plant compartments; Bock et al., 2007; Derrien et al., 2006). As described for the PLFAs, the carbohydrates may also be rapidly reproduced, but they may also be stabilized through interactions with Fe oxides, microaggregates, or captured in very fine pores, for instance (Amelung et al., 1997; 1999b; Cheshire, 1979; Eusterhues et al., 2005;

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Mean residence time, MRT (years)

Gleixner et al., 2002; see also mechanistic works on the ageing of pollutants, e.g., Hatzinger and Alexander, 1995; Laabs and Amelung, 2005; Luthy et al., 1997). Indeed, compound-specific 13C tracing within individual carbohydrates supported both assumptions. Sauheitl et al. (2005) found, for instance, that 1 d after slurry application more than 20% of slurry derived carbohydrates had entered the very surface soil, however, 1 day later, less than one fourth could still be recovered, because they have been eaten-up or leached or both. The MRTs averaged 30 h for dung-derived arabinose and 91 d for xylose, respectively (Glaser, 2005; Sauheitl et al., 2005). Also 13CO pulse labeling of wheat revealed that glucose, the major metabolite 2 in the first exudates released from the root after labeling, dissipated rapidly with an MRT of about 20 h (recalculated from Derrien et al., 2004). This is of similar magnitude as found for the majority of carbohydrates in abovementioned laboratory incubation studies (Derrien et al., 2007; see also Section 4.1.2). The carbohydrates produced in situ have significant longer residence times in soil. Computing the compound-specific MRT data available showed that for the plant-derived pentoses xylose and arabinose as well as for the microbe-derived hexoses rhamnose, mannose, and galactose (fucose not evaluated) MRT increased from 3 to 28 years with increasing time after vegetation change (Fig. 6). Hence, similar to the summary of data for lignin, aliphatic C, and PLFAs, MRT increased nonlinearly with increasing time

Pentoses Hexoses

60 50

r² = 0.83 (exponential rise to maximum) r² = 0.95 (sigmoidal development)

40 30 20 10 0 0

5 10 15 20 Time after crop labelling (years)

25

Figure 6 MRT of microbial hexoses and plant-derived pentoses as a function of the duration of cropping with new 13C label (free-air CO2 enrichment or C4-maize after wheat; data from Derrien et al., 2006; Glaser et al., 2006). Curve fitting such as exponential rise to maximum was performed with SigmaPlot for Windows versus 9.0, with y = MRTmax(1–exp{–bx}), and maximum MRT (MRTmax) = 27.7 years, and b = 0.101 years1).

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after crop conversion. Again it must be assumed that similar to the other compounds also carbohydrates persist in soils in at least two pools, a labile and a stable one (see also Derrien et al., 2007). The MRT of total carbohydrates assessed via GC-c-IRMS was not different to that obtained from pyrolysis GC measurements (data of Derrien et al., 2006 and Gleixner et al., 2002, combined in Fig. 7, see below). The much higher MRT found for total carbohydrates than for the individual ones reported in Fig. 7 was mainly due to high MRT of glucose attached to minerals (Derrien et al., 2006; glucose was not included in Fig. 6 because of its unspecific origin). The MRT of the other carbohydrates tended to decrease in the order pentoses, hexoses > bacterial amino sugars and pentoses > glucosamine common in fungi (Fig. 7, see below). This supports abovementioned findings from d13C-PLFA analyses: (1) the fate of microbialderived carbohydrates is tightly coupled to that of their plant-derived precursors, and (2) fungi appear to utilize primarily fresh C sources in soil (Fig. 5). 4.2.2.3. Proteins and other The fate of hopanoids and tetraether lipids, specific microbial proteins or D-amino acids or other more refractory microbial cell compounds has not yet been assessed utilizing chronosequence studies with C3/C4 vegetation changes to our knowledge. There are a few indications, however, that especially the N forms in soil may be pretty persistent. Gleixner et al. (2002) assigned soil proteins an average MRT of 54 years from d13C abundance analysis in specific GC-pyrolysis products of the Boigenville and Halle plots (see also review of Flessa et al., 2008). Bol et al. (2002) used the d15N signature of different amino acids as indicators of ancient management in the bronze ages. Amelung et al. (2006) concluded from the detection of racemized amino acids in biotic

Total organic carbon Alkanoic acids n-Alkanes Proteins Lignin PLFA gram− PLFA gram+ Total saccharides Bact. hexosamines Glucosamine Hexoses Pentoses 0

100 200 300 Mean residence time (years)

400

Figure 7 Box plots of MRT of major soil biomarkers and respective soil organic carbon (sites listed in Table 3). The black points are outliers beyond the 10th/90th percentiles.

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environments that significant parts of the soil protein pool are not seen by the soil microbial community and preserved several decades if not even longer (see also below, and Amelung, 2003).

4.3. Ageing phenomena Artificial labeling experiments followed by compound-specific stable isotope analyses did not take longer than several weeks or months. Also observed C3/C4 vegetation changes never exceeded a century. Chemical stabilization processes, however, are thought to extend to millennia (e.g., Buyanovski et al., 1994; Jenkinson and Rayner, 1977). Hence, none of the described methods mentioned above have succeeded in elucidating the fate of SOM in the very long-term run. Bol et al. (1996) and Huang et al. (1999) reported that radiocarbon ages of n-alkanes reached 10,000 years in British upland soils with aquic moisture regime. Assuming that there was no fungal resynthesis of these structures from old C remains, the authors therewith discovered a truly passive SOM fraction within these mainly anaerobic soils. In many other cases, however, microbes are able to utilize old C sources, even fossil C has been recovered in soil PLFA isolates (Flessa et al., 2008; Kramer and Gleixner, 2006). Hence, compound-specific radiocarbon dating can only trace true storage time of organic molecules in soil, when the biomarkers have not been produced from old C sources. To determine the true storage time of biomarkers, a method is required that indicates in situ ageing. For proteins and oligopeptides this might be achieved from amino acid racemization assessment. With increasing age and thus storage of dead proteins in the environment, the L-amino acids are inverted into their D-form until equilibrium is reached (Bada, 1985; see also Section 2). Indeed it was found that the D/L ratio of amino acids increased in British upland soils with increasing radiocarbon age (Amelung, 2003; Amelung et al., 2001). The D-content of alanine increased with radiocarbon ages, because of the production and preservation of the bacterial biomarker D-alanine. Thus, the D/L ratio of alanine was affected by microbial activity, but that of lysine was not (Fig. 8). Hence, D-lysine is a promising age marker. Compound-specific d13C analyses helped for better assignment of the identity of D-lysine because only biological D-amino acid formation but not abiotic racemization reactions result in significant isotope discrimination reactions. And in contrast to the enantiomers of aspartic acid, the natural d13C abundances of D- and L-lysine were not significantly different from another, thus giving support to the hypothesis that D-lysine was formed by slow abiotic racemization from its L-amino acid counterpart (Amelung, 2003). The existence of such racemization reactions indicates that SOM may be conserved for centuries, despite N deficiency frequently occurring in living terrestrial environments.

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D-amino acid (% L-enantiomer)


22 20 18 16 14 12 10 8 6 4 2 0

Alanine (gleysol) Lysine (gleysol) Alanine (spodosol) Lysine (spodosol)

r ² = 0.95

0

2000

4000

6000

8000

10000

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Radiocarbon age (years) Figure 8 Relationship of the D/L ratio of alanine and lysine with radiocarbon age of soil organic matter in British upland soils (redrawn from data of Amelung, 2003; Amelung et al., 1999c; Bol et al., 1996).

4.4. Fate of individual SOM compounds: A comparative synthesis Artificial labeling has only been conducted for a limited period of time. Incubation experiments in the laboratory usually had to stop after a few weeks to months, and the FACE studies have an observatory scale of almost one decade. (e.g., Bock et al., 2007). The natural labeling on the basis of C3/C4 vegetation change currently date back to one century (e.g., Lobe et al., 2005). Hence, there is a principle limitation on using stable isotope techniques for elucidating chemical stabilization processes in the very longterm (up to millennia) (see also Sections 2.2.3 and 4.3). The maximum MRT detected by the stable isotope approach was 350 years for some nalkanes (Poirier et al., 2006) and 380 years for bulk SOM (Kramer and Gleixner, 2006) in the very surface soil. Such long MRTs are already biased by the input of lignite and black C into the long-term research trials, esp. at Halle and Bad Lauchsta¨dt, Germany. Both lignite and black C partly deriving from fossil fuel combustion are significantly depleted in 14C and exhibit a d13C signature of the former C3 crops (e.g., Brodowski et al., 2007; Kramer and Gleixner, 2006; Laskov et al., 2002). If corrected for such inputs, MRT of bulk SOM also for the Halle SOM ranged between 100 and 160 years (Flessa et al., 2008; Rethemeyer et al., 2004) and was thus similar in magnitude to that found for Ap horizons of the US grasslands, for instance (Paul et al., 1997). Compared with the MRT of bulk SOC, that of the individual SOM constituents was generally of smaller to similar magnitude. Only for a few

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exceptions the MRT of the biomarkers was higher (Fig. 7; see outliers for n-alkanes). The outliers may be due to the recycling or contribution of lignite-derived C, but even the MRT of the detected compounds never exceeded 400 years. Hence, on the basis of compound-specific stable isotope analyses within individual biomarkers, we cannot support the existence of an inert SOM pool. There is not even any indication that selective preservation of organic molecules is a relevant mechanism of SOM dynamics in the long-term run (centuries to millennia), except perhaps for an observed accumulation of black C (Brodowski et al., 2007; Flessa et al., 2008; Marschner et al., 2008). However, also for black C organo-mineral interactions might contribute to its stability in soil (Brodowski et al., 2006). In line with these arguments, Fig. 7 reveals that the range of MRT found currently for carbohydrates was similar in magnitude to the turnover of lignin-derived phenols and alkanes, despite the latter structures being significantly more recalcitrant from the chemical point of view. We may therefore also conclude that chemical recalcitrance is not decisive for the medium-term fate of SOM (decades to centuries). The above-mentioned conclusion agrees with earlier suggestions that mainly the soil minerals and their aggregation control the fate of SOM in the mineral soil (e.g., Amelung et al., 1997; Christensen, 1996; Haider, 1992; Six et al., 2000; von Lu¨tzow et al., 2006). And indeed, all studies that differentiated between SOM bound to minerals and particulate SOM of the sand fractions confirmed that turnover rates increased as particle size decreased, that is, in the order coarse sand < fine sand < silt < clay (Derrien et al., 2006; Glaser et al., 2006; Queńeá et al., 2006; ). The MRT for particulate organic matter of the coarse sand fraction, known to consist mainly of fresh plant fragments with little degree of microbial alteration (Amelung et al., 1998, 1999a; Guggenberger et al., 1994) ranged from 4 to 13 years for different carbohydrates for instance, but reached 30 to 100 years for glucose and microbial sugars in the fine mineral fractions (Bock et al., 2007; Derrien et al., 2006; Glaser et al., 2007). Different mineralogy in different soil types may control the first ratelimiting release of SOM for biological use (Kemmitt et al., 2008). Indeed, the amount of old C used by different microorganisms varied for different soil types (Table 3). Unfortunately, there are only very few studies that compared the turnover of individual biomarkers under different site conditions. Only Heim and Schmidt (2007) reported that dissipation of lignins in arable soil (MRT 9–38 years) were less rapid than in grassland (MRT 5– 26 years). However, these findings were affected a the lower sampling depth in the grassland (10 vs. 30 cm), exhibiting higher microbial activity but lacking dilution with deeper soil materials by ploughing. Hence, assessing the rates of SOM genesis and transformation for different soil orders and major land use practices still warrants urgent attention.

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It is obvious, that for different biomarkers analyzed, MRT increased with increasing time after C3/C4 crop conversion. We even observed here that MRT increased nonlinearly as a function of the time after vegetation change (Figs. 4–6). We attribute this finding to non-steady-state conditions of the added C4-C. The system, itself, may be in steady-state, because C contents do hardly change when wheat is replaced by maize, for instance, however, the new C4-C added is mainly added as root debris. This debris decays with time. Some parts dissipate according to the equations outlined in Section 3. However, decomposition is not complete. Parts of the fast decomposing debris are released into dissolved organic matter that is trapped by minerals (Guggenberger and Kaiser, 2003; Guggenberger et al., 1998), other parts become physically protected in aggregates (e.g., Golchin et al., 1994; Ladd et al., 1993; Six et al., 2000). Also modeling of compoundspecific lignin and carbohydrate dynamics assumed that respective stable C subfractions of lignin and carbohydrates are formed (Derrien et al., 2007; Rasse et al., 2006). Whatever the underlying mechanisms are, first a medium turnover and at the very end even slow turnover C4-C pools are formed from the newly added fast turnover C4 vegetation inputs (Fig. 9).

C3

t0 = 0

t1 = t

t2 >> t1

δ C = −26 ‰

δ C = −12 ‰

δ C = −12 ‰

13

δ13C = −26.0 ‰

C4

13

δ13C = −22.9 ‰

C4

13

δ13C = −18.2 ‰

Time Key:

= Fast turnover C pool

= Medium turnover C pool

= Slow turnover C pool

Figure 9 Conceptional figure of stable pool formation after C3/C4 vegetation change: Fresh root debris dissipates with the kinetics of the fast turnover C pool (t0 < t). Simultaneously, a medium turnover pool is formed (t1 = t). As cropping time increases, there is now increasing decomposition from this stable pool (t2 t1), that is, the overall MRT increases. This increase of MRT extends as long as also the formation of slow turnover C4 pools has reached steady-state equilibrium conditions.

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This formation of medium- turnover and slow turnover C4-C pools may take years to decades. As time after crop change increases, there is thus an increasing contribution from the medium turnover and slow turnover C pools to MRT, that is, MRT increases as well. It increases as long as the reformation of the slow turnover pools is equivalent to its losses, that is, as long as the content of slow turnover C4-C pool will also reach steady state. Therefore, we have to conclude that almost none of the systems where C3/ C4 vegetation changes have been studied so far, overall steady-state conditions have been reached for C4-C. This is simply because the formation of stable C pools with low MRT has not yet been completed since the experiments started. As a result, the true MRTs are underestimated. A MRT comparable to 14C analyses will not be reached (see also Boutton et al., 1998). Perhaps the true MRT will never be assessed for both the 13C and 14C methods, because nonextractable bound-residues may always escape the analytical window, with unknown contribution to bulk turnover rates. Nevertheless, the results presented in Figs. 5 and 6 suggest that for carbohydrates and PLFAs, and possibly also for lignin, after about 30 to 40 years of cropping calculated MRT approached a maximum. Hence, when C3/C4 vegetation changes occurred more than 30–40 years ago, at least for these compounds reasonable estimates of MRT are achieved.

5. Conclusions and Perspectives Compound-specific stable isotope analyses in major biomarkers have enabled soil scientists to identify the microorganisms that control the fate and genesis of SOM, to elucidate their ecological niches and vulnerability against environmental changes, and to reconstruct and quantify the transformation and turnover of C in soil beyond the microbial life cycle. The summary of currently available data suggests that fungi frequently play a major role in initial substrate utilization, whereas gram-positive bacteria significantly feed also on older SOM. Frequently, there is a direct coupling of microbial performance to substrate inputs. The added SOC is recycled, new short-chain aliphatic substances and carbohydrates are formed and stabilized, that is, these structures reveal an apparently high MRT in soil despite their chemical lability. In contrast, other structures that cannot be resynthesized by bacteria and fungi, such as lignin, have much shorter residence times than may be expected from their recalcitrant nature. In some soils, fossil C inputs remained, such as from lignite dust or from the incomplete combustion of fossil fuels; in these cases calculations of MRTs may be erratic. Usually, the MRTs of different biomarkers are shorter than that of bulk SOM. In no case a biomarker MRT exceeded several hundred years.


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Hence, no compound could be detected that may explain the very longterm turnover times of chemically stabilized SOM. It is concluded that the hypothesis of selective preservation must be refuted, and is not a significant process in mineral soils. In contrast, bound residue formation adds to bulk MRT in a yet unresolved manner. The processes are likely influenced by soil type, but current data base is too small to defend or refute this assumption. Surprisingly, the MRTs of all compounds tended to increase with increasing duration after last C3/C4 vegetation change. We attribute this result to non-steady-state conditions of the new C4-derived C added. Current data suggest that it requires at least 30–40 years until medium to slow turnover C4-C pools are formed in C3 soil or vice versa. During this period, the newly formed stable carbon pools increasingly contribute to bulk SOM turnover assessment. Hence, unless full steady-state conditions are reached, the calculated MRTs are probably too short, and SOM turns over less rapidly than currently assessed in field incubations and short-term chronosequence studies. Therefore, we urgently support the appeal to establish and maintain long-term agricultural field experiments! Our study examining the current developments in characterizing SOM by combining biomarkers with stable isotope analyses represents like all reviews the ‘‘state of the art’’ at this time. Our aim was to highlight the potential of this combined approach for soil studies, as well as the excitement which these novel applications are providing to soil and environmental research, further strengthened by the continued dynamic and rapid development in this research area. In this perspective Section, we simply aim to highlight and comment on a few of such developing applications to point to some additional studies beyond the current scope of this review. This review focused on the combined use of biomarkers and compound specific d13C methods, as this is the most applied technique in soil and environmental studies (see Glaser, 2005; Medeiros and Simoneit, 2007). Recently, however more and more studies combine biomarkers with compound specific d15N (Berg et al., 2007; Bol et al., 1998, 2002, 2004), or dD (Krull et al., 2006; Sachse et al., 2006) or d32S, d33S, d34S, or d36S stable isotope analysis (Ono et al., 2006; Sephton and Gilmour, 2004), although for mentioned sulphur isotopes this is still limited to rocks or extraterrestrial materials. Compound-specific d18O analyses are still in the fledgling stages. Another area of development is the linkage of compound specific 13C and 14C measurements providing more detailed information on the source and residence time of individual biomarkers in soils (Kramer and Gleixner, 2006; Rethemeyer et al. 2004, 2005). However, it can be difficult and time consuming to obtain enough C for such 14C analysis. An alternative is to isolate a ‘‘whole group’’ of specific biomarker compounds, for example, aliphatic hydrocarbons (Bol et al., 1996; Huang et al., 1996, 1999), which

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can provide sufficient sample for 14C analysis. Again, when combined with the individual compound specific d13C analysis this can provide novel information on the sources and rates of processes that form the SOM. And if combined with racemization assessment, possibly even true storage times of proteins can be distinguished from their average cycling rates. Position-specific d13C variations within (biomarker) molecules, that is, the 13 d C value of individual C positions within a single simple molecule, can vary by up to 20 to 25% (Brenna et al., 1998; Hobbie and Werner, 2004; Sacks and Brenna, 2003; Schmidt and Gleixner, 1995; Schmidt et al., 1998), and hence provides additional information of the metabolic processes and origin of such molecules in soils. Similarly, large position-specific isotope differences (up to ca. 40%) have been observed for d15N of various N-groups in arginine and other amino acids (Sacks and Brenna, 2005; Schmidt et al., 1998). Although, it remains unclear in soil systems how biological isotope fractionations relate to more ‘‘classic’’ (Galimov, 1985) or modern thermodynamic concepts ( Joergenson, 2006), a new ‘‘isotope-based’’ tool box is available to help improving our understanding of the role and functioning of the microbial community and its food-web structure, as well as microbial proteins, DNA, RNA dynamics in soils. For example, there are novel isotope based separations (e.g., SIP; Buckley et al., 2007; Bull et al., 2000), isotope tracing/tagging techniques (isotope-coded protein tags, Schmidt et al., 2005; fluorescence in situ hybridization-microautoradiography (FISH); Ginige et al., 2005; Wagner et al., 2006), and isotope labeling approaches (Neufeld et al., 2007). Potential exists to link the above methods to compound specific isotope techniques other than GC, for example, irm-LC-MS; (Krummen et al., 2004), nano-SIMS (Godin et al., 2005; Herrmann et al., 2007) and laserablation IRMS (Passey and Cerling, 2006; Schulze et al., 2004), and to relate the isotopic information to spatio-temporal soil properties, such as obtained from imaging approaches for soil pore space using 129Xe nuclear resonance sprectroscopy (Filimonova et al., 2004, 2006) or X-ray microtomography (Nunan et al., 2006; O’Donnell et al., 2007), or from two-dimensional microand nano-scale observations using synchrotron-based Fourier-transform infrared (FTIR) and near-edge X-ray absorption fine structure (NEXAFS) spectroscopy (e.g., Lehmann et al., 2007). Clearly, we can be positive about future research combining biomarkers with stable isotope analyses techniques to dig deeper in how organic matter is formed, stabilized, and turned over in soils.

ACKNOWLEDGMENTS We thank D. Derrien, C. Moedl, A. Preger, L. Schwark, G. Welp, and G. Wiesenberg for helpful discussions. Parts of this work have been supported by the German Research Foundation.


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Glossary Artificial labeling labeling with source materials (generally enriched) in 13C, that is, values outside the natural range (ca. + 10 to 80%) Natural abundance labeling labeling using source materials within the natural range of 13C values Biomarker organic compound with a defined structure indicative of its producer. It may represent a larger group of molecules in living or dead organisms BPCAs benzene polycarboxylic acids; B3CAs = S hemimellitic, trimellitic, trimesic acids; B4CAs = S pyromellitic, prehnitic, mellophanic acids; B5CA = benzene pentacarboxylic acid; B6CA = mellitic acid C3 plant during photosynthesis in a C3 plant, CO2 is initially fixed into a 3-C compound called 3-phosphoglyceric acid (3-PGA), a reaction catalyzed by rubisco. Most plants are C3 plants C4 plant during photosynthesis in a C4 plant, CO2 is initially fixed into a 4-C compound (malic or aspartic acids), a reaction catalyzed by PEP carboxylase, 1–5% of plants are C4 plants, but they include important crop species: maize, sugarcane, and sorghum CAM plant can behave as C3 or C4 plant during photosynthesis, depending on CAM species, but at night behave as C4 plants. About 10% of plants are CAM, and they are mainly succulent plants C3 soil describes a soil that has been continuously under vegetation comprising C3 plants for a given period (e.g., years to decades) C4 soil describes a soil that has been continuously under vegetation comprising C4 plants for a given period (e.g., years to decades) Ci cinnamyl phenolic CuO oxidation products (p-coumaryl, ferulic acid) CSIA compound specific stable isotope analysis CT condensed tannins (proanthocyanidins)

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EA elemental analyzer used to determine the elemental composition (more precisely empirical formula) of a pure organic compound by combusting the sample under conditions where the resulting combustion products can be quantitatively analyzed FACE free air CO2 enrichment FAME fatty acid methyl ester GC gas chromatography is a chemical analysis method for separating individual compounds in a complex sample, where the mobile phase is a gas GDGTs glycerol dialkyl glycerol tetraethers HT hydrolyzable tannins (gallotannins and ellagitannins) IRMS isotope ratio mass spectrometry (IRMS) is a field of mass spectrometry concerned with measuring the relative abundance of atomic isotopes LA laser ablation the process of removing material from a solid (or occasionally liquid) surface by irradiating it with a laser beam LC liquid chromatography is a chemical analysis method for separating individual compounds in a complex sample in which the mobile phase is a liquid MB methane-oxidizing bacteria MALDI-TOF MS matrix assisted laser desorption/ionisation-time-of-flight mass spectrometer, a sophisticated technique that is very effective in the analyses of large biopolymers, such as proteins and tannin MRT Mean residence time, calculated as reciprocal of the rate constant (1/k). With this definition, MRT is 44% longer (by a factor of 1/ln2) than dissipation half life MS mass spectrometry is an analytical technique used to measure the mass-tocharge ratio of ions, as measured by a mass spectrometer. It is most generally used to find the composition of a physical sample by generating a mass spectrum representing the masses of sample components


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PAH polycyclic aromatic hydrocarbon PLFAs phospholipid fatty acids. These compounds are part of the microbial cell membrane and decay immediately after the organism dies. The PLFA composition varies among microorganisms. This makes them the most widely used biomarkers for characterizing the living structural microbial diversity in soil Py pyrolysis is a chemical decomposition of organic materials by heating in the absence of oxygen or any other reagents. It is used in soil studies to break down complex matter into simpler molecules for identification, for example, by pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) S syringyl phenolic CuO oxidation products (syringaldehyde, acetosyringaldehyde, syringig acid) SIMS secondary ion mass spectrometry is a technique where a solid sample is sputtered by primary ions of few kilo electron volts energy, a fraction of the particles then emitted from the solid sample target are ionized. These secondary ions can be analyzed with a mass spectrometer. Secondary ion emission by a solid surface under ion bombardment supplies information about the elemental, isotopic, and molecular composition of its uppermost atomic layers SIP stable isotope probing, an isotope labeling technique which relies on the fact that DNA and RNA labelled with 13C in an active organism can be distinguished from the unlabeled one. SOC soil organic carbon SOM soil organic matter TG-DSC-QMS-IRMS mass spectrometer-thermal balance system to enable thermogravimetry differential scanning calorimetry (TG-DSC) coupled on-line to isotope ratio mass spectrometer (IRMS) and quadrupole mass spectrometer (QMS) for the simultaneous determination of 13C stable isotope, gas analysis, mass balance and energy change for volatile minerals and soil carbon materials V vanillyl phenolic CuO oxidation products (vanillin, acetovanillone, vanillic acid)

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Index

A Accelerator mass spectrometers (AMS), 17 Aeolian input, significance of, 36–37 Agricultural innovations, 3 Aliphatic compounds, SOM cutan and suberan, 172 ester-bound biopolymers cutin and suberin, 172 extractable lipid, 171–172 specific plant biomarkers, 172–173 Aluminosilicates, nanoparticles, 127 Antibiotic sensitivity assays, 103–104 Arabidopsis ddm1, 65 Arabidopsis Medea (MEA) locus, 66 Atmospheric dusts, 36 Atomic absorption (AA) spectroscopy, 28 Atomic force microscopy (AFM), 132–133 B Bacterial assemblages biofilm interactions, 99–100 microbial distribution, 94 10 Be inventory, in soils, 20 B-I alleles, 67 Biofilm interactions bacterial assemblages, 99–100 bacterial movement, 101 cellulose-matrix type, 105 physical features, 101–102 Pseudomonas aeruginosa studies, 102–104 surface-active agents/biosurfactants, 105–106 Biomarkers black carbon (BC), 180 compound-specific stable isotope analyses ageing phenomena, 216–217 field studies, 201–216 incubation studies, 189–201 individual SOM compounds, 217–220 dead microbial biomass amino sugars and soil proteins, 178–180 hopanoids and tetraether lipids, 177–178 living microbial biomass ergosterol, 175 phospholipid fatty acids (PLFA), 175–176 multiple origin carbohydrates, 173 steroids and bile acids, 174

plant-derived C aliphatic compounds, 170–173 lignins, 169–170 tannins, 170 sources, 160–168 Biosurfactants, 105–106 Bisulfite sequencing, 73 Black carbon (BC), 129–131 Borlaug–Ruan International Internship program, 11–12 Borlaug’s new wheat varieties, 6 Brassica polyploids, 69 Brownian motion/diffusion, 101–102 C 13

C analysis, SOM arctic tundra, 198 C3 and C4 pools decomposition, 199 cultivable methane-oxidizing bacteria (MB), 197 environmental conditions, 199 flow chamber, 197 incubation time, 200 labeling experiments, 199–200 microorganisms response, 197–198 pollutants, 198 population, 196 SOM genesis and transformation, 198–199 Carbon isotopes, SOM analytical techniques complicated IRMS techniques, 183 elemental analyzer (EA) and gas chromatograph, 182 isotope ratio mass spectrometry (IRMS) methods, 181 artificial labeling techniques, 184–185 isotope fractionation and tracing, 183–184 natural labeling techniques bulk soil C change, 187–188 13 C labeling tracer approach, 185–186 photosynthetic pathway types, 186 soil carbon replacement, 186 steady state condition, 187 turnover time, 187–188 stable isotopes and their measurement units, 180–181 C dynamics ecosystem, 97–98 Cheaters, 93

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252

Index

Chemical weathering, 16 Chemotaxis, 101 Chernobyl nuclear accident, 21 Chromatin structure, 63–65 Chromomethylases (CMT), 61 clark kent alleles, 68 Colloidal translocation, 42 Colloids, 125–126 Colluvial flux (Qs), 38 Complex adaptive system (CAS) extracellular polysaccharides (EPS) production, 110–111 functional consequences, 111–112 physical and biological processes, 109–110 Compound-specific stable isotope analyses, SOM ageing phenomena, 216–217 D/L ratio, 216–217 field studies isotope labeled fertilizers, 202–206 turnover of microbial-derived biopolymers, 210–216 turnover of plant-derived biopolymers, 207–210 incubation studies aliphatic compounds, 189, 196 carbohydrates, 196 dead microbial biomass, 200–201 tracing of living biomass by 13C analyses, 196–200 individual SOM compounds artificial labeling, 217 C4-C pools, 220 mineralogy, 218 stable pool formation, 219 vs. MRT, 217–218 Concentration of atmospherically derived Pb (Pbatm), 25 Congressional Gold Medal, 2, 13 Contaminants inputs to soils, 29–30 lead pollution, 30–32 SCPs, pollution history of, 32–34 Cornfields of Iowa, 6 CpG and CpHpG methylation, 61 CpHpG DNA methylation, 65 Crystal nucleation theory, 140–141 D 14

C dating methods, 20 DEMETER (DME), 66 Depression era relief program, 4 Digital elevation model (DEM), 38 DNA glycosylase enzyme, 66 methyltransferase enzymes, 61 Domains rearranged methyltransferases (DRM), 61 DuPont Corporation, 5

E Enantiomers, 179 Environmental nanoscience, 148–149 Epialleles, and gene discovery, 73 Epidemics ecosystem, 98–99 Epigenetic inheritance basis of, 60 and crop improvement, 71–73 phenotypic examples of, 66 epigenetic alleles, 68 imprinting, 66–67 nucleolar dominance, 69 paramutation, 67–68 polyploid formation, 69 Epigenetics, 60 natural variation for genic epigenetic variation, 70–71 for methylation of repetitive elements, 70 phenomena in plants, 65–66 in quantitative inheritance and selection response, 72–73 Extracellular polysaccharides (EPS) complex adaptive system (CAS), 110 microbial activity, 108–109 physico-chemical properties, 103 F Facing Starvation, 5, 8 Farm-to-market roads, 4 Father of Green Revolution, 8 Field-effect transistor, 138–139 Forestry, 4 Fossil fuel, 34 Fungi and fungal hyphaes distribution, 94–95 soil–fungal interactions, 95–97 G Galactosamine, 178 General Foods Corporation, 10 Genetically modified crops, 12 Geochemical mass balance, 39 Geochemical tracers, 36 Global Pb production, 31 Green fluorescent protein (GFP)-gene expression, 104 Green Revolution, to Africa, 9 H Heterozygosity, 72 Histone modifications, 62–63, 65 octamers, 60 H3K9 and H3K27 methylation, 65 Humic and fulvic acids, nanoparticles, 127–128

253

Index

Hybridization, 72 Hydroxides, nanoparticles, 127 Hysteresis mechanism, 87–88 I Inductively coupled plasma atomic emission spectroscopy (ICP-AES), 28 Inductively coupled plasma mass spectrometry (ICP-MS), 28 International Maize and Wheat Improvement Center (CIMMYT), 6–7 International Rice Research Institute, Philippines, 7 International Symposium and Youth Institute, 10 Iowa State Capitol, 10–11 Iowa State Teacher’s College, 4 IR-8 rice, 7 L Langmuir isotherm, 23 Lcyc gene, 68 Lead biogeochemical properties of, 22–23 pollution, 30–32 tracer of organic matter dynamics, 45–47 Lichtfouse, 189, 196 Linaria vulgaris, 68 Loss rates, lead in organic layer, 47 M Maize and Wheat Improvement Center, Mexico, 9 Malnutrition, 7 Mass balance model of soil layer, 39–41 of soil pedon, 34–35 Mass fluxes, across boundaries of soil, 34–39 MEA protein, 67 Metal production, Europe and Asia, 30 Methylation, of DNA, 61–62, 65 5-Methylcytosine, 60 Mexico, confronting poverty in, 5–6 Mez1, ZmFie1, and Nrp1, maize genes, 67 Microbial-derived biopolymers neutral and amino sugars microbial recycling, 213 MRT of microbial hexoses and plantderived pentoses, 214 MRT of total carbohydrates, 215 phospholipid fatty acids cropping period, 211 isotope labeling, 210 isotopic shifts, 211 MRT of C, 212 soil depth, 213 proteins and others, 215–216

Microbial distribution and interactions bacterial assemblages functions, 94 complex adaptive system (CAS), 109–111 diffusion processes, 91–92 functional consequences, 111–112 fungi and fungal hyphaes, 94–97 microscale patchiness, 93–94 quorum sensing mechanism, 93 regulatory feedbacks, 107–109 spatial structure, 90–91 structural complexity, 106–107 Microscale patchiness, 93–94 Millennium Development Goals and AGRA, 9 Miracle rice, 7 Miracle seeds, 8 Moisture characteristic (MC) function, 84–85 mop1 gene, 64 Multicollector-inductively coupled plasma mass spectrometry (MC-ICP-MS), 28 Mutations, 65 N Nanominerals definition, 126–127 ferrihydrite role, 143–144 Nanoparticles behavioral science field-effect transistor, 138–139 surface reconstruction, 140 surface relaxation, 139 black carbon (BC) role, 129–131 crystal nucleation theory, 140–141 definition, 125 Earth-surface environment, 127–128 ferrihydrite role, 143–144 mobility, 144–146 oriented attachment, 143 pollutant transport and bioavailability, 146–147 properties, 135–136 stability, hydration and defects, 142–143 Stokes law, 137–138 surface free energy, 141–142 toxicity, 147–148 viruses and bacteria, 129 Young–Laplace equation, 136–137 Nanoscale secondary ion mass spectrometers (NanoSIMS), 133–134 Nano-scale techniques atomic force microscopy (AFM), 132–133 nanoscale secondary ion mass spectrometers (NanoSIMS), 133–134 TEM and scanning transmission electron microscopy (STEM), 132 X-ray absorption spectroscopy (XAS), 134–135 National Medal of Science, 2 NCAA Wrestling Hall of Fame, 3

254

Index

New Green Revolution, 13 Nobel Foundation, 8 Nobel Peace Prize, 2, 8 Norman Rockwell-esque farm, 3 Nucleolar dominance, 69 O Oriented attachment, nanoparticles, 143 P 206

207

Pb/ Pb ratios, 24–25, 44 Persistent organic pollutants (POPs), 49 Phospholipid fatty acids (PLFA), 175–176 Plant-derived biopolymers lignin-derived phenols, 207–208 n-alkanes and n-alkanoic acids bioaccessibility, 209 grassland sites, 210 MRT of aliphatic biomarkers, 209 origin, 208 Plant-derived cycling, 22 Podzolization, 45 Polycomb repressive complex2 (PRC2), 66 Polymerases, 64 Polyploidy, 69 Posttranscriptional gene silencing (PTGS), 64 Posttranslational modifications, to histone tails, 62 Proton induced X-ray emission (PIXE), 28 Pseudomonas aeruginosa biofilm development, 102–103 extracellular polysaccharides (EPS), 103–104 Q Quality protein maize, 9 Quorum sensing mechanism, 93 R Ribosomal RNA genes, in plants, 69 RISC complexes, 64 RNA-directed heritable silencing, 64 Rockefeller Foundation, 5–6 Rust, parasitic fungus, 5 S Sadhu element (At2g10410), 70 Sasakawa Africa Association, 9 SCPs. See Spheroidal carbonaceous particles Secondary ion mass spectrometry (SIMS), 28 Shuttle breeding, 6 Sodium enriched soils, 44 Soil catena, emergence of, 37 Soil erosion, 21, 37 and redistribution, 37–39 Soil erosion rate (E), 34–35, 37 Soil layer, vertical mass fluxes, 39

Soil matrix, tracking tracers in, 24–29 Soil organic matter (SOM) biomarkers black carbon (BC), 180 dead microbial biomass, 176–180 living microbial biomass, 175–176 multiple origin, 173–174 plant-derived C, 169–173 sources, 160–168 carbon isotopes analytical techniques, 181–183 artificial labeling techniques, 184–185 isotope fractionation and tracing, 183–184 natural labeling techniques, 185–188 stable isotopes and their measurement units, 180–181 compound-specific stable isotope analyses ageing phenomena, 216–217 field studies, 201–216 incubation studies, 189–201 individual SOM compounds, 217–220 isotope labeled fertilizers, 202–206 objective, 159 rationale composition analysis, 158 contents, 157 mathematical models, 158 Soils age, 47 biofilm interactions bacterial assemblages, 99–100 bacterial movement, 101 cellulose-matrix type, 105 physical features, 101–102 Pseudomonas aeruginosa studies, 102–104 surface-active agents/biosurfactants, 105–106 bioturbation, 42 C dynamics, 97–98 definition, 82–84 epidemics, 98–99 microbial distribution bacterial assemblages functions, 94 diffusion processes, 91–92 fungi and fungal hyphaes, 94–97 microscale patchiness, 93–94 quorum sensing mechanism, 93 spatial structure, 90–91 microbial interactions complex adaptive system (CAS), 109–111 functional consequences, 111–112 regulatory feedbacks, 107–109 structural complexity, 106–107 mixing rates, 43 moisture characteristic (MC), 84–85 nanoparticles behavioral science, 138–140 black carbon (BC) role, 129–131

255

Index

crystal nucleation theory, 140–141 Earth-surface environment, 127–128 ferrihydrite role, 143–144 mobility, 144–146 oriented attachment, 143 pollutant transport and bioavailability, 146–147 properties, 135–136 stability, hydration and defects, 142–143 Stokes law, 137–138 surface free energy, 141–142 toxicity, 147–148 viruses and bacteria, 129 Young–Laplace equation, 136–137 nano-scale techniques atomic force microscopy (AFM), 132–133 nanoscale secondary ion mass spectrometers (NanoSIMS), 133–134 TEM and scanning transmission electron microscopy (STEM), 132 X-ray absorption spectroscopy (XAS), 134–135 soil–water interfaces, 85–88 surface area, 89–90 water-film thickness, 88–89 Spheroidal carbonaceous particles (SCPs), 19 biogeochemical properties, 23–24 pollution history of, 32–34 tracer of organic matter dynamics, 45–47 Spodosols, 42 87 Sr/86Sr ratio, 20 Stable isotope probing (SIP), 159 Stokes law, 137–138 Strontium isotope technique, 20 SUMOlation, 62 Surface-active agents. See Biosurfactants Surface reconstruction process, 140 Surface relaxation process, 139 T TEM and scanning transmission electron microscopy (STEM), 132 Texas A&M University, 11 Thermal ion mass spectrometry (TIMS), 28 Thermonuclear-bomb-testing, 20 Tracers, tracking in soil matrix, 24

atmospheric lead, separation from geogenic lead, 24–28 counting SCPs, 29 excess concentration (Pbexcess), 26–27 excess Pb and geogenic Pb concentration, calculation, 26 methods for measurement, 28–29 210 Pb originating, determination of, 27–28 Pb (Pbatm) calculation, 25 weak acid extractions of Pb, 27 Tragopogon polyploids, 69 Transmission electron microscopy (TEM), 130–131 Trimethylation of lysine 27, 63 U Ubiquitination, 62 Ultisols, 44 UN Food and Agriculture Organization, 6 University of Minnesota, 4 University of Oslo, 8 U.S. Forest Service, 4 U.S. National Medal of Science, 13 V Vertical mass fluxes, within soil pedon, 39 constraining vertical clay translocation within, 44–45 constraining vertical mixing, 41–44 convection–diffusion models, 40 mass balance model, 39–41 VIM1 gene, 70 W World Food Day, 10 World Food Prize, 2, 9–10 World Food Summit, 12 X X-ray absorption spectroscopy (XAS), 134–135 X-ray fluorescence (XRF), 28 Y Young–Laplace equation, 136–137

Advances in Agronomy, Volume 100

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