V O L U M E S I X T Y ADVANCES IN MARINE BIOLOGY Advances in MARINE BIOLOGY Series Editor MICHAEL LESSER Department of Molecular, Cellular and Biomedical Sciences University of New Hampshire, Durham, USA Editors Emeritus LEE A. FUIMAN University of Texas at Austin CRAIG M. YOUNG Oregon Institute of Marine Biology Advisory Editorial Board ANDREW J. GOODAY Southampton Oceanography Centre SANDRA E. SHUMWAY University of Connecticut V O L U M E S I X T Y ADVANCES IN MARINE BIOLOGY Edited by MICHAEL LESSER Department of Molecular, Cellular and Biomedical Sciences University of New Hampshire, Durham, USA 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 32 Jamestown Road, London NW1 7BY, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands Linacre House, Jordan Hill, Oxford OX2 8DP, UK 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA First edition 2011 Copyright r 2011 Elsevier Ltd. 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. 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ISBN: 978-0-12-385529-9 ISSN: 0065-2881 For information on all Academic Press publications visit our website at elsevierdirect.com Printed and bound in UK 11 12 13 14 10 9 8 7 6 5 4 3 2 1 CONTRIBUTORS TO VOLUME 60 Scott C. France Department of Biology, University of Louisiana at Lafayette, Lafayette, LA, USA Alistair J. Hobday Wealth from Oceans Flagship, CSIRO Marine and Atmospheric Research, Hobart, Tasmania, Australia Brian Helmuth Department of Biological Sciences and Environment and Sustainability Program, University of South Carolina, Columbia, SC, USA Cristián J. Monaco Department of Biological Sciences and Environment and Sustainability Program, University of South Carolina, Columbia, SC, USA John C. Montgomery Leigh Marine Laboratory, University of Auckland, New Zealand Brian Palenik Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA Eric Pante Department of Biology, University of Louisiana at Lafayette, Lafayette, LA, USA David A. Ritz School of Zoology, University of Tasmania, Hobart, Australia Tatiana A. Rynearson Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA Anne Simpson Darling Marine Center, University of Maine, Walpole, ME, USA v vi Contributors Ashley J.W. Ward School of Biological Sciences, University of Sydney, Sydney, New South Wales, Australia Les Watling Department of Biology, University of Hawaii at Mānoa, Honolulu, HI, USA Darling Marine Center, University of Maine, Walpole, ME, USA CONTENTS Contributors to Volume 60 Series Contents for Last Fifteen Years v ix 1. Learning to Read the Oceans: Genomics of Marine Phytoplankton 1 Tatiana A. Rynearson and Brian Palenik 1. Introduction 2. Marine Cyanobacterial Genomics 3. Eukaryotic Genomics 4. Conclusions Acknowledgements References 2 7 14 28 30 30 2. Biology of Deep-Water Octocorals 41 Les Watling, Scott C. France, Eric Pante and Anne Simpson 1. Introduction 2. Classification 3. Phylogenetic Relationships 4. Biogeography 5. Distribution of the Three Major Deep-Sea Families 6. Symbionts 7. Predators 8. Food 9. Reproduction 10. Growth and Age 11. Dispersal 12. Threats and Conservation Issues Acknowledgements References 3. Tipping Points, Thresholds and the Keystone Role of Physiology in Marine Climate Change Research 42 43 48 51 68 82 95 95 96 105 108 109 110 111 123 Cristián J. Monaco and Brian Helmuth 1. Introduction 2. Weather, Climate and Climate Change from the Viewpoint of a Non-Human Organism 3. Physiological Response Curves 124 130 137 vii viii Contents 4. Indirect Effects of Climate Change: Species Interactions and Tipping Points 5. Putting the Pieces Together: Where Do We Go from Here? Acknowledgements References 4. Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 144 146 150 151 161 David A. Ritz, Alistair J. Hobday, John C. Montgomery and Ashley J.W. Ward 1. Introduction 2. Aggregation Principles and Features in Pelagic Ecosystems 3. Technology Breakthroughs in Experimental and Observational Methods 4. Theoretical Developments in Social Aggregation 5. Social Aggregation, Climate Change and Ocean Management 6. Conclusion Acknowledgements References 163 166 192 205 208 211 214 214 Subject Index Taxonomic Index 229 237 SERIES CONTENTS FOR LAST FIFTEEN YEARS* Volume 32, 1997. Vinogradov, M. E. Some problems of vertical distribution of meso- and macroplankton in the ocean. pp. 1 92. Gebruk, A. K., Galkin, S. V., Vereshchaka, A. J., Moskalev, L. I. and Southward, A. J. Ecology and biogeography of the hydrothermal vent fauna of the Mid-Atlantic Ridge. pp. 93 144. Parin, N. V., Mironov, A. N. and Nesis, K. N. Biology of the Nazca and Sala y Gomez submarine ridges, an outpost of the Indo-West Pacific fauna in the eastern Pacific Ocean: composition and distribution of the fauna, its communities and history. pp. 145 242. Nesis, K. N. Goniatid squids in the subarctic North Pacific: ecology, biogeography, niche diversity, and role in the ecosystem. pp. 243 324. Vinogradova, N. G. Zoogeography of the abyssal and hadal zones. pp. 325 387. Zezina, O. N. Biogeography of the bathyal zone. pp. 389 426. Sokolova, M. N. Trophic structure of abyssal macrobenthos. pp. 427 525. Semina, H. J. An outline of the geographical distribution of oceanic phytoplankton. pp. 527 563. Volume 33, 1998. Mauchline, J. The biology of calanoid copepods. pp. 1 660. Volume 34, 1998. Davies, M. S. and Hawkins, S. J. Mucus from marine molluscs. pp. 1 71. Joyeux, J. C. and Ward, A. B. Constraints on coastal lagoon fisheries. pp. 73 199. Jennings, S. and Kaiser, M. J. The effects of fishing on marine ecosystems. pp. 201 352. Tunnicliffe, V., McArthur, A. G. and McHugh, D. A biogeographical perspective of the deep-sea hydrothermal vent fauna. pp. 353 442. Volume 35, 1999. Creasey, S. S. and Rogers, A. D. Population genetics of bathyal and abyssal organisms. pp. 1 151. * The full list of contents for volumes 1 37 can be found in volume 38 ix x Series Contents for Last Fifteen Years Brey, T. Growth performance and mortality in aquatic macrobenthic invertebrates. pp. 153 223. Volume 36, 1999. Shulman, G. E. and Love, R. M. The biochemical ecology of marine fishes. pp. 1 325. Volume 37, 1999. His, E., Beiras, R. and Seaman, M. N. L. The assessment of marine pollution—bioassays with bivalve embryos and larvae. pp. 1 178. Bailey, K. M., Quinn, T. J., Bentzen, P. and Grant, W. S. Population structure and dynamics of walleye pollock, Theragra chalcogramma. pp. 179 255. Volume 38, 2000. Blaxter, J. H. S. The enhancement of marine fish stocks. pp. 1 54. Bergstro€m, B. I. The biology of Pandalus. pp. 55 245. Volume 39, 2001. Peterson, C. H. The ‘‘Exxon Valdez’’ oil spill in Alaska: acute indirect and chronic effects on the ecosystem. pp. 1 103. Johnson, W. S., Stevens, M. and Watling, L. Reproduction and development of marine peracaridans. pp. 105 260. Rodhouse, P. G., Elvidge, C. D. and Trathan, P. N. Remote sensing of the global light-fishing fleet: an analysis of interactions with oceanography, other fisheries and predators. pp. 261 303. Volume 40, 2001. Hemmingsen, W. and MacKenzie, K. The parasite fauna of the Atlantic cod, Gadus morhua L. pp. 1 80. Kathiresan, K. and Bingham, B. L. Biology of mangroves and mangrove ecosystems. pp. 81 251. Zaccone, G., Kapoor, B. G., Fasulo, S. and Ainis, L. Structural, histochemical and functional aspects of the epidermis of fishes. pp. 253 348. Volume 41, 2001. Whitfield, M. Interactions between phytoplankton and trace metals in the ocean. pp. 1 128. Series Contents for Last Fifteen Years xi Hamel, J.-F., Conand, C., Pawson, D. L. and Mercier, A. The sea cucumber Holothuria scabra (Holothuroidea: Echinodermata): its biology and exploitation as beche-de-Mer. pp. 129 223. Volume 42, 2002. Zardus, J. D. Protobranch bivalves. pp. 1 65. Mikkelsen, P. M. Shelled opisthobranchs. pp. 67 136. Reynolds, P. D. The Scaphopoda. pp. 137 236. Harasewych, M. G. Pleurotomarioidean gastropods. pp. 237 294. Volume 43, 2002. Rohde, K. Ecology and biogeography of marine parasites. pp. 1 86. Ramirez Llodra, E. Fecundity and life-history strategies in marine invertebrates. pp. 87 170. Brierley, A. S. and Thomas, D. N. Ecology of southern ocean pack ice. pp. 171 276. Hedley, J. D. and Mumby, P. J. Biological and remote sensing perspectives of pigmentation in coral reef organisms. pp. 277 317. Volume 44, 2003. Hirst, A. G., Roff, J. C. and Lampitt, R. S. A synthesis of growth rates in epipelagic invertebrate zooplankton. pp. 3 142. Boletzky, S. von. Biology of early life stages in cephalopod molluscs. pp. 143 203. Pittman, S. J. and McAlpine, C. A. Movements of marine fish and decapod crustaceans: process, theory and application. pp. 205 294. Cutts, C. J. Culture of harpacticoid copepods: potential as live feed for rearing marine fish. pp. 295 315. Volume 45, 2003. Cumulative Taxonomic and Subject Index. Volume 46, 2003. Gooday, A. J. Benthic foraminifera (Protista) as tools in deep-water palaeoceanography: environmental influences on faunal characteristics. pp. 1 90. Subramoniam,T. and Gunamalai,V. Breeding biology of the intertidal sand crab, Emerita (Decapoda: Anomura). pp. 91 182. Coles, S. L. and Brown, B. E. Coral bleaching—capacity for acclimatization and adaptation. pp. 183 223. xii Series Contents for Last Fifteen Years Dalsgaard J., St. John M., Kattner G., Müller-Navarra D. and Hagen W. Fatty acid trophic markers in the pelagic marine environment. pp. 225 340. Volume 47, 2004. Southward, A. J., Langmead, O., Hardman-Mountford, N. J., Aiken, J., Boalch, G. T., Dando, P. R., Genner, M. J., Joint, I., Kendall, M. A., Halliday, N. C., Harris, R. P., Leaper, R., Mieszkowska, N., Pingree, R. D., Richardson, A. J., Sims, D.W., Smith, T., Walne, A. W. and Hawkins, S. J. Long-term oceanographic and ecological research in the western English Channel. pp. 1 105. Queiroga, H. and Blanton, J. Interactions between behaviour and physical forcing in the control of horizontal transport of decapod crustacean larvae. pp. 107 214. Braithwaite, R. A. and McEvoy, L. A. Marine biofouling on fish farms and its remediation. pp. 215 252. Frangoulis, C., Christou, E. D. and Hecq, J. H. Comparison of marine copepod outfluxes: nature, rate, fate and role in the carbon and nitrogen cycles. pp. 253 309. Volume 48, 2005. Canfield, D. E., Kristensen, E. and Thamdrup, B. Aquatic Geomicrobiology. pp. 1 599. Volume 49, 2005. Bell, J. D., Rothlisberg, P. C., Munro, J. L., Loneragan, N. R., Nash, W. J., Ward, R. D. and Andrew, N. L. Restocking and stock enhancement of marine invertebrate fisheries. pp. 1 358. Volume 50, 2006. Lewis, J. B. Biology and ecology of the hydrocoral Millepora on coral reefs. pp. 1 55. Harborne, A. R., Mumby, P. J., Micheli, F., Perry, C. T., Dahlgren, C. P., Holmes, K. E., and Brumbaugh, D. R. The functional value of Caribbean coral reef, seagrass and mangrove habitats to ecosystem processes. pp. 57 189. Collins, M. A. and Rodhouse, P. G. K. Southern ocean cephalopods. pp. 191 265. Tarasov, V. G. Effects of shallow-water hydrothermal venting on biological communities of coastal marine ecosystems of the western Pacific. pp. 267 410. Series Contents for Last Fifteen Years xiii Volume 51, 2006. Elena Guijarro Garcia. The fishery for Iceland scallop (Chlamys islandica) in the Northeast Atlantic. pp. 1 55. Jeffrey, M. Leis. Are larvae of demersal fishes plankton or nekton? pp. 57 141. John C. Montgomery, Andrew Jeffs, Stephen D. Simpson, Mark Meekan and Chris Tindle. Sound as an orientation cue for the pelagic larvae of reef fishes and decapod crustaceans. pp. 143 196. Carolin E. Arndt and Kerrie M. Swadling. Crustacea in Arctic and Antarctic sea ice: Distribution, diet and life history strategies. pp. 197 315. Volume 52, 2007. Leys, S. P., Mackie, G. O. and Reiswig, H. M. The Biology of Glass Sponges. pp. 1 145. Garcia E. G. The Northern Shrimp (Pandalus borealis) Offshore Fishery in the Northeast Atlantic. pp. 147 266. Fraser K. P. P. and Rogers A. D. Protein Metabolism in Marine Animals: The Underlying Mechanism of Growth. pp. 267 362. Volume 53, 2008. Dustin J. Marshall and Michael J. Keough. The Evolutionary Ecology of Offspring Size in Marine Invertebrates. pp. 1 60. Kerry A. Naish, Joseph E. Taylor III, Phillip S. Levin, Thomas P. Quinn, James R. Winton, Daniel Huppert, and Ray Hilborn. An Evaluation of the Effects of Conservation and Fishery Enhancement Hatcheries on Wild Populations of Salmon. pp. 61 194. Shannon Gowans, Bernd Würsig, and Leszek Karczmarski. The Social Structure and Strategies of Delphinids: Predictions Based on an Ecological Framework. pp. 195 294. Volume 54, 2008. Bridget S. Green. Maternal Effects in Fish Populations. pp. 1 105. Victoria J. Wearmouth and David W. Sims. Sexual Segregation in Marine Fish, Reptiles, Birds and Mammals: Behaviour Patterns, Mechanisms and Conservation Implications. pp. 107 170. David W. Sims. Sieving a Living: A Review of the Biology, Ecology and Conservation Status of the Plankton-Feeding Basking Shark Cetorhinus Maximus. pp. 171 220. Charles H. Peterson, Kenneth W. Able, Christin Frieswyk DeJong, Michael F. Piehler, Charles A. Simenstad, and Joy B. Zedler. Practical Proxies for Tidal Marsh Ecosystem Services: Application to Injury and Restoration. pp. 221 266. xiv Volume 55, 2008. Annie Mercier and Annie Mercier and Annie Mercier and Annie Mercier and Series Contents for Last Fifteen Years Jean-Francois Hamel. Jean-Francois Hamel. Jean-Francois Hamel. Jean-Francois Hamel. Introduction. pp. 1 6. Gametogenesis. pp. 7 72. Spawning. pp. 73 168. Discussion. pp. 169 194. Volume 56, 2009. Philip C. Reid, Astrid C. Fischer, Emily Lewis-Brown, Michael P. Meredith, Mike Sparrow, Andreas J. Andersson, Avan Antia, Nicholas R. Bates, Ulrich Bathmann, Gregory Beaugrand, Holger Brix, Stephen Dye, Martin Edwards, Tore Furevik, Reidun Gangst, Hjalmar Hatun, Russell R. Hopcroft, Mike Kendall, Sabine Kasten, Ralph Keeling, Corinne Le Quere, Fred T. Mackenzie, Gill Malin, Cecilie Mauritzen, Jon Olafsson, Charlie Paull, Eric Rignot, Koji Shimada, Meike Vogt, Craig Wallace, Zhaomin Wang and Richard Washington. Impacts of the Oceans on Climate Change. pp. 1 150. Elvira S. Poloczanska, Colin J. Limpus and Graeme C. Hays. Vulnerability of Marine Turtles to Climate Change. pp. 151 212. Nova Mieszkowska, Martin J. Genner, Stephen J. Hawkins and David W. Sims. Effects of Climate Change and Commercial Fishing on Atlantic Cod Gadus morhua. pp. 213 274. Iain C. Field, Mark G. Meekan, Rik C. Buckworth and Corey J. A. Bradshaw. Susceptibility of Sharks, Rays and Chimaeras to Global Extinction. pp. 275 364. Milagros Penela-Arenaz, Juan Bellas and Elsa Vazquez. Effects of the Prestige Oil Spill on the Biota of NW Spain: 5 Years of Learning. pp. 365 396. Volume 57, 2010. Geraint A. Tarling, Natalie S. Ensor, Torsten Fregin, William P. Goodall-Copestake and Peter Fretwell. An Introduction to the Biology of Northern Krill (Meganyctiphanes norvegica Sars). pp. 1 40. Tomaso Patarnello, Chiara Papetti and Lorenzo Zane. Genetics of Northern Krill (Meganyctiphanes norvegica Sars). pp. 41 58. Geraint A. Tarling. Population Dynamics of Northern Krill (Meganyctiphanes norvegica Sars). pp. 59 90. John I. Spicer and Reinhard Saborowski. Physiology and Metabolism of Northern Krill (Meganyctiphanes norvegica Sars). pp. 91 126. Katrin Schmidt. Food and Feeding in Northern Krill (Meganyctiphanes norvegica Sars). pp. 127 172. Series Contents for Last Fifteen Years xv Friedrich Buchholz and Cornelia Buchholz. Growth and Moulting in Northern Krill (Meganyctiphanes norvegica Sars). pp. 173 198. Janine Cuzin-Roudy. Reproduction in Northern Krill. pp. 199 230. Edward Gaten, Konrad Wiese and Magnus L. Johnson. Laboratory-Based Observations of Behaviour in Northern Krill (Meganyctiphanes norvegica Sars). pp. 231 254. Stein Kaartvedt. Diel Vertical Migration Behaviour of the Northern Krill (Meganyctiphanes norvegica Sars). pp. 255 276. Yvan Simard and Michel Harvey. Predation on Northern Krill (Meganyctiphanes norvegica Sars). pp. 277 306. Volume 58, 2010. A. G. Glover, A. J. Gooday, D. M. Bailey, D. S. M. Billett, P. Chevaldonné, A. Colaço, J. Copley, D. Cuvelier, D. Desbruyères, V. Kalogeropoulou, M. Klages, N. Lampadariou, C. Lejeusne, N. C. Mestre, G. L. J. Paterson, T. Perez, H. Ruhl, J. Sarrazin, T. Soltwedel, E. H. Soto, S. Thatje, A. Tselepides, S. Van Gaever, and A. Vanreusel. Temporal Change in DeepSea Benthic Ecosystems: A Review of the Evidence From Recent TimeSeries Studies. pp. 1 96. Hilario Murua. The Biology and Fisheries of European Hake, Merluccius merluccius, in the North-East Atlantic. pp. 97 154. Jacopo Aguzzi and Joan B. Company. Chronobiology of Deep-Water Decapod Crustaceans on Continental Margins. pp. 155 226. Martin A. Collins, Paul Brickle, Judith Brown, and Mark Belchier. The Patagonian Toothfish: Biology, Ecology and Fishery. pp. 227 300. Volume 59, 2011. Charles W. Walker, Rebecca J. Van Beneden, Annette F. Muttray, S. Anne Böttger, Melissa L. Kelley, Abraham E. Tucker, and W. Kelley Thomas. p53 Superfamily Proteins in Marine Bivalve Cancer and Stress Biology. pp 1 36. Martin Wahl, Veijo Jormalainen, Britas Klemens Eriksson, James A. Coyer, Markus Molis, Hendrik Schubert, Megan Dethier, Anneli Ehlers, Rolf Karez, Inken Kruse, Mark Lenz, Gareth Pearson, Sven Rohde, Sofia A. Wikström, and Jeanine L. Olsen. Stress Ecology in Fucus: Abiotic, Biotic and Genetic Interactions. pp. 37 106. Steven R. Dudgeon and Janet E. Kübler. Hydrozoans and the Shape of Things to Come. pp. 107 144. Miles Lamare, David Burritt, and Kathryn Lister. Ultraviolet Radiation and Echinoderms: Past, Present and Future Perspectives. pp. 145 187. This page intentionally left blank C H A P T E R O N E Learning to Read the Oceans: Genomics of Marine Phytoplankton Tatiana A. Rynearson*,1 and Brian Palenik† Contents 1. Introduction 2. Marine Cyanobacterial Genomics 2.1. Microbial speciation in the marine environment 2.2. Environmental stress responses 2.3. Biogeochemical cycles: nitrogen fixation 2.4. Microbial interactions: sources of marine natural products 3. Eukaryotic Genomics 3.1. Introduction 3.2. Insights into the evolution of marine phytoplankton 3.3. Insights into the structural and functional diversity of eukaryotic marine phytoplankton 4. Conclusions Acknowledgements References 2 7 7 10 11 12 14 14 15 24 28 30 30 Abstract The phytoplankton are key members of marine ecosystems, generating about half of global primary productivity, supporting valuable fisheries and regulating global biogeochemical cycles. Marine phytoplankton are phylogenetically diverse and are comprised of both prokaryotic and eukaryotic species. In the last decade, new insights have been gained into the ecology and evolution of these important organisms through whole genome sequencing projects and more recently, through both transcriptomics and targeted metagenomics approaches. Sequenced genomes of cyanobacteria are generally small, ranging in size from 1.8 to 9 million base pairs (Mbp). Eukaryotic genomes, in general, have a much larger size range and those that have been sequenced range from 12 to 57 Mbp. Whole genome sequencing projects have revealed key features of the evolutionary history of marine phytoplankton, their varied * † 1 Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA Corresponding author: Email: rynearson@gso.uri.edu Advances in Marine Biology, Volume 60 ISSN: 0065-2881, DOI: 10.1016/B978-0-12-385529-9.00001-9 © 2011 Elsevier Ltd All rights reserved. 1 2 Rynearson and Palenik responses to environmental stress, their ability to scavenge and store nutrients and their unique ability to form elaborate cellular coverings. We have begun to learn how to read the ‘language’ of marine phytoplankton, as written in their DNA. Here, we review the ecological and evolutionary insights gained from whole genome sequencing projects, illustrate how these genomes are yielding information on marine natural products and informing nanotechnology as well as make suggestions for future directions in the field of marine phytoplankton genomics. 1. Introduction Planktonic habitats contain some of the most phylogenetically diverse assemblages on earth. A single liter of seawater can contain representatives of Archaea, Bacteria and all major kingdoms of Eukaryotes. The organisms that drive these diverse communities are the marine phytoplankton, comprised of both prokaryotic and eukaryotic species. They all share a common ability to photosynthesize and thus thrive in the upper, euphotic zone of the worlds’ oceans. Beyond photosynthesis and the fixation of inorganic carbon, marine phytoplankton possess a broad diversity of metabolic capabilities through which they also influence biogeochemical cycles. These include the ability to fix nitrogen, converting N2 gas into ammonia (Capone et al., 1997; Moisander et al., 2010), the ability to precipitate silicic acid in seawater into a cell wall impregnated with hydrated glass (Round et al., 1990) and the ability to precipitate calcite scales (Andersen, 2004). By definition, these organisms drift with the oceans’ tides and currents. Some species are motile (Waterbury et al., 1986; Worden et al., 2009), and thus may be able to take advantage of micro-habitats in the water column. Others are able to adjust their buoyancy, allowing them to descend to high nutrient waters, take up inorganic nutrients and then return to surface waters (Villareal et al., 1999). Together, the prokaryotic and eukaryotic phytoplankton generate roughly half of global primary production, support marine food webs, including valuable fisheries and play a key role in the regulation of global biogeochemical cycles (Nielsen, 1952; Strickland, 1965; Field et al., 1998). Currently, there are far more marine cyanobacterial genome sequences available than there are eukaryotic phytoplankton genome sequences, primarily due to differences in genome size and genome complexity. For example, the average cyanobacterial genome size is 2 3 million base pairs (Mbp), although they range up to 9 Mbp (Table 1.1). In contrast, eukaryotic genomes tend to be orders of magnitude larger. Completed genome projects have focused on species with relatively small genomes, ranging Table 1.1 Whole genome sequence summary data of cyanobacterial and eukaryotic marine phytoplankton, including genome size, percent GC content, arrangement of cells (single celled, in filaments or aggregates), ability to fix N2 and accession numbers RefSeq ID Organism Cyanobacterial genomes 58167 Acaryochloris marina MBIC11017 16707 Acaryochloris sp. CCMEE 5410 19291 Calothrix rhizosoleniae SC01 15661 Crocosphaera watsonii WH 0002 54123 Crocosphaera watsonii WH 8501 43697 Cyanobacterium UCYN-A 54675 Cyanobium sp. PCC 7001 59013 Cyanothece sp. ATCC 51142 59973 Cyanothece sp. ATCC 51472 54615 Cyanothece sp. CCY0110 13550 Dermocarpa sp. 0006 43137 Leptolyngbya sp. PCC 7375 54785 Leptolyngbya valderiana BDU 20041 60895 Lyngbya majuscula 3L 54161 Lyngbya sp. PCC 8106 54695 Microcoleus chthonoplastes PCC 7420 Size (Mbp) % GC 8.36 47 Shape Sphere, ellipse Sphere, ellipse Filament 6.24 37.1 1.40 2.83 5.46 5.40 5.88 5.00 8.90 0.09 31.1 68.7 37.9 Coccus 7.04 8.65 36.7 Coccus Arrangement of cells Motility N2 fixation Single GenBank accession no. CP000828 Single Single Unknown Yes Single Yes AADV00000000 Yes CP001842 ABSE00000000 CP000806 Single Single Single Filament 53.9 Filaments 41.1 45.4 Filament Aggregates Yes Yes Yes Yes No AAXW00000000 Anaerobic AAZV00000000 Yes AAVU00000000 ABRS00000000 (continued) Table 1.1 (continued ) RefSeq ID Organism 54171 Nodularia spumigena CCY9414 Prochlorococcus marinus str. AS9601 Prochlorococcus marinus str. MIT 9202 Prochlorococcus marinus str. MIT 9211 Prochlorococcus marinus str. MIT 9215 Prochlorococcus marinus str. MIT 9301 Prochlorococcus marinus str. MIT 9303 Prochlorococcus marinus str. MIT 9312 Prochlorococcus marinus str. MIT 9313 Prochlorococcus marinus str. MIT 9515 Prochlorococcus marinus str. NATL1A Prochlorococcus marinus str. NATL2A Prochlorococcus marinus subsp. marinus str. CCMP1375 58307 54709 58309 58819 58437 58305 58357 57773 58313 58423 58359 57995 % GC 5.32 41.3 Rod AAVW00000000 1.67 31.3 CP000551 1.69 31.1 Oval 1.70 38 CP000878 1.74 31.1 CP000825 1.64 31.3 CP000576 2.70 50 Oval Single No CP000554 1.71 31.2 Oval Single No CP000111 2.40 50.7 Oval Single No BX548175 1.70 30.8 CP000552 1.86 35 CP000553 1.84 35.1 Oval Singles No CP000095 1.75 36.4 Oval Single No AE017126 Shape Arrangement of cells Single Motility No N2 fixation GenBank accession no. Size (Mbp) ACDW00000000 57761 47033 13452 54225 46501 46503 58123 58319 58323 13558 19371 19373 19375 59137 54731 13654 54223 54221 54219 13642 54217 230 55973 57925 13552 Prochlorococcus marinus subsp. pastoris str. CCMP1986 Prochlorococcus sp. UH18301 Prochloron didemni Synechococcus sp. BL107 Synechococcus sp. CB0101 Synechococcus sp. CB0205 Synechococcus sp. CC9311 Synechococcus sp. CC9605 Synechococcus sp. CC9902 Synechococcus sp. Eum14 Synechococcus sp. M11.1 Synechococcus sp. M16.17 Synechococcus sp. MIT S9220 Synechococcus sp. PCC 7002 Synechococcus sp. PCC 7335 Synechococcus sp. RCC307 Synechococcus sp. RS9916 Synechococcus sp. RS9917 Synechococcus sp. WH 5701 Synechococcus sp. WH 7803 Synechococcus sp. WH 7805 Synechococcus sp. WH 8102 Synechococcus sp. WH 8109 Trichodesmium erythraeum IMS101 Trichodesmium thiebautii II-3 1.66 30.8 Oval 1.65 2.28 2.70 2.40 2.61 2.51 2.23 2.50 3.40 5.96 2.22 2.66 2.58 3.04 2.37 2.62 2.43 2.12 7.75 Single Oval Sphere No BX548174 No 54.3 52.4 Coccus 59.2 Coccus 54.2 Coccus 49.2 48.2 60.8 59.8 64.5 65.4 60.2 57.6 59.4 60.1 34.1 Single Coccus Coccus AATZ00000000 ADXL00000000 ADXM00000000 CP000435 CP000110 CP000097 Yes Yes Yes Yes No Coccus Single Yes Coccus Single Yes Coccus Coccus Filament Single Yes Yes Yes CP000951 Anaerobic ABRV00000000 CT978603 AAUA00000000 AANP00000000 AANO00000000 CT971583 AAOK00000000 BX548020 ACNY00000000 Yes CP000393 Yes Yes Filaments, aggregates Filaments (continued) Table 1.1 (continued ) Organism Size (Mbp) Aureococcus anophagefferens Emiliania huxleyi 56.70 167.7 NZ_ACCP00000000 Micromonas pusilla CCMP 1545 NC_013038 Micromonas sp. RCC299 NC_013054 NC_009355 Ostreococcus NC_009375 lucimarinus NC_014426 Ostreococcus tauri NC_014445 NZ_ABQD01000000 Phaeodactylum tricornutum NZ_AAFD02000000 Thalassiosira pseudonana 21.90 20.90 RefSeq ID % GC Shape Arrangement of cells Coccoid Coccoid Single Single 65 Coccoid 64 Motility N2 fixation GenBank accession no. Eukaryotic genomes Single No No Yes No (haploid) Yes No ACCP00000000 Coccoid Single Yes No ACCO00000000 713.20 60 Coccoid Single No No 712.60 59 Coccoid Single No No 732.40 48 Pennate Single No No CP000581 CP000601 CR954201 CR954220 ABQD01000000 727.40 47 Centric Single No No AAFD02000000 Empty cells indicate unknown data or incomplete genome sequencing projects. ACJI00000000 Learning to Read the Oceans: Genomics of Marine Phytoplankton 7 from 12 Mbp (Palenik et al., 2007) up to 57 Mbp (Gobler et al., 2011). The recent availability of marine phytoplankton genome sequences has prompted a wealth of research in diverse fields including ecology, evolution, biochemistry, and biotechnology. We will discuss the new insights gained from genome sequencing of eukaryotes and cyanobacteria separately, but in some cases the same kinds of research questions in marine biology are being addressed, but with different organisms. 2. Marine Cyanobacterial Genomics For two cyanobacterial groups alone, Prochlorococcus and Synechococcus, there are more than 300 (Prochlorococcus) and 500 (Synechococcus) hits in ISI Web of Knowledge to simple keyword searches with the genus name and ‘genome’. This does not include the publications on or using the genomes of other marine cyanobacteria. In NCBI there are currently 54 identified marine cyanobacterial genome projects, completed, in draft, or other stage of progress (Table 1.1). Useful sources for finding the status of marine cyanobacterial genomes or analyses specifically of these genomes are Cyanobase (http://genome.kazusa.or.jp/cyanobase), Cyanobike (http:// cyanobike-community.csbc.vcu.edu/welcome-frame.php) and cyanorak (http://www.sb-roscoff.fr/Phyto/cyanorak/). Other websites also maintain lists of genome projects such as GOLD Genomes Online (http://www. genomesonline.org/) or major genome sequencing centres such as JGI (http://www.jgi.doe.gov/ or http://www.jgi.doe.gov/genome-projects/). Many valuable websites are available for the integrated analyses of microbial genomes such as Microbes Online (http://www.microbesonline.org/) or IMG (http://img.jgi.doe.gov/cgi-bin/pub/main.cgi), but a discussion of these could take up a whole review. Given the nearly overwhelming amount of material being published using cyanobacterial genomes alone, this review will focus largely on the major topics that fascinate marine biologists. These are speciation, stress responses, biogeochemical cycles and organism interactions in the marine environment. 2.1. Microbial speciation in the marine environment The strikingly high abundance of cyanobacteria in marine waters was discovered only in 1979 (Johnson and Sieburth, 1979; Waterbury et al., 1979) and subsequent studies documented the importance of marine Synechococcus to primary productivity and their near-global distribution (Waterbury et al., 1986; Olson et al., 1990a). The understanding and classification of Prochlorococcus, a phylogenetically related ‘sister’ group of 8 Rynearson and Palenik Synechococcus lacking phycobilisomes, followed a similar trajectory after its discovery in 1988 (Chisholm et al., 1988; Olson et al., 1990b). The combination of the tremendous ecological importance of these organisms, the ability to bring some isolates into culture and the ability to characterize their abundance in the field has resulted in marine cyanobacteria being a major model system for understanding microbial diversity and speciation. Two recent reviews summarize the state of Prochlorococcus and Synechococcus biology (Scanlan et al., 2009; Partensky and Garczarek, 2010). Early on, molecular sequence evidence from field samples showed the presence of distinct genetic clusters of marine cyanobacteria (Palenik, 1994; Toledo and Palenik, 1997; Ferris and Palenik, 1998). These clusters sometimes occupied different portions of the water column, suggesting the presence of distinct species with different ecological niches. Due to the ongoing debate on the nature of microbial species, the marine cyanobacterial field has used the term ‘clades’ (Synechococcus) or ‘ecotypes’ (Prochlorococcus). Using single gene markers such as 16S rRNA, ITS, and rpoC, there are approximately 14 of these (Fuller et al., 2003; Ahlgren and Rocap, 2006) that have been given number or roman numeral designations, but new Synechococcus clades are still being described. In Prochlorococcus, two major ecotypes were first described based on their light physiology and ability to occupy either high- or low-light habitats (Moore et al., 1998). Six additional clades were later defined using cultured isolates and genetic evidence (Rocap et al., 2002). The identification of clades has gradually increased with further surveys and the use of powerful new metagenomics techniques (Martiny et al., 2009; Rusch et al., 2010). Marine cyanobacterial species/clades/ecotypes thus have distinct evolutionary histories. Subsequent work has shown that these clades have distinct biogeographical and temporal distributions (West et al., 2001; Toledo and Palenik, 2003; Zinser et al., 2006; Zwirglmaier et al., 2008; Tai and Palenik, 2009; Rusch et al., 2010). These studies suggest that speciation of marine cyanobacteria may be influenced by light, temperature, and nutrients and perhaps by interactions with phages and grazing predators. The point of this slightly lengthy introduction is that the era of genomics and metagenomics arrived in the context of a field describing diversity but with the still inadequate tools to understand it. Genomics and metagenomic approaches provide opportunities to examine the underlying mechanisms of speciation of marine cyanobacteria (and other major bacteria groups) seen in phylogenies built with single genes. These approaches also provide an opportunity to examine the genetic adaptations associated with different clades/ecotypes. To what extent is speciation driven by different gene complements or adaptation of individual genes to environmental conditions (e.g. adaptation of individual proteins to operate at different temperatures)? If the former, what genes differ between clades? What is the role of horizontal gene transfer in speciation? Learning to Read the Oceans: Genomics of Marine Phytoplankton 9 The first genomes of Synechococcus and Prochlorococcus were sequenced by the D.O.E. Joint Genome Institute and Genoscope and published together (Palenik et al., 2003; Rocap et al., 2003; Dufresne et al., 2008). Additional marine Synechococcus and Prochlorococcus genomes have been obtained from divergent clades and different ecosystems (Palenik et al., 2006) and a recent analysis (Dufresne et al., 2008) and review (Scanlan et al., 2009) of 11 Synechococcus genomes have been published. It should be noted that many cyanobacteria described as Synechococcus (e.g. the important cyanobacterial model Synechococcus PCC 7002) are completely unrelated to each other and not typically found in open marine waters. For Prochlorococcus 12 genomes have been completed (Kettler et al., 2007). The result of this wealth of genome studies has been the recognition that the abundant marine unicellular cyanobacteria typically have relatively small genomes ranging from 1.8 to 2.7 MB. Some Prochlorococcus isolates are thought to have a near-minimal genome for a free-living photoautotroph. Prochlorococcus has a core genome (found in all isolates) of about 1250 genes (Kettler et al., 2007). Marine Synechococcus have a larger core genome of about 1570 genes due to the genes required for synthesis, maintenance, and regulation of a phycobilisome used for light harvesting (Dufresne et al., 2008). Accessory genes, those found in some but not all isolates, then comprise another approximately 750 800 genes. These include genes involved in diverse pathways, but regulatory genes and transporters are particularly important. Unique genes are defined as those found only in the genome of a single Synechococcus or Prochlorococcus isolate, although these can be found in genomes outside this cyanobacterial lineage. The number of these genes is highly variable and is thought to be acquired through horizontal gene transfer. The category of unique genes also includes some ‘over-predicted’ or mis-identified genes, e.g. open reading frames that do not actually encode a gene at all. Unique genes could also include genes that have arisen through the processes of evolution operating on the genome and represent truly ‘novel’ genes found only in that clade, e.g. where a stretch of DNA acquires functional transcription and translation start sites. However, this is still somewhat speculative. As with other relatively small genomes, marine cyanobacteria have few regulatory genes such as two-component systems involving a histidine kinase sensor and a DNA-binding response regulator. There are approximately 7 15 histidine kinases and 6 18 response regulators in the Prochlorococcus and Synechococcus clades with Prochlorococcus typically having fewer and coastal Synechococcous strains having more (Palenik et al., 2006; Dufresne et al., 2008). Cyanobacteria that are phylogenetically distinct from these marine groups can have 10 times more regulatory genes. This genome information suggests that unicellular marine cyanobacteria have adapted to relatively constant environments that do not require many 10 Rynearson and Palenik rapid response ‘circuits’ or the fine-tuning that would be possible with a more complex regulatory repertoire. Recent genomic and metagenomic studies suggest the importance of horizontal gene transfer for driving cyanobacterial diversity (Kettler et al., 2007; Palenik et al., 2009; Zhaxybayeva et al., 2009). It appears that horizontal gene transfer contributes to the adaptation of isolates to specific environments. For example, phosphate acquisition genes appear to be horizontally acquired by strains in regions of low phosphate (Martiny et al., 2006). More provocative is the suggestion based on bioinformatics analyses of sequenced genomes that horizontal gene transfer has been very frequent between low-light Prochlorococcus and Synechococcus as suggested by many gene families whose phylogenetic relationships do not match the plurality tree of other gene families (Zhaxybayeva et al., 2009). 2.2. Environmental stress responses Little is known about how marine cyanobacteria regulate gene expression in response to changing environmental variables such as nitrogen, phosphate, or micronutrient concentrations. Marine cyanobacteria may regulate many processes similarly to all cyanobacteria or there may be adaptations to the marine environment that have necessitated specific regulatory adaptations. Certainly one of the early insights from whole genome sequences was that marine Synechococcus has a drastically reduced number of two-component regulatory systems compared to the major freshwater model systems Synechococcus PCC7942, Synechocystis, or Anabaena/Nostoc (Palenik et al., 2003), but it is not yet clear if this has resulted in some fundamental difference in regulation of nutrient and other metabolisms. Different cyanobacterial clades show significant differences in gene expression, a topic that has been largely unexplored in the literature on cyanobacteria (Palenik, 2011). To investigate gene regulation in two genetically distinct Synechococcus strains, Synechococcus sp. WH 8102 (clade III) and Synechococcus sp. 9311 (clade I), full genome microarrays have been used, consisting of a mix of PCR amplicons and 70 mer oligonucleotides (Su et al., 2006; Stuart et al., 2009; Tai et al., 2009; Tetu et al., 2009; Thomas et al., 2009). One of the interesting findings from microarray studies is that putatively horizontally acquired genes in Synechococcus are expressed and play important roles in stress responses. When Synechococcus CC9311 is copper stressed, it displays a response similar to osmotic shock, with changes in genes involved in osmolyte synthesis and transport. In addition, two operons of genes that appear to be horizontally acquired are highly upregulated. These may function in binding copper or responding to copper-generated oxidative stress (Stuart et al., 2009). In addition, in all microarray studies to date, many ‘hypothetical’ Learning to Read the Oceans: Genomics of Marine Phytoplankton 11 genes change their expression. These are genes that have no known function and may be found only in one or a few genomes. The high number of ‘hypothetical’ genes in cyanobacterial genomes is both frustrating and humbling since it reflects how far we have to go to understand these organisms. At the same time they will provide active areas of research for new scientists to the field. Prochlorococcus microarrays have also been used to look at gene expression. A light/dark cycle was used to examine the synchrony of gene expression and 1698 genes showed expression (Zinser et al., 2009). This included expression of some high light-inducible genes in response to the day/night cycle. Many known genes involved in stress responses were not expressed confirming their relevance to stress conditions. Nutrient and stress responses (including phage infection) have also been studied using microarrays and are gradually leading to a better understanding of cyanobacterial transcriptional responses to common environmental stresses (Martiny et al., 2006; Steglich et al., 2006; Tolonen et al., 2006; Lindell et al., 2007). 2.3. Biogeochemical cycles: nitrogen fixation Nitrogen fixation is a major source of ‘new’ nitrogen to the marine environment in contrast to the constant recycling of nitrogen that is also occurring. The major contributors to nitrogen fixation are the cyanobacteria, either free living or in symbioses with eukaryotes. Trichodesmium is a filamentous cyanobacterium, which has long been thought to be a major contributor to nitrogen fixation. Darwin is thought to have observed blooms of Trichodesmium while on the voyage of the Beagle, while numerous surveys have documented the abundance and potential nitrogen fixation rates of Trichodesmium in the environment (reviewed in Capone et al., 1997). Molecular phylogenetic studies now suggest that multiple species of Trichodesmium are present, with some of these showing differences in filament and colony morphology. The genome of one Trichodesmium isolate has been sequenced, Trichodesmium erythraeum IMS101 (Genbank accession CP000393), while another, Trichodesmium thiebautii II-3, is in progress. These cyanobacteria have large genomes compared to other cyanobacteria at around 7.75 MB. ISM101 also has a low %GC content at 34%. An overall analysis of the Trichodesmium genome is not readily available, but interesting insights have been obtained using available genome information. Examinations of iron metabolism (Chappell and Webb, 2010), phosphate metabolism (Orchard et al., 2009), and carbon/nitrogen metabolism (Levitan et al., 2010) have been carried out that relied on the availability of genome information. The genome has been used to analyze the proteome of Trichodesmium (Sandh et al., 2011) as well. In this technique, mass spectrometry data 12 Rynearson and Palenik from peptides of an organism of interest are identified using predicted proteins from an available genome. This technique will become increasingly used in the study of marine organisms, especially with the large number of genomes of model organisms becoming available. Largely through the survey work of Zehr and coworkers (most recently Moisander et al., 2010), it has become apparent that unicellular cyanobacteria capable of fixing nitrogen are present in diverse marine environments. These are largely found in two phylogenetically unrelated groups. One group is represented by the species Crocosphaera, while the other, UCYN-A, based on phylogenetic analysis of genes encoding the nitrogenase iron protein (nifH) was thought to be related to nitrogenfixing cyanobacteria of the genus Cyanothece. UCYN-A representatives were until recently more enigmatic and, to date, have yet to be cultivated. Using flow cytometry to sort cells from the UCYN-A type coupled with genomic reconstruction of the sorted population, Tripp et al. (2010) determined that this unicellular cyanobacteria is related to the nitrogenfixing cyanobacteria of the genus Cyanothece. UCYN-A interestingly appears to be only able to use photosynthesis to generate ATP. It thus must largely use fixed carbon it obtains from its environment. This is a fascinating ecological strategy about which we are likely to learn more in the future. The genus Cyanothece itself has been the object of a major genome initiative with multiple genomes being sequenced from both marine and freshwater environments (Welsh et al., 2008). These genomes provide a useful database to understand UCYN-A, but also more broadly the processes required for nitrogen fixation in unicellular cyanobacteria. Unicells typically must temporally regulate nitrogen fixation, an oxygen sensitive process, and photosynthesis which produces oxygen. Some Cyanothece species seem relatively easy to culture and may thus also be of importance for biotechnological applications. The Crocosphaera watsonii WH 8501 genome has been sequenced and is about 6.24 MB (Genbank accession AADV00000000). A second genome Crocosphaera watsonii WH 0002 is in progress. An overall analysis of these genomes has not been published. 2.4. Microbial interactions: sources of marine natural products Many microbes including the cyanobacteria live in close association with other organisms. This has been interesting to marine biologists simply as an important aspect of life in marine habitats, and it has also been of interest because cyanobacteria in such environments often make interesting ‘natural products’. These are secondary metabolites that could mediate interactions with other organisms positively or negatively and have useful potential for the development of new antibiotics, anticancer compounds, Learning to Read the Oceans: Genomics of Marine Phytoplankton 13 etc. (Kalaitzis et al., 2009; Jones et al., 2010; Nunnery et al., 2010). Three major marine cyanobacteria of relevance to this review are Prochloron, Acaryochloris, and Lyngbya which have had their genomes sequenced. These genomes tend to be larger than those of the small unicellular cyanobacteria such as Synechococcus and encode a more diverse suite of secondary metabolites. 2.4.1. Prochloron Prochloron is a relatively large cyanobacterium often existing in symbioses with tunicates (Lewin and Cheng, 1989). It synthesizes chlorophyll a and b as light-harvesting pigments. It is an example of the prochlorophytes, a polyphyletic group of cyanobacteria that have adopted the use of these pigments. The genome of Prochloron has been sequenced, although an indepth analysis of the genome does not yet seem available (Schmidt et al., 2005). Probably one of the most interesting insights from the genome to date has been the description of the biosynthesis of patellamide, a peptide-based natural product. This molecule is cytotoxic but the activity of this molecule is unknown. It could function in the symbioses of this cyanobacterium (Donia et al., 2006). Similar molecules have been found in the Trichodesmium genome (Schmidt et al., 2005; Sudek et al., 2006). 2.4.2. Lyngbya Lyngbya species are filamentous cyanobacteria and have been a rich source of marine natural products. They produce the extracellular sunscreen scytonemin which is a common and widespread indole-alkaloid among cyanobacteria. Some initial results from draft genomes of this organism are beginning to emerge, increasing our understanding of the biosynthetic pathways of the various natural products from Lyngbya and their regulation (Jones et al., 2010). 2.4.3. Acaryochloris marina Acaryochloris marina has been found growing in association with the cyanobacterium Prochloron, eukaryotic macroalgae, and in microbial mats (Swingley et al., 2008). In these environments, light levels are typically low as other organisms have harvested much of the available light. The unique niche of Acaryochloris sp. is that they are able to produce chlorophyll d as a light-harvesting pigment and this allows them to harvest far red light not used by other phototrophs for photosynthesis. Interestingly the genome of Acaryochloris is very large at 8.3 MB (47% GC) with nine single-copy plasmids and an unusual amount of gene duplication. Some potential genes involved in chlorophyll d biosynthesis were found. The genome contains over 170 genes related to those involved in bacterial two-component regulatory systems. This is a large number for a small 14 Rynearson and Palenik unicellular organism and may be involved in precisely regulating interactions with other organisms, although this is only speculation. The potential role of natural products in the interactions of cyanobacteria with other organisms is still obscure. One of the future major impacts of genomics in the field of marine biology will be the unravelling of their function and regulation. Genomics makes possible an understanding of the potential for natural product production, but as seen in the use of whole genome microarrays, a genome also makes possible many new kinds of experiments. Using genome-enabled techniques such as microarrays or proteomics will clearly lead to a better understanding of the regulation of natural product synthesis and its role in symbioses. 3. Eukaryotic Genomics 3.1. Introduction Eukaryotic marine phytoplankton are extraordinarily diverse. These single-celled organisms, or protists, range in size from 1 µm to 1 mm and are comprised of approximately 25,000 described species representing at least four major lineages; alveolates, prasinophytes, haptophytes and heterokonts (reviewed in Falkowski et al., 2004). A fifth lineage, the cryptophyta, can also be abundant in some marine habitats. The deep phylogenetic diversity of eukaryotic phytoplankton contrasts with that of terrestrial plants, which are dominated by a single clade (the Viridiplantae) (Fig. 1.1). Eukaryotic marine phytoplankton include phylogenetic lineages resulting from primary, secondary and tertiary endosymbiosis events. Their modes of energy acquisition span the range from obligate photoautotrophy to mixotrophy. This phylogenetic and physiological diversity translates into a wide range of potential genome sizes, structures, metabolic pathways, life histories and evolutionary relationships, all found within a single teaspoon of seawater. On the one hand, the phylogenetic and physiological diversity within the phytoplankton provides an enormous opportunity to use genomics to better understand both the ecology of aquatic organisms and the evolution of life on earth. On the other hand, this diversity presents significant challenges in terms of deciding which ‘model’ organisms to focus on, identifying genes via homology searches and determining the function of metabolic pathways. Of the four major lineages of eukaryotic phytoplankton, whole genome sequences are currently available for two; the heterokonts (Armbrust et al., 2004; Bowler et al., 2008; Gobler et al., 2011) and the prasinophytes (Palenik et al., 2007; Worden et al., 2009) (Table 1.1). Additional members of the heterokont and haptophyte lineages are in the Learning to Read the Oceans: Genomics of Marine Phytoplankton 15 Figure 1.1 Phylogenetic diversity of eukaryotic organisms. Dotted lines are used where branching order among lineages is unresolved. The four major lineages of eukaryotic phytoplankton (haptophytes, diatoms, dinoflagellates and prasinophytes) are deeply divergent, highlighting the genomic variation present within assemblages of eukaryotic phytoplankton. pipeline for sequencing or draft genomes are being analyzed (e.g. the haptophyte Emiliania huxleyi; http://genome.jgi-psf.org/Emihu1/Emihu1. home.html). Genomics, as discussed here, encompasses more than genome sequencing. It also includes transcriptional analyses of both sequenced (Allen et al., 2008; Mock et al., 2008) and unsequenced organisms (von Dassow et al., 2009), as well as targeted metagenomics of ecologically important but uncultured phytoplankton (Cuvelier et al., 2010). Below, we examine the insights gained from a range of genomic approaches to examine marine eukaryotic phytoplankton ecology and evolution. 3.2. Insights into the evolution of marine phytoplankton A key evolutionary feature of eukaryotic phytoplankton is the origin and spread of plastids by endosymbiosis. Primary endosymbiosis resulted from the engulfment of a photosynthetic cyanobacterium by a eukaryotic host cell (Fig. 1.2). The red algal lineages resulted from primary endosymbiosis 16 Rynearson and Palenik Figure 1.2 The origin and spread of plastids via endosymbiosis events. Both primary and secondary endosymbioses resulted in the large-scale transfer of genes from the plastid to the host nucleus. Tertiary symbiosis involved several different endosymbionts. Figure modified from Keeling et al. (2004). Learning to Read the Oceans: Genomics of Marine Phytoplankton 17 as did the green algal lineages (including one of the four major groups of marine phytoplankton, the prasinophytes). Primary endosymbiosis is thought to have occurred earlier than 1.5 billion years ago (Butterfield, 2000; Yoon et al., 2004). Red algal secondary endosymbiosis likely took place about 200 million years later and included different eukaryotic host cells leading to three of the four major groups of phytoplankton; alveolates, heterokonts and haptophytes (Fig. 1.2) (Yoon et al., 2004). Tertiary symbiosis is thought to have occurred in some dinoflagellates (alveolates) and appears to have included a range of endosymbionts, including heterokonts, haptophytes and green algae (reviewed in Falkowski et al., 2004). Interestingly, recent work suggests that this picture may not be as clearcut as described. For some members of the heterokonts, up to 16% of the nuclear coding potential likely derived from the green algal lineage, suggesting endosymbiosis in the heterokonts may have included both the commonly accepted red lineage as well as the green lineage (Moustafa et al., 2009). Signatures of a similar green lineage cryptic endosymbiosis have been observed in the haptophytes as well, suggesting that mosaic gene repertoires may be a key characteristic of many modern eukaryotic phytoplankton (Cuvelier et al., 2010). 3.2.1. The Heterokonts 3.2.1.1. Diatoms The most ecologically successful heterokonts are the diatoms. They occupy marine and freshwater environments as well as planktonic and benthic habitats (Round et al., 1990). They generate approximately 20% of the primary productivity on earth (Falkowski et al., 1998; Field et al., 1998) and play an important role in global biogeochemical cycles. It is estimated that tens of thousands of species of diatoms exist, making them the most species-rich group of eukaryotic phytoplankton (Mann and Droop, 1996). Diatoms can be subdivided into three major classes (Fig. 1.3): the Coscinodiscophyceae (radial centric diatoms), the Mediophyceae (multi-polar centrics plus some radial centric diatoms) and the Bacillariophyceae (pennate diatoms) (Medlin and Kaczmarska, 2004). Members of these classes differ with regards to cell shape (centric diatoms are radially symmetric, pennate diatoms are bilaterally symmetric), presence/absence of a raphe (in pennate diatoms) required for motility, structure and arrangement of the Golgi apparatus and chloroplast pyrenoid, mode of sexual reproduction and small subunit (SSU) rDNA phylogeny. The centric forms first appeared in the fossil record 180 million years ago, after the Permian Triassic boundary (Rothpletz, 1896). The Bacillariophyceae is the youngest of the three classes, appearing in the fossil record about 70 million years ago (Moshkovitz et al., 1983). Two complete genomes have been sequenced: Thalassiosira pseudonana, from the Mediophyceae (Armbrust et al., 2006) and Phaeodactylum tricornutum, from the Bacillariophyceae (Bowler et al., 2008) (Fig. 1.3). 18 Rynearson and Palenik Figure 1.3 Phylogenetic relationships among the diatoms based on the SSU rDNA and highlighting the three main classes of diatoms; the Coscinodiscophyceae (red), the Bacillariophyceae (blue) and the Mediophyceae (green) and the sister group to the diatoms (the Bolidophyceae, in black). The phylogenetic position of the two sequenced genomes, T. pseudonana and P. tricornutum are indicated by arrows. Line drawings illustrate the basic morphological characteristics of each class. From Bowler et al. (2008). The T. pseudonana and P. tricornutum genomes are similar in size (27.4 and 32.4 Mb, respectively) and predicted gene content (10,402 and 11,776, respectively) but share just 57% of their genes. Bowler et al. (2008) reported the divergence between the two diatoms as greater than that between Homo sapiens and Takifugu rubripes (pufferfish). This level of divergence is surprising given that the fish mammal divergence occurred over 480 million years before the two diatom classes diverged. This genome-wide observation is supported by estimates that nucleotide substitution rates in diatoms are as fast as 1% per 14 million years for the SSU rDNA (Kooistra and Medlin, 1996; Damste et al., 2004), a rate of up to three times faster than reported for metazoans. Among genes that the two diatom species do share, there is no major conservation of gene Learning to Read the Oceans: Genomics of Marine Phytoplankton 19 order, with the largest sets being microclusters of up to eight genes (Bowler et al., 2008). These genome characteristics suggest that diatoms are undergoing very rapid rates of evolution. Whole genome sequencing revealed two other intriguing evolutionary aspects of the diatoms: the presence of genes acquired from bacteria and of transposable elements. Bacteria likely played a large role in structuring the modern diatom genome. In the P. tricornutum genome, 7.5% of gene models appear to be transferred from bacteria, suggesting that horizontal gene transfer is pervasive in diatoms (Bowler et al., 2008). The bacterial genes may have conferred novel metabolic capabilities and interestingly, do not appear to have derived from any single bacterial clade. Rather, they are derived from heterotrophic bacteria and cyanobacteria. This includes the diazotrophs and the planctomycete bacteria, which have been found living in close association with diatoms (Carpenter and Janson, 2000; Zehr et al., 2000; Schmid, 2003). Interestingly, only 34% of the P. tricornutum genes of bacterial origin are also found in the T. pseudonana genome, suggesting ongoing accumulation and/or loss of bacterially derived genes. A potentially important force in structuring diatom genomes is the activity of transposable elements. As Maumus et al. (2009) noted, the activity of transposable elements might have a particular influence on genome evolution since any non-lethal retroelement insertion would be transmitted to subsequent generations through asexual reproduction. Up to 2% of the T. pseudonana genome contains what appear to be relics of transposable elements, with the most common being the long-terminal repeat retrotransposons (LTR-TRs) (Armbrust et al., 2004). The P. tricornutum genome also contains these same elements but the number of LTR-TRs is much higher (Bowler et al., 2008). Experiments with P. tricornutum have shown that the expression of LTR-TRs increases dramatically in response to stressful growth conditions (Maumus et al., 2009), illustrating that LTR-TRs are active and have the potential to significantly restructure genomic landscapes on relatively short timescales. Given the dramatic differences observed between the genomes of T. pseudonana and P. tricornutum, future genome sequencing should focus on the earlydiverging Coscinodiscophyceae and if possible the Bolidophyceae, a sister group to the diatoms. Genome-wide comparisons with these lineages may provide insights into the earliest acquisitions of bacterial genes and provide additional information on LTR-TR activities over time. In addition, future sequencing of both closely related diatom species and multiple strains within single species will provide additional perspective on the causes and consequences of rapid rates of evolution in diatoms. It appears that closely related diatoms show surprisingly different genome sizes possibly due to polyploidy (von Dassow et al., 2008; Koester et al., 2010). Genomes from closely related diatoms with different 20 Rynearson and Palenik genome sizes would provide insight into the processes of genome duplication. Within a single species, genetically distinct populations with different physiological capabilities have been identified (Rynearson and Armbrust, 2004; Rynearson et al., 2009; Casteleyn et al., 2010). Furthermore, individual populations can be comprised of thousands of genetically distinct strains (Rynearson and Armbrust, 2005). Genome sequencing of strains from different populations will provide insights into the process of evolution by natural selection across the genome. 3.2.1.2. Pelagophytes The Pelagophytes are members of the Heterokonts that can comprise a significant component of the phytoplankton in both open ocean habitats (Steinberg et al., 2001) and estuarine and coastal environments (Gobler et al., 2005). This lineage includes Aureococcus anophagefferens, a species that generates harmful algal blooms (HABs), known as ‘brown tides’ in estuaries both in South Africa and the eastern United States (reviewed in Gobler et al., 2005). This species generates a toxin that affects bivalves and, due to high cell densities, can shade and harm seagrass beds. Interestingly, this species was not known to produce HABs until 1985 and has since been implicated in annual HAB formation in several locations. Recently, the whole genome sequence of A. anophagefferens was sequenced revealing a unique genome composition that reflects the evolutionary signature of selection. Aureococcus anophagefferens is just 2 µm in diameter and has a genome size of 57 Mbp (Gobler et al., 2011). Although its cell size is much smaller than sequenced diatoms (5 11 µm), its genome size is measurably larger (Table 1.1). Correspondingly, the predicted gene number based on homology searches is 11,501, higher than that identified in either diatom species. Gobler et al. (2011) suggest that a higher gene number may provide a competitive advantage over other phytoplankton with fewer genes. One of the hallmark characteristics of the A. anophagefferens genome is a proliferation of genes encoding for the light-harvesting complex (LHC) proteins. This genome contains 1.5 3 times more LHC genes than other eukaryotic phytoplankton genomes that have been sequenced (Gobler et al., 2011). The LHC proteins confer a higher ability to capture photons by binding antenna chlorophyll and carotenoid pigments to augment the photosynthetic reaction centres (Green and Durnford, 1996). The enhanced ability to capture photons suggested by genomic data is supported by physiological experiments showing that the irradiance level required to reach maximal growth rate in A. anophagefferens is much lower than for other sequenced eukaryotic phytoplankton (Gobler et al., 2011). These physiological and genomic observations support the hypothesis that this species has evolved to outcompete co-occurring phytoplankton for light and to thrive in low-light environments such as turbid estuaries. Learning to Read the Oceans: Genomics of Marine Phytoplankton 21 The A. anophagefferens genome contains genes suggesting an evolutionary adaptation to bacterial attack. Over 163 genes were identified in the A. anophagefferens genome that are involved in the synthesis of putative compounds to deter competitors and predators, including ABC transporters, multi-drug ABC transporters and chloroquine transporters (Gobler et al., 2011). Some of these genes, such as the membrane attack complex gene and three phenanzine synthetase genes may encode for enzymes that confer defences against microbes and protistan grazers (Pierson et al., 1995; Rosado, 2007). The A. anophagefferens genome contains genes involved in putative antimicrobial or antigrazing activity that are largely absent from the genome sequences of other eukaryotic phytoplankton (e.g. phenanzine synthetases) or that are far more abundant than other genome sequences (e.g. ABC transporters). This unique gene complement suggests that A. anophagefferens has evolved a complex set of defences that allow it to thrive in the face of competition and predation in waters containing high levels of dissolved organic carbon and thus form dense blooms. 3.2.2. The Prasinophytes Prasinophytes are early-diverging members within the green plant lineage and thus retain genome characteristics that were likely present in the last common ancestor of the land plants and green algae (Lewis and McCourt, 2004). Most prasinophytes are only 2 µm in size or smaller and have just a single chloroplast and one mitochondrion (Chretiennot-Dinet et al., 1995). They can be motile with one or multiple flagella and include the smallest free-living eukaryotic organisms known at 0.95 µm in diameter. Four complete genomes (and one draft genome) have been sequenced from the Prasinophytes, all within the order Mamiellales and representing two genera, Ostreococcus (Derelle et al., 2006; Palenik et al., 2007) and Micromonas (Worden et al., 2009). A genome of the related species Bathycoccus is in progress. The field is also fortunate in having the genome of the model freshwater chlorophyte Chlamydomonas which has helped provide insights into prasinophyte biology (Merchant et al., 2007; Grossman et al., 2010). A comparison of the four prasinophyte genomes yields insights into the evolutionary history of the Mamiellales. The Micromonas genomes are 60 70% larger than those of Ostreococcus (Palenik et al., 2007; Worden et al., 2009). In turn, Ostreococcus appears to be more derived from the common ancestor of plants and prasinophytes. They share a core genome of 7137 genes (Worden et al., 2009), which is up to 93% of the Ostreococcus genome but just 67% of the Micromonas genome. The larger Micromonas genome has more transporter families and higher numbers of transporters than Ostreococcus and what appears to be more robust defences against heavy-metal toxicity and reactive oxygen species (Worden et al., 2009). Since Micromonas is motile, these adaptations may have significance for 22 Rynearson and Palenik microenvironments in which it finds itself. These characteristics suggest that Micromonas has evolved with a broader set of physiological responses than Ostreococcus. Despite the larger genome size, Micromonas has far fewer putatively functional transposable elements compared to Ostreococcus (Palenik et al., 2007; Worden et al., 2009). Interestingly, it appears that in Micromonas, aspects of the genome suggest that the activity and propagation of transposable elements may be actively hindered. Differences such as genome size, transcriptional activity and gene content (e.g. transporters) suggest significant differences in the forces shaping the Ostreococcus and Micromonas genomes over time. The two species sequenced within each genus show divergence both in terms of gene content and genome structure. For example, the two Ostreococcus species have an average amino acid identity of 70%. This is a higher level of divergence than that observed between Saccharomyces species (Palenik et al., 2007). In both genera, there are particular chromosomes or chromosomal regions characterized by an increased number of transposable elements, high intron content and low GC content, suggesting that these genomic regions are evolving rapidly and could be involved in speciation by preventing interstrain crossing (Worden et al., 2009). As described for diatoms above, there is evidence of horizontal transfer of bacterial genes in both genera. Genes of bacterial origin differ markedly between species. For example, bacterial genes, including one coding for the enzyme UDP-N acetyglucosamine, are located on a chromosome found only in Ostreococcus lucimarinus but not in Ostreococcus tauri, suggesting ongoing adaptation (Derelle et al., 2006; Palenik et al., 2007). These examples highlight some of the different evolutionary mechanisms that may be involved in speciation and adaptation in the prasinophytes. 3.2.3. The Haptophytes Haptophytes are thought to have originated over 800 million years ago (Medlin et al., 2008; Liu et al., 2010) and are broadly distributed and abundant in the modern ocean. This lineage includes the coccolithophorids, an ecologically important clade that includes the well-studied genus Emiliania. The coccolithophorids are covered in calcite plates, are able to form massive blooms in the open ocean and thus are thought to play an important role in biogeochemical cycling (Andersen, 2004). Recent work in open ocean habitats discovered thousands of haptophytes species, none of which corresponded to known or cultured lineages within the haptophytes (Liu et al., 2009). Furthermore, the newly identified haptophytes appear to be mixotrophs, combining photosynthesis with bacterivory. They produce organic plate scales and may be essential mediators of carbon fluxes in the oceans, like the coccolithophorids. The discovery of these uncultured, broadly distributed and biogeochemically important Learning to Read the Oceans: Genomics of Marine Phytoplankton 23 organisms suggests that the haptophytes have adapted to a broad range of habitats and employ several nutrient acquisition strategies. Targeted metagenomics of the newly discovered and uncultured haptophytes has yielded significant insights into haptophyte evolution. Using sorting flow cytometry and high throughput sequencing, Cuvelier et al. (2010) examined the genomic content of uncultured haptophytes from the subtropical North Atlantic. This study focused on the ,2 3 µm size fraction because it may generate about 25% of primary production in the Northeast Atlantic and appears to be overwhelmingly comprised of haptophytes (Jardillier et al., 2010). The combination of sorting flow cytometry and high throughput sequencing revealed that the uncultured haptophytes possess mosaic genome structures. As in diatoms and prasinophytes, there was evidence of potential horizontal gene transfer from bacteria. Furthermore, it appears that nuclear-encoded genes have a distinct evolutionary background compared to plastid genes. Plastid DNA recovered from the field was most similar to E. huxleyi and placed directly between the cryptophytes and stramenopiles, suggesting a red algal secondary endosymbiosis (Cuvelier et al., 2010). In contrast, many genes in the nucleus were most closely related to the prasinophytes, in the green lineage. In fact 55% of those genes were most closely related to the streptophytes, particularly early-diverging plants (Cuvelier et al., 2010). Currently, the haptophyte lineage is placed close to the stramenopiles, based primarily on plastid gene homology (Andersen, 2004). The observation from metagenomic data that many nuclear genes appear more similar to prasinophytes than stramenopiles suggests that either the current phylogenetic position of the haptophytes is incorrect or that, like diatoms, the haptophytes had an ancient cryptic endosymbiont. In contrast to the uncultured haptophytes described in Cuvelier et al. (2010), the coccolithophore E. huxleyi has been extensively examined both in culture and in the field. Fossil records indicate that the ecologically important E. huxleyi arose just 270,000 years ago (Thierstein et al., 1977) and has since proliferated into temperate and sub-polar waters, forming massive blooms that can be viewed from space. Consistent with this recent origin and subsequent rapid adaptation to a wide range of environmental conditions, expressed sequence tag (EST) libraries showed limited sequence variability among three geographically distant strains (von Dassow et al. 2009). Because of the sequence level similarity, von Dassow et al. (2009) hypothesize that adaptation in this haptophyte species may have involved changes in gene regulation and gene gain or loss. Whole genome sequencing for E. huxleyi is underway (http://genome. jgi-psf.org/Emihu1/Emihu1.home.html) and will likely yield insights into both mechanisms of adaptation as well as the placement of the haptophytes on the tree of life. 24 Rynearson and Palenik 3.3. Insights into the structural and functional diversity of eukaryotic marine phytoplankton Eukaryotic phytoplankton encompass a broad range of metabolic functions as well as a diversity of cellular ultrastructures. Both genomic and transcriptomic data have been used to examine these intriguing aspects of protist ecology. One of the challenges in examining metabolic and structural diversity in these organisms is the large number of novel genes identified in each newly sequenced genome. These novel genes have no homology to genes with known or suspected functions. Often, they are similar only to genes of unknown function in closely related organisms. Depending on the genome, the number of novel genes ranges from 20% to nearly 40% highlighting the limitations of homology-based approaches. A further challenge to understanding the metabolic potential of these organisms is that the computational or in silico identification of genes can be difficult. Differences in gene structure (e.g. novel splice sites, signal peptides and untranslated regions) are a challenge to gene-calling algorithms designed for higher plants and metazoans. One solution to this challenge has been the development of tiling arrays for sequenced genomes. For example, using tiling and gene-specific arrays, Mock et al. (2008) predicted 3470 new genes in the T. pseudonana genome that were not previously identified using standard gene-calling algorithms. The 33% increase in the total number of genes predicted in the T. pseudonana genome suggests that gene density and number are not fully captured using standard gene-finding algorithms. Although there are many unknowns, homology-based, genefinding algorithms have been used to identify thousands of genes. In the following section, we highlight how this genomic information has provided insights into nutrient acquisition strategies and biomineralization. 3.3.1. Nutrient acquisition Inorganic nutrient concentrations in the open ocean can be very low, leading to strong potential selection pressures to either reduce nutrient demand or access other nutrient sources. Functional adaptations to the open ocean environment include alterations to metabolic pathways and enzymes. For example, several lineages, including the picoprymnesiophytes, the prasinophytes and one diatom species (P. tricornutum), possess nickel-containing superoxide dismutases (SODs) instead of the common iron-containing SODs (Palenik et al., 2007; Cuvelier et al., 2010). Because photosynthesis generates toxic superoxide radicals, SODs are particularly important in antioxidant defence for photosynthetic organisms. The replacement of Fe-SODs by Ni-SODs may be an adaptation to open ocean environments where Ni concentrations are 10-fold higher than Fe concentrations (Dupont et al., 2010). Under low Fe conditions, the diatom P. tricornutum responds by upregulating antioxidants that do not Learning to Read the Oceans: Genomics of Marine Phytoplankton 25 require Fe, such as dehydroascorbate and tocopherol (Allen et al., 2008). Adaptation to low Fe concentrations also includes the ability to store Fe through the Fe storage protein ferritin and has been observed in both prasinophytes and pennate diatoms (Palenik et al., 2007; Marchetti et al., 2009). Alveolates appear to store Fe as well but do not possess ferritin, suggesting that this lineage may have a novel Fe-storage mechanism (Sutak et al., 2010). In addition to increased ability to store nutrients, there is likely an increased ability to scavenge nutrients at low concentrations using multiple transporters. The proliferation of multiple transporters has been observed in both diatoms (Armbrust et al., 2004) and prasinophytes (Worden et al., 2009) and may reflect different substrate affinities and/or differential regulation. When nutrients are present in excess, such as during transient pulses related to upwelling or water column mixing, some phytoplankton are able to take up nutrients in excess of immediate needs. For example, diatoms are able to take up excess nitrate where it is likely stored in vacuoles (Lomas and Gilbert, 2000). In plants, asparagines are an important storage compound with a high ratio of nitrogen to carbon (Lea and Miflin, 1980). Plants use asparagine synthetase (AS) under high nitrogen conditions, to convert glutamine and glutamate to asparagines. Interestingly, both diatom genomes contain AS genes with high levels of homology to higher plants, suggesting that diatom AS may function similarly to plant AS. As a result, diatoms may utilize these enzymes in asparagine synthesis for nitrogen storage. Alternate sources of nutrients are also utilized. For example, the prasinophyte, Ostreococcus, appears to lack the standard Fe-uptake system found in other protists. Instead, genes similar to prokaryotic siderophore-Fe uptake were observed (Palenik et al., 2007). Further research should help sort out the competing hypotheses of whether Ostreococcus acquires siderophore-Fe generated by prokaryotes or whether it is able to make and take up its own siderophore complexes. Similarly, the diatom P. tricornutum is able to take up siderophores (Soria-Dengg and Hortsman, 1995), and genes that may be involved in siderophore uptake are highly expressed under low Fe conditions (Allen et al., 2008). As with Ostreococcus, it is unknown if P. tricornutum is able to generate its own siderophores. Phytoplankton are thought to rely mainly on inorganic nutrients but organic nutrients are also accessed. For example, several genes belonging to picoprymnesiophytes have domains that could be involved in the uptake of large substrates such as proteins and nucleic acids (Cuvelier et al., 2010), suggesting that these organisms may be scavenging organic nutrients from the water column. 3.3.2. Construction of the cell wall The eukaryotic phytoplankton possess a diverse range of cell walls including the silica frustule of diatoms, the calcite plates of coccolithophores 26 Rynearson and Palenik and the cellulose plates of dinoflagellates. These structures are highly patterned, very precise and used to identify organisms at the species level. Given the highly controlled nanostructure of these cell walls, they have attracted the interest of the nanotechnology community (Poulsen and Kroeger, 2004). Thus far, genomics tools have been used to examine biomineralization in diatoms and in the coccolithophore E. huxleyi. The diatom cell wall is composed of two silica-containing halves each consisting of a valve connected to a series of overlapping girdle bands and coated by an organic matrix (Round et al., 1990). The organic casing is a key component of the cell wall, preventing silica dissolution in seawater (Bidle and Azam, 1999). The casing contains glycoproteins and hydroxylated amino acids (Volcani, 1981) and specific casing glycoproteins called frustulins (Kroeger et al., 1994). The T. pseudonana genome contains several frustulin genes and an abundant gene family that encodes prolyl-4 hydroxylases (Armbrust et al., 2004). Within the organic casing, the silica valves contain many precisely patterned openings that can be nanometres in diameter, suggesting very strict control of the biomineralization process (Fig. 1.4A and B). The T. pseudonana genome contains three silicic acid transporters (SITs) that move the dissolved form from the environment and into the cell (Armbrust et al., 2004). Two SITs are upregulated under silicic acid limitation, and a third shows no response to any type of limitation (Thamatrakoln and Hildebrand, 2007; Mock et al., 2008). Silica is precipitated in the silica deposition vesicle using molecules that are embedded within the precipitated silica matrix. These molecules include long-chain polyamines (Kroeger et al., 2000), highly modified phosphoproteins called silaffins (Kroeger et al., 2002) and acid proteins called silacidins (Wenzl et al., 2008). Biosynthesis of long-chain polyamines requires spermidine and spermine-synthase like enzymes, and over four times as many copies of genes that encode for these enzymes are present in the T. pseudonana genome compared to other sequenced organisms (Armbrust et al., 2004). Additional proteins have been identified that are involved in cell wall formation and putatively include cytoskeletal, vesicle trafficking and transport proteins as well as those involved in the protein protein interactions that are likely key to forming the mature cell wall (Frigeri et al., 2006). Because there are so few sequenced phytoplankton genomes and because diatom biomineralization is so different from other silicifying organisms, gene homology searches have limited utility. For example, in a whole genome tiling array study, 75 genes were identified that were upregulated in response to silicic acid limitation (Mock et al., 2008). The majority of these genes have no known function. Many of those same genes were upregulated both in response to silicic acid limitation and iron limitation, suggesting important physiological links between these two nutrients (Mock et al., 2008) including the potential for a common Learning to Read the Oceans: Genomics of Marine Phytoplankton 27 Figure 1.4 Scanning electron micrographs showing the silica-based cell walls of the diatom Ditylum brightwellii (A and B) and the calcite coccoliths of the haptophyte Emiliania huxleyi (C and D). Scale bars represent 5 µm (A and B) or 2 µm (C). Panels C and D courtesy J. Young, The Natural History Museum, London. halting of cell cycle progression. Recently, Scheffel et al. (2011) used a new approach to discover a novel class of silica morphogenesis proteins. This study used homology-independent searches based on particular amino acid domains combined with the presence of an N terminal ER signal peptide. Using this method, six novel proteins called cingulins were discovered and shown to be key components of silica biomineralization (Scheffel et al., 2011). Fewer studies have been conducted to examine the genetic basis of calcification in the haptophyte E. huxleyi. As with diatoms, the calcite plates, called coccoliths, are precisely patterned and can be used to identify coccolithophores to the species level both in modern and paleo oceans (Fig. 1.4C and D) (Saez et al., 2003). Emiliania huxleyi only produces calcite plates during the diploid, non-motile phase of its life cycle. The haploid phase is motile and non-calcifying. Both phases are capable 28 Rynearson and Palenik of rapid, asexual division. A recent transcriptome analysis of both haploid and diploid phases of the E. huxleyi life cycle have provided insights into calcification in these important primary producers (von Dassow et al., 2009). One aspect of calcification likely includes a large flux of Ca21 into coccolith deposition vesicles, where calcification occurs. von Dassow et al. (2009) found five gene clusters with homology to vacuolar-type Ca21 /H1 antiporters. Of those, expression of one appeared to be specific to the calcified phase of the life cycle. This same study also revealed six clusters that were similar to the K1 -dependent Na1 /Ca21 exchanger family of Ca21 pumps, two of which appeared specific to the calcified phase of the life cycle. Other transporter-type genes that were specific to the diploid, calcifying phase included a homolog to the SLC4 Cl2 /bicarbonate exchangers, which play a role in pH regulation in animal cells. A final phase of cell wall construction is excretion of plates after precipitation occurs. The diploid-specific expression of a homolog to the SNARE protein, which plays a role in vesicle fusion during exocytosis, may be involved in the highly coordinated excretion of large calcite plates from the cell (von Dassow et al., 2009). Previously, 45 transcripts with potential roles in biomineralization were identified using two diploid strains of E. huxleyi, one that calcified and one that did not (Quinn et al. 2006). Interestingly, the follow-up comparison by von Dassow et al. (2009) of one strain in both the haploid and the diploid phases could not confirm that the transcripts were specific to calcification. This suggests that Quinn et al. (2006) may have identified strain-specific differences in gene expression rather than biomineralization-specific expression signatures. Alternately, those genes identified by Quinn et al. (2006) may be subject to post-transcriptional control, allowing them to be expressed in both haploid and diploid cells but only playing a role in calcification of diploid cells. In addition to the transporters and vesicle fusion genes identified thus far, the ongoing whole genome sequencing of E. huxleyi will likely provide additional insights into calcification. 4. Conclusions The breadth of sequenced genomes highlights the phylogenetic and metabolic diversity that characterizes the marine phytoplankton. The recent description of new lineages, such as the uncultured picohaptophytes (Cuvelier et al., 2010) suggests that many ecologically important lineages have yet to be discovered. For example, a newly discovered and deeply branching clade of eukaryotes, the rappemonads, appears to be widespread in both freshwater and marine habitats and can form transient Learning to Read the Oceans: Genomics of Marine Phytoplankton 29 blooms in open ocean waters (Fig. 1.1) (Kim et al., 2011). These aspects of its ecology suggest that it may have unique adaptations to life in the plankton. Future efforts to culture and sequence the genomes of these and other yet undiscovered marine phytoplankton will reveal key aspects of their evolution and functional ecology. For many organisms with either large and/or complex genomes, particularly for eukaryotic marine phytoplankton such as the alveolates, transcriptome sequencing may be a more immediate and cost-effective route than whole genome sequencing to obtain ecological and evolutionary information. For example, EST analysis of the dinoflagellate Alexandrium minutum revealed 192 genes that were differentially expressed between isolates that produced paralytic shellfish poison (PSP) toxins and those that did not (Yang et al., 2010). In terms of the functional ecology of A. minutum, the 192 genes are putative candidates for genes involved in toxin synthesis and regulation or acclimation to intracellular PSP toxins. The sequences in the EST library also suggest that PSP toxins generated by this alveolate did not arise from a recent gene transfer from cyanobacteria, as was previously hypothesized (Yang et al., 2010). Other dinoflagellate EST sequencing projects have yielded further insights into toxin production (Toulza et al., 2010), predator prey interactions (Yang et al., 2011), genome architecture (Lin et al., 2010) and trophic status (Wisecaver and Hackett, 2010). As sequencing costs drop and sequencing throughput capabilities increase, transcriptome analyses of many marine phytoplankton species and even strains within species will soon become not only feasible but also commonplace. A common theme amongst eukaryotic and prokaryotic marine phytoplankton genomes is the presence of mosaic gene repertoires. As described above for eukaryotic phytoplankton, multiple lineages show the imprints of both horizontal gene transfer from bacteria and of cryptic endosymbioses. Additional genome sequences will reveal the extent and significance of horizontal gene transfer as well as the relative timing of different gene transfers amongst clades. The hypothesis that cryptic green algal endosymbioses occurred in both the heterokonts and haptophytes is intriguing but still tenuous given the paucity of red algal genome sequence data. Future sequencing of red algal genomes will allow far more extensive homology searches and provide further insights into the possibility of past cryptic endosymbioses. The ability to do genetics experiments with marine phytoplankton has been greatly accelerated by the availability of genome sequences. The use of transposon mutagenesis in cyanobacteria, in which random genes are inactivated, followed by selection for interesting mutant phenotypes, has greatly accelerated in recent years because mutations can be rapidly determined with only minor sequencing investment (McCarren and Brahamsha, 2005). Diatom genetics is similarly accelerating. Additional 30 Rynearson and Palenik investment in making genetics techniques both available and robust in a diverse array of marine phytoplankton will greatly help in understanding gene function, especially for the large number of hypothetical and conserved hypothetical genes. This will in turn help in understanding how specific genes function in global biogeochemical cycles. New approaches have emerged and will continue to emerge that rely on genome sequence data. For example, whole genome microarrays include a range of targets such as all annotated genes or even the whole genome (tiling arrays). New directions in this area will use genome information from diverse keystone species to create microarrays for use in characterizing environmental samples. mRNA sequencing approaches such as Illumina sequencing generate only short reads and can take advantage of available genomes to examine gene expression. For example, mRNA from two conditions are used to generate cDNA libraries that are sequenced, generating quantitative gene expression profiles that can be compared. Proteomics approaches are already available that use whole genome sequences to examine protein expression in phytoplankton by matching protein MS/MS spectra to those predicted from whole genome sequences (Gobler et al., 2011). The application of these emerging genomics techniques to phytoplankton research is likely to become widespread in the future. One challenge beyond the genomics revolution is to apply these techniques in the marine environment to examine phytoplankton physiology in situ. This will mean synthesizing many different types of data simultaneously such as real time physical and chemical environmental variables, community species composition, gene expression profiles and protein signatures. This systems-level approach has the potential to greatly expand our understanding of how phytoplankton function in their environment. ACKNOWLEDGEMENTS The authors thank C. Lane (URI) for generating the phylogenetic tree (Fig. 1.1) and M. Hildebrand (SIO) and B. Jenkins (URI) for comments on parts of the manuscript. This is supported by National Science Foundation (NSF) grant no. OCE0727227 (to TAR) and NSF grant no. MCB0731771 (to BP). REFERENCES Ahlgren, N. A. and Rocap, G. (2006). Culture isolation and culture-independent clone libraries reveal new marine Synechococcus ecotypes with distinctive light and N physiologies. Applied and Environmental Microbiology 72, 7193 7204. Allen, A. E., LaRoche, J., Maheswari, U., Lommer, M., Schauer, N., Lopez, P. J., Finazzi, G., Fernie, A. R. and Bowler, C. (2008). Whole-cell response of the pennate diatom Phaeodactylum tricornutum to iron starvation. Proceedings of the National Academy of Sciences of the United States of America 105, 10438 10443. Learning to Read the Oceans: Genomics of Marine Phytoplankton 31 Andersen, R. A. (2004). Biology and systematics of heterokont and haptophyte algae. American Journal of Botany 91, 1508 1522. Armbrust, E. V., Berges, J. A., Bowler, C., Green, B. R., Martinez, D., Putnam, N. H., Zhou, S. G., Allen, A. E., Apt, K. E., Bechner, M., Brzezinski, M. A., Chaal, B. K., Chiovitti, A., Davis, A. K., Demarest, M. S., Detter, J. C., Glavina, T., Goodstein, D., Hadi, M. Z., Hellsten, U., Hildebrand, M., Jenkins, B. D., Jurka, J., Kapitonov, V. V., Kroger, N., Lau, W. W. Y., Lane, T. W., Larimer, F. W., Lippmeier, J. C., Lucas, S., Medina, M., Montsant, A., Obornik, M., Parker, M. S., Palenik, B., Pazour, G. J., Richardson, P. M., Rynearson, T. A., Saito, M. A., Schwartz, D. C., Thamatrakoln, K., Valentin, K., Vardi, A., Wilkerson, F. P. and Rokhsar, D. S. (2004). The genome of the diatom Thalassiosira pseudonana: Ecology, evolution, and metabolism. Science 306, 79 86. Armbrust, E. V., Rynearson, T. A. and Jenkins, B. D. (2006). Genomic insights into diatom evolution and metabolism. In Genome Evolution of Eukaryotic Microbes (L. A. Katz and D. Bhattacharya, eds), pp. 201 213. Oxford University Press, Oxford. Bidle, K. D. and Azam, F. (1999). Accelerated dissolution of diatom silica by marine bacterial assemblages. Nature 397, 508 512. Bowler, C., Allen, A. E., Badger, J. H., Grimwood, J., Jabbari, K., Kuo, A., Maheswari, U., Martens, C., Maumus, F., Otillar, R. P., Rayko, E., Salamov, A., Vandepoele, K., Beszteri, B., Gruber, A., Heijde, M., Katinka, M., Mock, T., Valentin, K., Verret, F., Berges, J. A., Brownlee, C., Cadoret, J.-P., Chiovitti, A., Choi, C. J., Coesel, S., De Martino, A., Detter, J. C., Durkin, C., Falciatore, A., Fournet, J., Haruta, M., Huysman, M. J. J., Jenkins, B. D., Jiroutova, K., Jorgensen, R. E., Joubert, Y., Kaplan, A., Kroger, N., Kroth, P. G., La Roche, J., Lindquist, E., Lommer, M., MartinJezequel, V., Lopez, P. J., Lucas, S., Mangogna, M., McGinnis, K., Medlin, L. K., Montsant, A., Secq, M.-P. O.-L., Napoli, C., Obornik, M., Parker, M. S., Petit, J.-L., Porcel, B. M., Poulsen, N., Robison, M., Rychlewski, L., Rynearson, T. A., Schmutz, J., Shapiro, H., Siaut, M., Stanley, M., Sussman, M. R., Taylor, A. R., Vardi, A., von Dassow, P., Vyverman, W., Willis, A., Wyrwicz, L. S., Rokhsar, D. S., Weissenbach, J., Armbrust, E. V., Green, B. R., Van de Peer, Y. and Grigoriev, I. V. (2008). The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature 456, 239 244. Butterfield, N. J. (2000). Bangiomorpha pubescens n. gen., n. sp.: implications for the evolution of sex, multicellularity, and the Mesoproterozoic/Neoproterozoic radiation of eukaryotes. Paleobiology 26, 386 404. Capone, D. G., Zehr, J. P., Paerl, H. W., Bergman, B. and Carpenter, E. J. (1997). Trichodesmium, a globally significant marine cyanobacterium. Science 276, 1221 1229. Carpenter, E. and Janson, S. (2000). Intracellular cyanobacterial symbionts in the marine diatom Climacodium frauenfeldianum (Bacillariophyceae). Journal of Phycology 36, 540 544. Casteleyn, G., Leliaert, F., Backeljau, T., Debeer, A.-E., Kotaki, Y., Rhodes, L., Lundholm, N., Sabbe, K. and Vyverman, W. (2010). Limits to gene flow in a cosmopolitan marine planktonic diatom. Proceedings of the National Academy of Sciences 107, 12952 12957. Chappell, P. D. and Webb, E. A. (2010). A molecular assessment of the iron stress response in the two phylogenetic clades of Trichodesmium. Environmental Microbiology 12, 13 27. Chisholm, S. W., Olson, R. J., Zettler, E. R., Goericke, R., Waterbury, J. B. and Welschmeyer, N. A. (1988). A novel free-living Prochlorophyte abundant in the oceanic euphotic zone. Nature 334, 340 343. Chretiennot-Dinet, M.-J., Courties, C., Vaquer, A., Neveux, J., Claustre, H., Lautier, J. and Machado, M. C. (1995). A new marine picoeucaryote: Ostreococcus tauri gen. et sp. nov. (Chlorophyta, Prasinophyceae). Phycologia 34, 285 292. 32 Rynearson and Palenik Cuvelier, M. L., Allen, A. E., Monier, A., McCrow, J. P., Messie, M., Tringe, S. G., Woyke, T., Welsh, R. M., Ishoey, T., Lee, J.-H., Binder, B. J., DuPont, C. L., Latasa, M., Guigand, C. D., Buck, K. R., Hilton, J., Thiagarajan, M., Caler, E., Read, B., Lasken, R. S., Chavez, F. P. and Worden, A. Z. (2010). Targeted metagenomics and ecology of globally important uncultured eukaryotic phytoplankton. Proceedings of the National Academy of Sciences of the United States of America 107, 14679 14684. Damste, J. S. S., Muyzer, G., Abbas, B., Rampen, S. W., Masse, G., Allard, W. G., Belt, S. T., Robert, J.-M., Rowland, S. J., Moldowan, J. M., Barbanti, S. M., Fago, F. J., Denisevich, P., Dahl, J., Trindade, L. A. F. and Schouten, S. (2004). The rise of the rhizosolenid diatoms. Science 304, 584 587. Derelle, E., Ferraz, C., Rombauts, S., Rouze, P., Worden, A. Z., Robbens, S., Partensky, F., Degroeve, S., Echeynie, S., Cooke, R., Saeys, Y., Wuyts, J., Jabbari, K., Bowler, C., Panaud, O., Piegu, B., Ball, S. G., Ral, J. P., Bouget, F. Y., Piganeau, G., De Baets, B., Picard, A., Delseny, M., Demaille, J., Van de Peer, Y. and Moreau, H. (2006). Genome analysis of the smallest free-living eukaryote Ostreococcus tauri unveils many unique features. Proceedings of the National Academy of Sciences of the United States of America 103, 11647 11652. Donia, M. S., Hathaway, B. J., Sudek, S., Haygood, M. G., Rosovitz, M. J., Ravel, J. and Schmidt, E. W. (2006). Natural combinatorial peptide libraries in cyanobacterial symbionts of marine ascidians. Nature Chemical Biology 2, 729 735. Dufresne, A., Ostrowski, M., Scanlan, D. J., Garczarek, L., Mazard, S., Palenik, B. P., Paulsen, I. T., de Marsac, N. T., Wincker, P., Dossat, C., Ferriera, S., Johnson, J., Post, A. F., Hess, W. R. and Partensky, F. (2008). Unraveling the genomic mosaic of a ubiquitous genus of marine cyanobacteria. Genome Biology 9, R90. Dupont, C. L., Buck, K. N., Palenik, B. and Barbeau, K. (2010). Nickel utilization in phytoplankton assemblages from contrasting oceanic regimes. Deep Sea Research Part I: Oceanographic Research Papers 57, 553 566. Falkowski, P., Barber, R. and Smetacek, V. (1998). Biogeochemical controls and feedbacks on ocean primary production. Science 281, 200 206. Falkowski, P. G., Katz, M. E., Knoll, A. H., Quigg, A., Raven, J. A., Schofield, O. and Taylor, F. J. R. (2004). The evolution of modern eukaryotic phytoplankton. Science 305, 354 360. Ferris, M. J. and Palenik, B. (1998). Niche adaptation in ocean cyanobacteria. Nature 396, 226 228. Field, C. B., Behrenfeld, M. J., Randerson, J. T. and Falkowski, P. (1998). Primary production of the biosphere: Integrating terrestrial and oceanic components. Science 281, 237 240. Frigeri, L. G., Radabaugh, T. R., Haynes, P. A. and Hildebrand, M. (2006). Identification of Proteins from a Cell Wall Fraction of the Diatom Thalassiosira pseudonana. Molecular & Cellular Proteomics 5, 182 193. Fuller, N. J., Marie, D., Partensky, F., Vaulot, D., Post, A. F. and Scanlan, D. J. (2003). Clade-specific 16S ribosomal DNA oligonucleotides reveal the predominance of a single marine Synechococcus clade throughout a stratified water column in the Red Sea. Applied and Environmental Microbiology 69, 2430 2443. Gobler, C. J., Lonsdale, D. J. and Boyer, G. L. (2005). A synthesis and review of causes and impact of harmful brown tide blooms caused by the alga, Aureococcus anophagefferens. Estuaries 28, 726 749. Gobler, C. J., Berry, D. L., Dyhrman, S. T., Wilhelm, S. W., Salamov, A., Lobanov, A. V., Zhang, Y., Collier, J. L., Wurch, L. L., Kustka, A. B., Dill, B. D., Shah, M., VerBerkmoes, N. C., Kuo, A., Terry, A., Pangilinan, J., Lindquist, E. A., Lucas, S., Paulsen, I. T., Hattenrath-Lehmann, T. K., Talmage, S. C., Walker, E. A., Koch, F., Burson, A. M., Marcoval, M. A., Tang, Y.-Z., LeCleir, G. R., Coyne, K. J., Berg, Learning to Read the Oceans: Genomics of Marine Phytoplankton 33 G. M., Bertrand, E. M., Saito, M. A., Gladyshev, V. N. and Grigoriev, I. V. (2011). Niche of harmful alga Aureococcus anophagefferens revealed through ecogenomics. Proceedings of the National Academy of Sciences of the United States of America 108, 4352 4357. Green, B. and Durnford, D. (1996). The chlorophyll-carotenoid proteins of oxygenic photosynthesis. Annual Reviews of Plant Physiology and Plant Molecular Biology 47, 685 714. Grossman, A. R., Karpowicz, S. J., Heinnickel, M., Dewez, D., Hamel, B., Dent, R., Niyogi, K. K., Johnson, X., Alric, J., Wollman, F. A., Li, H. Y. and Merchant, S. S. (2010). Phylogenomic analysis of the Chlamydomonas genome unmasks proteins potentially involved in photosynthetic function and regulation. Photosynthesis Research 106, 3 17. Jardillier, L., Zubkov, M., Pearman, J. and Scanlan, D. (2010). Significant CO2 fixation by small prymnesiophytes in the subtropical and tropical northeast Atlantic Ocean. ISME Journal 4, 1180 1192. Johnson, P. W. and Sieburth, J. M. (1979). Chroococcoid cyanobacteria in the sea Ubiquitous and diverse phototrophic biomasss. Limnology and Oceanography 24, 928 935. Jones, A. C., Monroe, E. A., Eisman, E. B., Gerwick, L., Sherman, D. H. and Gerwick, W. H. (2010). The unique mechanistic transformations involved in the biosynthesis of modular natural products from marine cyanobacteria. Natural Product Reports 27, 1048 1065. Kalaitzis, J. A., Lauro, F. M. and Neilan, B. A. (2009). Mining cyanobacterial genomes for genes encoding complex biosynthetic pathways. Natural Product Reports 26, 1447 1465. Keeling, P. J., Archibald, J. M., Fast, N. M. and Palmer, J. D. (2004). “Comment on “The Evolution of Modern Eukaryotic Phytoplankton”.” Science 306, 2191. Kettler, G. C., Martiny, A. C., Huang, K., Zucker, J., Coleman, M. L., Rodrigue, S., Chen, F., Lapidus, A., Ferriera, S., Johnson, J., Steglich, C., Church, G. M., Richardson, P. and Chisholm, S. W. (2007). Patterns and implications of gene gain and loss in the evolution of Prochlorococcus. PLOS Genetics 3, 2515 2528. Kim, E., Harrison, J. W., Sudek, S., Jones, M. D. M., Wilcox, H. M., Richards, T. A., Worden, A. Z. and Archibald, J. M. (2011). Newly identified and diverse plastidbearing branch on the eukaryotic tree of life. Proceedings of the National Academy of Sciences of the United States of America 108, 1496 1500. Koester, J. A., Swalwell, J. E., von Dassow, P. and Armbrust, E. V. (2010). Genome size differentiates co-occurring populations of the planktonic diatom Ditylum brightwellii (Bacillariophyta). BMC Evolutionary Biology 10, 1. Kooistra, W. H. C. F. and Medlin, L. K. (1996). Evolution of the diatoms (Bacillariophyta) IV. A reconstruction of their age from small subunit rRNA coding regions and the fossil record. Molecular Phylogenetics and Evolution 3, 391 407. Kroeger, N., Bergsdorf, C. and Sumper, M. (1994). A new calcium binding glycoprotein family constitutes a major diatom cell wall component. EMBO Journal 13, 4676 4683. Kroeger, N., Deutzmann, R., Bergsdorf, C. and Sumper, M. (2000). Species-specific polyamines from diatoms control silica morphology. Proceedings of the National Academy of Sciences of the United States of America 97, 14133 14138. Kroeger, N., Lorenz, S., Brunner, E. and Sumper, M. (2002). Self-Assembly of highly phosphorylated silaffins and their function in biosilica morphogenesis. Science 298, 584 586. Lea, P. and Miflin, B. (1980). Transport and metabolism of asparagine and other nitrogen compounds within the plant. In The Biochemistry of Plants (P. Stumpt and E. Conn, eds), pp. 569 607. Academic Press, New York. Levitan, O., Sudhaus, S., LaRoche, J. and Berman-Frank, I. (2010). The influence of pCO(2) and temperature on gene expression of carbon and nitrogen pathways in Trichodesmium IMS101. PLOS One 5, e15104. 34 Rynearson and Palenik Lewin, R. A. and Cheng, L. (1989). Prochloron: A microbial enigma. Chapman and Hall, New York. Lewis, L. A. and McCourt, R. M. (2004). Green algae and the origin of land plants. American Journal of Botany 91, 1535 1556. Lin, S. J., Zhang, H. A., Zhuang, Y. Y., Tran, B. and Gill, J. (2010). Spliced leader-based metatranscriptomic analyses lead to recognition of hidden genomic features in dinoflagellates. Proceedings of the National Academy of Sciences of the United States of America 107, 20033 20038. Lindell, D., Jaffe, J. D., Coleman, M. L., Futschik, M. E., Axmann, I. M., Rector, T., Kettler, G., Sullivan, M. B., Steen, R., Hess, W. R., Church, G. M. and Chisholm, S. W. (2007). Genome-wide expression dynamics of a marine virus and host reveal features of co-evolution. Nature 449, 83 86. Liu, H., Probert, I., Uitz, J., Claustre, H., Aris-Brosou, S. P., Frada, M., Not, F. and de Vargas, C. (2009). Extreme diversity in noncalcifying haptophytes explains a major pigment paradox in open oceans. Proceedings of the National Academy of Sciences of the United States of America 106, 12803 12808. Liu, H., Aris-Brosou, S. P., Probert, I. and de Vargas, C. (2010). A time line of the environmental genetics of the Haptophytes. Molecular Biology and Evolution 27, 161 176. Lomas, M. W. and Gilbert, P. M. (2000). Comparisons of nitrate uptake, storage, and reduction in marine diatoms and flagellates. Journal of Phycology 36, 903 913. Mann, D. G. and Droop, S. J. M. (1996). Biodiversity, biogeography and conservation of diatoms. Hydrobiologia 336, 19 32. Marchetti, A., Parker, M. S., Moccia, L. P., Lin, E. O., Arrieta, A. L., Ribalet, F., Murphy, M. E. P., Maldonado, M. T. and Armbrust, E. V. (2009). Ferritin is used for iron storage in bloom-forming marine pennate diatoms. Nature 457, 467 470. Martiny, A. C., Coleman, M. L. and Chisholm, S. W. (2006). Phosphate acquisition genes in Prochlorococcus ecotypes: Evidence for genome-wide adaptation. Proceedings of the National Academy of Sciences of the United States of America 103, 12552 12557. Martiny, A. C., Tai, A. P. K., Veneziano, D., Primeau, F. and Chisholm, S. W. (2009). Taxonomic resolution, ecotypes and the biogeography of Prochlorococcus. Environmental Microbiology 11, 823 832. Maumus, F., Allen, A. E., Mhiri, C., Hanhua, H., Jabbari, K., Vardi, A., Grandbastien, M.-A. and Bowler, C. (2009). Potential impact of stress activated retrotransposons on genome evolution in a marine diatom. BMC Genomics 10, 624 642. McCarren, J. and Brahamsha, B. (2005). Transposon mutagenesis in a marine Synechococcus strain: Isolation of swimming motility mutants. Journal of Bacteriology 187, 4457 4462. Medlin, L. K. and Kaczmarska, I. (2004). Evolution of the diatoms: V. Morphological and cytological support for the major clades and a taxonomic revision. Phycologia 43, 245 270. Medlin, L. K., Sáez, A. G. and Young, J. R. (2008). A molecular clock for coccolithophores and implications for selectivity of phytoplankton extinctions across the K/T boundary. Marine Micropaleontology 67, 69 86. Merchant, S. S., Prochnik, S. E., Vallon, O., Harris, E. H., Karpowicz, S. J., Witman, G. B., Terry, A., Salamov, A., Fritz-Laylin, L. K., Marechal-Drouard, L., Marshall, W. F., Qu, L. H., Nelson, D. R., Sanderfoot, A. A., Spalding, M. H., Kapitonov, V. V., Ren, Q. H., Ferris, P., Lindquist, E., Shapiro, H., Lucas, S. M., Grimwood, J., Schmutz, J., Cardol, P., Cerutti, H., Chanfreau, G., Chen, C. L., Cognat, V., Croft, M. T., Dent, R., Dutcher, S., Fernandez, E., Fukuzawa, H., Gonzalez-Ballester, D., Gonzalez-Halphen, D., Hallmann, A., Hanikenne, M., Hippler, M., Inwood, W., Jabbari, K., Kalanon, M., Kuras, R., Lefebvre, P. A., Lemaire, S. D., Lobanov, A. V., Lohr, M., Manuell, A., Meir, I., Mets, L., Mittag, M., Mittelmeier, T., Moroney, J. V., Moseley, J., Napoli, C., Nedelcu, A. M., Niyogi, K., Novoselov, S. V., Paulsen, I. T., Learning to Read the Oceans: Genomics of Marine Phytoplankton 35 Pazour, G., Purton, S., Ral, J. P., Riano-Pachon, D. M., Riekhof, W., Rymarquis, L., Schroda, M., Stern, D., Umen, J., Willows, R., Wilson, N., Zimmer, S. L., Allmer, J., Balk, J., Bisova, K., Chen, C. J., Elias, M., Gendler, K., Hauser, C., Lamb, M. R., Ledford, H., Long, J. C., Minagawa, J., Page, M. D., Pan, J. M., Pootakham, W., Roje, S., Rose, A., Stahlberg, E., Terauchi, A. M., Yang, P. F., Ball, S., Bowler, C., Dieckmann, C. L., Gladyshev, V. N., Green, P., Jorgensen, R., Mayfield, S., MuellerRoeber, B., Rajamani, S., Sayre, R. T., Brokstein, P., Dubchak, I., Goodstein, D., Hornick, L., Huang, Y. W., Jhaveri, J., Luo, Y. G., Martinez, D., Ngau, W. C. A., Otillar, B., Poliakov, A., Porter, A., Szajkowski, L., Werner, G., Zhou, K. M., Grigoriev, I. V., Rokhsar, D. S., Grossman, A. R., Chlamydomonas, A. and Team, J. G. I. A. (2007). The Chlamydomonas genome reveals the evolution of key animal and plant functions. Science 318, 245 251. Mock, T., Samanta, M. P., Iverson, V., Berthiaume, C., Robison, M., Holtermann, K., Durkin, C., BonDurant, S. S., Richmond, K., Rodesch, M., Kallas, T., Huttlin, E. L., Cerrina, F., Sussman, M. R. and Armbrust, E. V. (2008). Whole-genome expression profiling of the marine diatom Thalassiosira pseudonana identifies genes involved in silicon bioprocesses. Proceedings of the National Academy of Sciences of the United States of America 105, 1579 1584. Moisander, P. H., Beinart, R. A., Hewson, I., White, A. E., Johnson, K. S., Carlson, C. A., Montoya, J. P. and Zehr, J. P. (2010). Unicellular cyanobacterial distributions broaden the oceanic N2 fixation domain. Science 327, 1512 1514. Moore, L. R., Rocap, G. and Chisholm, S. W. (1998). Physiology and molecular phylogeny of coexisting Prochlorococcus ecotypes. Nature 393, 464 467. Moshkovitz, S., Ehrlich, A. and Soudry, D. (1983). Siliceous microfossils of the Upper Cretaceous Mishash formation, Central Negev, Israel. Cretaceous Research 4, 73 194. Moustafa, A., Beszteri, B., Maier, U. G., Bowler, C., Valentin, K. and Bhattacharya, D. (2009). Genomic footprints of a cryptic plastid endosymbiosis in diatoms. Science 324, 1724 1726. Nielsen, E. S. (1952). Production of organic matter in the sea. Nature 169, 956 957. Nunnery, J. K., Mevers, E. and Gerwick, W. H. (2010). Biologically active secondary metabolites from marine cyanobacteria. Current Opinion in Biotechnology 21, 787 793. Olson, R. J., Chisholm, S. W., Zettler, E. R., Altabet, M. A. and Dusenberry, J. A. (1990a). Spatial and temporal distributions of prochlorophyte picoplankton in the NorthAtlantic ocean. Deep Sea Research Part A. Oceanographic Research Papers 37, 1033 1051. Olson, R. J., Chisholm, S. W., Zettler, E. R. and Armbrust, E. V. (1990b). Pigments, size, and distribution of Synechococcus in the North-Atlantic and Pacific oceans. Limnology and Oceanography 35, 45 58. Orchard, E. D., Webb, E. A. and Dyhrman, S. T. (2009). Molecular analysis of the phosphorus starvation response in Trichodesmium spp. Environmental Microbiology 11, 2400 2411. Palenik, B. (1994). Cyanobacterial community structure as seen from RNA-polymerase gene sequence analysis. Applied and Environmental Microbiology 60, 3212 3219. Palenik, B. (2011). Recent functional genomics studies in marine Synechococcus. In Functional Genomics and Evolution of Photosynthetic Systems (R. Burnap and W. Vermaas, eds), pp. 103 118. Springer, Amsterdam. Palenik, B., Brahamsha, B., Larimer, F. W., Land, M., Hauser, L., Chain, P., Lamerdin, J., Regala, W., Allen, E. E., McCarren, J., Paulsen, I., Dufresne, A., Partensky, F., Webb, E. A. and Waterbury, J. (2003). The genome of a motile marine Synechococcus. Nature 424, 1037 1042. Palenik, B., Ren, Q. H., Dupont, C. L., Myers, G. S., Heidelberg, J. F., Badger, J. H., Madupu, R., Nelson, W. C., Brinkac, L. M., Dodson, R. J., Durkin, A. S., Daugherty, S. C., Sullivan, S. A., Khouri, H., Mohamoud, Y., Halpin, R. and Paulsen, I. T. 36 Rynearson and Palenik (2006). Genome sequence of Synechococcus CC9311: Insights into adaptation to a coastal environment. Proceedings of the National Academy of Sciences of the United States of America 103, 13555 13559. Palenik, B., Grimwood, J., Aerts, A., Rouze, P., Salamov, A., Putnam, N., Dupont, C., Jorgensen, R., Derelle, E., Rombauts, S., Zhou, K., Otillar, R., Merchant, S. S., Podell, S., Gaasterland, T., Napoli, C., Gendler, K., Manuell, A., Tai, V., Vallon, O., Piganeau, G., Jancek, S. V., Heijde, M., Jabbari, K., Bowler, C., Lohr, M., Robbens, S., Werner, G., Dubchak, I., Pazour, G. J., Ren, Q., Paulsen, I., Delwiche, C., Schmutz, J., Rokhsar, D., Van de Peer, Y., Moreau, H. and Grigoriev, I. V. (2007). The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. Proceedings of the National Academy of Sciences of the United States of America 104, 7705 7710. Palenik, B., Ren, Q., Tai, V. and Paulsen, I. T. (2009). Coastal Synechococcus metagenome reveals major roles for horizontal gene transfer and plasmids in population diversity. Environmental Microbiology 11, 349 359. Partensky, F. and Garczarek, L. (2010). Prochlorococcus: Advantages and limits of minimalism. Annual Review of Marine Science 2, 305 331. Pierson, L., Gaffney, T., Lam, S and Gong, F. (1995). Molecular analysis of genes encoding phenazine biosynthesis in the biological control bacterium, Pseudomonas aureofaciens 30 84. FEMS Microbiology Letters 134, 299 307. Poulsen, N. and Kroeger, N. (2004). Silica morphogenesis by alternative processing of silaffins in the diatom Thalassiosira pseudonana. Journal of Biological Chemistry 279, 42993 42999. Quinn, P., Bowers, R. M., Zhang, X., Wahlund, T. M., Fanelli, M. A., Olszova, D. and Read, B. A. (2006). cDNA microarrays as a tool for identification of biomineralization proteins in the coccolithophorid Emiliania huxleyi (Haptophyta). Applied and Environmental Microbiology 72, 5512 5526. Rocap, G., Distel, D. L., Waterbury, J. B. and Chisholm, S. W. (2002). Resolution of Prochlorococcus and Synechococcus ecotypes by using 16S-23S ribosomal DNA internal transcribed spacer sequences. Applied and Environmental Microbiology 68, 1180 1191. Rocap, G., Larimer, F. W., Lamerdin, J., Malfatti, S., Chain, P., Ahlgren, N. A., Arellano, A., Coleman, M., Hauser, L., Hess, W. R., Johnson, Z. I., Land, M., Lindell, D., Post, A. F., Regala, W., Shah, M., Shaw, S. L., Steglich, C., Sullivan, M. B., Ting, C. S., Tolonen, A., Webb, E. A., Zinser, E. R. and Chisholm, S. W. (2003). Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature 424, 1042 1047. Rosado, C. J. (2007). A common fold mediates vertebrate defense and bacterial attack. Science 317, 1548 1551. Rothpletz, A. (1896). Über die Flysch-Fucoiden und einige andere fossile Algen, sowie über liasische, Diatomeen führende Horenschwämme. Zeitschrift der Deutschen Geologischen Gesellschaft 48, 854 914. Round, F. E., Crawford, R. M. and Mann, D. G. (1990). The Diatoms. Cambridge University Press, Cambridge. Rusch, D. B., Martiny, A. C., Dupont, C. L., Halpern, A. L. and Venter, J. C. (2010). Characterization of Prochlorococcus clades from iron-depleted oceanic regions. Proceedings of the National Academy of Sciences of the United States of America 107, 16184 16189. Rynearson, T. A. and Armbrust, E. V. (2004). Genetic differentiation among populations of the planktonic marine diatom Ditylum brightwellii (Bacillariophyceae). Journal of Phycology 40, 34 43. Rynearson, T. A. and Armbrust, E. V. (2005). Maintenance of clonal diversity during a spring bloom of the centric diatom Ditylum brightwellii. Molecular Ecology 14, 1631 1640. Rynearson, T. A., Lin, E. O. and Armbrust, E. V. (2009). Metapopulation structure in the planktonic diatom Ditylum brightwellii (Bacillariophyceae). Protist 60, 11 121. Learning to Read the Oceans: Genomics of Marine Phytoplankton 37 Saez, A. G., Probert, I., Geisen, M., Quinn, P., Young, J. R. and Medlin, L. K. (2003). Pseudo-cryptic speciation in coccolithophores. Proceedings of the National Academy of Sciences of the United States of America, 1 6. Sandh, G., Ran, L. A., Xu, L. H., Sundqvist, G., Bulone, V. and Bergman, B. (2011). Comparative proteomic profiles of the marine cyanobacterium Trichodesmium erythraeum IMS101 under different nitrogen regimes. Proteomics 11, 406 419. Scanlan, D. J., Ostrowski, M., Mazard, S., Dufresne, A., Garczarek, L., Hess, W. R., Post, A. F., Hagemann, M., Paulsen, I. and Partensky, F. (2009). Ecological genomics of marine picocyanobacteria. Microbiology and Molecular Biology Reviews 73, 249 299. Scheffel, A., Poulsen, N., Shian, S. and Kroeger, N. (2011). Nanopatterned protein microrings from a diatom that direct silica morphogenesis. Proceedings of the National Academy of Sciences of the United States of America 108, 3175 3180. Schmid, A. M. M. (2003). Endobacteria in the diatom Pinnularia (Bacillariophyceae). I. “Scattered ct-nucleoids” explained: DAPI-DNA complexes stem from exoplastidial bacteria boring into the chloroplasts. Journal of Phycology 39, 122 138. Schmidt, E. W., Nelson, J. T., Rasko, D. A., Sudek, S., Eisen, J. A., Haygood, M. G. and Ravel, J. (2005). Patellamide A and C biosynthesis by a microcin-like pathway in Prochloron didemni, the cyanobacterial symbiont of Lissoclinum patella. Proceedings of the National Academy of Sciences of the United States of America 102, 7315 7320. Soria-Dengg, S. and Hortsman, U. (1995). Ferrioxamines B and E as iron sources for the marine diatom Phaeodactylum tricornutum. Marine Ecology Progress Series 127, 269 277. Steglich, C., Futschik, M., Rector, T., Steen, R. and Chisholm, S. W. (2006). Genomewide analysis of light sensing in Prochlorococcus. Journal of Bacteriology 188, 7796 7806. Steinberg, D. K., Carlson, C. A., Bates, N. R., Johnson, R. J., Michaels, A. F. and Knap, A. H. (2001). Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): A decade-scale look at ocean biology and biogeochemistry. Deep Sea Research Part II: Topical Studies in Oceanography 48, 1405 1447. Strickland, J. D. (1965). Phytoplankton and marine primary production. Annual Review of Microbiology 19, 127 162. Stuart, R. K., Dupont, C. L., Johnson, D. A., Paulsen, I. T. and Palenik, B. (2009). Coastal strains of marine Synechococcus species exhibit increased tolerance to copper shock and a distinctive transcriptional response relative to those of open-ocean strains. Applied and Environmental Microbiology 75, 5047 5057. Su, Z. C., Mao, F. L., Dam, P., Wu, H. W., Olman, V., Paulsen, I. T., Palenik, B. and Xu, Y. (2006). Computational inference and experimental validation of the nitrogen assimilation regulatory network in cyanobacterium Synechococcus sp. WH 8102. Nucleic Acids Research 34, 1050 1065. Sudek, S., Haygood, M. G., Youssef, D. T. A. and Schmidt, E. W. (2006). Structure of trichamide, a cyclic peptide from the bloom-forming cyanobacterium Trichodesmium erythraeum, predicted from the genome sequence. Applied and Environmental Microbiology 72, 4382 4387. Sutak, R., Slapeta, J., San Roman, M., Camadro, J.-M. and Lesuisse, E. (2010). Nonreductive iron uptake mechanism in the marine alveolate Chromera velia. Plant Physiology 154, 991 1000. Swingley, W. D., Chen, M., Cheung, P. C., Conrad, A. L., Dejesa, L. C., Hao, J., Honchak, B. M., Karbach, L. E., Kurdoglu, A., Lahiri, S., Mastrian, S. D., Miyashita, H., Page, L., Ramakrishna, P., Satoh, S., Sattley, W. M., Shimada, Y., Taylor, H. L., Tomo, T., Tsuchiya, T., Wang, Z. T., Raymond, J., Mimuro, M., Blankenship, R. E. and Touchman, J. W. (2008). Niche adaptation and genome expansion in the chlorophyll d-producing cyanobacterium Acarylochloris marina. Proceedings of the National Academy of Sciences of the United States of America 105, 2005 2010. 38 Rynearson and Palenik Tai, V. and Palenik, B. (2009). Temporal variation of Synechococcus clades at a coastal Pacific ocean monitoring site. ISME Journal 3, 903 915. Tai, V., Paulsen, I. T., Phillippy, K., Johnson, D. A. and Palenik, B. (2009). Wholegenome microarray analyses of Synechococcus-Vibrio interactions. Environmental Microbiology 11, 2698 2709. Tetu, S. G., Brahamsha, B., Johnson, D. A., Tai, V., Phillippy, K., Palenik, B. and Paulsen, I. T. (2009). Microarray analysis of phosphate regulation in the marine cyanobacterium Synechococcus sp. WH 8102. ISME Journal 3, 835 849. Thamatrakoln, K. and Hildebrand, M. (2007). Analysis of Thalassiosira pseudonana silicon transporters indicates distinct regulatory levels and transport activity through the cell cycle. Eukaryotic Cell 6, 271 279. Thierstein, H. R., Geitzenauer, K. R. and Molfino, B. (1977). Global synchroneity of late quaternary coccolith datum levels validation by oxygen isotopes. Geology 5, 400 404. Thomas, E.V., Phillippy, K.H., Brahamsha, B., Haaland, D.M., Timlin, J.A., Elbourne, L. D.H., Palenik, B. and Paulsen, I.T. (2009). Statistical analysis of microarray data with replicated spots: A case study with Synechococcus WH8102. Comparative and Functional Genomics 2009, 11 pp. Toledo, G. and Palenik, B. (1997). Synechococcus diversity in the California current as seen by RNA polymerase (RpoC1) gene sequences of isolated strains. Applied and Environmental Microbiology 63, 4298 4303. Toledo, G. and Palenik, B. (2003). A Synechococcus serotype is found preferentially in surface marine waters. Limnology and Oceanography 48, 1744 1755. Tolonen, A. C., Aach, J., Lindell, D., Johnson, Z. I., Rector, T., Steen, R., Church, G. M. and Chisholm, S. W. (2006). Global gene expression of Prochlorococcus ecotypes in response to changes in nitrogen availability. Molecular Systems Biology 2, 1 15. Toulza, E., Shin, M. S., Blanc, G., Audic, S., Laabir, M., Collos, Y., Claverie, J. M. and Grzebyk, D. (2010). Gene expression in proliferating cells of the dinoflagellate Alexandrium catenella (Dinophyceae). Applied and Environmental Microbiology 76, 4521 4529. Tripp, H. J., Bench, S. R., Turk, K. A., Foster, R. A., Desany, B. A., Niazi, F., Affourtit, J. P. and Zehr, J. P. (2010). Metabolic streamlining in an open-ocean nitrogen-fixing cyanobacterium. Nature 464, 90 94. Villareal, T. A., Pilskaln, C., Brzezinski, M., Lipschultz, F., Dennett, M. and Gardner, G. B. (1999). Upward transport of oceanic nitrate by migrating diatom mats. Nature 397, 423 425. Volcani, B. E. (1981). In Silicon and Siliceous Structures in Biological Systems (T. L. Simpson and B. E. Volcani, eds), pp. 157 200. Springer, Berlin. von Dassow, P., Petersen, T. W., Chepurnov, V. A. and Armbrust, E. V. (2008). Inter- and intraspecific relationships between nuclear DNA content and cell size in selected members of the centric diatom genus Thalassiosira (Bacillariophyceae). Journal of Phycology 44, 335 349. von Dassow, P., Ogata, H., Probert, I., Wincker, P., Da Silva, C., Audic, S., Claverie, J.-M. and de Vargas, C. (2009). Transcriptome analysis of functional differentiation between haploid and diploid cells of Emiliania huxleyi, a globally significant photosynthetic calcifying cell. Genome Biology 10, R114. Waterbury, J., Watson, S., Valois, F. and Franks, D. (1986). Biological and ecological characterization of the marine unicellular cyanobacterium Synechococcus. Canadian Bulletin of Fisheries and Aquatic Sciences 214, 71 120. Waterbury, J. B., Watson, S. W., Guillard, R. R. L. and Brand, L. E. (1979). Widespread occurrence of a unicellular, marine, planktonic, cyanobacterium. Nature 277, 293 294. Learning to Read the Oceans: Genomics of Marine Phytoplankton 39 Welsh, E. A., Liberton, M., Stoeckel, J., Loh, T., Elvitigala, T., Wang, C., Wollam, A., Fulton, R. S., Clifton, S. W., Jacobs, J. M., Aurora, R., Ghosh, B. K., Sherman, L. A., Smith, R. D., Wilson, R. K. and Pakrasi, H. B. (2008). The genome of Cyanothece 51142, a unicellular diazotrophic cyanobacterium important in the marine nitrogen cycle. Proceedings of the National Academy of Sciences of the United States of America 105, 15094 15099. Wenzl, S., Hett, R., Richthammer, P. and Sumper, M. (2008). Silacidins: Highly acidic phosphopeptides from diatom shells assist in silica precipitation in vitro. Angewandte Chemie International Edition 47, 1729 1732. West, N. J., Schonhuber, W. A., Fuller, N. J., Amann, R. I., Rippka, R., Post, A. F. and Scanlan, D. J. (2001). Closely related Prochlorococcus genotypes show remarkably different depth distributions in two oceanic regions as revealed by in situ hybridization using 16S rRNA-targeted oligonucleotides. Microbiology 147, 1731 1744. Wisecaver, J. H. and Hackett, J. D. (2010). Transcriptome analysis reveals nuclear-encoded proteins for the maintenance of temporary plastids in the dinoflagellate Dinophysis acuminata. BMC Genomics 11, 366. Worden, A. Z., Lee, J.-H., Mock, T., Rouze, P., Simmons, M. P., Aerts, A. L., Allen, A. E., Cuvelier, M. L., Derelle, E., Everett, M. V., Foulon, E., Grimwood, J., Gundlach, H., Henrissat, B., Napoli, C., McDonald, S. M., Parker, M. S., Rombauts, S., Salamov, A., Von Dassow, P., Badger, J. H., Coutinho, P. M., Demir, E., Dubchak, I., Gentemann, C., Eikrem, W., Gready, J. E., John, U., Lanier, W., Lindquist, E. A., Lucas, S., Mayer, K. F. X., Moreau, H., Not, F., Otillar, R., Panaud, O., Pangilinan, J., Paulsen, I., Piegu, B., Poliakov, A., Robbens, S., Schmutz, J., Toulza, E., Wyss, T., Zelensky, A., Zhou, K., Armbrust, E. V., Bhattacharya, D., Goodenough, U. W., Van de Peer, Y. and Grigoriev, I. V. (2009). Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes Micromonas. Science 324, 268 272. Yang, I., John, U., Beszteri, S., Glockner, G., Krock, B., Goesmann, A. and Cembella, A. (2010). Comparative gene expression in toxic versus non-toxic strains of the marine dinoflagellate Alexandrium minutum. BMC Genomics 11, 248. Yang, I., Selander, E., Pavia, H. and John, U. (2011). Grazer-induced toxin formation in dinoflagellates: A transcriptomic model study. European Journal of Phycology 46, 66 73. Yoon, H. S., Hackett, J. D., Ciniglia, C., Pinto, G. and Bhattacharya, D. (2004). A molecular timeline for the origin of photosynthetic eukaryotes. Molecular Biology and Evolution 21, 809 818. Zehr, J. P., Carpenter, E. and Villareal, T. A. (2000). New perspectives on nitrogen-fixing microorganisms in tropical and subtropical oceans. Trends in Microbiology 8, 68 73. Zhaxybayeva, O., Doolittle, W. F., Papke, R. T. and Gogarten, J. P. (2009). Intertwined evolutionary histories of marine Synechococcus and Prochlorococcus marinus. Genome Biology and Evolution 2009, 325 339. Zinser, E. R., Coe, A., Johnson, Z. I., Martiny, A. C., Fuller, N. J., Scanlan, D. J. and Chisholm, S. W. (2006). Prochlorococcus ecotype abundances in the North Atlantic Ocean as revealed by an improved quantitative PCR method. Applied and Environmental Microbiology 72, 723 732. Zinser, E. R., Lindell, D., Johnson, Z. I., Futschik, M. E., Steglich, C., Coleman, M. L., Wright, M. A., Rector, T., Steen, R., McNulty, N., Thompson, L. R. and Chisholm, S. W. (2009). Choreography of the transcriptome, photophysiology, and cell cycle of a minimal photoautotroph Prochlorococcus. PLOS One, 4. Zwirglmaier, K., Jardillier, L., Ostrowski, M., Mazard, S., Garczarek, L., Vaulot, D., Not, F., Massana, R., Ulloa, O. and Scanlan, D. J. (2008). Global phylogeography of marine Synechococcus and Prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes. Environmental Microbiology 10, 147 161. This page intentionally left blank C H A P T E R T W O Biology of Deep-Water Octocorals Les Watling*,†,1, Scott C. France‡, Eric Pante‡ and Anne Simpson† Contents 1. Introduction 2. Classification 2.1. Order Alcyonacea 2.2. Order Pennatulacea 2.3. Order Helioporacea 3. Phylogenetic Relationships 4. Biogeography 4.1. North Atlantic 4.2. Indo-West Pacific 4.3. Hawaii 4.4. Japan 4.5. Aleutian Islands of Alaska 4.6. Other regions 5. Distribution of the Three Major Deep-Sea Families 5.1. Chrysogorgiidae 5.2. Isididae 5.3. Primnoidae 6. Symbionts 6.1. Deep-water coral hosts and their invertebrate symbionts 6.2. Characteristics of the invertebrate symbionts 6.3. Commensalism, parasitism or mutualism 6.4. Host fidelity 7. Predators 8. Food 9. Reproduction 9.1. Reproductive strategies 9.2. Gonochorism and sex ratio 9.3. Gametogenesis 42 43 44 46 48 48 51 51 63 64 66 67 68 68 68 74 77 82 85 91 94 95 95 95 96 96 100 101 * Department of Biology, University of Hawaii at Mānoa, Honolulu, HI, USA Darling Marine Center, University of Maine, Walpole, ME, USA ‡ Department of Biology, University of Louisiana at Lafayette, Lafayette, LA, USA 1 Corresponding author: Email: watling@hawaii.edu † Advances in Marine Biology, Volume 60 ISSN: 0065-2881, DOI: 10.1016/B978-0-12-385529-9.00002-0 © 2011 Elsevier Ltd All rights reserved. 41 42 Les Watling et al. 9.4. Sexual maturity and fecundity 9.5. Spawning and larval development 10. Growth and Age 11. Dispersal 12. Threats and Conservation Issues Acknowledgements References 103 104 105 108 109 110 111 1. Introduction To most people, the concept of a deep-water coral is an oxymoron. In fact, the existence of these species was for a long time known only to a handful of scientists and a large number of fishermen. In North America there was little knowledge of the existence of these corals in the minds of the general public as well as the broader scientific community (Breeze et al., 1997). However, fishermen have complained for at least two centuries about their long fishing lines getting tangled in the coral thickets. An early record, given by Edward Forbes (1859) in his Natural History of the European Seas, is as follows: The great tree Alcyonium [probably Paragorgia arborea or Primnoa resedaeformis], a branched zoophyte of leathery texture, is a very wonderful and characteristic production of the abysses of the Boreal Seas. The lines of the fishermen, when fishing for the redfish, or uër, become entangled in its branches, and draw up fragments of considerable dimensions, so large, indeed, that the people of the country believe it to grow to the size of forest-trees, an exaggeration in all probability, but nevertheless one founded in unusual magnitude (p. 71). Deep-water corals must have been quite abundant in those early days, or at least sufficiently abundant to be the predominant creatures brought up from considerable depths (remembering that true biological sampling of the upper slope depths did not begin until the Norwegians sampled at 300 400 fathoms in the 1850s). In Forbes’ book, he names the deepest zone of life in the sea after these deep-dwelling corals: Last and lowest of our regions of submarine existence is that of deep sea corals, so named on account of the great stony zoophytes characteristic of it in the oceanic seas of Europe (p. 26). Documentation of the distribution of deep-water corals advanced considerably through the latter part of the 1800s and into the twentieth century, especially due to the efforts of the Challenger Expedition (Wright and Studer, 1889), the sampling of the US Coast and Geodetic Survey Steamer ‘Blake’ (Verrill, 1883; Agassiz, 1888) in the Caribbean, Biology of Deep-Water Octocorals 43 the US Fish Commission Steamer ‘Albatross’ expeditions in the Pacific (Nutting, 1908, 1912), the German ‘Valdivia’ Deep-Sea Expedition (Kükenthal, 1919) and the ‘Siboga’ Expedition to Indonesia (Versluys, 1902, 1906; Nutting, 1910a,b,c,d,e, 1911). By the time of Kükenthal’s (1924) volume summarizing the state of knowledge of the gorgonian octocorals, 226 species had been discovered living in deep water, representing 29% of the 781 species in the monograph. There has been a surge of interest in deep-sea octocorals over the last decade or so. It was quickly recognized that deep-sea corals of all kinds were vulnerable to the impacts of fishing that was expanding from the continental shelf to the slope, seamounts and ridges. While there is much now known about deep-sea octocorals, the literature is scattered and needs to be summarized so we can better understand what we know, and what we yet need to know. In this review, we will document what is known about taxonomy, phylogeny, biogeography, ecology and reproductive biology of deep-sea octocorals, using both published and as yet unpublished information, and will highlight areas where knowledge is especially lacking. We will focus primarily on gorgonian octocorals because they are the predominant octocoral group in the deep sea, and we will not deal extensively with the pennatulaceans (sea pens) except to compare with other octocorals. Sea pens are quite diverse in the deep sea (Williams, 1995) and have recently been reviewed by Williams (2011). 2. Classification The Octocorallia are a diverse subclass of anthozoans characterized by the subdivision of the polyp by eight mesenteries, each division giving rise to a tentacle adorned with lateral pinnules, and tissues containing sclerites (these are calcitic micro-skeletal elements). The most widely accepted taxonomic scheme for octocorals, used for the latter half of the twentieth century, divided the subclass into 4 orders, the Helioporacea, Alcyonacea, Gorgonacea and Pennatulacea. Bayer (1981) noted that distinctions between most orders and suborders were blurred by intermediate taxa that resulted in a continuum of colonial organization and skeletal structure, an observation supported by molecular phylogenies (McFadden et al., 2006). Bayer (1981) thus combined Gorgonacea into the Alcyonacea, and suggested there was no taxonomic significance to named suborders, although they are still referenced as ‘subordinal groups’ for convenience. Subsequently, Grasshoff (1999) proposed the suborder Calcaxonia for five families that have a solid axial skeleton (Bayer’s ‘restricted Holaxonia’ plus Isididae), most of which are distributed in deep water. 44 Les Watling et al. Most families (30 of 45, or 67%) of octocorals have representatives living in the deep sea, and some show maximum diversity at depths .200 m. We present below the classification of the subclass Octocorallia, highlighting the genera with species known to occur deeper than 200 m. 2.1. Order Alcyonacea (Suborder) Protoalcyonaria: two families, three genera; deep-water species in Taiaroidae (Taiaroa). Taiaroa is noteworthy in that it is the only known octocoral that does not form colonies, but rather lives as large solitary polyps anchored in deep-water sediments around New Zealand (Bayer and Muzik, 1976). (Suborder) Stolonifera: Six families, 26 genera of soft corals that grow from ribbon-like stolons; deep-water species are mostly in Clavulariidae, which is the most diverse but likely polyphyletic family in the suborder (Bathytelesto, Clavularia (Fig. 2.1D)). (Suborder) Alcyoniina: Five families of fleshy soft corals lacking an axial skeleton, 74 genera; mostly shallow tropical, but deep-water species are found in Alcyoniidae (Alcyonium, Anthomastus) and Nephtheidae (Capnella, Drifa, Duva, Gersemia, Scleronephthya). (Suborder) Scleraxonia (Fig. 2.1): Seven families, 30 genera of colonies with an axial skeleton (or layers) composed of sclerites; deep-water species in four families, Briareidae (Lignopsis), Anthothelidae (Anthothela, Victorgorgia), Subergorgiidae (Rosgorgia) and Coralliidae (Corallium, Paracorallium), and two families exclusively in deep water: Paragorgiidae (Paragorgia, Sibogagorgia) and Parisididae (Parisis). (Suborder) Holaxonia (Fig. 2.1): Four families, 67 genera, of gorgonian sea fans characterized by an organic central axis with varying amounts of calcareous material deposited in loculi; axes with very little calcareous material are described as ‘woody’; mostly shallow tropical, but with deepwater species known from all families, Acanthogorgiidae (Acanthogorgia, Calcigorgia, Cyclomuricea), Gorgoniidae (Eunicella, Leptogorgia), Keroeididae (Keroeides, Lignella, Thelogorgia) and Plexauridae (Acanthacis, Alaskagorgia, Anthomuricea, Astromuricea, Bayergorgia, Bebryce, Caliacis, Calicogorgia, Cryogorgia, Dentomuricea, Echinomuricea, Hypnogorgia, Mesogligorgia, Muricea, Muriceides, Muriceopsis, Paramuricea, Placogorgia, Pseudoplexaura, Scleracis, Swiftia, Thesea, Villogorgia). Suborder Calcaxonia: Five families, 98 genera, of gorgonians with a solid axis composed of large amounts of non-scleritic calcareous material; most genera and species in the three largest families, Chrysogorgiidae, Primnoidae and Isididae, are found in deep, cold waters, and are discussed in more detail below. Ellisellidae are most common at shelf depths, but species in some genera extend beyond 200 m depth (Ctenocella, Ellisella, Heliana, Nicella, Riisea, Viminella). Within the Chrysogorgiidae (Fig. 2.2), 45 Biology of Deep-Water Octocorals (A) (B) (C) (D) (E) (F) (G) (H) Figure 2.1 Miscellaneous octocorals. (A) A colony of the bubblegum coral Paragorgia johnsoni 1.2 m wide is host to more than a dozen Asteroschema ophiuroids and several crinoids (Manning Seamount, 1333 m depth). (B) A Paragorgia johnsoni colony that has fallen is apparently being grazed upon by several asteroids (Manning Seamount, 1335 m depth). We observed little change in this scene between two visits 18 months apart: the grazing asteroids are only several centimetres away from where first observed, the Paragorgia polyps remained alive, and still intertwined on its branches were Asteroschema ophiuroids (see inset). (C) A colony of the bubblegum coral Paragorgia coralloides is almost completely overgrown by a colonial zoanthid (larger, lighter colored polyps) but still retains several of its typical commensal ophiuroids (Asteroschema). A pycnogonid appears to be feeding on the zoanthid polyps 46 Les Watling et al. genera with deep-sea species include Chrysogorgia, Metallogorgia, Radicipes, Pseudochrysogorgia, Iridogorgia and Rhodaniridogorgia. The Primnoidae (Fig. 2.3) are very diverse in the deep sea, including the genera Ainigmaptilon, Arthrogorgia, Callogorgia, Calyptrophora, Candidella, Fanellia, Narella, Primnoa and Thouarella. Bamboo corals, family Isididae (Fig. 2.4), are represented in the deep sea by three of the four subfamilies, Mopseinae, Circinisidinae and Keratoisidinae. The latter contains eight genera, all of which are deep-sea dwelling, the most common being Acanella, Isidella, Keratoisis and Lepidisis. Incertae sedis: The family Dendrobrachiidae is monogeneric (Dendrobrachia), with 5 species distributed between 230 and 1080 m. The genus is unusual among octocorals in that tissues lack sclerites and the axis is elaborated into ridges and spines, which led to it being initially classified as a black coral (Hexacorallia, Antipatharia); its relationship to other octocorals is still unknown (López-González and Cunha, 2010). 2.2. Order Pennatulacea Sea pens; among the 14 families and 34 genera of sea pens, only one family and six genera are not known to extend their vertical distribution to depths deeper than 200 m. Suborder Sessiliflorae: 11 families, 22 genera; deep-water species common in all families except Renillidae (the sea pansies); Veretillidae (Lituaria, Veretillum, Cavernularia), Echinoptilidae (Actinoptilum, Echinoptilum), Kophobelemnidae (Kophobelemnon, Sclerobelemnon), Funiculinidae (Funiculina), Protoptilidae (Distichoptilum, Protoptilum), Stachyptilidae (Stachyptilum), Scleroptilidae (Scleroptilum), Chunellidae (Calibelemnon, Amphiacme, Chunella), Umbellulidae (Umbellula), Anthoptilidae (Anthoptilum). Suborder Subselliflorae: Three families, 12 genera; deep-water species in all families, Halipteridae (Halipteris), Virgulariidae (Stylatula, Acanthoptilum, (Balanus Seamount, 1790 m depth). (D) At 1750 m depth on Balanus Seamount a large, still living, colony of Corallium sp. is overgrown by a variety of other cnidarians and sponges, including the purple stoloniferous octocoral Clavularia rudis, gold-coloured zoanthids, solitary cup corals and hydroids. Dozens of ophiuroids (pinkish, flat against the coral) and crinoids (yellowish, arms up into the water column) are using the colony as a perch. (E) Diversity is high on an elevated ridge at 1430 m depth on Manning Seamount. At least six octocoral (bamboo corals Lepidisis sp., Keratoisis sp.; primnoids Calyptrophora ?microdentata, C. ?clinata; soft coral Anthomastus sp.; bubblegum coral Paragorgia sp.) and two black coral (Leiopathes sp., Parantipathes sp.) species can be seen. (F) At 1827 m on Rehoboth Seamount, a wall is dominated by colonies of Acanthogorgia armata. (G) The plexaurid Paramuricea grayi with ophiuroid Asteroschema sp. at 1353 m on the Bahama Escarpment. (H) The soft coral Anthomastus sp. at 2110 m on Nashville Seamount; sponges are to its left. 47 Biology of Deep-Water Octocorals (A) (B) (C) (D) (E) (F) (G) (H) Figure 2.2 Chrysogorgiidae. (A) Several 2 3 m tall Iridogorgia magnispiralis colonies spiral over meter-high Metallogorgia melanotrichos (dense ball-shaped crown atop a monopodial axis) and Paramuricea sp. (yellow-coloured sea fans) at 2200 m on Nashville Seamount. (B) Close-up of Iridogorgia fontinalis showing the spiraling central axis characteristic of the genus (Caloosahatchee Seamount, 1325 m). (C) Octocorals, including the chrysogorgiid Metallogorgia melanotrichos, and sponges are restricted to an outcrop of hard substrate at 1800 m on Corner Seamount. (D) Close-up of Metallogorgia melanotrichos showing its commensal brittle star Ophiocreas oedipus tightly wound at the center of branching crown. (E) A colony of Chrysogorgia tricaulis at 2000 m on Corner Seamount. (F) Close-up of Chrysogorgia tricaulis showing some of the characteristic features of chrysogorgiids: the metallic gold-coloured skeleton clearly visible through a thin layer of tissue, small, widely spaced delicate polyps and regular bifurcating branching (Corner Seamount, 2068 m). 48 Les Watling et al. Virgularia, Scytaliopsis), Pennatulidae (Pennatula, Gyrophyllum, Crassophyllum, Pteroeides). 2.3. Order Helioporacea Two families, two genera; monomorphic octocorals with a rigid aragonitic skeleton, including the monotypic tropical shallow-water blue coral (Helioporidae) and the stoloniferous Lithotelestidae, the latter includes deep-water species (Epiphaxum). 3. Phylogenetic Relationships The nineteenth century concept of an azoic deep sea lifeless below 545 m has long been abandoned, and the discovery of high species diversity in the bathyal benthos instead raised questions on the origin of deep-sea species (Hessler and Wilson, 1983; Lindner et al., 2008; Raupach et al., 2009). As noted above, 67% of octocoral families have representatives living in the deep sea, and some families are restricted to, or show a diversity maximum, below 200 m. In particular, the calcaxonian families Chrysogorgiidae, Isididae and Primnoidae are common, abundant and diverse in deeper waters, although shallow-water species are also known from each group. McFadden et al. (2006) analysed two mitochondrial genes (msh1 and nad2) to generate a phylogeny that included representatives of 103 genera from 28 of the 45 families of the subclass Octocorallia, including 10 deep-water species from the Chrysogorgiidae, Isididae and Primnoidae. In their phylogeny, these 10 species grouped to a single clade, in three strongly supported subclades that corresponded to each family, suggesting a single radiation from a common deep-sea ancestor. However, while the McFadden et al. study broadly sampled families across the subclass, many genera within the deep-sea clades were missing and no shallow-water species from those families were included. More recently, France and his students have greatly expanded the taxon sampling within these families (France et al., 2010; Pante and France, in preparation, unpublished data). An interesting result is that Chrysogorgiidae and Isididae are polyphyletic, but it is the inclusion of shallow-water taxa in these families that appears to be the cause: the strictly deep-water The pale round structure is the egg casing of a cirriteuthid octopod. (G) Dense meadow of the unbranched chrysogorgiid Radicipes sp. at .2100 m in Adak Canyon on the Aleutian Ridge. (H) Close-up of Radicipes sp. at 2014 m on Caloosahatchee Seamount. 49 Biology of Deep-Water Octocorals (A) (B) (C) (D) (E) Figure 2.3 Primnoidae. (A) Aggregation of the unbranched primnoid Calyptrophora clinata, yellow-coloured paramuriceid sea fans, and Acanella bamboo corals (smaller, bushy colour) near the summit of Kukenthal Peak (1217 m) on Corner Seamount. (B) A large planar primnoid, Calyptrophora microdentata (left), with a crinoid and several ophiuroids among its branches, at 2229 m on Nashville Seamount; a spiraling chrysogorgiid, Rhodaniridogorgia fragilis, is at lower right. (C) Thouarella hilgendorfi is a primnoid with ‘bottlebrush’-shaped colonies (Corner Seamount, 1805 m). (D) Tens of ophiocanthid ophiuroids clamber over the primnoid Candidella imbricata at 1980 m on Corner Seamount. (E) Narella pauciflora with single ophiuroid at 1265 m in the Northwest Providence Channel, Bahamas. 50 Les Watling et al. (A) (B) (D) (C) (E) (F) (G) Figure 2.4 Isididae, Keratoisidinae. (A) Three species of bamboo coral at the top of a 300 m tall wall in Little Abaco Canyon (1614 m depth). Inset shows the characteristic axial skeleton of isidids where a proteinaceous node (dark) alternates with a calcareous internode (white). Branching may arise from the node or, as shown here, the internode. (B) A field of whip-like bamboo corals, Lepidisis sp., stand 2 3 m tall (Balanus Seamount, 1560 m). (C) A candelabra-like bamboo coral branches at the nodes; chirostylid crabs live on the Biology of Deep-Water Octocorals 51 genera cluster on robust monophyletic clades, lending further support to an extensive in situ deep-sea radiation. In the McFadden et al. (2006) study, the deep-water calcaxonian families also showed a close relationship with sea pens (order Pennatulacea), another group dominated by deep-sea species. Williams (1993) hypothesized that sea pens arose from alcyoniid soft coral ancestors in the shallow-water tropics, and subsequently diversified into deeper water; however, molecular phylogenies suggest a relationship with the calcaxonian family Ellisellidae (McFadden et al., 2010). Dolan’s (2008) analysis of mitochondrial genes gave strong support to the idea that highly derived taxa occur in both shallow and deep water and that many species may have differentiated and dispersed from the deep sea to the shallows. Phylogenetic data are also available for the scleraxonian families Paragorgiidae and Coralliidae, which are almost exclusively deep water with only one known shallow-water species, Corallium rubrum, the precious red coral of the Mediterranean that has been harvested for more than 200 years for the jewelery trade. These two families are distinct morphologically, but genetically are very similar, and cluster as a monophyletic group in molecular phylogenies (Herrera et al., 2010), again indicating significant evolutionary radiation of octocorals in the deep sea. 4. Biogeography In this section, the major areas of study of deep-sea gorgonians and sources of species descriptions are summarized. 4.1. North Atlantic Octocorals have been known from deep water in the North Atlantic longer than from any other part of the world, although the Challenger expedition showed that octocorals could be found in the depths of all oceans. Because the taxonomic work on the group began in the North Atlantic it colony (Northwest Providence Channel, 1263 m). (D) Upper: A colony of the bushy Acanella arbuscula is attached by a holdfast to a pavement-like substrate (Caloosahatchee Seamount, 1688 m); Lower: another A. arbuscula lies on the substrate showing the rootlike holdfast typical of soft sediment-dwelling colonies (Caloosahatchee Seamount, 1316 m). (E) A lyrate colony with nodal branching at 1889 m on Balanus Seamount. (F) A bramble-like colony without visible nodes at 2246 m on Nashville Seamount. (G) A large planar, fan-like colony extends off a wall in Little Abaco Canyon at 1900 m. 52 Les Watling et al. is assumed that the fauna of the area is the most well known. While this is partially true (seamounts and the Mid-Atlantic Ridge were not sampled until the twentieth century), the other consequence of this early work is that most of the taxonomic descriptions were very limited (i.e., not very detailed or illustrated) and several species were described multiple times. Modern researchers have been finding many new species in areas previously inaccessible by standard ship-deployed sampling devices, and species are now being redescribed using modern illustrative techniques (such as scanning electron microscopy, for example). The first comprehensive list of North Atlantic octocoral species was compiled by Grasshoff (1982, 1985) as a part of a study of the octocorals and black corals of the Bay of Biscay, northeastern Atlantic. This was followed by the list and maps of Watling and Auster (2005) who put all records to that time into a GIS database. We have updated the Watling and Auster database (Table 2.1) including many new species described from the New England (NES) and Corner Rise (CR) seamounts as well as additional species from the Gulf of Mexico and Bahamas Escarpment. Only a few new species have been described from the northeastern Atlantic. Most of the records from both the eastern and western North Atlantic are from the late 1800s and early 1900s. Along the American east coast deep-water corals have been known since at least 1862 when Verrill noted the presence of a Primnoa on Georges Bank (Verrill, 1862). Several other deep-water coral species from depths greater than 200 fathoms off the coasts of New England and Nova Scotia were documented by Verrill during the latter part of the nineteenth century (Verrill, 1878a, b, 1879, 1884). Many specimens were captured during dredging programs instituted by the U.S. Fish Commission (as the National Marine Fisheries Service was known in those days), but an equally large number of specimens were brought to Verrill’s attention by schooner captains who had pulled the corals from the bottom while tub trawling. Photographic transects of the slope and canyon faunas south of Georges Bank recorded over 25 species of both hard corals and octocorals with several taxa dominant in the overall megafaunal community (Hecker et al., 1980, 1983; Valentine et al., 1980; Hecker, 1990). The NES and CR Seamount groups were extensively sampled by us using the submersible ‘Alvin’ and the remotely operated vehicle (ROV) ‘Hercules’ from 2003 to 2005. Eight seamounts in the NES and five peaks on three CR seamounts were sampled during a total of 34 dives and 226 h of bottom time. Depths sampled ranged from 3900 m at Retriever Seamount to 700 m on Corner Seamount. The average minimum and maximum depths were 1650 and 1970 m, respectively. We expected that most of the species sampled would be those already known from the North Atlantic, but that was not the case. In fact, of the approximately 45 IIA Western North Atlantic, East Canada to Cape Hatteras “Stoloniferous forms” Clavulariidae Anthelia borealis (Koren and Danielssen, 1883) Anthelia fallax Broch, 1912 Clavularia alba (Grieg, 1888) Clavularia griegii Madsen, 1944 Clavularia levidensis Madsen, 1944 Clavularia modesta (Verrill, 1874) Clavularia venustella Madsen, 1944 Clavularia arctica (Sars, 1860) Clavularia marioni (Von Koch, 1891) Clavularia rudis (Verrill, 1922) Sarcodictyon roseum (Philippi, 1842) Scleranthelia rugosa (Pourtalès, 1867) Scyphopodium ingolfi (Madsen, 1944) Telesto fructiculosa Dana, 1846 Telestula septentrionalis Madsen, 1944 IIC New England and Corner Seamounts, Bermuda X IIB Boreal Eastern Atlantic, northern MidAtlantic Ridge IA IIIA Western North LusitanianAtlantic, Cape Mediterranean Hatteras to Florida Straits, Antilles X X X X X X X X X X X X X Biology of Deep-Water Octocorals Table 2.1 Deep-sea species of octocorals from the North Atlantic Ocean arranged according to the geographical regions of Cairns and Chapman (2001) X X X X X X X X 6 1 X 7 2 X 8 53 (continued) 54 Table 2.1 (continued ) IIA Western North Atlantic, East Canada to Cape Hatteras IIC New England and Corner Seamounts, Bermuda IIB Boreal Eastern Atlantic, northern MidAtlantic Ridge IA IIIA Western North LusitanianAtlantic, Cape Mediterranean Hatteras to Florida Straits, Antilles “Massive body forms” Paralcyoniidae Paralcyonium spinulosum Delle Chiaje, 1822 X 0 Alcyoniidae Alcyonium acaule Marion, 1878 Alcyonium coralloides (Pallas, 1766) Alcyonium digitatum Linnaeus, 1758 Alcyonium palmatum Pallas, 1766 Anthomastus agassizii Verrill 1922 Anthomastus grandiflorus Verrill, 1878 Ceratocaulon wandeli Jungersen, 1892 X 1 X X X X X X X X X X 3 1 2 1 0 X X X 3 X X X 3 X X 6 X 0 1 Les Watling et al. Nephtheidae Capnella florida (Rathke, 1806) Capnella glomerata (Verrill, 1869) Gersemia rubiformis (Ehrenberg, 1834) 0 X X X X X 1 Paragorgiidae Paragorgia boschmai Bayer, 1964 Paragorgia coralloides Bayer, 1993 Paragorgia arborea (Linnaeus, 1758) Paragorgia johnsoni Gray, 1862 X Biology of Deep-Water Octocorals “Scleraxonia” Anthothelidae Anthothela grandiflora (M. Sars, 1856) Titanidium suberosum (Ellis and Solander, 1786) Victorgorgia josephinae Lopex-Gonzalez and Briand, 2002 1 1 1 2 X X X 1 Coralliidae Corallium bathryrubrum Simpson and Watling 2010 Corallium maderense (Johnson, 1899) Corallium medea Bayer, 1964 Corallium johnsoni Gray, 1860 Corallium niobe Bayer, 1964 Corallium rubrum (Linnaeus, 1758) Corallium tricolor (Johnson, 1899) X X 2 1 X 2 X X 2 X X X X 0 2 X 0 2 X X X X 5 (continued) 55 “Holaxonia” Acanthogorgiidae Acanthogorgia armata Verrill, 1878 Acanthogorgia aspera Pourtalès, 1867 Acanthogorgia hirsuta Gray, 1857 Acanthogorgia pico Grasshoff, 1973 Acanthogorgia schrammi (Duchassaing and Michelotti, 1864) Acanthogorgia sp. 56 Table 2.1 (continued ) IIA Western North Atlantic, East Canada to Cape Hatteras IIC New England and Corner Seamounts, Bermuda X X IA IIIA Western North LusitanianAtlantic, Cape Mediterranean Hatteras to Florida Straits, Antilles X X X X X 1 X 2 0 2 2 X X X X X X X X X Les Watling et al. Plexauridae Bebryce mollis Molippi, 1842 Dentomuricea meteor Grasshoff, 1977 Muricea pendula Verrill, 1868 Muriceides kuekenthali (Broch, 1912) Muriceides lepida Carpine and Grasshoff, 1975 Muriceides paucituberculata (Marion, 1882) Paramuricea ‘purple’ Paramuricea clavata (Risso, 1826) Paramuricea grandis Verrill, 1883 IIB Boreal Eastern Atlantic, northern MidAtlantic Ridge X X X X Biology of Deep-Water Octocorals X X X X X X X X X X X X X X X X X X X 4 4 4 X X 21 X X X 57 Paramuricea grayi (Johnson, 1861) Paramuricea macrospina (Koch, 1882) Paramuricea biscaya Grasshoff, 1977 Paramuricea candida Grasshoff, 1977 Paramuricea placomus (Linnaeus, 1758) X Placogorgia massiliensis Carpine and Grasshoff, 1975 Placogorgia becena Grasshoff, 1977 Placogorgia coronata Carpine and Grasshoff, 1975 Placogorgia graciosa (Tixier-Durivault and d’Hond, 1975) Placogorgia intermedia (Thomson, 1927) Placogorgia terceira Grasshoff, 1977 Spinimuricea atlantica (Johnson, 1862) X Swiftia casta (Verrill, 1883) Swiftia pourtalesii Deichmann, 1936 Swiftia borealis (Kramp, 1930) Swiftia dubia (Thomson, 1929) Swiftia pallida Madsen, 1970 Swiftia rosea (Grieg, 1887) Thesea talismani Grasshoff, 1986 Villogorgia bebrycoides (Koch, 1887) 3 Gorgoniidae Eunicella filiformis Studer, 1879 Eunicella gazella Studer, 1878 Eunicella labiata Thomson, 1927 (continued) 58 Table 2.1 (continued ) IIA Western North Atlantic, East Canada to Cape Hatteras IIC New England and Corner Seamounts, Bermuda IIB Boreal Eastern Atlantic, northern MidAtlantic Ridge Eunicella modesta Verrill, 1883 Eunicella verrucosa (Pallas, 1766) Leptogorgia sarmentosa (Esper, 1791) X 0 0 0 1 X X 5 X X X X X X X X X X X X X X Les Watling et al. “Calcaxonia” Chrysogorgiidae Chrysogorgia abludo Pante and Watling, in press Chrysogorgia agassizii (Verrill, 1883) Chrysogorgia artospira Pante and Watling, in press Chrysogorgia averta Pante and Watling, in press Chrysogorgia campanula Madsen, 1944 Chrysogorgia desbonni Duchassaing and Michelotti, 1864 Chrysogorgia elegans (Verrill, 1883) Chrysogrgia fewkesii Verrill, 1883 Chrysogorgia herdendorfi Cairns, 2001 Chrysogorgia multiflora Deichmann, 1936 Chrysogorgia quadriplex Thomson, 1927 Chrysogorgia spiculosa (Verrill, 1883) IA IIIA Western North LusitanianAtlantic, Cape Mediterranean Hatteras to Florida Straits, Antilles X X X X X X X X X X X X X 3 X 10 0 Ellisellidae Ctenocella (Ellisella) schmitti (Bayer, 1961) Ctenocella (Ellisella) paraplexauroides Stiasny, 1936 Ctenocella (Viminella) flagellum ( Johnson, 1863) Nicella granifera (Kölliker, 1865) X X X 10 8 X X X 0 0 0 1 X 3 X X 59 Primnoidae Acanthoprimnoa goesi (Aurivillius, 1931) Acanthoprimnoa pectinata Cairns and Bayer, 2004 Biology of Deep-Water Octocorals Chrysogorgia squamata (Verrill, 1883) Chrysogorgia thyrsiformis Deichmann, 1936 Chrysogorgia triacaulis Pante and Watling, in press Distichogorgia sconsa Bayer, 1979 Iridogorgia fontinalis Watling, 2007 Iridogorgia magnispiralis Watling, 2007 Iridogorgia splendens Watling, 2007 Iridogorgia pourtalesii Verrill, 1883 Metallogorgia melanotrichos (Wright and Studer, 1889) Radicipes sp. Radicipes challengeri (Wright, 1885) Radicipes gracilis (Verrill, 1884) Rhodaniridogorgia fragilis Watling, 2007 (continued) 60 Table 2.1 (continued ) IIA Western North Atlantic, East Canada to Cape Hatteras IIB Boreal Eastern Atlantic, northern MidAtlantic Ridge IA IIIA Western North LusitanianAtlantic, Cape Mediterranean Hatteras to Florida Straits, Antilles X X X X X X X X X X X X X X X X X X X X X X X X X X X X Les Watling et al. Callogorgia americana Cairns and Bayer 2002 Callogorgia gracilis (Milne Edwards and Haime, 1857) Callogorgia linguimaris Cairns and Bayer, 2002 Callogorgia verticillata (Pallas, 1766) Calyptrophora gerdae Bayer, 2001 Calyptrophora microdentata Pasternak, 1985 Calyptrophora trilepis (Pourtalès, 1868) Calyptrophora antilla Bayer, 2001 Calyptrophora clinata Cairns, 2007 Candidella imbricata (Johnson, 1862) Convexella jungerseni (Madsen, 1944) Narella alvinae Cairns and Bayer, 2003 Narella bellissima (Kükenthal, 1915) Narella pauciflora Deichmann, 1936 Narella spectabilis Cairns and Bayer, 2003 Narella laxa Deichmann, 1936 Narella regularis (Duchassaing and Michelotti, 1860) Narella versluysi (Hickson, 1909) IIC New England and Corner Seamounts, Bermuda Biology of Deep-Water Octocorals Paracalyptrophora carinata Cairns and Bayer, 2004 Paracalyptrophora duplex Cairns and Bayer, 2004 Paracalyptrophora simplex Cairns and Bayer, 2004 Paracalyptrophora josephinae (Lindström, 1877) Paranarella watlingi Cairns, 2007 Parastenella atlantica Cairns, 2007 Plumarella aculeata Cairns and Bayer, 2004 Plumarella aurea (Deichmann, 1936) Plumarella dichotoma Cairns and Bayer, 2004 Plumarella laxiramosa Cairns and Bayer, 2004 Plumarella pellucida Cairns and Bayer, 2004 Plumarella pourtalesii (Verrill, 1883) Primnoa resedaeformis (Gunnerus, 1763) X Primnoella polita Deichmann, 1936 Primnoella jungerseni Madsen, 1944 Thouarella bipinnata Cairns, 2006 X Thouarella grasshoffi Cairns, 2006 Thouarella hilgendorfi (Studer, 1879) 3 Isididae Acanella arbuscula (Johnson, 1862) X X X X X X X X X X X X X X X X X X X X 11 3 X X 26 X X 10 X 61 (continued) 62 Table 2.1 (continued ) IIA Western North Atlantic, East Canada to Cape Hatteras Total Alcyonacea IIB Boreal Eastern Atlantic, northern MidAtlantic Ridge IA IIIA Western North LusitanianAtlantic, Cape Mediterranean Hatteras to Florida Straits, Antilles X X X X X X X X X X X X X X X 4 28 X X X X 6 40 3 24 5 57 X X X X X X 11 85 Les Watling et al. Chelidonisis aurantiaca Studer, 1891 Eknomisis dalioi Watling and France, 2011 Isidella longiflora (Verrill, 1883) Isidella elongata (Esper, 1788) Isidella lofotensis Sars, 1868 Keratoisis ornata Verrill, 1878 Keratoisis flexibilis (Pourtalès, 1868) Keratoisis grayi Wright, 1869 Lepidisis cyanae Grasshoff, 1986 Lepidisis caryophyllia Verrill, 1883 Lepidisis longiflora Verrill, 1883 Lepidisis macrospiculata (Kükenthal, 1915) Lyrate bamboo Triple junction bamboo Bamboo whip Bramble bamboo IIC New England and Corner Seamounts, Bermuda Biology of Deep-Water Octocorals 63 species tentatively identified so far, only 11 were previously known. New species of Coralliidae (Simpson and Watling, 2011), Chrysogorgiidae (Cairns, 2001; Watling, 2007; Pante and Watling, in press) and Primnoidae (Cairns, 2006, 2007a) have been described, and work is just beginning on the Keratoisidinae subfamily of Isididae (Watling and France, in press). There were a number of early expeditions sampling octocorals in the northeastern Atlantic that produced a moderately comprehensive look at the fauna both in the Mediterranean and in the Lusitanian Province (which ranges from Northwest Africa to off the British Isles (Studer, 1901; Thomson, 1927; Grasshoff, 1986). Eleven new species of octocorals were also discovered during the Biaçores sampling program (Tixier-Durivault and d’Hondt, 1974). More recently, major European Union projects are imaging deep-sea corals in situ, with collections that will improve our understanding of their distribution and biogeography, e.g. HERMES and HERMIONE programs (Weaver et al., 2009), APLABES (Mediterranean; Corselli, 2010) and MAR-ECO (Mortensen et al., 2008). 4.2. Indo-West Pacific Knowledge of deep-water octocorals of the Indo-West Pacific region is meagre, and contrasts with the wealth of information on shallow-water taxa (Costello et al., 2010). Focusing on the global biogeography of seamount fauna, Clark et al. (2010) identified the Indian and Central Pacific oceans as areas that were particularly under-sampled by deep-sea biologists. Investigations of the deep waters of the Indo-West Pacific started with the great exploratory cruises of the nineteenth and early twentieth centuries, most notably those of the ‘Challenger’, the ‘Siboga’, and the ‘Albatross’. More contemporarily, the Muséum National d’Histoire Naturelle, Paris, France (MNHN) and the Institut de Recherche pour le Développement (France) have organized a series of cruises aimed at exploring and characterizing the deep-benthic fauna of the southwest Pacific Ocean. Since the inception of the Tropical Deep Sea Benthos Program (formerly MUSORSTOM) in the early 1980s, over 3600 stations have been sampled between about 100 and 1500 m depth from Taiwan to the Marquesas Islands (Bouchet et al., 2008). Deep-water octocorals are well represented within these collections (Pante and France, unpublished observations), and even midway through the sampling program Bayer and Stefani (1988b) exclaimed the collections were ‘la plus extraordinaire du siècle’. However, to date only a few of the deep-water octocorals have been described (Bayer and Stefani, 1987a, 1988a, b; Bayer, 1990; Pante and France, 2010) and much remains to be done. Butler et al. (2010) reviewed data on the marine biodiversity of the Australian EEZ, and noted that waters below 1200 m were seldom 64 Les Watling et al. sampled but when they were, a wealth of unknown species was revealed. A recent cruise on the shelf and slope of Western Australia (100 1000 m, Williams et al., 2010) produced 141 species of soft corals, 80% of which are estimated to be new to science (Butler et al., 2010). Data from cold-water octocorals of the Indian Ocean are particularly meagre. Thomson and Henderson (1906) produced early monographic works based on collections from the Royal Indian Marine Survey Ship ‘Investigator’, and Grasshoff (1988) reported 27 species of gorgonians from the vicinity of St. Paul and Amsterdam Islands in the southern Indian Ocean, none of which were known from Antarctic or subAntarctic waters or South Africa. Rather, where the comparative data were available, species had an affinity with temperate and subtropical tropical regions, and in particular with the northeastern Atlantic. Ingole and Koslow (2005) reviewed information on the deep-sea ecosystems of the area and noted that although the overall benthic assemblages appeared diverse, ‘virtually nothing is known of such major groups as (. . .) soft corals’ (see also Wafar et al., 2011). Faunal assemblages from seamounts, a common habitat for deep-water octocorals, are particularly understudied (Sautya et al., 2011). Despite the chronic lack of data from the Indo-West Pacific, and particularly from the Coral Triangle (the shallow epicentre of marine diversity), deep waters of this region appear to shelter highly diverse octocoral assemblages. For example, the Malay Archipelago alone contains an estimated 25 species of Chrysogorgia (Chrysogorgiidae), which is more than 40% of the current species richness of this genus. Corals tend to dominate seamounts in the southwest Pacific (Samadi et al., 2007), and observations made on the northwest end of the Norfolk Ridge (southeast of New Caledonia; Pante and Samadi, unpublished observations) suggest that chrysogorgiid and primnoid octocorals have patchy distributions and greatly contribute to the overall biomass of some seamount summits. In addition, chrysogorgiid corals (Chrysogorgia and Radicipes) have been mentioned as early colonists on trawled Tasmanian seamounts (Althaus et al., 2009). 4.3. Hawaii Hawaii is a popular tourist destination due in large part to its shallow coral reefs and abundant coral reef fishes. But, in contrast to many tropical reef areas, Hawaii has only four shallow octocoral species, one of which may be introduced. On the other hand, the diversity of deep-sea octocorals is extremely high, with 70 species confirmed (Table 2.2), and many others awaiting description. Studies on deep-sea octocorals of Hawaii began with sampling by the US Fisheries Commission Steamer ‘Albatross’ around the main Hawaiian Islands in 1902. Fifty-two species were obtained during this expedition 65 Biology of Deep-Water Octocorals Table 2.2 Species of deep-sea octocorals recorded from the Hawaiian Archipelago Family Clavulariidae Clavularia grandiflora (Nutting, 1908) Family Telestidae Telestula spiculicola (Nutting, 1908) Telestula corrugata (Nutting, 1908) Family Alcyoniidae Anthomastus fisheri Bayer, 1952 Family Coralliidae Corallium abyssale Bayer, 1956 Corallium kishinouyei Bayer, 1996 Corallium laauense Bayer, 1956 Corallium niveum Bayer, 1956 Corallium regale Bayer, 1956 Corallium secundum Dana, 1846 Paracorallium tortuosum (Bayer, 1956) Family Paragorgiidae Paragorgia dendroides Bayer, 1956 Family Siphonogorgiidae Siphonogorgia alexanderi (Nutting, 1908) Siphonogorgia collaris Nutting, 1908 Family Anthothelidae Anthothela nuttingi Bayer, 1956 Family Keroeididae Keroeides pallida Hiles, 1899 Keroeides fallax Bayer, 1956 Keroeides mosaica Bayer, 1956 Family Plexauridae Villogorgia tenuis (Nutting, 1908) Bebryce brunnea (Nutting, 1908) Paramuricea hawaiiensis Nutting, 1908 Pseudothesea sp.cf. P. plaeoderma Nutting, 1910 Thesea sp.cf. T. ramosa Nutting, 1910 Paracis miyajimai (Kinoshita, 1909) Paracis spinifera (Nutting, 1912) Anthomuricea tenuispina Nutting, 1908 Anthomuricea sp. cf. A. divergens Kükenthal, 1919 Anthomuricea sp. cf. A. reticulata Nutting, 1910 Cyclomuricea flabellata Nutting, 1908 Family Acanthogorgiidae Acanthogorgia sp. Family Primnoidae Thouarella (Diplocalyptra) biserialis (Nutting, 1908) Thouarella (Euthouarella) hilgendorfi (Studer, 1878) Plumarella circumoperculum Cairns, 2010 Callogorgia robusta Versluys, 1906 Callogorgia formosa (Kükenthal, 1907) Callogorgia gilberti Nutting, 1908 Fanellia tuberculota (Versluys, 1906) Fanellia euthyeia Bayer and Stefani, 1989 Fanellia medialis Bayer and Stefani, 1989 Narella dichotoma (Versluys, 1906) Narella bowersi (Nutting, 1908) Narella ornata Bayer, 1995 Narella gigas (Cairns and Bayer, 2007) Narella alata Cairns and Bayer, 2007 Narella vermifera Cairns and Bayer, 2007 Narella macrocalyx Cairns and Bayer, 2007 Narella muzikae Cairns and Bayer, 2007 Narella hawaiinensis Cairns and Bayer, 2007 Paracalyptrophora echinata Cairns, 2009 (continued) 66 Les Watling et al. Table 2.2 (continued ) Paracalyptrophora hawaiinensis Cairns, 2009 Calyptrophora wyvillei Wright, 1885 Calyptrophora angularis (Nutting, 1908) Calyptrophora clarki Bayer, 1951 Calyptrophora pileata Cairns, 2009 Calyptrophora alpha Cairns, 2009 Parastenella bayeri Cairns, 2010 Candidella gigantea (Wright and Studer, 1889) Candidella helminthophora (Nutting, 1908) Family Chrysogorgiidae Rhodaniridogorgia superba (Nutting, 1908) Radicipes spiralis (Nutting, 1908) Chrysogorgia japonica (Wright and Studer, 1889) Chrysogorgia papillosa Kinoshita, 1913 Chrysogorgia scintillans Bayer and Stefani, 1988 Chrysogorgia stellata Nutting, 1908 Metallogorgia melanotrichos (Wright and Studer, 1889) Family Isididae Lepidisis olapa Muzik, 1978 Isidella trichotoma Bayer, 1990 Keratoisis flabellum (Nutting, 1908) Acanella dispar Bayer, 1990 (Nutting, 1908). No further collection occurred until the ‘Sango’ Expedition of the early 1970s (Grigg and Bayer, 1976) whose focus was documenting the distribution of precious corals around the islands. In all a further 41 new species or new species records were added to the Hawaiian fauna. However, much of this material has not been formally described. Muzik (1978) described one new species of bamboo coral, but the 18 new plexaurid species in her dissertation (Muzik, 1979) were never formally published. Recent additions to the Hawaiian fauna have all been in the family Primnoidae, with major revisions by Cairns and Bayer (2008) and Cairns (2009, 2010) adding 12 species. At least six new genera and species of bamboo corals and five new species of chrysogorgiids are in the process of being described from collections recently made in the northwest Hawaiian Islands. It is expected that many more new species will be found as further collections are made. 4.4. Japan Much taxonomic work has been done on octocorals from Japan, primarily from Sagami Bay, a large and deep embayment near Tokyo. The ‘Challenger’ occupied three deep stations in the Sagami Bay area. Large collections of alcyonaceans were produced by Dr. Franz Doflein, who sampled a few stations between 120 and 800 m in 1904 1905 (Doflein, 1906), and Sixten Bock’s Expedition to Japan in 1914. These specimens were described by Kükenthal (1909), Kükenthal and Gorzawsky (1908) and Biology of Deep-Water Octocorals 67 Aurivillius (1931). In 1906, the US Fish Commission Steamer ‘Albatross’ sampled extensively in the Japanese seas, occupying 27 stations deeper than 300 m, and producing 31 species of deep-dwelling octocorals, including pennatulaceans (Nutting, 1912). Additional sampling in the Sagami Bay area, especially the Okinose Bank from 360 to 750 m, produced more new species of Primnoidae and Chrysogorgiidae (Kinoshita, 1908, 1913). Matsumoto et al. (2007) note that to date 260 octocoral species are known from Japan, of which 144 are gorgonians and 36 pennatulaceans, and of those 120 can be found in the upper bathyal to abyssal depths. Matsumoto et al. note that more than 190 species recorded from the area over 100 years ago have not been re-collected during modern surveys. Apparently the Okinose Bank area, which produced most of the deep-water species, could not be sampled due to gear restrictions and high ship traffic. 4.5. Aleutian Islands of Alaska Knowledge of Alaskan and Aleutian corals dates back to the late 1800s, and recognition of the rich diversity of octocorals began with Nutting’s (1912) description of material collected during the ‘Albatross’ expeditions to the northwest Pacific. Since the 1970s, National Oceanic and Atmospheric Administration (NOAA) fisheries scientists have been documenting the diversity of cold-water corals through by-catch (non-target species taken by bottom trawls) survey data, and more recently on exploratory cruises using ROVs and submersibles (Heifetz et al., 2005; Stone and Shotwell, 2007). The estimated diversity includes 9 species of soft corals, 6 stoloniferans, 10 pennatulaceans, and more than 60 gorgonians. Gorgonians (i.e. sea fans belonging to the Calcaxonia and Holaxonia) dominate the sessile invertebrate fauna concentrated on the hard substrates of the continental shelf and upper slopes of the Aleutian Islands (Stone, 2006; Stone and Shotwell, 2007), especially on islands west of 169 W where exposed hard substrates are more common (Heifetz et al., 2005). The most abundant families are the Primnoidae, Plexauridae and Isididae, respectively (Stone and Shotwell, 2007). Some genera are known only from Alaskan waters (e.g. monotypic plexaurids Cryogorgia and Alaskagorgia) or the far northern Pacific from Alaska-to-Japan (e.g. acanthogorgiid Calcigorgia (three spp.) and primnoid Arthrogorgia (four spp.)) (Sánchez and Cairns, 2004; Williams, 2005; Dautova, 2007). Many of the typically deep-sea plexaurid and primnoid genera here also range into waters shallower than 200 m. Of particular note is Primnoa pacifica, which can be found as shallow as 9 m depth in Alaskan fjords, the shallowest known record for this quintessentially deep-sea family (Cairns and Bayer, 2005). Stone et al. (2005) suggest these are deep-water emergents, which is supported by molecular phylogenies (S. France, unpublished data), and that P. pacifica may be a pioneer species in recently deglaciated habitats. 68 Les Watling et al. 4.6. Other regions Broch (1935) documented 16 species (five new) from a Russian Expedition to the northern Sea of Japan and the Sea of Okhotsk. Jamieson et al. (2007) compiled a list of 21 species of alcyonaceans from Canadian waters off British Columbia. Because of the affinities of many of these species with those from Alaska, they expect the fauna to include many more species. Cairns (2007b) listed 22 species (six newly described therein) of calcaxonians from the eastern Pacific between the equator and the Gulf of Alaska. Deep-water octocorals have been collected for many years from Antarctic waters (Kükenthal, 1912), however, the fauna has never been treated in a comprehensive manner (Bayer, 1993). Besides the works cited by Bayer (1993), additional species have been described in recent years by LópezGonzález and Gili (2000, 2001, 2005) and López-González et al. (2002). 5. Distribution of the Three Major Deep-Sea Families 5.1. Chrysogorgiidae 5.1.1. Systematics and evolution Deep-water chrysogorgiids have been known since the family was first described in 1883 by Verrill, based on material collected during the Blake Expedition. The Chrysogorgiidae is currently recognized as an assemblage of 14 genera ranging from rare, putatively local endemics (e.g. Distichogorgia off Northern Florida) to globally distributed genera (e.g. Chrysogorgia and Metallogorgia). The depth distribution of these genera ranges from shallow-water, reef-dwelling corals (e.g. Stephanogorgia) to deep stenobathic (e.g. Iridogorgia 567 2311 m) and eurybathic (Chrysogorgia, 10 4492 m) genera. A molecular phylogenetic analysis based on 12 genera (Pante and France, in preparation) revealed that the family is polyphyletic, with only Chrysogorgia, Pseudochrysogorgia, Iridogorgia, Rhodaniridogorgia, Radicipes and Metallogorgia forming a monophyletic clade (here termed the Monophyletic Chrysogorgiidae Clade, or MCC). These genera all comprise species of cold, deep waters. While morphological data suggest that Chrysogorgia (the widest-ranging genus within the MCC) has been sampled above 100 m, there is no genetic support to suggest that chrysogorgiids of the MCC can be found above that depth. There is strong support for close evolutionary relationships among the MCC, the Primnoidae and the isidid subfamily Keratoisidinae. The latter two groups are also Biology of Deep-Water Octocorals 69 predominantly found in the deep sea, suggesting that the MCC evolved and diversified in deep waters (Pante and France, in preparation). 5.1.2. Morphology Genera of the MCC (Fig. 2.2) are recognized by their strongly calcified, iridescent skeleton, thin coenenchyme and slender polyps invariably armoured by relatively smooth, simple sclerites that are recognized by a circular light extinction pattern under polarized light (Bayer, 1956; for a complete diagnosis, see Cairns, 2001). The axis of most colonies spiral or coil. In Iridogorgia the axial skeleton spirals with branches originating along one side, but in Rhodaniridogorgia the branches originate in a spiral around a twisted axis (Watling, 2007). While major differences in colony organization separate genera of the MCC, cases of convergence and plasticity have been observed. Although Iridogorgia is described as having undivided branches, colonies with bifurcating branches have been observed (Watling, unpublished observations). Colony morphology and branching pattern can change with growth, as in Metallogorgia melanotrichos, where adult colonies shed branches along the axial skeleton, retaining only the upper branches in the adult (Mosher and Watling, 2009). In Chrysogorgia, the branching sequence (the direction and tightness of the spiral formed by branches emerging from the main stem) varies along the axial skeleton, and can therefore change with age (Cairns, 2001). Pante and Watling (2012, in press) compared variation among Chrysogorgia specimens at the intra- and inter-specific levels, based on material collected on the New England and Corner Seamounts (northwestern Atlantic), and found complete congruence between genetic variability at the mtDNA msh1 gene and the following morphological characters: branching sequence and interbranch distance along the axial skeleton, branch morphology, polyp shape and presence of polyps on the main stem, and zonation of sclerite types. However, some incongruence between genetics and morphology was observed for specimens recently collected from the Bahamas Escarpment. 5.1.3. Biogeography To explore biogeographic patterns within the MCC, a database of worldwide records was built based on the literature, museum records and our own collections. Information on a total of 913 biogeographic locations and 2111 colonies was compiled. The distribution of MCC taxa was compared against the global distribution of octocorals. The location of all sampling records for octocorals provides an estimate of sampling effort at a global scale, and constitutes a null model of where chrysogorgiids could be found. Information on over 16,800 octocoral samples was compiled from museum records, the literature and our collections. All duplicate biogeographic locations were removed, providing a total of 7304 unique 70 Les Watling et al. Figure 2.5 Occurrence records for species of the genus Radicipes. Figure 2.6 Occurrence records for species of the genus Chrysogorgia. biogeographic locations where octocorals were found (details on data sources in Pante, 2011). Most species of the MCC were described based on only a few specimens, and biogeographic patterns inferred from the known distribution of colonies and species may therefore be highly biased. Of the 89 species in the MCC (including species variants and unpublished species descriptions), 32% are known from single specimens, and more than 85% are known from 10 colonies or less (Pante, 2011). Despite these limitations, some patterns emerge. The most striking is perhaps the broad distributions (latitudinal and bathymetric) of Radicipes (Fig. 2.5) and Chrysogorgia Biology of Deep-Water Octocorals 71 Figure 2.7 Occurrence records for species of the genus Metallogorgia. Figure 2.8 Occurrence records for species of the genus Iridogorgia. (Fig. 2.6) compared to Metallogorgia (Fig. 2.7), Iridogorgia (Fig. 2.8) and Rhodaniridogorgia (Fig. 2.9). Indeed, in a phylogeny inferred from DNA sequences, these last three genera form a strongly supported monophyletic clade (Pante and France, in preparation), and the biogeographic distribution of these genera is therefore linked to their inferred evolutionary history. All genera of the MCC, except Pseudochrysogorgia (this genus being known from only five colonies collected in the Coral Sea and NE of New Zealand), are widely distributed in the North Atlantic and the 72 Les Watling et al. Figure 2.9 Occurrence records for species of the genus Rhodaniridogorgia. Pacific. Very little is known of the distribution of MCC species in the South Atlantic, the Indian Ocean, East Pacific and Antarctica. For instance, only seven species are known from the Indian Ocean (five Chrysogorgia and two Radicipes), but this region is overall poorly known for octocorals. In our database fewer than 70 deep-water (.200 m) stations containing octocorals come from the Indian Ocean, so the apparent rareness of chrysogorgiids is likely an artifact of limited sampling. Chrysogorgiids are apparently also rare in Antarctic waters (only two MCC species were sampled Chrysogorgia antarctica and Radicipes sp.). Significant sampling has been conducted in this region (420 stations sampled poleward of 60 S contained octocoral specimens), and the depth of these stations (20 5043 m) fully overlaps with the known bathymetric distribution of the MCC. The distribution of MCC species is significantly skewed toward the northern hemisphere, with a peak of biodiversity around 25 N. In Chrysogorgia, 55% of 71 species and variants are found between 0 N and 30 N. Overall, this pattern closely mirrors the overall distribution of octocorals. Chrysogorgia has the widest latitudinal range and was found from Antarctica (C. antarctica, 76.5 S) to the Denmark Straight (C. agassizii, 64.7 N). Radicipes is almost equally widely distributed, being found between 62.1 S (Radicipes sp.) and 62.95 N (R. gracilis). Iridogorgia displays the narrowest latitudinal range, all seven nominal species being found between 0 N and 39 N, however, an unidentified colony has been collected from 15.98 S, in the Vanuatu Archipelago. Metallogorgia has been sampled between 42.72 S and 39.96 N. This genus currently contains four nominal species, one (M. melanotrichos) being distributed across the entire latitudinal, longitudinal and bathymetric range of the genus, but Biology of Deep-Water Octocorals 73 the three other species are rare. Although genetic and morphological evidence support the validity of at least two species (M. melanotrichos and M. macrospina; Pante and France, 2010), the legitimacy of M. splendens (see Deichmann, 1936) and M. tenuis still needs to be tested. As with latitudinal patterns, the bathymetric distribution of MCC genera and species closely mirrors overall sampling efforts and the distribution of known octocorals. Once again, Chrysogorgia is the most widely distributed of the MCC genera. The shallowest depth at which Chrysogorgia was sampled is unclear, because key reports are based on trawled specimens for which large depth ranges (e.g. 0 1000 m) were recorded. However, some records of shallow collections of Chrysogorgia exist: a specimen of C. curvata was sampled between 37 and 55 m in Hawaii (USNM 91906). A total of nine species were recorded from waters shallower than 200 m, and genetic fingerprinting confirmed the presence of Chrysogorgia as shallow as 101 m in the Gulf of Mexico (Pante and France, in preparation). The deepest collection of Chrysogorgia comes from between 4163 and 4492 m on Derickson Seamount (Gulf of Alaska; USNM 1081181). Maximum species diversity in this genus is observed between 500 and 700 m depth, where half of the species and variants in the genus can be observed; 80% are found between 500 and 2000 m. Similarly, Radicipes has a very broad bathymetric range, and most species (six out of seven described) can be observed between 700 and 1400 m depth. Iridogorgia and Metallogorgia have a narrower bathymetric distribution, found no shallower than 567 m and no deeper than 2311 m. Species diversity in Iridogorgia peaks between 1300 and 1400 m, where six of seven described species can be observed. Between 1000 and 1100 m depth, three of the four species of Metallogorgia can be observed. Chrysogorgiid corals are found on hard and soft substrates. Both Radicipes and Chrysogorgia include species that are characterized by a strongly calcified holdfast adapted to anchoring in soft sediments. Some species of Chrysogorgia, and all species of Iridogorgia, Metallogorgia and Pseudochrysogorgia have a discoidal holdfast that is attached to hard substrates. In situ observations of chrysogorgiid corals on seamounts and slopes have confirmed the presence of chrysogorgiids on hard and soft substrates. Radicipes and Chrysogorgia can be locally abundant. Fields of Radicipes were observed with ROVs at .2800 m depth on Aleutian Islands slopes in Alaska (Fig. 2.2; France and Watling, unpublished observations) and Pedra Seamount (Fig. 2.4B of Clark et al., 2010). Althaus et al. (2009) reported densities of 2.34 colonies m 22 of Radicipes sp. (a new species being described by Phil Alderslade) on Pedra and Mongrel Seamounts. Similarly, dozens to hundreds of colonies of Chrysogorgia were sampled at single dredging stations on seamounts of the Norfolk Ridge (Pante and Samadi, unpublished). 74 Les Watling et al. MCC species are known from continental and island slopes, seamounts and canyons. The available body of evidence suggests that the distribution of species is influenced by depth and the nature of the substrate, rather than geological setting (e.g. seamount versus island slope; Pante, 2011). In a global genetic survey of Chrysogorgia from seamounts, continental and island slopes, Pante (2011) found little evidence for seamount-scale endemism, geographic restriction being most likely due to sampling artifacts. However, Atlantic Chrysogorgia haplotypes tended to be more widely distributed than their Pacific counterparts. This pattern may reflect different dispersal abilities of Atlantic and Pacific species, or conversely, be an artifact of sampling across depth gradients. Indeed, the Atlantic material available for genetic study comes from much deeper waters than the material collected in the southwest Pacific. 5.2. Isididae The bamboo coral family Isididae (Fig. 2.4) currently is divided into four subfamilies, the Isidinae, Keratoisidinae, Mopseinae and Circinisidinae (Alderslade, 1998). The Isidinae includes the type species of the family, Isis hippuris, which is confined to shallow tropical waters and two rarely encountered deep-sea genera (Chelidonisis, Muricellisis). Mopseins and circinisidins are found in the southern hemisphere in both shallow warm as well as cold and deep waters, while only the globally distributed keratoisidins are found exclusively in deep and cold waters. Kükenthal (1919, 1924) suggested that the family was probably polyphyletic, with the arrangement of proteinaceous nodes alternating with calcareous internodes arising four separate times. Beginning with Studer (1887), students of the group gradually proposed subdividing the family into several subfamilies, culminating in the scheme proposed by Alderslade (1998). Placing all the genera into these subfamilies is not without problems, however. For example, Orstomisis has retractile polyps, as do the three genera of the Isidinae, but in Orstomisis the sclerites are all rods as they are in the Keratoisidinae, while the Isidinae are characterized by six-, eight-radiates and tuberculate capstans and clubs. Sclerisis has been considered a keratoisidin only because the polyp sclerites are arranged longitudinally; otherwise its sclerites are more similar to genera in the Mopseinae, and it is unlike most of the keratoisidins in not having pharyngeal sclerites. Alderslade (1998) recently thoroughly revised the subfamilies Mopseinae and Circinisidinae (adding 16 new genera), but the most recent treatments that include Keratoisidinae have been regional and incomplete (Grant, 1976; Bayer and Stefani, 1987a). Alderslade (1998) defines the Keratoisidinae as having ‘sclerites in the form of more or less prickly rods or spindles, longitudinally arranged on the polyps’ (p. 20). Bayer and Stefani (1987b) suggested an additional sclerite character for the subfamily the presence of small Biology of Deep-Water Octocorals 75 double stars or thorny rods in the pharyngeal wall. These specialized, very small, sclerites are not present in Caribisis, Sclerisis or Australisis, and so the inclusion of these genera in the Keratoisidinae is questionable. A diversity of colony forms is represented among the genera currently included in the subfamily Keratoisidinae (Fig. 2.4). For example, Keratoisis, as originally constituted, included branched fan, ‘open bushy’ species, and unbranched whip-like forms. Lepidisis, as described by Verrill (1883), included both unbranched forms and some that branched at the nodes. Isidella is sparsely branched from the nodes and is more or less planar. Acanella has a verticillate bushy form with multiple branches occurring at the nodes. A group of genera, including Australisis, Orstomisis and Sclerisis, have a dense bushy growth form, but Orstomisis branches at the nodes (and a new Hawaiian species recently acquired is fan-like), whereas the other two branch at the internodes. There has been much debate in the literature about whether colony morphology, and in particular branching, should be used to define Keratoisidinae genera. France (2007) analysed DNA sequences of the mitochondrial msh1 gene (1426 nucleotides) from 35 isidid colonies to address the issue of whether the genera Lepidisis and Keratoisis should be distinguished solely on the basis of ‘colony branching’, as was proposed by Muzik (1978), and advanced in subsequent taxonomic keys (Bayer, 1981, 1990). The msh1 phylogeny did not support a diagnosis of Lepidisis and Keratoisis based on colony branching. Additionally, even this limited sampling of 35 Keratoisidinae suggested at least 14 species were distributed among six major clades, when only four genera were thought to be included at the start of the study. Furthermore, the ‘colonies unbranched’ character state is not a synapomorphy of any clade, but rather is distributed throughout the tree alongside branching colonies, which suggests that this is one morphological character that should not be used in generic diagnoses. The subfamily Keratoisidinae currently contains 57 species in eight genera, although our collections contain about 25 additional undescribed species, and there are many more in the museum collections we have surveyed. At least two of the genera, Keratoisis and Lepidisis, are problematic and require extensive revision. One new genus from the New England seamounts has recently been described by Watling and France (2011) and another from Australian waters by Alderslade (in preparation), further highlighting the unexplored diversity of this deep-sea family. The most widespread genera are Acanella (Fig. 2.10), Isidella (Fig. 2.11), Lepidisis (Fig. 2.12) and Keratoisis (Fig. 2.13). Acanella contains nine species currently known from the North Atlantic, central North Pacific and Indo-West Pacific regions. There do not seem to be many taxonomic issues in this genus, although at present there is no way to adequately distinguish the North Atlantic species, A. arbuscula and A. eburnea. 76 Les Watling et al. Figure 2.10 Occurrence records for species of the genus Acanella. Figure 2.11 Occurrence records for species of the genus Isidella. On the other hand, Isidella, Keratoisis and Lepidisis all have major taxonomic problems that need resolving. Isidella was originally described from the northeast Atlantic and Mediterranean, and now includes species from the central and North Pacific, the latter being the most problematic because it is not similar to the Atlantic species morphologically. Lepidisis was created by Verrill (1883) to accommodate three species collected by the Steamer ‘Blake’ in deep water from the US east coast and Caribbean. Unfortunately, Verrill’s original description was incorrect with respect to sclerite characters and this error, along with Muzik’s (1978) assertion that the genus was characterized by an unbranched, whip-like form, has resulted in species from widely disparate areas being added to the genus. Biology of Deep-Water Octocorals 77 Figure 2.12 Occurrence records for species of the genus Lepidisis. Figure 2.13 Occurrence records for species of the genus Keratoisis. The genus Keratoisis is also very broadly distributed and seems to be a convenient group in which to place any species that branches from the internodes. Several polyp morphologies can be seen amongst these species suggesting that Keratoisis, too, is a genus in need of revision. 5.3. Primnoidae Cairns and Bayer (2009) call the Primnoidae the ‘quintessential deepwater octocoral family’. It has a vertical distribution spanning 8 5850 m, the deepest known being Convexella krampi, collected from the Kermadec Trench. Primnoids are most common at bathyal-slope depths and there 78 Les Watling et al. are only a few records from shallow water. Primnoids are immediately recognizable by examining the polyps, which are more or less covered by an armour of scale-like sclerites, many of which are ornamented with spines, ridges, granules or tubercles. Colonies can be characterized by eight branching patterns (dichotomous planar, dichotomous lyriform, dichotomous bushy, dichotomous sparse, sympodial, pinnate opposite, pinnate alternate, bottlebrush or unbranched); although these colony shapes are often used to key out taxa, they may be highly variable even within a genus and have little phylogenetic value (Cairns and Bayer, 2009). Primnoids may grow to large size; colonies of Primnoa can reach 2 m in height and several metres in width (Fig. 2.3). With 36 genera and 233 valid species, Primnoidae is the fourth largest octocorallian family. The family was first described by Milne Edwards (1857), and by the time of Wright and Studer’s (1889) report on the Alcyonaria of the ‘Challenger’ Expedition, there were already 14 described genera. Cairns and Bayer (2009) found cladistic support to suggest that the primnoids originated in the Antarctic, where they are the dominant (16 genera) gorgonian family (López-González et al., 2003). Some of these Antarctic genera are monotypic (Aglaoprimnoa, Armadillogorgia, Arntzia, Onogorgia, Tokoprimno, Dasystenella) but others have diversified (Ainigmaptilon (five spp., Fig. 2.14), Metafannyella (four), Fannyella (four)). A few primnoid genera are truly cosmopolitan (Narella (Fig. 2.15), Thouarella (Fig. 2.16), Parastenella (Fig. 2.17) and Callogorgia (Fig. 2.18)). Several genera have Atlantic Pacific distributions, often involving sister taxa (Calyptrophora (Fig. 2.19), Candidella (Fig. 2.20), Plumarella (Fig. 2.21) and Primnoa (Fig. 2.22)). Others, such as Fanellia (Fig. 2.23) and Arthrogorgia (Fig. 2.24, squares) are strictly Pacific in Figure 2.14 Occurrence records for species of the genus Ainigmaptilon. Biology of Deep-Water Octocorals 79 Figure 2.15 Occurrence records for species of the genus Narella. Figure 2.16 Occurrence records for species of the genus Thouarella. distribution. Two genera, Fannyella (Fig. 2.24, triangles) and Primnoella (Fig. 2.25) seem to be associated with Antarctic Bottom and Intermediate Waters. Only two genera that have more than one species show evidence of local endemism (Pseudoplumarella with five spp. in eastern Australia, Perissogorgia with seven spp. off New Caledonia). The genus Narella is the most species-rich in the family (38 spp.), and another good example of a deep-sea evolutionary radiation. All but two of the species are found at .200 m depth (to 4594 m) with the exceptions being N. irregularis from Japan (137 m) and an undescribed species 80 Les Watling et al. Figure 2.17 Occurrence records for species of the genus Parastenella. Figure 2.18 Occurrence records for species of the genus Callogorgia. from New Caledonia collected at 55 m. Two other species have depth ranges that span shelf to slope: N. gilchristi (90 340 m in the southwestern Indian Ocean) and N. regularis (159 792 m, Lesser Antilles) (Cairns and Bayer, 2008). Despite the genus being known from all ocean basins, current records show groups of regionally endemic species: nine species are known only from the vicinity of the Hawaiian Islands, nine from the Indo-West Pacific, five from Japan, five from the Gulf of Alaska and five from the western Atlantic (Cairns and Bayer, 2008). Interestingly, despite the evidence for an Antarctic origin for the family, N. gaussi (2450 m) Biology of Deep-Water Octocorals 81 Figure 2.19 Occurrence records for species of the genus Calyptrophora. Figure 2.20 Occurrence records for species of the genus Candidella. is the only Narella species known from that region. The five species from the Gulf of Alaska were described by Cairns and Baco (2007) and were the first representatives of the genus found in the North Pacific. All the specimens were collected from seamounts, between 2377 and 4594 m, during three exploratory submersible cruises between 2002 and 2004, and are further evidence of how poorly known is the deep-water octocoral fauna in the under-sampled parts of the world. Cairns and Baco (2007) predicted that, based on the large number of species described from such a relatively small collection, many more species will be discovered. They also noted a problem inherent in the taxonomy of many deep-water octocorals: that the genus is in need of revision as almost 82 Les Watling et al. Figure 2.21 Occurrence records for species of the genus Plumarella. Figure 2.22 Occurrence records for species of the genus Primnoa. every species is known only from its type material, which in most cases consists of just a few specimens. 6. Symbionts Deep-sea octocoral colonies are often large (10 500 cm in height) so would seem to offer a wide range of biogenic habitats to other invertebrate species. However, symbionts are not always observed on deep-sea Biology of Deep-Water Octocorals 83 Figure 2.23 Occurrence records for species of the genus Fanellia. Figure 2.24 Occurrence records for species of the genus Arthrogorgia (squares) and Fannyella (triangles). octocorals, in fact, they may be quite rare. This may be partly due to early methods of sampling in the deep sea where invertebrates from dredge samples were sorted into large taxonomic categories, thus likely separating host and symbiont, and partly because octocorals have a variety of defenses and so may not be very good hosts. In this review, we will not deal with symbionts that are single-celled inhabitants of octocoral tissue, such as species of Symbiodinium, primarily because all octocorals in deep water are azooxanthellate. We will, instead, focus on those invertebrate species that seem to be, if not obligate 84 Les Watling et al. Figure 2.25 Occurrence records for species of the genus Primnoella. symbionts, found most frequently on the octocoral host. Thus, species that have been recorded from octocorals, but that we know occur widely in the deep-sea environment, are not included. Examples of the latter might be comatulid crinoids and certain ophiuroids. Also not included are those species that might be referred to as ‘calciphiles’ that colonize bare calcareous substrates. Barnacles, both verrucids and lepadids, are typical of this category. Many bamboo corals, for example, are typically colonized by barnacles and other invertebrates when the overlying coenenchyme tissue has died and the calcareous axis is exposed. Invertebrate symbionts are routinely classified as commensalistic, parasitic or mutualistic according to the relationship of the ‘guest’ to the ‘host’. This is typically categorized as 1/0 for commensals, 1/2 for parasites and 1/1 for mutualists, among the various relationships along the symbiotic continuum (Lewis, 1985), the symbols indicating the effect of the association on the fitness of the guest and the host. In all cases, the fitness of the guest is enhanced by the association, but the fitness of the host may be unaffected, negatively impacted or improved. Of the 31 families of alcyonacean octocorals (i.e. excluding the pennatulids and helioporaceans), invertebrate symbionts have routinely been found on only 17 (Table 2.3; Watling, unpublished). Six of those families occur in warm, mostly shallow waters, seven have representatives hosting symbionts in both warm and cold waters, and only four families have species with symbionts only in cold water. The symbionts found in species from cold waters are listed in Table 2.4. For the most part, the type of symbiont is also moderately restricted; that is, most are classified as commensals, but a few are considered to be parasites. 85 Biology of Deep-Water Octocorals Table 2.3 Summary of known symbionts on all octocorals Family Alcyoniina Alcyoniidae Nephtheidae Nidaliidae Xeniidae Scleraxonia Anthothelidae Subergorgiidae Melithaeidae Paragorgiidae Briareidae Coralliidae Holaxonia Gorgoniidae Acanthogorgiidae Plexauridae Calcaxonia Ellisellidae Primnoidae Chrysogorgiidae Isididae Major groups of symbionts Water temperature Worms, copepods Worms, copepods, decapods, molluscs, brittle stars Crab Worms, copepods, crabs, fish Warm and cold Warm only Macrouran crustaceans, bivalves, brittle stars Copepods Copepods, decapods, brittle stars Cnidarians, worms, copepods, amphipods, brittle stars Copepods Copepods, worms Warm and cold Warm only Warm only Warm only Warm only Cold only Warm and cold Cold only Copepods Worms, brittle stars, amphipods, copepods, molluscs Cnidarians, worms, bivalves, acorn and ascothoracid barnacles, copepods, brittle stars Warm only Warm and cold Worms, barnacles, copepods, brittle stars Worms, copepods, amphipods, brittle stars Brittle stars, ascothoracid barnacles, copepods, shrimp, chirostylids, pycnogonids, annelids, anemones Worms, acorn and ascothoracid barnacles, copepods Warm and cold Warm and cold Cold only Cold only Warm and cold 6.1. Deep-water coral hosts and their invertebrate symbionts 6.1.1. Alcyoniina (true soft corals) Only the species Anthomastus grandiflorus has been shown to harbour a commensal, the polychaete (scale worm), Harmothoe acanellae. 86 Les Watling et al. Table 2.4 Cold-water octocorals with known symbionts Coral host Symbiont group “Alcyoniina” Family Alcyoniidae Anthomastus Scale worm grandiflorus “Scleraxonia” Family Anthothelidae Victorgorgia Brittle star josephinae Family Paragorgiidae Paragorgia Amphipod arborea Paragorgia arborea Paragorgia arborea Zoanthid Copepod Paragorgia Sphaerodorid arborea worm Paragorgia Anemone arborea Family Coralliidae Corallium Scale worm imperiale Corallium Scale worm johnsoni Corallium niobe Scale worm Corallium secundum Corallium sp. Copepod Scale worm “Holaxonia” Family Acanthogorgiidae Acanthogorgia Scale worm armata Acanthogorgia Aplacophoran armata Acanthogorgia Scale worm aspera Symbiont name Type Reference Harmothoe acanellae C Ditlevsen (1917) Asteroschema sp. C López-González and Briand (2002) Pleustidae 4 C Epizoanthus norvegicus Gorgonophilus canadensis P Buhl-Mortensen and Watling (unpublished) Dons (1944) P Sphaerodorum C guilbaulti Synanthus mirabilis C Gorgoniapolynoe guadalupensis Gorgoniapolynoe caeciliae Gorgoniapolynoe caeciliae Herpyllobiidae gen? sp? Gorgoniapolynoe muzikae Harmothoe acanellae Strophomenia agassizi Gorgoniapolynoe caeciliae C C C P Buhl-Mortensen and Mortensen (2004) Martin and Britayev (1998) Verrill (1922) Pettibone (1991a) Pettibone (1991a) Pettibone (1991a) Stock (1986) C Pettibone (1991a) C Martin and Britayev (1998) Heath (1918) C C Pettibone (1991a) (continued) 87 Biology of Deep-Water Octocorals Table 2.4 (continued ) Coral host Symbiont group Acanthogorgia Scale worm bocki Family Plexauridae Placogorgia sp. Ascothoracid “Calcaxonia” Family Ellisellidae Ellisella Brittle star barbadensis Family Chrysogorgiidae Chrysogorgia Ascothoracid desbonni Chrysogorgia Ascothoracid elegans Chrysogorgia Ascothoracid elegans Chrysogorgia Ascothoracid elegans Chrysogorgia Ascothoracid orientalis Chrysogorgia Ascothoracid papillosa Chrysogorgia Ascothoracid quadriplex Chrysogorgia sp. Ascothoracid Chrysogorgia sp. Ascothoracid Chrysogorgia sp. Ascothoracid cf. papillosa Iridogorgia Shrimp splendens Metallogorgia Brittle star melanotrichos Radicipes Brittle star pleurocristatus. Radicipes verrilli Brittle star Family Primnoidae Primnoa Amphipod resedaeformis Symbiont name Type Reference Gorgoniapolynoe muzikae C Pettibone (1991a) Gorgonolaureus muzikae C Grygier (1981b) Asteroschema tenue C Thalassomembracis bayeri Cardomanica longispinata Cardomanica quadricornuta Thalassomembracis acanthosphaericus Cardomanica andersoni Thalassomembracis tetraedos Thalassomembracis atlanticus Thalassomembracis conquistador Thalassomembracis orientalis Thalassomembracis bilobis Bathypalaemonella serratipalma Ophiocreas oedipus Emson and Woodley (1987) C Grygier (1984) C Grygier (1984) C Lowry (1985) C Grygier (1984) C Lowry (1985) C Grygier (1984) C Grygier (1984) C Grygier (1984) C Grygier (1984) C Grygier (1984) C Watling (2010) C Asteronyx loveni C Mosher and Watling (2009) Fujita (2001) Asteronyx loveni C Fujita (2001) Amatiguakius forsberghi C Coleman and Barnard (1991) (continued) 88 Les Watling et al. Table 2.4 (continued ) Coral host Symbiont group Symbiont name Type Reference Arthrogorgia sp. Amphipod Pleustid 4 C Arthrogorgia sp. Amphipod Stenopleustes sp. C Arthrogorgia sp. Amphipod Stenothoidae C Callogorgia gilberti Callogorgia sp. Copepod Lamipinna P Buhl-Mortensen and Watling (unpublished) Buhl-Mortensen and Watling (unpublished) Buhl-Mortensen and Watling (unpublished) Cairns (2010) Copepod P Grygier (1980) Callogorgia sp. Scale worm Sphaerippe caligicola Gorgoniapolynoe uschakovi Gorgoniapolynoe muzikae Gorgoniapolynoe caeciliae Gorgoniapolynoe galapagensis Gorgoniapolynoe bayeri Epizoanthus norvegicus Hemilepidia versluysii Unidentified C Pettibone (1991a) Pettibone (1991a) Pettibone (1991a) Pettibone (1991a) Pettibone (1991a) Dons (1944) Candidella Scale worm helminthophora Candidella Scale worm imbricata Narella ambigua Scale worm Narella clavata Scale worm Primnoa resedaeformis Thouarella hilgendorfi Thouarella hilgendorfi Thouarella laxa Thouarella sp. Zoanthid Scale worm Copepod Scale worm Scale worm Thouarella Scale worm variabilis Family Isididae Acanella Anemone arbuscula Acanella Scale worm arbuscula C C C C P C P Unidentified Polynoe thouarellicola C C Polyeunoa laevis C Amphianthus inornatus Harmothoe acanellae C C Martin and Britayev (1998) Cairns (2010) Versluys (1906) HartmannSchröder (1989) Pettibone (1969) Bronsdon et al. (1993) Ditlevsen (1917) (continued) 89 Biology of Deep-Water Octocorals Table 2.4 (continued ) Coral host Symbiont group Symbiont name Type Reference Acanella arbuscula Acanella arbuscula Acanella arbuscula Mopsea gracilis Primnoisis formosa Sclerisis macquariana Minusis pseudoplana Minusis granti Ascothoracid Isidascus bassindalei C Moyse (1983) Copepod Lamipella acanellae P Grygier (1983) Anemone Sagartia acanella C Verrill (1883) Copepod Copepod Isidicola antarctica Isidicola antarctica P P Gravier (1914) Gravier (1914) Scale worm C Scale worm Tottonpolynoe symantipatharia unidentified C Scale worm unidentified C Pettibone (1991b) Alderslade (1998) Alderslade (1998) Type: C 5 commensal; P 5 parasite. 6.1.2. Scleraxonia Commensals and parasites have been observed on deep-water representatives of three families, the Anthothelidae, Paragorgiidae and Coralliidae. The only deep-water anthothelid known to host a commensal is Victorgorgia josephinae, which was collected with a single Asteroschema sp. on its branches. Paragorgia arborea grows into very large colonies, and branches with living tissue may be colonized by amphipods, copepods or anemones, some of which form galls (Buhl-Mortensen and Mortensen, 2004). Parts of the colony where sediment has accumulated or surface tissue has died may be inhabited by a large number of typically ‘fouling’ species. The closely related P. johnsoni tends to be inhabited by the brittle star, Asteroschema clavigera, on the seamounts of the Northwest Atlantic (Cho and Shank, 2010). In both the Atlantic and the Pacific, specimens of P. coralloides are often overgrown by an unknown zoanthid (Fig. 2.1C). In the northwestern Atlantic, the zoanthid covers the main branches and many of the minor ones, but does not cover an entire branch. In addition, P. coralloides with zoanthids may still host at least one A. clavigera. Species of the genus Corallium, the precious corals, have a layer of coenenchyme that varies in thickness from species to species. If the coenenchyme layer is thick enough, it can be inhabited by polychaetes of the genus Gorgoniapolynoe. The mechanism is unknown, but the presence of 90 Les Watling et al. the worm results in galleries being formed in the coenenchyme tissue, thus changing the shape of the coral branch. Each species of worm appears to live on only one or two species of coral (Pettibone, 1991a). 6.1.3. Holaxonia There are about 64 species of Acanthogorgia (van Ofwegen, 2010), many of which occur at continental shelf depths in the Atlantic and Western Pacific, while a few can be found in deep cold waters, for example in the Antarctic and Atlantic. The axis is generally covered in a thin layer of coenenchyme, so commensals usually live among the branches, which may be quite dense, rather than within the tissue of the host. In deep waters, commensal species include an aplacophoran, and several species of scale worms, including the genus Gorgoniapolynoe (personal observations). Shallow tropical plexaurid species can be host to a wide variety of invertebrates, mostly crustaceans and worms, but only a few commensals have been documented from plexaurids in deep water. In the North Atlantic, the most common commensal is the brittle star, Asteroschema clavigera, which occurs on paramuriceids living on seamounts or along the continental slope (Fig. 2.1G; Cho and Shank, 2010). Paramuriceids of continental shelf habitats, even though the waters are cold, such as the Gulf of Maine or fjords of Norway, do not have brittle stars living with them. A few, very small scale worms have been found living on paramuriceids from the New England seamounts, but neither the coral nor the worm have yet been identified (personal observations). 6.1.4. Calcaxonia This group has a calcareous axis that is covered by a layer of coenenchyme tissue of varying thickness but is predominantly relatively thin. As with most of the holaxonians, commensals thus need to find a place to live either on or among the branches of the host. On chrysogorgiids, some symbionts, such as ascothoracid barnacles (Grygier, 1984), are attached directly to a branch or the central axis, but most are mobile forms that take up residence among the branches. For example, in the North Atlantic, the shrimp, Bathypalaemonella serratipalma, lives most of its adult life among the branches of Iridogorgia splendens (Watling, 2010). The shrimp seems to prefer species of Iridogorgia with moderately close spacing of the branches. It has not been found on other Iridogorgia species, but it occurs occasionally on Chrysogorgia tricaulis on the New England and Corner Rise Seamounts (Pante and Watling, 2012, in press). Chrysogorgia species are occasionally home to scale worms, and the colonies are sometimes used as a substrate to which eggs can be attached by unknown species of octopus and fish (Fig. 2.2F). Perhaps the most remarkable commensal relationship known is that of the brittle star, Ophiocreas oedipus, and its host chrysogorgiid, Metallogorgia melanotrichos Biology of Deep-Water Octocorals 91 (Fig. 2.2D; Mosher and Watling, 2009). A single brittle star appears to find its coral host when they both are very young; they grow up together, the association lasting until the coral dies. Chrysogorgia polyps can harbour young pycnogonids (Stock, 1953 cited in Bayer, 1956), annelid worms, copepods and unknown crustaceans (Versluys, 1902; Weber, 1902; Nutting, 1908). Some Chrysogorgia colonies from the southwestern Pacific (New Caledonia and New Zealand) were found covered with ring sea anemones (Pante, personal observations). A number of organisms have found living on primnoids advantageous (Fig. 2.3D, E). Commensals include polychaete worms, amphipods and brittle stars, and Cairns (2010) noted Thouarella hilgendorfi and Callogorgia gilberti harboured parasitic lamippid copepods in some polyps whose morphology is subsequently highly modified. Some primnoids, such as the genus Thouarella, often host scale worms that live freely on the central axis of the colony (Kükenthal, 1912). Only the association of the polynoid worm, Gorgoniapolynoe caeciliae, with the primnoid, Candidella imbricata, has been studied in any detail (Eckelbarger et al., 2005). Gorgoniapolynoe caeciliae appears to settle on the coral colony when the worm is about 25 segments long. Somehow it induces the coral to change the morphology of the basal polyp sclerites, creating an ‘arbor vita’-like tunnel along the branch. As the worm grows, presumably more sclerites from additional polyps are involved. Eckelbarger et al. (2005) proposed that the worms reached sexual maturity in their second or third year on the colony. Most scale worms do not live for more than 5 years, so there must be a large amount of turnover of worms on an individual C. imbricata colony. It is not known whether the coral sheds the oversize sclerites used to make the tunnel of the worm, or whether they are kept for a subsequent worm to inhabit. Only a few species of bamboo corals (Isididae) are known to host symbionts. The keratoisidin, Acanella arbuscula, may be host to anemones, scale worms, ascothoracid barnacles, a shrimp and occasionally a parasitic copepod. Two species of mopseins are host to parasitic copepods, and two species in one genus host a scale worm. For the most part, however, bamboo corals are generally completely devoid of commensal species, and perhaps also of parasites. We collected 68 bamboo coral specimens on the New England and Corner Rise Seamounts. Of those, only 14 of 23 Acanella arbuscula specimens hosted any invertebrate commensal, the most common being a scale worm on the older colonies. 6.2. Characteristics of the invertebrate symbionts There are about 32 phyla of marine invertebrates, only a small number of which have species that form symbiotic relationships with octocorals, whether in shallow water or the deep sea. Those phyla include the Cnidaria, Annelida, Mollusca, Arthropoda (Crustacea) and Echinodermata. 92 Les Watling et al. Two major groups of cnidarians occur on deep-sea octocorals, the anemones and zoanthids. Anemones occur sporadically attached to the branches of an octocoral colony. A prominent example is Stephanuge acanellae which wraps its pedal disc around the branch of the bamboo coral, Acanella arbuscula. Another common anemone is an unidentified species that is found on many deep-sea octocorals in the northwestern Atlantic, for example, in the genera Acanthogorgia, Corallium and Paramuricea (personal observations). Ocaña et al. (2004) note the presence of ring anemones (provisionally assigned to Actinostolidae) on gorgonians (mostly calcaxonians) from deep water off New Zealand. Zoanthids can be very abundant on deep-sea octocorals, but their taxonomy has generally been ignored, largely because there does not seem to be a consistent set of morphological characters that can be used to easily distinguish the species. One example of a very successful zoanthid is the so-far unidentified species that colonizes Paragorgia coralloides (Fig. 2.1C; personal observations). It is nearly universally present on this octocoral and is probably best characterized as a parasite. While we do not know how the octocoral gets colonized by the zoanthid, it does appear that the zoanthid grows over the tissue and polyps of the octocoral. Parrish and Roark (2009) show an image of the zoanthid, Gerardia sp., that has just begun the colonization of a bamboo coral. This zoanthid, known as the precious gold coral, will grow to quite large size and there will eventually be no external indication that a bamboo coral was the substrate on which the early development of the zoanthid started. Of the 80 or so families of marine polychaetes, only 29 are involved in some sort of symbiotic relationship with other invertebrates, and of those, only the Polynoidae, Sphaerodoridae and Syllidae are associated with gorgonians (Martin and Britayev, 1998), whether in shallow or deep water. No polychaetes have been found to be parasitic on gorgonians. In deep water, the commensal polychaetes cause the host to modify its morphology somewhat, resulting in either modified sclerites, or expansion of coenenchyme tissue. Beyond that, however, only the species, Gorgoniapolynoe caeciliae, has been studied in any detail. This worm is moderately small, generally less than 14 mm in length, and inhabits the branches of Candidella imbricata, Corallium niobe and Acanthogorgia aspera. By far the greatest numbers of worms are found on C. imbricata, where as many as 120 specimens were removed from a piece of the colony (Eckelbarger et al., 2005). Candidella imbricata has a narrow branch axis and the polyps generally occur as opposite pairs. When the worm is present, the basal sclerites of each pair of affected polyps are modified into very large curved structures that effectively create a tunnel inhabited by the worm. Usually 7 10 pairs of polyps are involved. Corallium, however, has very small sclerites, so the zone of habitation by the worm is modified Biology of Deep-Water Octocorals 93 in the form of expanded coenenchyme tissue, which forms a tunnel for the worm (Simpson and Watling, 2011). From histological analysis, Eckelbarger et al. (2005) determined that G. caeciliae most likely spawns annually and fertilization is external. Pettibone (1991a) described a species in this genus, G. pelagica, that was collected in the plankton off Bermuda. She suggested, on the basis of its small size and certain morphological features, that G. pelagica might be the pelagic juvenile stage of one of the other species, probably G. caeciliae. This corresponds well with the observations of Eckelbarger et al. (2005) who never found worms with fewer than 23 body segments on any coral. Molluscs are vastly under-represented as symbionts of deep-water octocorals. Only one aplacophoran species has been found living on Acanthogorgia armata by Heath (1918). Ascothoracid barnacles, copepods, amphipods, galatheids, chirostylids and a shrimp characterize the crustacean symbionts of deep-water octocorals. Most live freely or are attached to the surface of the colony, but some copepods may form and inhabit galls. The taxonomy and distribution of ascothoracids has been well documented by Grygier in a series of papers (deep-sea species in Grygier, 1981a, b, 1984, 1991). One occurs on a plexaurid, one on the bamboo coral, Acanella arbuscula, but all others were found on various species of Chrysogorgia. None of the ascothoracids are particularly numerous, with only one or two individuals being found on a host colony. All of the copepods found on deep-water octocorals were considered to be parasites, being found either in galls or in the gastrovascular cavity of some polyps. Other crustaceans were relatively rare. Amphipods seem to be rare on octocorals in the Atlantic, but ongoing work in the North Pacific suggests that amphipods may be quite diverse and have high host fidelity (Watling and Buhl-Mortensen, personal observation). In contrast to shallow-water octocorals, only one shrimp is currently known to have a commensal relationship with a deep-water octocoral. In the North Atlantic, the shrimp, Bathypalaemonella serratipalma, was found to be a regular inhabitant of the chrysogorgiid, Iridogorgia splendens (Watling, 2010). Among the echinoderms, only the ophiuroids form associations with octocorals. In deep water, brittle stars living with octocorals are all from the order Euryalae. Some, like the Asteroschematidae (Emson and Woodley, 1987) or Asteronychidae (Fujita and Ohta, 1988; Fujita, 2001), have especially flexible arms and are commonly found with some arms strongly coiled around the branches of the octocoral host while the others are extended into the water for prey capture (Figs 2.2D, 2.3E). Little is known about the life history of Euryalae, but Mosher and Watling (2009) showed that the brittle star, Ophiocreas oedipus, settles on the octocoral, Metallogorgia melanotrichos, when the octocoral is very young, and the two grow and mature together. The brittle star does not appear to die until after the octocoral has died (personal observation). 94 Les Watling et al. 6.3. Commensalism, parasitism or mutualism Combes (2005) argues that what has routinely been called ‘commensalism’, is in fact a subset of either parasitism or mutualism where the costs or benefits to one partner are so minute as to be unmeasurable. It may be that for deep-sea species, obtaining such measurements is nearly impossible. Almost all of the relationships described above are considered to be of a commensalistic nature, that is, the cost to the host is negligible while the guest clearly benefits. Only the associations involving copepods are routinely thought to be parasitic in nature. Considering costs to the colony as a whole, however, one could argue, for example, that the worm, Gorgoniapolynoe caeciliae, costs the host colony as much energy in making enlarged sclerites or adding coenenchyme tissue, as a tiny copepod extracts from the few feeding polyps in which they are found. On the other hand, it is not known whether the presence of the worm reduces the fecundity of the coral, whereas the presence of a copepod in the cavity of the polyp usually means that the polyp will not produce eggs or sperm. However, the benefit to the worm is very strong. It can live in a tunnel created by the host, it is most likely provided with food in the form of organic particles adhering to the mucus secreted by the host, and it therefore can put most or all of its energy into reproduction. For all of the associations described, the primary benefit to the guest seems to be that of protection from predation. Octocorals generally do not have stinging cnidae, but most shallow-water gorgonians, and the few deep-water species that have been examined, are heavily loaded with secondary metabolites such as terpenoids, which make the coral unpalatable (Puglisi et al., 2002). If the guest can tolerate the chemical environment provided by the host, it most likely will find a refuge from predators. Interestingly, the one group for whom this is of only a limited benefit is the ophiuroids. Euryalids tend to feed on small zooplankton captured from the water column so their arms, which they unravel from the coral and extend into the water, are vulnerable to predators. In the northwestern Atlantic we found the asteroschematids living on Paramuricea sp. often had regenerating arms (personal observation). Another benefit for at least some of the symbionts, is the ability to gather food more effectively. The ophiuroids are most likely feeding on small zooplankton such as copepods and by living on the gorgonian they position themselves higher in the water column, away from the sluggish currents in the lower part of the benthic boundary layer. In addition, there are undoubtedly flow effects associated with the fan-like design of the coral that may also help to deliver food items to the brittle star. Biology of Deep-Water Octocorals 95 6.4. Host fidelity Most of the symbionts live on a narrow range of host species. Interestingly, multiple host species may be in the same geographical area, on the same seamount perhaps, but may not be very closely related phylogenetically. The worm, Gorgoniapolynoe caeciliae, for example, can be found on the primnoid Candidella imbricata in the suborder Calcaxonia, but also on some species of Corallium in the suborder Scleraxonia. On the other hand, it is not known why Gorgoniapolynoe lives on only a few of the more than 20 species of Corallium. The most extreme case of host fidelity is the ophiuroid, Ophiocreas oedipus, who lives only on the chrysogorgiid, Metallogorgia melanotrichos, and somehow no other symbionts are allowed to take up residence (Mosher and Watling, 2009). Indeed, among .200 observations from both the Atlantic and Pacific, we have never observed a living M. melanotrichos without its O. oedipus symbiont or with more than one symbiont. 7. Predators There is little evidence that deep-water octocorals are preyed on by either fish or invertebrates. However, one group that seems to have evolved to consume bamboo corals, at least, are the goniasterid hippasterine sea stars (Mah, 2006; Mah et al., 2010). Members of several genera in this subfamily have been observed feeding on bamboo corals of the genus Keratoisis or its relatives, and a few collected specimens have been observed with the sclerites from the coral in their stomachs. On the New England seamounts, a pycnogonid, probably Bathypallenopsis mollisima, was seen feeding on an unidentified bamboo whip (Roger Bamber identification from photograph). 8. Food Food habits of deep-water octocorals have not been a subject of much study. Orejas et al. (2002a) examined the gastrovascular cavity contents of polyps of A. bathyproctus collected at .400 m from the Antarctic Peninsula. All of the more than 100 polyps examined from eight colonies had remnants of a salp, Salpa thompsoni, in their guts. Orejas et al. (2003) also examined gastrovascular cavity contents of a bamboo coral, Primnoisis 96 Les Watling et al. antarctica, and a primnoid, Primnoella sp., from about 100 m water depth in the eastern Weddell Sea, and found that both species largely consumed phytoplankton (diatoms and dinoflagellates, respectively). Shallow-water gorgonians are known to consume a range of items, including detrital particulate organic matter (POM), invertebrate eggs, and phytoplankton (Tsounis et al., 2006), picoplankton and autotrophic nanoplankton (Picciano and Ferrier-Pagès, 2007), so it is likely that deep-water species are consumers of planktonic particles and small organisms as well. Sherwood et al. (2008), using C and N isotopes, concluded that Paragorgia arborea had the isotopic signature of macrozooplankton and so must be consuming fresh phytodetritus, whereas Primnoa resedaeformis was supplementing its diet with microzooplankton, and Acanthogorgia armata, A. grandiflorus and the bamboo corals were feeding at much higher trophic levels. 9. Reproduction In the deep sea, gonochorism, continual (or aperiodic) reproduction and larval brooding are general patterns observed among invertebrates (Gage and Tyler, 1991). A number of deep-sea octocoral species appear to conform to this pattern, while others show evidence of seasonal reproduction, and broadcast spawning of gametes. There is a general paucity of knowledge about reproductive processes in deep-sea octocorals. Our understanding of population connectivity, biogeographic patterns and the ability of octocoral species and communities to recover from disturbance is limited by lack of data on reproduction. Currently fewer than a dozen published studies are devoted to describing various aspects of reproductive biology in less than two dozen deep-sea species of octocorals. Of course, our ability to study reproductive life history in deep-sea octocorals is limited by the inherent logistical difficulties and related expenses of accessing these animals in their remote habitats. 9.1. Reproductive strategies Even with the limited data currently available it is clear that deep-sea octocorals exhibit sexual reproductive strategies in common with their shallow-water counterparts. However, forms of asexual reproduction (budding, fission, parthenogenesis, etc.) that occur frequently in many shallow-water species have yet to be observed among deep-sea octocorals. Two basic types of sexual reproduction are known in octocorals: (1) broadcast spawning, with fertilization and development in the water Biology of Deep-Water Octocorals 97 column; and (2) brooding, where fertilization of eggs occurs either in, or on, the maternal colony. In the latter type of reproduction, embryos develop internally (in autozooids, siphonozooids or specialized brood chambers), or adhere to the external surface of the adult colony. Data from shallow-water species suggest that, in general, the frequency of brooding versus broadcast spawning varies within the Octocorallia by taxonomic order. Sea pens (order Pennatulacea), including all known deep-sea species (Rice et al., 1992; Tyler et al., 1994; Eckelbarger et al., 1998; Pires et al., 2009), reproduce exclusively by means of broadcast spawning (Table 2.5). Such a pattern suggests that sexual reproduction via broadcast spawning may represent a phylogenetic constraint within this lineage. In contrast, soft corals and gorgonian-type octocorals (order Alcyonacea) appear to have a greater degree of flexibility in reproductive strategy, exhibiting both broadcast spawning and brooding, sometimes within the same genus (e.g. Alcyonium, Hartnoll, 1975; McFadden et al., 2001). Among the limited number of deep-sea alcyonacean species studied, brooding occurs consistently in soft corals from the families Alcyoniidae or Nephtheidae and among a few Antarctic primnoids (Table 2.5). Larvae develop internally in all known brooders. The location of the planula within a maternal colony varies for different species. Developing gametogenic cells and larvae are most commonly retained in the autozooid polyp cavities as observed in the Antarctic primnoids, Fannyella rossii, Fannyella spinosa, an unidentified Thouarella sp. (Orejas et al., 2002b) and Thouarella variabilis (Brito et al., 1997). North Atlantic soft corals including Duva florida (Sun et al., 2009a) and A. grandiflorus also brood larvae in their autozooids (Mercier and Hamel, 2011), while in the Pacific A. ritteri larvae develop in siphonozooids. A unique type of brooding polyp was recently observed by Sun et al. (2009b) in North Atlantic Drifa species. The exceptionally large larvae present in these species are brooded in enlarged polyps that are 3 times the size of autozooids and lack tentacles. At the time of this review, deep-sea broadcast-spawning octocorals can be found among the Pennatulacea (sea pens) and most alcyonacean suborders, although the number of species for whom the reproductive strategy is known is still pretty small. Broadcast spawning calcaxonian taxa include Primnoa resedaeformis (Primnoidae), and the deep-sea bamboo corals (Isididae) Keratoisis ornata (Mercier and Hamel, 2011) and Acanella arbuscula (Beazley, 2011). Scleraxonians belonging to the Hawaiian precious coral species, Corallium lauuense and Corallium secundum, are likely broadcast spawners (Waller and Baco, 2007). At least two species from the North Atlantic in the holaxonian genus, Paramuricea, also appear to share this strategy (Simpson, in preparation). From the limited data currently available for octocorals, it remains unclear to what degree reproductive strategy is influenced by selective pressures and/or dictated by phylogenetic constraints, especially in Species 98 Table 2.5 Reproductive data for deep-water octocorals Reproductive strategy Gametogenic cycle Oocytes/polyp Spawning (gamete or planula release) Source Broadcast spawning Broadcast spawning Broadcast spawning Broadcast spawning Continuous Up to 90 Continuous Pires et al. (2009) Continuous Not reported Continuous? Rice et al. (1992) Continuous Not reported Continuous? Periodic? Not reported Indeterminate Eckelbarger et al. (1998) Tyler et al. (1994) Broadcast spawning Brooder? Periodic or quasi- 18.8 1/2 16.2, continuous max 75 Periodic 3 1/2 2 Indeterminate Beazley (2011) Autumn winter Brooding Periodic Orejas et al. (2002b) Mercier and Hamel (2011) Anthomastus ritteri Brooding Corallium lauuense Broadcast spawning? Broadcast spawning? Unknown Continuous or Average 5.3 quasi-continuous oocytes and larvae Periodic or quasi- Not reported continuous Periodic or quasi- Not reported continuous Periodic 1.2 1/2 0.08 Order Pennatulacea Anthoptilum murrayi Kophobelemnon stelliferum Pennatula aculeata Umbellula lindahi Order Alcyonacea Acanella arbuscula Ainigmaptilon antarcticum Anthomastus grandiflorus Dasystenella acanthina Fall (timing may vary slightly by region/ environment) Continuous Fall? Fall? Indeterminate seasonal spawning Cordes et al. (2001) Waller and Baco (2007) Waller and Baco (2007) Orejas et al. (2007) Les Watling et al. Corallium secundum Not reported Continuous? Not reported Drifa sp. Brooding Continuous? Not reported Duva sp. not published Brooding Fannyella rossii Brooding Periodic Not reported 1.5 1/2 0.06 Fannyella spinosa Brooding Periodic 1.4 1/2 0.08 Keratoisis ornata Broadcast spawning Broadcast spawning Periodic 10 60 Continuous? Thouarella sp. Brooding Periodic ,500 m depth 84.3 1/2 3.1, max 107; .500 m depth 45.5 1/2 1.7, max 65 1.1 1/2 0.1 Thouarella variabilis Brooding Continuous or 1 mature oocyte quasi-continuous at a time, total not reported Primnoa resedaeformis Continuous with concentrated planulation periods: December January, and April and June Continuous with peak in planula release September December; smaller peak in spring in colonies (,500 m) Indeterminate Annual (possibly during austral summer) Annual (possibly during austral summer) Annual (late summer) No evidence of spawning periodicity Indeterminate Austral summer Sun et al. (2010a) Sun et al. (2010a) Sun et al. (2009a) Orejas et al. (2007) Orejas et al. (2007) Mercier and Hamel (2011) Mercier and Hamel (2011) Orejas et al. (2007) Brito et al. (1997) 99 Brooding Biology of Deep-Water Octocorals Drifa glomerata 100 Les Watling et al. deep-sea alcyonaceans (gorgonians and soft corals). Environmental conditions that characterize many deep-sea habitats such as limited energetic inputs, low temperature and thermal stability, reduced or absent temporal or seasonal signals, restrict both the energetic budget available for reproduction as well as any external cues for gametogenic development and spawning synchrony (Gage and Tyler, 1991; Young, 2003). Under such conditions, internal fertilization and brooding are predicted to be favoured in many deep-sea environments. However, the existence of broadcast spawning among sea pens and gorgonian-type octocorals, suggests that further work is needed to provide a greater understanding of the role of intrinsic and extrinsic factors on reproductive strategies in this group. 9.2. Gonochorism and sex ratio In most octocorals, including all known deep-sea species, gonochorism occurs at the colony level. In a comprehensive review of reproduction, Kahng et al. (submitted) report 89% of octocoral species are gonochoristic and 9% are simultaneous hermaphrodites. Hermaphroditism appears to occur mostly in a limited number of shallow-water, soft coral species, especially in the families Alcyoniidae and Xeniidae. The only account to date of hermaphroditism in a deep-water octocoral is for a yetundescribed Drifa sp. (Alcyoniidae) from the North Atlantic (Sun et al., 2009a, 2010a), although it is not uncommon to encounter the occasional, aberrant hermaphroditic polyp or even colony that is otherwise predominantly dioecious (Simpson, personal observation). Populations of both shallow- and deep-water octocorals most commonly exhibit a 1:1 sex ratio, a pattern that is understood to represent optimum energetic allocation to reproduction under conditions of random mating (Williams, 1975). Due to the traditionally limited scope of deep-sea sampling, population sex ratios have been calculated for only a few species including the sea pens, Anthoptilum murrayi and Kophobelemnon stelliferum, the gorgonians, Ainigmaptilon antarcticum and Acanella arbuscula and the soft coral, A. grandiflorus. Surveys of sea pens and gorgonians found typical 1:1 sex ratios (Rice et al., 1992; Orejas et al., 2002b; Pires et al., 2009; Beazley, 2011), while the soft coral, A. grandiflorus, exhibited a strongly female-biased sex ratio approximating 4:1. The latter was sampled at bathyal depths along the eastern Canadian shelf and slope (Mercier and Hamel, 2011). Other alcyonacean species from this region, including the soft coral, Drifa glomerata (Sun et al., 2010a), and the gorgonians, P. resedaeformis and K. ornata (Sun et al., 2009b; Mercier and Hamel, 2011), may also exhibit highly skewed sex ratios as no male colonies have been collected. Beazley (2011) notes that female-biased sex ratios represent the most common deviation from (opposite sex) parity. Biology of Deep-Water Octocorals 101 The proportion of males to females in a population has implications for fertilization success, especially for populations occurring at low densities and in the deep sea where temporal signals and energy budgets for reproductive processes may be limited. 9.3. Gametogenesis Basic features of reproductive anatomy and gametogenesis appear highly conserved in the Octocorallia. True ‘gonads’ are absent, and reproductive cells develop along gametogenic areas of the ventral and lateral (nonasulcul) mesenteries (Fautin and Mariscal, 1991). The most detailed account to date of reproductive morphology and gametogenesis in a deep-water octocoral is Eckelbarger et al.’s (1998) study of the sea pen, Pennatula aculeata. That study found that germ cells develop surrounded by mesoglea with an overlying layer of ‘follicle cells’, which are presumed to have a nutritive function. Studies of oogenesis in deep-sea octocorals have identified four or five stages of oocyte development. Fully mature oocytes are generally large (,600 µm) and contain abundant quantities of yolk (Eckelbarger et al., 1998). In the bamboo coral, Keratoisis ornata, there is evidence that not all early phase oocytes reach maturity and some are apparently resorbed, possibly becoming a nutrient source for other developing oocytes (Mercier and Hamel, 2011). In colonies containing gametes, especially large, mature oocytes, it is not unusual for all the space in the gastrovascular cavity to be occupied by reproductive products, which presumably inhibits an individual polyp’s ability to feed. Thus, nutrition derived from the breakdown of ‘supernumerary’ gametes, or from food sources transported from other regions of the colony, may be essential for gametogenesis. Similar to oogenesis, spermatogenesis is most often divided into three to four basic stages (Cordes et al., 2001; Beazley, 2011). In many shallowwater octocoral species, the process of spermatogenesis is usually shorter in duration than oogenesis. Limited observations do not yet provide clear evidence of such a pattern in the deep sea. Gametogenesis is often highly asynchronous in deep-sea octocorals, with several stages of developing oocytes or spermatocysts (containing developing male gametes) present in a single polyp (Rice et al., 1992; Cordes et al., 2001; Beazley, 2011). Asynchronous development of gametes suggests a state of aperiodic or quasi-continuous reproduction that is often characteristic of deep-sea animals; however, this pattern of gametogenic development is also widespread among shallow-water species suggesting that continuous gametogenesis is, perhaps, a phylogenetic feature typical of octocorals. Additionally, Orejas (2001) provides evidence that trophic control of gametogenesis and reproductive processes is widespread in octocorals. Among deep-water species a few, including the soft 102 Les Watling et al. coral, A. grandiflorus, and gorgonians, K. ornata, F. rossi and F. spinosa, show marked synchronicity of gamete development suggesting periodic, and possibly seasonal reproduction (Orejas et al., 2007; Mercier and Hamel, 2011). Gametogenesis and spawning in populations of A. grandiflorus and K. ornata from bathyal depths along the eastern Canadian shelf and slope (from Newfoundland, Labrador and the lower Arctic) appears to be regulated by environmental factors. In K. ornata, initiation of gametogenesis in the spring is correlated with increasing day length and seawater temperatures, both factors known to be important in synchronizing reproductive cycles in shallow-water octocorals (Benayahu, 1997 cited in Mercier and Hamel, 2011). Further evidence of the influence of environmental cues on the reproductive cycle was seen in A. grandiflorus, where the onset of oogenesis shifted across latitudes, with colonies from Arctic regions showing the greatest delay. In deep-sea coral habitats beyond the continental shelf edge and slope, such as seamounts and mid-ocean ridges, day length and seasonal temperature signals may be virtually absent. Instead deep-sea animals may rely on other temporal environmental cues such as seasonal phytodetritus falls, benthic storm turbulence, eddy kinetic energy and possibly changes in light intensity (Tyler, 1988) to regulate reproductive and other life history processes. In octocorals, it is not uncommon for the process of gametogenesis, especially oogenesis, to extend beyond an annual cycle. In many species, prolonged gametogenesis gives rise to overlapping cycles of gamete development (Benayahu and Loya, 1986; Kruger et al., 1998; Orejas et al., 2007). Prolonged cycles of oogenesis (up to 2 years) may be required to synthesize large, yolky eggs (300 1200 µm) produced by octocorals (Benayahu and Loya, 1986; Harrison and Wallace, 1990; GutierrezRodriguez and Lasker, 2004; Orejas et al. 2007), especially in deep-sea species where energy available for reproduction may be limited. However, oocyte size is not always directly linked to duration of oogenesis and some shallow-water species (Benayahu and Loya, 1984, 1986), as well as a few from deep water, including A. grandiflorus and K. ornata, produce large eggs during relatively short gametogenic cycles (Mercier and Hamel, 2011) (Table 2.1), suggesting factors other than oocyte size influence the duration of oogenesis. Orejas et al. (2007) suggest that a prolonged oogenic cycle in the deep water Antarctic gorgonian, Dasystenella acanthina, may allow this species to adjust the timing of oocyte maturation so that the process may be partially decoupled from primary oocyte emergence (Brazeau and Lasker, 1989). The ability to adjust the timing of oocyte maturity during the developmental cycle may present a mechanism that allows some deep-sea octocorals to compensate for the possible lack of highly synchronized spawning activity arising from limited or weak temporal signals in these environments. Biology of Deep-Water Octocorals 103 9.4. Sexual maturity and fecundity Reproductive maturity and fecundity are linked to colony size in octocorals. Studies of shallow-water species have shown that young colonies often allocate energetic resources solely to somatic growth at the expense of reproduction until a ‘threshold’ size is attained (Kapela and Lasker, 1999; Gutierrez-Rodriguez and Lasker, 2004). Since mortality rates are strongly size dependent (Lasker, 1990), such a growth strategy presumably minimizes the time a colony spends in size classes presenting the greatest survival risk. Although predation pressures in shallow-water environments presumably differ from those at great depth, the deep-sea chrysogorgiids, Metallogorgia melanotrichos and Iridogorgia magnispiralis, seem to conform to the pattern of size-linked reproductive maturity. Colonies of both species do not appear to be reproductively active until at least an intermediate growth stage is reached (Mosher and Watling, 2009; Simpson, in preparation). In contrast, the bamboo coral, A. arbuscula, reaches sexual maturity at an early stage of somatic growth, with colonies ,3 cm in height containing developing gametes (Beazley, 2011). There is limited evidence that temperature may also influence the timing of reproductive maturity. Sun et al. (2010a) reported that colonies of the soft coral, D. glomerata, occurring in deeper waters (200 330 m) with warmer temperatures are reproductively active at smaller sizes compared to shallower colonies (100 200 m) exposed to colder waters. Life history theory predicts that species occupying stable environments where food resources are limited should exhibit slow growth, delayed onset of maturity and a long life span (K-strategist) (Young, 2003). From the limited data currently available it appears that some deep-sea octocorals fit the description of a K-strategist, however, the relationship between environmental features, colony size, age and reproductive maturation remains uncertain. A clearer pattern emerges for colony height, or more generally colony size and fecundity, which are positively correlated in deep-water octocorals (Cordes et al., 2001; Beazley, 2011; Mercier and Hamel, 2011). In A. arbuscula, increasing fecundity with colony size appears to arise from changing colony morphology, whereby larger colonies often are more highly branched and/or contain more polyps per branch area (Beazley, 2011) suggesting that colony size does not necessarily increase the reproductive output of individual polyps (Babcock, 1990; Hall and Hughes, 1996). Contrary to the once widely held prediction that environmental conditions in the deep sea reduce reproductive output, deep-sea octocorals appear to maintain levels of fecundity that are on par with related shallowwater species (Cordes et al., 2001; Orejas et al., 2007; Pires et al., 2009; Beazley, 2011). Reproductive output in both shallow- and deep-water corals appears to decrease with depth (Beiring and Lasker, 2000; Tsounis et al., 104 Les Watling et al. 2006a; Beazley, 2011; Mercier and Hamel, 2011), presumably due to diminishing food resources, and subsequent reduction in energy allocated to reproduction. Orejas et al. (2007) argue that colony growth form (e.g. ‘bottlebrush’, ‘fan-like’), may impact fecundity to a greater degree than the environmental conditions. It is possible that flow effects around and through the colony could result in differential delivery of food particles, thus resulting in varying reproductive output within a colony. In a comparative study of deep water, Antarctic primnoids with ‘sea fan’ or ‘bottlebrush’ colony form, Orejas et al. (2007) found no significant differences in polyp-level reproductive output in species with fan-like, dichotomously branching colony morphologies, whereas the ‘bottlebrush’ species, Dasystenella acanthina, exhibited a higher number of sexual products in proximal and central polyps in the middle to lower region of the colony. A similar pattern was observed by Brito et al. (1997) in Thouarella variabilis, another Antarctic primnoid with a ‘bottlebrush’ growth form. The latter authors proposed that polyps near the central axis showing the highest reproductive output might rely on energy from feeding (non-reproductive) polyps along the colony edge. A different pattern of reproductive output was observed in colonies of the bushy (but not bottlebrush) isidid, Acanella arbuscula, where distal polyps exhibited the highest levels of fecundity (Beazley, 2011), possibly due to an enhanced nutritional state created by increased prey capture rates. Fecundity may also be constrained by polyp volume (Brito et al., 1997), and general colony growth form dictating relative number of polyps and branches per colony (Beazley, 2011). 9.5. Spawning and larval development Photoperiod, temperature and productivity peaks have all been identified as temporal cues that may synchronize spawning activities in deep-water octocorals (Orejas et al., 2002b; Sun et al., 2010a; Mercier and Hamel, 2011); however, at present, many important questions remain unanswered about the timing and duration of gamete release. The influence of photoperiod and temperature are greatly reduced or absent in many deep-sea coral habitats, suggesting that other factors such as periodic phytodetrital pulses may be important. Synchronization of spawning activities enhances fertilization success among benthic free-spawners (Orejas, 2001). A number of species, including the sea pens, Anthoptilum murrayi and Kophobelemnon stelliferum, and the soft corals, A. ritteri and D. glomerata, appear to engage in both continuous, or nearly continuous, gametogenesis and spawning. Among asynchronous or continuous spawners, brooding may represent a more effective/efficient strategy for fertilization by presenting a larger target (colony or polyp) relative to the size of an individual oocyte. Brooding and internal fertilization are widespread among the temperate and cold-water octocorals, including Biology of Deep-Water Octocorals 105 many deep-water species; however, some apparently continuously spawning deep-sea species, like the sea pens, A. murrayi and K. stelliferum, apparently broadcast their gametes (Rice et al., 1992; Pires et al., 2009). Large oocyte sizes among deep-sea octocorals have led to the widespread assumption that larvae are lecithotrophic (Edwards and Moore, 2009). Larval development studies in the brooding soft coral species, A. ritteri (Cordes et al., 2001), D. glomerata and Drifa sp. (Sun et al., 2010a,b), and general observation of larvae in A. grandiflorus (Mercier and Hamel, 2011), support this assumption. There is evidence that lecithotrophic octocoral planulae may have long competency periods, delaying settlement as long as 2 4 months (Cordes et al., 2001; Sun et al., 2010b). In addition to providing for development of lecithotrophic larvae, large egg size may enhance fertilization among broadcast spawning species by providing a larger target for sperm (Levitan, 1993). Among brooding species the timing of planula release has been linked to lunar rhythms, temperature and productivity peaks. In Gersemia fruticosa and Drifa sp. from the northwest Atlantic, planulation was significantly correlated with the full and waning phases of the moon, respectively (Sun et al., 2010; Mercier and Hamel, 2011; Mercier et al., 2011). In A. grandiflorus from the same region, maturation of gametes and planulae occurred seasonally, culminating in planulation during summer and/or fall, coincident with the annual maximum in seawater temperature or initial decrease in temperature during the fall (Mercier and Hamel, 2011). In the same region, planulation in D. glomerata occurred throughout the year, with a peak in December January correlated with maximum temperature at that depth and an increase in photoperiod. A second peak in planula release occurred from April early June, especially in populations ,200 m (Sun et al., 2010b). This planulation event took place just prior to the onset of the spring bloom in the region. Larval behaviour has been observed in the soft coral species, D. glomerata and an unidentified Drifa sp. The two species exhibited different pre-settlement behaviours. Larvae from the Drifa sp. moved between the benthos and the water column by readily changing buoyancy, whereas D. glomerata larvae largely crawled on the bottom (Sun et al., 2010b). Planulae from both species showed a preference for settlement on natural rough surfaces coated with biofilm. 10. Growth and Age Growth of arborescent gorgonians can be measured in two dimensions: axial growth, the elongation of the main stem and branches, and 106 Les Watling et al. radial growth, the thickening of the main stem and branches. In gorgonians, this growth involves asexual propagation of polyps and deposition of an axial skeleton. The skeleton is composed of gorgonin, a horny proteinaceous material, embedded to varying degrees with calcium carbonate. Taxa within the predominantly deep-sea suborder Calcaxonia have skeletons that are more heavily calcified, and so have been the subject of several studies on ageing and skeletal growth. Considering the large number of species described from the deep sea, there are very few that document changes in morphological characteristics with age. Most octocorals grow via one of two mechanisms: monopodial, where a single terminal (axial) polyp grows upward budding off daughter polyps below it, or sympodial, where a polyp buds one or two daughter polyps of equal size, thus creating a zig-zag pattern of colony branches (Bayer, 1973). Mosher and Watling (2009) showed that the chrysogorgiid, Metallogorgia melanotrichos, radically changed colony form as it grew. Very young colonies possessed side branches along the central axis, but these branches were gradually lost as the colony matured. Adults possessed only a crown of branches, and the density of polyps and subdivisions of the crown branches increased as the colony approached old age. Juveniles of a few other species have been seen during some of the dives on the New England Seamounts. For example, the chrysogorgiid, Iridogorgia magnispiralis, starts life as a loosely coiled colony, with the youngest stages not quite completing a single coil (personal observation). On the other hand, young colonies of Paragorgia johnsoni look like miniature versions of the larger adults. As the colony grows branches are added and the central axis is thickened so that it can handle the weight and stress of the additional branches (personal observation). In short, much more work needs to be done to document morphological changes with growth in deep-sea gorgonians. Age estimates of deep-dwelling gorgonians collected range from about 30 to more than 400 years (Table 2.6). While there has been some attempt to use growth rings as a method of determining colony age (Sherwood and Edinger, 2009), for the most part either 14C or 210Pb decay rates have been the method of choice. Annual growth rings appear to be a feature of growth of Primnoa resedaeformis (Andrews et al., 2002; Sherwood and Edinger, 2009), but in the woody axis of species of Paramuricea, Sherwood and Edinger (2009) estimated growth rings were laid down once a decade. Most of the corals examined were between 50 and 100 years old, with a few, either in really deep water or subfossil specimens, were several hundred years old. Sherwood and Edinger (2009) suggest that the large number of younger specimens found in their area might be due to the fact that fishing has removed a large fraction of the very old colonies. For bamboo corals, Thresher (2009) suggested that radial growth increased linearly with ambient temperature when the water was between 2 C and 5 C. Below Table 2.6 Age and growth estimates for deep-sea gorgonian octocorals Growth (radial) mm y 21 Growth (axial) cm y 21 Method Colony age (years) Sample depth (m) Reference Paramuricea spp. 0.09 0.200 0.56 0.58 14 71, 103 814, 850 Corallium rubrum 0.35 1 20.15 30 40 15 60 Corallium secundum Corallium sp. Primnoa resedaeformis Primnoa resedaeformis Primnoa resedaeformis 0.17 0.36 0.44 0.09 1.00 67 71 67 .200 .100 years 210 .300 91 109 450 1482 263 450 414 Acanella arbuscula 0.07 1.00 30 100 526 Isidella tentaculum Keratoisis sp. Keratoisis sp. Keratoisis sp. Keratoisis ornata 0.10 0.051 0.057 0.05 0.05 0.074 Pb Pb 210 Pb/226Ra 210 Pb 14 C 53 98 282 131 110 400 94 874 1425, 1574 1425 1000 601 Keratoisis ornata 0.075 14 170 230 1193 Lepidisis sp. Unidentified bamboo 0.18 0.05 0.16 210 20 45 75 208 690 800 634 720 Sherwood and Edinger (2009) Marschal et al. (2004) Roark et al. (2006) Andrews et al. (2005) Andrews et al. (2002) Risk et al. (2002) Sherwood and Edinger (2009) Sherwood and Edinger (2009) Andrews et al. (2009) Andrews et al. (2009) Andrews et al. (2005) Thresher et al. (2004) Sherwood and Edinger (2009) Sherwood and Edinger (2009) Tracey et al. (2007) Roark et al. (2005) 0.43 1.6 2.32 C Labeling with stain 14 C 210 Pb 210 Pb 14 C Growth rings 14 C 210 210 0.19 0.44 0.93 14 C Pb/226Ra C Biology of Deep-Water Octocorals Species 107 108 Les Watling et al. 2 C, growth occurred at 10 15 µm y 21, and above 5 C, growth plateaued at about 165 µm y 21. Growth rates were also noticeably slower in water greater than 1200 m deep. 11. Dispersal Estimates of dispersal distances and population structure based on genetic data are few for deep-sea octocorals. There are two major problems that confront researchers. The first is access to adequate sample sizes for population-level analyses. Collections via submersible or ROV consume a significant fraction of bottom time due to the constraints of sampling octocoral colonies with a manipulator arm while holding station at 100s to 1000s of metres depth, which severely limits the number of samples that can effectively be gathered (it is not unusual for a single collection to take 20 30 min from the time of observation to the time the sample is stowed in a biobox; not surprisingly, the most time-effective sampling comes when aggregations of corals are found such that minimum redeployment of the remotely operated vehicle (ROV) or human occupied vehicle (HOV) is required). Larger numbers of colonies may be collected using ship-deployed samplers such as dredges and scientific trawls if they happen upon aggregations of corals, although the condition of samples recovered is typically poorer than those targeted by less invasive means, and their dispersion in the habitat is unknown. A second major problem stems from the slow rate of evolution in octocorals of mitochondrial markers that are commonly used in population genetic studies of other taxa (McFadden et al., 2010), and these markers cannot be used for studies of intra-specific variation. In the absence of reliable access to sufficient sample sizes of populations, there has been reluctance among researchers to develop the more variable, but relatively costly, microsatellite markers. Mitochondrial markers have been used to look at species distributions. Thoma et al. (2009) examined the distribution of msh1 haplotypes from six genera of octocorals collected from seamounts spanning 1700 km in the western North Atlantic. Among the well-sampled haplotypes they found no evidence for endemism at the seamount or seamount-chain scale, and three chrysogorgiid haplotypes were seen also in specimens collected in the Pacific (Hawaii, Solomon Islands, Kermadec Ridge). Smith et al. (2004) sequenced portions of the large subunit rRNA (16S) and an intergenic region in bamboo corals (Keratoisidinae) and found haplotypes that were distributed from the southwest Pacific to Hawaii and the eastern Pacific, and one from the genus Acanella that was shared between New Biology of Deep-Water Octocorals 109 Zealand and the northwest Atlantic. These data suggest that at least some species may have broad geographic distributions spanning ocean basins, although an alternative explanation is that the markers are not sufficient to detect recent species divergence events, in which case these may be geminate taxa. Genetic and morphological data both support the idea that deep-water chrysogorgiid (MCC) species are excellent dispersers. The maximum distance between MCC msh1 haplotypes is directly correlated with sampling effort (Thoma et al., 2009; Pante, 2011), and all genera of the MCC (except the rare Pseudochrysogorgia) harbour a pan-distributed haplotype. Among the 56 species and variants that are known from more than one geographic location, 82% have a maximum geographical spread .1000 km, and all species found at more than three geographic locations have a maximum geographical spread .780 km. Only two published studies have applied microsatellite markers to analyses of deep-water octocoral populations. Baco and Shank (2005) examined Corallium lauuense populations between 385 and 535 m depth from eight sites spanning 1200 km in the Hawaiian Archipelago. Although the microsatellites showed the expected higher levels of genetic diversity compared to mitochondrial-based markers, there was no significant population structure, despite some low levels of differentiation in pairwise comparisons of populations. Costantini et al. (2010) genotyped a very small number of samples of Mediterranean Corallium from between 585 and 819 m depth in the Strait of Sicily. They showed that the deep specimens differed from shallow-water collections of Corallium rubrum (20 40 m depth), but, perhaps more importantly, that microsatellite primers developed for the deep-water C. lauuense could be used in the Mediterranean Corallium species, suggesting that development and testing of microsatellites in closely related shallow-water octocorals (e.g. Paramuricea, Agell et al. 2009; Primnoa, S. France, unpublished data) will benefit future deep-water studies once the sampling issues are resolved. 12. Threats and Conservation Issues Octocorals are a common component of the benthic communities on seamounts and ridges throughout the world. As such they come into direct contact with bottom trawls being used to catch a variety of deepsea fish, especially species such as orange roughy (Hoplostethus atlanticus), black scabbardfish (Aphanopus carbo) and toothfishes (Dissostichus spp.), among many others (Rogers and Gianni, 2010). Loss of coral communities from heavily fished seamounts has been documented in Tasmania 110 Les Watling et al. (Koslow et al., 2001), New Zealand (Clark and O’Driscoll, 2003; Clark and Koslow, 2007), as well as in the North Atlantic (Waller et al., 2007; Watling et al., 2007). In all cases, where bottom trawling was frequent, no corals or other epifauna could be found, abundant scrape marks were visible, and the seamount rock surface was bare. Althaus et al. (2009) revisited some of the seamounts where fishing impacts were documented by Koslow et al. (2001). Impacts of fishing for orange roughy extended from the peaks at about 700 m water depth, to the sides at 1300 m. The seamounts with a strong fishing history were species poor or still bare more than 10 years after fishing effort was drastically reduced. An undescribed species of Chrysogorgiidae was found in some areas, but it could not be determined whether, because of its small size, it had not been impacted by the trawl gear, or it was a recent recruit. Similarly, Williams et al. (2010) found minor indications of recovery of the New Zealand seamount coral communities 5 years after trawling ceased, again with chrysogorgiids being present in some locations, but it is unknown whether they are indicators of survival or recruitment. In the North Atlantic, small sponges and plexaurid gorgonians were seen growing on the otherwise bare substrate of a seamount where there may have been no trawling for about 20 years (personal observations). Deep-water octocorals are one of the primary groups of organisms covered under the term ‘vulnerable marine ecosystem’, or VME. As a result of United Nations General Assembly (UNGA) Resolutions 61/105 and 64/72, nations must develop plans to safeguard VMEs when fishing on the high seas (Rogers and Gianni, 2010). Deep-water octocorals are seemingly found everywhere in the world ocean, and so are likely to be a VME component on seamounts, ridges, and plateaus, as well as continental slopes, throughout the world. It is, therefore, important that we document their taxonomic diversity and understand aspects of their biology, from acting as hosts for other species to their reproductive characteristics, so that management can be better informed and these long-lived organisms protected. ACKNOWLEDGEMENTS Much of the information in this review has come from expeditions we made in the North Atlantic courtesy of grants from the NOAA Ocean Exploration program. We would like to express our deepest thanks to NOAA for having made those expeditions possible. We also would like to thank our colleagues on those trips, P.J. Auster, R. Waller, T. Shank, J. Moore and L. Mullineaux for helping to collect specimens and for stimulating discussions while at sea. Further discussions on some of the topics covered here were had with J. Thoma, C. McFadden, A. Baco-Taylor, K. Morris, S. Cairns, J. Sanchez, R. Stone, L. Buhl-Mortensen and J. Guinotte. J. Thoma and H. Ylitalo-Ward provided valuable help with data gathering, species descriptions, etc., and J. Guinotte helped with preparation of the maps. Biology of Deep-Water Octocorals 111 REFERENCES Agassiz, A. (1888). Three cruises of the United States Coast and Geodetic Steamer “Blake” in the Gulf of Mexico, in the Caribbean Sea, and along the Atlantic coast of the United States, from 1877 to 1880. 2 vols., Houghton, Mifflin & Company, Boston. Agell, G., Rius, M. and Pascual, M. (2009). Isolation and characterization of eight polymorphic microsatellite loci for the Mediterranean gorgonian Paramuricea clavata. Conservation Genetics 10, 2025 2027. Alderslade, P. (1998). Revisionary systematics in the gorgonian family Isididae, with descriptions of numerous new taxa (Coelenterata: Octocorallia). Records of the Western Australian Museum S55, 1 359. Althaus, F., Williams, A., Schlacher, T. A., Kloser, R. J., Green, M. A., Barker, B. A., Bax, N. J., Brodie, P. and Schlacher-Hoenlinger, M. A. (2009). Impacts of bottom trawling on deep-coral ecosystems of seamounts are long-lasting. Marine Ecology Progress Series 397, 279 294. Andrews, A. H., Cordes, E. E., Mahoney, M. M., Munk, K., Coale, K. H., Cailliet, G. M. and Heifetz, J. (2002). Age, growth and radiometric age validation of a deepsea, habitat-forming gorgonian (Primnoa resedaeformis) from the Gulf of Alaska. Hydrobiologia 471, 101 110. Andrews, A., Cailliet, G., Kerr, L., Coale, K., Lundstrom, C. and DeVogelaere., A. (2005). Investigations of age and growth for three deep-sea corals from the Davidson Seamount off central California. In Cold-Water Corals and Ecosystems (A. Freiwald and J. M. Roberts, eds), pp. 1021 1038. Springer-Verlag, Berlin. Andrews, A. H., Stone, R. P., Lundstrom, C. C. and DeVogelaere, A. P. (2009). Growth rate and age determination of bamboo corals from the northeastern Pacific Ocean using refined 210Pb dating. Marine Ecology Progress Series 397, 173 185. Aurivillius, M. (1931). The gorgonarians of Dr. Sixteen Bock’s Expedition to Japan and Bonin Islands, 1914. Kungliska Svenska Vetenskapsakademiens Handlingar 3 ser. 9, 1 337. Babcock, R. (1990). Reproduction and development of the blue coral Heliopora coerulea (Alcyonaria: Coenothecalia). Marine Biology 104, 75 481. Baco, A. R. and Shank, T. M. (2005). Population genetic structure of the Hawaiian precious coral Corallium lauuense (Octocorallia: Coralliidae) using microsatellites. In ColdWater Corals and Ecosystems (A. Freiwald and J. M. Roberts, eds), pp. 663 678. Springer-Verlag, Berlin. Bayer, F.M. (1956). Octocorallia. In Treatise on Invertebrate Paleontology (R. Moore, ed.). Volume F, Coelenterata, F166 F230. Bayer, F. M. (1973). Colonial organization in octocorals. In Animal Colonies (R. S. Boardman, A. H. Cheetham and W. A. Oliver, Jr., eds), pp. 69 93. Dowden, Hutchinson & Ross, Inc., Stroudsburg, PA. Bayer, F. M. (1981). Key to the genera of Octocorallia exclusive of Pennatulacea (Coelenterata: Anthozoa), with diagnosis of new taxa. Proceedings of the Biological Society of Washington 94, 902 947. Bayer, F. M. (1990). A new isidid octocoral (Anthozoa: Gorgonacea) from New Caledonia, with descriptions of other new species from elsewhere in the Pacific Ocean. Proceedings of the Biological Society of Washington 103, 205 228. Bayer, F. M. (1993). A new scleraxonian octocoral (Coelenterate: Anthozoa) from Antarctic waters. Precious Corals and Octocoral Research 2, 11 18. Bayer, F. M. and Muzik, K. M. (1976). A New Solitary Octocoral, Taiaroa tauhou gen. et sp. nov. (Coelenterata: Protoalcyonaria) from New Zealand. Journal of the Royal Society of New Zealand 6, 499 515. Bayer, F. M. and Stefani, J. (1987a). Isididae de Nouvelle-Calédonie (clé des genres et description de 6 espèces nouvelles). Bulletin du Muséum National d’Histoire Naturelle, Paris, 4e série 1, 47 106. 112 Les Watling et al. Bayer, F. M. and Stefani, J. (1987b). New and previously known taxa of isidid octocorals (Coelenterata: Gorgonacea), partly from Antarctic waters. Proceedings of the Biological Society of Washington 100, 937 991. Bayer, F. and Stefani, J. (1988a). A new species of Chrysogorgia (Octocorallia: Gorgonacea) from New Caledonia, with descriptions of some other species from the Western Pacific. Proceedings of the Biological Society of Washington 101, 257 279. Bayer, F. M. and Stefani, J. (1988b). Primnoidae (Gorgonacea) de Nouvelle-Calédonie. Bulletin du Muséum National d’Histoire Naturelle, Paris, 4e série 3, 449 518. Beazley, L. I. (2011). Reproductive Biology of the Deep-Water Gorgonian Coral Acanella arbuscula from the Northwest Atlantic. Dalhousie University, Halifax, M.Sc thesis. Beiring, E. A. and Lasker, H. R. (2000). Egg production by colonies of a gorgonian coral. Marine Ecology Progress Series 196, 169 177. Benayahu, Y. and Loya, Y. (1984). Life history studies on the Red Sea soft coral Xenia macrospiculata Gohar, 1940. I. Annual dynamics of gonadal development. Biological Bulletin 166, 32 43. Benayahu, Y. and Loya, Y. (1986). Sexual reproduction of a soft coral: Synchronous and brief annual spawning of Sarcophyton glaucum (Quoy and Gaimard, 1833). Biological Bulletin 170, 32 42. Bouchet, P., Héros, V., Lozouet, P. and Maestrati, P. (2008). A quarter-century of deepsea malacological exploration in the South and West Pacific: Where do we stand? How far to go? In Tropical Deep-Sea Benthos 25 (V. Héros, R. H. Cowie and P. Bouchet, eds), pp. 9 40. Brazeau, D. A. and Lasker, H. R. (1989). The reproductive cycle and spawning in a Caribbean gorgonian. Biological Bulletin 176, 1 7. Breeze, H., Davis, D. S., Butler, M. and Kostylev, V. (1997). Distribution and Status of Deep Sea Corals Off Nova Scotia, Ecology Action Center, Halifax, NS, Canada Marine Issues Committee Special Publication Number 158 p. Brito, T. A. S., Tyler, P. A. and Clarke, A. (1997). Reproductive biology of the Antarctic octocoral Thouarella variabilis Wright & Studer, 1889. In Coelenterate Biology: Proceedings of the 6th International Congress of Coelenterate Biology (J. C. den Hartog, ed), pp. 63 69. Nationaal Natuurhistorisch Museum, Leiden. Broch, H. (1935). Oktokorallen des Nordlichsten Pazifischen Ozeans un ihre beziehungen zur Atlantishcen Fauna. Avhandlinger utgitt av Det Norske Videnskaps-Akademi i Oslo. Matematisk-naturvidenskapelig klasse 1935, No. 1: 1 53. Bronsdon, S. K., Tyler, P. A., Rice, A. L. and Gage, J. R. (1993). Reproductive biology of two epizoic anemones from the deep north-eastern Atlantic Ocean. Journal of the Marine Biological Association of the United Kingdom 73, 531 542. Buhl-Mortensen, L. and Mortensen, P. B. (2004). Gorgonophilus canadensis n. gen., n. sp. (Copepoda: Lamippidae), a gall forming endoparasite in the octocoral Paragorgia arborea (L., 1758) from the Northwest Atlantic. Symbiosis 37, 155 268. Butler, A. J., Rees, T., Beesley, P. and Bax, N. J. (2010). Marine biodiversity in the Australian region. PLoS One 5, e11831. Cairns, S. D. (2001). Studies on western Atlantic Octocorallia (Coelenterata : Anthozoa). Part 1: The genus Chrysogorgia Duchassaing & Michelotti, 1864. Proceedings of the Biological Society of Washington 114, 746 787. Cairns, S. D. (2006). Studies on western Atlantic Octocorallia (Coelenterata: Anthozoa). Part 6: The genera Primnoella Gray, 1858, Thouarella Gary, 1870; Dasystenella Versluys, 1906. Proceedings of the Biological Society of Washington 119, 161 194. Cairns, S. D. (2007a). Studies on western Atlantic Octocorallia (Coelenterata: Anthozoa). Part 8: New records of Primnoidae from the New England and Corner Rise seamounts. Proceedings of the Biological Society of Washington 120, 243 263. Biology of Deep-Water Octocorals 113 Cairns, S. D. (2007b). Calcaxonian octocorals (Cnidaria: Anthozoa) from eastern Pacific seamounts. Proceedings of the California Academy of Sciences 58, 511 541. Cairns, S. D. (2009). Review of the Octocorallia (Cnidaria: Anthozoa) from Hawaı́i and adjacent seamounts: Part 2. Genera Paracalyptrophora Kinoshita, 1908; Candidella Bayer, 1954; and Calyptrophora Gray, 1866. Pacific Science 63, 413 448. Cairns, S. D. (2010). Review of the Octocorallia (Cnidaria: Anthozoa) from Hawaı́i and adjacent seamounts. Part 3. Genera Thouarella, Plumarella, Callogorgia, Fanellia, and Parastenella. Pacific Science 64, 413 440. Cairns, S. D. and Baco, A. (2007). Review and five new Alaskan species of deepwater octocoral Narella (Octocorallia: Primnoidae). Systematics and Biodiversity 5, 391 407. Cairns, S. D. and Bayer, F. M. (2005). A review of the genus Primnoa (Octocorallia: Gorgonacea: Primnoidae), with the description of two new species. Bulletin of Marine Science 77, 225 256. Cairns, S. D. and Bayer, F. M. (2008). A review of the Octocorallia (Cnidaria: Anthozoa) from Hawaı́i and adjacent seamounts: the genus Narella Gray, 1870. Pacific Science 62, 83 115. Cairns, S. D. and Bayer, F. M. (2009). A generic revision and phylogenetic analysis of the Primnoidae (Cnidaria: Octocorallia). Smithsonian Contributions to Zoology 629, 1 79. Cairns, S. D. and Chapman, R. E. (2001). Biogeographic affinities of the North Atlantic deep-water Scleractinia. In Proceedings of the First International Symposium on Deep-Sea Corals (J. H. M. Willison, J. Hall, S. E. Gass, E. L. R. Kenchington, M. Butler and P. Doherty, eds), pp. 30 57. Ecology Action Centre, Halifax. Cho, W. and Shank, T. M. (2010). Incongruent patterns of genetic connectivity among four ophiuroid species with differing coral host specificity on North Atlantic seamounts. Marine Ecology 31(suppl. 1):121 143. Clark, M. R. and Koslow, J. A. (2007). Impacts of fisheries on seamounts. In Seamounts: Ecology, Fisheries and Conservation (T. J. Pitcher, T. Morato, P. J. B. Hart, M. R. Clark, N. Haggan and R. S. Santos, eds), pp. 413 441. Blackwell Publishing, Oxford. Clark, M. R. and O’Driscoll, R. L. (2003). Deepwater fisheries and aspects of their impact on seamount habitat in New Zealand. Journal of Northwest Atlantic Fisheries Science 31, 441 458. Clark, M. R., Rowden, A. A., Schlache, T., Williams, A., Consalvey, M., Stocks, K. I., Rogers, A. D., O’Hara, T. D., White, M., Shank, T. M. and Hall-Spencer, J. M. (2010). The ecology of seamounts: Structure, function, and human impacts. Annual Review of Marine Science 2, 253 278. Coleman, C. O. and Barnard, J. L. (1991). Amatiguakius forsberghi, a new genus and species from Alaska (marine Amphipoda: Epimeriidae). Proceedings of the Biological Society of Washington 104, 279 287. Combes, C. (2005). The Art of Being a Parasite. University of Chicago Press, Chicago. Costantini, F., Taviani, M., Remia, A., Pintus, E., Schembri, P. J. and Abbiati, M. (2010). Deep-water Corallium rubrum (L., 1758) from the Mediterranean Sea: preliminary genetic characterisation. Marine Ecology 31, 261 269. Cordes, E. E., Nybakken, J. W. and VanDykhuizen, G. (2001). Reproduction and growth of Anthomastus ritteri (Octocorallia: Alcyonacea) from Monterey Bay, California, USA. Marine Biology 138, 491 501. Corselli, C. (2010). Introduction: Cold-water coral communities in the Mediterranean Sea. Deep Sea Research Part II: Topical Studies in Oceanography 57, 323 325. Costello, M. J., Coll, M., Danovaro, R., Halpin, P., Ojaveer, H. and Miloslavich, P. (2010). A census of marine biodiversity knowledge, resources, and future challenges. PLoS One 5, e12110. Dautova, T. N. (2007). Gorgonians (Anthozoa: Octocorallia) of the Northwestern Sea of Japan. Russian Journal of Marine Biology 33, 297 304. 114 Les Watling et al. Deichmann, E. (1936). The Alcyonaria of the western part of the Atlantic Ocean. Memoirs of the Museum of Comparative Zoology, Harvard 53, 1 317. Ditlevsen, H. (1917). Annelids. I. Ingolf Reports 4, 1 71. Doflein, F. (1906). Ostasienfahrt. Erlebnisse und beobachtungen eines naturforschers in China, Japan und Ceylon. B. G. Teubner, Leipzig, 511 p. Dolan, E. (2008). Phylogenetics, Systematics and Biogeography of Deep-Sea Pennatulacea (Anthozoa: Octocorallia): Evidence from Molecules and Morphology. University of Southampton, School of Ocean and Earth Science, Southampton, U.K. Doctoral Thesis, p 195. Dons, C. (1944). Norges korallrev. Det Kongelige Norske Videnskabers Selskabs Forhandlinger 16, 37 82. Eckelbarger, K. J., Tyler, P. A. and Langton, R. W. (1998). Gonadal morphology and gametogenesis in the sea pen Pennatula aculeata (Anthozoa: Pennatulacea) from the Gulf of Maine. Marine Biology 132, 677 690. Eckelbarger, K. J., Watling, L. and Fournier, H. (2005). Reproductive biology of the deep-sea polychaete Gorgoniapolynoe caeciliae (Polynoidae), a commensal species associated with octocorals. Journal of the Marine Biological Association of the United Kingdom 85, 1425 1433. Edwards, D. C. B. and Moore, C. G. (2009). Reproduction in the sea pen Funiculina quadrangularis (Anthozoa: Pennatulacea). Estuarine, Coastal, and Shelf Science 82, 161 168. Emson, R. H. and Woodley, J. D. (1987). Submersible and laboratory observations on Asteroschema tenue, a long-armed euryaline brittle star epizoic on gorgonians. Marine Biology 96, 31 45. Fautin, D. G. and Mariscal, R. N. (1991). Cnidaria: Anthozoa. In Microscopic Anatomy of Invertebrates, vol 2: Placozoa, Porifera, Cnidaria, and Ctenophora (F. H. Harrison, ed), pp. 267 358. Wiley, New York. Forbes, E. (1859). A Natural History of the European Seas, John Van Voorst, London. France, S. C. (2007). Genetic analysis of bamboo corals (Cnidaria: Octocorallia: Isididae): Does lack of colony branching distinguish Lepidisis from Keratoisis? Bulletin of Marine Science 81, 323 333. France, S.C., Pante, E., Brugler, M.R. and van der Ham, J.L. (2010). On the evolution of deep-sea octocorals and antipatharians: Patterns revealed from molecular phylogenies. Society for Integrative and Comparative Biology Annual Meeting 2010, Seattle, Washington (http://www.sicb.org/meetings/2010/schedule/abstractdetails.php3? id=226; accessed 5-12-2011) Fujita, T. (2001). Submersible observations on the euryaline brittle star, Asteronyx loveni (Echinodermata, Ophiuroidea), living in association with a gorgonaceans coral. In Echinoderms 2000 (M. Barker, ed), pp. 267 272. Swets and Zellinger, Lisse. Fujita, T. and Ohta, S. (1988). Photographic observations of the life style of a deep-sea ophiuroid Asteronyx loveni (Echinodermata). Deep-Sea Research 35, 2029 2043. Gage, J. D. and Tyler, P. A. (1991). Deep-Sea Biology: A Natural History of Organisms at the Deep-Sea Floor, Cambridge University Press, Cambridge. Grant, R. (1976). The marine fauna of New Zealand: Isididae (Octocorallia: Gorgonacea) from New Zealand and the Antarctic. New Zealand Oceanographic Institute Memoir 66, 1 54. Grasshoff, M. (1982). Die Gorgonaria, Pennatularia, und Antipatharia des Tiefwassers der Biskaya (Cnidaria, Anthozoa). Bulletin Muse´um national d’Histoire Naturelle Paris, 4e série (1981) 3, 731 766. Grasshoff, M. (1985). Die Gorgonaria, Pennatularia und Antipatharia des Tiefswassers der Biskaya. III. Ergänzungen. In Peuplements profonds du Golfe de Gascogne (L. Laubier and C. Monniot, eds), pp. 229 310. IFREMER, Brest. Biology of Deep-Water Octocorals 115 Grasshoff, M. (1986). Die Gorgonaria de Expeditionen von “Travailleur” 1880 1882 und “Talisman” 1883 (Cnidaria, Anthozoa). Bulletin Muséum national d’Histoire Naturelle, Paris, 4e série 8, 9 38. Grasshoff, M. (1988). The geographical and bathymetric distribution of the Gorgonacea and Antipatharia (Cnidaria, Anthozoa) of St. Paul and Amsterdam Islands (Indian Ocean). Mesoge´e 48, 115 124. Grasshoff, M. (1999). The shallow water gorgonians of New Caledonia and adjacent islands (Coelenterata, Octocorallia). Senckenbergiana Biologica 78, 1 121. Gravier, Ch. (1914). Isidicola antarctica, Crustacé parasite de quelques Isidae de l’Antarctique Sud-américaine, Deuxième Expédition Antarctique Française (1908 1910). Les Alcyonaires. Appendice, 99 100. Grigg, R. W. and Bayer, F. M. (1976). Present knowledge of the systematics and zoogeography of the order Gorgonacea in Hawaii. Pacific Science 30, 167 175. Grygier, M. J. (1980). Two new lamippid copepods parasitic on gorgonians from Hawaii and the Bahamas. Proceedings of the Biological Society of Washington 93, 662 673. Grygier, M. J. (1981a). Redescription of Gorgonolaureus bikiniensis (Crustacea: Ascothoracica), with a reevaluation of it familial affinities. Micronesica 17, 67 76. Grygier, M. J. (1981b). Gorgonolaureus muzikae sp. nov. (Crustacea: Ascothoracica) parasitic on a Hawaiian gorgonian, with special reference to its protandric hermaphroditism. Journal of Natural History 15, 1019 1045. Grygier, M. J. (1983). An endoparasitic lamippid copepod in Acanella from the North Atlantic. Crustaceana 45, 176 182. Grygier, M. J. (1984). Ascothoracida (Crustacea: Maxillopoda) parasitic on Chrysogorgia (Gorgonacea) in the Pacific and western Atlantic. Bulletin of Marine Science 34, 141 169. Grygier, M. J. (1991). Additions to the ascothoracidan fauna of Australia and South-east Asia (Crustacea, Maxillopoda): Synagogidae (part), Lauridae and Petrarcidae. Records of the Australian Museum 43, 1 46. Gutierrez-Rodriguez, C. and Lasker, H. R. (2004). Reproductive biology, development, and planula behavior in the Caribbean gorgonian Pseudopterogorgia elisabethae. Invertebrate Biology 123, 54 67. Hall, V. R. and Hughes, T. P. (1996). Reproductive strategies of modular organisms: Comparative studies of reef-building corals. Ecology 77, 950 963. Harrison, P. L. and Wallace, C. C. (1990). Reproduction, dispersal and recruitment of scleractinian corals. In Ecosystems of the World 25, Coral Reefs (Z. Dubinsky, ed), pp. 133 207. Elsevier Science Publishers, Amsterdam. Hartmann-Schröder, G. (1989). Polynoe thouarellicola n. sp. aus der Antarktis, assoziiert mit Hornkorallen, und Wiederbeschreibung von Polynoe antarctica Kinberg, 1858 (Polychaeta, Polynoidae). Zoologisches Anzeiger 222, 205 221. Hartnoll, R. G. (1975). The annual cycle of Alcyonium digitatum. Estuarine, Coastal, and Shelf Science 3, 71 78. Heath, H. (1918). Solenogastres from the eastern coast of North America. Memoirs of the Museum of Comparative Zoology 45, 187 261. Hecker, B. (1990). Variation in megafaunal assemblages on the continental margin south of New England. Deep-Sea Research 37, 37 57. Hecker, B., Blechschmidt, G. and Gibson, P. (1980). Final Report Canyon Assessment Study in the Mid- and North Atlantic Areas of the U.S. Outer Continental Shelf. U.S. Department of the Interior, Bureau of Land Management, Washington, D.C. Contract No. BLM-AA551-CT8-49. Hecker, B., Logan, D.T., Gandarillas, F.E. and Gibson, P.R. (1983). Megafaunal assemblages in Lydonia Canyon, Baltimore Canyon, and selected slope areas. Canyon and 116 Les Watling et al. Slope Processes Study, Vol. 3, Final Report for U.S. Department of the Interior, Minerals Management Service Contract 14-12-001-29178. Heifetz, J., Wing, B. L., Stone, R. P., Malecha, P. W. and Courtney, D. L. (2005). Corals of the Aleutian Islands. Fisheries Oceanography 14(Suppl. 1):131 138. Herrera, S., Baco, A. and Sánchez, J. A. (2010). Molecular systematics of the bubblegum coral genera (Paragorgiidae, Octocorallia) and description of a new deep-sea species. Molecular Phylogenetics and Evolution 55, 123 135. Hessler, R. R. and Wilson, G. D. F. (1983). The origin and biogeography of malacostracan crustaceans in the deep sea. In The Emergence of the Biosphere (R. W. Sims, J. H. Price and P. E. S. Whalley, eds), pp. 227 254. Academic Press, New York. Ingole, B. and Koslow, J. A. (2005). Deep-sea ecosystems of the Indian Ocean. Indian Journal of Marine Sciences 34, 27 34. Jamieson, G. S., Pellegrin, N. and Jesson, S. (2007). Taxonomy and zoogeography of coldwater corals in coastal British Columbia. In Conservation and Adaptive Management of Seamount and Deep-Sea Coral Ecosystems (R. Y. George and S. D. Cairns, eds), pp. 215 229. Rosenstiel School of Marine and Atmospheric Science, University of Miami. Kapela, W. and Lasker, H. R. (1999). Size-dependent reproduction in the Caribbean gorgonian Pseudoplexaura porosa. Marine Biology 135, 107 114. Kinoshita, K. (1908). Primnoidae von Japan. Journal of the College of Science, Tokyo Imperial University 23, 1 74. Kinoshita, K. (1913). Studien über einige Chrysogorgiiden Japans. Journal of the College of Science, Tokyo Imperial University 33, 1 47. Koslow, J. A., Gowlett-Holmes, K., Lowry, J. K., O’Hara, T., Poore, G. C. B. and Williams, A. (2001). Seamount benthic macrofauna off southern Tasmania: Community structure and impacts of trawling. Marine Ecology Progress Series 213, 111 125. Kruger, A., Schleyer, M. H. and Benayahu, Y. (1998). Reproduction in Anthelia glauca (Octocorallia: Xeniidae). I. Gametogenesis and larval brooding. Marine Biology 131, 423 432. Kükenthal, W. (1909). Japansiche Gorgoniden. II Teil. Die Familien der Plexauriden, Chrysogorgiden und Melitoiden. In Beitrage sur Naturgeschichte Ostasiens (F. Doflein, ed), Bayer Akademische Wissenschaftliche pp. 1 78. Abhandlungen math.-physik. Klasse K., Supplement Band 1. Kükenthal, W. (1912). Die Alcyonaria der Deutschen Südpolar-Expedition 1901 1903. Deutsche Südpolar Expedition 1901 1903 im Auftrage des Reichsamtes des Innern 13, 289 347. Kükenthal, W. (1919). Gorgonaria. Wissenschaftliche Ergebnisse der deutschen TiefseeExpedition auf dem Dampfer “Valdivia”, 1898 1899 13, 1 946. pls. 30 89. Kükenthal, W. (1924). Coelenterata Gorgonaria. Das Tierreich 47, xxviii 478. Kükenthal, W. and Gorzawsky, H. (1908). Japansiche Gorgoniden. I Teil. Die Familien der Primnoiden, Muriceiden und Acanthogorgiden. In Beitrage sur Naturgeschichte Ostasiens (F. Doflein, ed), Bayer Akademische Wissenschaftliche pp. 1 78. Abhandlungen math.-physik. Klasse K., Supplement Band 1. Lasker, H. R. (1990). Clonal propagation and population dynamics of a gorgonian coral. Ecology 71, 1578 1589. Levitan, D. R. (1993). The importance of sperm limitation to the evolution of egg size in marine invertebrates. American Naturalist 141, 517 536. Lewis, D. H. (1985). Symbiosis and mutualism: Crisp concepts and soggy semantics. In The Biology of Mutualism: Ecology and Evolution (D. H. Boucher, ed), pp. 29 39. Croom Helm Ltd., Kent, U.K. Lindner, A., Cairns, S. D. and Cunningham, C. W. (2008). From offshore to onshore: Multiple origins of shallow-water corals from deep-sea ancestors. PLoS ONE 3, e2429. Biology of Deep-Water Octocorals 117 López-González, P. J. and Briand, P. (2002). A new scleraxonian genus from Josephine Bank, north-eastern Atlantic (Cnidaria, Octocorallia). Hydrobiologia 482, 97 105. López-González, P. J. and Cunha, M. R. (2010). Two new species of Dendrobrachia Brook, 1889 (Cnidaria: Octocorallia: Dendrobrachiidae) from the north-eastern Atlantic and western Mediterranean. Scientia Marina 74, 423 434. López-González, P. J. and Gili, J.-M. (2000). A new octocoral genus (Cnidaria: Anthozoa) from Antarctic waters. Polar Biology 23, 452 458. López-González, P. J. and Gili, J.-M. (2001). Rosgorgia inexspectata, new genus and species of Subergorgiidae (Cnidaria, Octocorallia) from off the Antarctic Peninsula. Polar Biology 24, 122 126. López-González, P. J. and Gili, J.-M. (2005). Two new dimorphic soft-coral species (Anthozoa: Octocorallia) from Antarctica. Hydrobiologia 544, 143 153. López-González, P. J., Gili, J.-M. and Orejas, C. (2002). A new primnoid genus (Anthozoa: Octocorallia) from the Southern Ocean. Scientia Marina 66, 383 397. López-González, P. J., Rodriguez, E. and Vert, N. (2003). Biogeography and ecology of Cnidaria. In The Expedition ANTARKTIS XIX/5 (LAMPOS) of RV “Polarstern” in 2002 (W. Arntz and T. Brey, eds), pp. 13 18. Berichte zur Polar und Meeresforschung. No. 462 Lowry, J. K. (1985). Cardomanica andersoni n. gen. n. sp. from the Western Tasman Sea with notes on species from tropical western Atlantic Ocean (Crustacea: Ascothoracida: Synagogidae). Records of the Australian Museum 37, 317 323. Mah, C. (2006). Phylogeny and biogeography of the deep-sea goniasterid Circeaster (Echinodermata, Asteroidea, Goniasteridae) including descriptions of six new species. Zoosystema 28, 917 954. Mah, C., Nizinski, M. and Lundsten, L. (2010). Phylogenetic revision of the Hippasterinae (Goniasteridae; Asteroidea): Systematics of deep sea corallivores, including one new genus and three new species. Zoological Journal of the Linnaean Society 160, 266 301. Marschal, C., Garrabou, J., Harmelin, J. G. and Pichon, M. (2004). A new method for measuring growth and age in the precious red coral, Corallium rubrum (L.). Coral Reefs 23, 423 432. Martin, D. and Britayev, T. A. (1998). Symbiotic polychaetes: review of known species. Oceanography and Marine Biology: An Annual Review 36, 217 340. Matsumoto, A. K., Iwase, F., Imahara, Y. and Namikawa, H. (2007). Bathymetric distribution and biodiversity of cold-water octocorals (Coelenterata: Octocorallia) in Sagami Bay and adjacent waters of Japan. In Conservation and Adaptive Management of Seamount and Deep-Sea Coral Ecosystems (R. Y. George and S. D. Cairns, eds), pp. 231 251. Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, USA. McFadden, C. S., Donahue, R., Hadland, B. K. and Weston, R. (2001). A molecular phylogenetic analysis of reproductive trait evolution in the soft coral genus Alcyonium. Evolution 55, 54 67. McFadden, C. S., France, S. C., Sánchez, J. A. and Alderslade, P. (2006). A molecular phylogenetic analysis of the Octocorallia (Cnidaria: Anthozoa) based on mitochondrial protein-coding sequences. Molecular Phylogenetics and Evolution 41, 513 527. McFadden, C. S., Sanchez, J. A. and France, S. C. (2010). Molecular phylogenetic insights into the evolution of Octocorallia: A review. Integrative and Comparative Biology 50, 389 410. Mercier, A. and Hamel, J.-F. (2011). Contrasting reproductive strategies in three deep-sea octocorals from eastern Canada: Primnoa resedaeformis, Keratoisis ornata, and Anthomastus grandiflorus. Coral Reefs 30, 337 350. Mercier, A., Sun, Z., Baillon, S. and Hamel, J.-F. (2011). Lunar rhythms in the deep sea: Evidence from the reproductive periodicity of several marine invertebrates. Journal of Biological Rhythms 26, 82 86. 118 Les Watling et al. Milne-Edwards, H. and Haime, J. (1857). Histoire naturelles des Coralliaires ou Polypes proprements dits. Tome, 1 3. Paris. Mortensen, P. B., Buhl-Mortensen, L., Gebruk, A. V. and Krylova, E. M. (2008). Occurrence of deep-water corals on the Mid-Atlantic Ridge based on MAR-ECO data. Deep-Sea Research II 55, 142 152. Mosher, C. and Watling, L. (2009). Partners for life: A brittle star and its octocoral host. Marine Ecology Progress Series 397, 81 88. Moyse, J. (1983). Isidascus bassindalei gen. nov., sp. nov. (Ascothoracida: Crustacea) from North-East Atlantic with a note on the origin of barnacles. Journal of the Marine Biological Association of the United Kingdom 63, 161 180. Muzik, K. (1978). A bioluminescent gorgonian, Lepidisis olapa, new species (Coelenterata, Octocorallia) from Hawaii. Bulletin of Marine Science 28, 735 741. Muzik, K. (1979). A Systematic Revision of the Hawaiian Paramuriceidae and Plexauridae (Coelenterata: Octocorallia), University of Miami, 227 pp. Ph.D. Dissertation. Nutting, C. C. (1908). Descriptions of the Alcyonaria collected by the U.S. Bureau of Fisheries Steamer Albatross in the vicinity of the Hawaiian Islands in 1902. Proceedings of the U.S. National Museum 35, 543 601. Nutting, C. C. (1910a). The Gorgonacea of the Siboga Expedition III. The Muriceidae. Siboga-Expeditie Monographie Monographie XIII, 108. pls. 1 22. Nutting, C. C. (1910b). The Gorgonacea of the Siboga Expedition IV. The Plexauridae. Siboga-Expeditie Monographie Monographie XIII, 20. pls. 1 4. Nutting, C. C. (1910c). The Gorgonacea of the Siboga Expedition V. The Isidae. SibogaExpeditie Monographie XIII, 24. pls. 1 6. Nutting, C. C. (1910d). The Gorgonacea of the Siboga Expedition VI. The Gorgonellidae. Siboga-Expeditie Monographie XIII, 39. pls. 1 11. Nutting, C. C. (1910e). The Gorgonacea of the Siboga Expedition VII. The Gorgonidae. Siboga-Expeditie Monographie Monographie XIII, 10. pls. 1 3. Nutting, C. C. (1911). The Gorgonacea of the Siboga Expedition VIII. The Scleraxonia. Siboga-Expeditie Monographie Monographie XIII, 62. pls. 1 12. Nutting, C. C. (1912). Descriptions of the Alcyonaria collected by the U.S. Fisheries steamer “Albatross,” mainly in Japanese waters, during 1906. Proceedings of the United States National Museum 43, 1 104. Ocaña, O., den Hartog, J. C. and van Ofwegen, L. P. (2004). Ring sea anemones, an overview (Cnidaria, Anthozoa, Actinaria). Graellsia 60, 143 154. Orejas, C. (2001). Role of benthic cnidarians in energy transfer processes in the southern Ocean marine ecosystem (Antarctica). Berichte zur Polar- und Meeresforschung 395, 1 186. Orejas, C., Gili, J. M., Lopez-Gonzalez, P. J. and Arntz, W. E. (2002a). Feeding strategies and diet composition of four Antarctic cnidarian species. Polar Biology 24, 620 627. Orejas, C., Lopez-Gonzalez, P. J., Gili, J. M., Teixido, N., Gutt, J. and Arntz, W. E. (2002b). Patterns of density and reproductive ecology of the Antarctic octocoral Ainigmaptilon antarcticum in the Weddell Sea. Marine Ecology Progress Series 231, 101 114. Orejas, C., Gili, J. M. and Arntz, W. E. (2003). Role of small-plankton communities in the diet of two Antarctic octocorals (Primnoisis antarctica and Primnoella sp.). Marine Ecology Progress Series 250, 105 116. Orejas, C., Gili, J. M., Lopez-Gonzalez, P. J., Hasemann, C. and Arntz, W. E. (2007). Reproduction patterns of four Antarctic octocorals in the Weddell Sea: An interspecific, shape, and latitudinal comparison. Marine Biology 150, 551 563. Pante, E. (2011). Biogeography and Evolution of Chrysogorgiid Corals, University of Louisiana at Lafayette, Lafayette, LA Ph.D. thesis. Biology of Deep-Water Octocorals 119 Pante, E. and France, S. C. (2010). Pseudochrysogorgia bellona n. gen. n. sp.: A new genus and species of chrysogorgiid octocoral (Coelenterata: Anthozoa) from the Coral Sea. Zoosystema 32, 595 612. Pante, E. and Watling, L. (2012, in press). Chrysogorgia from the New England and Corner Seamounts: Atlantic Pacific connections. Journal of the Marine Biological Association of the United Kingdom. Parrish, F. A. and Roark, E. B. (2009). Growth validation of gold coral Gerardia sp. in the Hawaiian Archipelago. Marine Ecology Progress Series 397, 163 172. Pettibone, M. H. (1969). The genera Polyeunoa McIntosh, Hololepidella Willey, and three new genera (Polychaeta, Polynoidae). Proceedings of the Biological Society of Washington 82, 43 62. Pettibone, M. H. (1991a). Polynoids commensal with gorgonian and stylasterid corals, with a new genus, new combinations, and new species (Polychaeta: Polynoidae: Polynoinae). Proceedings of the Biological Society of Washington 104, 688 713. Pettibone, M. H. (1991b). Polynoid polychaetes commensal with antipatharian corals. Proceedings of the Biological Society of Washington 104, 714 726. Picciano, M. and Ferrier-Pagès, C. (2007). Ingestion of pico- and nanoplankton by the Mediterranean red coral, Corallium rubrum. Marine Biology 150, 773 782. Pires, D. O., Castro, C. B. and Silva, J. C. (2009). Reproductive biology of the deep-sea pennatulacean Anthoptilum murrayi (Cnidaria, Octocorallia). Marine Ecology Progress Series 397, 103 112. Puglisi, M. P., Paul, V. J., Biggs, J. and Slattery, M. (2002). Co-occurrence of chemical and structural defenses in the gorgonian corals of Guam. Marine Ecology Progress Series 239, 105 114. Raupach, M. J., Mayer, C., Malyutina, M. and Wagele, J. W. (2009). Multiple origins of deep-sea Asellota (Crustacea: Isopoda) from shallow waters revealed by molecular data. Proceedings of the Royal Society B-Biological Sciences 276, 799 808. Rice, A. L., Tyler, P. A. and Paterson, G. J. L. (1992). The pennatulid Kophobelemnon stelliferum (Cnidaria: Octocorallia) in the Porcupine Seabight (North-east Atlantic Ocean). Journal Marine Biological Association of United Kingdom 72, 417 434. Risk, M. J., Heikoop, J. M., Snow, M. G. and Beukens, R. (2002). Lifespans and growth patterns of two deep-sea corals: Primnoa resedaeformis and Desmophyllum cristagalli. Hydrobiologia 471, 125 131. Roark, E. B., Guilderson, T. P., Flood-Page, S., Dunbar, R. B., Ingram, B. L., Fallon, S. and McCulloch, M. (2005). Radiocarbon-based ages and growth rates of bamboo corals from the Gulf of Alaska. Geophysical Research Letters 32, 1 5. Roark, E. B., Guilderson, T. P., Dunbar, R. B. and Ingram, B. L. (2006). Radiocarbonbased ages and growth rates of Hawaiian deep-sea corals. Marine Ecology Progress Series 327, 1 14. Rogers, A. D. and Gianni, M. (2010). The implementation of UNGA Resolutions 61/ 105 and 64/72 in the management of deep-sea fisheries on the high seas. Report Prepared for the Deep-Sea Conservation Coalition, International Programme on the State of the Ocean, 97. Samadi, S., Schlacher, T. and Richer de Forges, B. (2007). Seamount benthos. In Seamounts: Ecology, Fisheries and Conservation (T. J. Pitcher, T. Morato, P. J. B. Hart, M. R. Clark, N. Haggan and R. S. Santos, eds), pp. 119 140. Blackwell Publishing, Oxford. Sánchez, J. A. and Cairns, S. D. (2004). An unusual new gorgonian coral (Anthozoa: Octocorallia) from the Aleutian Islands, Alaska. Zoologische Mededelingen, Leiden 78, 265 274. 120 Les Watling et al. Sautya, S., Ingole, B., Ray, D., Stohr, S., Samudrala, K., Raju, K. A. K. and Mudholkar, A. (2011). Megafaunal community structure of Andaman seamounts including the backarc basin a quantitative exploration from the Indian Ocean. PLoS ONE 6, e16162. Sherwood, O. and Edinger, E. N. (2009). Ages and growth rates of some deep-sea gorgonian and antipatharian corals of Newfoundland and Labrador. Canadian Journal of Fisheries and Aquatic Sciences 66, 142 152. Sherwood, O., Jamieson, R. E., Edinger, E. N. and Wareham, V. E. (2008). Stable C and N isotopic composition of cold-water corals from the Newfoundland and Labrador continental slope: Examination of trophic, depth and spatial effects. Deep-Sea Research I 55, 1392 1402. Simpson, A. and Watling, L. (2011). Precious corals (Coralliidae) from north-western Atlantic seamounts. Journal of the Marine Biological Association of the United Kingdom 91, 369 382. Smith, P. J., McVeagh, S. M., Mingoia, J. T. and France, S. C. (2004). Mitochondrial DNA sequence variation in deep-sea bamboo corals (Keratoisidinae) species in the Southwest and Northwest Pacific Ocean. Marine Biology 144, 253 261. Stock, J. H. (1986). Cases of hyperassociation in the Copepoda (Herpyllobiidae and Nereicolidae). Systematic Parasitology 8, 71 81. Stone, R. P. (2006). Coral habitat in the Aleutian Islands of Alaska: Depth distribution, fine-scale species associations, and fisheries interactions. Coral Reefs 25, 229 238. Stone, R.P., Andrews, A. and Mondragon, J. (2005). Deepwater Emergence of Red Tree Coral (Primnoa sp.) in Glacier Bay, Alaska. Third International Symposium on Deep-Sea Corals, Miami, FL (ftp://ftp.afsc.noaa.gov/posters/pStone02_red-tree-coral. pdf ). Stone, R. P. and Shotwell, S. K. (2007). State of deep coral ecosystems in the Alaska Region: Gulf of Alaska, Bering Sea and Aleutian Islands. In The State of Deep Coral Ecosystems of the United States (S. E. Lumsden, T. F. Hourigan, A. W. Bruckner and G. Dorr, eds), pp. 65 108. NOAA Technical Memorandum CRCP-3. Silver Spring, Maryland. Studer, T. and Wright, E. P. (1887). Versuch eines Systemes der Alcyonaria. Archiv für Naturgeschichte 53, 1 74. Studer, T. (1901). Alcyonaires provenant des campagnes de l’Hirondelle (1886 1888). Résultats des campagnes scientifiques accomplies sur son yacht par Albert Ier, Prince Souverain de Monaco 20, 1 64. Sun, Z., Hamel, J.-F. and Mercier, A. (2009a). Reproductive biology of deep-sea corals in the Newfoundland and Labrador region. In The Ecology of Deep-Sea Corals of Newfoundland and Labrador Waters: Biogeography, Life History, Biogeochemistry, and Relation to Fishes (K. Gilkinson and E. Edinger, eds), pp. 23 33. Canadian Technical Report of Fisheries and Aquatic Sciences No. 2830. Sun, Z., Hamel, J.-F. and Mercier, A. (2009b). Planulation of deep-sea octocorals in the NW Atlantic. Coral Reefs 28, 781. Sun, Z., Hamel, J.-F., Edinger, E. and Mercier, A. (2010a). Reproductive biology of the deep-sea Drifa glomerata in the northwest Atlantic. Marine Biology 157, 863 873. Sun, Z., Hamel, J.-F., Edinger, E. and Mercier, A. (2010b). Planulation periodicity, settlement preferences and growth of two deep-sea octocorals from the northwest Atlantic. Marine Ecology Progress Series 410, 71 87. Thoma, J. N., Pante, E., Brugler, M. R. and France, S. C. (2009). Deep-sea octocorals and antipatharians show no evidence of seamount-scale endemism in the NW Atlantic. Marine Ecology Progress Series 397, 25 35. Thomson, J. A. (1927). Alcyonaires provenant des campagnes scientifiques du Prince Albert Ier de Monaco. Résultats des campagnes scientifiques accomplies sur son yacht par Albert Ier, Prince Souverain de Monaco 73, 1 87. Biology of Deep-Water Octocorals 121 Thomson, J. A. and Henderson, W. D. (1906). An account of the alcyonarians collected by the Royal Indian Marine Survey Ship Investigator in the Indian Ocean. The Alcyonarians of the deep sea. The Indian Museum, Calcutta, 132. Thresher, R. E. (2009). Environmental and compositional correlates of growth rate in deep-water bamboo corals (Gorgonacea: Isididae). Marine Ecology Progress Series 397, 187 196. Thresher, R. E., Rintoul, S. R., Koslow, J. A., Weidman, C., Adkins, J. and Proctor, C. (2004). Oceanic evidence of climate change in southern Australia over the last three centuries. Geophysics Research Letters 31, L07212. Tixier-Durivault, A. and d’Hondt, M.-J. (1974). Les octocoralliaires de la champagne Biaçores. Bulletin du Muse´um National d’Histoire Naturelle 3e se´ries 174, 1361 1433. Tracey, D. M., Neil, H., Marriott, P., Andrews, A. H., Cailliet, G. M. and Sanchez, J. A. (2007). Age and growth of two genera of deep-sea bamboo corals (family Isididae) in New Zealand waters. Bulletin of Marine Science 81, 393 408. Tsounis, G., Rossi, S., Aranguren, M., Gili, J.-M. and Arntz, W. (2006). Effects of spatial variability and colony size on the reproductive output and gonadal development of the Mediterranean red coral (Corallium rubrum L.). Marine Biology 148, 513 527. Tsounis, G., Rossi, S., Laudien, J., Bramanti, L., Fernández, N., Gili, J.-M. and Arntz, W. (2006a). Diet and seasonal prey capture rates in the Mediterranean red coral (Corallium rubrum L.). Marine Biology 149, 313 325. Tyler, P. A. (1988). Seasonality in the deep sea. Oceanography and Marine Biology Annual Review 26, 227 258. Tyler, P. A., Bronsdon, S. K., Young, C. M. and Rice, A. L. (1994). Ecology and gametogenesis biology of genus Umbellula (Pennatulacea) in the North Atlantic Ocean. International Review of Hydrobiology 80, 187 199. Valentine, P. C., Uzmann, J. R. and Cooper, R. A. (1980). Geology and biology of Oceanographer submarine canyon. Marine Geology 38, 283 312. van Ofwegen, L. (2010). Acanthogorgia Gray, 1857. Accessed through: World Register of Marine Species at http://www.marinespecies.org/aphia.php?p=taxdetails&id=125293 accessed on 2011-04-12. Verrill, A. E. (1862). Notice of a Primnoa from Georges Bank. Proceedings of the Essex Institute, Salem, Massachusetts 3, 127 129. Verrill, A. E. (1878a). Notice of recent additions to the marine fauna of the eastern coast of North America. American Journal of Science and Arts, Ser. 3 16, 207 215. Verrill, A. E. (1878b). Notice of recent additions to the marine fauna of the eastern coast of North America. American Journal of Science and Arts, Ser. 3 16, 371 379. Verrill, A. E. (1879). Notice of recent additions to the marine fauna of the eastern coast of North America. American Journal of Science and Arts, Ser. 3 17, 472 474. Verrill, A. E. (1883). Reports on the Anthozoa, and on some additional species dredged by the “Blake” in 1877 1879, and by the U. S. Fish Commission Steamer “Fish Hawk” in 1880 82. Bulletin of the Museum of Comparative Zoology, Harvard 11, 1 72. Verrill, A. E. (1884). Notice of the remarkable marine fauna occupying the outer banks of the southern coast of New England. American Journal of Science, Ser. 3 28, 213 220. Verrill, A.E. (1922). The Actinaria of the Canadian Arctic Expedition, with notes on interesting species from Hudson Bay and other Canadian localities. Report of the Canadian Arctic Expedition, 1913 1918. Vol. VIII. Mollusks, Echinoderms, Coelenterates, etc., Part G: Alcyonaria and actinaria, pp. 89 165. Versluys, J. (1902). Die Gorgoniden der Siboga Expedition I. Die Chrysogorgiidae. Siboga-Expeditie Monographie XIII, 120. Versluys, J. (1906). Die Gorgoniden der Siboga Expedition II. Die Primnoidae. SibogaExpeditie Monographie XIII, 178. pls. 1 10. 122 Les Watling et al. Wafar, M., Venkataraman, K., Ingole, B., Ajmal Khan, S. and Lokabharathi, P. (2011). State of knowledge of coastal and marine biodiversity of Indian Ocean countries. PLoS ONE 6, e14613. Waller, R. G. and Baco, A. R. (2007). Reproductive morphology of three species of deep-water precious corals from the Hawaiian archipelago: Gerardia sp., Corallium secundum, and Corallium lauuense. Bulletin of Marine Science 81, 533 542. Waller, R., Watling, L., Auster, P. J. and Shank, T. (2007). Anthropogenic impacts on the Corner Rise Seamounts, NW Atlantic Ocean. Journal of the Marine Biological Association of the United Kingdom 87, 1075 1076. Watling, L. (2007). A review of the genus Iridogorgia (Octocorallia : Chrysogorgiidae) and its relatives, chiefly from the North Atlantic Ocean. Journal of the Marine Biological Association of the United Kingdom 87, 393 402. Watling, L. (2010). Notes on the habitat of the deep-sea caridean shrimp, Bathypalaemonella serratipalma Pequegnat. In Studies on Malacostraca: Lipke Bijdeley Holthuis Memorial Volume (C. H. Fransen, S. de Grave and P. L. Ng, eds), pp. 707 714. E.J. Brill, Netherlands. Watling, L. and Auster, P. J. (2005). Distribution of deep-water Alcyonacea off the northeast coast of the United States. In Cold-Water Corals and Ecosystems (A. Freiwald and J. M. Roberts, eds), pp. 279 296. Springer-Verlag, Berlin. Watling, L. and France, S.C. (in press). A new genus and species of bamboo coral (Octocorallia: Isididae: Keratoisidinae) from the New England seamounts. Bulletin of the Yale Peabody Museum. Watling, L., Waller, R. and Auster, P. J. (2007). Corner Rise seamounts: the impact of deep-sea fisheries. ICES Insight 44, 10 14. Weaver, P. P. E., Boetius, A., Danovaro, R., Freiwald, A., Gunn, V., Heussner, S., Morato, T., Schewe, I. and van den Hove, S. (2009). The future of integrated deep-sea research in Europe: The HERMIONE project. Oceanography 22, 179 191. Weber, M. (1902). Siboga Expeditie Introduction et description de l’expedition. Monographie Siboga-Expeditie 1, 1 159. Williams, A., Althaus, F., Dunstan, P. K., Poore, G. C., Bax, N. J., Kloser, R. J. and McEnnulty, F. R. (2010). Scales of habitat heterogeneity and megabenthos biodiversity on an extensive australian continental margin (100 1100 m depths). Marine Ecology 31 (suppl. 1):222 236. Williams, G. C. (1975). Sex and Evolution. Princeton University Press, Princeton. Williams, G. C. (1993). Biotic diversity, biogeography and phylogeny of pennatulacean octocorals associated with coral reefs in the Indo-Pacific. Proceedings of the Seventh International Coral Reef Symposium, 1992 2, 729 735. Williams, G. C. (1995). Living genera of sea pens (Coelenterata: Octocorallia: Pennatulacea): Illustrated key and synopses. Zoological Journal of the Linnaean Society 113, 93 140. Williams, G. C. (2005). New taxa of Octocorals (Anthozoa: Octocorallia) from the Northeastern Pacific Ocean. Proceedings of the California Academy of Sciences 56, 53 65. Williams, G. C. (2011). The Global Diversity of Sea Pens (Cnidaria: Octocorallia: Pennatulacea). PLoS ONE 6(7): e22747. doi:10.1371/journal.pone.0022747. Wright, E. P. and Studer, T. (1889). Report on the Alcyonaria collected by H.M.S. Challenger during the years 1873 76. Report on the scientific results of the voyage of the H.M.S. Challenger during the years 1873 1876. Zoology 31, 1 314. 43 pls. Young, C. M. (2003). Reproduction, development and life-history traits. In Ecosystems of the Deep Oceans (P. A. Tyler, ed), pp. 381 426. Ecosystems of the world, Vol. 28. Elsevier, Amsterdam. C H A P T E R T H R E E Tipping Points, Thresholds and the Keystone Role of Physiology in Marine Climate Change Research Cristián J. Monaco1 and Brian Helmuth Contents 1. Introduction 1.1. Non-Linearities, tipping points and concepts of scale 2. Weather, Climate and Climate Change from the Viewpoint of a NonHuman Organism 3. Physiological Response Curves 3.1. Thermal physiology of marine organisms 4. Indirect Effects of Climate Change: Species Interactions and Tipping Points 5. Putting the Pieces Together: Where Do We Go from Here? Acknowledgements References 124 128 130 137 139 144 146 150 151 Abstract The ongoing and future effects of global climate change on natural and human-managed ecosystems have led to a renewed interest in the concept of ecological thresholds or tipping points. While generalizations such as poleward range shifts serve as a useful heuristic framework to understand the overall ecological impacts of climate change, sophisticated approaches to management require spatially and temporally explicit predictions that move beyond these oversimplified models. Most approaches to studying ecological thresholds in marine ecosystems tend to focus on populations, or on non-linearities in physical drivers. Here we argue that many of the observed thresholds observed at community and ecosystem levels can potentially be explained as the product of non-linearities that occur at three scales: (a) the mechanisms by which individual organisms interact with their ambient habitat, (b) the non-linear relationship between organismal physiological performance and Department of Biological Sciences and Environment and Sustainability Program, University of South Carolina, Columbia, SC, USA 1 Corresponding author: Email: monacocj@email.sc.edu Advances in Marine Biology, Volume 60 ISSN: 0065-2881, DOI: 10.1016/B978-0-12-385529-9.00003-2 © 2011 Elsevier Ltd All rights reserved. 123 124 Monaco and Helmuth variables such as body temperature and (c) the indirect effects of physiological stress on species interactions such as competition and predation. We explore examples at each of these scales in detail and explain why a failure to consider these non-linearities many of which can be counterintuitive can lead to Type II errors (a failure to predict significant ecological responses to climate change). Specifically, we examine why ecological thresholds can occur well before concomitant thresholds in physical drivers are observed, i.e. how even small linear changes in the physical environment can lead to ecological tipping points. We advocate for an integrated framework that combines biophysical, ecological and physiological methods to generate hypotheses that can be tested using experimental manipulation as well as hindcasting and nowcasting of observed change, on a spatially and temporally explicit basis. 1. Introduction Anthropogenic climate change is among the most critical threats facing the world’s natural and human-managed ecosystems (Rockstrom et al., 2009). Numerous studies have documented geographic shifts in species range boundaries (Beaumont and Hughes, 2002; Parmesan and Yohe, 2003; Zacherl et al., 2003), alterations in phenology (Parmesan, 2006; Mitchell et al., 2008) and episodes of mass mortality (Harley, 2008; Harley and Paine, 2009) related to climate change. While temperature is one of the more obvious drivers of these patterns (Tomanek, 2008; Pörtner, 2010; Somero, 2010), stressors such as ocean acidification (OA) (Fabry, 2008; Hoegh-Guldberg and Bruno, 2010) have also been shown to have significant ecological impacts. The biological effects of climate change in turn have enormous economic and societal implications (Climate Change Science Program, CCSP, 2008; Millennium Ecosystem Assessment, 2005). Subsequently, and as highlighted recently by Hoegh-Guldberg and Bruno (2010), there has been an increase in the number of peer-reviewed papers examining climate change and its consequences to the natural world and to human society (Fig. 3.1). While generalizations such as poleward migrations of species range boundaries or upward shifts in altitudinal distribution in mountain environments have served as a useful heuristic framework for exploring the ecological impacts of climate change, recent evidence suggests that these generalizations may be violated in nature more often than has previously been appreciated. For example, Crimmins et al. (2011) found that despite increases in ambient air temperature, the distribution of 64 plant species shifted downward in elevation during the last ca. 75 years. This pattern was explainable because the negative physiological impacts of increased temperature were overridden by the positive impacts of increases in precipitation, which had also occurred during this time period. Similarly, Tipping Points and the Keystone Role of Physiology 125 Figure 3.1 Number of peer-reviewed articles related to climate change and tipping points that have been indexed by the ISI Web http://apps.webofknowledge.com/WOS_ GeneralSearch_input.do?SID5S2P9GLPhpF6fKNgfIEp&product5WOS&search_mode5 GeneralSearch of Science between 1970 and 2010. (A) Annual number of papers indexed with the topic ‘climate change’ or ‘global warming’, restricted to all fields related to biology as well as those restricted to aquatic biology. As a control for both the increase in journals as well as bias due to the database itself, the number of citations for the term ‘Drosophila’ is also presented. Note that while there is a large overall increase in publications after 1990 (as indicated by the ‘control’), the increase in the number of these publications appears to be slowing; in contrast, the increase in the rate of publication of climate-related papers began much later, and is increasing rapidly, as shown by Hoegh-Guldberg and Bruno (2010). (B) General biology and aquatic biology articles that have utilized the concepts ‘tipping points’, ‘ecological thresholds’ or ‘stable states’ independently, or in combination with ‘climate change’ or ‘global warming’ in papers restricted to the ecological and environmental literature. numerous studies have now shown that geographic patterns of physiological stress do not always follow simple latitudinal gradients, but rather exhibit ‘mosaic’ patterns over geographic scales (Helmuth et al., 2002, 2006; Holtmeier and Broll, 2005; Finke et al., 2007; Place et al., 2008; 126 Monaco and Helmuth Mislan et al., 2009; Pearson et al., 2009), suggesting that our expectation of what to expect in coming decades may not always be poleward shifts in species range boundaries, but at least in some cases may be localized extinctions even well within range boundaries. Moreover, recent studies have documented geographic variability in physiological tolerance (Pearson et al., 2009) and have experimentally shown evidence of local adaptation (Kuo and Sanford, 2009). All of these studies suggest that a detailed understanding of the mechanisms underlying the complex interaction between changes in the physical environment and organismal and ecological responses is vital if we are to predict future patterns of biodiversity, distribution and abundance, and that simple generalizations are not always effective as working null hypotheses. Moreover, increasingly sophisticated adaptation planning by a wide array of decision makers demands quantitative predictions of ecological impacts of climate change, often at fine spatial and temporal resolutions, along with associated estimates of uncertainty. For example, the emplacement of protected areas (Hoffman, 2003), predictions of fishery, crop and livestock productivity (CCSP, 2008) and estimates of the spread of disease and invasive species (Chown and Gaston, 2008; Kearney et al., 2008; US EPA, 2008) all demand quantitative, spatially and temporally explicit predictions of how climate change is likely to impact organisms and ecosystems (Ludwig et al., 2001). Predictions that are based on simple generalizations are highly unlikely to be able to capture the spatial and temporal complexity of the real world in order to effectively plan and prepare for ongoing and future climate change impacts. Furthermore, the public’s perception of climate change, and their trust in the scientific community’s understanding of climate change, is significantly affected by the frequency by which scientific predictions are borne out. Simple generalizations, while logically tractable, are not always the expected outcome, as shown by Crimmins et al. (2011). Nevertheless, when patterns contrary to sweeping generalizations are reported, they are often viewed by ‘climate skeptics’ as evidence of the falsehood of climate change or, worse, of the duplicity of scientists. In a recent opinion piece, Parmesan et al. (2011) stated that any attempts to attribute individual responses to climate change were ‘ill advised,’ suggesting that because such cause and effect linkages were too complex, the scientific community should instead focus on overarching trends. In stark contrast, however, data collected by communication experts have shown that when scientists make explicit predictions that can then be tested in a transparent manner, even at the risk of failure, this builds trust between the public and the scientific community (Goodwin and Dahlstrom, 2011). Thus, explicit, testable predictions made using mechanistic approaches (Helmuth et al., 2005) not only provide useful information to decision makers, and potentially elucidate important principles by which weather and climate affect organisms Tipping Points and the Keystone Role of Physiology 127 and ecosystems, but may also play an important role in discourse between scientists and the public. While several recent authors have highlighted the importance and power of cross-disciplinary research when exploring the ecological impacts of climate change (Wiens and Graham, 2005; Stenseth, 2007; Chown and Gaston, 2008; Denny and Helmuth, 2009; Hofmann et al., 2010), in many ways such collaborations have yet to be fully realized. To a large extent, environmental data are collected, archived and disseminated with little or no thought given to their potential biological applications (Helmuth et al., 2010). Similarly, many physiological studies are conducted with only a limited ecological context, and vice versa (Denny and Helmuth, 2009). When such studies are used in the decision-making process, they also frequently fail to take the needs of end-users into consideration (Agrawala et al., 2001). Here we discuss some of the underlying reasons why an understanding of the detailed mechanisms by which weather and climate (both terrestrial and oceanic) affects organisms and ecosystems can fundamentally alter our predictions of the ecological impacts of climate change. We focus primarily on coastal marine invertebrates using the concept of ‘tipping points’ (ecological thresholds) to explore the importance of these details. We advocate for an interdisciplinary, mechanistic approach, explicitly embedded within a collaborative framework that combines assessments of physiological performance, organismal biology and population and community ecology performed at different scales. Specifically, we argue that many of the observed instances of tipping points are explainable given non-linearities in how environmental signals are translated into physiological responses, and subsequently how those physiological responses drive species interactions and population dynamics that affect ecosystem-level patterns. We divide these non-linearities into three broad categories: (a) the translation of environmental parameters (‘habitat’, as in Kearney, 2006) into niche-level processes such as body temperature; (b) the physiological consequences of these niche-level processes and (c) the indirect, ecological effects of physiological stress on species interactions. The goal of this chapter is not to provide an exhaustive review of studies of ecological thresholds in marine ecosystems: a recent special issue of Marine Ecology Progress Series (Osman et al., 2010) provides an excellent overview of the current state of the field. Nor is it our intention to imply that studies or approaches that focus on populations or ecosystems rather than on individual organisms and physiological responses are flawed. For example, catastrophic physical disturbance such as damage from hurricanes obviously can play a large role in driving phase shifts and may have no connection to physiological performance (although, physiological performance may contribute to recovery from such events, sensu Highsmith et al., 1980). However, current discussions of the concept of ecological thresholds/tipping points seldom incorporate the physiological performance of the constituent organisms, despite an increasing number of studies focused on ecological thresholds in 128 Monaco and Helmuth the context of global climate change (Fig. 3.1B). For example, the CCSP of the United States, recently released the Synthesis and Assessment Product 4.2 (SAP 4.2), Thresholds of Climate Change in Ecosystems, a comprehensive report that specifically elaborates on our current knowledge of ecological thresholds at the ecosystem level and provides guidelines for resource managers who are forced to contend with the uncertain scenarios presented by global climate change. The document lists areas where further research is needed to fill the many gaps in our current understanding of the causes and consequences of ecological thresholds (CCSP, 2009), and advocates for an integrative approach as a means for dealing with cross-scale interaction processes. Notably, however, because the report primarily focuses on large-scale ecosystem responses, it generally fails to recognize the importance of examining the impacts of weather and climate at organismal scales (Somero, 2010). Our goal is therefore to demonstrate how much can potentially be learned through an integrated approach that includes an understanding of the mechanisms underlying thresholds, including biophysical interactions between organisms and their environment, and physiological consequences of climate change at organismal scales (Sanford, 2002a; Pörtner et al., 2006). 1.1. Non-Linearities, tipping points and concepts of scale A system is said to be non-linear when its inputs and outputs are disproportionate to one another (Hilbert, 2002). Within an ecosystem context, the term input thus refers to any relevant abiotic variable, e.g. precipitation or air temperature, or a biotic component such as a keystone predator (Menge et al., 1994), and the output is any physiological or ecological process such as an organism’s phenology, reproductive output or ecological interactions (Sanford, 2002a; Pincebourde et al., 2008). The CCSP SAP 4.2 report defines an ecological threshold (tipping point) as ‘the point where there is an abrupt change in an ecosystem quality, property, or phenomenon or where small changes in an environmental driver produce large, persistent responses in an ecosystem, which is not likely to return to the previous more stable state’ (CCSP, 2009). Studies of ecological thresholds are extremely valuable for planning conservation strategies, as they can shed light on an ecosystems’ sensitivity to environmental change (Littler and Littler, 2007; Briske et al., 2010). Studies performed at the community and ecosystem levels of organization have also detected the presence of tipping points that set the boundary between different ecological states (Scheffer et al., 2001), although other authors have suggested that the simple dichotomy between alternative stable states, while intellectually appealing, may be an oversimplification of a much more complicated process (Dudgeon et al., 2010). Long-term observations of community-level dynamics have allowed for both empirical and theoretical descriptions of major phase shifts and/or alternative Tipping Points and the Keystone Role of Physiology 129 stable states (Hare and Mantua, 2000; Casini et al., 2009; Dudgeon et al., 2010) in marine systems such as coral reefs (Idjadi et al., 2006) and rocky intertidal communities (Petraitis et al., 2009). Characterizing these higherlevel changes has proven useful for ecologists and wildlife managers, since they provide an overall understanding of the main environmental variables shaping natural systems. So far, most studies of threshold effects in marine systems have measured and modelled processes at community and ecosystem scales. Empirical studies that only concentrate on higher-level dynamics can seldom tease apart the underlying factors driving systems to change, which often restricts conclusions to pure documentations of the observed patterns. Of course, a focus on community- and ecosystem-level processes does not mean that authors do not recognize the importance of underlying biological processes at the scale of the organism (Hewitt and Thrush, 2010). For example, Norkko et al. (2010) manipulated disturbance via hypoxia at scales ranging from 1 to 16 m2 and monitored recovery by infaunal and epifaunal organisms living in soft substrate communities. Their results highlighted the importance of considering life-history characteristics (epifaunal/infaunal) and mobility (dispersal) of the organisms in driving the resilience and recovery of the benthic community. There are also excellent examples of the power of considering physiological performance in the context of ecological thresholds (Littler and Littler, 2007; Hofmann et al., 2010). Most notably, recent studies of OA squarely place an emphasis on measuring the effects of decreases in pH on growth and survival of ecologically key species when attempting to understand and predict ecological consequences at large scales (Fabry, 2008, Hofmann et al., 2010). For example, McNeil and Matear (2008) measured and modelled levels of carbonate (CO23 2 ) and pH in the southern ocean, and then projected the impact on rates of calcification by key planktonic species. Their results suggested that the pteropod Limacina helicina Phipps, 1774, was likely to be severely impacted during larval development. This species is ecologically important, comprising up to B65% of the total zooplankton in the Ross Sea, and the thecosome shells of this and other pteropod species are thought to be a major contributor to the carbonate flux of the deep ocean south of the Polar Front (Hunt et al., 2008). Understanding the physiological responses of calcifying organisms such as pteropods, corals (Hoegh-Guldberg et al., 2007) and coccolithophores (Fabry, 2008) to changes in ocean pH thus clearly has significant ecological consequences, and therefore physiological performance is likely to have a direct impact on the probability of phase transitions/ecological thresholds occurring. Many recent studies have further emphasized the importance of considering physiological performance in determining local and biogeographic patterns of distribution (Somero, 2005; Helmuth et al., 2006; Pörtner et al., 2006; Gedan and Bertness, 2009), an approach often termed 130 Monaco and Helmuth ‘macrophysiology’ (Chown et al., 2004; Chown and Gaston, 2008). For example, Wethey and Woodin (2008) compared long-term records of sea surface temperature against known physiological thresholds of the barnacle Semibalanus balanoides and showed that observed range shifts were consistent with winter temperatures known to cause reproductive failure in this species. However, studies that span scales from molecular to biogeographical remain rare (Pörtner et al., 2006; Denny and Helmuth, 2009; Pearson et al., 2009; Hofmann et al., 2010). Moreover, a number of studies have shown that organisms may be living close to their physiological tolerances even well within their range limits (Sagarin and Somero, 2006; Place et al., 2008; Beukema et al., 2009), and have warned that, conversely, physiological stress is not always the limiting factor at species range edges (Davis et al., 1998a,b). Thus, while understanding the relationship between the physiological performance of key species and ecological thresholds may not always be simple (Hutchins et al., 2007; Crain et al., 2008), a failure to consider these effects can potentially lead to Type II errors, i.e. we may be surprised by sudden phase shifts due to non-linearities that ultimately originate at (sub)organismal scales. Understanding when such events are likely to occur, and the mechanisms that lead to their occurrence, is therefore critical. Perhaps even more importantly, as recently discussed by Mumby et al. (2011), tipping points may be preceded by significant alterations in ecosystem function that, while not meeting the definition of a ‘threshold’, nevertheless may have catastrophic ecological, economic and societal implications. For example, declines in ecosystem services may occur well before threshold events are observed. As a result, Mumby et al. (2011) argue that while threshold events are important, we must not lose focus on the importance of predicting declines in other metrics of ecosystem performance. In this review we explore how small, often linear changes in physical drivers may potentially lead to large, nonlinear responses in ecological systems. As a result, ecological tipping points may theoretically occur long before any comparable changes in the physical environment are observed. However, such methods may also be applied to the agenda set forth by Mumby et al. (2011), in that they provide a mechanistic framework that can be used to predict potential ‘trouble areas’ not just in terms of threshold events but also in terms of declining ecosystem function. 2. Weather, Climate and Climate Change from the Viewpoint of a Non-Human Organism Climate change is a global phenomenon, but to an organism the ‘world’ can be exceedingly small. Consider for example an intertidal barnacle. As a cyprid floating in the water column, only the immediate Tipping Points and the Keystone Role of Physiology 131 conditions of pH, temperature and food surrounding the animal affect its physiology. To that larval animal, it does not matter if it is entrained in a gyre or in the nearshore swash zone per se, but rather what its location within either of those larger-scale phenomena means to its immediate physical and biological environment. As the animal moves onshore, it encounters levels of turbulence, temperature, pH and nutrients different from those in the immediate nearshore environment (Pineda and Lopez, 2002; Pfister et al., 2007; Wootton et al., 2008). Importantly, those conditions likely could not have been predicted given measurements made just offshore (Pfister et al., 2007). Eventually, as the larva reaches the intertidal zone where it settles, metamorphoses and grows into an adult barnacle, it experiences not only the conditions of the subtidal environment, but also those of the terrestrial environment during low tide, conditions which can vary markedly with intertidal zonation height (Wethey, 1983). Perhaps not surprisingly, all point to the fact that the physical environment for these animals, like that for many others, is highly spatially and temporally heterogeneous (Denny et al., 2004, 2011). Nevertheless, in many cases, measurements conducted at moderate to large spatial scales, e.g. by satellite and buoy, appear to provide considerable insight into large-scale ecological processes (Schoch et al., 2006; Blanchette et al., 2008; Gouhier et al., 2010). Similar concordance appears over large temporal scales, and when looking at the ecological influences of climate indices such as the El Niño Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) and the Pacific Decadal Oscillation (PDO; Stenseth et al., 2002, 2003; Forchhammer and Post, 2004). However, as pointed out by Hallet et al. (2004), the seemingly superior ability of these large-scale indices to predict biological responses than higher frequency weather data may lie in a failure to take mechanism into account. Using detailed data from a population of Soay sheep, Hallet et al. (2004) showed that high rainfall, high winds or low temperatures could all contribute to the mortality of sheep, either immediately or with a lag. In other words, the association between each of these variables and the timing of mortality varied significantly between years, so that overall there appeared to be no pattern. Without an understanding of the underlying physiological mechanistic drivers, simple correlations between any single variable such as rainfall and mortality failed to uncover any relationship, giving the false impression that climatic indices were a better predictor of ecological response than were weather variables. However, when variables such as rainfall and air temperature were used in an integrated context that considered not only their direct physiological effects but also their indirect effects on food, etc., a highly significant relationship emerged (Hallet et al., 2004) that had greater explanatory power than did climatic indices. Such may be the case for many environmental factors that are often dismissed as irrelevant due to their apparent lack of 132 Monaco and Helmuth concordance with biological processes: by assuming a direct relationship between variables such as solar radiation, air and water temperature, or food availability and population responses, we fail to consider that these variables interact in non-linear ways, and are filtered through the functional traits of the organisms that they are affecting (Kearney, 2006; Kearney et al., 2010). A case in point is how weather variables are translated into physiologically and ecologically relevant terms such as body temperature (Kearney, 2006). While we may be interested in climate change, it is weather (as affected by climate) that drives physiological responses. Global climate change encompasses change in numerous ‘environmental signals’ (Helmuth, 2009) including ocean pH, sea level, salinity and temperature. Importantly, however, the only environmental signals that matter to an organism directly are those that the organism experiences, i.e. those at the level of the niche (Kearney, 2006). The physiological niche of an organism is driven by the interaction of an organism’s morphology, size and behaviour with its local microhabitat, and is therefore often very different from measurements of large-scale, habitat-level parameters such as air or water temperature (Marshall et al., 2010; Helmuth et al., 2006, 2011). Thus, two species inhabiting the same microhabitat may experience radically different physiological drivers such as body temperature. For example, as has been shown for many species in both terrestrial and intertidal environments, the flux of heat is driven by the interaction of multiple environmental factors, including solar radiation, wind speed, air temperature and relative humidity (Porter and Gates, 1969; Bell, 1995; Marshall et al., 2010). Moreover, the characteristics of the organism mass, colour, surface wetness, etc. significantly alter heat flux so that two organisms exposed to identical environmental parameters can experience markedly different body temperatures (Broitman et al., 2009). In some cases, the difference between body temperature and environmental temperature can be fairly minor, e.g. when animals continually live in the shade or in deep water environments. In other cases, the difference in temperature between an ectothermic organism and its surroundings is quite remarkable. The body temperatures of ectothermic animals in the sun are generally significantly hotter than the temperature of the surrounding air (Marshall et al., 2010). Only in cases where animals lose heat through the evaporation of water, or through infrared radiation at night, is body temperature likely to be significantly lower than that of air or surface temperature (Bell, 1995; Helmuth, 2002). These observations are significant, because they suggest that for many organisms (e.g. those that are unable to evaporatively cool or for which desiccation is a limitation), air temperature is likely to set the lower limit to body temperature during the day, and solar radiation then increases body temperature above that minimum. Critically, this also means that convective heat transfer serves to bring the temperature of the Tipping Points and the Keystone Role of Physiology 133 Figure 3.2 Translation of habitat-level parameters such as air temperature to niche-level parameters (which drive physiology) such as body temperature. The figure shows a steadystate heat budget for a generic ectotherm under identical conditions of solar radiation and cloud cover, over a range of air temperatures, and with wind speeds of (A) 0.25 m s 21, (B) 1.0 m s 21 and (C) 2.5 m s 21. In this simplified example, body temperature increases linearly with increasing air temperature. Notably, the y intercept varies with wind speed, so that a 5 C increase in air temperature results in shifts between very different magnitudes of body temperature. animal closer to that of the surrounding air, i.e. cooler, even in the absence of evaporation. For example, recent modelling suggests that predicted increases in the mean wind field along the west coast of the United States may in some cases counteract the effect of increases in ambient air temperature on animal body temperature, at least for animals with dry surfaces (Helmuth et al., 2011). In contrast, for animals with wet surfaces (seastars, seaweeds), the forecasted increase in mean wind may have a greater physiological impact through increased desiccation stress (Bell, 1995). Importantly, this does not mean that changes in parameters such as air temperature are unimportant; if all other environmental factors remain unchanged, increases in air temperature will lead to increases in body temperature (Fig. 3.2). However, in some cases the importance of variability or long-term change in air temperature can be overridden by other factors such as wind speed, wave splash (Helmuth et al., 2011) or the timing of when low tide occurs (Mislan et al., 2009). Moreover, these results indicate that increases in body temperature cannot be based on changes in any one environmental parameter. Figure 3.2 shows the results of a simple heat budget model for a generic ectotherm, in which all parameters are identical except for wind speed. In the three simplified scenarios shown, body temperature increases linearly with air temperature, i.e. the coefficient of determination is 1.0. However, the y intercept varies markedly depending on which value of wind speed is used. In the first scenario (wind speed 5 0.25 m s 21), an increase in air temperature from 15 C to 20 C leads to an increase in body temperature from 33 C to 38 C; in the second scenario, the same change in air 134 Monaco and Helmuth temperature, but with a wind speed of 1.00 m s 21, leads to a change in body temperature from 30 C to 35 C; in the third scenario, with a wind speed of 2.50 m s 21, the same change in air temperature leads to a change in body temperature from 26 C to 31 C. Clearly, simply measuring the temperature of the habitat (air), or even the change in the air temperature, is not sufficient to assess how the changing environment will impact the organism. In the steady-state scenario shown in Fig. 3.2, all parameters except wind speed are held constant, and body temperature increases linearly with air temperature. Under natural field conditions it is unclear how often this holds true, but in the few cases where explicit comparisons have been made it appears that the relationship between air and body temperature can be extremely poor. Marshall et al. (2010) compared maximum air temperature at low tide to the body temperature of intertidal snails and found that there was no correlation between the two temperatures; at times the temperature of the animal could be 22 C above that of the air. Helmuth et al. (2011) compared maximum air temperature at low tide to the temperature of biomimetic loggers designed to mimic the temperature of intertidal mussels and reported a coefficient of determination of only 0.14, with differences of up to 19 C between air and animal temperature. They also showed that mussel (logger) temperatures were frequently high even on days when air temperatures were low and vice versa. Broitman et al. (2009) compared the temperatures of predators (Pisaster ochraceus Brandt, 1835) to those of their prey (Mytilus californianus Conrad, 1837) under identical microclimatic conditions at four sites, two on each end of Santa Cruz Island, CA. They found that on the west side of the island, the temperatures of predators and prey were very similar, but on the east side of the island, the body temperatures of the predator and prey were very different from one another, even though in all cases both species were exposed to identical weather conditions at each site. Again, these results point to the interaction between multiple weather variables, and between weather and the functional traits of the organism (Kearney et al., 2010) in driving the body temperature of ectotherms and demonstrate the highly non-linear nature of the relationship between ‘habitat’ and ‘niche’ (Kearney, 2006). While arguments regarding heat flux apply most directly to terrestrial organisms and intertidal organisms exposed to air at low tide, studies have shown that in shallow water, solar radiation can raise the temperature of coral tissue by several degrees when rates of convection (i.e. water flow) are sufficiently low (Fabricius, 2006; Jimenez et al., 2008). Analogous arguments can also be made for the exchange of gas and nutrients in subtidal environments, where the flux of these substances is driven not only by concentration gradients but also by fluid flow (Lesser et al., 1994). As elegantly discussed by Patterson (1992), the characteristics of fluid flow Tipping Points and the Keystone Role of Physiology 135 Figure 3.3 Typical mass transfer coefficient relationship as a function of flow speed, in which small increases initially make a large difference, but past a threshold have little effect (heat transfer coefficients show a very similar relationship). around the respiratory and feeding structures of subtidal organisms determine rates of exchange of oxygen, bicarbonate and nutrients through their effects on the diffusion boundary layer (Shashar et al., 1996). A generic equation to describe the exchange of any mass item thus includes a mass transfer coefficient (hm), an empirically derived parameter that describes the interaction of an organism with the surrounding flow: dm=dt ¼ hm AðCo 2 Ci Þ (3.1) where dm/dt is the rate of mass flux; hm is the mass transfer coefficient, A is the area over which exchange occurs, and Co and Ci are the concentrations of the mass item of interest (e.g. O2) outside and inside of the organism, respectively. The mass transfer coefficient changes non-linearly with increasing flow (Fig. 3.3), and is lower for ‘streamlined’ animals than for animals with a bluff body. (The equation for convective heat exchange is functionally identical, except that a heat transfer coefficient, hc, is used to describe the effect of morphology on the rate of heat exchange, which is affected by a temperature gradient rather than a concentration gradient.) Numerous studies have documented the important role of mass flux in driving the physiological ecology of benthic organisms. Both laboratory (Nakamura et al., 2003) and field studies (Nakamura and van Woesik, 2001; Finelli et al., 2006) have shown that increased mass flux reduces the rate of coral bleaching through the removal of excess oxygen, which reduces oxidative stress (Lesser, 1996, 1997, 2004). As with heat exchange in the terrestrial environment, the morphology of an organism can significantly affect fluid flow, so that two organisms exposed to identical flows, 136 Monaco and Helmuth and/or identical concentrations of gases, can experience very different rates of passive gas uptake. Lenihan et al. (2008) showed an effect of reef structure (height and morphology) on rates of bleaching in Moorea, French Polynesia. At a much smaller scale, Finelli et al. (2007) found that intracolony variability in coral bleaching could be explained by differences in flow, which in turn was affected by coral morphology. Thomas and Atkinson (1997) showed that rates of flow and surface roughness controlled rates of ammonium uptake by corals. These results demonstrate why simply measuring the concentration of oxygen, bicarbonate or ammonium is not sufficient to estimate rates of exchange; two organisms exposed to identical ‘habitats’ (gas or nutrient concentrations) will experience markedly different rates of uptake depending on how their functional traits/morphology interact with local flow. Directly analogous studies have shown the non-linear relationship between water flow and the risk of dislodgement by sessile organisms, especially those in wave-swept environments (Denny and Gaylord, 2010). The force of drag acting upon an organism varies as the square of water velocity, so that small changes in flow can result in large changes in the force of drag acting on an organism: Drag ¼ 1/2ρACd U 2 (3.2) where A is the area upon which the fluid acts, ρ is fluid density, Cd is the drag coefficient, and U is fluid velocity. As above, the Cd is a function of organism morphology, and a bluff body has a much higher Cd than does a streamlined organism. Similarly, the force of lift also scales with the square of U (Denny and Gaylord, 2010). Numerous studies have examined the interactions between the fluid environment and the risk of dislodgement of sessile organisms, noting that one prediction of climate change in many regions is an increase in wave height (Boller and Carrington, 2007; Carrington et al., 2009). While some studies have suggested that the relationship between increasing wave height and onshore wave velocity may be more complex than expected due to the tendency of larger waves to break farther offshore (Helmuth and Denny, 2003) and because of the overwhelming importance of small-scale topography in affecting local flows (Denny et al., 2004), these results nevertheless point to the potentially important, highly non-linear relationship between small increases in wave height, water flow and the risk of dislodgement. Thus, almost never are interactions between organisms and their physical environment, or interactions between the physical parameters acting on organisms, linear in their impacts. For example, heat and mass transfer coefficients, which describe the interactions between the morphology of an organism (or colony) and the surrounding fluid in driving heat or mass (e.g. gases and nutrients), are usually a nearly asymptotic function, in which small changes in fluid velocity initially lead to a large change in Tipping Points and the Keystone Role of Physiology 137 exchange of mass or heat. After a point, however, further increases in flow lead to very little change in heat or mass flux (Fig. 3.3). Small increases in wind speed initially have a large effect of cooling through convection, but once wind serves to bring the temperature of an organism close to the temperature of the surrounding air, very little change occurs with further increases in wind speed. Likewise, small increases in water flow can initially lead to large increases in gas or nutrient flux, but past some threshold make little difference. Conversely, when conditions of wind or flow decrease, initial changes may result in little change when the range of conditions corresponds to the ‘plateau’ region of the curve, but below some threshold, rapid changes may ensue with even subtle drops in flow. Note that these relationships starkly contrast with the relationship between water flow and drag, which increases exponentially. In essence, therefore, these non-linearities thus create tipping points as environmental parameters at the habitat level are translated into changes at the niche level (Kearney, 2006): linear changes in parameters such as flow speed, wave height or air temperature can lead to non-linear changes in physiologically relevant parameters such as body temperature or gas flux, and thus to the likelihood of reproductive failure and mortality of key species. 3. Physiological Response Curves Although changes in niche-level responses such as body temperature and oxygen exchange are the proximal drivers of physiological response, ultimately cellular- and subcellular-level reactions will determine the consequences of those environmental changes (Somero, 2010). For example, Carrington (2002) has shown that the attachment strength of mussels (Mytilus edulis Linnaeus, 1758) varies seasonally, and is twofold higher in winter than in summer. However, the match between wave force and attachment strength is not perfect, and during hurricane season mussels are only weakly attached. Their results suggest a potential energetic trade-off between the production of byssal threads and energy devoted to reproduction (Carrington, 2002), and emphasize the critical importance of measuring not only environmental variables but also the vulnerability of organisms to their physical environment (Helmuth et al., 2005). One of the best ways to describe an organism’s response to changing environmental conditions is through the use of physiological performance curves. Physiological performance curves have long been used to define the complex relationships between organism responses related to fitness, such as growth, reproduction and survival and factors such as body temperature (although, notably, many studies mistakenly have used habitat 138 Monaco and Helmuth Figure 3.4 Standard physiological performance curve, using some aspect of performance related to fitness as a function of body temperature. Pmax is maximum (optimum) performance, and Tmax is the body temperature at which it occurs. CTmin and CTmax are the critical minimum and maximum temperatures, respectively. Performance breadth is defined based on a fixed percentage of Pmax (here shown as a fairly small percentage for emphasis). temperature as the independent axis, incorrectly assuming that it is equivalent to body temperature). Performance curves describe both an organism’s physiological limits to survival and the conditions under which that organism can survive and reproduce (Huey and Stevenson, 1979; Angilletta et al., 2002, 2003). Importantly, these curves are almost always ‘left skewed’ in that fairly large increases in body temperature (or other factor) above some lower threshold generally lead to only modest changes in performance (Fig. 3.4) until a maximal level of performance (Pmax) is reached, at body temperature Tmax (or Topt; Dewitt and Friedman, 1979; Angilletta, 2009). Above that optimum, however, performance declines rapidly with increasing temperature. Thus, at temperatures above Tmax, small changes in body temperature can have significant impacts on survival and reproduction. Returning to the scenario presented in Fig. 3.2, when a biophysical approach using a heat budget model is combined with a physiological performance curve, the importance of considering the non-linearities involved in how habitat-level parameters are translated into physiological responses becomes apparent. Under conditions of wind speed 5 2.5 m s 21, a 5 C increase in air temperature from 15 C to 20 C leads to an increase in body temperature from 26 C to 31 C (Fig. 3.5A). When this change in body (not air) temperature is translated to a physiological (thermal) performance Tipping Points and the Keystone Role of Physiology 139 Figure 3.5 Importance of considering multiple environmental variables when predicting shifts in performance. (A) Influence of wind speed on how air temperature is translated to organism’s body temperature, as illustrated in figure 3.2. (B) Effect of body temperature change on the organism’s performance. Under conditions of wind speed of 2.5 m s 21, an increase in air temperature of 5 C (blue) leads to an increase in performance. Under conditions of wind speed of 0.25 m s 21, an increase in air temperature of 5 C (red) leads to death. curve, this leads to an increase in physiological performance, as the temperature of this hypothetical animal is brought closer to Tmax (Fig. 3.5B). In contrast, an increase in air temperature from 15 C to 20 C but coupled with a wind speed of 0.25 m s 21 leads to an increase in animal body temperature from 33 C to 38 C enough to shift the animal’s temperature from a point near its optimum to its lethal limit (Fig. 3.5, in red). 3.1. Thermal physiology of marine organisms Describing an organism’s physiological performance curve under a range of physical conditions is considered a fairly straightforward analysis that can provide valuable information on how individual organisms respond to their environment, the energetic trade-offs that emerge from specific responses (Angilletta et al., 2003), and the evolutionary consequences of such responses (Kingsolver et al., 2004). However, while this approach has been used with great success by terrestrial ecologists, marine ecologists have rarely described a species’ performance throughout its entire thermal range, especially for invertebrates. Although some excellent examples of marine species performance curves can be found in the literature (Table 3.1), marine invertebrate physiologists have primarily focused either on identifying thermal limits (CTmax and CTmin), or on contrasting the effects of a few habitat temperatures covering only portions of a species’ whole thermal range. Here we follow Angilletta’s (2009, p. 36) Phylum Nucella lamellosa adult Nucella lamellosa juvenile Nucella ostrina Mytilus sp. Semibalanus balanoides Semibalanus balanoides Chthamalus stellatus Balanus perforatus Balanus perforatus Balanus crenatus Elminius modestus Lepas anatifera Balanus improvisus Balanus amphitrite Balanus balanus Botryllus schlosseri Botryllus schlosseri Botrylloides violaceus Salmo trutta juvenile Salmo trutta juvenile Oncorhynchus nerka juvenile Oncorhynchus nerka adult Oncorhynchus nerka Performance trait Crawling rate Crawling rate Crawling rate Speed of cilia Cirral activity Cirral activity Cirral activity Cirral activity Cirral activity Cirral activity Cirral activity Cirral activity Cirral activity Cirral activity Cirral activity Reproductive output Growth rate Growth rate Growth rate Feeding rate Growth Growth Food intake Pmax 21 40 cm h 30 cm h21 23 cm h21 334 mm s21 0.63 beats s21 0.56 beats s21 1 beats s21 0.9 beats s21 0.94 beats s21 1 beats s21 2.2 beats s21 0.28 beats s21 0.11 beats s21 0.14 beats s21 0.48 beats s21 2 larvae colony 21 week 21 28 zooids colony 21 70 day 21 20 zooids colony 21 70 day 21 0.3 g day 21 (1 g fish) 1.25 attempts min 21 25% wet weight day 21 15% wet weight day 21 27% body dry weight Tmax CTmin CTmax Citation 20 5 20 5 10 32.5 21 18.4 30 25.2 30.3 21.3 24.2 19.8 30 29.9 20.2 25 20 20 16.8 17.3 15 15 17 0 0 0 ,0 ,2.3 ,1.8 4.6 6 6 ,4.3 2 0.5 22 6 22 15 5 5 1.24 ,2.6 ,1 ,1 ,5 30 30 25 40 31 31.5 37.5 36 35.2 25.5 33 33 35.5 38.4 30 .25 .25 .25 24.74 .24 14 14 24 1 1 1 2 3 3 3 3 3 3 3 4 4 4 4 5 5 5 6 6 7 7 7 Temperatures are in  C. References: (1) Bertness and Schneider (1976), (2) Gray (1923), (3) Southward (1955a,b), (4) Southward (1957), (5) Epelbaum et al. (2009), (6) Ojanguren et al. (2001) and (7) Brett (1971). Monaco and Helmuth Mollusca Mollusca Mollusca Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Chordata Chordata Chordata Chordata Chordata Chordata Chordata Chordata Species 140 Table 3.1 Physiological performance traits of marine animals from a review of the literature Tipping Points and the Keystone Role of Physiology 141 definition of performance as ‘any measure of the organism’s capacity to function, usually expressed as a rate or probability’. The decision of which specific trait(s) to evaluate is of great importance for the conclusions that one can draw from response curves. In essence, one should consider the species’ niche-dependent requirements and measure those traits that most closely contribute to fitness (i.e. lifetime reproductive success). For instance, foraging rates and/or success are considered proxies of an organism’s fitness, but the way these are estimated is entirely dependent upon the organism’s life history. For example, the adult seastar Leptasterias polaris Müller and Troschel, 1842, a major subtidal predator inhabiting soft sediments in the northern Gulf of St. Lawrence (Canada), relies on its ability to sense the odour of infaunal prey to guide its digging efforts and to forage efficiently (Thompson et al., 2005). Using odour sensitivity as a performance trait for L. polaris is consistent with its ecological niche; this trait would reflect a potential for survival and reproduction under different environmental conditions (e.g. current speed and water temperature). In contrast, for predators that rely on other traits to forage efficiently such as vision or speed, measuring odour sensitivity as a performance trait would not tell us anything about the mechanisms that drive their ecological responses. Recent studies with marine species of crabs, fish, bivalves and polychaetes have used the concept of oxygen and capacity-limited thermal tolerance (Pörtner et al., 1999; Sommer and Pörtner, 1999; Frederich and Pörtner, 2000; Pörtner and Farrell, 2008; Kassahn et al., 2009; ) to explain the mechanism that regulates both an organism’s thermotolerance windows (CTs) and thermal optima (Tmax ). The theory states that detrimental temperatures bring about insufficient oxygen supply and transport to tissues, which coupled with high baseline oxygen demand at elevated temperatures likely shapes the typical ‘left-skewed’ thermal performance curve (Fig. 3.4; Angilletta, 2009). Traditional life-history traits used to describe physiological performance curves include lifetime reproduction (e.g. fecundity and reproductive output), growth (e.g. change in size or body mass), feeding/assimilation (e.g. feeding rate and chemosensory ability), development rate and locomotion (e.g. speed and distance covered). Some investigators have also included survival probability or any proxy for it such as righting response or burrowing capacity (Angilletta, 2009); we believe such studies are necessary for identifying temperature critical limits, but because they do not provide quantifiable information of an organism’s potential reproductive contribution (other than zero or ‘maybe’), we opt not to consider them as strict fitness-related performance traits. From the traditionally measured traits listed above, lifetime reproduction is considered the most closely related to fitness; unfortunately quantifying the lifetime contribution of an individual through 142 Monaco and Helmuth reproduction is not an easy task (Angilletta, 2009), especially for broadcasting species with pelagic life-cycle stages, which are common in coastal marine ecosystems (Thorson, 1950). An example of a reproductive performance curve is given by Epelbaum et al. (2009) (Table 3.1), who tested the interactive effects of water temperature and salinity on two invasive species of colonial ascidians now present throughout much of the northeast Pacific, Botryllus schlosseri Pallas, 1766, and Botryllus violaceus Oka, 1927 (Lambert, 2005). The study included virtually the entire thermotolerance window, of the species (Brunetti et al., 1980), and captured the conditions where reproduction and growth would be maximized (Table 3.1). It is worth noting that, for B. schlosseri, CTmin is greater when reproduction is used as the performance trait than when growth is used (Table 3.1). This occurs because reproductive output, unlike growth, is stalled when temperatures are lower than 20 C, a trade-off commonly observed in marine invertebrates (Sebens, 2002; Sibly and Atkinson, 1994). Because body size is a relatively straightforward parameter to measure, growth is commonly used as a performance trait. In addition, growth is regarded as a fairly accurate proxy for fitness. An organism’s growth represents a net yield, which results from the difference between energy costs (metabolic cost) and benefits (ingestion/assimilation rate) (Levinton, 1983; Sanford, 2002a). Thus, aquaculture-driven research has dedicated a considerable amount of effort to understanding energetic constraints on a number of marine and freshwater species of interest (e.g. salmon and trout species). For example, Ojanguren et al. (2001) elegantly describe thermal performance curves for juvenile activity levels, feeding attempts and growth rates (Table 3.1). Feeding and/or assimilation rates have also been extensively used as proxies for temperature-dependent fitness. The ecological importance of feeding rates is overarching as it not only provides an organism-specific condition index but also sheds direct light on processes that occur at higher (i.e. population and community) levels (Paine, 1966). Southward (1955a,b, 1957) studied cirral activity, a proxy for feeding rates of different intertidal barnacle species that inhabit rocky shores of the United Kingdom. He provided extensive data on the effects of temperature throughout their thermotolerance windows (Table 3.1), revealing a clear fit with a typical performance curve’s shape (Fig. 3.6). Furthermore, his research draws attention to differences in performance in relation to additional sources of variability, including geographic origin of the species and populations (including the potential for thermal adaptation and acclimation) and intertidal height. For example, he showed how the species Balanus perforatus and Chthamalus stellatus exhibited higher Pmax and Tmax than S. (Balanus) balanoides, in accordance with their more southern distribution (Southward 1955a,b). Notably, as highlighted above, studies that only concentrate on feeding rates Tipping Points and the Keystone Role of Physiology 143 Figure 3.6 Performance curve based on barnacle cirral beat frequency, as reported by Southward (1955a,b). may not be sufficient to explain the entire story. Although feeding rates are known to increase with temperature presumably allowing for higher growth, reproduction and fitness the energetic costs (metabolic rate) are known to rise exponentially with temperature as well, compromising the overall contribution of increased feeding rates shown by the organism (Levinton, 1983; Sanford, 2002a). In sum, while many examples exist in the literature of the effects of environmental factors on traits related to fitness and performance, in relatively few cases do we have complete performance curves for marine organisms, and especially non-commercial invertebrates. Many studies have focused on extremes of temperature, or (especially in the case of aquaculture studies) on optima. This area thus represents a major gap in our knowledge, but is one that could be filled relatively easily. It is a critical area. For example, the outcome of field manipulations that compare the influence of environmental parameters on physiological or ecological performance depends entirely on where the conditions of the two sites lie on the performance curve; e.g. an increase in habitat temperature could lead to an increase in performance, a decrease in performance, or no change at all depending entirely on what combination of conditions were used (Fig. 3.5). While axiomatic in hindsight, often field manipulations fail to take such relationships into consideration or, when they do, they do not measure the conditions of the organism directly, but rather rely on proxies such as air or water temperature that may or may not accurately represent the physiological status of the organism (Figs 3.2 and 3.5). 144 Monaco and Helmuth 4. Indirect Effects of Climate Change: Species Interactions and Tipping Points Key to our argument is the concept that changes in the population dynamics of one or a few species can lead to community-level phase shifts/ tipping points, and that the reproductive failure or large-scale mortality of a species will not simply result in its replacement by a functional equivalent. Clearly this argument will not apply to all organisms or ecosystems. For example, there are many discussions in the literature of functional redundancy, especially in planktonic communities, and of the role of guilds. However, many examples of the important role played by a few key species have been shown, as in the case of organisms at the base of food webs such as the Antarctic pteropod (example given earlier). The concept of keystone species, first introduced by Paine (1969), has become a cornerstone of ecological theory (Power et al., 1996), and while its universality has been questioned (Strong, 1992), experimental manipulations have shown that it serves as a useful heuristic tool for examining interaction strengths within food webs (Menge et al., 1994), and the disproportionately large importance that some species have in relation to their abundance, i.e. the definition of a keystone species (Power et al., 1996). In some definitions, the keystone species concept is remarkably similar to that of a tipping point. For example, in his criticism of the keystone species concept, Strong (1992) defines keystone species as ‘taxa with such top-down dominance that their removal causes precipitous change in the [eco]system’. In a now-famous experiment Paine (1974) experimentally removed the predatory seastar Pisaster over a period of 5 years from a rocky intertidal shore. The removal of this keystone predator resulted in the competitive dominance of the primary space occupier M. californianus, which then excluded over 25 other species of invertebrates and benthic algae from the shore (although, as noted by Suchanek (1992), the presence of mussel beds also increases the diversity of fauna living within the bed). Estes and Palmisano (1974) have shown that the removal of sea otters results in rapid expansion of sea urchin populations, which in turn destroy macroalgae, resulting in urchin barrens. On coral reefs, the presence of herbivores determines whether reefs undergo a phase shift from a coral-dominated reef to an algal-dominated reef (but see Dudgeon et al., 2010). Hoey and Bellwood (2009) quantified rates of browsing on Sargassum on the Great Barrier Reef and found that despite the fact that the reef supported 50 herbivorous fish species and 6 macroalgal browsing species, a single species, Naso unicornis Forsskål, 1775, was responsible for B95% of the algae removed via grazing. Thus, this single species determined to a large extent the phase transition from a coral-dominated to a macroalgal-dominated reef. Tipping Points and the Keystone Role of Physiology 145 Forty years after the introduction of the term, the definition of a keystone species is still debated, and has taken on new urgency given its implications for conservation management (Clemente et al., 2010; Navarrete et al., 2010). The discussion over whether keystone species should receive special management status continues, and several authors have suggested that the concept be expanded to include any species that has a large impact on their assemblage, whether out of proportion to their biomass or not (Mills et al., 1993; Davic, 2003). For example, in a 1993 review, Mills et al. described five different types of keystone species: keystone predators, keystone prey, keystone mutualists, keystone hosts and keystone modifiers. Keystone predators typically act by removing competitive dominants or other consumers, as described above. Keystone prey species affect community diversity through their impacts on the populations of their predators; via high rates of reproduction, these species are able to sustain populations of predators, thereby reducing the density of other prey species (Holt, 1977). Keystone mutualists are species that are critical to mutualistic relationships, e.g. pollinators and seed dispersers. Keystone hosts are the organisms that in turn support those pollinators and dispersers, e.g. plants. While Mills et al. (1993) did not list any marine examples of these latter two categories, zooxanthellae and their coral hosts may potentially lend themselves to these definitions. Finally, Mills et al. (1993) defined keystone modifiers as species that significantly altered the physical habitat without necessarily having any trophic relationship with other species. The archetypical example given was that of North American beavers, which through the creation of dams flood the landscape, thereby impacting all other members of the assemblage. Jones et al. (1994) expanded on this latter definition to explore the concept of organisms as physical ecosystem engineers. Ecosystem engineers physically modify, maintain or create habitat, and in doing so directly or indirectly control the availability of resources to other species. Thus, for example, ecosystem engineers such as trees, corals, tube worms and bed-forming animals such as mussels and oysters all create living space for other organisms. Suchanek (1992) noted 135 species living in beds of the mussel M. californianus. Some polychaete species (e.g. Diopatra neapolitana Delle Chiaje, 1841) can build large emergent tubes that can alter flow regimes, stabilize sediment, and drive patterns of biodiversity by providing refugia for other species from predation (Woodin, 1981). Other species (e.g. Arenicola marina Linnaeus, 1758) are bioturbators that create disturbance that leads to decreases in biodiversity (Berke et al., 2010). In all of these examples, one species has a large effect on the ecological community, and thus any sudden change in levels of physiological stress, reproduction or mortality that affect the behaviour and/or population dynamics of those species is likely to have a cascading ecological impact (Connell et al., 2011; Kordas et al., 2011). In many cases these impacts are likely to exhibit a threshold effect as well. Below critical population 146 Monaco and Helmuth densities, populations can exhibit an Allee effect (depensation) in which negative rates of per capita growth begin to occur (Stoner and Ray-Culp, 2000). For example, below critical densities spawning success of urchins has been shown to decline (Levitan et al., 1992). Recent work has shown that small populations, especially those at the edge of species ranges (‘frayed edges’), are more highly susceptible to environmental change (i.e. less physiologically resilient) due to lower genetic variance (Pearson et al., 2009). Results such as these are worrisome, because they suggest that thresholds may occur even more rapidly once some minimum threshold in genetic variance, and hence a lower adaptive capacity, is surpassed. Cumulatively these studies point to the need for a better understanding not only of the direct physiological effects of climate change but also the indirect effects on species interactions. Multiple studies have examined the indirect effects of changes in climaterelated factors on species interactions (Poloczanska et al., 2008; Yamane and Gilman, 2009; Connell et al., 2011; Kordas et al., 2011). Wethey (1984) experimentally altered the outcome of competitive interactions by shading two species of intertidal barnacles, demonstrating that the dominant competitor was restricted from the more physiologically challenging high intertidal zone by thermal and/or desiccation stress. Schneider et al. (2010) compared the survival of two species of mussels (Mytilus trossulus Gould, 1850, and Mytilus galloprovincialis Lamarck, 1819) under varying conditions of aerial exposure and food availability and reported differential survival under stressful aerial conditions, suggesting a role of environmental stress in driving the distribution of these two species, one of which (M. galloprovincialis) is an invasive. Sanford (1999, 2002a) showed that rates of predation by the seastar P. ochraceus on the intertidal mussel M. californianus were positively correlated with water temperature. Pincebourde et al. (2008) expanded upon this work and showed that the aerial body temperature of Pisaster also affected feeding rates. Following short (1 2 day) exposures to elevated temperatures, increasing aerial body temperatures led to higher feeding rates. However, following longer (8 day) exposures to temperatures that were high yet still realistic when compared to what was observed in the field, feeding rates decreased by up to 40%, and led to decreases in seastar growth (Pincebourde et al., 2008). 5. Putting the Pieces Together: Where Do We Go from Here? Given the complicated interactions between organisms and their physical environments, the physiological mechanisms by which environmental factors drive organism behaviour, fitness and survival, and the indirect effects of Tipping Points and the Keystone Role of Physiology 147 these impacts on species interactions, is there any hope for a mechanistic framework? Multiple models have been proposed to explore the relationship between abiotic stressors and species interactions. For example, consumer stress models posit that top predators are more affected by physiological stress than are their prey (Menge and Sutherland, 1987). In contrast, prey stress models suggest that prey experience higher levels of physiological stress than do their predators (Menge and Olson, 1990). A quantitative understanding of relative physiological stress levels of predator and prey under both normal and extreme field conditions is thus vital to the application of these theories (Petes et al., 2008). Critically, the concepts presented in this chapter suggest not only that patterns in the field may be more complex than anticipated, but they also may be highly context dependent. That is, a site that is physiologically stressful for one species may not necessarily be so for another, for several reasons (Fig. 3.7). For example, a prey species may be physiologically more stressed than its predator because the predator has a thermal performance curve with higher optimum and lethal temperature limits. However, a prey species may also be more stressed than its predator simply because, under the same environmental conditions, the predator maintains a lower body temperature (Fig. 3.7). This appears to be the case with the intertidal seastar P. ochraceus and its mussel prey M. californianus (Petes et al., 2008). Largely because of its wet surface and large thermal inertia (Pincebourde et al., 2009), Pisaster appears to maintain a temperature that is either the same or lower than that of its prey (Broitman et al., 2009). Even if the two species have similar performance curves (e.g., Pisaster and Mytilus appear to have similar lethal limits: Pincebourde et al., 2008; Denny et al., 2011), they will experience very different levels of stress under identical field conditions simply because the predator is able to maintain a lower body temperature. Thus, neither measurements of performance curves nor measurements of body temperature alone are enough to determine relative (or even absolute) stress levels in the field; and certainly measurements of habitat alone are insufficient. Predicting physiological stress levels under field conditions is of course no simple matter due to potential interactions between multiple stressors. Crain et al. (2008) reviewed 171 studies that manipulated two or more stressors in marine ecosystems. Their meta-analysis showed an overall synergistic interaction effect, suggesting that the cumulative effects of multiple stressors are likely to be worse than expected based on the independent impacts of each stressor. Moreover, they found that cumulative effects could be additive, synergistic or antagonistic. Thus, in some cases, stressors either ameliorated one another, or one stressor had such a large effect the addition of a second stressor had no additional impact. Still, some studies have shown that when there is an overwhelming effect of one stressor, explicit predictions can be made that can then be tested under field conditions. Hofmann et al. (2010) outlined an approach that links differential susceptibility to OA, including physiological plasticity, 148 Monaco and Helmuth Figure 3.7 Translation of changes in the physical environment into ecological responses for two prey species (Mytilus spp.) and two consumers (Pisaster and Nucella). Because characteristics such as the size, morphology, colour and mass of an organism drives rates of heat flux, two organisms exposed to identical microclimates can have very different body temperatures. Each organism also has a unique physiological response to body temperature (thermal performance curve). The relative performance of interacting organisms can then influence the outcome of competitive interactions, or of rates of predation. Thus, for example, under elevated temperatures ‘prey stress’ can occur either because in any particular environment, the predator is able to maintain a lower body temperature, as appears to be the case between Pisaster and Mytilus (‘Prey Stress 1’), and/or because of a higher physiological tolerance of the consumer to temperature (‘Prey Stress 2’). to spatially explicit measurements of ocean pH in order to assess global patterns of calcification. Terrestrial ecologists have long used mechanistic heat budget models to generate temporally and spatially explicit maps of body temperature that can then be compared against known tolerance limits derived from controlled laboratory and field studies (Kearney and Porter, 2009). More recently these approaches have been applied to Tipping Points and the Keystone Role of Physiology 149 intertidal ecosystems (Gilman et al., 2006; Kearney et al., 2010; Denny et al., 2011; Helmuth et al., 2011). Such studies hold a distinct advantage over correlative (‘climate envelope’) models in that they can potentially incorporate local adaptation (Kuo and Sanford, 2009) and acclimation (Somero, 2010); however, they also are much more time- and data intensive. Most recently, biophysical models have been connected to Dynamic Energy Budget (DEB) models (Kooijman, 2009) as a means of accurately estimating the effects of changing levels of food and temperature on growth, reproductive output and survival (Kearney et al., 2010; Sará et al., 2011). Unlike other conceptual models, DEB models recognize that organisms live, as the name implies, in a dynamic environment. Thus, for example, Fig. 3.4 implies that it is a simple matter to define performance level at any given temperature (or combination of food and temperature, etc.). In reality, however, except for organisms in environments such as the deep sea or polar waters, organisms seldom live at a fixed temperature. Even in relatively thermally stable environments such as coral reefs, water temperatures fluctuate by up to several degrees due to solar heating of surface waters (Leichter et al., 2006; Castillo and Lima, 2010). Fluctuations in body temperature of 25 C or more are not uncommon in intertidal environments (Finke et al., 2009; Marshall et al., 2010). In reality, therefore, organismal performance changes throughout the course of the day as body temperature changes, although mobile organisms can potentially ameliorate much of that variability through microhabitat selection. As a corollary, the cumulative effects of thermal variability have been shown to significantly affect an organism’s long-term performance. For example, Sanford (2002b) compared feeding and growth rates of the intertidal predators P. ochraceus and Nucella canaliculata Duclos, 1832, held in tanks at three temperature treatments: constant ‘cold’ (9 C), constant ‘warm’ (12 C), and a 14-day fluctuating regime (9 12 C) simulating recurrent upwelling conditions. His experiments revealed greater growth under a fluctuating thermal environment, which led him to speculate that a continuous displacement in body temperature around the Tmax of a thermal performance curve would explain such results. Importantly, as theoretical models (Katz et al., 2005) and empirical studies (Easterling et al., 2000) have demonstrated that climate change involves increases in temperature variability, models designed to forecast organisms’ response to climate change need to explicitly consider scenarios with different thermal amplitudes (Folguera et al., 2009). The use of a single temperature to represent the physiological state of an animal over the course of a day (or even longer time period) is therefore generally untenable, especially when average values are used. DEB bypasses such limitations through a dynamic approach. 150 Monaco and Helmuth Kearney et al. (2010) successfully combined a biophysical heat budget model with a DEB model to study the effects of aerial and aquatic temperature on the intertidal mussel M. californianus. While they did not use the model to explore potential future effects of climate change, they did demonstrate the efficacy of the approach in predicting patterns of reproductive output and growth. Thus, the model could easily be combined with projections of future climate scenarios to predict the conditions under which this major space occupier would be most likely to experience mortality and/or reproductive failure, thus leading to a major phase shift in the intertidal ecosystem. Alternatively, it could be used as part of a sensitivity analysis to determine which environmental parameters are most likely to impact this key species (Helmuth et al., 2011). Mechanistic forecasting approaches such as these thus hold considerable promise, especially if and when they can begin to incorporate indirect effects such as predation (Sanford, 2002a; Petes et al., 2008; Pincebourde et al., 2008; Yamane and Gilman, 2009). However, their ability to predict such complex interactions can only be assessed empirically; and because it is imperative that we test such models now rather than wait to see what will happen under future climate scenarios, our best option is thus to use nowcasting and hindcasting as hypothesis-testing frameworks (Helmuth et al., 2006; Wethey and Woodin, 2008). Specifically, using our understanding of the sensitivity of organisms to changes in environmental parameters, we can develop much more sophisticated predictions of the likely effects of changes in the physical environment that can then be tested using experimental manipulations (Firth and Williams, 2009). The combination of both organismal- and suborganismal scale measurements and models with studies at population and community scales will provide a much more comprehensive view of the drivers of ecological thresholds than will simple correlations between environmental change and community responses. More to the point, an understanding of the world as viewed by the organisms we study will place us in a much better stead if we are to have any hope of predicting, and hopefully averting, some of the most severe impacts of anthropogenic change in coming decades. ACKNOWLEDGEMENTS The authors were supported by grants from NSF (OCE-0926581) and NASA (NNX07AF20G). We would particularly like to thank Michael Lesser for his help and patience throughout the process of writing this manuscript, and Andrés Monaco for assistance in the creation of figures. We are grateful to Carol Blanchette, Bernardo Broitman, Nicholas Burnett, Nicholas Colvard, Jerry Hilbish, Josie Iacarella, Michael Kearney, Allison Matzelle, Laura Petes, Gianluca Sará, Allison Smith, David Wethey and Sally Woodin for the many discussions that contributed significantly to the ideas presented in this manuscript. Tipping Points and the Keystone Role of Physiology 151 REFERENCES Agrawala, S., Broad, K. and Guston, D. H. (2001). Integrating climate forecasts and societal decision making: Challenges to an emergent boundary organization. Science Technology and Human Values 26, 454 477. Angilletta, M. J. (2009). Thermal Adaptation: A Theoretical and Empirical Synthesis. Oxford University Press, New York. Angilletta, M. J., Niewiarowski, P. H. and Navas, C. A. (2002). The evolution of thermal physiology in ectotherms. Journal of Thermal Biology 27, 249 268. Angilletta, M. J., Wilson, R. S., Navas, C. A. and James, R. S. (2003). Tradeoffs and the evolution of thermal reaction norms. Trends in Ecology & Evolution 18, 234 240. Beaumont, L. J. and Hughes, L. (2002). Potential changes in the distributions of latitudinally restricted Australian butterfly species in response to climate change. Global Change Biology 8, 954 971. Bell, E. C. (1995). Environmental and morphological influences on thallus temperature and desiccation of the intertidal alga Mastocarpus papillatus Kützing. Journal of Experimental Marine Biology and Ecology 191, 29 55. Bertness, M. D. and Schneider, D. E. (1976). Temperature relations of Puget Sound thaids in reference to their intertidal distribution. Veliger 19, 47 58. Berke, S. K. (2010). Functional groups of ecosystem engineers: A proposed classification with comments on current issues. Integrative and Comparative Biology 50, 147 157. Beukema, J. J., Dekker, R. and Jansen, J. M. (2009). Some like it cold: Populations of the tellinid bivalve Macoma balthica (L.) suffer in various ways from a warming climate. Marine Ecology Progress Series 384, 135 145. Blanchette, C. A., Miner, M. M., Raimondi, P. T., Lohse, D., Heady, K. E. K. and Broitman, B. R. (2008). Biogeographical patterns of rocky intertidal communities along the Pacific coast of North America. Journal of Biogeography 35, 1593 1607. Boller, M. L. and Carrington, E. (2007). Interspecific comparison of hydrodynamic performance and structural properties among intertidal macroalgae. Journal of Experimental Biology 210, 1874 1884. Brett, J. R. (1971). Energetic responses of salmon to temperature study of some thermal relations in physiology and freshwater ecology of sockeye salmon (Oncorhynchus nerka). American Zoologist 11, 99 113. Briske, D. D., Washington-Allen, R. A., Johnson, C. R., Lockwood, J. A., Lockwood, D. R., Stringham, T. K. and Shugart, H. H. (2010). Catastrophic thresholds: A synthesis of concepts, perspectives, and applications. Ecology and Society 15, 37. Broitman, B. R., Szathmary, P. L., Mislan, K. A. S., Blanchette, C. A. and Helmuth, B. (2009). Predator prey interactions under climate change: The importance of habitat vs body temperature. Oikos 118, 219 224. Brunetti, R., Beghi, L., Bressan, M. and Marin, M. G. (1980). Combined effects of temperature and salinity on colonies of Botryllus schlosseri and Botfylloides leachi (Ascidiacea) from the Venetian Lagoon. Marine Ecology Progress Series 2, 303 314. Carrington, E. (2002). Seasonal variation in the attachment strength of blue mussels: Causes and consequences. Limnology and Oceanography 47, 1723 1733. Carrington, E., Moeser, G. M., Dimond, J., Mello, J. J. and Boller, M. L. (2009). Seasonal disturbance to mussel beds: Field test of a mechanistic model predicting wave dislodgment. Limnology and Oceanography 54, 978 986. Casini, M., Hjelm, J., Molinero, J.-C., Lövgren, J., Cardinale, M., Bartolino, V., Belgrano, A. and Kornilovs, G. (2009). Trophic cascades promote threshold-like shifts in pelagic marine ecosystems. Proceedings of the National Academy of Sciences of the United States of America 106, 197 202. Castillo, K. D. and Lima, F. P. (2010). Comparison of in situ and satellite-derived (MODIS-Aqua/Terra) methods for assessing temperatures on coral reefs. Limnology and Oceanography: Methods 8, 107 117. 152 Monaco and Helmuth CCSP (2008). The effects of climate change on agriculture, land resources, water resources, and biodiversity. In A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research (A.J.P. Backlund, D. Schimel, J. Hatfield, K. Boote, P. Fay, L. Hahn, C. Izaurralde, B.A. Kimball, T. Mader, J. Morgan, D. Ort, W. Polley, A. Thomson, D. Wolfe, M. Ryan, S. Archer, R. Birdsey, C. Dahm, L. Heath, J. Hicke, D. Hollinger, T. Huxman, G. Okin, R. Oren, J. Randerson, W. Schlesinger, D. Lettenmaier, D. Major, L. Poff, S. Running, L. Hansen, D. Inouye, B.P. Kelly, L. Meyerson, B. Peterson and R. Shaw, eds), pp. 332. US Environmental Protection Agency, Washington, DC. CCSP (2009). Thresholds of climate change in ecosystems. In A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research (D.B. Fagre, C.W. Charles, C.D. Allen, C. Birkeland, F.S. Chapin III, P.M. Groffman, G.R. Guntenspergen, A.K. Knapp, A.D. McGuire, P.J. Mulholland, D.P.C. Peters, D.D. Roby and G. Sugihara, eds), pp. 156. USG Survey, Reston, VA. Chown, S. L. and Gaston, K. J. (2008). Macrophysiology for a changing world. Proceedings of the Royal Society of London Series B 275, 1469 1478. Chown, S. L., Gaston, K. J. and Robinson, D. (2004). Macrophysiology: Large-scale patterns in physiological traits and their ecological implications. Functional Ecology 18, 159 167. Clemente, S., Hernández, J. C., Rodrı́guez, A. and Brito, A. (2010). Identifying keystone predators and the importance of preserving functional diversity in sublittoral rockybottom areas. Marine Ecology Progress Series 413, 55 67. Connell, S. D., Russell, B. D. and Irving, A. D. (2011). Can strong consumer and producer effects be reconciled to better forecast ‘catastrophic’ phase-shifts in marine ecosystems? Journal of Experimental Marine Biology and Ecology 400, 296 301. Crain, C. M., Kroeker, K. and Halpern, B. S. (2008). Interactive and cumulative effects of multiple human stressors in marine systems. Ecology Letters 11, 1304 1315. Crimmins, S. M., Dobrowski, S. Z., Greenberg, J. A., Abatzoglou, J. T. and Mynsberge, A. R. (2011). Changes in climatic water balance drive downhill shifts in plant species’ optimum elevations. Science 331, 324 327. Davic, R. D. (2003). Linking keystone species and functional groups: A new operational definition of the keystone species concept. Conservation Ecology 7, r11. Davis, A. J., Jenkinson, L. S., Lawton, J. H., Shorrocks, B. and Wood, S. (1998a). Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783 786. Davis, A. J., Lawton, J. H., Shorrocks, B. and Jenkinson, L. S. (1998b). Individualistic species responses invalidate simple physiological models of community dynamics under global environmental change. Journal of Animal Ecology 67, 600 612. Denny, M. and Gaylord, B. (2010). Marine ecomechanics. Annual Review of Marine Science 2, 89 114. Denny, M. W. and Helmuth, B. (2009). Confronting the physiological bottleneck: A challenge from ecomechanics. Integrative and Comparative Biology 49, 197 201. Denny, M. W., Helmuth, B., Leonard, G. H., Harley, C. D. G., Hunt, L. J. H. and Nelson, E. K. (2004). Quantifying scale in ecology: Lessons from a wave-swept shore. Ecological Monographs 74, 513 532. Denny, M. W., Dowd, W. W., Bilir, L. and Mach, K. J. (2011). Spreading the risk: Small-scale body temperature variation among intertidal organisms and its implications for species persistence. Journal of Experimental Marine Biology and Ecology 400, 175 190. Dewitt, C. B. and Friedman, R. M. (1979). Significance of skewness in ectotherm thermoregulation. American Zoologist 19, 195 209. Dudgeon, S. R., Aronson, R. B., Bruno, J. F. and Precht, W. F. (2010). Phase shifts and stable states on coral reefs. Marine Ecology Progress Series 413, 201 216. Tipping Points and the Keystone Role of Physiology 153 Easterling, D. R., Meehl, G. A., Parmesan, C., Changnon, S. A., Karl, T. R. and Mearns, L. O. (2000). Climate extremes: Observations, modeling, and impacts. Science 289, 2068 2074. Epelbaum, A., Herborg, L. M., Therriault, T. W. and Pearce, C. M. (2009). Temperature and salinity effects on growth, survival, reproduction, and potential distribution of two non-indigenous botryllid ascidians in British Columbia. Journal of Experimental Marine Biology and Ecology 369, 43 52. Estes, J. A. and Palmisano, J. F. (1974). Sea otters - Their role in structuring seashore communities. Science 185, 1058 1060. Fabricius, K. E. (2006). Effects of irradiance, flow, and colony pigmentation on the temperature microenvironment around corals: Implications for coral bleaching? Limnology and Oceanography 51, 30 37. Fabry, V. J. (2008). Marine calcifiers in a high-CO2 ocean. Science 320, 1020 1022. Finelli, C. M., Helmuth, B. S. T., Pentcheff, N. D. and Wethey, D. S. (2006). Water flow controls oxygen transport and photosynthesis in corals: Potential links to coral bleaching. Coral Reefs 25, 47 57. Finelli, C. M., Helmuth, B. S., Pentcheff, N. D. and Wethey, D. S. (2007). Intracolony variability in photosynthesis by corals is affected by water flow: Role of oxygen flux. Marine Ecology Progress Series 349, 103 110. Finke, G. R., Navarrete, S. A. and Bozinovic, F. (2007). Tidal regimes of temperate coasts and their influences on aerial exposure for intertidal organisms. Marine Ecology Progress Series 343, 57 62. Finke, G. R., Bozinivic, F. and Navarrete, S. A. (2009). A mechanistic model to study the thermal ecology of a southeastern Pacific dominant intertidal mussel and implications for climate change. Physiological and Biochemical Zoology 82, 303 313. Firth, L. B. and Williams, G. A. (2009). The influence of multiple environmental stressors on the limpet Cellana toreuma during the summer monsoon season in Hong Kong. Journal of Experimental Marine Biology and Ecology 375, 70 75. Folguera, G., Bastı́as, D. A. and Bozinovic, F. (2009). Impact of experimental thermal amplitude on ectotherm performance: Adaptation to climate change variability? Comparative Biochemistry and Physiology Part A, Molecular & Integrative Physiology 154, 389 393. Forchhammer, M. C. and Post, E. (2004). Using large-scale climate indices in climate change ecology studies. Population Ecology 46, 1 12. Frederich, M. and Pörtner, H. O. (2000). Oxygen limitation of thermal tolerance defined by cardiac and ventilatory performance in spider crab, Maja squinado. American Journal of Physiology Regulatory Integrative and Comparative Physiology 279, R1531 R1538. Gedan, K. B. and Bertness, M. D. (2009). Experimental warming causes rapid loss of plant diversity in New England salt marshes. Ecology Letters 12, 842 848. Gilman, S. E., Wethey, D. S. and Helmuth, B. (2006). Variation in the sensitivity of organismal body temperature to climate change over local and geographic scales. Proceedings of the National Academy of Sciences of the United States of America 103, 9560 9565. Goodwin, J. and Dahlstrom, M. F. (2011). Good reasons for trusting climate science communication. American Meteorological Society (abstract), Washington, DC . Gouhier, T. C., Guichard, F. and Menge, B. A. (2010). Ecological processes can synchronize marine population dynamics over continental scales. Proceedings of the National Academy of Sciences of the United States of America 107, 8281 8286. Gray, J. (1923). The mechanism of ciliary movement. III. The effect of temperature. Proceedings of the Royal Society of London Series B Containing Papers of a Biological Character 95, 6 15. 154 Monaco and Helmuth Hallett, T. B., Coulson, T., Pilkington, J. G., Clutton-Brock, T. H., Pemberton, J. M. and Grenfell, B. T. (2004). Why large-scale climate indices seem to predict ecological processes better than local weather. Nature 430, 71 75. Hare, S. R. and Mantua, N. J. (2000). Empirical evidence for North Pacific regime shifts in 1977 and 1989. Progress in Oceanography 47, 103 145. Harley, C. D. G. (2008). Tidal dynamics, topographic orientation, and temperature-mediated mass mortalities on rocky shores. Marine Ecology Progress Series 371, 37 46. Harley, C. D. G. and Paine, R. T. (2009). Contingencies and compounded rare perturbations dictate sudden distributional shifts during periods of gradual climate change. Proceedings of the National Academy of Sciences of the United States of America 106, 11172 11176. Helmuth, B. (2002). How do we measure the environment? Linking intertidal thermal physiology and ecology through biophysics. Integrative and Comparative Biology 42, 837 845. Helmuth, B. (2009). From cells to coastlines: How can we use physiology to forecast the impacts of climate change? Journal of Experimental Biology 212, 753 760. Helmuth, B. and Denny, M. W. (2003). Predicting wave exposure in the rocky intertidal zone: Do bigger waves always lead to larger forces? Limnology and Oceanography 48, 1338 1345. Helmuth, B. S., Harley, C. D. G., Halpin, P., O’Donnell, M., Hofmann, G. E. and Blanchette, C. (2002). Climate change and latitudinal patterns of intertidal thermal stress. Science 298, 1015 1017. Helmuth, B., Carrington, E. and Kingsolver, J. G. (2005). Biophysics, physiological ecology, and climate change: Does mechanism matter? Annual Review of Physiology 67, 177 201. Helmuth, B., Broitman, B. R., Blanchette, C. A., Gilman, S., Halpin, P., Harley, C. D. G., O’Donnell, M. J., Hofmann, G. E., Menge, B. and Strickland, D. (2006). Mosaic patterns of thermal stress in the rocky intertidal zone: Implications for climate change. Ecological Monographs 76, 461 479. Helmuth, B., Broitman, B. R., Yamane, L., Gilman, S. E., Mach, K., Mislan, K. A. S. and Denny, M. W. (2010). Organismal climatology: Analyzing environmental variability at scales relevant to physiological stress. Journal of Experimental Biology 213, 995 1003. Helmuth, B., Yamane, L., Lalwani, S., Matzelle, A., Tockstein, A. and Gao, N. (2011). Hidden signals of climate change in intertidal ecosystems: What (not) to expect when you are expecting. Journal of Experimental Marine Biology and Ecology 400, 191 199. Hewitt, J. E. and Thrush, S. F. (2010). Empirical evidence of an approaching alternate state produced by intrinsic community dynamics, climatic variability and management actions. Marine Ecology Progress Series 413, 267 276. Highsmith, R. C., Riggs, A. C. and D’Antonio, C. M. (1980). Survival of hurricane-generated coral fragments and a disturbance model of reef calcification/growth rates. Oecologia 46, 322 329. Hilbert, D. W. (2002). Non-linear systems. In “Encyclopedia of Global Environmental Change, The Earth System: Biological and Ecological Dimensions of Global Environmental Change” (T. Munn, H. A Mooney and J. G. Canadell, eds), vol. II, pp. 450 455. John Wiley & Sons, Chichester. Hoegh-Guldberg, O. and Bruno, J. F. (2010). The impact of climate change on the world’s marine ecosystems. Science 328, 1523 1528. Hoegh-Guldberg, O., Mumby, P. J., Hooten, A. J., Steneck, R. S., Greenfield, P., Gomez, E., Harvell, C. D., Sale, P. F., Edwards, A. J. Caldeira, K., et al. (2007). Coral reefs under rapid climate change and ocean acidification. Science 318, 1737 1742. Hoey, A. S. and Bellwood, D. R. (2009). Limited functional redundancy in a high diversity system: Single species dominates key ecological process on coral reefs. Ecosystems 12, 1316 1328. Tipping Points and the Keystone Role of Physiology 155 Hoffman, J. (2003). Temperate marine. In “Buying Time: A User’s Manual for Building Resistance and Resilience to Climate Change in Natural Systems” (L. J. Hansen, J. L. Biringer and J. R. Hoffman, eds), pp. 123 155. Hofmann, G. E., Barry, J. P., Edmunds, P. J., Gates, R. D., Hutchins, D. A., Klinger, T. and Sewell, M. A. (2010). The effect of ocean acidification on calcifying organisms in marine ecosystems: An organism-to-ecosystem perspective. Annual Review of Ecology Evolution and Systematics 41, 127 147. Holt, R. D. (1977). Predation, apparent competition, and the structure of prey communities. Theoretical Population Biology 12, 197 229. Holtmeier, F.-K. and Broll, G. (2005). Sensitivity and response of northern hemisphere altitudinal and polar treelines to environmental change at landscape and local scales. Global Ecology and Biogeography 14, 395 410. Huey, R. B. and Stevenson, R. D. (1979). Integrating thermal physiology and ecology of ectotherms: A discussion of approaches. American Zoologist 19, 357 366. Hunt, B. P. V., Pakhomov, E. A., Hosie, G. W., Siegel, V., Ward, P. and Bernard, K. (2008). Pteropods in Southern Ocean ecosystems. Progress in Oceanography 78, 193 221. Hutchins, D. A., Fu, F.-X., Zhang, Y., Warner, M. E., Feng, Y., Portune, K., Bernhardt, P. W. and Mulholland, M. R. (2007). CO2 control of Trichodesmium N2 fixation, photosynthesis, growth rates, and elemental ratios: Implications for past, present, and future ocean biogeochemistry. Limnology and Oceanography 52, 1293 1304. Idjadi, J., Lee, S., Bruno, J., Precht, W., Allen-Requa, L. and Edmunds, P. (2006). Rapid phase-shift reversal on a Jamaican coral reef. Coral Reefs 25, 209 211. Jimenez, I. M., Kühl, M., Larkum, A. W. D. and Ralph, P. J. (2008). Heat budget and thermal microenvironment of shallow-water corals: Do massive corals get warmer than branching corals? Limnology and Oceanography 53, 1548 1561. Jones, C. G., Lawton, J. H. and Shachak, M. (1994). Organisms as ecosystem engineers. Oikos 69, 373 386. Kassahn, K. S., Crozier, R. H., Pörtner, H. O. and Caley, M. J. (2009). Animal performance and stress: Responses and tolerance limits at different levels of biological organisation. Biological Reviews 84, 277 292. Katz, R. W., Brush, G. S. and Parlange, M. P. (2005). Statistics of extremes: Modeling ecological disturbances. Ecology 36, 1124 1134. Kearney, M. (2006). Habitat, environment and niche: What are we modelling? Oikos 115, 186 191. Kearney, M. and Porter, W. (2009). Mechanistic niche modelling: Combining physiological and spatial data to predict speciesranges. Ecology Letters 12, 334 350. Kearney, M., Phillips, B. L., Tracy, C. R., Christian, K. A., Betts, G. and Porter, W. P. (2008). Modelling species distributions without using species distributions: The cane toad in Australia under current and future climates. Ecography 31, 423 434. Kearney, M., Simpson, S. J., Raubenheimer, D. and Helmuth, B. (2010). Modelling the ecological niche from functional traits. Philosophical Transactions of the Royal Society of London Series B 365, 3469 3483. Kingsolver, J. G., Izem, R. and Ragland, G. J. (2004). Plasticity of size and growth in fluctuating thermal environments: Comparing reaction norms and performance curves. Integrative and Comparative Biology 44, 450 460. Kooijman, S. A. L. M. (2009). Dynamic Energy Budget Theory for Metabolic Organisation. Cambridge University Press, Cambridge. Kordas, R. L., Harley, C. D. G. and O’Connor, M. I. (2011). Community ecology in a warming world: The influence of temperature on interspecific interactions in marine systems. Journal of Experimental Marine Biology and Ecology 400, 218 226. 156 Monaco and Helmuth Kuo, E. S. L. and Sanford, E. (2009). Geographic variation in the upper thermal limits of an intertidal snail: Implications for climate envelope models. Marine Ecology Progress Series 388, 137 146. Lambert, G. (2005). Ecology and natural history of the protochordates. Canadian Journal of Zoology 83, 34 50. Leichter, J. J., Helmuth, B. and Fischer, A. M. (2006). Variation beneath the surface: Quantifying complex thermal environments on coral reefs in the Caribbean, Bahamas and Florida. Journal of Marine Research 64, 563 588. Lenihan, H. S., Adjeroud, M., Kotchen, M. J., Hench, J. L. and Nakamura, T. (2008). Reef structure regulates small-scale spatial variation in coral bleaching. Marine Ecology Progress Series 370, 127 141. Lesser, M. P. (1996). Elevated temperatures and ultraviolet radiation cause oxidative stress and inhibit photosynthesis in symbiotic dinoflagellates. Limnology and Oceanography 41, 271 283. Lesser, M. P. (1997). Oxidative stress causes coral bleaching during exposure to elevated temperatures. Coral Reefs 16, 187 192. Lesser, M. P. (2004). Experimental biology of coral reef ecosystems. Journal of Experimental Marine Biology and Ecology 300, 217 252. Lesser, M. P., Weis, V. M., Patterson, M. R. and Jokiel, P. L. (1994). Effects of morphology and water motion on carbon delivery and productivity in the reef coral, Pocillopora damicornis (Linnaeus): Diffusion barriers, inorganic carbon limitation, and biochemical plasticity. Journal of Experimental Marine Biology and Ecology 178, 153 179. Levinton, J. S. (1983). The latitudinal compensation hypothesis: Growth data and a model of latitudinal growth differentiation based upon energy budgets I. Interspecific comparison of Ophryotrocha (Polychaeta: Dorvilleidae). Biological Bulletin 165, 686 698. Levitan, D. R., Sewell, M. A. and Chia, F.-S. (1992). How distribution and abundance influence fertilization success in the sea urchin Strongylocentrotus franciscanus. Ecology 73, 248 254. Littler, M. M. and Littler, D. S. (2007). Assessment of coral reefs using herbivory/nutrient assays and indicator groups of benthic primary producers: A critical synthesis, proposed protocols, and critique of management strategies. Aquatic Conservation: Marine and Freshwater Ecosystems 17, 195 215. Ludwig, D., Mangel, M. and Haddad, B. (2001). Ecology, conservation, and public policy. Annual Review of Ecology and Systematics 32, 481 517. Marshall, D. J., McQuaid, C. D. and Williams, G. A. (2010). Non-climatic thermal adaptation: Implications for species’ responses to climate warming. Biology Letters 6, 669 673. McNeil, B. I. and Matear, R. J. (2008). Southern Ocean acidification: A tipping point at 450-ppm atmospheric CO2. Proceedings of the National Academy of Sciences of the United States of America 105, 18860 18864. Menge, B. A. and Olson, A. M. (1990). Role of scale and environmental factors in regulation of community structure. Trends in Ecology and Evolution 5, 52 57. Menge, B. A. and Sutherland, J. P. (1987). Community regulation: Variation in disturbance, competition, and predation in relation to environmental stress and recruitment. The American Naturalist 130, 730 757. Menge, B. A., Berlow, E. L., Blanchette, C. A., Navarrete, S. A. and Yamada, S. (1994). The keystone species concept: Variation in interaction strength in a rocky intertidal habitat. Ecological Monographs 64, 249 286. Millennium Ecosystem Assessment, (2005). Ecosystems and Well-Being: Opportunities and Challenges for Business and Industry. World Resources Institute, Washington, DC, pp. 29. Mills, L. S., Soulé, M. E. and Doak, D. F. (1993). The keystone-species concept in ecology and conservation. Bioscience 43, 219 224. Tipping Points and the Keystone Role of Physiology 157 Mislan, K. A. S., Wethey, D. S. and Helmuth, B. (2009). When to worry about the weather: Role of tidal cycle in determining patterns of risk in intertidal ecosystems. Global Change Biology 15, 3056 3065. Mitchell, N. J., Kearney, M. R., Nelson, N. J. and Porter, W. P. (2008). Predicting the fate of a living fossil: How will global warming affect sex determination and hatching phenology in tuatara? Proceedings of the Royal Society of London Series B 275, 2185 2193. Mumby, P. J., Iglesias-Prieto, R., Hooten, A. J., Sale, P. F., Hoegh-Guldberg, O., Edwards, A. J., Harvell, C. D., Gomez, E. D., Knowlton, N., Hatziolos, M. E., Kyewalyanga, M. S. and Muthiga, N. (2011). Revisiting climate thresholds and ecosystem collapse. Frontiers in Ecology and the Environment 9, 94 96. Nakamura, T. and van Woesik, R. (2001). Water-flow rates and passive diffusion partially explain differential survival of corals during the 1998 bleaching event. Marine Ecology Progress Series 212, 301 304. Nakamura, T., Yamasaki, H. and van Woesik, R. (2003). Water flow facilitates recovery from bleaching in the coral Stylophora pistillata. Marine Ecology Progress Series 256, 287 291. Navarrete, S. A., Gelcich, S. and Castilla, J. C. (2010). Long-term monitoring of coastal ecosystems at Las Cruces, Chile: Defining baselines to build ecological literacy in a world of change. Revista Chilena de Historia Natural 83, 143 157. Norkko, J., Norkko, A., Thrush, S. F., Valanko, S. and Suurkuukka, H. (2010). Conditional responses to increasing scales of disturbance, and potential implications for threshold dynamics in soft-sediment communities. Marine Ecology Progress Series 413, 253 266. Ojanguren, A. F., Reyes-Gavilán, F. G. and Braña, F. (2001). Thermal sensitivity of growth, food intake and activity of juvenile brown trout. Journal of Thermal Biology 26, 165 170. Osman, R. W., Munguia, P. and Zajac, R. N. (2010). Ecological thresholds in marine communities: Theory, experiments and management. Marine Ecology Progress Series 413, 185 187. Paine, R. T. (1966). Food web complexity and species diversity. The American Naturalist 100, 368 378. Paine, R. T. (1969). The Pisaster Tegula interaction: Prey patches, predator food preference, and intertidal community structure. Ecology 50, 950 961. Paine, R. T. (1974). Intertidal community structure: Experimental studies on the relationship between a dominant competitor and its principal predator. Oecologia 15, 93 120. Parmesan, C. (2006). Ecological and evolutionary responses to recent climate change. Annual Review of Ecology Evolution and Systematics 37, 637 669. Parmesan, C. and Yohe, G. (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37 42. Parmesan, C., Duarte, C., Poloczanska, E., Richardson, A. J. and Singer, M. C. (2011). Overstretching attribution. Nature Climate Change, 2 4. Patterson, M. R. (1992). A chemical engineering view of cnidarian symbioses. American Zoologist 32, 566 582. Pearson, G. A., Lago-Leston, A. and Mota, C. (2009). Frayed at the edges: Selective pressure and adaptive response to abiotic stressors are mismatched in low diversity edge populations. Journal of Ecology 97, 450 462. Petes, L. E., Mouchka, M. E., Milston-Clements, R. H., Momoda, T. S. and Menge, B. A. (2008). Effects of environmental stress on intertidal mussels and their sea star predators. Oecologia 156, 671 680. Petraitis, P., Methratta, E., Rhile, E., Vidargas, N. and Dudgeon, S. (2009). Experimental confirmation of multiple community states in a marine ecosystem. Oecologia 161, 139 148. 158 Monaco and Helmuth Pfister, C. A., Wootton, J. T. and Neufield, C. J. (2007). Relative roles of coastal and oceanic processes in determining physical and chemical characteristics of an intensively sampled nearshore system. Limnology and Oceanography 52, 1767 1775. Pincebourde, S., Sanford, E. and Helmuth, B. (2008). Body temperature during low tide alters the feeding performance of a top intertidal predator. Limnology and Oceanography 53, 1562 1573. Pincebourde, S., Sanford, E. and Helmuth, B. (2009). An intertidal sea star adjusts thermal inertia to avoid extreme body temperatures. The American Naturalist 174, 890 897. Pineda, J. and López, M. (2002). Temperature, stratification and barnacle larval settlement in two Californian sites. Continental Shelf Research 22, 1183 1198. Place, S. P., O’Donnell, M. J. and Hofmann, G. E. (2008). Gene expression in the intertidal mussel Mytilus californianus: Physiological response to environmental factors on a biogeographic scale. Marine Ecology Progress Series 356, 1 14. Poloczanska, E. S., Hawkins, S. J., Southward, A. J. and Burrows, M. T. (2008). Modeling the response of populations of competing species to climate change. Ecology 89, 3138 3149. Porter, W. P. and Gates, D. M. (1969). Thermodynamic equilibria of animals with environment. Ecological Monographs 39, 227 244. Pörtner, H.-O. (2010). Oxygen- and capacity-limitation of thermal tolerance: A matrix for integrating climate-related stressor effects in marine ecosystems. Journal of Experimental Biology 213, 881 893. Pörtner, H. O. and Farrell, A. P. (2008). Physiology and climate change. Nature 322, 690 692. Pörtner, H. O., Hardewig, I. and Peck, L. S. (1999). Mitochondrial function and critical temperature in the Antarctic bivalve, Laternula elliptica. Comparative Biochemistry and Physiology Part A, Molecular & Integrative Physiology 124, 179 189. Pörtner, H. O., Bennett, A. F., Bozinivic, F., Clarke, A., Lardies, M. A., Lucassen, M., Pelster, B., Schiemer, F. and Stillman, J. H. (2006). Trade-offs in thermal adaptation: The need for a molecular to ecological integration. Physiological and Biochemical Zoology 79, 295 313. Power, M. E., Tilman, D., Estes, J. A., Menge, B. A., Bond, W. J., Mills, L. S., Daily, G., Castilla, J. C., Lubchenco, J. and Paine, R. T. (1996). Challenges in the quest for keystones. Bioscience 46, 609 620. Rockstrom, J., Steffen, W., Noone, K., Persson, A., Chapin, F. S., Lambin, E. F., Lenton, T. M., Scheffer, M., Folke, C. Schellnhuber, H. J., et al. (2009). A safe operating space for humanity. Nature 461, 472 475. Sagarin, R. D. and Somero, G. N. (2006). Complex patterns of expression of heat-shock protein 70 across the southern biogeographical ranges of the intertidal mussel Mytilus californianus and snail Nucella ostrina. Journal of Biogeography 33, 622 630. Sanford, E. (1999). Regulation of keystone predation by small changes in ocean temperature. Science 283, 2095 2097. Sanford, E. (2002a). Water temperature, predation, and the neglected role of physiological rate effects in rocky intertidal communities. Integrative and Comparative Biology 42, 881 891. Sanford, E. (2002b). The feeding, growth, and energetics of two rocky intertidal predators (Pisaster ochraceus and Nucella canaliculata) under water temperatures simulating episodic upwelling. Journal of Experimental Marine Biology and Ecology 273, 199 218. Sará, G., Kearney, M. and Helmuth, B. (2011). Combining heat-transfer and energy budget models to predict thermal stress in Mediterranean intertidal mussels. Chemistry and Ecology 27, 135 145. Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. and Walker, B. (2001). Catastrophic shifts in ecosystems. Nature 413, 591 596. Tipping Points and the Keystone Role of Physiology 159 Schneider, K. R., Van Thiel, L. E. and Helmuth, B. (2010). Interactive effects of food availability and aerial body temperature on the survival of two intertidal Mytilus species. Journal of Thermal Biology 35, 161 166. Schoch, G. C., Menge, B. A., Allison, G., Kavanaugh, M., Thompson, S. A. and Wood, S. (2006). Fifteen degrees of separation: Latitudinal gradients of rocky intertidal biota along the California Current. Limnology and Oceanography 51, 2564 2585. Sebens, K. P. (2002). Energetic constraints, size gradients, and size limits in benthic marine invertebrates. Integrative and Comparative Biology 42, 853 861. Shashar, N., Kinane, S., Jokiel, P. L. and Patterson, M. R. (1996). Hydromechanical boundary layers over a coral reef. Journal of Experimental Marine Biology and Ecology 199, 17 28. Sibly, R. M. and Atkinson, D. (1994). How rearing temperature affects optimal adult size in ectotherms. Functional Ecology 8, 486 493. Somero, G. N. (2005). Linking biogeography to physiology: Evolutionary and acclimatory adjustments of thermal limits. Frontiers in Zoology 2, 1. Somero, G. N. (2010). The physiology of climate change: How potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers’. Journal of Experimental Biology 213, 912 920. Sommer, A. and Pörtner, H. O. (1999). Exposure of Arenicola marina to extreme temperatures: Adaptive flexibility of a boreal and a subpolar population. Marine Ecology Progress Series 181, 215 226. Southward, A. J. (1955a). On the behaviour of barnacles: II. The influence of habitat and tide-level on cirral activity. Journal of the Marine Biological Association of the UK 34, 423 433. Southward, A. J. (1955b). On the behaviour of barnacles: I. The relation of cirral and other activities to temperature. Journal of the Marine Biological Association of the UK 34, 403 422. Southward, A. J. (1957). On the behaviour of barnacles: III. Further observations on the influence of temperature and age on cirral activity. Journal of the Marine Biological Association of the UK 36, 323 334. Stenseth, N. C. (2007). Canadian hare lynx dynamics and climate variation: Need for further interdisciplinary work on the interface between ecology and climate. Climate Research 34, 91 92. Stenseth, N. C., Mysterud, A., Ottersen, G., Hurrell, J. W., Chan, K.-S. and Lima, M. (2002). Ecological effects of climate fluctuations. Science 297, 1292 1296. Stenseth, N. C., Ottersen, G., Hurrell, J. W., Mysterud, A., Lima, M., Chan, K.-S., Yoccoz, N. G. and Adlandsvik, B. (2003). Studying climate effects on ecology through the use of climate indices: The North Atlantic Oscillation, El Niño Southern Oscillation and beyond. Proceedings of the Royal Society of London Series B 270, 2087 2096. Stoner, A. W. and Ray-Culp, M. (2000). Evidence for Allee effects in an over-harvested marine gastropod: Density-dependent mating and egg production. Marine Ecology Progress Series 202, 297 302. Strong, D. R. (1992). Are trophic cascades all wet? Differentiation and donor-control in speciose ecosystems. Ecology 73, 747 754. Suchanek, T. H. (1992). Extreme biodiversity in the marine environment: Mussel bed communities of Mytilus californianus. Northwest Environmental Journal 8, 150 152. Thomas, F. I. M. and Atkinson, M. J. (1997). Ammonium uptake by coral reefs: Effects of water velocity and surface roughness on mass transfer. Limnology and Oceanography 42, 81 88. Tomanek, L. (2008). The importance of physiological limits in determining biogeographical range shifts due to global climate change: The heat shock response. Physiological and Biochemical Zoology 81, 709 717. 160 Monaco and Helmuth Thompson, M., Drolet, D. and Himmelman, J. (2005). Localization of infaunal prey by the sea star Leptasterias polaris. Marine Biology 146, 887 894. Thorson, G. (1950). Reproductive and larval ecology of marine bottom invertebrates. Biological Review 25, 1 45. US Environmental Protection Agency (EPA), (2008). Effects of Climate Change for Aquatic Invasive Species and Implications for Management and Research. National Center for Environmental Assessment, Washington, DC, pp. 337. Wethey, D. S. (1983). Geographic limits and local zonation: The barnacles Semibalanus (Balanus) and Chthamalus in New England. Biological Bulletin 165, 330 341. Wethey, D. S. (1984). Sun and shade mediate competition in the barnacles Chthamalus and Semibalanus: A field experiment. Biological Bulletin 167, 176 185. Wethey, D. S. and Woodin, S. A. (2008). Ecological hindcasting of biogeographic responses to climate change in the European intertidal zone. Hydrobiologia 606, 139 151. Wiens, J. J. and Graham, C. H. (2005). Niche conservatism: Integrating evolution, ecology, and conservation biology. Annual Review of Ecology Evolution and Systematics 36, 519 539. Woodin, S. A. (1981). Disturbance and community structure in a shallow water sand flat. Ecology 62, 1052 1066. Wootton, J. T., Pfister, C. A. and Forester, J. D. (2008). Dynamic patterns and ecological impacts of declining ocean pH in a high-resolution multi-year dataset. Proceedings of the National Academy of Sciences of the United States of America 105, 18848 18853. Yamane, L. and Gilman, S. E. (2009). Opposite responses by an intertidal predator to increasing aquatic and aerial temperatures. Marine Ecology Progress Series 393, 27 36. Zacherl, D., Gaines, S. D. and Lonhart, S. I. (2003). The limits to biogeographical distributions: Insights from the northward range extension of the marine snail, Kelletia kelletii (Forbes, 1852). Journal of Biogeography 30, 913 924. C H A P T E R F O U R Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates David A. Ritz*,1, Alistair J. Hobday†, John C. Montgomery‡ and Ashley J.W. Wardy Contents 1. Introduction 2. Aggregation Principles and Features in Pelagic Ecosystems 2.1. Origins of sociality 2.2. Significance and benefits of social aggregation 2.3. Structure and functions of social aggregations 2.4. Association patterns within aggregations 2.5. Sensing the behaviour of neighbours 2.6. Social networks 3. Technology Breakthroughs in Experimental and Observational Methods 3.1. Video and motion analysis software 3.2. Optical plankton counters and holography 3.3. Acoustic technology 3.4. Electronic tags 3.5. Future technology challenges 4. Theoretical Developments in Social Aggregation 5. Social Aggregation, Climate Change and Ocean Management 6. Conclusion 6.1. Do reviews stimulate new work? 6.2. Future needs and synthesis Acknowledgements References 163 166 170 171 176 183 184 190 192 192 197 198 203 204 205 208 211 211 212 214 214 * School of Zoology, University of Tasmania, Hobart, Australia Wealth from Oceans Flagship, CSIRO Marine and Atmospheric Research, Hobart, Tasmania, Australia ‡ Leigh Marine Laboratory, University of Auckland, New Zealand y School of Biological Sciences, University of Sydney, Sydney, New South Wales, Australia 1 Corresponding author: Email: David.Ritz@utas.edu.au † Advances in Marine Biology, Volume 60 ISSN: 0065-2881, DOI: 10.1016/B978-0-12-385529-9.00004-4 © 2011 Elsevier Ltd All rights reserved. 161 162 David A. Ritz et al. Abstract Aggregations of organisms, ranging from zooplankton to whales, are an extremely common phenomenon in the pelagic zone; perhaps the best known are fish schools. Social aggregation is a special category that refers to groups that self-organize and maintain cohesion to exploit benefits such as protection from predators, and location and capture of resources more effectively and with greater energy efficiency than could a solitary individual. In this review we explore general aggregation principles, with specific reference to pelagic organisms; describe a range of new technologies either designed for studying aggregations or that could potentially be exploited for this purpose; report on the insights gained from theoretical modelling; discuss the relationship between social aggregation and ocean management; and speculate on the impact of climate change. Examples of aggregation occur in all animal phyla. Among pelagic organisms, it is possible that repeated cooccurrence of stable pairs of individuals, which has been established for some schooling fish, is the likely precursor leading to networks of social interaction and more complex social behaviour. Social network analysis has added new insights into social behaviour and allows us to dissect aggregations and to examine how the constituent individuals interact with each other. This type of analysis is well advanced in pinnipeds and cetaceans, and work on fish is progressing. Detailed three-dimensional analysis of schools has proved to be difficult, especially at sea, but there has been some progress recently. The technological aids for studying social aggregation include video and acoustics, and have benefited from advances in digitization, miniaturization, motion analysis and computing power. New techniques permit three-dimensional tracking of thousands of individual animals within a single group which has allowed novel insights to within-group interactions. Approaches using theoretical modelling of aggregations have a long history but only recently have hypotheses been tested empirically. The lack of synchrony between models and empirical data, and lack of a common framework to schooling models have hitherto hampered progress; however, recent developments in this field offer considerable promise. Further, we speculate that climate change, already having effects on ecosystems, could have dramatic effects on aggregations through its influence on species composition by altering distribution ranges, migration patterns, vertical migration, and oceanic acidity. Because most major commercial fishing targets schooling species, these changes could have important consequences for the dependent businesses. Key Words: social aggregation; pelagic zone; marine; association; social networks; technology; climate change; modelling Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 163 1. Introduction The marine pelagic zone is defined as the water column, usually in the open sea. Further divisions of the water column into epipelagic and mesopelagic can be made; however, here we use the term generally. It differs from the coastal marine domains with regard to ecological patterns; high alpha diversity, low beta diversity; apparent lack of keystone predators; few examples of trophic cascades; and little apparent competition for space. The marine pelagic environment represents 99% of the biosphere volume (Angel, 1993). In addition to supplying more than 80% of the fish consumed by humans (Pauly et al., 2002), pelagic ecosystems account for almost half of the photosynthesis on Earth (Field et al., 1998). Just as productivity in the pelagic ocean is not uniform, individuals are not distributed evenly, and clustering is the norm. Because of the lack of geological substrate, as in coastal regions, many pelagic species are highly mobile as individuals or populations. In this review, we focus on examples from species living in the upper 200 m, which is also known as the euphotic zone. Animals need to eat to survive, and in mobile pelagic ecosystems this means finding prey. However, the average concentration of resources in the world’s oceans is insufficient for growth and survival of a variety of marine species, ranging from planktonic larvae to top predators (Steele, 1980; Levin, 1992; Genin et al., 2005). Therefore, their survival depends on encountering dense patches of prey that, in the case of zooplankton, form aggregations that vary in size along a continuum of spatial scales from 107 to 101 m (Fig. 4.1) (Haury et al., 1978; Mackas et al., 1985, Nicol, 2006). Steele’s (1980) analysis showed that the patchiness resulting from aggregation increases with trophic level (Fig. 4.2). This seems to be a consequence of the fact that the higher the trophic level, the less the response to the detailed structure of the local environment, and a greater ability to use largescale ocean features such as currents or fronts. The higher the trophic level, the less are the organisms dependent on short-term events such as storms, which markedly affect phytoplankton production, and active behaviour plays a more dominant role in generating patchiness. This prey aggregation, in turn, aggregates their predators at the same locations. But why is phytoplankton, the base of the food chain, patchy? The main limitations on primary production are physical and chemical (i.e. light and nutrient concentrations). Variations in the distribution of light and nutrients occur both temporally and spatially in the ocean. The higher the trophic level, the lower the physical environment plays in driving spatial variability of standing stock, and the more behavioural processes assume importance (Steele, 1980; Folt and Burns, 1999). A challenge for the predators then is first to locate these patchy prey aggregations and to remain 164 David A. Ritz et al. Figure 4.1 The Stommel diagram, overlain to show the scales that can be sampled with various platforms, and features such as fronts. From Kaiser et al. (2005), with permission from Oxford University Press. within them until it is no longer profitable to continue feeding. Arearestricted search patterns for food are widespread phenomena among pelagic predators from copepods to whales indicating that many predators are adapted to find and exploit aggregated prey (Steele, 1980; Leising and Franks, 2000; Leising, 2001; De Robertis, 2002). While aggregation is ubiquitous at all scales in pelagic ecosystems, it is not simply a passive process where individuals gather together to exploit a food source and separate once the food has been eaten. The numerous additional benefits of group living ensure that groups of many different species remain cohesive for non-feeding periods though membership may change. These benefits are usually reported as protection from predators, facilitation of foraging and feeding, access to centralized information, energy saving and facilitation of mate finding and reproduction (Wilson, 1975; Ritz, 1994; Hamner and Parrish, 1997; Heppner, 1997; Krause and Ruxton, 2002). Persistent animal aggregation has been called a central problem in ecological and evolutionary theory (Levin, 1997; Flierl et al., 1999) because of the apparently conflicting requirements of short-term selfishness and longerterm group benefits. It may be that the study of the ‘social histories of genetic aggregations and organelle symbioses’ can resolve this dilemma (Frank, 2007). We contribute to the analysis of social aggregation by Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 165 Figure 4.2 Patchiness resulting from aggregation increases with trophic level. Modified from Steele (1980). reviewing the social behaviour of invertebrates and fish living in the upper 200 m of the pelagic environment, but where appropriate, we use examples from marine birds and mammals. This review builds on Ritz (1994), and thus we restricted the present review to post-1994 discoveries except where reference to earlier papers is necessary for clarity or because of previous omission. Because the scope of this review has been expanded to include fish and, where appropriate, other vertebrates, relevant pre-1994 papers are also included for these groups. We explore general aggregation principles (Section 2), describe a range of new technologies and provide examples of the insights gained from their use (Section 3), and from theoretical modelling (Section 4). In Section 5 we discuss the relationship between social aggregation and ocean management and speculate on the possible impact of climate change. Since this review complements Ritz (1994), we also examine whether the post-1994 literature on the subject of social aggregation indicates if the earlier review stimulated research in directions identified as being particularly worthy of further study. We did this by using search terms associated with the previously identified gaps for the subsequent period. We conclude with areas ripe for further research to advance understanding of social aggregation (Section 6). We note that review papers offer an opportunity for synthesis, comparison, gap analysis and identification of new areas for attention. Explicit guidelines to achieve these objectives in a repeatable and transparent fashion have been codified for medical reviews by Roberts et al. (2006), who also note that ecological reviews often fail to measure up to these criteria. Many of these criteria helped to shape this review, but in particular, identification of the sources of evidence and how they were obtained allows assessment as to whether the material included is likely to be comprehensive with respect to a topic of interest. Depending on the 166 David A. Ritz et al. presentation of this material, this allows repeatability in future. We performed a comprehensive search for relevant material using several search engines: ISI Web of Science, Google Scholar, Science Daily using the following terms: Social aggregation in pelagic environments; group dynamics; three-dimensional analysis of pelagic aggregations; modelling pelagic aggregations; pelagic aggregations and ocean management; pelagic aggregations and climate change, and the contractions of these words. We did not to restrict our literature search to specific journals, as we were concerned we might miss important insights and contributions in journals covering alternative disciplines. Additional materials were obtained from reference lists in papers located using our search procedure, our personal reference collections, and from discussion with expert colleagues. In this way we accessed relevant breakthroughs in the study of social insects and humans. Grey literature is difficult to access with traditional search tools (e.g. Biological Abstracts), but increasing use of the Internet allows searching using the same keywords for posted grey literature. 2. Aggregation Principles and Features in Pelagic Ecosystems Before concentrating on social aggregation, some general points about aggregation are relevant. For example, the importance of aggregation for energy transfer is often ignored. This energy transfer can be trophic, or spatial, connecting habitats and allowing biological processes to be enhanced in ‘non-productive’ areas. Hydrodynamic patterns can concentrate resources (Alldredge and Hamner, 1980) while migrating animals cause cross-habitat redistribution of carbon and nutrients (Young et al., 1996). Furthermore it has been shown that schooling animals, by their swimming actions, are an important source of fine-scale turbulence in the ocean (Huntley and Zhou, 2004). They found that estimated rates of kinetic energy production by animal schools are all of the same order, i.e. 1025 W kg 21, irrespective of size (see Table 4.1). Based on these data it appears that animal-induced turbulence is comparable in magnitude to energy dissipation resulting from major storms. In fact, according to Dewar et al. (2006), the biosphere generates enough power to stir the ocean. More recent work by Katija and Dabiri (2009) shows that such fine-scale turbulence is primarily dissipated as heat. These authors highlight an alternative mechanism of mixing originally suggested by Darwin (1953), which depends on animal shape and ‘drift volume’, i.e. the volume of fluid that migrates with the animal as it swims. Importantly, the drift volume of adjacent animals in an aggregation may increase the effective size of their combined boundary layers, enhancing the possibility of vertical mixing. Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 167 Table 4.1 Kinetic energy production (Ep(W kg 21)) by a range of schooling species Species η Mass (kg) Abundance Speed (no. m 23) (m s 21) Euphausia superba Engraulis japonicus Engraulis mordax Sardinops saqax Clupea harengus Pollachius virens Thunnus albacares Tursiops truncatus Thunnus thynnus Orcin us orca Physeter macrocephalus 0.0002 0.002 0.010 0.033 0.30 2.30 77 21.5 318 1645 19850 30000 1294 115 29.4 4.7 0.25 0.0035 0.0010 6.5 3 1024 8.9 3 1025 4.5 3 1026 0.05 0.09 0.14 0.19 0.35 1.05b 1.59 3.35c 1.30 3.95c 2.08d 0.11a 0.22 0.24 0.26 0.30 0.39 0.44 0.85c 0.48 0.87c 0.83e Ep (W kg 21) 2.6431025 1.4131025 1.0631025 1.3131025 3.9031025 2.7031025 4.4931025 2.803l025 5.3631025 4.0331025 6.7731025 a from Torres (1984). average swimming speed of free-swimming saithe schools (Pedersen, 2001). c direct measurement of cruising speed and propulsive efficiency (Fish, 1998). d from Rice (1989). e approximated from measurements on the white whale Delphinapterous leucas (Fish, 1998). Reproduced with permission from Huntley and Zhou (2004). b The disadvantage of group living includes predator attraction, local depletion of food resources, competition for food and spread of disease (Parrish and Edelstein-Keshet, 1999; Hoare and Krause, 2003) and the trade-offs have been examined using a range of evolutionary models. These studies often advocate greater integration between empirical work, theoretical and modelling approaches (see Parrish and Edelstein-Keshet, 1999). Aggregations in the pelagic ecosystem may occur as a result of several processes: 1. Passive aggregation including the concentrating effects of circulation such as fronts from river plumes, Langmuir circulation and internal waves (Flierl et al., 1999; Banas et al., 2004), and over abrupt topographies, such as the shelf break and seamounts (Boehlert and Genin, 1987), and coral reefs (Genin et al., 1988, 1994). 2. Active and non-social aggregation including independent attraction of conspecific individuals to a food resource (e.g. copepods, Poulet and Ouellet, 1982); or to a light source (Yen and Bundock, 1997); predators may gather at the same natural features (Klimley et al., 2003; Hobday and Campbell, 2009) as well as artificial structures such as fish aggregation devices (FADs) (Freon and Dagorn, 2000). 3. Active and social aggregation that includes groups that ‘self-organize’ and maintain cohesion because of the many derived benefits (Ritz, 1994; Krause and Ruxton, 2002). Parrish and Edelstein-Keshet (1999) 168 David A. Ritz et al. Figure 4.3 Examples of aggregations of invertebrates, fish and marine mammals. (A) Schooling krill, Nyctiphanes australis; (B) mysids, Paramesopodopsis rufa; (C) squid, Sepioteuthis sepiodea; (D) school of Real Bastard Trumpeter, Mendosoma lineatum; (E) school of northern bluefin tuna, Thunnus thynnus; (F) pod of dolphins, Tursiops truncatus. (A) Photo by Rudi Kuiter; (B) photo by Jon Bryan; (C) photo by Ruth Byrne; (D) photo by Ron Mawbey; (E) photo by Bill Pearcy; (F) photo by Simon Talbot. define social animal aggregations as those that ‘self-organize’ as opposed to aggregations that form in response to external cues e.g. light or food. It is this active social aggregation that is the focus of this review. This subset of aggregation is sometimes termed congregation (Turchin, 1997) and occurs in a range of invertebrates and vertebrates as shown in Fig. 4.3. Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 169 Ritz (1994) presented many examples of aggregations of pelagic invertebrates (his Table 1) with their spatial and temporal attributes. Figure 4.4 shows the spatial and temporal scales of aggregation of Antarctic krill (Euphausia superba) and illustrates the range of descriptive terms applied to groups of this species (see also Box 4.1). Temporal scale Spatial scale Concentration Mths Patches Wks Hrs Macro >1000 km Super-swarms ρ : few 1000 g m–3 t:100-250mm l: up to several km Swarms ρ : few 10 to several 100gm–3 t: 1-20 m l: several 10s m Cohesive aggregations Meso 10– 1000 km Layers and Scattered forms ρ :10gm–3 (approx): t: large l: many km Irregular forms ρ : few 100s g m–3 t: 10 cm Dispersed aggregations Non-aggregate forms ρ : <0.1 g m–3 Micro <10 km Non-aggregated forms Figure 4.4 Nomenclature of aggregations of Antarctic krill, Euphausia superba. ρ 5 density; t 5 thickness; l 5 length. Reproduced with permission from Miller and Hampton (1989). Box 4.1 Terms used to define aquatic invertebrate groups (after Ritz, 1994, and Folt and Burns, 1999) and social groups of aquatic vertebrates (after Pitcher and Parrish, 1993; Shane et al., 1986) as used in this review. Swarm: used here to mean a discrete integrated social group with members evenly spaced, but not polarized (aligned in the same direction). School: discrete integrated social group in which members are polarized and displaying synchrony of movement. A school need not always imply that all individuals are facing the same direction; social squid can swim both backwards and forward. Shoal: a (usually) larger grouping within which are contained subgroups conforming to the definitions of swarm and school. Pod (primary group): term confined to smallest social groupings of cetaceans that remain intact for days or weeks. Herd (secondary group): temporary (minutes or hours) aggregations of primary groups of cetaceans. 170 David A. Ritz et al. In the rest of this review we will concentrate on social aggregation. Sociality means living in groups which, in turn, creates and intensifies two opposing forces: on the one hand cooperation with conspecific neighbours, and on the other competition for local resources (Frank, 2007). The former can increase efficiency and aid in competition with other groups; the latter may promote conflict. This antagonism between cooperation and competition is a recurring theme in studies of social aggregation. The title of Frank’s (2007) paper, ‘All of life is social’, reflects the fact that multicellularity arose through genetic aggregations and cellular symbioses. But what factors make social aggregation, as exemplified by fish or krill schools, such a conspicuous feature of the pelagic zone? Before addressing this question we consider how sociality may have arisen. 2.1. Origins of sociality Aggregation occurs in all phyla but where does sociality begin? In one sense ‘all of life is social’ (Frank, 2007), in that multicellularity owes its existence to amalgamation and symbiotic cooperation of single-celled organisms. According to the argument outlined below, animals (or cells) must be able to recognize conspecifics to the extent that stable networks develop. How do animals recognize each other? Recognition of self versus non-self must have arisen early in the evolution of multicellularity, perhaps to protect these cellular aggregations against invasion by competing neighbours (Frank, 2007). There is evidence that olfactory cues discriminated by the major histocompatibility complex are important in vertebrates (Krause and Ruxton, 2002; Villinger and Waldman, 2008). It has been reported recently that there is an analogous system in invertebrates (Cadavid et al., 2004). Recent work on social insects has led to the postulation that development of complex societies arose initially through natural group dynamics, i.e. not a genetic selection for particular traits favouring social behaviour but a tendency towards network development within groups (Fewell, 2003). Relatively simple connections between individuals in a group can create patterns of behaviour of increasing complexity in the same way as simple decision rules create complex behaviour in computer-generated aggregations. If true, this suggests that networks are an essential precursor to social behaviour. It has been suggested that the repeated co-occurrence of stable pairs may have been an important prerequisite for the evolution of cooperative behaviour and reciprocal altruism (Milinski, 1987; Croft et al., 2005). Relatively simple connections between individuals in a group can create patterns of behaviour of increasing complexity. Organized societies occurring within many different taxa may have arisen through the agency of these simple local interactions self-organizing into global networks (Glance and Huberman, 1994; Fewell, 2003). It has long been recognized Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 171 that inclusive fitness operates within groups whose members share genes (Hamilton, 1964). However, it is now suggested that multilevel selection operates not just because members of groups are related but that densely connected networks exist within aggregations (Fewell, 2003). This permits rapid and efficient information transfer and flexible responses. It is important to explain that multilevel selection does not imply that some groups are more successful (fitter) than others and contribute more groups to the next generation. Instead group-level selection implies that the fittest groups contribute the most individuals to the next generation. Behavioural traits possessed by individuals need to influence the behaviour of others in the group to be relevant to group selection (Krause and Ruxton, 2002). Behavioural characteristics leading to social networks are not necessarily restricted to vertebrates. According to Webster and Fiorito (2001), socially guided behaviour, conforming to a framework developed for social vertebrates, can be found in a wide range of non-insect invertebrate phyla. However, among marine taxa, only Crustacea, Gastropoda and Cephalopoda displayed behaviour typical of social learning, i.e. the acquisition of novel behaviour due to observation of, or interaction with, a conspecific. This might indicate more sophisticated social behaviour in these groups compared to ‘lower’ invertebrate animals. 2.2. Significance and benefits of social aggregation The commonly stated benefits of group living are facilitation of foraging and feeding; reproductive facilitation (including sharing parental care in the case of some cetaceans), protection from predators and energy saving (Wilson, 1975; Ritz, 2000; Krause and Ruxton, 2002). Other authors add maintenance of position in the environment (Clutter, 1969), habitat defence (Hurley, 1977) and access to centralized information (Parrish et al., 2002). Benefits of synchronous breeding and release of young within aggregations of mysids are described by Johnston and Ritz (2001). The benefits listed above are not divorced from one another; in fact an ultimate advantage of aggregation may be energy saving in the broadest sense, i.e. efficiency in foraging, food capture, locomotion, protection from predators, etc. It is likely that any adaptation that conserves energy will be favored by selection and fixed into the genetic blueprint (Ritz and Swadling, 2006). Thus if energy can be conserved at the same time as efficient food gathering and escape from predation, this will ultimately be advantageous for the species. Cohen and Ritz (2003) found that mysids in small uncohesive groups were more likely to expend energy (tailflips) when exposed to a threat in the form of a fish kairomone than those in swarms. A kairomone is a chemical substance released externally that benefits the recipient without benefitting the emitter. The energy saving 172 David A. Ritz et al. benefits of swarm membership by mysids were confirmed by Ritz et al. (2001) who found that larger swarms expended less energy than smaller ones, which, in turn, saved more energy than un-aggregated individuals (Ritz, 2000). The conversion of these energy savings into enhanced fitness is assumed to follow, but empirical evidence is lacking to date. The foregoing discussion raises some important questions, e.g. do different forces promoting patchiness and social aggregation dominate at different scales (Flierl et al., 1999)? Patterns apparent at large scale are not independent of those at small scale and vice versa. Large populations of individuals can self-organize into pattern-generating aggregations (Parrish and Edelstein-Keshet, 1999; Parrish et al., 2002; Viscido et al., 2004). Many of the patterns seen in real-life schools and swarms are apparent in computer generated models based on a few simple rules (see Boids: http:// www.red3d.com/cwr/boids/ and Efloys: http://arieldolan.com/ofiles/ eFloys.html). An unresolved question is ‘are all of these emergent patterns evolutionarily advantageous?’ Can we reconcile the short-term selfishness of individuals with the maximum group benefit of maintaining a cohesive unit? There is potential for great complexity of trade-offs and constant tension determining decision-making. Examples can be found among krill in the decision to migrate vertically, and whether it is advantageous to school while doing so (De Robertis, 2002; Burrows and Tarling, 2004). Silversides (Menidia menidia) changed their schooling behaviour according to light levels during periods of twilight, which appeared to be associated with predation threat and availability of food (Major, 1977). Vertically migrating mesopelagic fish may apparently elect to form schools when light levels at night are high enough to favour visual predators but not otherwise (Kaartvedt et al., 1996; see Fig. 4.5 reproduced from their paper). This suggests there are some disadvantages to schooling, e.g. increased visibility to and attraction of the attention of predators; also decreased per capita share of food resources. Active and social groups are those that ‘self-organize’ (characteristic group patterns that arise from decentralized behaviour), and maintain cohesion because the derived benefits outweigh the costs (Ritz, 1994; Krause and Ruxton, 2002). As defined in Box 4.1, social aggregations include swarms, schools, shoals, pods and herds. Regardless of the terminology, social aggregations can be recognized by their coordinated movement, persistence in time, reactions of individuals to the group and by the fitness benefits provided by mutual attraction. Animal aggregations occur where large numbers of individuals are to be found gathered in close spatial and temporal proximity. Examples of aggregations can be found in virtually all animal phyla (Parrish and Edelstein-Keshet, 1999; Parrish et al., 2002). Such aggregations may consist entirely of conspecifics; however, there are also many examples of multispecies aggregations, e.g. in fish (Allan, 1986), and crustaceans Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 173 Figure 4.5 Echogram showing abrupt changes in vertical distribution of krill, planktivorous (Norway Pout) and piscivorous (Pearlside) fish in response to changes in light penetration. Reproduced from Kaartvedt et al. (1996). (Ohtsuka et al., 1995), and even mixtures of the two taxa (McFarland and Kotchian, 1982). Aggregations may form in response simply to the distribution of resources in the habitat, but the term ‘social aggregation’ refers specifically to groups of individuals that are brought together, either wholly or in part, by social attraction. Social aggregations are an extremely common phenomenon with considerable ecological and economic importance. For example, well over half the world’s fishes, including the overwhelming majority of the commercially harvested species, form social aggregations at some stage during their lives (Shaw, 1978). Theoretically, a social aggregation could consist at a minimum of two individuals (e.g. spawning cuttlefish); however, most marine social aggregations are substantially larger than this, indeed some may often encompass billions of individuals (e.g. krill). The scale of such aggregations can be dramatic DeBlois and Rose (1996) reported shoals of cod (Gadus morhua) of over 10 km in length, migrating groups of mullet (Liza aurata and L. saliens) stretch for over 100 km (Probatov, 1953), while Radakov (1973) estimated the volume of some Atlantic herring (Clupea harengus) shoals at up to 5 km3. The concept of the optimal group size has been the subject of considerable theoretical debate (see Sibly, 1983; 174 David A. Ritz et al. Giraldeau and Gillis, 1985; Giraldeau and Caraco, 2000); however, few empirical data exist to test theoretical predictions (Willis, 2008). In marine environments, many of the larger aggregations that may be observed are so-called free-entry systems, where group members have little or no ability (and indeed little incentive) to control group membership. In these cases, group sizes are highly dynamic and groups frequently split and reform according to the context, a property which has led to them being described as ‘fission fusion’ societies (Couzin, 2006). ‘Restricted-entry’ groups are far less common in the marine environment, and the examples that exist are all of vertebrates, perhaps most notably the small aggregations of coral reef fishes (Sale, 1971; Forrester, 1991; Whiteman and Cote, 2004) and social groups of cetaceans (Gowans et al., 2001; Lusseau 2003; Hartman et al., 2008). There exists considerable variation, both within and between species, in sociality. Some authors draw a distinction between facultative and obligate sociality; however, such distinctions are somewhat arbitrary since the extent to which any individual manifests social attraction is likely to vary with ontogenetic stage and with context (Ritz, 1994). Nonetheless, some animals exhibit considerable stress if separated from conspecifics: Atlantic herring that have been experimentally isolated from conspecifics have been reported to die as a result (Gerasimov, 1962). While this is an extreme example, many social species do manifest stress-related changes in behaviour and/or physiology if isolated. For example, Ritz et al. (2003) showed that heart rate of an individual Antarctic krill was high when isolated but slowed significantly when it was tethered at normal schooling distance from a conspecific and was presumably able to access social cues. The extent to which marine animals form social aggregations may also be highly dependent upon the environment. For example, in heterogeneous environments, shoal cohesion often decreases (Mochek, 1987). Many fish, amongst them cod (Gadus morhua), sergeant majors (Abudefduf saxatilis) and grey snappers (Lutjanus griseus), exhibit shoaling behaviour when in mid-water, but the shoals break up towards the bottom of the water column or when in nearshore areas (Pavlov and Kasumyan, 2000). Furthermore, shoals of fish characteristically break up at night as light intensity decreases (Higgs and Fuiman, 1996). While some species consistently form aggregations throughout their lives, many others are more social during some stages of their life history than others. The larvae of many pelagic fish typically do not begin to shoal until metamorphosis (Fuiman and Magurran, 1994). The importance of the development of the central nervous system in relation to schooling behaviour in larval and juvenile striped jack (Pseudocaranx dentex) was highlighted by Masuda and Tsukamoto (1999), who reported the emergence of mutual social attraction among individuals at around 12 mm in length. Interestingly, Antarctic krill first begin to show social attraction at Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 175 around the same length (Hamner et al., 1989), even though final size is very different. The expression of strong social attraction towards conspecifics during the juvenile phase is a common pattern in fishes before becoming increasingly solitary as they grow (Pavlov and Kasumyan, 2000). Indeed, many marine species form aggregations during vulnerable early life stages, since grouping behaviour is often suggested to confer valuable antipredator benefits upon group members (Ritz, 1994). The opposite trend is manifest in spiny lobsters where juveniles are typically solitary and adults very often live in groups (Ratchford and Eggleston, 1998). Butler et al. (1999) demonstrated that attraction to conspecific chemical cues in spiny lobsters only occurs once individuals reach a given size. The larvae (and indeed the adults) of many marine species aggregate as plankton; however, it is arguable whether this is to any great extent due to social attraction. Banas et al. (2004) consider that the interaction between individuals in many zooplankton swarms (as opposed to schools) is of secondary importance. For example, Leising and Yen (1997) contend that density of copepod swarms in their experiments was controlled by avoidance reactions to chance close-range encounters. The same authors found five species of copepod to be insensitive to proximity of conspecifics except at very close range. Extrapolating this view, in their modelling studies, Banas et al. (2004) regard a swarm not as an interaction between individuals, but between each animal and its local stimulus field. Of course, this interpretation does not necessarily reduce the importance of social attraction. Swarms can be social and density-dependent or nonsocial and density-independent. Most modelling studies of swarming/ schooling behaviour are based on a resolution of forces of attraction, repulsion and alignment (Couzin, 2006). The degree to which individuals respond to each other and over what distance is probably a function of sensory capability but also the need to maintain a hydrodynamic ‘territory’ (or flow field) that is an essential part of the feeding current, and by distortion of which an individual gains information about approaches by other animals. Social aggregation in general is less well studied in pelagic invertebrates, despite their amenable sizes for experimental laboratory work. The label ‘plankton’, with its connotations of passivity, has probably seriously hindered the study of social behaviour of ‘true’ zooplankton and micronekton (Ritz, 1994). The term plankton is not a taxonomic unit and encompasses a huge diversity of species and forms. It seems likely that a spectrum of capacity to manifest social behaviour exists in which most copepods would occupy one end, and live in swarms, with little social interaction between individuals except to ensure that empty space around themselves is maintained. On the other end, mysids and euphausiids exhibit a full range of social interaction with neighbours. Unfortunately, many authors still group the larger more active constituents, e.g. krill and 176 David A. Ritz et al. mysids, with the smaller ones, e.g. up to the size of most copepods, and ascribe little in the way of social interaction. Alldredge et al. (1984) claimed that zooplankton swarms with a nearest neighbour distance (NND) of more than a few body lengths were rare in nature. Later results (Jiang et al., 2002) explain why this is true for copepods. Larger interindividual distances of 7 14 body lengths ensure that hydrodynamic interactions between neighbours are minimized so that feeding currents and detection of nearby individuals are not compromised. Jiang et al. (2002) showed that copepods gain no energetic benefits when in close proximity to conspecifics. In contrast, O’Brien (1989) showed that mysid and euphausiid NNDs were on average 1 2 body lengths, and energetic benefits of swimming close to neighbours were demonstrated by Ritz (2000) and Patria and Wiese (2004), and communication benefits by Wiese (1996). The characteristics of aggregations of zooplankton are sometimes suggested to vary considerably between different species (Banas et al., 2004). Despite these assertions, there exist many similarities between the aggregation behaviour of many different marine species. For example, Hamner (1985) and O’Brien and Ritz (1988) describe behaviour of swarms and schools of krill and mysids as being strongly reminiscent of fish schools. Escape manoeuvres of groups of the two taxa require a high degree of synchrony between individuals, probably requiring a combination of vision, chemoreception and mechanoreception. Furthermore, the possibility of any clear distinction between the aggregation behaviour of copepods and that of mysids and krill seems unlikely since even copepods (Labidocera pavo) can be found forming schools (Omori and Hamner, 1982) which surely requires some inter-individual coordination. Differences in NNDs between aggregations of copepods and krill/mysids may be due to the different feeding methods, lack of any energetic benefit in close alignment in the former and/or the differences in the reliance on vision. The eyes of most copepods and other non-Malacostracan crustacean zooplankters do not have lenses and do not have an image-forming capacity (Eloffson, 1966). However, it is perhaps significant to note that Labidocera have ‘remarkable eyes’ (Omori and Hamner, 1982) that have a dorsal pair of spherical lenses serving a single mobile eyecup (Land, 1988). Land suggests that scanning movements of the rhabdoms in the eyecup are concerned with visual detection of conspecifics. 2.3. Structure and functions of social aggregations From an evolutionary point of view, it is predicted that for aggregations to persist, the benefits of group membership must, on average, outweigh the costs. The benefits and costs of grouping generally are dealt with extensively in other texts (Krause and Ruxton, 2002) and so here we Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 177 provide only limited pelagic examples to illustrate the commonality with non-pelagic taxa and to emphasize social benefits and costs. According to Flierl et al. (1999), many groups in marine pelagic systems, with the exception of marine mammals, seem to be large, relatively transient and loosely knit. This is not necessarily the case for fish schools or schools of mysids and euphausiids, where the aggregation can be longlived even if membership changes over time (O’Brien, 1988; Parrish and Edelstein-Keshet, 1999). Social grouping implies some appreciation by individuals of their membership of the group and its consequences. In other words, individuals respond to conspecifics in a density-dependent manner (Flierl et al., 1999; Burrows and Tarling, 2004; Hensor et al., 2005; Grunbaum, 2006). A group formed initially by random encounters may grow by density-dependent interactions between members with eventual size being determined by mean payoff to individuals in the group (Parrish and Edelstein-Keshet, 1999). An example might be in food acquisition whereby all group members are disadvantaged if the group outgrows the food resource and individual rations decrease. Social organization then is an emergent property (group characteristic arising from decentralized interactions) that develops from the network structure (see below). Research on the influence of the social group on metabolic processes including growth, feeding efficiency and energy use has been neglected (Ritz, 2002). Ritz (2000) predicted that growth rate in schools or swarms of krill and mysids was likely to be much greater than in isolated individuals or small groups; however, it was some years before evidence was forthcoming. Atkinson et al. (2006) showed that when growth of freshly caught krill is recorded by the instantaneous rate method, a technique that allows insight into the recent social behaviour, the maximum values were much higher than most of the previously published rates. These high rates are likely due to the benefits of feeding within a school immediately before capture. Analysis by Ritz (1997) showed that food capture rate by mysid swarms of different sizes varied significantly when per capita food availability was constant and inhomogeneous. Furthermore Ritz (2000) noted that ingestion rates of Antarctic krill measured in freshly caught animals by faecal egestion were 3 times higher than those fed in laboratory tanks (Pakhomov et al., 1997). He suggested that the difference was due to the former having fed within aggregations immediately before capture, whereas the krill in laboratory tanks rarely form aggregations (Kawaguchi et al., 2010). It appears that krill (and mysids) not only save energy in social groups but may also feed more efficiently (see also Ritz, 1997). The latter is a well-established principle in fish. For example, fish are known to forage more successfully on spatially variable food patches when searching in social groups (Ryer and Olla, 1992). The issue of food competition in aggregations may be more acute for vertebrates than for 178 David A. Ritz et al. Figure 4.6 (A) Time series of body outlines of trout superimposed on vorticity and velocity vector plots of the wake produced by a cylinder located in the flow (left to right). (B) Midlines for seven consecutive tailbeats. (C) Phase between body and vortices where 180 represents slaloming in between vortices and 0 and 360 represent vortex interception. After Liao et al. (2003a,b). invertebrates. Ritz (2000) made the point that fish in schools in the sea are more likely to be food limited than particle-feeding coastal mysids. Many animals that aggregate socially have been demonstrated to save energy while aggregated. Birds (pelicans) in V-formation showed a significant decrease in heart rate and the energy saving may have been because they were able to spend more time gliding and less flapping (Weimerskirch et al., 2001). Fish (trout) have been shown to slalom between experimentally generated vortices (Kármán gait) using only their anterior axial muscles. By synchronizing their body kinematics in this way, they may use very little energy and gain a hydrodynamic advantage beyond that gained by simple drafting (Fig. 4.6). Thus it is possible that any favourable hydrodynamic consequences generated by the aggregation itself or its interaction with surfaces (ground effect) could be exploited to save energy. 2.3.1. Patchiness in zooplankton Many authors have documented the uneven or patchy distribution of zooplankton in marine and freshwater (Hardy and Gunther, 1935; Steele and Henderson, 1981; Folt and Burns, 1999). It has taken several decades for the contribution of plankton behaviour in generating this patchiness Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 179 to be fully appreciated (Ritz, 1994; Folt and Burns, 1999). These authors describe general principles of invertebrate aggregations, and both papers highlight the importance of biological factors in creating and maintaining planktonic aggregations. By contrast, Siegel and Kalinowski (1994) provide Table 4.2 listing suggested causes of aggregation by Antarctic krill. It is particularly noteworthy for how few authors credit behaviour as a primary contributor to aggregation. Nonetheless, the conclusion that social behaviour is the main driver of variability in small-scale density distribution of Antarctic krill is strongly supported by a comparison of variance spectra of phytoplankton, temperature and krill (Weber et al., 1986; Verdy and Flierl, 2009). At small scales, the krill spectrum is flatter than the others indicating that factors other than environmental ones are generating the observed patchiness in density distribution (Fig. 4.7). Folt and Burns (1999) list four behavioural mechanisms that can result in zooplankton patchiness: (1) diel vertical migration, (2) predator avoidance, (3) food finding and (4) mating behaviour. Genin (2004) gives five further mechanisms contributing to patchiness by which plankton, micronekton and fish can become aggregated above abrupt topographic features, all driven by (1) ocean currents where long residence upwelled water enriches primary production which propagates up the food web; (2) daily accumulations where topography blocks morning descent of zooplankton, e.g. over seamounts; (3) behavioural response of zooplankton to upwelling currents; (4) behavioural response to downwelling currents; (5) enhanced population growth by residents due to current amplification driven by abrupt topographies. These mechanisms do not necessarily imply any social interaction between individuals but may provide opportunities for closer attraction. Hamner (1988) observed that almost any animal behaviour can generate patchiness, but more sophisticated behaviour is required for social aggregation. Recent work by Genin et al. (2005), using sophisticated multibeam acoustic equipment, has demonstrated that zooplankters,5 mm actively swim against upwelling and downwelling currents in an effort to maintain depth. In this way, they aggregate at fronts. The value of maintaining depth in this way is not yet clear, but may serve to keep them within food-rich zones and prevent them straying into less favourable depths. Any behaviour that actively or passively leads to individual distributions becoming clumped could result in a tendency to remain in a group once the many benefits are manifested. Swimming against currents of up to 1 cm s 21 at rates of .10 body lengths s 21 (Genin et al., 2005) is energetically expensive but might be less so if the individuals formed aggregations. Ritz (2000) and Ritz unpublished observation have shown that mysid swarms expend between 3 and 7 times less energy than small groups of individuals swimming uncohesively. A possible explanation is that swimming action by 180 David A. Ritz et al. Table 4.2 Suggested causes of aggregation in Antarctic krill, Euphausia superba Author Main causes and environmental limits Region Marr (1962) Inflow of the current branches from the Weddell Sea Presence of system currents flowing in opposite direction Occurrence of eddies South Georgia No elements of environmental limits directly influence distribution Environmental parameters and social behaviour Oxygen limits West Atlantic Maslennikov (1972) Wolnomicjski et al. (1978) Rukusa-Suszczewski (1978) Mauchline (1980) Kils (1979) Wilek et al. (1981) Stein and RakusaSuszczewski (1984) Kalinowski and Witek (1985b); Witek et al. (1988) Hydrodynamic forces, thermocline No physical and chemical variability Phytoplankton (2 20 km spatial scale) Kalinowski and Witek Presence of daylight for patch (1985a) formation Loeb and Shulenberger Temperature (infusion of cold (1987) water) and wind direction Everson and Murphy Hydrodynamic processes (passive (1987) current-borne movement) El-Sayed (1988) Phytoplankton at scales 2 20 km Priddle et al. (1988) South Georgia Theoretical consideration Laboratory experiments West Atlantic No elements of environmental parameters T, S, O2, nutrients, phytoplankton Bransfield Topography of bottom, which Strait influences direction of water masses and hydrodynamic process Hydrodynamic processes and social West Atlantic behaviour Hampton (1985) Weber and El-Sayed (1985) Weber et al. (1986) Murphy et al. (1988) South Georgia Environmental phenomena and food availability Environmental phenomena and active reaction for disadvantageous environmental conditions Indian sector Indian sector West Atlantic Elephant Island King George Island Southern Ocean Southern Ocean South Georgia Bransfield Strait (continued) Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 181 Table 4.2 (continued ) Author Main causes and environmental limits Region Maslennikov and Solyankin (1988) Makarov et al. (1988) Variability of hydrological conditions Stable eddies and influence of water masses West Atlantic West Atlantic Reproduced with permission from Siegel and Kalinowski (1994). Figure 4.7 Mean spectral plots for krill, fluorescence and temperature. . After Weber et al. (1986); http://plankt.oxfordjournals.org/content/14/10/1397.full.pdf cohesive groups generates favourable currents that could be exploited by members to reduce the cost of forward propulsion and/or to minimize the rate of sinking by relatively dense crustaceans. Patria and Wiese (2004) showed that swimming krill (Meganyctiphanes norvegica) generated vortex 182 David A. Ritz et al. rings behind the tail that could be exploited by following krill for this purpose. Note that this benefit would only make sense if the krill were in school formation, i.e. individuals polarized and neighbours sufficiently close to perceive and take advantage of currents moving in the same direction as themselves. Ritz (2000) showed that energetic benefit accrued in mysid swarms, i.e individuals, was not polarized. In this case it may have been the powerful downdraft generated collectively by the group that induced an updraft at the margins of the swarm. This could have been used to counteract sinking. Buskey (1998) reported that mysids frequently changed vertical position in the school while maintaining their horizontal position in a current. Energetic benefit may derive from specific relative positions of individuals in groups (Parrish and Edelstein-Keshet, 1999; Svendsen et al., 2003). Thus it would be logical for individuals not to adopt fixed positions within the group but to continually make excursions in order to maximize whatever benefits accrued at any given moment (Ritz, 1997). 2.3.2. Diel vertical migration Diel vertical migration (DVM) is generally held to represent a trade-off between the functions of food gathering and avoiding predators (Kaartvedt et al., 1996). The typical pattern is a dawn descent into deeper, darker levels during the day and an ascent towards the surface beginning around dusk. Ritz (1994) was the first to suggest that since social aggregation serves the same purposes, species that form swarms or schools may find DVM redundant. This could account for the confusing and sometimes contradictory evidence for vertical migration in Antarctic krill (Miller and Hampton, 1989). It appears from work by De Robertis (2002) that there are situations in which the normally social euphausiid Euphausia pacifica performs DVM but does not form social aggregations, i.e. one is redundant in the presence of the other. De Robertis suggests that DVM may be favoured over social behaviour in open-water zooplankton because of well-developed vertical gradients in predation risk. However, Antarctic krill are strongly social in open-water situations and exhibit variable DVM (O’Brien, 1987; Daly and Macaulay, 1991). Further evidence is supplied by Kaartvedt et al. (1996). The DVM behaviour of fish and krill (mainly Thysanoessa inermis) varied according to the light conditions in upper shelf waters and the predation risk. In brief intervals at dawn and dusk, known as ‘anti-predation windows’, there is sufficient light for planktivorous fish to locate prey, but not enough to render these fish vulnerable to piscivores. ‘Anti-predation windows’ may occur at other times due to the passage of fronts bringing water of higher turbidity. At these times planktivorous Norway Pout may migrate vertically to forage on krill, whereas when the light penetration through overlying waters is greater, the planktivores remain in safer, deeper layers but out of contact with their prey. It is unclear whether the planktivores migrate in schools. Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 183 These examples serve to illustrate the great flexibility of behavioural strategies and the fitness benefits they may confer. 2.4. Association patterns within aggregations Association patterns within and between groups of marine animals are seldom entirely random. At a very basic level, multispecies aggregations are very often dominated by a small number of species (Krause et al., 1996; 2005), suggesting either species-level recognition and an association preference for conspecifics, or that this emerges passively from similarities in activity patterns within but not between species (Conradt and Roper, 2000). Beyond this, group membership may be structured by phenotype; fish shoals for example frequently assort by body length, species, colour and parasite load (Krause et al., 2000). The fact that invertebrate aggregations commonly consist of a narrow individual size range (Watkins et al., 1992) suggest that they too possess a certain level of cognitive ability permitting pairing with other morphologically similar individuals (Wilson and Dugatkin, 1997). Watkins and Murray (1998) reported that biological characteristics between adjacent swarms of Antarctic krill (Euphausia superba) vary in individual size, maturity stage, moult and feeding state. Young et al. (1994) show that Daphnia clones have differing swarm-forming tendencies and suggest that clone-mates can recognize each other probably by chemical cues. This appears to be a fruitful subject for further research. Any advantages of group fidelity may be partly related to the fact that individuals associate with others of similar phenotype. In addition to this, research has shown that individuals manifest social association preferences for some conspecifics over others, in the absence of any clear phenotypic differences (Milinski et al., 1990; Ward and Hart, 2003). ‘Familiarity’, as this subgrouping phenomenon is known, acts to enhance the benefits of shoaling, further reducing the per capita risk of predation (Chivers et al., 1995) and improving foraging performance (Ward and Hart, 2005). In consequence, it might be expected that this would act in concert with other mechanisms (Conradt and Roper, 2000) to stabilize association patterns over time. Yet the data gathered from field studies on this topic are equivocal; it appears that there is no general rule regarding shoal fidelity among free-ranging schooling fish. Evidence exists for shoal fidelity in three-spine sticklebacks, Gasterosteus aculeatus (Ward et al., 2002), and Klimley and Holloway (1999) reported the co-occurrence of individual yellowfin tuna in time and space. By contrast, banded killifish (Fundulus diaphanus) appeared to show no consistent shoal fidelity (Hoare et al., 2000) despite showing a strong tendency to assort with fish of similar phenotypic characteristics. Of the few other studies in which the movement of marked fish among and between shoals have been followed, Helfman (1984) found low shoal fidelity among yellow perch (Perca flavescens), and Hilborn (1991) 184 David A. Ritz et al. showed that schools of skipjack tuna (Katsuwonus pelamis) mixed rapidly and were not composed of the same individuals for more than a few weeks (see also Willis and Hobday, 2007). There are even fewer examples of experiments testing shoal fidelity in invertebrate aggregations, although Twining et al. (2000) demonstrated homing behaviour and shoal fidelity in a mysid (Mysidium gracile). On a larger scale, studies of migrating fish populations do suggest a tendency for stable association patterns to develop (McKinnell et al., 1997; Hay and McKinnell, 2002). McKinnell et al. (1997) reported that steelhead trout (Oncorhynchus mykiss) form long-term associations at sea throughout their migration. Furthermore, Hay and McKinnell’s (2002) remarkable study of the movements of more than half a million Pacific herring (Clupea pallasii) over a period of 14 years concluded that individuals formed stable temporal and spatial associations. Nonetheless, it would be difficult to conclude that familiarity alone is entirely responsible for the observed patterns. Group fidelity in fish migrations may be influenced by any of several different mechanisms, including kin- or population-specific recognition (Quinn and Tolson, 1986), activity synchronization (Conradt and Roper, 2001), population-specific migration traditions (Warner, 1988) or pheromonal attraction (Baker and Montgomery, 2001). Alternatively, because migrating fish tend to remain in large, temporally stable shoals, as opposed to the smaller and looser aggregations characteristic of many of the shallow water species studied by behavioural ecologists, patterns of association in migrating fish may be explained simply by long-term shoal cohesion. 2.5. Sensing the behaviour of neighbours The sensory dimensions of social aggregation include the sensory basis of group formation (about which little is known), and the behaviour of individuals, and hence the group, under different contexts such as feeding and predation threat. Understanding the sensory basis of behaviour is one of the first steps to understanding the underlying neural algorithms driving the behaviour in individuals and hence the ‘behaviour’ of the aggregation. The focus of this section will be to review the sensory basis of fish schooling, as one of the most important, and highly coordinated, examples of social aggregation. This discussion will then be extended to consider the similarities and differences between fish schooling, and invertebrate aggregations, and briefly the sensory communication within marine mammal aggregations. The sensory basis of fish behaviour has been reviewed in Montgomery and Carton (2008). Vision, lateral line and hearing are the strongest candidates for schooling coordination although olfaction may also play an important role. Olfaction is certainly implicated in schooling coordination for group spawning. Pheromones play a role in maturation timing, and Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 185 spawning synchrony by ensuring that male spawning readiness is linked to female maturation and spawning. Observations of spawning behaviour in the sparid (Pagrus auratus) also describe males following females prior to and during their vertical spawning run (JM, personal observation). However, for the more demanding expressions of schooling coordination, the high temporal and spatial resolution characteristics of visual and mechanosensory (lateral line and acoustic) senses are required. The sensory basis of schooling behaviour during feeding will first be considered, followed by the more challenging context of tight schooling coordination in response to predator threat. Both examples provide interesting case studies in the conflicting demands of mid-water camouflage and schooling communication and coordination. In most coral and temperate reef habitats, schools of plankton-feeding fish form over reefs. The reefs provide hydrodynamic conditions that can concentrate and transport plankton to the schools, and in some cases the reef also provides shelter from predators of the fish. This discussion concentrates on those species such as mackerel (Trachurus? sp.) that are often reef associated, but that depend more on schooling rather than reef shelter for predator defence (a parallel example exists for mysids; see Flynn and Ritz, 1999). These species also depend on the strategy of reflective camouflage to reduce their visibility to predators in open water (Denton, 1970, 1980; Johnsen and Sosik, 2003). Clearly camouflage and visual communication are conflicting requirements; visual communication signals must to some extent undermine effective camouflage. Reflective camouflage also only operates effectively within strict physical constraints, including fish orientation. When the camouflage is compromised by change in orientation, or a sudden turn, the resulting visual stimulus can provide a basis for visual communication. A mirror in the water column will be difficult to see, only if it is vertical and far enough away from the surface to be in a vertically symmetrical light field. Only under these conditions will the reflected light match the background space light and reduce the contrast between target and space light and hence the effective visual range of detection. The reflective camouflage of pelagic fishes operates on a similar principle (Denton, 1970, 1980). Reflective platelets in the skin and the scales of the fish are oriented parallel to the dorso-ventral axis of the fish so that when the fish is in its normal orientation they form an array of vertical mirrors. This reflective camouflage works best when the fish is horizontal in the anterio-posterior axis, with its dorsal surface pointing to the most intense downwelling light. Deviations from this position increase the target contrast against background space light for an observer (Dare, 2008). The relevance of these considerations for social aggregation is that the demands for effective foraging can over-ride camouflage and provide schooling conspecifics with visual signals that convey information on feeding opportunity and success. 186 David A. Ritz et al. Reflective camouflage is a strategy used by many larger mobile (hence muscular) fish, but zooplankton (including ichthyoplankton) typically employ an alternate strategy based on transparency. Transparent prey are by definition hard to see; however, sighting of transparent prey can be improved by viewing them just beyond angles in Snell’s window (Janssen, 1981). Snell’s window is a phenomenon by which an underwater viewer sees everything above the surface through a cone of light of width of about 96 . This phenomenon is caused by refraction of light entering water and is governed by Snell’s law. The area outside Snell’s window will either be completely dark or will show a reflection of underwater objects (Janssen, 1981). Tilting up by approximately 40 to achieve this forward sighting line improves feeding success, but necessarily increases the predator fish contrast against the background and hence visibility to conspecifics and predators. Moreover, the slight flaring of the gills and opening of the mouth associated with suction feeding also provides a clear visual signal, or light flash, to other school members conveying feeding success. In essence, by attending to visual contrast and flashes, members of a school can gain information on which parts of the school are feeding and how successfully. It is debatable as to whether this represents a signal in the sense of being purposely sent, but if one was looking for a benefit to the sender, the shift of the school towards favourable feeding areas may provide some ‘safety in numbers’ advantage for them. Finally, from a sensory perspective this behaviour is almost exclusively visually mediated. The polarization of the school facing into the current (positive rheotaxis) would likely be visually mediated, though maybe with a lateral line component (Montgomery et al., 1997), but the dynamics of schooling coordination in relation to targeting food patches would be exclusively mediated by visual signals. By contrast, the tight coordination of schools under predation threat requires more complex multimodal sensory communication. Under the threat of predation, observations and studies of complex schooling behaviour (Pitcher, 1993; Pitcher and Parrish, 1993) tend to evoke descriptions of the school as a ‘super organism’. Discrete ‘behaviours’ of the school are observable such as ‘splits, vacuoles and flash expansion’. The sensory basis of these ‘school behaviours’ is extremely hard to study, but is almost certainly due to the sensory detection of an attack by the fish on the ‘front line’ via the visual looming stimulus of the predator, and/or the associated pressure pulse of a lunging strike. The behaviour of the school will result from the way in which this information propagates into the school, both directly, and as a result of the avoidance response of the ‘front line’ fish. Thus, communication between neighbours is likely central to the schooling response to attack, but it is also key to understanding the schooling coordination under the threat of predation. For this latter case, there is good experimental evidence and theoretical reasoning to support the active involvement of both vision and Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 187 the octavolateralis sensory systems (lateral line and hearing) (Partridge and Pitcher, 1980; Gray and Denton, 1991; Montgomery et al., 1995; Faucher et al., 2010). The main sensory requirements for coordination relate to initiating and maintaining the close association of individuals, combined with highly effective collision avoidance. We know rather little about the sensory mechanisms underpinning initiation of tight schooling formations. The perception of threat by individual members of the school and the communication of that threat may simply be a function of an abrupt change in direction of those fish aware of the threat and a reduction in NND in that part of the group. An abrupt change in behaviour in one part of the school could propagate through the school visually, but it is also feasible that acoustic communication is involved. This could be both passive on the part of the signaller, resulting simply from the abrupt turn or acceleration (Gray and Denton, 1991; Denton and Gray, 1993), but could also include active acoustic communication. Some schooling fish such as mackerel and tuna produce sound through stridulation of the gill rakers; however, the behavioural context of this sound production is not known (Allen and Demer, 2003). Once initiated, the maintenance of close NND is mediated by both visual and lateral line stimuli. Partridge and Pitcher (1980) have shown that blinded fish can still swim in formation and Faucher et al. (2010) have shown that in some fish the lateral line may be necessary for tightly coordinated schooling behaviour. Liao (2007) also provides an excellent discussion of the theoretical rationale for a hydrodynamic basis to school structure. The prediction is that a fish located behind and in between two preceding members of the school can take advantage of the average reduced velocity associated with the thrust wakes of those ahead. In effect, fish in schools can benefit from flow refuging (exploiting regions of reduced flow) and vortex capture (harnessing the energy of environmental vortices). Direct experimental determination of vortex capture, associated energetic benefits and its sensory basis have not been done for schooling fish. However, individual fish swimming in a flume have been shown to use lateral line information to position themselves in an energetically favourable position behind a cylinder (Montgomery et al., 2003), and to entrain to shed vortices from a bluff object in the flow (Liao et al., 2003a,b). Figure 4.6 reproduced from Liao et al.’s paper illustrates this. Visual communication in schooling has been extensively studied by Rowe and Denton (1997) and Denton and Rowe (1994, 1998). Their analysis is that the same substrate of reflective surfaces that provides for midwater camouflage, supplemented by additional reflective and non-reflective surfaces (such as the double yellow reflective dots on the tail: see Fig. 4.9), can provide strong communication signals to nearest neighbours. Additional reflective and non-reflective surfaces include highly silvered patches on the tail, the dorsal lateral line which is a non-sensory reflective open canal (Rowe and Denton, 1997) and the bands of reflection that 188 David A. Ritz et al. Figure 4.8 School of Trachurus novaezelandiae showing double yellow reflective dots on tail. Photo by John Montgomery. can mask underlying black stripes (Denton and Rowe, 1998). The combination of light reflection and colour changes can provide large changes in appearance for relatively small changes in roll, pitch and yaw. These changes could in theory signal a fish’s movement and/or position relative to its neighbours. These visual communication signals are thought to combine with hydrodynamic and acoustic stimuli to provide for the impressive collision avoidance capability of schooling fish. Crustacean species such as Antarctic krill (Euphausia superba) and mysids also undertake coordinated swimming in formation (O’Brien, 1988; Patria and Wiese, 2004; Kawaguchi et al., 2010). Wiese (1996) has reviewed the available information on the role of vision and mechanoreception in the control of schooling in krill. He concludes that the evidence strongly supports a mechanosensory basis for control of this behaviour. Even though blinded krill were unable to school (Strand and Hamner, 1990), this seems to be simply a result of the inability to orient the body to a fixed vertical axis in space i.e. to the axis of light from the surface. In contrast, fish can continue to school with loss of either vision or lateral line but not when deprived of both (Partridge and Pitcher, 1980). Yen et al. (2003) and Patria and Wiese (2004) have also described the vortex wake behind a tethered Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 189 Figure 4.9 (A) Communities and subcommunities in a dolphin social network. Vertices shaded in black are all part of one community, while all other vertices are part of the second community. The second community is subdivided into three subcommunities represented by white, light grey and dark grey shading. (B) Social network of a population of guppies. All guppies from two interconnected pools were collected, marked and released. Over the next 2 weeks approximately 20 shoals were captured daily and fish that belonged to the same shoal were connected in the network. (A) After Lusseau and Newman (2004); (B) after Couzin et al. (2006). 190 David A. Ritz et al. krill and the latter authors demonstrated the ability of antennular sensors to detect these oscillations at normal schooling distances and entrain a synchronous pleopod beat in a following animal. From these and other studies (e.g. Wiese and Ebina, 1995), it appears that the vortex wake generated by swimming krill could be used by followers both as a source of information, both hydromechanical and chemical, about the neighbour ahead, and also as a means of reducing their energy expenditure. Acoustic signals travel much better than visual ones in the ocean, so it is not surprising that highly mobile marine mammals have exploited this communication channel for social purposes (Tyack, 2000). Social communication within and between groups of delphinid cetaceans includes not only vision and tactile stimulation, but also sound production in the form of narrow-band, frequency-modulated signals (whistles) (Dudzinski et al., 2002). These types of signals are relatively easily localized, with directional characteristics which, together with their variability and power, make them particularly suited to contact calls, the pod-specific dialects of resident Orcas or the signature whistle of bottlenose dolphins (Tyack, 2000). Echolocation by clicks, on the other hand, is thought to be used more for foraging and navigation (Dudzinski et al., 2002). Communication among groups of Spinner dolphins, which feed mainly at night, may differ from this general pattern. Apparently these dolphins do not use whistles while hunting for prey (Benoit-Bird and Whitlow, 2009). Instead they used a series of clicks with the highest click rates just prior to foraging. The authors suggested that this may be a strategy to limit communication only within the group and to avoid betraying the location of rich food resources to other predators (e.g. tuna) which can also hear whistles but not clicks. 2.6. Social networks Recent application of social networks analysis to the study of animal behaviour and ecology has allowed novel and intriguing insights into populations of aggregating animals (Croft et al., 2008). As Croft et al. (2005) note ‘Social network theory can help bridge the gap between interactions at the local and global level and provides a framework for the study of sociality’. In a social network analysis a graph is often used to describe the interactions between individuals. These individuals are represented as nodes in the graph, and the lines joining them represent social ties. Most nodes are connected by at least one short path, and nodes in the network with a high number of connections are known as hubs. One class of social network, the so-called small-world network, is characterized by a comparatively short average path length between nodes and a large number of hubs or ‘cliques’ of interacting individuals. These qualities are important in facilitating the spread of such things as information, genes or even disease across Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 191 population. Such small-world networks (Watts and Strogatz, 1998) exist within large fish aggregations (Croft et al., 2005; Couzin et al., 2007), birds (Ballerini et al., 2008) and in dolphin pods (Lusseau, 2003; Lusseau and Newman, 2004) (Fig. 4.9). The implications of these small-world networks, in information transfer and speed of spreading of disease, are now starting to be seriously studied at a range of levels of biological organization, from the individual to the population, in marine mammals. Arguably the main strength of this method is to examine how individual interactions scale to structure population-level processes, which allows us to examine self-organization, and potentially the transmission of genes, information and disease (Watts and Strogatz, 1998; Latora and Marchiori, 2001; Cross et al., 2004). In the context of the present review, social network analysis allows us to dissect aggregations to look at the structures within, particularly how the constituents of an aggregation interact with one another. Many of the aggregations discussed here are examples of ‘fission fusion’ groups (Couzin et al., 2006) where, as the name suggests, aggregations split into smaller groups at certain times before later coalescing into larger aggregations once more (Pearson, 2009). Aggregations, therefore, are very often made up of a mosaic of smaller functional subgroups, a phenomenon that may be elucidated using social network analysis. For example, a study of social networks in bottlenose dolphins (Tursiops truncatus) reported that the population of these animals off the east coast of Scotland was composed of two largely separate social units (Wiszniewski et al., 2009). Indeed, most of the work carried out on social networks in the marine environment has focused on pinnipeds (Wolf et al., 2007) and cetaceans (Slooten et al., 1993; Chilvers and Corkeron, 2002; Ottensmeyer and Whitehead, 2003; Lusseau, 2003). This may be easier in these species as the individuals are large, and can be recognized via photo studies. Such work has greatly extended our knowledge of the function of cetacean social groups in particular. For example, a study by Lusseau and Newman (2004) identified different sex and age-structured social patterns in a New Zealand dolphin population, and perhaps most interestingly, reported the existence of key individuals (‘brokers’) within the population that linked separate subgroups (Fig. 4.9A). Such findings are important not only for our understanding of the social dynamics of these animals, but behavioural studies of association patterns and social networks in these animals offer insights that may be used to inform key management and conservation decisions in marine animals (Williams and Lusseau, 2006; Higham et al., 2009; Williams et al., 2009). In the coming years, it is hoped that social network analysis will enable us to gain greater understanding of the social dynamics of a broader range of marine animals. Complex biological structures such as social groups consistently show attributes of networks that have non-random systems of connectivity. Several authors point to the fact that emergent properties are common to 192 David A. Ritz et al. groups of simple, identical units even non-biological ones, e.g. molecules and spin magnets, and caution that appearance of pattern may not necessarily signify adaptive behaviour (Parrish and Edelstein-Keshet, 1999: Parrish et al., 2002). Others stress that an acceptance of social groups as networks is an important preliminary to an understanding of fitness of individuals and groups (Fewell, 2003). Wilson and Dugatkin (1997) show that assortative interactions within or among groups can create conditions of highly non-random variation providing material for group selection. The idea of group selection was an unthinkable and heretical concept until just a few decades ago (Wilson and Dugatkin, 1997). A more modern view is that selection operates on a nested hierarchy of units (see Wilson and Sober, 1994, for a review of this concept). As noted in Section 1, group selection implies that the fittest groups contribute not more groups to the next generation, but more individuals which are themselves predisposed to form successful groups. 3. Technology Breakthroughs in Experimental and Observational Methods Aggregative behaviour is of profound importance to both the ecology and economical exploitation of the pelagic environment, but the remoteness and opacity of the environment has limited the observations that can inform behavioural understanding. With regard to exploitation these limitations may have, in fact, prevented the complete over-exploitation of important commercial fish species such as tuna (Sibert et al., 2006), and near elimination of other species such as great whales (Clapham and Baker, 2003; Roman and Palumbi, 2003). However, technology currently available considerably improves our observational abilities, so due caution to avoid continued non-sustainable exploitation needs to be considered. In the following subsections we review insights on social aggregation that have been generated using modern technology including (i) video and motion analysis software, (ii) optical plankton counters, (iii) acoustics and (iv) electronic tagging. 3.1. Video and motion analysis software 3.1.1. Historical use In earlier sections we argue that aggregative behaviour is of profound importance to the ecology and economy of the pelagic environment. Aquatic animals aggregate for a variety of reasons including reproduction, defence against predators, food finding and energy savings (Ritz 1994, Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 193 2000). Classically, research has focused on the attributes of a group as a whole, with little consideration given to individuals within the group. Recent studies highlight the fact that it is the behaviour of individuals that collectively determine the behavioural trajectory of the aggregation. Detailed analysis of behaviour of individuals and small groups can thus illuminate the behaviour of the much less tractable larger aggregations (Parrish and Edelstein-Keshet, 1999; Banas et al., 2004). Within any aggregation relative positions of individual members are not static, but usually change constantly, moving from the centre to the edge and back again (Hamner and Parrish, 1997). Until recently studying individuals within the context of the entire swarm or school was difficult, particularly for small planktonic animals, and researchers generally concentrated either on analysing behaviour in the two-dimensional plane or examining swarming/schooling individuals under artificial, laboratory conditions. The common first step in studies of social behaviour in aggregations is to capture images of the aggregation. Video and motion analytical tools were first tested in the laboratory, with isolated individuals in controlled situations. Behaviour of pelagic crustaceans has been recorded in three dimensions using a variety of photographic and video methods to examine mating (Doall, 1998; Strickler, 1998), escape responses (Buskey et al., 2002) and attack volume (Doall et al., 2002). However, these were usually measured in detail under laboratory conditions where small groups or individuals were isolated from the rest of the aggregation often in a small volume of water. Group attributes in the form of NNDs, bearings and angles of elevation have been measured using still photographs (O’Brien et al., 1986), but these methods provided only approximations of parameters such as velocity. Obtaining threedimensional trajectories of specific individuals for extended periods was also difficult, with data typically generated under highly artificial conditions (Parrish et al., 2002). In the past, most analysis of objects in three-dimensional space (photogrammetry) employed film cameras because only these cameras could provide sufficiently high image quality and geometric reliability. However, digital still cameras and digital video cameras now offer image resolution approaching that of film and, more importantly, provide an imaging geometry that is sufficiently stable for photogrammetric purposes. Modern digital video cameras offer the possibility of capturing stereo-video and obtaining accurate three-dimensional measurements of moving targets (Osborn, 1997). These early studies also tracked a limited number of individuals, with individuals identified by eye in sequential images. Thus, while a focus has been on the spatial and temporal description of aggregations, these early studies have also shown that energetic benefits to individuals in aggregations often cannot be inferred from analysis of isolated individuals. 194 David A. Ritz et al. 3.1.2. Technological developments in video and image analysis Increased computing power, digital storage capability and modern digital cameras have allowed an order of magnitude increase in analytical power and offer new insights into the spatial and temporal mechanistics of aggregation. A selection of technological aids used in studying zooplankton aggregations was provided by Folt and Burns (1999). Here we update this list and focus on technology that is particularly applicable to, or was developed for the purpose of, recording social aggregative behaviour. A second challenge has been to develop technology to allow observations of aggregations at sea. Kils (1992) described a small, free-drifting system for recording in situ predator prey interactions between juvenile herring schools and copepod swarms. One particular objective of the study was to determine how herring schools search and encounter prey under the reduced visibility conditions in the Baltic caused inter alia by recent enhancement of phytoplankton blooms. The hardware, known as ecoSCOPE, consists of two optical endoscopes mounted on a remotely operated vehicle. Microlayers containing the herring schools are located from 40 m away using a scanning sonar also mounted on the ROV. Images of the predators are collected by one charge coupled device array and prey by a second one. The field system is accompanied by a software package dynIMAGE that allows the user to process images in a way that compensates for system swaying caused by microturbulence. Using this system, Kils (1992) was able to provide one of the first recordings of fish schools capturing prey in the field, describing copepod captures at a rate of 2.4 s 21 by herring feeding within layers containing high (up to 850 l 21) concentrations of copepods. Recent advances in digital video and its miniaturization and low cost have resulted in a range of new equipment for in situ and laboratory observations. Kawaguchi et al. (2010) present a method that combines footage captured with dual digital stereo-video cameras with a commercially available motion analysis system, WinAnalyse (Intec), which enables sophisticated studies of the movement of aquatic animals, including the analysis of complex interactions among individuals in an aggregation. An introduction to the basic stereophotogrammetry, including specifics on calibration and precision of the hardware and software, is provided. They demonstrate the capability of this equipment by describing qualitative behaviour of laboratory schools of Antarctic krill and testing hypotheses about the effects of light and food. For example, the method allows a comparison of NNDs and swimming speeds in different regions of the swarm. Krill swam in polarized groups and responded cohesively to objects that produced a sharp contrast but not to those that were less distinct. Schools broke up when they encountered dense phytoplankton patches but aggregated more tightly when kept in a white featureless background. Viscido et al. (2004) used similar equipment to compare behaviour of real and simulated fish schools. Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 195 Some parallels with krill schools are apparent, e.g. there was a clear relationship between group speed and polarity; in both cases polarized groups swam faster than non-polarized ones. This equipment would permit researchers to test in situ, e.g. whether individuals at the edge of a swarm are likely to be travelling predominantly in vertical trajectories compared to those in the centre that are hypothesized to be travelling predominantly in a more horizontal direction. This would enable those at the edges to exploit favourable currents generated by the swimming movements of swarm members (Ritz, 2000). A breakthrough in the three-dimensional analysis of thousands of individuals in a group was reported by Cavagna et al. (2008a,b). These authors studied flocks of starlings and devised computerized techniques to identify corresponding images of individuals from stereoscopic pairs of pictures taken by cameras placed 25 m apart and about 100 m from the birds (Fig. 4.10). This analysis yielded several important conclusions, among them: i. Each bird interacts with a fixed number of neighbours irrespective of their distance. In other words, the birds interacted with neighbours according to topological distance are not metric. One of the consequences seems to be that birds under attack from predators do not lose cohesion when the flock rapidly changes shape, density and direction. ii. Each bird interacted with a maximum of seven neighbours. This may represent a cognitive limit for starlings although there is some evidence that it may be a more widespread limit for other social species. The techniques described by Cavagna et al. (2008a,b) lend themselves to the study of aggregations of other social species with the promise of more robust estimates of behavioural characteristics. They also report methods for removing bias due to individuals at the borders of the aggregation. Neglect of this factor can cause erroneous conclusions especially in small groups which, hitherto, have necessarily been the subject of three-dimensional analysis. A new approach in visualization of aggregations was recently introduced by Myriax Pty Ltd. (http://eonfusion.myriax.com/). It is called Eonfusion and permits aggregations to be readily displayed in four dimensions, x, y, z coordinates and time. An example of a krill school is shown in Fig. 4.11, and other examples can be found at http://www.youtube. com/watch?v 5 CF8pb1a9gvA&feature 5 player_embedded. Recent developments include the video plankton recorder (VPR) (Fig. 4.12) that is essentially a towed underwater video microscope that images, identifies, counts and sizes plankton, and other particles in the size range 100 µm 5 cm (IGBP Science 5). Some of the newest VPR platforms permit high-speed towing (10 knots) out of the wake of the ship, a moored autonomous profiler to obtain high-resolution time series of water column plankton, and autonomous 196 David A. Ritz et al. Figure 4.10 Upper panels are actual photos of a flock of starlings taken by a pair of stereoscopic cameras placed 25 m apart and about 100 m from the birds. Square boxes indicate corresponding birds in the two pictures. Lower panels are three-dimensional reconstructions of the flock from four different perspectives. After Cavagna and Giardina (2008). Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 197 Figure 4.11 (A) Visualization of a school of Antarctic krill (Euphausia superba) using Eonfusion. The positions (x,y,z) were used to derive metrics, e.g. swimming speed, direction, acceleration and nearest neighbour data. These were used to explore behaviour of individuals over time. (B) Krill connected to their nearest neighbour (for distances ,50 mm). Reproduced with permission from Eonfusion: Tim Pauly; Australian Antarctic Division: Rob King, So Kawaguchi; University of Tasmania: Jon Osborn; Georgia Tech: David Murphy, Jeanette Yen, Donald Webster. underwater vehicles to provide remote spatial sampling of plankton and environmental variables (IGBP Science, 2003). Together these image capture systems offer a wide range of potential approaches to gather the primary data on aggregations. 3.2. Optical plankton counters and holography Since its first appearance in 1992 (Checkley et al., 1997), use of the optical plankton counter has increased exponentially to provide quantitative 198 David A. Ritz et al. Figure 4.12 The housing for a VPR, with a very streamlined shape, sits ready for deployment off the back of a research vessel (Gulf of Maine Area Program GoMA). . http:// www.coml.org/investigating/observing/vprs. measurements of abundance and sizes of meso-zooplankton. In addition it has evolved to become an important piece of equipment in multidisciplinary studies for acquiring and displaying data from a range of sensors (Cass-Calay, 2003). Spatial and temporal zooplankton distributions can be recorded by instruments mounted on a range of underwater towed frames (Zhou and Tande, 2002). At present, however, OPCs have limited value in the study of social aggregations because of issues of avoidance, coincidences in measurements, image resolution and depth of field. A submersible holographic system has been used to visualize the instantaneous in situ three-dimensional distribution of copepods and particles .10 µm in a 1 l volume (Fig. 4.13; Malkiel et al., 1999). Results show clear evidence of clustering at almost all depths sampled. One great advantage of holographic imaging systems over other optical systems is their ability to resolve small particles, e.g. plankters over a much larger sample volume. The authors give as an example the comparison between a planar imaging system that can resolve a 20 µm object with a depth of field of 0.6 mm at a given wavelength, whereas a holographic system under similar conditions would resolve the same object with a depth of field over 100 times larger. This potentially makes holography a valuable tool for in situ studies of pelagic aggregations. 3.3. Acoustic technology Traditional acoustics involves the detection of sound waves reflected from a target (active) or emitted from a target imbedded with an acoustic Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates Sam pling or irr volum e M irr o r M 199 Co Exp an len ding ses r se llim len ating ses La r lte l fi a ati Sp s se en yl ela R Mir ror ra me Ca Mir ror Figure 4.13 Holocamera: optical setup of in-line holography. Redrawn from Malkiel et al. (1999). transmitter. Detection of ‘natural’ sound from a target (passive acoustics) has also shown promise for detection of large objects, and we discuss it briefly with regard to pelagic species in a following subsection. Verifying the species or individual represented by the acoustic signal has been a primary challenge and as a result there have been several attempts to combine optical and acoustic sensors for the purpose of identifying and quantifying zooplankton in situ. For example, Jaffe et al. (1998) introduced the optical acoustical submersible imaging system in which a digital still camera was mounted on their FISH-TV sonar array. The camera is triggered to capture an image within the sonar beams when the target strength exceeds 290 dB. Using this equipment, the authors reported imaging 375,000 individual zooplankters, many as small as 1 mm (Genin et al., 2005). In a variation on a theme, Warren et al. (2001) used an analogue video camera to aim their acoustic array, mounted on an ROV, at individual zooplankters, i.e. siphonophores, 200 David A. Ritz et al. euphausiids or other taxa. These ‘images’ of individuals can then be used as for conventional optics to derive measures of in situ school parameters related to social aggregation such as NND. Traditional acoustics have been limited to resolving pelagic zooplankton and fish at distances of 10s to 100s of metres from the vessel. A further limitation is that single-beam downward-looking echosounders sample only a narrow cone of water beneath the vessel (Cox et al., 2009). Thus they may undersample activity just outside this cone, e.g. predators interacting with target schools. Increasingly, multibeam echosounders (MBEs) have been used that not only broaden the width of the swath sampled but also enable direct observation of volume and surface area of aggregations leading to more accurate threedimensional estimates of individuals (Cox et al., 2009). This equipment is well suited to resolving pelagic aggregations in situ, and has been used to analyse three-dimensional structure of anchovy and sardine schools (Gerlotto and Paramo, 2003; Gerlotto et al., 2004), and also to study predator prey interactions between Atlantic puffins and herring (Axelsen et al., 2001). Cox et al. (2009) used an MBE to study interactions between swarms of Antarctic krill and penguins and fur seals. Kaartvedt et al. (2009) showed that detailed in situ behaviour of individual mesopelagic fish could be resolved by multibeam acoustics deployed from a stationary vessel (Fig. 4.14). This study revealed a variety of DVM behaviour amongst myctophid fish including normal, reverse migration and non-migration of some individuals. Techniques using submerged echosounders offer exciting prospects for studying deep-living aggregations non-intrusively, assuming that the target species are not themselves responding to the echoes. The GLOBEC’s Southern Ocean programme employs a Bio-Optical Multi-frequency Acoustical and Physical Environmental Recorder (BIOMAPER-II) to map abundance and distribution of Antarctic phytoplankton, zooplankton and especially krill (Wiebe et al., 2002). BIOMAPER can record data from 500 m or more of the water column at a time. It is towed at speeds up to 10 knots and data are fed continuously to the surface via conducting cable. The instrument uses a five-frequency sonar system, a VPR and an environmental sensor system that measures water temperature, salinity, oxygen, chlorophyll and light levels. The most astounding breakthrough in recent years is that of continental shelf-scale imaging (Makris et al., 2006), that has revealed huge fish shoals covering many square kilometres and containing tens of millions of fish. An example of the trace from the ocean acoustic waveguide remote sensing (OAWRS) system is shown in Fig. 4.15. This technique relies on the continental shelf environment acting as an acoustic waveguide, making it possible to survey areas roughly one million times greater than conventional fish-finding methods. The technology utilizes Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 201 Figure 4.14 (A) Echo traces of individual mesopelagic fish. Graduated scale at right of picture refers to backscattering strength (dB). (B) Example of three-dimensional movement and swimming speed of a target ascribed to Benthosema glaciale (framed in A) at 380 m depth. Graduated scale at right of picture refers to swimming speed (m s 21). Reprinted with permission from Kaartvedt et al. (2009). a moored acoustic source and a towed receiving array. Sound propagates over long ranges via trapped modes that suffer only cylindrical spreading loss rather than spherical loss suffered by more conventional sonar. Using this technique, unprecedented imaging of fish shoals is possible providing details of behaviour such as school formation, fragmentation and movement. Once species can be discriminated, this technology offers exciting potential to look at the interaction between schools as a basic unit of analysis, perhaps using the density within a school as a measure of behavioral response. 202 David A. Ritz et al. (A) (B) α 100 m 100 m β Distance (km) –6 (C) (D) –9 100 m 100 m –12 γ –15 δ –18 0 3 6 9 Distance (km) 12 Fish/m3 1 0.2 0.05 0.01 0.001 (E) Depth (m) 70 80 90 α 100 0 1 β 2 3 4 γ 5 6 Range (km) δ 7 8 9 Figure 4.15 (A D) Comparison of OAWRS with conventional fish-finding sonar (CFFS). A sequence of areal density (fish m 22) images taken roughly 10 min apart is shown. The corresponding CFFS is overlain in light blue (see colour plate). CFFS position for the given OAWRS image is indicated by a circle. (E) Range-depth profile of fish volumetric density (fish m 23) measured along the transect in (A D). After Makris et al. (2006). Traditional optical visualization is compromised by poor visibility, which acoustics can also overcome, although acoustic methods can also be compromised when the water contains many particles. A promising new instrument for use in turbid water is the acoustic camera (DIDSONt Dual Frequency Identification Sonar, i.e. either 1.8 and 1.1 MHz or 0.70 and 1.2 MHz) that has been used to count and identify fish, and to reveal schooling behaviour of salmon in hatchery ponds (Belcher et al., 2002). Depending on the frequencies, it can image objects from 1 out to 80 m range. The near video quality of the image allows observation of fish behaviour in turbid water and at night near natural and manmade structures. Furthermore, the equipment can be used close to the banks of rivers or streams where deployment of other acoustic instruments is problematic. Since the sonar beam is emitted perpendicular Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 203 to the flow, fish can be counted and sized as they pass through the field of view. DIDSONt is small and portable requiring only 30 W of power. 3.4. Electronic tags Both visual and acoustic in situ observations of pelagic social aggregation typically require the presence of the scientist or attending vessel. For larger pelagic species, an alternative is to implant or attach data recorders that can transmit data or be recovered at a later time. The primary goal of most electronic tagging studies has been to understand the movements of individual animals, which with sufficient sample size are assumed to be representative of the population (Hobday et al., 2009). The study of individual behaviour has been a focus, aggregation behavior less often, and social behavior rarely. Three types of tag are in common use: archival tags, satellite transmitting tags and acoustic tags, all of which could provide information on social behaviors in aggregations. Archival tags are usually internally implanted in an individual, and collect a variety of data (depth/pressure, internal/external temperature, light), while the animal is at liberty (Gunn and Block, 2001). Following recovery of the animal, data can be processed to determine daily position (Welch and Eveson, 1999; Teo et al., 2004). Errors in calculated position can still be in the order of hundreds of kilometers which has prevented insights into group behaviors (Nielsen et al., 2009). Depth resolution is more accurate, and so simultaneous movements in depth of tagged animals could be used to determine coherence in group behaviors. A modification of the basic archival tag has been a range of satellite archival tags which are externally attached and transmit data continuously (e.g. SPLASH, SPOT; Weng et al., 2005) or detach from the animal after a period of time and transmit summarized data to satellites (PSAT; Block et al., 2001; Patterson et al., 2008). Position estimates are similarly coarse, and these tags have been of little use for studies of social aggregation. However, both types of archival tag have been widely used on a range of pelagic species with social aggregations, including fishes, sharks, marine mammals and birds, and even a few large invertebrates (Nomuru jellyfish) and squid (Gilly et al., 2006), and so potential remains to use these tags within their technological constraints to achieve breakthroughs in documenting social behaviors. Acoustic tags transmit while attached to the animal, which can be actively tracked by an attending researcher (Block et al., 1997; Davis and Stanley 2002) or monitored using fixed acoustic receivers (Heupel et al., 2006; Hobday et al., 2009). Advantages of using fixed receivers include the increased time over which multiple individuals can be simultaneously monitored subsequent to the tagging event. Thus, there is increased likelihood of detecting natural behaviors and recurrent grouping of individuals. The 204 David A. Ritz et al. resolution of the position estimates (tens to hundreds of metres) also allows greater certainty that individuals are clustering (Klimley and Holloway, 1999). Aggregations of pelagic fishes are common, either with each other, with floating objects, or with topographic features such as seamounts (Holland et al., 2009). This behaviour is increasingly being exploited by fishermen who each year release thousands of floating FADs throughout the oceans (Hallier and Gaertner, 2008). These devices typically attract tunas, but also a range of other species including sharks, billfish and sea turtles (Dagorn and Fréon, 1999; Gaertner et al., 2002; Hallier and Gaertner, 2008). Ongoing research is aimed at a greater understanding of the relationship between aggregation/association of fish at FADs and the potential impact of fisheries (Hallier and Gaertner, 2008). Evolution of electronic tags for this purpose has been rapid and there are plans to test the feasibility of using acoustic data to determine whether the fish are, indeed, schooling, or if they are responding to the floating structure (Taquet et al., 2007; Soria et al., 2009). Another recently developed device, the CHAT (Communicating History Acoustic Transmitter) or Business Card tag, has the potential to exchange data between fish and then remotely transfer archived data from the fish to listening stations deployed on the seabed or on buoys (Holland et al., 2009). Whether or not data on schooling behaviour could be logged would depend on successful design of suitable sensors, but would be an important innovation in monitoring aggregations at sea, as well as contributing vital information to ecologists and fisheries scientists (Holland and Dagorn, 2009). While most electronic tagging studies have a single-species focus, Goňi et al. (2009) acoustically tracked juvenile albacore in the Bay of Biscay and simultaneously collected prey distribution data from echosounders. Although tuna depth distribution did not relate to prey distribution, the combination of these technologies is suitable for developing fine-scale understanding of tuna schooling and foraging behavior, and may yield interesting results in future. 3.5. Future technology challenges Traditional technologies based on visualization have provided most of the insights regarding pelagic social aggregations to date, with increases in usage of acoustic technology providing recent breakthroughs for a range of species. The challenge for understanding pelagic social behavior is developing systems that can be used in the open sea, often remote from the observing researcher. Use of electronic tags is likely to be restricted to the larger species for the foreseeable future, and so information on the smaller taxa is likely to come from combining technologies, such as Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 205 camera systems with traditional acoustics. The resolution of the data in time is sufficient in existing technologies, with millisecond reporting commonplace. Spatial resolution can be considerably improved, and while geolocation may have inherent limitations (Welch and Eveson, 1999; Nielsen et al., 2009), alternative technologies that allow tags to communicate will overcome the uncertainty in individual position estimates, by confirming co-location. With regard to tags, a number of companies are supplying technology, yet these are generally incompatible. Given the scale of the pelagic ocean, and the infrequent encounters between independently tagged animals, opportunities are likely to be missed if tags cannot communicate. Commercial considerations are important and likely prevent complete compatibility between technologies; however, a middle ground may to use a common signal for ‘chat’ between tags and maintain individual coding for the primary data collection (Grothues, 2009). 4. Theoretical Developments in Social Aggregation ‘A key purpose of modelling is to distinguish behavioural cause from organizational effect by studying the consequences of various hypothetical social interaction rules’ Parrish et al. (2002) We do not propose to give a comprehensive review of theoretical modelling of aggregations here. Instead we will describe the kinds of models that have been applied to the problem to date, outline the results obtained and identify areas and directions where further work is warranted. Levin (1997) discussed conceptual issues posed by modelling aggregations, particularly in relation to scale, and Parrish et al. (2002) provided a recent review of theoretical modelling approaches particularly as they relate to fish schools. The current state of modelling animal aggregations and its relation to empirical data has been described with great clarity by Giardina (2008). In general, three major frameworks have been used to model animal aggregations (Parrish and Edelstein-Keshet, 1999). These three types differ in (i) spatial and temporal scale of the analysis, (ii) the kind of information individuals use to aggregate and (iii) the mathematical complexity (Giardina, 2008). First, individual-based models (IBMs or Lagrangian) are based on individual trajectories with attributes such as location, genotype, phenotype, physiological and behavioural status, and allow rule-based responses to environmental data in order to explore the dynamics that lead to formation of groups. For example, fish joining a school would assume a particular 206 David A. Ritz et al. angular position with respect to another individual and a particular direction that could be modified by the environment (e.g. current strength) (Adioui et al., 2003). The individual unit could be an individual fish, or a school of fishes. An aggregation has been shown to result when individuals follow three simple rules: move in the same direction as neighbours, remain close to them and avoid collisions (Giardina, 2008). Second, Eulerian models deal with populations as the base unit. They typically consider each unit at a geographically fixed location instead of simulating each individual and, in the case of a school, predict emergent group properties such as population density. Space is not georeferenced and the number of individuals inside each cell is followed in time. Environmental drivers can also be applied to each unit, which responds according to a set of dynamical equations. This type of model is mostly applied when investigating population evolution on long time scales or over large spatial scales, and has made a strong contribution to fisheries science. A third approach relies on discrete simulations using a range of individual behavioural rules and motion (an example is the use of cellular automata). This method, it is argued, gives a clear, visual prediction of how individual behaviour contributes to that of the group. Rules that do not lead to group formation can be modified or rejected, allowing some form of hypothesis testing. Automatic selection of the rules based on ‘reproductive fitness’ occurs in models using genetic algorithms which have been used to explore schooling behaviour (Giske et al., 1998). Lagrangian and Eulerian concepts have been combined (Adioui et al., 2003) and further refined to better forecast three-dimensional movement patterns of individual salmon in the ELAM (Eulerian Lagrangian Agent method) (Goodwin et al., 2006). These studies have shown that the ELAM framework is well suited to describing large-scale patterns in hydrodynamics and water quality at the same time as much smaller scales at which individual fish make movement decisions. This ability to simultaneously handle dynamics at multiple scales allows ELAM models to realistically represent fish movements within aquatic systems. This method seems to hold promise for future ecological modelling of fish schools. An Eulerian approach has been used to model the effects of environmental conditions, krill fishery and natural predation on Antarctic krill growth, distribution, vertical migration, feeding, etc. (Alonzo and Mangel, 2001; Alonzo et al., 2003). These authors used a dynamic statevariable model to predict the effect of changes in predation risk on behaviour and spatial distribution of Antarctic krill. However, these models did not incorporate the influence of schooling behaviour, which could be a key factor affecting krill population abundance (Willis, 2007a). The problem has been that, it has been impossible to reproduce schooling behaviour of Antarctic krill in laboratory conditions and thus gather Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 207 reliable empirical data with which to paramaterize models. Recently, however, routine generation of schooling behaviour in krill in aquarium tanks has been achieved (Kawaguchi et al., 2010) which may allow progress in population modelling. Research on other social aquatic species suggests that individual behaviour is so closely tied to that of the aggregation that data extrapolated from individuals isolated from their schools will be misleading. A good example is metabolism, where it has been demonstrated that individuals in aggregations consume far less energy per unit mass than isolated individuals (Ritz, 2000; Ritz et al., 2001). IBMs and computer-generated artificial life creatures (e.g. boids and later offshoots such as efloys) have been successful in reproducing the selforganizing characteristics of aggregations of flocking birds and marine species, particularly fishes. But are these emergent properties of the group functionally important? Parrish and Edelstein-Keshet (1999) caution that, although simple rules can generate lifelike behaviour, there is no guarantee that living systems follow simple rules. More experimental data gained by tracking individuals in small groups is needed (Parrish and EdelsteinKeshet, 1999). In fact, one clear conclusion from this overview is that the feedback between empirical observations and modelling, that is so important to scientific progress, has been seriously hampered by lack of data on the former (Giardina, 2008). The theoretical study of animal aggregations has a long history, but attempts to characterize the relationships between individual-level behaviour and group-level patterns have been hampered by lack of a common framework to schooling models (Parrish et al., 2002). They argue in favour of a distinction between group- and population-level characteristics that are inevitable consequences when many identical particles become aggregated in space (epiphenomena or ‘pattern’), and true emergent properties, that benefit members because of their membership of the group. Viscido et al. (2007) continued this theme by analysing the factors contributing to fish school formation and maintenance. In a simulation study they found that several, mostly social, factors were important in giving rise to emergent properties: notably a repulsion factor is necessary to prevent collisions, a neutral zone must exist in which there is neither attraction nor repulsion, a modest alignment impulse strong enough to induce polarity is necessary, number and weighting of influential neighbours is critically important, as is speed of motion. A topological response to neighbours, i.e. focal individual interacting with a fixed number of neighbours irrespective of distance, has also been shown in simulations of starling flocks (Ballerini et al., 2008) but only recently demonstrated empirically (see Cavagna et al., 2010) (see also Fig. 4.10). Bode et al. (2010) suggested an alternative interpretation of the behavioural rules governing individuals moving in aggregations that is distance based rather than topological. They hypothesize that an individual under threat must minimize its ‘oddity’ by increasing the rate at which it 208 David A. Ritz et al. Figure 4.16 Simplified food web of the Great Australian Bight, Australia. In this ecosystem representation, southern bluefin tuna are represented in two separate boxes, lone tuna and tuna in schools, illustrating the different trophic relationships related to school membership. With permission from Willis (2007b). updates its position and orientation within the group, leading to an overall increase in synchronization and uniformity. This is a subject rich with possibilities for future study. Ecosystem modelling, such as trophic modelling via Ecopath, has also ignored the implications of social aggregation. However, Willis (2007b) has recently examined ways in which to incorporate social aggregation and behaviour into an ecosystem model of southern bluefin tuna (Thunnus maccoyii) in the Great Australian Bight (see Fig. 4.16). This simple solution, including a schooling and non-schooling category, may be suitable for discrete behaviours, but it unlikely to succeed when a continuum of states is possible. Given that factors such as feeding success, predator vulnerability and reproductive capacity differs across the spectrum of solitary to aggregated individuals, inclusion of aggregation state in ecosystem models, such as ecopath is likely to influence ecosystem understanding. 5. Social Aggregation, Climate Change and Ocean Management The direct impacts of climate change on species and populations include changes in distribution, abundance, phenology and physiology. The pelagic ocean is well buffered from some of the impacts of climate Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 209 change; however, some secondary processes, including aggregation, will likely be affected. Climate impacts will also be expressed indirectly via changes in the habitats where aggregation can occur, as well as directly on the aggregating species. For example, changes in CO2 concentration that change acidity of the ocean, or changes in ocean circulation that control oxygen concentrations, may limit the waters where aggregation can occur. Given the many instances of change in distributions of marine species (Occhipinti-Ambrogi, 2007; Last et al., 2011), species composition is changing regionally. A change in local water temperature could change the competitive advantages of one species over another. If one species lived in schools and the other did not, this could have a profound influence not only on the food web but also potentially on commercial fisheries. While climate change has already begun to impact waters around the world (Harley et al., 2006; Cao and Caldeira, 2008), the variation in future predictions and the spatial and temporal resolution of predictions make forecasting biological change difficult. The response to historical climate variability is a window to the future, and so we first describe some of the documented responses of aggregations to climate variability at a range of scales. We explore the implication of these changes for pelagic trophic linkages (e.g. changes in energy that pass from aggregations of zooplankton to fish/seals/whales). Disruption to physiological abilities, such as smell, has been shown to affect homing, predator and conspecific detection in larval fishes (Munday et al., 2009). If senses important to establishing aggregations are disrupted, then benefits to populations discussed in earlier sections will be reduced, with possible ecosystem impacts. One outcome of global warming is that warmer seawater will have markedly decreased viscosity. For example, a rise in temperature from 0 C to 5 C decreases viscosity of seawater by about 15%. This could possibly lead to aggregation at a smaller size if, as Ritz (2000) suggests, currents generated by the group serve to offset the tendency to sink in heavier than water animals, i.e. crustaceans. Antarctic krill only begin to aggregate when they reach late furcilia stage at a length of around 10 mm (Hamner et al., 1989). Ritz (2000) suggests that this is the size at which the cost of resisting sinking becomes too great to remain solitary. On the other hand, increasing acidity of oceanic waters could result in crustaceans and molluscs (e.g. pteropods) precipitating less calcium carbonate in their cuticles and shells and becoming less dense, which may offset the decreasing viscosity. Polar marine ecosystems are particularly vulnerable to climate change because of the effects that small increases in temperature can have on the critical interfacial habitat between ice and water (Smetacek and Nicol, 2005). The atmosphere around the Antarctic Peninsula region has been 210 David A. Ritz et al. warming faster than any other part of the planet and a concomitant decrease in sea ice extent and krill population has been documented (Atkinson et al., 2004). However, the effect of climate warming in this region could be more subtle and complex than described by these authors given the influence of ocean/atmosphere interactions such as El Nino Southern Oscillation (Loeb et al., 2009). Overall, the impact of climate change on social aggregation is unknown but may deliver ecosystem surprises. What effects might climate variability have on social species? In the first place, warming might change species distributions, but it is not clear whether warming per se would affect aggregative behaviour directly. However, there is a range of indirect consequences of warmer oceans. For example, it seems inconceivable that DVM would not be affected. If DVM is traded for aggregation in certain circumstances, could climate change push it in a particular direction? One serious consequence of climate change for social aggregatuons might be through an affect on seasonal migrations. Barbaro et al. (2009) have modelled the migration of the Icelandic Capelin stock, an important commercial resource. The most significant factor in determining the route of migration was oceanic temperature and the way the fish schools responded to it. In a warming ocean, the migration routes and aggregation tendency of fishes may change. The possible effects of both predator and climate-change-induced alterations in schooling behaviour of krill need to be considered for sustainable management. Predation risk is commonly held to be an important stimulus for aggregation in krill (Ritz, 1994; Kaartvedt et al., 1996; Folt and Burns, 1999; De Robertis, 2002), but the urgency for aggregation can be overridden, e.g. if predation risk is low, if light levels are low enough to frustrate visual predators or possibly if energetic considerations dictate that vertical migration is a more economical option. The urge to aggregate with similar individuals is very strong (Bakun and Cury, 1999), but clearly there is great flexibility in this behaviour (Bertrand et al., 2006) and possibilities for tradeoffs are many. Willis (2007a) proposes that decimation of whales in the Southern Ocean has led not to widely predicted large increases in krill stocks but to a change of vertical migratory behaviour that resulted in lower krill abundance. If the disappearance of a large proportion of the whale population had been the direct result of climate change rather than man induced, this would have profound implications for our strategies to prepare for the effects of a changing climate. A second and more immediate threat for pelagic systems comes from commercial fisheries which are already inducing profound changes in fish populations (Heino and Dieckmann, 2009). Since the majority of commercially important fish are those living in schools (Pauly et al., 2005), evolutionary changes induced by fishing will also affect the food web dynamics. Fisheries management can benefit from including information Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 211 on social behaviour to better manage fisheries. The basin hypothesis, that fish stocks collapse to a core area of habitat as population declines (MacCall, 1990), is in part due to social interactions between aggregations. Mixed-species or mixed age-class aggregations provide information on population status. Effective closed area management is dependent on identifying the vulnerable stages of an animal’s life, and social theory can identify these stages. Introduction of exotic species into new regions could disrupt competitive interactions and predator prey relationships. The net result of such introductions could be complicated by aggregative behaviour of the newcomer or the ousted competitor, making prey availability more problematic. This could extend to human fisheries activities which are highly dependent on schooling behaviour of prey species (Quinn and Deriso, 1999; Cury et al., 2000). 6. Conclusion 6.1. Do reviews stimulate new work? In his earlier review of social aggregation in pelagic invertebrates, Ritz (1994) suggested that several topics deserved particular attention, including: 1. General behavioural studies regarding individuals in aggregations. 2. Determining genetic relatedness among individuals in aggregations. 3. Evaluating whether particle capture is more successful by aggregated than by solitary individuals. This was considered to be challenging, as rigorous assessment requires experimental reproduction of patchy food distribution instead of the more common homogeneous distribution used in laboratory containers. 4. Effectiveness of aggregations with regard to successful mate finding and reproduction. 5. Experimental studies of decision-making, e.g. trade-offs between aggregation or other behavioral choices. Evaluating if this earlier review was successful in promoting research in specific areas is worthwhile before suggesting additional areas for future study, as such insight can help modify the way such suggestions can be made. This type of analysis is rare in review papers (Roberts et al., 2006) but in our view is a worthy consideration. Using the search tools available in ISI (http://apps.isiknowledge.com), publication trends for these topics were analysed. Comparing pre-1995 and post-1995 is difficult as the electronic indexing of publications was incomplete in the early period, so here we consider the effort against the suggested areas in Ritz (1994). We 212 David A. Ritz et al. also do not claim a direct influence, only that these topics did receive attention in the subsequent years, and although we concede that the results are somewhat inconclusive, they do suggest that for some topic areas a different approach to the one suggested by Ritz (1994) might lead to breakthroughs. Since 1994 a total of 76 papers have been published with a focus on ‘pelagic invertebrates’ (search term: pelagic SAME invertebrates); of these 8 (11%) had a behavioural component (topic 1), four (5%) a genetic component (topic 2), although none focused on the relationship between individuals in an aggregation, and 16 (21%) considered feeding aspects, again few compared aggregated against solitary individuals (topic 3). Papers focusing on reproductive advantages as a consequence of aggregation (topic 4) and trade-offs in decision-making (topic 5) have not received any experimental attention, according to our search approach (but see the example described below). Thus, some of these topics are still worthy of attention some 15 years on. By way of example, one suggestion made by Ritz (1994) has acted as a catalyst for further research. Ritz (1994) suggested that because schooling behaviour sub-served predator protection and facilitated foraging and feeding, social species might find vertical migration redundant since it too serves the same functions. Although this work was not experimental in nature, it can be regarded as an example under topic 5 (trade-offs). Observational research by De Robertis (2002) and De Robertis et al. (2003) suggested that Euphausia pacifica did not form subsurface social aggregations in certain environments, instead relying on DVM for protection from fish predators. The fact that E. pacifica forms schools in other environments (Mauchline, 1980; Hanamura et al., 1984) highlights the flexibility of the behavioural repertoire, and indicates that flexibility in behavior can result from environmental differences. On a related theme, Kaartvedt et al. (1996) suggested the significance of ‘anti-predation windows’ that may encourage vertical migration of the predators (Norway Pout) at times of low visibility, e.g. dawn and dusk, when it is less risky to chase prey (krill) closer to the surface. At other times, vertical migration is suppressed and fish remain in deeper water. If social aggregation is a cost, e.g. if schools are more likely to attract the attention of piscivorous predators, then it might be suggested that schooling would be abandoned while migrating vertically. This argument could be negated if the energy saved by migrating in schools is more than offset by the increased risk of predation. 6.2. Future needs and synthesis Benefits of aggregation in a wide range of systems are well known, and it is no surprise that aggregation is also commonly found amongst pelagic species. There are many aspects of aggregations in the ocean still in need of research, e.g. energy saving: larger swarms expended less energy than smaller Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 213 ones, which, in turn, saved more energy than un-aggregated individuals (Ritz, 2000). The conversion of these energy savings into enhanced fitness is assumed to follow, but empirical evidence is lacking to date. Network analysis is a relatively new field that should lead to more breakthroughs in future, particularly for understanding social structure and membership persistence. Social networks have been demonstrated in aggregations of fish, birds and mammals. There is ample scope for extending this kind of analysis to other aggregations including invertebrates. Understanding sensory processes, and their role in forming, maintaining and dispersal of groups is an area ripe for future research. Conflicts between sensory inputs and processes (e.g. camouflage and communication) can occur, and the decision-making processes of individuals could be studied empirically using the latest optical or acoustic technology. Or it might lend itself to analysis by simulation or modelling. Technologies for the study of aggregation have progressed markedly in recent decades, and offer insights in the future, using tools such as simultaneous acoustic imaging of individuals (Kaartvedt et al., 2009) and electronic communicating tags (Holland et al., 2009). We expect threedimensional analysis to extend to thousands of individuals within marine aggregations to occur soon, as has been reported for terrestrial birds (Cavagna et al., 2008a,b). Membership of aggregations is often claimed to be an effective strategy in successful mate finding and reproduction (topic 4 in Section 6.1). We could find no evidence of progress since Ritz (1994) suggested that more experimental evidence was needed. This is perhaps an area that might be advanced through modelling studies, to examine the benefit in terms of fitness, and help refine experimental design. Ultimately, tests on real species would be desirable. Modelling studies (topic 5 in Section 6.1) are the basis of a suggestion that predatory risk-induced changes in behaviour could lead to major changes in population abundance (Alonzo and Mangel, 2001; Alonzo et al., 2003; Willis, 2007a). Using a dynamic state-variable model, Alonzo and Mangel (2001) predict that, in the face of extreme temperatures and/ or predation risk, Antarctic krill will shrink in size and spatial distribution may change. In Alonzo et al. (2003) they use the same approach to try to understand the relationship between krill fisheries and penguin foraging success in the Antarctic. The model suggests that a change in krill behaviour is likely to cause stronger effects of the fishery on penguins than can be explained solely by the percentage of biomass removed. This is because, as offshore krill are depleted by fishing, the deeper location of inshore krill near penguins, which are land based for reproduction, will make them less accessible to diving penguins. Note, though, that the absence of the influence of schooling behaviour in these models might alter the predator prey dynamics (Ritz, 2002). 214 David A. Ritz et al. Experimental studies of decision-making in the face of conflicting signals also seem rare in recent decades, and perhaps modelling or simulation studies might be timely to explore such trade-offs. Impacts of climate change on aggregations are difficult to predict though we do not anticipate direct effects. However, there are many possibilities for indirect consequences. These include the consequence of likely changes in water viscosity with increasing temperature; decreased acidity with increasing dissolved CO2; and distribution changes both geographical and vertical. With unprecedented rates of environmental change, the ability of pelagic species to adapt is questionable, and so we expect more attention to this area in the coming years. Aggregation in the pelagic zone is common, and likely important in the survival and well-being of many species, including humans who, via fisheries, rely heavily on aggregation to efficiently catch food. In fact, the average encounter rate of non-aggregated prey would lead to starvation in many predators (e.g. whales). With many of the world’s commercial target species already overfished, the importance of aggregation should not be underestimated in a functioning pelagic zone. ACKNOWLEDGEMENTS The encouragement of the former editor for Advances in Marine Biology, David Sims, in seeking this review and the comments of the editorial board in focusing the scope are appreciated. We thank all of our colleagues who allowed us to reproduce their published and unpublished figures. REFERENCES Adioui, M., Treuil, J. P. and Arino, O. (2003). Alignment in a fish school: A mixed Lagrangian Eulerian approach. Ecological Modelling 167, 19 32. Allan, J. R. (1986). The influence of species composition on behaviour in mixed species cyprinid shoals. Journal of Fish Biology 29(Suppl. A):97 106. Alldredge, A. L. and Hamner, W. M. (1980). Recurring aggregation of zooplankton by a tidal current. Estuarine and Coastal Marine Science 10, 31 37. Alldredge, A. L., Robison, B. H., Fleminger, A., Torres, J. J., King, M. and Hamner, W. M. (1984). Direct sampling and in situ observation of a persistent copepod aggregation in the mesopelagic zone of the Santa Barbara Basin. Marine Biology 80, 75 81. Allen, S. and Demer, D. A. (2003). Detection and characterization of yellowfin and bluefin tuna using passive-acoustical techniques. Fisheries Research 63, 393 403. Alonzo, S. H. and Mangel, M. (2001). Survival strategies and growth of krill: Avoiding predators in space and time. Marine Ecology Progress Series 209, 203 217. Alonzo, S. H., Switzer, P. V. and Mangel, M. (2003). An ecosystem-based approach to management: Using individual behaviour to predict the indirect effects of Antarctic krill fisheries on penguin foraging. Journal of Applied Ecology 40, 692 697. Angel, M. (1993). Biodiversity of the pelagic ocean. Conservation Biology 7, 760 772. Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 215 Atkinson, A., Siegel, V., Pakhomov, E. and Rothery, P. (2004). Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100 103. Atkinson, A., Shreeve, R. S., Hirst, A. G., Rothery, P., Tarling, G. A., Pond, D. W., Korb, R. E., Murphy, E. J. and Watkins, J. L. (2006). Natural growth rates in Antarctic krill (Euphausia superba): II. Predictive models based on food, temperature, body length, sex, and maturity stage. Limnology and Oceanography 51, 973 987. Axelsen, B. E., Anker-Nilssen, T., Fossum, P. and Nøttestad, L. (2001). Pretty patterns but a simple strategy: Predator prey interactions between juvenile herring and Atlantic puffins observed with multibeam sonar. Canadian Journal of Zoology 79, 1586 1596. Baker, C. F. and Montgomery, J. C. (2001). Species-specific attraction of migratory banded kokopu juveniles to adult pheromones. Journal of Fish Biology 58, 1221 1229. Bakun, A. and Cury, P. (1999). The ‘school trap’: A mechanism promoting large-amplitude out-of-phase population oscillations of small pelagic fish species. Ecology Letters 2, 349 351. Ballerini, M., Cabibbo, R., Candelier, A., Cavagna, E, Cisbani, I., Giardina, V., Lecomte, A., Orlandi, G., Parisi, A., Procaccini, M., Viale, M. and Zdravkovic, V. (2008). Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proceedings of the National Academy of Sciences of the United States of America 105, 1232 1237. Banas, N. S., Wang, D.-P. and Yen, J. (2004). Experimental validation of an individualbased model for zooplankton swarming. In Scales in aquatic ecology: Measurements, analysis, modeling (L. J. Seuront and P. G. Strutton, eds), pp. 161 180. CRC Press, Boca Raton, FL. Barbaro, A., Einarsson, B., Birnir, B., Sigurðsson, S., Valdimarsson, H., Pálsson, O. K., Sveinbjörnsson, S. and Sigurðsson, P. (2009). Modelling and simulations of the migration of pelagic fish. ICES Journal of Marine Science 66, 826 838. Belcher, E., Hanot, W, and Burch, J. (2002). Dual-Frequency Identification Sonar (DIDSON). Proceedings of the International Symposium on Underwater Technology, April 16 19, 187 192. Benoit-Bird, K. J. and Whitlow, W. L. (2009). Phonation behavior of cooperatively foraging spinner dolphins. Journal of the Acoustical Society of America 125, 539 546. Bertrand, A., Barbieri, M. A., Gerlotto, F., Leiva, F. and Cordova, J. (2006). Determinism and plasticity of fish schooling behaviour as exemplified by the South Pacific jack mackerel Trachurus murphyi. Marine Ecology Progress Series 311, 145 156. Block, B. A., Booth, D. T. and Carey, F. G. (1992). Depth and temperature of the blue marlin, Makaira nigricans, observed by acoustic telemetry. Marine Biology 114, 175 183. Block, B. A., Keen, J. E., Castillo, B., Dewar, H., Freund, E. V., Marcinek, D. J., Brill, R. W. and Farwell, C. (1997). Environmental preferences of yellowfin tuna (Thunnus albacares) at the northern extent of its range. Marine Biology 130, 119 132. Block, B. A., Dewar, H., Blackwell, S. B., Williams, T. D., Prince, E. D., Farwell, C. J., Boustany, A., Teo, S. L. H., Seitz, A., Walli, A. and Fudge, D. (2001). Migratory Movements, Depth Preferences, and Thermal Biology of Atlantic Bluefin Tuna. Science 293, 1310 1314. Bode, N. W. F., Faria, J. J., Franks, D. W. and Krause, J. (2010). How perceived threat increases synchronization in collectively moving animal groups. Proceedings of the Royal Society Series B Biological Sciences 277, 3065 3070. Boehlert, G. W. and Genin, A. (1987). A review of the effects of seamounts on biological processes. In Seamount, Islands and Atolls (Geophysical Monograph) (B. H. Keating, P. Fryer, R. Batiza and G. W. Boehlert, eds), pp. 319 334. American Geophysical Union, Washington D.C. 216 David A. Ritz et al. Burrows, M. T. and Tarling, G. (2004). Effect of density dependence on diel vertical migration of populations of northern krill: A genetic algorithm model. Marine Ecology Progress Series 277, 209 220. Buskey, E. (1998). Components of mating behavior in planktonic copepods. Journal of Marine Systems 15, 13 21. Buskey, E. J., Lenz, P. H. and Hartline, D. K. (2002). Escape behavior of planktonic copepods in response to hydrodynamic disturbances: High speed video analysis. Marine Ecology Progress Series 235, 135 146. Butler, M. J., Macdermid, A. B. and Booth, J. D. (1999). The cause and consequence of ontogenetic changes in social aggregation in New Zealand spiny lobsters. Marine Ecology Progress Series 188, 179 191. Cadavid, L. F., Powell, A. E., Nicotra, M. L., Moreno, M. and Buss, L. W. (2004). An invertebrate histocompatibility complex. Genetics 167, 357 365. Cao, L. and Caldeira, K. (2008). Atmospheric CO2 stabilization and ocean acidification. Geophysical Research Letters 35, L19609. Cass-Calay, S. L. (2003). The feeding ecology of larval Pacific hake (Merluccius productus) in the California Current region: An updated approach using a combined OPC/ MOCNESS to estimate prey biovolume. Fisheries Oceanography 12, 34 48. Cavagna, A. and Giardina, I. (2008). The seventh starling. Significance 5, 62 66. Cavagna, E., Giardina, I., Orlandi, G., Parisi, A., Procaccini, A., Viale, M. and Zdravkovic, V. (2008). The STARFLAG handbook on collective animal behaviour. Part I: Empirical methods. Animal Behaviour 76, 217 236. Cavagna, E., Giardina, I., Orlandi, G., Parisi, A. and Procaccini, A. (2008). The STARFLAG handbook on collective animal behaviour. 2: Three-dimensional analysis. Animal Behaviour 76, 237 248. Cavagna, A., Cimarelli, A., Giardina, I., Parisi, G., Santagati, R., Stefanini, F. and Tavarone, R. (2010). From empirical data to inter-individual interactions: Unveiling the rules of collective animal behavior. Mathematical Models and Methods in Applied Science 20, 1491 1510. Checkley, D. M. J., Ortner, P. B., Settle, L. R. and Cummings, S. R. (1997). A continuous, underway fish egg sampler. Fisheries Oceanography 6, 58 73. Chilvers, B. L. and Corkeron, P. J. (2002). Association patterns of bottlenose dolphins (Tursiops aduncus) off Point Lookout, Queensland, Australia. Canadian Journal of Zoology 80, 973 979. Chivers, D. P., Brown, G. E. and Smith, R. J. F. (1995). Familiarity and shoal cohesion in fathead minnows (Pimephales promelas) implications for antipredator behavior. Canadian Journal of Zoology 73, 955 960. Clapham, P. J. and Baker, C. S. (2003). How many whales were killed in the Southern Hemisphere in the 20th century? CAMLR SC/53/O 14, 1 3. Clutter, R. I. (1969). The microdistribution and social behaviour of some pelagic mysid shrimps. Journal of Experimental Marine Biology and Ecology 3, 125 155. Cohen, P. J. and Ritz, D. A. (2003). Role of kairomones in feeding interactions between seahorses and mysids. Journal of the Marine Biological Association of the UK 83, 633 638. Conradt, L. and Roper, T. J. (2000). Activity synchrony and social cohesion: A fission fusion model. Proceedings of the Royal Society of London Series B Biological Sciences 267, 2213 2218. Couzin, I. D. (2006). Behavioral ecology: Social organization in fission fusion societies. Current Biology 16, R169 R171. Couzin, I. D., James, R., Croft, D. P. and Krause, J. (2006). Social organization and information transfer in schooling. In Fish Cognition and Behaviour (C. Brown, K. N. Laland and J. Krause, eds), Blackwell Publishing, Oxford. Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 217 Cox, M. J., Warren, J. D., Cutter, G. R. and Brierley, A. S. (2009). Multibeam echosounder observations reveal interactions between Antarctic krill and air-breathing predators. Marine Ecology Progress Series 378, 199 209. Croft, D., James, R., Ward, A. J. W., Botham, M. S. and Mawdsley, D. (2005). Assortative interactions and social networks in fish. Oecologia 143, 211 219. Croft, D. P., James, R. and Krause, J. (2008). Exploring Animal Social Networks. Princeton University Press, Princeton. Cross, P. C., Lloyd-Smith, J. O., Bowers, J. A., Hay, C. T., Hofmeyr, M. and Getz, W. M. (2004). Integrating association data and disease dynamics in a social ungulate: Bovine tuberculosis in African buffalo in the Kruger National Park. Annales Zoologici Fennici 41, 879 892. Cury, P., Bakun, A., Jarre, A., Quiñones, R. A., Shannon, L. J. and Verheye, H. M. (2000). Small pelagics in upwelling systems: Patterns of interaction and structural changes in ‘wasp-waist’ ecosystems. ICES Journal of Marine Science 57, 603 618. Dagorn, L. and Fréon, P. (1999). Tropical tuna associated with floating objects: A simulation study of the meeting point hypothesis. Canadian Journal of Fisheries and Aquatic Sciences 56, 984 993. Daly, K. L. and Macaulay, M. C. (1991). Influence of physical and biological mesoscale dynamics on the seasonal distribution and behavior of Euphausia superba in the Antarctic marginal ice zone. Marine Ecology Progress Series 79, 37 66. Dare, J.E. (2008). Remaining Unseen in the Pelagic World: The Conflict between Camouflage and Feeding in Trachurus novaezelandiae. MSc Thesis, University of Auckland. Darwin, C. (1953). Note on hydrodynamics. Proceedings of the Cambridge Philosophical Society Biological Science 49, 342 354. Davis, T. L. O. and Stanley, C. A. (2002). Vertical and horizontal movements of southern bluefin tuna (Thunnus maccoyii) in the Great Australian Bight observed with ultrasonic telemetry. Fishery Bulletin 100, 448 465. De Robertis, A. (2002). Small-scale spatial distribution of the euphausiid Euphausia pacifica and the overlap with planktivorous fishes. Journal of Plankton Research 24, 1207 1220. DeBlois, E. M. and Rose, G. A. (1996). Cross-shoal variability in the feeding habits of migrating Atlantic cod (Gadus morhua). Oecologia 108, 192 196. Denton, E. J. (1970). Review lecture: On the organization of reflecting surfaces in some marine animals. Philosophical Transactions of the Royal Society of London Series B Biological Sciences 258, 285 313. Denton, E. J. (1980). Reflectors in fishes. Scientific American 242, 65 72. Denton, E. J. and Gray, J. A. B. (1993). Stimulation of the acoustico-lateralis system of clupeid fish by external sources and their own movements. Philosophical Transactions of the Royal Society of London Series B Biological Sciences 341, 113 127. Denton, E. J. and Rowe, D. M. (1994). Reflective communication between fish, with special reference to the greater sand eel, Hyperoplus lanceolatus. Philosophical Transactions of the Royal Society of London Series B Biological Sciences 344, 221 237. Denton, E. J. and Rowe, D. M. (1998). Bands against stripes on the backs of mackerel, Scomber scombrus. Proceedings of the Royal Society of London Series B Biological Sciences 265, 1051 1058. Dewar, W. K., Bingham, R. J., Iverson, R. L., Nowacek, D. P., St. Laurent, L. C. and Wiebe, P. H. (2006). Does the marine biosphere mix the ocean? Journal of Marine Research 64, 541 561. Doall, M. H. (1998). Locating a mate in 3D: The case of Temora longicornis. Philosophical Transactions of the Royal Society of London Series B Biological Sciences 353, 681 689. 218 David A. Ritz et al. Doall, M., Strickler, J., Fields, D. and Yen, J. (2002). Mapping the free-swimming attack volume of a planktonic copepod, Euchaeta rimana. Marine Biology 140, 881 882. Dudzinski, K. M., Douaze, E. and Thomas, J. (2002). Communication. In Encyclopedia of Marine Mammals (W. F. Perrin, B. Würsig and H. C. M. Thewissen, eds), Academic Press, Inc., Burlington, MA, USA. Eloffson, R. (1966). The nauplius eye and frontal organs of the non-malacostracans (Crustacea). Sarsia 25, 1 128. Faucher, K., Parmentier, E., Becco, C., Vandewalle, N. and Vandewalle, P. (2010). Fish lateral system is required for accurate control of shoaling behaviour. Animal Behaviour 79, 679 687. Fewell, J. H. (2003). Social insect networks. Science 301, 1867 1870. Field, C. B., Behrenfeld, M. J., Randerson, J. T. and Falkowski, P. (1998). Primary Production of the Biosphere. Integrating Terrestrial and Oceanic Components Science 281, 237 240. Flierl, G., Grunbaum, D., Levin, S. and Olson, D. (1999). From individuals to aggregations: The interplay between behavior and physics. Journal of Theoretical Biology 196, 397 454. Flynn, A. J. and Ritz, D. A. (1999). Effect of habitat complexity and predatory style on capture success of fish feeding on aggregated prey. Journal of the Marine Biological Association of the U.K. 79, 487 494. Folt, C. L. and Burns, C. W. (1999). Biological drivers of zooplankton patchiness. Trends in Ecology and Evolution 14, 300 305. Forrester, G. E. (1991). Social rank, individual size and group composition as determinants of food-consumption by humbug damselfish, Dascyllus aruanus. Animal Behaviour 42, 701 711. Frank, S. A. (2007). All of life is social. Current Biology 17, 648 650. Freon, P. and Dagorn, L. (2000). Review of fish associative behaviour: Towards a generalization of the meeting point hypothesis. Reviews in Fish Biology and Fisheries 10, 183 207. Fuiman, L. A. and Magurran, A. E. (1994). Development of predator defenses in fishes. Reviews in Fish Biology and Fisheries 4, 145 183. Gaertner, D., Menard, F., Develter, C., Ariz, J. and Delagardo de Molina, A. (2002). Bycatch of billfishes by the European tuna purse-seine fishery in the Atlantic Ocean. Fishery Bulletin 100, 683 689. Genin, A. (2004). Bio-physical coupling in the formation of zooplankton and fish aggregations over abrupt topographies. Journal of Marine Systems 50, 3 20. Genin, A., Greene, C., Haury, L., Wiebe, P., Gal, G., Kaartvedt, S., Meir, E., Fey, C. and Dawson, J. (1994). Zooplankton patch dynamics: Daily gap formation over abrupt topography. Deep-Sea Research 41, 941 951. Genin, A., Haury, L. R. and Greenblatt, P. (1988). Interactions of migrating zooplankton with shallow topography: Predation by rockfish and intensification of patchiness. DeepSea Research 35, 151 175. Genin, A., Jaffe, J. S., Reef, R., Richter, C. and Franks, P. J. S. (2005). Swimming against the flow: A mechanism of zooplankton aggregation. Science 308, 860 862. Gerasimov, V. V. (1962). Feeding behavior of Murmansk herring in school and out of school in aquarium conditions. Trudy Murmanskogo Morskogo Biologicheskogo Instituta 2, 254 259. Gerlotto, F. and Paramo, J. (2003). The three dimensional morphology and internal structure of Clupeids schools as observed using vertical scanning multibeam sonar. Aquatic Living Resources 16, 113 122. Gerlotto, F., Castillo, J., Saavedra, A., Barbieri, M. A., Espejo, M. and Cotel, P. (2004). Three-dimensional structure and avoidance behaviour of anchovy and common sardine schools in central southern Chile. ICES Journal of Marine Science 61, 1120 1126. Giardina, I. (2008). Collective behavior in animal groups: Theoretical models and empirical studies. Human Frontier Science Project Journal 2, 205 219. Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 219 Gilly, W. F., Markaida, U., Baxter, C. H., Block, B. A., Boustany, A., Zeidberg, L., Reisenbichler, K., Robison, B., Bazzino, G. and Salinas, C. (2006). Vertical and horizontal migrations by the jumbo squid Dosidicus gigas revealed by electronic tagging. Marine Ecology Progress Series 324, 1 17. Giraldeau, L.-A. and Caraco, T. (2000). Social Foraging Theory. Princeton University Press, Princeton. Giraldeau, L.-A. and Gillis, D. (1985). Optimal group size can be stable: A reply to Sibly. Animal Behaviour 33, 666 667. Giske, J., Huse, G. and Fiksen, O. (1998). Modelling spatial dynamics of fish. Reviews in Fish Biology and Fisheries 8, 57 91. Glance, N. S. and Huberman, B. A. (1994). The dynamics of social dilemmas. Scientific American 270, 58 63. Goñi, N., Arregui, I., Lezama, A., Arrizabalaga, H. and Moreno, G. (2009). Small scale vertical behaviour of juvenile albacore in relation to their biotic environment in the Bay of Biscay. In Tagging and Tracking of Marine Animals with Electronic Devices (J. Nielsen, H. Arrizabalaga, N. Fragosa, A. Hobday, M. E. Lutcavage and J. Sibert, eds), Vol. 2, Springer Academic Publishers, Dordrecht, The Netherlands. Goodwin, N. B., Grant, A., Perry, A. L., Dulvy, N. K. and Reynolds, J. D. (2006). Life history correlates of density-dependent recruitment in marine fishes. Canadian Journal of Fisheries and Aquatic Sciences 63, 494 509. Gowans, S., Whitehead, H. and Hooker, S. K. (2001). Social organization in northern bottlenose whales, Hyperoodon ampullatus: Not driven by deep-water foraging? Animal Behaviour 62, 369 377. Gray, J. A. B. and Denton, E. J. (1991). Fast pressure pulses and communication between fish. Journal of the Marine Biological Association of United Kingdom 71, 83 106. Grothues, T.M. (2009). A Review of Acoustic Telemetry Technology and a Perspective on its Diversification Relative to Coastal Tracking Arrays. In Tagging and Tracking of Marine Animals with Electronic Devices Reviews: Methods and Technologies in Fish Biology and Fisheries, Vol. 9, 77-90. Springer Science, Netherlands. Grunbaum, D. (2006). Align in the sand. Science 312, 1320 1322. Gunn, J. and Block, B. (2001). Advances in acoustic, archival, and satellite tagging of tunas. In Tuna: Physiology, Ecology and Evolution (B. A. Block and E. D. Stevens, eds), pp. 167 224. Academic Press, San Diego, CA. Hallier, J.-P. and Gaertner, D. (2008). Drifting fish aggregation devices could act as an ecological trap for tropical tuna species. Marine Ecology Progress Series 353, 255 264. Hamilton, W. D. (1964). The genetical evolution of social behaviour. Journal of Theoretical Biology 7, 1 52. Hamner, W. M. (1985). The importance of ethology for investigations of marine zooplankton. Bulletin of Marine Science 37, 414 424. Hamner, W. M. (1988). Behaviour of plankton and patch formation in pelagic ecosystems. Bulletin of Marine Science 43, 752 757. Hamner, W. M. and Parrish, J. K. (eds), (1997). Animal Groups in Three Dimensions: How Species Aggregate Cambridge University Press, Cambridge. Hamner, W. M., Hamner, P. P., Obst, B. S. and Carleton, J. H. (1989). Field observations on the ontogeny of schooling of Euphausia superba furciliae and its relationship to ice in Antarctic waters. Limnology and Oceanography 34, 451 456. Hanamura, Y., Endo, Y. and Taniguchi, A. (1984). Underwater observations on the surface swarm of a euphausiid, Euphausia pacifica in Sendai Bay, northeastern Japan. La Mer 22, 63 68. Hardy, A. C. and Gunther, E. R. (1935). The plankton of the South Georgia whaling grounds and adjacent waters 1926 27. Discovery Reports 11, 1 456. 220 David A. Ritz et al. Harley, C. D. G., Hughes, A. R., Hultgren, K., Miner, B. G., Sorte, C. J. B., Thornber, C. S., Rodriguez, L. F., Tomanek, L. and Williams, S. L. (2006). The impacts of climate change in coastal marine systems. Ecology Letters 9, 228 241. Hartman, K. L., Visser, F. and Hendricks, A. J. E. (2008). Social structure of Risso’s dolphins (Grampus griseus) at the Azores: A stratified community based on highly associated social units. Canadian Journal of Zoology 86, 294 306. Haury, L. R., McGowan, J. A. and Wiebe, P. H. (1978). Patterns and processes in the time-space scales of plankton distributions. In Spatial Patterns in Plankton Communities (J. H. Steele, ed), pp. 277 327. Plenum Press, New York. Hay, D. E. and McKinnell, S. M. (2002). Tagging along: Association among individual Pacific herring (Clupea pallasii) revealed by tagging. Canadian Journal of Fisheries and Aquatic Sciences 59, 1960 1968. Heino, M. and Dieckmann, U. (2009). Fisheries-induced evolution in Encyclopedia of Life Sciences. John Wiley & Sons, Chichester, UK. Helfman, G. (1984). School fidelity in fishes: The yellow perch pattern. Animal Behaviour 32, 673 689. Hensor, E., Couzin, I. D., James, R. and Krause, J. (2005). Modelling density-dependent fish shoal distributions in the laboratory and field. Oikos 110, 344 352. Heppner, F. (1997). Three-dimensional structure and dynamics of bird flocks. In Animal Groups in Three Dimensions: How Species Aggregate (J. K. Parrish and W. M. Hamner, eds), pp. 68 87. Cambridge University Press, Cambridge. Heupel, M. R., Semmens, J. M. and Hobday, A. J. (2006). Automated acoustic tracking of aquatic animals: Scales, design and deployment of listening station arrays. Marine and Freshwater Research 57, 1 13. Higgs, D. M. and Fuiman, L. A. (1996). Light intensity and schooling behaviour in larval gulf menhaden. Journal of Fish Biology 48, 979 991. Higham, J., Bejder, L. and Lusseau, D. (2009). An integrated and adaptive management model to address the long-term sustainability of tourist interactions with cetaceans. Environmental Conservation 35, 294 302. Hilborn, R. (1991). Modeling the stability of fish schools: Exchange of individual fish between schools of skipjack tuna (Katsuwonus pelamis). Canadian Journal of Fisheries and Aquatic Science 48, 1081 1091. Hoare, D. and Krause, J. (2003). Social organisation, shoal structure and information transfer. Fish and Fisheries 4, 269 279. Hoare, D. J., Ruxton, G. D., Godin, J.-G. J. and Krause, J. (2000). The social organization of free-ranging fish shoals. Oikos 89, 546 554. Hobday, A. J. and Campbell, G. (2009). Topographic preferences and habitat partitioning by pelagic fishes in southern Western Australia. Fisheries Research 95, 332 340. Hobday, A. J., Kawabe, R., Takao, Y., Miyashita, K. and Itoh, T. (2009). Correction of an abundance index using acoustic tag data for juvenile southern bluefin tuna in southern Western Australia. In Tagging and Tracking of Marine Animals with Electronic Devices II. Reviews: Methods and Technologies in Fish Biology and Fisheries (J. Nielsen, J. R. Sibert, A. J. Hobday, M. E. Lutcavage, H. Arrizabalaga and N. Fragosa, eds), pp. 405 422. Springer, The Netherlands. Holland, K. N., Meyer, C. G. and Dagorn, L. C. (2009). Inter-animal telemetry: Results from first deployment of acoustic ‘business card’ tags. Endangered Species Research 10, 287 293. Huntley, M. E. and Zhou, M. (2004). Influence of animals on turbulence in the sea. Marine Ecology Progress Series 273, 65 79. Hurley, A. C. (1977). Mating behaviour of the squid Loligo opalescens. Marine Behaviour and Physiology 4, 195 203. IGBP Science 5 (2003). Marine Ecosystems and Global Change. (M. Barange and R. Harris, eds). Stockholm: IGBP, 32 pp. Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 221 Jaffe, J. S., Ohman, M. D. and Roberts, A. D. (1998). OASIS in the sea: Measurement of the acoustic reflectivity of zooplankton with concurrent optical imaging. Deep-Sea Research II 45, 1239 1253. Janssen, J. (1981). Searching for zooplankton just outside Snell’s window. Limnology and Oceanography 26, 1168 1171. Jiang, H., Osborn, T. R. and Meneveau, C. (2002). Hydrodynamic interaction between two copepods: A numerical study. Journal of Plankton Research 24, 235 253. Johnsen, S. and Sosik, H. M. (2003). Cryptic coloration and mirrored sides as camouflage strategies in near-surface pelagic habitats: Implications for foraging and predator avoidance. Limnology and Oceanography 48, 1277 1288. Johnston, N. and Ritz, D. A. (2001). Synchronous development and release of broods by the swarming mysids Anisomysis mixta australis, Paramesopodopsis rufa and Tenagomysis tasmaniae (Mysidacea: Crustacea). Marine Ecology Progress Series 223, 225 233. Kaartvedt, S., Melle, W., Knutsen, T. and Skjoldal, H. R. M (1996). Vertical distribution of fish and krill beneath water of varying optical properties. Marine Ecology Progress Series 136, 51 58. Kaartvedt, S., Røstad, A., Klevjer, T. A. and Staby, A. (2009). Use of bottom-mounted echo sounders in exploring behavior of mesopelagic fishes. Marine Ecology Progress Series 395, 109 118. Kaiser, M. J., Attrill, M. J., Jennings, S., Thomas, D. N., Barnes, D. K. A., Brierley, A. S., Polunin, N., Raffaelli, D. G., Williams, P. J. and le, B (2005). Marine Ecology Processes, Systems and Impacts. Oxford University Press, Oxford. Katija, K. and Dabiri, J. O. (2009). A viscosity-enhanced mechanism for biogenic ocean mixing. Nature 460, 624 627. Kawaguchi, S., King, R., Meijers, R., Osborn, J. E., Swadling, K. M., Ritz, D. A. and Nicol, S. (2010). An experimental aquarium for observing the schooling behaviour of Antarctic krill (Euphausia superba). Deep-Sea Research II 57, 683 692. Kils, U. (1992). The ecoSCOPE and dynIMAGE: Microscale Tools for in situ Studies of Predator Prey Interactions. Archiv fur Hydrobiologie Beih 36, 83 96. Klimley, A. P. and Holloway, C. F. (1999). School fidelity and homing synchronicity of yellowfin tuna. Thunnus albacares. Marine Biology 133, 307 317. Klimley, A. P., Jorgensen, S. J., Muhlia-Melo, A. and Beavers, S. C. (2003). The occurrence of yellowfin tuna (Thunnus albacares) at Espiritu Seamount in the Gulf of California. Fishery Bulletin 101, 686 692. Krause, J. and Ruxton, G. D. (2002). Living in groups. Oxford University Press, Oxford 240 pp. Krause, J., Godin, J. G. J. and Brown, D. (1996). Phenotypic variability within and between fish shoals. Ecology 77, 1586 1591. Krause, J., Hoare, D. J., Croft, D., Lawrence, J., Ward, A. J. W., Ruxton, G. D., Godin, J. G. J. and James, R. (2000). Fish shoal composition: Mechanisms and constraints. Proceedings of the Royal Society of London Series B Biological Sciences 267, 2011 2017. Krause, J., Ward, A. J. W., Jackson, A. L., Ruxton, G. D., James, R. and Currie, S. (2005). The influence of differential swimming speeds on composition of multi-species fish shoals. Journal of Fish Biology 67, 866 872. Land, M. (1988). The optics of animal eyes. Contemporary Physics 29, 435 455. Last, P. R., White, W. T., Gledhill, D. C., Hobday, A. J., Brown, R., Edgar, G. J. and Pecl, G. T. (2011). Long-term shifts in abundance and distribution of a temperate fish fauna: A response to climate change and fishing practices. Global Ecology and Biogeography 20, 58 72. Latora, V. and Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters 87 222 David A. Ritz et al. Leising, A. W. (2001). Copepod foraging in patchy habitats and thin layers using a 2-D individual-based model. Marine Ecology Progress Series 216, 167 179. Leising, A. W. and Franks, P. J. S. (2000). Copepod vertical distribution within a spatially variable food source: A simple foraging-strategy model. Journal of Plankton Research 22, 999 1024. Leising, A. W. and Yen, J. (1997). Spacing mechanisms within light-induced copepod swarms. Marine Ecology Progress Series 155, 127 135. Levin, S. A. (1992). The problem of pattern and scale in ecology. Ecology 73, 1943 1967. Levin, S. A. (1997). Conceptual and methodological issues in the modeling of biological aggregations. In Animal Groups in Three Dimensions (J. K. Parrish and W. M. Hamner, eds), pp. 247 256. Cambridge University Press, Cambridge. Liao, J. C. (2007). A review of fish swimming mechanics and behaviour in altered flows. Philosophical Transactions of the Royal Society Series B 362, 1973 1993. Liao, J. C., Beal, D. N., Lauder, G. V. and Triantafyllou, M. S. (2003). The Karman gait: Novel kinematics of rainbow trout swimming in a vortex street. Journal of Experimental Biology 206, 1059 1073. Liao, J. C., Beal, D. N., Lauder, G. V. and Triantafyllou, M. S. (2003). Fish exploiting vortices decrease muscle activity. Science 302, 1566 1569. Loeb, V. J., Hofmann, E. E., Klinck, J. M., Holm-Hansen, O. and White, W. B. (2009). ENSO and variability of the Antarctic Peninsula pelagic marine ecosystem. Antarctic Science 21, 135 148. Lusseau, D. (2003). The emergent properties of a dolphin social network. Proceedings of the Royal Society of London Series B Biological Sciences (Suppl.) 270, S186 S188. Lusseau, D. and Newman, M. E. J. (2004). Identifying the role that animals play in their social networks. Proceedings of the Royal Society of London Series B Biological Sciences (Suppl.) 271, S477 S481. MacCall, A.D. (1990). Dynamic geography of marine fish populations. Washington Sea Grant Program (Seattle) 153pp. Mackas, D. L., Denham, K. L. and Abbott, M. R. (1985). Plankton patchiness: Biology in the physical vernacular. Bulletin of Marine Science 37, 652 674. Major, P. F. (1977). Predator prey interactions in schooling fishes during periods of twilight: A study of the silverside Pranesus insularum in Hawaii. Fisheries Bulletin US 75, 415 426. Makris, N., Ratilal, P., Symonds, D., Jagannathan, S., Lee, S. and Nero, R. (2006). Fish population and behavior revealed by instantaneous continental shelf-scale imaging. Science 311, 660 663. Malkiel, E., Alquaddoomi, O. and Katz, J. (1999). Measurements of plankton distribution in the ocean using submersible holography. Measurement Science and Technology 10, 1142 1152. Masuda, R. and Tsukamoto, K. (1999). School formation and concurrent developmental changes in carangid fish with reference to dietary conditions. Environmental Biology of Fishes 56, 243 252. Mauchline, J. (1980). The Biology of Mysids and Euphausiids. Advances in Marine Biology 18, 1 681. McFarland, W. N. and Kotchian, N. M. (1982). Interaction between schools of fish and mysids. Behavioural Ecology and Sociobiology 11, 71 76. McKinnell, S., Pella, J. J. and Dahlberg, M. L. (1997). Population-specific aggregations of steelhead trout (Oncorhynchus mykiss) in the North Pacific Ocean. Canadian Journal of Fisheries and Aquatic Sciences 54, 2368 2376. Milinski, M. (1987). Tit-for-tat in sticklebacks and the evolution of cooperation. Nature 325, 433 437. Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 223 Milinski, M., Pfluger, D., Kulling, D. and Kettler, R. (1990). Do sticklebacks cooperate repeatedly in reciprocal pairs? Behavioural Ecology and Sociobiology 27, 17 21. Miller, D. G. M. and Hampton, I. (1989). Biology and Ecology of the Antarctic krill. SCAR and SCOR Scott Polar Institute, Cambridge. Mochek, A. D. (1987). Ethological Organization of Coastal Fish Communities. Nauka, Moscow [in Russian] Montgomery, J.C. and Carton, A.G. The senses: chemosensory, visual and octavolateralis. In: Fish Behaviour. (C. Magnhagen, V.A. Braithwaite, E. Forsgren and B.G. Kapoor eds.) Enfield, New Hampshire.Science Publishers: pp. 3-32. Montgomery, J. C., Coombs, S. and Halstead, M. B. D. (1995). Biology of the mechanosensory lateral line in fishes. Reviews in Fish Biology and Fisheries 5, 399 416. Montgomery, J. C., Baker, C. F. and Carton, A. G. (1997). The lateral line can mediate rheotaxis in fish. Nature 389, 960 963. Montgomery, J. C., McDonald, F., Baker, C. F., Carton, A. G. and Ling, N. (2003). Sensory integration in the hydrodynamic world of rainbow trout. Proceedings of the Royal Society of London Series B Biological Sciences 270(Suppl. 2):195 197. Munday, P. L., Dixson, D. L., Donelson, J. M., Jones, G. P., Pratchett, M. S., Devitsina, G. V. and Doving, K. B. (2009). Ocean acidification impairs olfactory discrimination and homing ability of a marine fish. Proceedings of the National Academy of Sciences of the United States of America 106, 1848 1852. Nicol, S. (2006). Krill, Currents, and Sea Ice: Euphausia superba and its Changing Environment. BioScience 56, 111 120. Nielsen, J., Sibert, J. R., Hobday, A. J., Lutcavage, M. E., Arrizabalaga, H. and Fragosa, N. (2009). ‘Tagging and Tracking of Marine Animals with Electronic Devices’ Reviews: Methods and Technologies in Fish Biology and Fisheries. Springer, Netherlands. O’Brien, D. P. (1987). Direct observations of the behaviour of Euphausia superba and Euphausia crystallorophias (Crustacea: Euphausiacea) under pack ice during the Antarctic spring of 1985. Journal of Crustacean Biology 7, 437 448. O’Brien, D. P. (1988). Direct observations of clustering (schooling and swarming) behaviour in mysids (Crustacea: Mysidacea). Marine Ecology Progress Series 42, 235 246. O’Brien, D. P. (1989). Analysis of the internal arrangement of individuals within crustacean aggregations (Euphausiacea, Mysidacea). Journal of Experimental Marine Biology and Ecology 128, 1 30. O’Brien, D. P. and Ritz, D. A. (1988). The escape responses of gregarious mysids (Crustacea; Mysidacea): Towards a general classification of escape responses in aggregated crustaceans. Journal of Experimental Marine Biology and Ecology 116, 257 272. O’Brien, D. P., Tay, D. and Zwart, P. R. (1986). Laboratory method of analysis of swarming behaviour in macroplankton: Combination of a modified flume tank and stereophotographic techniques. Marine Biology 90, 517 527. Occhipinti-Ambrogi, A. (2007). Global change and marine communities: Alien species and climate change. Marine Pollution Bulletin 55, 342 352. Ohtsuka, S., Inagaki, H., Onbe, T., Gushima, K. and Yoon, Y. H. (1995). Direct observations of groups of mysids in shallow coastal waters of western Japan and southern Korea. Marine Ecology Progress Series 123, 33 44. Omori, M. and Hamner, W. M. (1982). Patchy distribution of zooplankton: behaviour, population assessment and sampling problems. Marine Biology 72, 193 200. Osborn, J. (1997). Analytical and digital photogrammetry. In Animal Groups in Three Dimensions (J. K. Parrish and W. M. Hamner, eds), pp. 36 60. Cambridge University Press, Cambridge. Ottensmayer, C. A. and Whitehead, H. (2003). Behavioural evidence for social units in long-finned pilot whales. Canadian Journal of Zoology 81, 1327 1338. 224 David A. Ritz et al. Pakhomov, E. A., Perissinotto, R., Froneman, P. W. and Miller, D. G. M. (1997). Energetics and feeding dynamics of Euphausia superba in the South Georgia region during the summer of 1994. Journal of Plankton Research 19, 399 423. Parrish, J. K. and Edelstein-Keshet, L. (1999). Complexity, pattern and evolutionary trade-offs in animal aggregation. Science 284, 99 101. Parrish, J. K., Viscido, S. V. and Grunbaum, D. (2002). Self-organized fish schools: An examination of emergent properties. Biological Bulletin 202, 296 305. Partridge, B. L. and Pitcher, T. J. (1980). The sensory basis of fish schools: Relative roles of lateral line and vision. Journal of Comparative Physiology 135, 315 325. Patria, M. P. and Wiese, K. (2004). Swimming in formation in krill (Euphausiacea), a hypothesis: Dynamics of the flow field, properties of antennular sensor systems and a sensory-motor link. Journal of Plankton Research 26, 1315 1325. Patterson, T. A., Evans, K., Carter, T. I. and Gunn, J. S. (2008). Movement and behaviour of large southern bluefin tuna (Thunnus maccoyii) in the Australian region determined using pop-up satellite archival tags. Fisheries Oceanography 17, 352 367. Pauly, D., Christensen, V., Guénette, S., Pitcher, T. J., Sumaila, U. R., Walters, C. J., Watson, R. and Zeller, D. (2002). Towards sustainability in world fisheries. Nature 418, 689 695 . Pauly, D., Watson, R. and Alder, J. (2005). Global trends in world fisheries: Impacts on marine ecosystems and food security. Philosophical Transactions of the Royal Society Series B 360, 5 12. Pavlov, D. S. and Kasumyan, A. O. (2000). Patterns and mechanisms of schooling behavior of fish: A review. Journal of Ichthyology 40, S163 S231. Pearson, H. C. (2009). Influences on dusky dolphin (Lagenorhynchus obscurus) fission fusion dynamics in Admiralty Bay, New Zealand. Behavioural Ecology and Sociobiology 63, 1437 1446. Pitcher, T. J. and Parrish, J. K. (1993). Functions of shoaling behaviour in teleosts. In Behaviour of Teleost Fishes (T. Pitcher, ed), pp. 363 439. Chapman & Hall, London. Pitcher, T. J. (ed) (1993). Behaviour of Teleost Fishes 2nd edn. Chapman & Hall, London. Poulet, S. A. and Ouellet, G. (1982). The role of amino acids in the chemosensory swarming and feeding of marine copepods. Journal of Plankton Research 4, 341 359. Probatov, S. N. (1953). The results of the air exploring of the Caspian mullet and the possibilities of its catch on the routes of migration. Fisheries 8, 18 22. Quinn, T. J. and Deriso, R. B. (1999). Quantitative Fish Dynamics. Oxford University Press, Oxford. Quinn, T. P. and Tolson, G. M. (1986). Evidence of chemically mediated population recognition in coho salmon (Oncorhynchus kisutch). Canadian Journal of Zoology 64, 84 87. Radakov, D. V. (1973). Schooling in the Ecology of Fish. John Wiley & Sons, New York. Ratchford, S. G. and Eggleston, D. B. (1998). Size- and scale-dependent chemical attraction contribute to an ontogenetic shift in sociality. Animal Behaviour 56, 1027 1034. Ritz, D. A. (1994). Social aggregation in pelagic invertebrates. Advances in Marine Biology 30, 155 216. Ritz, D. A. (1997). Costs and benefits as a function of group size: Experiments on a swarming mysid, Paramesopodopsis rufa Fenton. In Animal Groups in Three dimensions: How Species Aggregate (W. M. Hamner and J. K. Parrish, eds), pp. 194 206. Cambridge University Press, Cambridge. Ritz, D. A. (2000). Is social aggregation in aquatic crustaceans a strategy to conserve energy? Canadian Journal Fisheries and Aquatic Science 57, 59 67. Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 225 Ritz, D. A. (2002). Comment on Alonzo & Mangel (2001) survival strategies and growth of krill: Avoiding predators in space and time. Marine Ecology Progress Series 244, 307 308. Ritz, D. A., Foster, E. G. and Swadling, K. M. (2001). Benefits of swarming: mysids in larger swarms save energy. Journal of the Marine Biological Association of the United Kingdom 81, 543 544. Ritz, D. A. and Swadling, K. M. (2006). Energy savings at school. JMBA Global Marine Environment 3, 10 11. Ritz, D. A., Cromer, L., Swadling, K. M., Nicol, S. and Osborn, J. (2003). Heart rate as a measure of stress in Antarctic krill, Euphausia superba. Journal of the Marine Biological Association of the United Kingdom 83, 329 330. Roberts, P. D., Stewart, G. B. and Pullin, A. S. (2006). Are review articles a reliable source of evidence to support conservation and environmental management? A comparison with medicine. Biological Conservation 132, 409 423. Roman, J. and Palumbi, S. R. (2003). Whales Before Whaling in the North Atlantic. Science 301, 508 510. Rowe, D. M. and Denton, E. J. (1997). The physical basis of reflective communication between fish, with special reference to the horse mackerel, Trachurus trachurus. Philosophical Transactions of the Royal Society of London Series B Biological Sciences 352, 531 549. Ryer, C. H. and Olla, B. L. (1992). Social mechanisms facilitating exploitation of spatially variable ephemeral food patches in a pelagic marine fish. Animal Behavior 44, 69 74. Sale, P. F. (1971). Extremely limited home range in a coral reef fish, Dascyllus aruanus (Pisces: Pomacentridae). Copeia 1971, 324 327. Shane, S. H., Wells, R. S. and Würsig, B. (1986). Ecology, behavior and social organization of the bottlenose dolphin: A review. Marine Mammal Science 2, 34 63. Shaw, E. (1978). Schooling fishes. American Scientist 66, 166 175. Sibert, J. R., Hampton, J., Kleiber, P. and Maunder, M. (2006). Biomass, Size, and Trophic Status of Top Predators in the Pacific Ocean. Science 314, 1773 1776. Sibly, R. M. (1983). Optimal group size is unstable. Animal Behaviour 31, 947 948. Siegel, V. and Kalinowski, J. (1994). Krill demography and small-scale processes: A review. In Southern Ocean Ecology the Biomass Perspective (S. Z. El-Sayed, ed), pp. 145 164. Cambridge University Press, Cambridge. Slooten, E., Dawson, S. M. and Whitehead, H. (1993). Associations among photographically identified Hector’s Dolphins. Canadian Journal of Zoology 71, 2311 2318. Smetacek, V. and Nicol, S. (2005). Polar ocean ecosystems in a changing world. Nature 437, 362 368. Soria, M., Dagorn, L., Potin, G. and Freon, P. (2009). First field-based experiment supporting the meeting point hypothesis for schooling in pelagic fish. Animal Behavior 78, 1441 1446. Steele, J. H. (1980). Patterns in plankton. Oceanus 23, 2 8. Steele, J. H. and Henderson, E. W. (1981). A simple plankton model. American Naturalist 117, 676 691. Strand, S. W. and Hamner, W. M. (1990). Schooling behaviour of Antarctic krill (Euphausia superba) in laboratory aquaria: Reactions to chemical and visual stimuli. Marine Biology 106, 355 360. Strickler, J. R. (1998). Observing free-swimming copepods mating. Philosophical Transactions of the Royal Society of London Series B 353, 671 680. Svendsen, J. C., Skov, J., Bildsoe, M. and Steffensen, J. F. (2003). Intra-school positional preference and reduced tail beat frequency in trailing positions in schooling roach under experimental conditions. Journal of Fish Biology 62, 834 846. 226 David A. Ritz et al. Taquet, M., Dagorn, L. C., Gaertner, J.-C., Girard, C., Aumerruddy, R., Sancho, G. and Itano, D. (2007). Behavior of dolphinfish (Coryphaena hippurus) around drifting FADs as observed from automated acoustic receivers. Aquatic Living Resources 20, 323 330. Teo, S. L. H., Boustany, A., Blackwell, S., Walli, A., Weng, K. C. and Block, B. A. (2004). Validation of geolocation estimates based on light level and sea surface temperature from electronic tags. Marine Ecology Progress Series 283, 81 98. Turchin, P. (1997). Quantitative Analysis of Movement. Sinauer Associates, Sunderland, MA. Twining, B. S., Gilbert, J. J. and Fisher, N. S. (2000). Evidence of homing behavior in the coral reef mysid Mysidium gracile. Limnology and Oceanography 45, 1845 1849. Tyack, P. L. (2000). Animal behavior: dolphins whistle a signature tune. Science 289, 1310 1311. Verdy, A. and Flierl, G. R. (2009). Evolution and social behavior in krill. Deep-Sea Research II 55, 472 484. Villinger, J. and Waldman, B. (2008). Self-referent MHC type matching in frog tadpoles. Proceedings of the Royal Society of London Series B Biological Sciences 275, 1225 1230. Viscido, S. V., Parrish, J. K. and Grunbaum, D. (2004). Individual behavior and emergent properties of fish schools: A comparison of observation and theory. Marine Ecology Progress Series 273, 239 249. Viscido, S. V., Parrish, J. K. and Grunbaum, D. (2007). Factors influencing the structure and maintenance of fish schools. Ecological Modelling 206, 153 165. Ward, A. J. W. and Hart, P. J. B. (2003). The effects of kin and familiarity on interactions between fish. Fish and Fisheries 4, 348 358. Ward, A. J. W. and Hart, P. J. B. (2005). Foraging benefits of shoaling with familiars may be exploited by outsiders. Animal Behaviour 69, 329 335. Ward, A. J. W., Botham, M. S., Hoare, D. J., James, R., Broom, M., Godin, J.-G. J. and Krause, J. (2002). Association patterns and shoal fidelity in the three-spined stickleback. Proceedings of the Royal Society of London Series B Biological Sciences 269, 2451 2455. Warner, R. R. (1988). Traditionality of mating-site preferences in a coral-reef fish. Nature 335, 719 721. Warren, J. D., Stanton, T. K., Benfield, M. C., Wiebe, P. H., Chu, D. and Sutor, M. (2001). In situ measurements of acoustic target strengths of gas-bearing siphonophores. ICES Journal of Marine Science 58, 740 749. Watkins, J. J. and Murray, A. W. A. (1998). Layers of Antarctic krill, Euphausia superba: Are they just long krill swarms? Marine Biology 131, 237 247. Watkins, J. L., Buchholz, F., Priddle, J., Morris, D. J. and Ricketts, C. (1992). Variation in reproductive status of Antarctic krill swarms: Evidence for a size-related sorting mechanism? Marine Ecology Progress Series 82, 163 174. Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature 393, 440 442. Weber, L., El-Sayed, S. Z. and Hampton, I. (1986). The variance spectra of phytoplankton, krill and water temperature in the Antarctic Ocean south of Africa. Deep-Sea Research 33, 1327 1343. Webster, S. J. and Fiorito, G. (2001). Socially guided behaviour in non-insect invertebrates. Animal Cognition 4, 69 79. Weimerskirch, H., Martin, J., Clerquin, Y., Alexandre, P. and Jiraskova, S. (2001). Energy saving in flight formation. Nature 413, 697 698. Welch, D. W. and Eveson, J. P. (1999). An assessment of light-based geoposition estimates from archival tags. Canadian Journal of Fisheries and Aquatic Sciences 56, 1317 1327. Weng, K. C., Castilho, P. C., Morrissette, J. M., Landeira-Fernandez, A. M., Holts, D. B., Schallert, R. J., Goldman, K. J. and Block, B. A. (2005). Satellite tagging and cardiac physiology reveal niche expansion in salmon sharks. Science 310, 104 106. Social Aggregation in the Pelagic Zone with Special Reference to Fish and Invertebrates 227 Whiteman, E. A. and Cote, I. M. (2004). Dominance hierarchies in group-living cleaning gobies: Causes and foraging consequences. Animal Behaviour 67, 239 247. Wiebe, P. H., Greene, C. H., Benfield, M. C., Sosik, H. M., Austin, T. C., Warren, J. D and Hammar, T. (2002). BIOMAPER-II: An integrated instrument platform for coupled biological and physical measurements in coastal and oceanic regimes. IEEE Journal of Oceanic Engineering 27, 700 716. Wiese, K. (1996). Sensory capacities of euphausiids in the context of schooling. Marine and Freshwater Behaviour and Physiology 28, 183 194. Wiese, K. and Ebina, Y. (1995). The propulsion jet of Euphausia superba (Antarctic krill) as a potential communication signal among conspecifics. Journal of the Marine Biological Association of the UK 75, 43 54. Williams, R. and Lusseau, D. (2006). A killer whale social network is vulnerable to targeted removals. Biology Letters 2, 497 500. Williams, R., Lusseau, D. and Hammond, P. S. (2009). The role of social aggregations and protected areas in killer whale conservation: The mixed blessing of critical habitat. Biological Conservation 142, 709 719. Willis, J.K. (2007a). Could whales have maintained a high abundance of krill? Evolutionary Ecology Research 9, 1 12. Willis, J.K. (2007b). Building Models of Pelagic Marine Ecosystems PhD thesis, University of Tasmania. Willis, J. (2008). Simulation model of universal law of school size distribution applied to southern bluefin tuna (Thunnus maccoyii) in the Great Australian Bight. Ecological Modelling 213, 33 44. Willis, J. and Hobday, A. J. (2007). Influence of upwelling on movement of southern bluefin tuna (Thunnus maccoyii) in the Great Australian Bight. Marine and Freshwater Research 58, 699 708. Wilson, D. S. and Dugatkin, L. A. (1997). Group selection and assortative interactions. American Naturalist 149, 336 351. Wilson, D. S. and Sober, E. (1994). Reintroducing group selection to the human behavioral sciences. Behavioral and Brain Sciences 17, 585 654. Wilson, E. O. (1975). Sociobiology. Harvard University Press, Boston, MA. Wiszniewski, J., Allen, S. J. and Moller, L. M. (2009). Social cohesion in a hierarchically structured embayment population of Indo-Pacific bottlenose dolphins. Animal Behavior 77, 1449 1457. Wolf, J. B. W., Mawdsley, D., Trillmich, F. and James, R. (2007). Social structure in a colonial mammal: Unravelling hidden structural layers and their foundations by network analysis. Animal Behaviour 74, 1293 1302. Yen, J. and Bundock, E. A. (1997). Aggregative behavior in zooplankton: Phototactic swarming in four developmental stages of Coullana canadensis (Copepoda, Harpacticoida). In Animal Groups in Three Dimensions: How Species Aggregate (J. K. Parrish and W. M. Hamner, eds), pp. 143 162. Cambridge University Press, Cambridge. Yen, J., Brown, J. and Webster, D. R. (2003). Analysis of the flow field of the krill. Euphausia pacifica. Marine and Freshwater Behavior and Physiology 36, 307 319. Young, S., Watt, P. J., Grover, J. P. and Thomas, D. (1994). The unselfish swarm? Journal of Animal Ecology 63, 611 618. Young, J. W., Bradford, R. W., Lamb, T. D. and Lyne, V. D. (1996). Biomass of zooplankton and micronekton in the southern bluefin tuna fishing grounds off eastern Tasmania, Australia. Marine Ecology Progress Series 138, 1 14. Zhou, M. and Tande, K. (eds) (2002). Optical Plankton Counter Workshop. GLOBEC Report 17, 1 67pp. This page intentionally left blank SUBJECT INDEX Note: Page numbers followed by f and t indicate figures and tables, respectively. A Abiotic stressors and species interactions, relationship between, 149 Abudefduf saxatilis, 176 Acanella, 75 76, 75f Acanella arbuscula, 91, 92, 93, 100 Acanthogorgia, 90 Acanthogorgia armata, 93, 96 Acanthogorgia aspera, 92 Acaryochloris marina, 13 14 Age, of octocorals, 105 108, 107t Ainigmaptilon, 78, 78f ‘Albatross,’ 64, 67 Alcyonacea, 44 46, 47f, 49f. See also Octocorallia Alcyoniidae, 97, 100 Aleutian Islands, Alaska octocorals in, 67 Allee effect, 148 Amphipods, 93 Anemones, 91, 92 Antarctic krill (Euphausia superba) coordinated swimming in formation, 190 heart rate of, when isolated, 176 ingestion rate, 179 social attraction towards conspecifics, 177 spatial and temporal scales, 171 Antarctic pteropod, 146 Anthomastus grandiflorus, 84 Anthoptilum murrayi, 100, 104 Arenicola marina, 147 Arthrogorgia, 78, 83f AS. See Asparagine synthetase (AS) Asparagine synthetase (AS), 25 Asteroschema clavigera, 89, 90 Aureococcus anophagefferens, 20 21 B Bacillariophyceae, 17, 18f Balanus perforatus, 144 Barnacle, impact of climate change on, 132 133 Bathypalaemonella serratipalma, 90, 93 Biological effects of climate change ecological impacts, 126, 129 poleward migrations of species, 126 128 Biological responses, during higher frequency weather, 133 134 Bottlenose dolphins (Tursiops truncatus), 193 Broadcast spawning, 96, 97, 100 Brooding, 96, 97, 105 ‘Brown tides,’ 20 C Calcaxonia, 44, 90 91, 106 Callogorgia, 78, 80f Callogorgia gilberti, 91 Calyptrophora, 78, 81f Candidella, 78, 81f Candidella imbricata, 91, 92, 95 Cell walls eukaryotic phytoplankton, 25 28 ‘Challenger,’ 66 Chlamydomonas, 21 Chrysogorgia, 69, 70f, 72 73, 90 Chrysogorgiidae, 44, 47f, 68 74 biogeography, 69 73, 70f, 71f 72f morphology, 68 69 systematics/evolution, 68 229 230 Chthamalus stellatus, 144 Circinisidinae, 74 Clades, 8 Clavulariidae, 44 Clupea harengus, 175 Cnidaria, 91, 92 Commensalism, 94 Community and ecosystem scales, studies of threshold effects, 130 132 Congregation, 170 Convexella krampi, 77 Coral bleaching, and effect of climate change, 138 Corallium, 89 Corallium lauuense, 109 Corallium niobe, 92 Corallium rubrum, 51 Corner Rise (CR), 52 Coscinodiscophyceae, 17, 18f CR. See Corner Rise (CR) Crocosphaera, 12 Crocosphaera watsonii, 12 Cryptophyta, 14 Cyanobacteria, 2, 7 14 abundance of, 7 8 genetic clusters of, 8 microbial interactions, 12 14 nitrogen fixation, 11 12 regulatory systems, environmental variables and, 10 11 speciation of, 7 10 status of, sources for finding, 7 Cyanothece, 12 D Deep-water octocorals. See Octocorals Dendrobrachiidae, 46 Diatoms, 17 20, 18f cell walls, 26 classes of, 17, 18f genomes, 18 20 Diel vertical migration (DVM), 184 185 Diopatra neapolitana, 147 Dolphin social network, 191 Drag coefficient, 138 Subject Index Drifa glomerata, 100 Dynamic Energy Budget (DEB) models, for estimating body temperature changes, 151 152 E Ecological influences, of climate body temperature, 134 135 Dynamic Energy Budget (DEB) models, 151 152 impact of ambient air temperature, 135 136 of predators and prey, 136 cellular- and subcellular-level reactions, 139 difference in temperature between an ectothermic organism and its surroundings, 134 environmental signals, of climate change, 134 heat flux, 134 heat transfer coefficients, 136 138 indices, 133 interactions between fluid environment and dislodgement of sessile organisms, 138 keystone species, 146 148 nutrient flux, 139 species interactions, 146 148 variability in coral bleaching, 138 in wave-swept environments, 138 Ecological threshold (tipping point), concept of, 129 changes at community and ecosystem level, 130 131 effects of decreases in pH on growth and survival, 131 epifaunal/infaunal characteristics and dispersal of organisms, 131 impact on rates of calcification, 131 physiological performance, 131, 132 definition, 130 at intertidal zonation height, 133 role in planning conservation strategies, 130 131 231 Subject Index Ecotypes, 8 Ellisellidae, 44 El Ninõ Southern Oscillation (ENSO), 133 Emiliania huxleyi, 22, 23, 26, 27 28 Epifaunal/infaunal organisms, 131 EST. See Expressed sequence tag (EST) Eukaryotic phytoplankton, 2 cell walls, 25 28 evolution of, 15 17, 16f gene structure, 24 genomics, 14 28 lineages of, 14 15 metabolic functions, 24 28 nutrient acquisition, 24 25 overview, 14 15 phylogenetic/physiological diversity of, 14 15, 15f Euphotic zone, 165 Evolution Chrysogorgiidae, 68 of eukaryotic phytoplankton, 15 17, 16f Expressed sequence tag (EST), 23 F Fanellia, 78, 83f Fannyella, 78, 83f Fannyella rossii, 97 Fannyella spinosa, 97 Feeding/assimilation rates, 144 145 Fluid velocity, 138 Forbes, Edward, 42 G Gadus morhua, 175, 176 Gametogenesis, octocorals, 101 102 Gene-calling algorithms, 24 Genes in Synechococcus, 10 transfer, cyanobacterial diversity, 10 unique, 9 Genomics of cyanobacterial, 7 14 eukaryotic phytoplankton, 14 28 of phytoplankton, 1 28 speciation of marine cyanobacteria, 8 Gonochorism, octocorals, 100 101 Gorgoniapolynoe, 89, 90 Gorgoniapolynoe caeciliae, 91, 92, 94 Growth, of octocorals, 105 106, 107t H HABs. See Harmful algal blooms (HABs) Haptophytes, 22 23 Harmful algal blooms (HABs), 20 Harmothoe acanellae, 84 Hawaii, octocorals in, 64 66, 65t 66t Herd (secondary group), 171 Heterokonts, 14 15, 17 21 diatoms, 17 20, 18f pelagophytes, 20 21 Holaxonia, 44, 90 Homo sapiens, 18 Host species, 95 I Incertae sedis, 46 Indo-West Pacific region, octocorals in, 63 64 Ingestion/assimilation rates, 145 Iridogorgia, 68, 69, 71f, 72, 73, 90 Iridogorgia magnispiralis, 106 Isidella, 74, 76, 76f Isididae, 46, 50f, 74 77 Isidinae, 74 Isis hippuris, 74 J Japan, octocorals in, 66 67 K Kairomone, 173 Keratoisidinae, 74 Keratoisis, 74, 75, 77f Keratoisis ornata, 101 Keystone hosts, 147 Keystone modifiers, 147 Keystone mutualists, 147 Keystone prey species, 147 232 Keystone species, impact of climate change, 146 148 Kophobelemnon stelliferum, 100, 104 L Lepidisis, 74, 75, 76f Leptasterias polaris, 143 LHC proteins. See Light-harvesting complex (LHC) proteins Light-harvesting complex (LHC) proteins, 20 Limacina helicina, 131 Liza aurata, 175 Liza saliens, 175 Long-terminal repeat retrotransposons (LTR-TRs), 19 LTR-TRs. See Long-terminal repeat retrotransposons (LTR-TRs) Lutjanus griseus, 176 Lyngbya, 13 M Macrophysiology, 132 Marine cyanobacteria. See Cyanobacteria Marine Ecology Progress Series, 129 Marine phytoplankton. See Phytoplankton Mbp. See Million base pairs (Mbp) MCC. See Monophyletic Chrysogorgiidae Clade (MCC) Mediophyceae, 17, 18f Metallogorgia melanotrichos, 69, 71f, 72, 73, 90, 95, 106 Micromonas, 21 22 Million base pairs (Mbp), 2 Mitochondrial markers, 108 Mobile pelagic ecosystems, 165 Monophyletic Chrysogorgiidae Clade (MCC), 68 73, 109 Monopodial, 106 Mopseins, 74 Mussels, 139 Mutualism, 94 Mysid (Mysidium gracile), 186 Mytilus californianus, 146, 148, 152 Mytilus galloprovincialis, 148 Subject Index Mytilus trossulus, 148 N Narella, 78, 79f Naso unicornis, 146 Natural History of the European Seas, 42 Nephtheidae, 97 NES. See New England (NES) New England (NES), 52 Nitrogen fixation, 11 12 Non-linearities, of ecosystem, 129, 132, 134 body temperature changes, 134 136 between water flow and the risk of dislodgement by sessile organisms, 138 North American beavers, 147 North Atlantic octocorallia, 51 63, 53t 62t octocorals in, 51 63, 53t 62t North Atlantic Oscillation (NAO), 133 Nucella canaliculata, 151 O Ocean acidification (OA), 126 Octocorallia, 43 44, 48, 97, 101 Octocorals age/growth of, 105 108, 107t in Aleutian Islands of Alaska, 67 biology of, 41 110 classification, 43 48 dispersal, 108 109 distribution of, documentation, 42 food habits of, 95 96 gametogenesis, 101 102 gonochorism/sex ratio, 100 101 in Hawaii, 64 66, 65t 66t in Indo-West Pacific region, 63 64 in Japan, 66 67 in North Atlantic, 51 63, 53t 62t overview of, 42 43 phylogenetic relationships, 48, 51 predators, 95 reproductive strategies, 96 100, 98t 99t 233 Subject Index sexual maturity/fecundity, 103 104 with symbionts, 82 95, 85t, 86t 89t threats/conservation issues, 109 110 Ophiocreas oedipus, 90, 95 Orstomisis, 74 Ostreococcus, 21 22, 25 P Pacific Decadal Oscillation (PDO), 133 Pacific herring (Clupea pallasii), 186 Paragorgia arborea, 85, 96 Paragorgia coralloides, 92 Paragorgia johnsoni, 106 Parasitism, 94 Parastenella, 78, 80f Pelagic zone, defined, 165 Pelagophytes, 20 21 Pennatula aculeata, 101 Phaeodactylum tricornutum, 17, 18, 19, 25 Phylogenetic diversity, of eukaryotic phytoplankton, 14 15, 15f Physiological diversity, of eukaryotic phytoplankton, 14 15, 15f Physiological performance curves concept of oxygen and capacitylimited thermal tolerance, 143 feeding and/or assimilation rates, 144 145 foraging rates, 143 lifetime reproduction, 143 144 of marine species, 141 145 literature review, 142t response of body temperature, 140 141 standard, 140 Physiological stress levels, influence of climate change, 148 150 Phytoplankton eukaryotic. See Eukaryotic phytoplankton genomics of, 1 28 metabolic capabilities, 2 overview of, 2, 3t 6t, 7 prokaryotic, 2 Pisaster, 146, 149 Pisaster ochraceus, 149, 151 Plumarella, 78, 82f Pod (primary group), 171 Poleward migrations of species, 126 128 Polychaete species, 147 Prasinophytes, 14, 21 22 Prey aggregations, 165 foraging behavior, 166 Prey stress models, 149 Primary endosymbiosis, 15 17, 16f Primnoa, 78, 82f Primnoa resedaeformis, 96, 106 Primnoella, 78, 84f Primnoidae, 44, 46, 49f, 77 81 Primnoisis antarctica, 95 96 Prochlorococcus, 7 8, 9, 11 ecotypes in, 8 Prochloron, 13 Prokaryotic phytoplankton, 2 Protoalcyonaria, 44 Pseudocaranx dentex, 176 Pteropod species, impact of changes in pH, 131 R Radicipes, 72, 73 Reflective camouflage strategy, 187 188 Reproduction, octocorals, 96 105 Reproductive performance curve, 143 144 Rhodaniridogorgia, 68, 72f Ross Sea, 131 S Salpa thompsoni, 95 Sargassum, 146 School, 171 Scleraxonia, 44 Scleraxonia, 84 Sclerisis, 74 Sea pens, 46, 97, 105 Sea surface temperature and physiological performance, 132 234 Semibalanus balanoides, 132 Sensory dimensions, of social aggregation, 186 192 communication within and between groups, 192 coordinated swimming in formation, 189 192 reflective camouflage, 187 188 spawning behaviour, 186 187 Sessile organisms, and ecological influences of climate, 138 Sessiliflorae, 46 Sex ratio, octocorals, 100 101 Sexual fecundity, octocorals, 103 104 Sexual maturity, octocorals, 103 104 Shoal, 171 Shoal fidelity, 185 186 Silicic acid transporters (SITs), 26 SITs. See Silicic acid transporters (SITs) Snell’s law, 188 Soay sheep, impact of climate change, 133 Social aggregation, in pelagic environments active and, 169 170 active and non-social, 169 of Antarctic krill (Euphausia superba), 171, 176 177, 179 association patterns within and between groups, 185 186 density-dependent interactions, 179 experimental and observational methods acoustics, 200 205 electronic tagging, 205 206 future development, 206 207 optical plankton counters, 199 200 video and motion analysis software, 194 199 food competition, 179 180 group fidelity, 185 186 and impacts of climate change, 210 213 origin, 172 173 passive, 169 Subject Index principles and features drift volume, 168 169 energy transfer, 168 rates of kinetic energy production, 168 169 sensory dimensions, 186 192 significance and benefits, 173 178 efficiency in foraging, 174 175 optimal group size, 175 176 social attraction towards conspecifics, 177 social networks analysis, 192 194 structure and functions diel vertical migration (DVM), 184 185 patchiness in zooplankton, 180 184 theoretical developments, 207 210 of zooplankton, 178 Social networks, 192 194 SODs. See Superoxide dismutases (SODs) Speciation, of cyanobacterial, 7 10 Steelhead trout (Oncorhynchus mykiss), 186 Stephanuge acanellae, 92 Stolonifera, 44 Subselliflorae, 46 Superoxide dismutases (SODs), 24 Swarm, 171 Symbiodinium, 82 Symbionts, octocorals with, 82 95, 85t, 86t 89t characteristics of, 91 93 Synechococcus, 7, 8, 9 genes in, 10 T Taiaroa, 44 Takifugu rubripes, 18 Temperature changes and reproduction, 132 Thalassiosira pseudonana, 17, 18, 19, 24, 26 Thouarella, 78, 79f, 91 Thouarella hilgendorfi, 91 Threats, octocorals, 109 110 235 Subject Index Thresholds of Climate Change in Ecosystems, 130 Trichodesmium, 11 12 Tropical Deep Sea Benthos Program, 63 U UCYN-A, 12 UDP-N acetyglucosamine, 22 Unique genes, 9 V Victorgorgia josephinae, 84 VME. See Vulnerable marine ecosystem (VME) Vulnerable marine ecosystem (VME), 110 W Weather variables and physiological responses, 133 139 X Xeniidae, 100 Z Zoanthids, 92 Zooplankton patchy distribution of, 180 184 reflective camouflage strategy, 188 This page intentionally left blank TAXONOMIC INDEX Note: Page numbers followed by f and t indicate figures and tables, respectively. A Abudefduf saxatilis, 176 Acanella, 46, 49f, 75, 76, 108 Acanella arbuscula, 51, 61t, 75, 88t, 89t, 91, 92, 93, 97, 98t, 100, 103, 104, 107t Acanella dispar, 66t Acanella eburnea, 75 Acanthacis, 44 Acanthogorgia, 44, 90, 92 Acanthogorgia armata, 46f, 56t, 86t, 93, 96 Acanthogorgia aspera, 56t, 86t, 92 Acanthogorgia bocki, 87t Acanthogorgia hirsuta, 56t Acanthogorgia pico, 56t Acanthogorgia schrammi, 56t Acanthogorgia sp., 56t, 65t Acanthoprimnoa goesi, 59t Acanthoprimnoa pectinata, 59t Acanthoptilum, 46 Acaryochloris, 13 Acaryochloris marina, 3, 13 Acaryochloris sp., 3t, 13 Actinoptilum, 46 Aglaoprimnoa, 78 Ainigmaptilon, 46, 78 Ainigmaptilon antarcticum, 98t, 100 Alaskagorgia, 44, 67 Alcyoniina, 85t Alcyonium, 44, 97 Alcyonium acaule, 54t Alcyonium coralloides, 54t Alcyonium digitatum, 54t Alcyonium palmatum, 54t Alexandrium minutum, 29 Amatiguakius forsberghi, 87t Amphiacme, 46 Amphianthus inornatus, 88t Anabaena, 10 Anthelia borealis, 53t Anthelia fallax, 53t Anthomastus, 44, 46f Anthomastus agassizii, 54t Anthomastus bathyproctus, 95 Anthomastus fisheri, 65t Anthomastus grandiflorus, 54t, 85, 86t, 96, 97, 98t, 100, 102, 105 Anthomastus ritteri, 97, 98t, 104, 105 Anthomastus sp., 46 Anthomuricea, 44 Anthomuricea divergens, 65t Anthomuricea reticulata, 65t Anthomuricea sp., 65t Anthomuricea tenuispina, 65t Anthoptilum, 46 Anthoptilum murrayi, 98t, 100, 104, 105 Anthothela, 44 Anthothela grandiflora, 55t Anthothela nuttingi, 65t Aphanopus carbo, 109 Arenicola marina, 147 Armadillogorgia, 78 Arntzia, 78 Arthrogorgia, 46, 67, 78, 83 Arthrogorgia sp., 88t Asteronyx loveni, 87t Asteroschema, 45f Asteroschema clavigera, 89, 90 Asteroschema sp., 46f, 86t, 89 Asteroschema tenue, 87t Astromuricea, 44 Aureococcus anophagefferens, 6t, 20, 21 Australisis, 75 B Balanus amphitrite, 142t Balanus balanus, 142t 237 238 Balanus crenatus, 142t Balanus improvisus, 142t Balanus perforatus, 142t, 144 Bathycoccus, 21 Bathypalaemonella serratipalma, 87t, 90, 93 Bathypallenopsis mollisima, 95 Bathytelesto, 44 Bayergorgia, 44 Bebryce, 44 Bebryce brunnea, 65t Bebryce mollis, 56t Benthosema glaciale, 203f Botrylloides violaceus, 142t Botryllus schlosseri, 142t, 144 Botryllus violaceus, 144 C Calcaxonia, 85t Calcigorgia, 44, 67 Caliacis, 44 Calibelemnon, 46 Calicogorgia, 44 Callogorgia, 46, 78, 80 Callogorgia americana, 60t Callogorgia formosa, 65t Callogorgia gilberti, 65t, 88t, 91 Callogorgia gracilis, 60t Callogorgia linguimaris, 60t Callogorgia robusta, 65t Callogorgia sp., 88t Callogorgia verticillata, 60t Calothrix rhizosoleniae, 3t Calyptrophora, 46f, 78, 81 Calyptrophora alpha, 66t Calyptrophora angularis, 66t Calyptrophora antilla, 60t Calyptrophora clarki, 66t Calyptrophora clinata, 46, 49f, 60t Calyptrophora gerdae, 60t Calyptrophora microdentata, 46, 49f, 60t Calyptrophora pileata, 66t Calyptrophora trilepis, 60t Calyptrophora wyvillei, 66t Candidella, 46, 78, 81 Candidella gigantea, 66t Candidella helminthophora, 66t, 88t Taxonomic Index Candidella imbricata, 49f, 60t, 88t, 91, 92, 95 Capnella florida, 54t Capnella glomerata, 54t Capnella, 44 Cardomanica andersoni, 87t Cardomanica longispinata, 87t Cardomanica quadricornuta, 87t Caribisis, 75 Cavernularia, 46 Ceratocaulon wandeli, 54t Chelidonisis aurantiaca, 62t Chelidonisis, 74 Chlamydomonas, 21 Chrysogorgia abludo, 58t Chrysogorgia, 46, 64, 68, 69, 70, 72, 73, 74, 90, 91, 93 Chrysogorgia agassizii, 58t, 72 Chrysogorgia antarctica, 72 Chrysogorgia artospira, 58t Chrysogorgia averta, 58t Chrysogorgia campanula, 58t Chrysogorgia curvata, 73 Chrysogorgia desbonni, 58t, 87t Chrysogorgia elegans, 58t, 87t Chrysogorgia herdendorfi, 58 Chrysogorgia japonica, 66t Chrysogorgia multiflora, 58t Chrysogorgia orientalis, 87t Chrysogorgia papillosa, 66t, 87t Chrysogorgia quadriplex, 58t, 87t Chrysogorgia scintillans, 66t Chrysogorgia sp., 87t Chrysogorgia spiculosa, 58t Chrysogorgia squamata, 59t Chrysogorgia stellata, 66t Chrysogorgia thyrsiformis, 59t Chrysogorgia triacaulis, 59t Chrysogorgia tricaulis, 47f, 90 Chrysogrgia fewkesii, 58 Chthamalus stellatus, 142t, 144 Chunella, 46 Clavularia, 44 Clavularia alba, 53t Clavularia arctica, 53t Clavularia grandiflora, 65t Clavularia griegii, 53t 239 Taxonomic Index Clavularia levidensis, 53t Clavularia marioni, 53t Clavularia modesta, 53t Clavularia rudis, 46f, 53t Clavularia venustella, 53t Clupea harengus, 169t, 175 Clupea pallasii, 186 Convexella jungerseni, 60t Convexella krampi, 77 Corallium, 44, 89, 92, 95, 109 Corallium abyssale, 65t Corallium bathryrubrum, 55t Corallium imperiale, 86t Corallium johnsoni, 55t, 86t Corallium kishinouyei, 65t Corallium laauense, 65t Corallium lauuense, 97, 98t, 109 Corallium maderense, 55t Corallium medea, 55t Corallium niobe, 55t, 86t, 92 Corallium niveum, 65t Corallium regale, 65t Corallium rubrum, 51, 55t, 107t, 109 Corallium secundum, 65t, 86t, 97, 98t, 107t Corallium sp., 46f, 86t, 107t Corallium tricolor, 55t Crassophyllum, 48 Crocosphaera watsonii, 3t, 12 Crocosphaera, 12 Cryogorgia, 44, 67 Ctenocella, 44 Ctenocella (Ellisella) paraplexauroides, 59t Ctenocella (Ellisella) schmitti, 59t Ctenocella (Viminella) flagellum, 59t Cyanobacterium, 3t Cyanobium sp., 3t Cyanothece, 12 Cyanothece sp., 3t Cyclomuricea, 44 Cyclomuricea flabellata, 65t D Daphnia, 185 Dasystenella, 78 Dasystenella acanthina, 98t, 102, 104 Dendrobrachia, 46 Dentomuricea meteor, 56t Dentomuricea, 44 Dermocarpa sp., 3t Diopatra neapolitana, 147 Dissostichus spp., 109 Distichogorgia sconsa, 59t Distichogorgia, 68 Distichoptilum, 46 Ditylum brightwellii, 27f Drifa, 44, 97 Drifa glomerata, 99t, 103, 104, 105 Drifa sp., 99t, 100, 105 Drosophila, 127f Duva, 44 Duva florida, 97 Duva sp., 99t E Echinomuricea, 44 Echinoptilum, 46 Eknomisis dalioi, 62t Ellisella barbadensis, 87t Ellisella, 44 Elminius modestus, 142t Emiliania huxleyi, 6t, 15, 23, 26, 27f, 28 Emiliania, 22 Engraulis japonicus, 169t Engraulis mordax, 169t Epiphaxum, 48 Epizoanthus norvegicus, 86t, 88t Eunicella, 44 Eunicella filiformis, 57t Eunicella gazella, 57t Eunicella labiata, 57t Eunicella modesta, 58t Eunicella verrucosa, 58t Euphausia pacifica, 184, 214 Euphausia superba, 169t, 171f, 182t, 185, 190f, 199f F Fanellia euthyeia, 65t Fanellia medialis, 65t Fanellia tuberculota, 65t Fanellia, 46, 78, 83 Fannyella rossi, 102 240 Fannyella rossii, 97, 99t Fannyella spinosa, 97, 99t, 102 Fannyella, 78, 79, 83 Fundulus diaphanus, 185 Funiculina, 46 G Gadus morhua, 175, 176 Gasterosteus aculeatus, 185 Gerardia sp., 92 Gersemia fruticosa, 105 Gersemia rubiformis, 54t Gersemia, 44 Gorgoniapolynoe, 89, 90, 95 Gorgoniapolynoe bayeri, 88t Gorgoniapolynoe caeciliae, 86t, 88t, 91, 92, 93, 94, 95 Gorgoniapolynoe galapagensis, 88t Gorgoniapolynoe guadalupensis, 86t Gorgoniapolynoe muzikae, 86t, 87t, 88t Gorgoniapolynoe pelagica, 93 Gorgoniapolynoe uschakovi, 88t Gorgonolaureus muzikae, 87t Gorgonophilus canadensis, 86t Gyrophyllum, 48 H Halipteris, 46 Harmothoe acanellae, 85, 86t, 88t Heliana, 44 Hemilepidia versluysii, 88t Holaxonia, 85t Hoplostethus atlanticus, 109 Hypnogorgia, 44 I Iridogorgia, 46, 68, 69, 71, 72, 73, 90 Iridogorgia fontinalis, 47f, 59t Iridogorgia magnispiralis, 47f, 59t, 103, 106 Iridogorgia pourtalesii, 59t Iridogorgia splendens, 59t, 87t, 90, 93 Isidascus bassindalei, 89 Isidella, 46, 75, 76 Isidella elongata, 62t Isidella lofotensis, 62t Isidella longiflora, 62 Taxonomic Index Isidella tentaculum, 107t Isidella trichotoma, 66t Isidicola antarctica, 89 Isis hippuris, 74 K Katsuwonus pelamis, 186 Keratoisis, 46, 75, 76, 77, 95 Keratoisis flabellum, 66 Keratoisis flexibilis, 62 Keratoisis grayi, 62 Keratoisis ornata, 62, 97, 99t, 100, 101, 102, 107t Keratoisis sp., 46, 107t Keroeides, 44 Keroeides fallax, 65t Keroeides mosaica, 65t Keroeides pallida, 65t Kophobelemnon stelliferum, 98t, 100, 104, 105 Kophobelemnon, 46 L Labidocera pavo, 178 Labidocera, 178 Lamipella acanellae, 89 Lamipinna, 88t Leiopathes sp., 46 Lepas anatifera, 142t Lepidisis, 46f, 75, 76, 77 Lepidisis caryophyllia, 62t Lepidisis cyanae, 62t Lepidisis longiflora, 62t Lepidisis macrospiculata, 62t Lepidisis olapa, 66t Lepidisis sp., 46, 50f, 107t Leptasterias polaris, 143 Leptogorgia sarmentosa, 58t Leptogorgia, 44 Leptolyngbya sp., 3t Leptolyngbya valderiana, 3t Lignella, 44 Lignopsis, 44 Limacina helicina, 131 Lituaria, 46 Liza aurata, 175 Liza saliens, 175 241 Taxonomic Index Lutjanus griseus, 176 Lyngbya majuscula, 3t Lyngbya, 13 Lyngbya sp., 3t M Meganyctiphanes norvegica, 183 Mendosoma lineatum, 170f Menidia menidia, 174 Mesogligorgia, 44 Metafannyella, 78 Metallogorgia macrospina, 73 Metallogorgia melanotrichos, 47f, 59t, 66t, 69, 72, 73, 87t, 90, 93, 95, 103, 106 Metallogorgia splendens, 73 Metallogorgia tenuis, 73 Metallogorgia, 46, 68, 71, 72, 73 Microcoleus chthonoplastes, 3t Micromonas, 21, 22 Micromonas pusilla, 6t Micromonas sp., 6t Minusis granti, 89t Minusis pseudoplana, 89t Mopsea gracilis, 89t Muricea, 44 Muricea pendula, 56t Muriceides, 44 Muriceides kuekenthali, 56t Muriceides lepida, 56t Muriceides paucituberculata, 56t Muricellisis, 74 Muriceopsis, 44 Mysidium gracile, 186 Mytilus, 149, 150f Mytilus californianus, 136, 146, 147, 148, 149, 152 Mytilus edulis, 139 Mytilus galloprovincialis, 148 Mytilus sp., 142t Mytilus spp., 150f Mytilus trossulus, 148 N Narella, 46, 78, 79, 81 Narella alata, 65t Narella alvinae, 60t Narella ambigua, 88t Narella bellissima, 60t Narella bowersi, 65t Narella clavata, 88t Narella dichotoma, 65t Narella gaussi, 80 Narella gigas, 65t Narella gilchristi, 80 Narella hawaiinensis, 65t Narella irregularis, 79 Narella laxa, 60t Narella macrocalyx, 65t Narella muzikae, 65t Narella ornata, 65t Narella pauciflora, 49f, 60t Narella regularis, 60t, 80 Narella spectabilis, 60t Narella vermifera, 65t Narella versluysi, 60t Naso unicornis, 146 Nicella, 44 Nicella granifera, 59t Nodularia spumigena, 4t Nostoc, 10 Nucella, 150f Nucella canaliculata, 151 Nucella lamellosa, 142t Nucella ostrina, 142t Nyctiphanes australis, 170f O Oncorhynchus mykiss, 186 Oncorhynchus nerka, 142t Onogorgia, 78 Ophiocreas oedipus, 47f, 87t, 90, 93, 95 Orcin us orca, 169t Orstomisis, 74, 75 Ostreococcus, 21, 22, 25 Ostreococcus lucimarinus, 6t, 22 Ostreococcus tauri, 6t, 22 P Pagrus auratus, 187 Paracalyptrophora carinata, 61t Paracalyptrophora duplex, 61t Paracalyptrophora echinata, 65t Paracalyptrophora hawaiinensis, 66t Paracalyptrophora josephinae, 61t 242 Paracalyptrophora simplex, 61t Paracis miyajimai, 65t Paracis spinifera, 65t Paracorallium, 44 Paracorallium tortuosum, 65t Paragorgia, 44, 45f, 46f Paragorgia arborea, 55t, 86t, 89, 96 Paragorgia boschmai, 55t Paragorgia coralloides, 45f, 55t, 89, 92 Paragorgia dendroides, 65t Paragorgia johnsoni, 45f, 55t, 89, 106 Paragorgia sp., 46 Paralcyonium spinulosum, 54t Paramesopodopsis rufa, 170f Paramuricea, 44, 56t, 92, 97, 106, 109 Paramuricea biscaya, 57t Paramuricea candida, 57t Paramuricea clavata, 56t Paramuricea grandis, 56t Paramuricea grayi, 46f, 57t Paramuricea hawaiiensis, 65t Paramuricea macrospina, 57t Paramuricea placomus, 57t Paramuricea sp., 47f, 94 Paramuricea spp., 107t Paranarella watlingi, 61t Parantipathes sp., 46 Parastenella, 78, 80 Parastenella atlantica, 61t Parastenella bayeri, 66t Parisis, 44 Pennatula, 48 Pennatula aculeata, 98t Perca flavescens, 185 Perissogorgia, 79 Phaeodactylum tricornutum, 6t, 17, 18f, 19, 24, 25 Physeter macrocephalus, 169t Pisaster, 146, 148, 149, 150f Pisaster ochraceus, 136, 149, 151 Placogorgia, 44 Placogorgia becena, 57t Placogorgia coronata, 57t Placogorgia graciosa, 57t Placogorgia intermedia, 57t Placogorgia massiliensis, 57t Placogorgia sp., 87t Taxonomic Index Placogorgia terceira, 57t Plumarella, 78, 82 Plumarella aculeata, 61t Plumarella aurea, 61t Plumarella circumoperculum, 65t Plumarella dichotoma, 61t Plumarella laxiramosa, 61t Plumarella pellucida, 61t Plumarella pourtalesii, 61t Pollachius virens, 169t Polyeunoa laevis, 88t Polynoe thouarellicola, 88t Primnoa, 46, 52, 78, 82, 109 Primnoa pacifica, 67 Primnoa resedaeformis, 61t, 87t, 88t, 96, 97, 99t, 106, 107t Primnoella, 79, 84 Primnoella jungerseni, 61t Primnoella polita, 61t Primnoella sp., 96 Primnoisis antarctica, 96 Primnoisis formosa, 89t Prochlorococcus, 7, 8, 9, 10, 11 Prochlorococcus marinus, 4t Prochlorococcus marinus subsp. marinus, 4t Prochlorococcus marinus subsp. pastoris, 5t Prochlorococcus sp., 5t Prochloron, 13 Prochloron didemni, 5t Protoptilum, 46 Pseudocaranx dentex, 176 Pseudochrysogorgia, 46, 68, 71, 73, 109 Pseudoplexaura, 44 Pseudoplumarella, 79 Pseudothesea plaeoderma, 65t Pseudothesea sp., 65t Pteroeides, 48 R Radicipes, 46, 64, 68, 70, 72, 73 Radicipes challengeri, 59t Radicipes gracilis, 59t, 72 Radicipes pleurocristatus, 87t Radicipes sp., 48f, 59t, 72, 73 Radicipes spiralis, 66t Rhodaniridogorgia, 46, 68, 69, 71, 72 Rhodaniridogorgia fragilis, 49f, 59t 243 Taxonomic Index Rhodaniridogorgia superba, 66t Riisea, 44 Rosgorgia, 44 Synechococcus, 7, 8, 9, 10, 13 Synechococcus sp., 5t, 10 Synechocystis, 10 S Saccharomyces, 22 Sagartia acanella, 89 Salmo trutta, 142t Salpa thompsoni, 95 Sarcodictyon roseum, 53t Sardinops saqax, 169t Sargassum, 146 Scleracis, 44 Scleranthelia rugosa, 53t Scleraxonia, 85t Sclerisis, 74, 75 Sclerisis macquariana, 89t Sclerobelemnon, 46 Scleronephthya, 44 Scleroptilum, 46 Scyphopodium ingolfi, 53t Scytaliopsis, 48 Semibalanus (Balanus) balanoides, 144 Semibalanus balanoides, 142t Sepioteuthis sepiodea, 170f Sibogagorgia, 44 Siphonogorgia alexanderi, 65t Siphonogorgia collaris, 65t Sphaerippe caligicola, 88t Sphaerodorum guilbaulti, 86t Spinimuricea atlantica, 57t Stachyptilum, 46 Stenopleustes sp., 88t Stephanogorgia, 68 Stephanuge acanellae, 92 Strophomenia agassizi, 86t Stylatula, 46 Swiftia, 44 Swiftia borealis, 57t Swiftia casta, 57t Swiftia dubia, 57t Swiftia pallida, 57t Swiftia pourtalesii, 57t Swiftia rosea, 57t Symbiodinium, 83 Synanthus mirabilis, 86t Synechococcous, 9 T Taiaroa, 44 Takifugu rubripes, 18f Telesto fructiculosa, 53t Telestula corrugata, 65t Telestula septentrionalis, 53t Telestula spiculicola, 65t Thalassiosira pseudonana, 6t, 17, 18f, 19, 24, 26 Thalassomembracis acanthosphaericus, 87t Thalassomembracis atlanticus, 87t Thalassomembracis bayeri, 87t Thalassomembracis bilobis, 87t Thalassomembracis conquistador, 87t Thalassomembracis orientalis, 87t Thalassomembracis tetraedos, 87t Thelogorgia, 44 Thesea, 44 Thesea ramosa, 65 Thesea sp., 65 Thesea talismani, 57t Thouarella, 46, 78, 79, 91 Thouarella (Diplocalyptra) biserialis, 65 Thouarella (Euthouarella) hilgendorfi, 65 Thouarella bipinnata, 61t Thouarella grasshoffi, 61t Thouarella hilgendorfi, 49f, 61t, 88t, 91 Thouarella laxa, 88t Thouarella sp., 88t, 97, 99t Thouarella variabilis, 88t, 97, 99t, 104 Thunnus albacares, 169t Thunnus maccoyii, 210 Thunnus thynnus, 169t, 170f Thysanoessa inermis, 184 Titanidium suberosum, 55t Tokoprimno, 78 Tottonpolynoe symantipatharia, 89 Trachurus novaezelandiae, 190f Trachurus sp., 187 Trichodesmium, 11, 13 Trichodesmium erythraeum, 5t, 11 Trichodesmium thiebautii, 5t, 11 Tursiops truncatus, 169t, 170f, 193 244 U Umbellula, 46 Umbellula lindahi, 98t V Veretillum, 46 Victorgorgia, 44 Taxonomic Index Victorgorgia josephinae, 55t, 86t, 89 Villogorgia, 44 Villogorgia bebrycoides, 57t Villogorgia tenuis, 65t Viminella, 44 Virgularia, 48