1932

Abstract

Empirical data on food web dynamics and predator-prey interactions underpin ecosystem models, which are increasingly used to support strategic management of marine resources. These data have traditionally derived from stomach content analysis, but new and complementary forms of ecological data are increasingly available from biochemical tracer techniques. Extensive opportunities exist to improve the empirical robustness of ecosystem models through the incorporation of biochemical tracer data and derived indices, an area that is rapidly expanding because of advances in analytical developments and sophisticated statistical techniques. Here, we explore the trophic information required by ecosystem model frameworks (species, individual, and size based) and match them to the most commonly used biochemical tracers (bulk tissue and compound-specific stable isotopes, fatty acids, and trace elements). Key quantitative parameters derived from biochemical tracers include estimates of diet composition, niche width, and trophic position. Biochemical tracers also provide powerful insight into the spatial and temporal variability of food web structure and the characterization of dominant basal and microbial food web groups. A major challenge in incorporating biochemical tracer data into ecosystem models is scale and data type mismatches, which can be overcome with greater knowledge exchange and numerical approaches that transform, integrate, and visualize data.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-marine-121916-063256
2018-01-03
2024-10-15
Loading full text...

Full text loading...

/deliver/fulltext/marine/10/1/annurev-marine-121916-063256.html?itemId=/content/journals/10.1146/annurev-marine-121916-063256&mimeType=html&fmt=ahah

Literature Cited

  1. Ackman RG, Tocher C, McLachlan J. 1968. Marine phytoplankter fatty acids. J. Fish. Board Can. 25:1603–20 [Google Scholar]
  2. Anderson MJ, Walsh DC. 2013. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing. Ecol. Monogr. 83:557–74 [Google Scholar]
  3. Aragay G, Pons J, Merkoçi A. 2011. Recent trends in macro-, micro-, and nanomaterial-based tools and strategies for heavy-metal detection. Chem. Rev. 111:3433–58 [Google Scholar]
  4. Arthur KE, Kelez S, Larsen T, Choy CA, Popp BN. 2014. Tracing the biosynthetic source of essential amino acids in marine turtles using δ13C fingerprints. Ecology 95:1285–93 [Google Scholar]
  5. Aumont O, Ethé C, Tagliabue A, Bopp L, Gehlen M. 2015. PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies. Geosci. Model. Dev. 8:2465–513 [Google Scholar]
  6. Bearhop S, Adams CE, Waldron S, Fuller RA, MacLeod H. 2004. Determining trophic niche width: a novel approach using stable isotope analysis. J. Anim. Ecol. 73:1007–12 [Google Scholar]
  7. Bec A, Perga ME, Koussoroplis A, Bardoux G, Desvilettes C. et al. 2011. Assessing the reliability of fatty acid–specific stable isotope analysis for trophic studies. Methods Ecol. Evol. 2:651–59 [Google Scholar]
  8. Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ. et al. 2013. GenBank. Nucleic Acids Res 41:D36–42 [Google Scholar]
  9. Bergé J-P, Barnathan G. 2005. Fatty acids from lipids of marine organisms: molecular biodiversity, roles as biomarkers, biologically active compounds, and economical aspects. Marine Biotechnology I R Ulber, Y Le Gal 49–125 Adv. Technol. Eng./Biotechnol 96 Berlin: Springer [Google Scholar]
  10. Bernhard M, Andreae M. 1984. Transport of trace metals in marine food chains. Changing Metal Cycles and Human Health JO Nriagu 143–67 Berlin: Springer [Google Scholar]
  11. Berry O, Bulman C, Bunce M, Coghlan M, Murray DC, Ward RD. 2015. Comparison of morphological and DNA metabarcoding analyses of diets in exploited marine fishes. Mar. Ecol. Prog. Ser. 540:167–81 [Google Scholar]
  12. Blanchard JL, Heneghan RF, Everett JD, Trebilco R, Richardson AJ. 2017. From bacteria to whales: using functional size spectra to model marine ecosystems. Trends Ecol. Evol. 32:174–86 [Google Scholar]
  13. Blanchard JL, Jennings S, Law R, Castle MD, McCloghrie P. et al. 2009. How does abundance scale with body size in coupled size‐structured food webs?. J. Anim. Ecol. 78:270–80 [Google Scholar]
  14. Blum JD, Popp BN, Drazen JC, Choy CA, Johnson MW. 2013. Methylmercury production below the mixed layer in the North Pacific Ocean. Nat. Geosci. 6:879–84 [Google Scholar]
  15. Boecklen WJ, Yarnes CT, Cook BA, James AC. 2011. On the use of stable isotopes in trophic ecology. Annu. Rev. Ecol. Evol. Syst. 42:411–40 [Google Scholar]
  16. Bolnick DI, Yang LH, Fordyce JA, Davis JM, Svanbäck R. 2002. Measuring individual‐level resource specialization. Ecology 83:2936–41 [Google Scholar]
  17. Borgå K, Kidd KA, Muir DC, Berglund O, Conder JM. et al. 2012. Trophic magnification factors: considerations of ecology, ecosystems, and study design. Integr. Environ. Assess. Manag. 8:64–84 [Google Scholar]
  18. Bourdaud P, Gascuel D, Bentorcha A, Brind'Amour A. 2016. New trophic indicators and target values for an ecosystem-based management of fisheries. Ecol. Indic. 61:588–601 [Google Scholar]
  19. Bradley CJ, Wallsgrove NJ, Choy CA, Drazen JC, Hetherington ED. et al. 2015. Trophic position estimates of marine teleosts using amino acid compound specific isotopic analysis. Limnol. Oceanogr. Methods 13:476–93 [Google Scholar]
  20. Brett MT, Eisenlord ME, Galloway AWE. 2016. Using multiple tracers and directly accounting for trophic modification improves dietary mixing-model performance. Ecosphere 7:e01440 [Google Scholar]
  21. Budge SM, Iverson SJ, Koopman HN. 2006. Studying trophic ecology in marine ecosystems using fatty acids: a primer on analysis and interpretation. Mar. Mamm. Sci. 22:759–801 [Google Scholar]
  22. Budge SM, Springer AM, Iverson SJ, Sheffield G. 2007. Fatty acid biomarkers reveal niche separation in an Arctic benthic food web. Mar. Ecol. Prog. Ser. 336:305–9 [Google Scholar]
  23. Budge SM, Wooller M, Springer A, Iverson SJ, McRoy C, Divoky G. 2008. Tracing carbon flow in an arctic marine food web using fatty acid-stable isotope analysis. Oecologia 157:117–29 [Google Scholar]
  24. Burger J, Tsipoura N, Niles LJ, Gochfeld M, Dey A, Mizrahi D. 2015. Mercury, lead, cadmium, arsenic, chromium and selenium in feathers of shorebirds during migrating through Delaware Bay, New Jersey: comparing the 1990s and 2011/2012. Toxics 3:63–74 [Google Scholar]
  25. Bustamante P, Caurant F, Fowler S, Miramand P. 1998. Cephalopods as a vector for the transfer of cadmium to top marine predators in the north-east Atlantic Ocean. Sci. Total Environ. 220:71–80 [Google Scholar]
  26. Carreon‐Martinez L, Heath DD. 2010. Revolution in food web analysis and trophic ecology: diet analysis by DNA and stable isotope analysis. Mol. Ecol. 19:25–27 [Google Scholar]
  27. Chiaradia A, Forero MG, McInnes JC, Ramírez F. 2014. Searching for the true diet of marine predators: incorporating Bayesian priors into stable isotope mixing models. PLOS ONE 9:e92665 [Google Scholar]
  28. Chikaraishi Y, Ogawa NO, Kashiyama Y, Takano Y, Suga H. et al. 2009. Determination of aquatic food‐web structure based on compound‐specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. Methods 7:740–50 [Google Scholar]
  29. Chouvelon T, Spitz J, Caurant F, Mendez-Fernandez P, Autier J. et al. 2012. Enhanced bioaccumulation of mercury in deep-sea fauna from the Bay of Biscay (north-east Atlantic) in relation to trophic positions identified by analysis of carbon and nitrogen stable isotopes. Deep-Sea Res. I 65:113–24 [Google Scholar]
  30. Choy CA, Davison PC, Drazen JC, Flynn A, Gier EJ. et al. 2012. Global trophic position comparison of two dominant mesopelagic fish families (Myctophidae, Stomiidae) using amino acid nitrogen isotopic analyses. PLOS ONE 7:e50133 [Google Scholar]
  31. Choy CA, Popp BN, Hannides C, Drazen JC. 2015. Trophic structure and food resources of epipelagic and mesopelagic fishes in the North Pacific Subtropical Gyre ecosystem inferred from nitrogen isotopic compositions. Limnol. Oceanogr. 60:1156–71 [Google Scholar]
  32. Choy CA, Popp BN, Kaneko JJ, Drazen JC. 2009. The influence of depth on mercury levels in pelagic fishes and their prey. PNAS 106:13865–69 [Google Scholar]
  33. Christensen V, Pauly D. 1992. ECOPATH II—a software for balancing steady-state ecosystem models and calculating network characteristics. Ecol. Model. 61:169–85 [Google Scholar]
  34. Christensen V, Walters CJ. 2004. Ecopath with Ecosim: methods, capabilities and limitations. Ecol. Model. 172:109–39 [Google Scholar]
  35. Clark JS, Gelfand AE. 2006. A future for models and data in environmental science. Trends Ecol. Evol. 21:375–80 [Google Scholar]
  36. Colwell RK, Futuyma DJ. 1971. On the measurement of niche breadth and overlap. Ecology 52:567–76 [Google Scholar]
  37. Cummings DO, Buhl J, Lee RW, Simpson SJ, Holmes SP. 2012. Estimating niche width using stable isotopes in the face of habitat variability: a modelling case study in the marine environment. PLOS ONE 7:e40539 [Google Scholar]
  38. Dalerum F, Angerbjörn A. 2005. Resolving temporal variation in vertebrate diets using naturally occurring stable isotopes. Oecologia 144:647–58 [Google Scholar]
  39. Dalsgaard J, John MS, Kattner G, Müller-Navarra D, Hagen W. 2003. Fatty acid trophic markers in the pelagic marine environment. Adv. Mar. Biol. 46:225–340 [Google Scholar]
  40. Davenport SR, Bax NJ. 2002. A trophic study of a marine ecosystem off southeastern Australia using stable isotopes of carbon and nitrogen. Can. J. Fish. Aquat. Sci. 59:514–30 [Google Scholar]
  41. de Carvalho CC, Caramujo M-J. 2014. Fatty acids as a tool to understand microbial diversity and their role in food webs of Mediterranean temporary ponds. Molecules 19:5570–98 [Google Scholar]
  42. De Troch M, Boeckx P, Cnudde C, Van Gansbeke D, Vanreusel A. et al. 2012. Bioconversion of fatty acids at the basis of marine food webs: insights from a compound-specific stable isotope analysis. Mar. Ecol. Prog. Ser. 465:53–67 [Google Scholar]
  43. DeAngelis DL, Gross LJ. 1992. Individual-Based Models and Approaches in Ecology New York: Chapman & Hall [Google Scholar]
  44. Décima M, Landry MR, Bradley CJ, Fogel ML. 2017. Alanine δ15N trophic fractionation in heterotrophic protists. Limnol. Oceanogr. 62:2308–22 [Google Scholar]
  45. Deehr RA, Luczkovich JJ, Hart KJ, Clough LM, Johnson BJ, Johnson JC. 2014. Using stable isotope analysis to validate effective trophic levels from Ecopath models of areas closed and open to shrimp trawling in Core Sound, NC, USA. Ecol. Model. 282:1–17 [Google Scholar]
  46. Dehn L-A, Follmann EH, Thomas DL, Sheffield GG, Rosa C. et al. 2006. Trophic relationships in an Arctic food web and implications for trace metal transfer. Sci. Total Environ. 362:103–23 [Google Scholar]
  47. Dijkman NA, Boschker HT, Middelburg JJ, Kromkamp JC. 2009. Group-specific primary production based on stable-isotope labeling of phospholipid-derived fatty acids. Limnol. Oceanogr. Methods 7:612–25 [Google Scholar]
  48. Dowd M. 2007. Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo. J. Mar. Syst. 68:439–56 [Google Scholar]
  49. Drazen J. 2007. Depth related trends in proximate composition of demersal fishes in the eastern North Pacific. Deep-Sea Res. I 54:203–19 [Google Scholar]
  50. Driscoll DL, Appiah-Yeboah A, Salib P, Rupert DJ. 2007. Merging qualitative and quantitative data in mixed methods research: how to and why not. Ecol. Environ. Anthropol. 3:19–28 [Google Scholar]
  51. El-Shahawi M, Hamza A, Bashammakh A, Al-Saggaf W. 2010. An overview on the accumulation, distribution, transformations, toxicity and analytical methods for the monitoring of persistent organic pollutants. Talanta 80:1587–97 [Google Scholar]
  52. Erhardt EB, Bedrick EJ. 2013. A Bayesian framework for stable isotope mixing models. Environ. Ecol. Stat. 20:377–97 [Google Scholar]
  53. Everett JD, Baird ME, Buchanan P, Bulman C, Davies C. et al. 2017. Modeling what we sample and sampling what we model: challenges for zooplankton model assessment. Front. Mar. Sci. 4:77 [Google Scholar]
  54. Every SL, Pethybridge HR, Fulton CJ, Kyne PM, Crook DA. 2017. Niche metrics suggest euryhaline and coastal elasmobranchs provide trophic connections among marine and freshwater biomes in northern Australia. Mar. Ecol. Prog. Ser. 565:181–96 [Google Scholar]
  55. Farquhar GD, Ehleringer JR, Hubick KT. 1989. Carbon isotope discrimination and photosynthesis. Annu. Rev. Plant Biol. 40:503–37 [Google Scholar]
  56. Follows MJ, Dutkiewicz S, Grant S, Chisholm SW. 2007. Emergent biogeography of microbial communities in a model ocean. Science 315:1843–46 [Google Scholar]
  57. Freeman KH, Hayes J. 1992. Fractionation of carbon isotopes by phytoplankton and estimates of ancient CO2 levels. Glob. Biogeochem. Cycles 6:185–98 [Google Scholar]
  58. Fry B. 2007. Stable Isotope Ecology Berlin: Springer [Google Scholar]
  59. Fulton EA. 2010. Approaches to end-to-end ecosystem models. J. Mar. Syst. 81:171–83 [Google Scholar]
  60. Fulton EA, Gorton R. 2014. Adaptive Futures for SE Australian Fisheries and Aquaculture: Climate Adaptation Simulations Hobart, Aust.: CSIRO [Google Scholar]
  61. Fulton EA, Link JS, Kaplan IC, Savina‐Rolland M, Johnson P. et al. 2011. Lessons in modelling and management of marine ecosystems: the Atlantis experience. Fish Fish 12:171–88 [Google Scholar]
  62. Fulton EA, Smith AD, Johnson CR. 2004. Biogeochemical marine ecosystem models I: IGBEM—a model of marine bay ecosystems. Ecol. Model. 174:267–307 [Google Scholar]
  63. Fulton EA, Smith AD, Punt AE. 2005. Which ecological indicators can robustly detect effects of fishing?. ICES J. Mar. Sci. 62:540–51 [Google Scholar]
  64. Galloway AW, Brett MT, Holtgrieve GW, Ward EJ, Ballantyne AP. et al. 2015. A fatty acid based Bayesian approach for inferring diet in aquatic consumers. PLOS ONE 10:e0129723 [Google Scholar]
  65. Galloway AW, Britton‐Simmons KH, Duggins DO, Gabrielson PW, Brett MT. 2012. Fatty acid signatures differentiate marine macrophytes at ordinal and family ranks. J. Phycol. 48:956–65 [Google Scholar]
  66. Galloway AW, Eisenlord M, Dethier M, Holtgrieve G, Brett M. 2014. Quantitative estimates of isopod resource utilization using a Bayesian fatty acid mixing model. Mar. Ecol. Prog. Ser. 507:219–32 [Google Scholar]
  67. Galloway AW, Winder M. 2015. Partitioning the relative importance of phylogeny and environmental conditions on phytoplankton fatty acids. PLOS ONE 10:e0130053 [Google Scholar]
  68. Germain LR, Koch PL, Harvey J, McCarthy MD. 2013. Nitrogen isotope fractionation in amino acids from harbor seals: implications for compound-specific trophic position calculations. Mar. Ecol. Prog. Ser. 482:265–77 [Google Scholar]
  69. Gilbert JA, Steele JA, Caporaso JG, Steinbrück L, Reeder J. et al. 2012. Defining seasonal marine microbial community dynamics. ISME J 6:298–308 [Google Scholar]
  70. Gladyshev MI, Sushchik NN, Kalachova GS, Makhutova ON. 2012. Stable isotope composition of fatty acids in organisms of different trophic levels in the Yenisei River. PLOS ONE 7:e34059 [Google Scholar]
  71. Gladyshev MI, Sushchik NN, Makhutova O, Kalachova GS. 2014. Trophic fractionation of isotope composition of polyunsaturated fatty acids in the trophic chain of a river ecosystem. Dokl. Biochem. Biophys. 454:4–5 [Google Scholar]
  72. Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V. et al. 2006. A standard protocol for describing individual-based and agent-based models. Ecol. Model. 198:115–26 [Google Scholar]
  73. Grimm V, Railsback SF. 2005. Individual-Based Modeling and Ecology Princeton, NJ: Princeton Univ. Press [Google Scholar]
  74. Grüss A, Schirripa MJ, Chagaris D, Velez L, Shin Y-J. et al. 2016. Estimating natural mortality rates and simulating fishing scenarios for Gulf of Mexico red grouper (Epinephelus morio) using the ecosystem model OSMOSE-WFS. J. Mar. Syst. 154:264–79 [Google Scholar]
  75. Guiet J, Poggiale J-C, Maury O. 2016. Modelling the community size-spectrum: recent developments and new directions. Ecol. Model. 337:4–14 [Google Scholar]
  76. Hammerschlag-Peyer CM, Yeager LA, Araújo MS, Layman CA. 2011. A hypothesis-testing framework for studies investigating ontogenetic niche shifts using stable isotope ratios. PLOS ONE 6:e27104 [Google Scholar]
  77. Hannides CC, Popp BN, Choy CA, Drazen JC. 2013. Midwater zooplankton and suspended particle dynamics in the North Pacific Subtropical Gyre: a stable isotope perspective. Limnol. Oceanogr. 58:1931–46 [Google Scholar]
  78. Hannides CC, Popp BN, Landry MR, Graham BS. 2009. Quantification of zooplankton trophic position in the North Pacific Subtropical Gyre using stable nitrogen isotopes. Limnol. Oceanogr. 54:50–61 [Google Scholar]
  79. Harvey HR. 2006. Sources and cycling of organic matter in the marine water column. Marine Organic Matter: Biomarkers, Isotopes and DNA JK Volkman 1–25 Berlin: Springer [Google Scholar]
  80. Harvey HR, Tuttle JH, Bell JT. 1995. Kinetics of phytoplankton decay during simulated sedimentation: changes in biochemical composition and microbial activity under oxic and anoxic conditions. Geochim. Cosmochim. Acta 59:3367–77 [Google Scholar]
  81. Hayes J, Freeman KH, Popp BN, Hoham CH. 1990. Compound-specific isotopic analyses: a novel tool for reconstruction of ancient biogeochemical processes. Org. Geochem. 16:1115–28 [Google Scholar]
  82. Hedges JI, Baldock JA, Gelinas Y, Lee C, Peterson M, Wakeham SG. 2001. Evidence for non-selective preservation of organic matter in sinking marine particles. Nature 409:801–4 [Google Scholar]
  83. Hetherington ED, Olson RJ, Drazen JC, Lennert‐Cody CE, Ballance LT. et al. 2017. Spatial food‐web structure in the eastern tropical Pacific Ocean based on compound‐specific nitrogen isotope analysis of amino acids. Limnol. Oceanogr. 62:541–60 [Google Scholar]
  84. Heymans JJ, Coll M, Link JS, Mackinson S, Steenbeek J. et al. 2016. Best practice in Ecopath with Ecosim food-web models for ecosystem-based management. Ecol. Model. 331:173–84 [Google Scholar]
  85. Hill SL, Watters GM, Punt AE, McAllister MK, Quéré CL, Turner J. 2007. Model uncertainty in the ecosystem approach to fisheries. Fish Fish 8:315–36 [Google Scholar]
  86. Hobson KA. 1999. Tracing origins and migration of wildlife using stable isotopes: a review. Oecologia 120:314–26 [Google Scholar]
  87. Hobson KA, Clark RG. 1992. Assessing avian diets using stable isotopes II: factors influencing diet-tissue fractionation. Condor189–97 [Google Scholar]
  88. Hobson KA, Wassenaar LI. 2008. Tracking Animal Migration with Stable Isotopes San Diego, CA: Academic [Google Scholar]
  89. Hoen DK, Kim SL, Hussey NE, Wallsgrove NJ, Drazen JC, Popp BN. 2014. Amino acid 15N trophic enrichment factors of four large carnivorous fishes. J. Exp. Mar. Biol. Ecol. 453:76–83 [Google Scholar]
  90. Hollowed AB, Holsman K, Kristiansen T. 2016. PICES/ICES Workshop on “Modelling effects of climate change on fish and fisheries.”. PICES Press 24:120–23 [Google Scholar]
  91. Hughes CE, Crawford J. 2013. Spatial and temporal variation in precipitation isotopes in the Sydney Basin, Australia. J. Hydrol. 489:42–55 [Google Scholar]
  92. Hurlbert SH. 1978. The measurement of niche overlap and some relatives. Ecology 59:67–77 [Google Scholar]
  93. Hussey NE, MacNeil MA, McMeans BC, Olin JA, Dudley SF. et al. 2014. Rescaling the trophic structure of marine food webs. Ecol. Lett. 17:239–50 [Google Scholar]
  94. Hynes H. 1950. The food of fresh-water sticklebacks (Gasterosteus aculeatus and Pygosteus pungitius), with a review of methods used in studies of the food of fishes. J. Anim. Ecol. 19:36–58 [Google Scholar]
  95. Hyslop E. 1980. Stomach contents analysis—a review of methods and their application. J. Fish Biol. 17:411–29 [Google Scholar]
  96. Iverson SJ. 2009. Tracing aquatic food webs using fatty acids: from qualitative indicators to quantitative determination. Lipids in Aquatic Ecosystems M Kainz, MT Brett, MT Arts 281–308 Berlin: Springer [Google Scholar]
  97. Iverson SJ, Field C, Don Bowen W, Blanchard W. 2004. Quantitative fatty acid signature analysis: a new method of estimating predator diets. Ecol. Monogr. 74:211–35 [Google Scholar]
  98. Iverson SJ, Piché J, Blanchard W. 2011. Hawaiian monk seals and their prey: assessing characteristics of prey species fatty acid signatures and consequences for estimating monk seal diets using quantitative fatty acid signature analysis NOAA Tech. Memo. NMFS-PIFSC-23, Pac. Mar. Fish. Sci. Cent., Natl. Mar. Fish. Serv., Natl. Ocean. Atmos. Adm. Honolulu, HI: [Google Scholar]
  99. Jackson AL, Inger R, Parnell AC, Bearhop S. 2011. Comparing isotopic niche widths among and within communities: SIBER – Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80:595–602 [Google Scholar]
  100. Jackson LJ, Trebitz AS, Cottingham KL. 2000. An introduction to the practice of ecological modeling. AIBS Bull 50:694–706 [Google Scholar]
  101. Jarman SN, Deagle B, Gales N. 2004. Group‐specific polymerase chain reaction for DNA‐based analysis of species diversity and identity in dietary samples. Mol. Ecol. 13:1313–22 [Google Scholar]
  102. Jarman SN, Gales N, Tierney M, Gill P, Elliott N. 2002. A DNA‐based method for identification of krill species and its application to analysing the diet of marine vertebrate predators. Mol. Ecol. 11:2679–90 [Google Scholar]
  103. Jennings S, Barnes C, Sweeting CJ, Polunin NV. 2008. Application of nitrogen stable isotope analysis in size‐based marine food web and macroecological research. Rapid Commun. Mass Spectrom. 22:1673–80 [Google Scholar]
  104. Jennings S, Van Der Molen J. 2015. Trophic levels of marine consumers from nitrogen stable isotope analysis: estimation and uncertainty. ICES J. Mar. Sci. 72:2289–300 [Google Scholar]
  105. Jennings S, Warr KJ, Mackinson S. 2002. Use of size-based production and stable isotope analyses to predict trophic transfer efficiencies and predator-prey body mass ratios in food webs. Mar. Ecol. Prog. Ser. 240:11–20 [Google Scholar]
  106. Joensen H, Grahl-Nielsen O. 2004. Stock structure of Sebastes mentella in the North Atlantic revealed by chemometry of the fatty acid profile in heart tissue. ICES J. Mar. Sci. 61:113–26 [Google Scholar]
  107. Jørgensen SE. 2016. Handbook of Ecological Models Used in Ecosystem and Environmental Management Boca Raton, FL: CRC [Google Scholar]
  108. Kandel S, Heer J, Plaisant C, Kennedy J, van Ham F. et al. 2011. Research directions in data wrangling: visualizations and transformations for usable and credible data. Inf. Vis. 10:271–88 [Google Scholar]
  109. Kennedy MC, O'Hagan A. 2001. Bayesian calibration of computer models. J. R. Stat. Soc. B 63:425–64 [Google Scholar]
  110. King R, Read D, Traugott M, Symondson W. 2008. Molecular analysis of predation: a review of best practice for DNA‐based approaches. Mol. Ecol. 17:947–63 [Google Scholar]
  111. Kline TC Jr.. 1999. Temporal and spatial variability of 13C/12C and 15N/14N in pelagic biota of Prince William Sound, Alaska. Can. J. Fish. Aquat. Sci. 56:94–117 [Google Scholar]
  112. Landry MR, Décima MR. 2017. Protistan microzooplankton and the trophic position of tuna: quantifying the trophic link between micro- and mesozooplankton in marine foodwebs. ICES J. Mar. Sci. 74:1885–92 [Google Scholar]
  113. Larsen T, Ventura M, Andersen N, O'Brien DM, Piatkowski U, McCarthy MD. 2013. Tracing carbon sources through aquatic and terrestrial food webs using amino acid stable isotope fingerprinting. PLOS ONE 8:e73441 [Google Scholar]
  114. Lassalle G, Bourdaud P, Saint-Béat B, Rochette S, Niquil N. 2014a. A toolbox to evaluate data reliability for whole-ecosystem models: application on the Bay of Biscay continental shelf food-web model. Ecol. Model. 285:13–21 [Google Scholar]
  115. Lassalle G, Chouvelon T, Bustamante P, Niquil N. 2014b. An assessment of the trophic structure of the Bay of Biscay continental shelf food web: comparing estimates derived from an ecosystem model and isotopic data. Prog. Oceanogr. 120:205–15 [Google Scholar]
  116. Layman CA, Araujo MS, Boucek R, Hammerschlag‐Peyer CM, Harrison E. et al. 2012. Applying stable isotopes to examine food‐web structure: an overview of analytical tools. Biol. Rev. 87:545–62 [Google Scholar]
  117. Layman CA, Arrington DA, Montaña CG, Post DM. 2007. Can stable isotope ratios provide for community-wide measures of trophic structure?. Ecology 88:42–48 [Google Scholar]
  118. Layman CA, Giery ST, Buhler S, Rossi R, Penland T. et al. 2015. A primer on the history of food web ecology: fundamental contributions of fourteen researchers. Food Webs 4:14–24 [Google Scholar]
  119. Lehodey P, Senina I, Murtugudde R. 2008. A spatial ecosystem and populations dynamics model (SEAPODYM)—modeling of tuna and tuna-like populations. Prog. Oceanogr. 78:304–18 [Google Scholar]
  120. Levins R. 1968. Evolution in Changing Environments: Some Theoretical Explorations Princeton, NJ: Princeton Univ. Press [Google Scholar]
  121. Lindeman RL. 1942. The trophic‐dynamic aspect of ecology. Ecology 23:399–417 [Google Scholar]
  122. Link JS, Ihde T, Harvey C, Gaichas SK, Field J. et al. 2012. Dealing with uncertainty in ecosystem models: the paradox of use for living marine resource management. Prog. Oceanogr. 102:102–14 [Google Scholar]
  123. Litzow MA, Bailey KM, Prahl FG, Heintz R. 2006. Climate regime shifts and reorganization of fish communities: the essential fatty acid limitation hypothesis. Mar. Ecol. Prog. Ser. 315:1–11 [Google Scholar]
  124. Logan JM, Jardine TD, Miller TJ, Bunn SE, Cunjak RA, Lutcavage ME. 2008. Lipid corrections in carbon and nitrogen stable isotope analyses: comparison of chemical extraction and modelling methods. J. Anim. Ecol. 77:838–46 [Google Scholar]
  125. Lorrain A, Graham BS, Ménard F, Popp B, Bouillon S. et al. 2009. Nitrogen and carbon isotope values of individual amino acids: a tool to study foraging ecology of penguins in the Southern Ocean. Mar. Ecol. Prog. Ser. 391:293–306 [Google Scholar]
  126. Lorrain A, Graham BS, Popp BN, Allain V, Olson RJ. et al. 2015. Nitrogen isotopic baselines and implications for estimating foraging habitat and trophic position of yellowfin tuna in the Indian and Pacific Oceans. Deep-Sea Res. II 113:188–98 [Google Scholar]
  127. Lozupone CA, Hamady M, Kelley ST, Knight R. 2007. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. 73:1576–85 [Google Scholar]
  128. Lu Z, Fisk AT, Kovacs KM, Lydersen C, McKinney MA. et al. 2014. Temporal and spatial variation in polychlorinated biphenyl chiral signatures of the Greenland shark (Somniosus microcephalus) and its arctic marine food web. Environ. Pollut. 186:216–25 [Google Scholar]
  129. Madigan DJ, Litvin SY, Popp BN, Carlisle AB, Farwell CJ, Block BA. 2012. Tissue turnover rates and isotopic trophic discrimination factors in the endothermic teleost, Pacific bluefin tuna (Thunnus orientalis). PLOS ONE 7:e49220 [Google Scholar]
  130. Mariani P, Visser AW. 2010. Optimization and emergence in marine ecosystem models. Prog. Oceanogr. 84:89–92 [Google Scholar]
  131. Masbou J, Point D, Guillou GL, Sonke JE, Lebreton B, Richard P. 2015. Carbon stable isotope analysis of methylmercury toxin in biological materials by gas chromatography isotope ratio mass spectrometry. Anal. Chem. 87:11732–38 [Google Scholar]
  132. Maury O. 2010. An overview of APECOSM, a spatialized mass balanced “Apex Predators ECOSystem Model” to study physiologically structured tuna population dynamics in their ecosystem. Prog. Oceanogr. 84:113–17 [Google Scholar]
  133. McCarthy MD, Lehman J, Kudela R. 2013. Compound-specific amino acid δ15N patterns in marine algae: tracer potential for cyanobacterial vs. eukaryotic organic nitrogen sources in the ocean. Geochim. Cosmochim. Acta 103:104–20 [Google Scholar]
  134. McClelland JW, Montoya JP. 2002. Trophic relationships and the nitrogen isotopic composition of amino acids in plankton. Ecology 83:2173–80 [Google Scholar]
  135. McElhany P, Steel EA, Avery K, Yoder N, Busack C, Thompson B. 2010. Dealing with uncertainty in ecosystem models: lessons from a complex salmon model. Ecol. Appl. 20:465–82 [Google Scholar]
  136. McGlory C, Galloway SD, Hamilton DL, McClintock C, Breen L. et al. 2014. Temporal changes in human skeletal muscle and blood lipid composition with fish oil supplementation. Prostaglandins Leukot. Essent. Fatty Acids 90:199–206 [Google Scholar]
  137. McMahon KW, Collier C, Lavery PS. 2013a. Identifying robust bioindicators of light stress in seagrasses: a meta-analysis. Ecolog. Indic. 30:7–15 [Google Scholar]
  138. McMahon KW, Hamady LL, Thorrold SR. 2013b. A review of ecogeochemistry approaches to estimating movements of marine animals. Limnol. Oceanogr. 58:697–714 [Google Scholar]
  139. McMahon KW, McCarthy MD. 2016. Embracing variability in amino acid δ15N fractionation: mechanisms, implications, and applications for trophic ecology. Ecosphere 7:e01511 [Google Scholar]
  140. McMahon KW, Thorrold SR, Houghton LA, Berumen ML. 2016. Tracing carbon flow through coral reef food webs using a compound-specific stable isotope approach. Oecologia 180:809–21 [Google Scholar]
  141. Menden-Deuer S, Kiørboe T. 2016. Small bugs with a big impact: linking plankton ecology with ecosystem processes. J. Plankton Res. 38:1036–43 [Google Scholar]
  142. Méndez-Fernandez P, Simon-Bouhet B, Bustamante P, Chouvelon T, Ferreira M. et al. 2017. Inter-species differences in polychlorinated biphenyls patterns from five sympatric species of odontocetes: Can PCBs be used as tracers of feeding ecology. Ecol. Indic. 74:98–108 [Google Scholar]
  143. Milessi AC, Danilo C, Laura R-G, Daniel C, Sellanes J, Rodriguez-Gallego L. 2010. Trophic mass-balance model of a subtropical coastal lagoon, including a comparison with a stable isotope analysis of the food-web. Ecol. Model. 221:2859–69 [Google Scholar]
  144. Miller TW, Brodeur RD, Rau GH. 2008. Carbon stable isotopes reveal relative contribution of shelf-slope production to the northern California Current pelagic community. Limnol. Oceanogr. 53:1493 [Google Scholar]
  145. Minagawa M, Wada E. 1984. Stepwise enrichment of 15N along food chains: further evidence and the relation between δ15N and animal age. Geochim. Cosmochim. Acta 48:1135–40 [Google Scholar]
  146. Moore JW, Semmens BX. 2008. Incorporating uncertainty and prior information into stable isotope mixing models. Ecol. Lett. 11:470–80 [Google Scholar]
  147. Munschy C, Bodin N, Potier M, Héas-Moisan K, Pollono C. et al. 2016. Persistent organic pollutants in albacore tuna (Thunnus alalunga) from Reunion Island (Southwest Indian Ocean) and South Africa in relation to biological and trophic characteristics. Environ. Res. 148:196–206 [Google Scholar]
  148. Mutshinda CM, Finkel ZV, Widdicombe CE, Irwin AJ. 2016. Ecological equivalence of species within phytoplankton functional groups. Funct. Ecol. 30:1714–22 [Google Scholar]
  149. Navarro J, Coll M, Louzao M, Palomera I, Delgado A, Forero MG. 2011. Comparison of ecosystem modelling and isotopic approach as ecological tools to investigate food webs in the NW Mediterranean Sea. J. Exp. Mar. Biol. Ecol. 401:97–104 [Google Scholar]
  150. Neubauer P, Jensen OP. 2015. Bayesian estimation of predator diet composition from fatty acids and stable isotopes. PeerJ 3:e920 [Google Scholar]
  151. Nielsen JM, Clare EL, Hayden B, Brett MT, Kratina P. 2017. Diet tracing in ecology: method comparison and selection. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.12869 [Crossref] [Google Scholar]
  152. Nielsen JM, Popp BN, Winder M. 2015. Meta-analysis of amino acid stable nitrogen isotope ratios for estimating trophic position in marine organisms. Oecologia 178:631–42 [Google Scholar]
  153. Nilsen M, Pedersen T, Nilssen EM, Fredriksen S. 2008. Trophic studies in a high-latitude fjord ecosystem—a comparison of stable isotope analyses (δ13C and δ15N) and trophic-level estimates from a mass-balance model. Can. J. Fish. Aquat. Sci. 65:2791–806 [Google Scholar]
  154. Olsen E, Fay G, Gaichas S, Gamble R, Lucey S, Link JS. 2016. Ecosystem model skill assessment. Yes we can!. PLOS ONE 11:e0146467 [Google Scholar]
  155. O'Sullivan G, Sandau C. 2014. Environmental Forensics for Persistent Organic Pollutants Amsterdam: Elsevier [Google Scholar]
  156. Oxtoby L, Budge S, Iken K, Brien DO, Wooller M. 2016. Feeding ecologies of key bivalve and polychaete species in the Bering Sea as elucidated by fatty acid and compound-specific stable isotope analyses. Mar. Ecol. Prog. Ser. 557:161–75 [Google Scholar]
  157. Pacella SR, Lebreton B, Richard P, Phillips D, DeWitt TH, Niquil N. 2013. Incorporation of diet information derived from Bayesian stable isotope mixing models into mass-balanced marine ecosystem models: a case study from the Marennes-Oléron Estuary, France. Ecol. Model. 267:127–37 [Google Scholar]
  158. Parnell AC, Inger R, Bearhop S, Jackson AL. 2010. Source partitioning using stable isotopes: coping with too much variation. PLOS ONE 5:e9672 [Google Scholar]
  159. Parnell AC, Phillips DL, Bearhop S, Semmens BX, Ward EJ. et al. 2013. Bayesian stable isotope mixing models. Environmetrics 24:387–99 [Google Scholar]
  160. Parrish CC. 2013. Lipids in marine ecosystems. ISRN Oceanogr 2013:604045 [Google Scholar]
  161. Parrish CC, Nichols PD, Pethybridge HR, Young JW. 2015. Direct determination of fatty acids in fish tissues: quantifying top predator trophic connections. Oecologia 177:85–95 [Google Scholar]
  162. Parrish FA, Howell EA, Antonelis GA, Iverson SJ, Littnan CL. et al. 2012. Estimating the carrying capacity of French Frigate Shoals for the endangered Hawaiian monk seal using Ecopath with Ecosim. Mar. Mamm. Sci. 28:522–41 [Google Scholar]
  163. Pauli JN, Steffan SA, Newsome SD. 2015. It is time for IsoBank. BioScience 65:229–30 [Google Scholar]
  164. Pauly D, Christensen V, Dalsgaard J, Froese R, Torres F. 1998. Fishing down marine food webs. Science 279:860–63 [Google Scholar]
  165. Perhar G, Arhonditsis GB, Brett MT. 2012. Modelling the role of highly unsaturated fatty acids in planktonic food web processes: a mechanistic approach. Environ. Rev. 20:155–72 [Google Scholar]
  166. Persson J, Vrede T. 2006. Polyunsaturated fatty acids in zooplankton: variation due to taxonomy and trophic position. Freshw. Biol. 51:887–900 [Google Scholar]
  167. Peterson BJ, Fry B. 1987. Stable isotopes in ecosystem studies. Annu. Rev. Ecol. Syst. 18:293–320 [Google Scholar]
  168. Pethybridge HR, Butler E, Cossa D, Daley R, Boudou A. 2012. Trophic structure and biomagnification of mercury in an assemblage of deepwater chondrichthyans from southeastern Australia. Mar. Ecol. Prog. Ser. 451:163–74 [Google Scholar]
  169. Pethybridge HR, Parrish CC, Morrongiello J, Young JW, Farley JH. et al. 2015. Spatial patterns and temperature predictions of tuna fatty acids: tracing essential nutrients and changes in primary producers. PLOS ONE 10:e0131598 [Google Scholar]
  170. Phillips DL. 2012. Converting isotope values to diet composition: the use of mixing models. J. Mammal. 93:342–52 [Google Scholar]
  171. Phillips DL, Gregg JW. 2003. Source partitioning using stable isotopes: coping with too many sources. Oecologia 136:261–69 [Google Scholar]
  172. Phillips DL, Inger R, Bearhop S, Jackson AL, Moore JW. et al. 2014. Best practices for use of stable isotope mixing models in food-web studies. Can. J. Zool. 92:823–35 [Google Scholar]
  173. Piché J, Iverson SJ, Parrish FA, Dollar R. 2010. Characterization of forage fish and invertebrates in the northwestern Hawaiian Islands using fatty acid signatures: species and ecological groups. Mar. Ecol. Prog. Ser. 418:1–15 [Google Scholar]
  174. Plagányi ÉE. 2007. Models for an ecosystem approach to fisheries FAO Fish. Tech. Paper No. 477, Food Agric. Organ UN, Rome: [Google Scholar]
  175. Plagányi ÉE, Punt AE, Hillary R, Morello EB, Thebaud O. et al. 2014. Multispecies fisheries management and conservation: tactical applications using models of intermediate complexity. Fish Fish 15:1–22 [Google Scholar]
  176. Polis GA, Strong DR. 1996. Food web complexity and community dynamics. Am. Nat. 147:813–46 [Google Scholar]
  177. Polovina JJ. 1984. Model of a coral reef ecosystem. Coral Reefs 3:1–11 [Google Scholar]
  178. Popp BN, Graham BS, Olson RJ, Hannides CC, Lott MJ. et al. 2007. Insight into the trophic ecology of yellowfin tuna, Thunnusalbacares, from compound‐specific nitrogen isotope analysis of proteinaceous amino acids. Terr. Ecol. 1:173–90 [Google Scholar]
  179. Post DM. 2002. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83:703–18 [Google Scholar]
  180. Ramsvatn S. 2013. Investigating coastal ecosystem structure and dynamics using Ecopath mass-balance modelling and stable isotope data PhD Thesis, Dep. Arctic Mar. Biol. Univ. Tromsø Nor.: [Google Scholar]
  181. Redfield AC. 1963. The influence of organisms on the composition of sea-water. The Sea: Ideas and Observations on Progress in the Study of the Seas 2 The Composition of Seawater: Comparative and Descriptive Oceanography MN Hill 26–77 New York: Interscience [Google Scholar]
  182. Rekadwad BN, Gonzalez JM, Khobragade CN. 2016. Genomic analysis of a marine bacterium: bioinformatics for comparison, evaluation, and interpretation of DNA sequences. BioMed Res. Int. 2016:7215379 [Google Scholar]
  183. Richoux NB, Jaquemet S, Bonnevie BT, Cherel Y, McQuaid CD. 2010. Trophic ecology of grey-headed albatrosses from Marion Island, Southern Ocean: insights from stomach contents and diet tracers. Mar. Biol. 157:1755–66 [Google Scholar]
  184. Rooker J, Secor D. 2004. Stock structure and mixing of Atlantic bluefin tuna: evidence from stable δ13C and δ18O isotopes in otoliths. Collect. Vol. Sci. Pap. ICCAT 56:1115–20 [Google Scholar]
  185. Rossi S, Sabatés A, Latasa M, Reyes E. 2006. Lipid biomarkers and trophic linkages between phytoplankton, zooplankton and anchovy (Engraulis encrasicolus) larvae in the NW Mediterranean. J. Plankton Res. 28:551–62 [Google Scholar]
  186. Roughgarden J. 1983. Competition and theory in community ecology. Am. Nat. 122:583–601 [Google Scholar]
  187. Sardenne F, Bodin N, Chassot E, Amiel A, Fouché E. et al. 2016. Trophic niches of sympatric tropical tuna in the western Indian Ocean inferred by stable isotopes and neutral fatty acids. Prog. Oceanogr. 146:75–88 [Google Scholar]
  188. Säwström C, Hyndes GA, Eyre BD, Huggett MJ, Fraser MW. et al. 2016. Coastal connectivity and spatial subsidy from a microbial perspective. Ecol. Evol. 6:6662–71 [Google Scholar]
  189. Schmidt K, Atkinson A, Petzke K-J, Voss M, Pond DW. 2006. Protozoans as a food source for Antarctic krill, Euphausia superba: complementary insights from stomach content, fatty acids, and stable isotopes. Limnol. Oceanogr. 51:2409–27 [Google Scholar]
  190. Schmidt SN, Olden JD, Solomon CT, Zanden M. 2007. Quantitative approaches to the analysis of stable isotope food web data. Ecology 88:2793–802 [Google Scholar]
  191. Schmittner A, Somes CJ. 2016. Complementary constraints from carbon (13C) and nitrogen (15N) isotopes on the glacial ocean's soft‐tissue biological pump. Paleoceanography 31:669–93 [Google Scholar]
  192. Semmens BX, Ward EJ, Moore JW, Darimont CT. 2009. Quantifying inter-and intra-population niche variability using hierarchical Bayesian stable isotope mixing models. PLOS ONE 4:e6187 [Google Scholar]
  193. Senn DB, Chesney EJ, Blum JD, Bank MS, Maage A, Shine JP. 2010. Stable isotope (N, C, Hg) study of methylmercury sources and trophic transfer in the northern Gulf of Mexico. Environ. Sci. Technol. 44:1630–37 [Google Scholar]
  194. Shannon L, Coll M, Bundy A, Gascuel D, Heymans JJ. et al. 2014. Trophic level-based indicators to track fishing impacts across marine ecosystems. Mar. Ecol. Prog. Ser. 512:115–40 [Google Scholar]
  195. Shin Y-J, Cury P. 2004. Using an individual-based model of fish assemblages to study the response of size spectra to changes in fishing. Can. J. Fish. Aquat. Sci. 61:414–31 [Google Scholar]
  196. Smith A, Fulton E, Hobday A, Smith D, Shoulder P. 2007. Scientific tools to support the practical implementation of ecosystem-based fisheries management. ICES J. Mar. Sci. 64:633–39 [Google Scholar]
  197. Smith M, Fulton E, Day R, Shannon L, Shin Y-J. 2015. Ecosystem modelling in the southern Benguela: comparisons of Atlantis, Ecopath with Ecosim, and OSMOSE under fishing scenarios. Afr. J. Mar. Sci. 37:65–78 [Google Scholar]
  198. Spitz J, Mourocq E, Schoen V, Ridoux V. 2010. Proximate composition and energy content of forage species from the Bay of Biscay: high- or low-quality food?. ICES J. Mar. Sci. 67:909–15 [Google Scholar]
  199. Sterner RW, Elser JJ. 2002. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere Princeton, NJ: Princeton Univ. Press [Google Scholar]
  200. Strandberg U, Taipale S, Hiltunen M, Galloway A, Brett M, Kankaala P. 2015. Inferring phytoplankton community composition with a fatty acid mixing model. Ecosphere 6:1–18 [Google Scholar]
  201. Syväranta J, Lensu A, Marjomäki TJ, Oksanen S, Jones RI. 2013. An empirical evaluation of the utility of convex hull and standard ellipse areas for assessing population niche widths from stable isotope data. PLOS ONE 8:e56094 [Google Scholar]
  202. Tollit DJ, Schulze AD, Trites AW, Olesiuk PF, Crockford SJ. et al. 2009. Development and application of DNA techniques for validating and improving pinniped diet estimates. Ecol. Appl. 19:889–905 [Google Scholar]
  203. Travers M, Shin Y-J, Jennings S, Cury P. 2007. Towards end-to-end models for investigating the effects of climate and fishing in marine ecosystems. Prog. Oceanogr. 75:751–70 [Google Scholar]
  204. Trueman C, MacKenzie K, Palmer M. 2012. Identifying migrations in marine fishes through stable‐isotope analysis. J. Fish Biol. 81:826–47 [Google Scholar]
  205. Turner TF, Collyer ML, Krabbenhoft TJ. 2010. A general hypothesis‐testing framework for stable isotope ratios in ecological studies. Ecology 91:2227–33 [Google Scholar]
  206. Uusitalo L, Lehikoinen A, Helle I, Myrberg K. 2015. An overview of methods to evaluate uncertainty of deterministic models in decision support. Environ. Model. Softw. 63:24–31 [Google Scholar]
  207. Valentini A, Pompanon F, Taberlet P. 2009. DNA barcoding for ecologists. Trends Ecol. Evol. 24:110–17 [Google Scholar]
  208. Vanderklift MA, Ponsard S. 2003. Sources of variation in consumer-diet δ15N enrichment: a meta-analysis. Oecologia 136:169–82 [Google Scholar]
  209. Volkman J, Jeffrey S, Nichols P, Rogers G, Garland C. 1989. Fatty acid and lipid composition of 10 species of microalgae used in mariculture. J. Exp. Mar. Biol. Ecol. 128:219–40 [Google Scholar]
  210. Wada E, Hattori A. 1976. Natural abundance of 15N in particulate organic matter in the North Pacific Ocean. Geochim. Cosmochim. Acta 40:249–51 [Google Scholar]
  211. Wang W-X. 2002. Interactions of trace metals and different marine food chains. Mar. Ecol. Prog. Ser. 243:295–309 [Google Scholar]
  212. Warszawski L, Frieler K, Huber V, Piontek F, Serdeczny O, Schewe J. 2014. The inter-sectoral impact model intercomparison project (ISI–MIP): project framework. PNAS 111:3228–32 [Google Scholar]
  213. West JB, Bowen GJ, Dawson TE, Tu KP. 2010. Isoscapes Berlin: Springer [Google Scholar]
  214. Williams CT, Buck CL. 2010. Using fatty acids as dietary tracers in seabird trophic ecology: theory, application and limitations. J. Ornithol. 151:531–43 [Google Scholar]
  215. Winemiller KO, Polis GA. 1996. Food webs: What can they tell us about the world?. Food Webs: Integration of Patterns and Dynamics GA Polis, KO Winemiller 1–22 Berlin: Springer [Google Scholar]
  216. Woodward G, Ebenman B, Emmerson M, Montoya JM, Olesen JM. et al. 2005. Body size in ecological networks. Trends Ecol. Evol. 20:402–9 [Google Scholar]
  217. Wright SW, Jeffrey S. 2006. Pigment markers for phytoplankton production. Marine Organic Matter: Biomarkers, Isotopes and DNA JK Volkman 71–104 Berlin: Springer [Google Scholar]
  218. Wu Y, Wang N, Zhang J, Wan R, Dai F, Jin X. 2016. Compound-specific isotopes of fatty acids as indicators of trophic interactions in the East China Sea ecosystem. Chin. J. Oceanol. Limnol. 34:1085–96 [Google Scholar]
  219. Yang T-H, Somero GN. 1993. Effects of feeding and food deprivation on oxygen consumption, muscle protein concentration and activities of energy metabolism enzymes in muscle and brain of shallow-living (Scorpaena guttata) and deep-living (Sebastolobus alascanus) scorpaenid fishes. J. Exp. Biol. 181:213–32 [Google Scholar]
  220. Yoshikawa C, Yamanaka Y, Nakatsuka T. 2005. An ecosystem model including nitrogen isotopes: perspectives on a study of the marine nitrogen cycle. J. Oceanogr. 61:921–42 [Google Scholar]
  221. Young JW, Hunt BP, Cook TR, Llopiz JK, Hazen EL. et al. 2015a. The trophodynamics of marine top predators: current knowledge, recent advances and challenges. Deep-Sea Res. II 113:170–87 [Google Scholar]
  222. Young JW, Olson RJ, Ménard F, Kuhnert PM, Duffy LM. et al. 2015b. Setting the stage for a global-scale trophic analysis of marine top predators: a multi-workshop review. Rev. Fish Biol. Fish. 25:261–72 [Google Scholar]
  223. Zeng Y-H, Luo X-J, Yu L-H, Chen H-S, Wu J-P. et al. 2013. Using compound-specific stable carbon isotope analysis to trace metabolism and trophic transfer of PCBs and PBDEs in fish from an e-waste site, South China. Environ. Sci. Technol. 47:4062–68 [Google Scholar]
  224. Zinger L, Gobet A, Pommier T. 2012. Two decades of describing the unseen majority of aquatic microbial diversity. Mol. Ecol. 21:1878–96 [Google Scholar]
/content/journals/10.1146/annurev-marine-121916-063256
Loading
/content/journals/10.1146/annurev-marine-121916-063256
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error