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Using stable isotope data to advance marine food web modelling

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Abstract

Marine ecosystem models that incorporate fisheries and climate change are essential for forecasting and guiding sustainable ecosystem management decisions. A key challenge in developing and applying ecosystem models that are able to provide robust predictions for management is to accurately represent the structure and dynamics of food webs. Ecosystem models vary in complexity and formulation and there is no set method routinely used to evaluate the skill of a model to correctly represent food web characteristics. One approach for evaluation is the comparison of modelled food web attributes with measures of stable isotope composition of taxa. While this approach has been used in some studies, its full potential has not been realised. Critically, directly modelling the assumed underlying processes that give rise to stable isotope signatures in ecosystem models has only just begun to be explored. Here, we examine the process of building ecosystem models and assess the potential for incorporating stable isotope results into this process, including the evaluation of model skill. We consider both size- and species- based ecosystem modelling approaches for their potential in this regard. We discuss that whilst conceptually achievable, in practice this is highly challenging through highlighting the advances and challenges in using stable isotope data, including the implications of precision associated with isotope-based measurements. We conclude with a proposed framework for explicitly integrating stable isotope data into both size- and species- based ecosystem models as an example of how signatures may be more powerfully used in the modelling process, and highlight key needs for future work.

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References

  • Addison PFE et al (2013) Practical solutions for making models indispensable in conservation decision-making. Divers Distrib 19:490–502. https://doi.org/10.1111/ddi.12054

    Article  Google Scholar 

  • Andersen KH, Jacobsen NS, Farnsworth KD (2015) The theoretical foundations for size spectrum models of fish communities. Can J Fish Aquat Sci 73:575–588

    Article  Google Scholar 

  • Barange M, Field JG, Harris RP, Hofmann E, Perry RI, Werner F (2010) Marine ecosystems and global change. Oxford University Press, Oxford

    Book  Google Scholar 

  • Barange M et al (2014) Impacts of climate change on marine ecosystem production in societies dependent on fisheries. Nat Clim Change 4:211–216

    Article  Google Scholar 

  • Barnes C, Maxwell D, Reuman DC, Jennings S (2010) Global patterns in predator–prey size relationships reveal size dependency of trophic transfer efficiency. Ecology 91:222–232

    Article  PubMed  Google Scholar 

  • Blanchard JL, Jennings S, Law R, Castle MD, McCloghrie P, Rochet MJ, Benoît E (2009) How does abundance scale with body size in coupled size-structured food webs? J Anim Ecol 78:270–280

    Article  PubMed  Google Scholar 

  • Blanchard JL et al (2012) Potential consequences of climate change for primary production and fish production in large marine ecosystems. Phil Trans R Soc B 367:2979–2989

    Article  PubMed  Google Scholar 

  • Blanchard JL, Andersen KH, Scott F, Hintzen NT, Piet G, Jennings S (2014) Evaluating targets and trade-offs among fisheries and conservation objectives using a multispecies size spectrum model. J Appl Ecol 51:612–622

    Article  Google Scholar 

  • 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 32:174–186

    Article  PubMed  Google Scholar 

  • Boecklen WJ, Yarnes CT, Cook BA, James AC (2011) On the use of stable isotopes in trophic ecology. Annu Rev Ecol Evol Syst 42:411–440

    Article  Google Scholar 

  • Brett M, Eisenlord M, Galloway A (2016) Using multiple tracers and directly accounting for trophic modification improves dietary mixing-model performance. Ecosphere 7:e01440. https://doi.org/10.1002/ecs2.1440

    Article  Google Scholar 

  • Cabana G, Rasmussen JB (1996) Comparison of aquatic food chains using nitrogen isotopes. Proc Natl Acad Sci 93:10844–10847

    Article  CAS  PubMed  Google Scholar 

  • Caut S, Angulo E, Courchamp F (2009) Variation in discrimination factors (Δ15N and Δ13C): the effect of diet isotopic values and applications for diet reconstruction. J Appl Ecol 46:443–453

    Article  CAS  Google Scholar 

  • Christensen V, Pauly D (1992) ECOPATH II—a software for balancing steady-state ecosystem models and calculating network characteristics. Ecol Model 61:169–185

    Article  Google Scholar 

  • Christensen V, Walters CJ (2004) Ecopath with Ecosim: methods, capabilities and limitations. Ecol Model 172:109–139

    Article  Google Scholar 

  • Dame JK, Christian RR (2008) Evaluation of ecological network analysis: validation of output. Ecol Model 210:327–338

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Dowd M (2007) Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo. J Mar Syst 68:439–456

    Article  Google Scholar 

  • Du J, Cheung WW, Zheng X, Chen B, Liao J, Hu W (2015) Comparing trophic structure of a subtropical bay as estimated from mass-balance food web model and stable isotope analysis. Ecol Model 312:175–181

    Article  CAS  Google Scholar 

  • Flynn KJ, Mitra A, Bode A (2018) Toward a mechanistic understanding of trophic structure: inferences from simulating stable isotope ratios. Mar Biol 165:147

    Article  PubMed  PubMed Central  Google Scholar 

  • Fry B (2007) Stable isotope ecology, vol 521. Springer, New York

    Google Scholar 

  • Fulton EA (2010) Approaches to end-to-end ecosystem models. J Mar Syst 81:171–183

    Article  Google Scholar 

  • Fulton EA, Link JS (2014) Modelling approaches for marine ecosystem-based management. In: Fogarty MJ, McCarthy JJ (eds) The sea: marine ecosystem-based management, vol 16. Harvard University Press, Cambridge, pp 121–170

    Google Scholar 

  • Gregr EJ, Chan KM (2014) Leaps of faith: how implicit assumptions compromise the utility of ecosystem models for decision-making. Bioscience 65:43–54

    Article  Google Scholar 

  • Guiet J, Poggiale J-C, Maury O (2016) Modelling the community size-spectrum: recent developments and new directions. Ecol Model 337:4–14

    Article  Google Scholar 

  • Gurney LJ, Pakhomov EA, Christensen V (2014) An ecosystem model of the Prince Edward Island archipelago. Ecol Model 294:117–136

    Article  Google Scholar 

  • Hall CA, Day JW Jr (1977) Ecosystem modeling in theory and practice: an introduction with case histories. Wiley, New York

    Google Scholar 

  • Handegard NO et al (2013) Towards an acoustic-based coupled observation and modelling system for monitoring and predicting ecosystem dynamics of the open ocean. Fish Fish 14:605–615

    Article  Google Scholar 

  • Healy K, Guillerme T, Kelly S, Inger R, Bearhop S, Jackson AL (2017) SIDER: an R package for predicting trophic discrimination factors of consumers based on their ecology and phylogenetic relatedness. Ecography https://doi.org/10.1111/ecog.03371

  • Hill S, Murphy E, Reid K, Trathan P, Constable A (2006) Modelling Southern Ocean ecosystems: krill, the food-web, and the impacts of harvesting. Biol Rev 81:581–608

    Article  CAS  PubMed  Google Scholar 

  • Hill SL, Keeble K, Atkinson A, Murphy EJ (2012) A foodweb model to explore uncertainties in the South Georgia shelf pelagic ecosystem. Deep Sea Res Part 2 Top Stud Oceanogr 59:237–252

    Article  Google Scholar 

  • Hussey NE et al (2014) Rescaling the trophic structure of marine food webs. Ecol Lett 17:239–250

    Article  PubMed  Google Scholar 

  • Hyder K et al (2015) Making modelling count-increasing the contribution of shelf-seas community and ecosystem models to policy development and management. Mar Policy 61:291–302

    Article  Google Scholar 

  • IPCC (2014) Climate Change 2014: Impacts, adaptation and vulnerability. Part A: global and sectoral aspects: contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

  • Jabot F, Giraldo C, Lefebvre S, Dubois S (2017) Are food web structures well represented in isotopic spaces? Funct Ecol 31:1975–1984

    Article  Google Scholar 

  • Jennings S, Cogan S (2015) Nitrogen and carbon stable isotope variation in northeast Atlantic fishes and squids. Ecology 96:2568

    Article  Google Scholar 

  • Jennings S, Collingridge K (2015) Predicting consumer biomass, size-structure, production, catch potential, responses to fishing and associated uncertainties in the world’s marine ecosystems. PLoS ONE 10:e0133794

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jennings S, Mackinson S (2003) Abundance–body mass relationships in size-structured food webs. Ecol Lett 6:971–974

    Article  Google Scholar 

  • Jennings S, Warr K (2003a) Environmental correlates of large-scale spatial variation in the δ15N of marine animals. Mar Biol 142:1131–1140

    Article  Google Scholar 

  • Jennings S, Warr KJ (2003b) Smaller predator-prey body size ratios in longer food chains. Proc R Soc Lond B Biol Sci 270:1413–1417

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Jennings S, Mélin F, Blanchard JL, Forster RM, Dulvy NK, Wilson RW (2008) Global-scale predictions of community and ecosystem properties from simple ecological theory. Proc R Soc Lond B Biol Sci 275:1375–1383

    Article  Google Scholar 

  • Kavanagh P, Newlands N, Christensen V, Pauly D (2004) Automated parameter optimization for Ecopath ecosystem models. Ecol Model 172:141–149

    Article  Google Scholar 

  • Kline TC, Pauly D (1998) Cross-validation of trophic level estimates from a mass-balance model of Prince William Sound using 15 N/14 N data. In: Funk F, Quinn II T, J, Heifetz J, Powers J, E, Schweigert JF, Sullivan PJ, Zhang C, I (eds) Fishery Stock Assessment Models. Alaska Sea Grant College Program Report, Number AAK-SG-98-01, University of Alaska, Fairbanks, pp 693–702

  • Krouse HR, Grinenko VA (1991) Stable isotopes: natural and anthropogenic sulphur in the environment. Wiley, Chichester

    Google Scholar 

  • Lassalle G, Chouvelon T, Bustamante P, Niquil N (2014) 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 120:205–215

    Article  Google Scholar 

  • Law R, Plank MJ, James A, Blanchard JL (2009) Size-spectra dynamics from stochastic predation and growth of individuals. Ecology 90:802–811

    Article  PubMed  Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • Layman CA et al (2012) Applying stable isotopes to examine food-web structure: an overview of analytical tools. Biol Rev 87:545–562

    Article  PubMed  Google Scholar 

  • MacKenzie K, Longmore C, Preece C, Lucas C, Trueman C (2014) Testing the long-term stability of marine isoscapes in shelf seas using jellyfish tissues. Biogeochemistry 121:441–454

    Article  Google Scholar 

  • Magozzi S, Yool A, Vander Zanden H, Wunder M, Trueman C (2017) Using ocean models to predict spatial and temporal variation in marine carbon isotopes. Ecosphere 8:e01763

    Article  Google Scholar 

  • Martínez del Rio C, Wolf N, Carleton SA, Gannes LZ (2009) Isotopic ecology ten years after a call for more laboratory experiments. Biol Rev 84:91–111

    Article  Google Scholar 

  • Mathisen OA, Sands NJ (1999) Ecosystem modeling of Becharof Lake, a sockeye salmon nursery lake in Southwestern Alaska. Ecosystem approaches for fisheries management Alaska Sea Grant College Program, University of Alaska Fairbanks, Fairbanks, Alaska Rep No AK-SG-99-01, pp 685–703

  • Maury O, Faugeras B, Shin Y-J, Poggiale J-C, Ari TB, Marsac F (2007) Modeling environmental effects on the size-structured energy flow through marine ecosystems. Part 1: the model. Prog Oceanogr 74:479–499

    Article  Google Scholar 

  • McCauley DJ et al. (2018) On the prevalence and dynamics of inverted trophic pyramids and otherwise top-heavy communities. Ecol Lett https://doi.org/10.1111/ele.12900

  • McMahon KW, McCarthy MD (2016) Embracing variability in amino acid δ15N fractionation: mechanisms, implications, and applications for trophic ecology. Ecosphere 7:e01511. https://doi.org/10.1002/ecs2.1511

    Article  Google Scholar 

  • Merino G et al (2012) Can marine fisheries and aquaculture meet fish demand from a growing human population in a changing climate? Glob Environ Change 22:795–806

    Article  Google Scholar 

  • Milessi AC, Danilo C, Laura R-G, Daniel C, Javier S, Rodríguez-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 221:2859–2869

    Article  CAS  Google Scholar 

  • 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 48:1135–1140

    Article  CAS  Google Scholar 

  • Moore JW, Semmens BX (2008) Incorporating uncertainty and prior information into stable isotope mixing models. Ecol Lett 11:470–480

    Article  PubMed  Google Scholar 

  • Murphy E et al (2012) Developing integrated models of Southern Ocean food webs: including ecological complexity, accounting for uncertainty and the importance of scale. Prog Oceanogr 102:74–92

    Article  Google Scholar 

  • 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 Bio Ecol 401:97–104

    Article  Google Scholar 

  • 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 65:2791–2806

    Article  CAS  Google Scholar 

  • O’Connell TC (2017) ‘Trophic’and ‘source’amino acids in trophic estimation: a likely metabolic explanation. Oecologia 184:317–326

    Article  PubMed  PubMed Central  Google Scholar 

  • Olsen E, Fay G, Gaichas S, Gamble R, Lucey S, Link JS (2016) Ecosystem model skill assessment. Yes we can! PloS ONE 11:e0146467

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 267:127–137

    Article  CAS  Google Scholar 

  • Parkyn SM, Collier KJ, Hicks BJ (2001) New Zealand stream crayfish: functional omnivores but trophic predators? Freshw Biol 46:641–652

    Article  Google Scholar 

  • Parnell AC et al (2013) Bayesian stable isotope mixing models. Environmetrics 24:387–399

    Google Scholar 

  • Pauli JN, Steffan SA, Newsome SD (2015) It is time for IsoBank. Bioscience 65:229–230

    Article  Google Scholar 

  • Peterson BJ, Fry B (1987) Stable isotopes in ecosystem studies. Annu Rev Ecol Evol Syst 18:293–320

    Article  Google Scholar 

  • Pethybridge HR, Choy CA, Polovina JJ, Fulton EA (2018) Improving marine ecosystem models with biochemical tracers. Annu Rev Mar Sci 10:199–228

    Article  Google Scholar 

  • Phillips DL, Gregg JW (2003) Source partitioning using stable isotopes: coping with too many sources. Oecol 136:261–269

    Article  Google Scholar 

  • Pinkerton MH, Bradford-Grieve JM (2014) Characterizing foodweb structure to identify potential ecosystem effects of fishing in the Ross Sea, Antarctica. ICES J Mar Sci 71:1542–1553

    Article  Google Scholar 

  • Plagányi ÉE (2007) Models for an ecosystem approach to fisheries, vol 477. Food and Agriculture Organization, Rome

    Google Scholar 

  • Polovina JJ (1984) Model of a coral reef ecosystem. Coral Reefs 3:1–11

    Article  Google Scholar 

  • Polunin NV, Pinnegar JK (2000) Trophic-level dynamics inferred from stable isotopes of carbon and nitrogen. Fish Down Mediterr Food Webs 12:69–72

    Google Scholar 

  • Post DM (2002) Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83:703–718

    Article  Google Scholar 

  • Raymond B et al (2011) A Southern Ocean dietary database. Ecology 92:1188

    Article  Google Scholar 

  • Rogers A, Blanchard JL, Mumby PJ (2014) Vulnerability of coral reef fisheries to a loss of structural complexity. Curr Biol 24:1000–1005

    Article  CAS  PubMed  Google Scholar 

  • Rose KA et al (2010) End-to-end models for the analysis of marine ecosystems: challenges, issues and next steps. Mar Coast Fish 2:115–130

    Article  Google Scholar 

  • Rykiel EJ (1996) Testing ecological models: the meaning of validation. Ecol Model 90:229–244

    Article  Google Scholar 

  • Scott F, Blanchard JL, Andersen KH (2014) mizer: an R package for multispecies, trait-based and community size spectrum ecological modelling. Methods Ecol Evol 5:1121–1125

    Article  PubMed  PubMed Central  Google Scholar 

  • 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

    Article  Google Scholar 

  • Somes CJ et al. (2010) Simulating the global distribution of nitrogen isotopes in the ocean. Global Biogeochem Cycles 24:GB4019. https://doi.org/10.1029/2009gb003767

  • Spence MA, Blackwell PG, Blanchard JL (2015) Parameter uncertainty of a dynamic multispecies size spectrum model. Can J Fish Aquat Sci 73:589–597

    Article  Google Scholar 

  • Stock BC, Semmens BX (2016) Unifying error structures in commonly used biotracer mixing models. Ecology 97:2562–2569

    Article  PubMed  Google Scholar 

  • Stowasser G, Atkinson A, McGill R, Phillips R, Collins MA, Pond D (2012) Food web dynamics in the Scotia Sea in summer: a stable isotope study. Deep Sea Res Part 2 Top Stud Oceanogr 59:208–221

    Article  CAS  Google Scholar 

  • Tagliabue A, Bopp L (2008) Towards understanding global variability in ocean carbon-13. Global Biogeochem Cycles 22:GB1025

    Article  CAS  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteor Soc 93:485–498

    Article  Google Scholar 

  • Travers M, Watermeyer K, Shannon L, Shin Y-J (2010) Changes in food web structure under scenarios of overfishing in the southern Benguela: comparison of the Ecosim and OSMOSE modelling approaches. J Mar Syst 79:101–111

    Article  Google Scholar 

  • Trebilco R, Baum JK, Salomon AK, Dulvy NK (2013) Ecosystem ecology: size-based constraints on the pyramids of life. Trends Ecol Evol 28:423–431

    Article  PubMed  Google Scholar 

  • Trebilco R, Dulvy NK, Anderson SC, Salomon AK (2016) The paradox of inverted biomass pyramids in kelp forest fish communities. Proc R Soc Lond B Biol Sci 283:20160816. https://doi.org/10.1098/rspb.2016.0816

    Article  Google Scholar 

  • Trueman CN, McGill RA, Guyard PH (2005) The effect of growth rate on tissue-diet isotopic spacing in rapidly growing animals. An experimental study with Atlantic salmon (Salmo salar). Rapid Commun Mass Sp 19:3239–3247

    Article  CAS  Google Scholar 

  • Vander Zanden HB, Soto DX, Bowen GJ, Hobson KA (2016) Expanding the isotopic toolbox: applications of hydrogen and oxygen stable isotope ratios to food web studies. Front Ecol Evol 4:20. https://doi.org/10.3389/fevo.2016.00020

    Article  Google Scholar 

  • Vizzini S, Mazzola A (2003) Seasonal variations in the stable carbon and nitrogen isotope ratios (13C/12C and 15N/14N) of primary producers and consumers in a western Mediterranean coastal lagoon. Mar Biol 142:1009–1018

    Article  CAS  Google Scholar 

  • Walters C, Christensen V, Pauly D (1997) Structuring dynamic models of exploited ecosystems from trophic mass-balance assessments. Rev Fish Biol Fish 7:139–172

    Article  Google Scholar 

  • West JB, Bowen GJ, Dawson TE, Tu KP (2010) Isoscapes: understanding movement, pattern, and process on Earth through isotope mapping. Springer, New York

    Book  Google Scholar 

  • Woodson CB, Schramski JR, Joye SB (2018) A unifying theory for top-heavy ecosystem structure in the ocean. Nat Commun 9:23

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Woodward G et al (2010) Individual-based food webs: species identity, body size and sampling effects. In: Woodward G (ed) Integrative ecology: from molecules to ecosystems. Academic Press, London, pp 211–266

    Chapter  Google Scholar 

  • Woodworth-Jefcoats PA, Polovina JJ, Howell EA, Blanchard JL (2015) Two takes on the ecosystem impacts of climate change and fishing: comparing a size-based and a species-based ecosystem model in the central North Pacific. Prog Oceanogr 138:533–545

    Article  Google Scholar 

  • Yeakel JD, Bhat U, Elliott Smith EA, Newsome SD (2016) Exploring the isotopic niche: isotopic variance, physiological incorporation, and the temporal dynamics of foraging. Front Ecol Evol 4:1

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Australian government’s Cooperative Research Centre Program through the Antarctic Climate and Ecosystems Cooperative Research Centre (ACE CRC) and through the Australian Antarctic Science Program (Projects 4347 and 4366). SAM acknowledges funding from the AAD-UTAS Quantitative Antarctic Science Program, and the Australian Research Training Program. RT was supported by the RJL Hawke Postdoctoral Fellowship. We thank Christopher Griffiths, Dr Andrea Walters, Dr Clive Trueman, Dr Kevin J Flynn and five anonymous reviewers for their helpful comments on previous versions of the manuscript.

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  1. Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 129, 20 Castray Esplanade, Battery Point, Hobart, TAS, 7001, Australia

    Stacey A. McCormack & Julia L. Blanchard

  2. Antarctic Climate and Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, TAS, 7001, Australia

    Stacey A. McCormack, Rowan Trebilco, Jessica Melbourne-Thomas & Andrew Constable

  3. Australian Antarctic Division, Department of the Environment and Energy, 203 Channel Highway, Kingston, TAS, 7050, Australia

    Jessica Melbourne-Thomas & Andrew Constable

  4. CSIRO [Commonwealth Scientific and Industrial Research Organisation] Oceans and Atmosphere Flagship, GPO Box 1538, Hobart, TAS, 7001, Australia

    Elizabeth A. Fulton

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  1. Stacey A. McCormack
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Correspondence to Stacey A. McCormack.

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Glossary

Ecosystem-based management

Natural resource management that recognizes the interconnectedness and interdependent nature of ecosystem components as well as the importance of ecosystem structures and functions

Ecosystem model

A theoretical representation of an ecological system (incorporating processes on the scale from an individual population, to an ecological community or even an entire biome), which is studied to gain understanding of the real system (Hall and Day 1977)

Evaluation

The process of assessing a model’s skill in representing the real world from the perspective of its intended use. There has been much debate around the use of the term “validation” in regards to ecosystem models (e.g. Rykiel 1996) particularly when addressing dynamic or predictive models. Many studies have emphasized that simulation models are unable to replicate (in a forecast sense) complex ecosystems due to unavoidable assumptions and generality, therefore cannot truly be “validated” (Gregr and Chan 2014). However, other authors feel this is an argument in semantics, as ecosystem models have many levels of validation, with forecast being only one such level. Equally valid is the testing of the model’s ability to capture the core patterns and dynamic relationships of causality through space and time (Fulton 2010). We use the term evaluation here to encompass these concepts

Food-web model

A subset of ecosystem models, applied for quantifying direct and indirect trophic interactions, for comparing food web properties, and for evaluating food web responses to human pressures and environmental change

Model skill

A term that originated from biophysical models that has recently been applied to marine ecosystem models (Olsen et al. 2016). The ability of a model to reproduce the true system state inferred from the best estimate available (e.g. another model or data collected directly from the system of interest)

Parameterisation

The process of deciding and defining the parameters necessary for a complete ecosystem model

Preferred PPMR

The preferred prey size of predators, as a proportion of their body weight. This prey size preference is used in parameterising size-based food web models

Realised PPMR

Predator-prey mass ratio that arises from a combination of preferred PPMR and availability. Realised PPMR is what is estimated from isotope data, and is the PPMR used as a parameter in macroecological models (Jennings and Collingridge 2015)

Signatures

Independent sets of biological or ecological measurements derived from field observations

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McCormack, S.A., Trebilco, R., Melbourne-Thomas, J. et al. Using stable isotope data to advance marine food web modelling. Rev Fish Biol Fisheries 29, 277–296 (2019). https://doi.org/10.1007/s11160-019-09552-4

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