Ecosystem Model in Data-Poor Situations

  • Hiroshi OkamuraEmail author
  • Momoko Ichinokawa
  • Osamu Komori
Part of the Fisheries Science Series book series (FISHSS)


Ecosystem assessment is one of the most interesting topics in ecology and fisheries science; modeling is an essential and indispensable part of ecosystem assessment. We briefly review existing ecosystem models that are employed around the world and present a new ecosystem model that can be applied in data-poor situations, e.g., when diet-composition data are unavailable. The new model is based on a multivariate state-space model with an allometric relationship between the biological parameters and body mass. The model generally does not require unrealistic assumptions, such as equilibrium prior to fishing and mass balance during a certain period. The simulation study demonstrated that the model outperformed a single-species assessment in terms of the inference of biological reference points. As an illustration, we applied the model to environmental index data and three species in the western North Pacific, which are known to show conspicuous species replacement (chub mackerel, sardine, and anchovy). The model can be extensively applied to various multispecies data in data-poor situations.


Allometric relationship Body size Ecosystem modeling Gompertz–Fox model Random effects State-space modeling Species replacement 



This research was supported by JST, CREST.


  1. Barange M, Merino G, Blanchard JL, Scholtens J, Harle J, Allison EH, Allen JI, Holt J, Jennings S (2014) Impacts of climate change on marine ecosystem production in societies dependent on fisheries. Nature Clim Change 4:211–216CrossRefGoogle Scholar
  2. Bogstad B, Hauge KH, Ulltang O (1997) MULTSPEC: a multi-species model for fish and marine mammals in the Barents Sea. J N Atl Fish Sci 22:317–341CrossRefGoogle Scholar
  3. Branch T, Watson R, Fulton EA, Jennings S, McGilliard CR, Pablico GT, Ricard D, Tracey SR (2010) The trophic fingerprint of marine fisheries. Nature 468:431–435CrossRefPubMedGoogle Scholar
  4. Carlin BP, Clark JS, Gelfand AE (2005) Elements of hierarchical Bayesian inference. In: Clark JS, Gelfand AE (eds) Hierarchical modelling for the environmental sciences. Oxford University Press, Oxford, pp 3–24Google Scholar
  5. Charnov EL (1993) Life history invariants. Oxford University Press, OxfordGoogle Scholar
  6. Christensen V, Pauly D (1992) ECOPATH II—a software for balancing steady-state ecosystem models and calculating network characteristics. Ecol Model 61:169–185CrossRefGoogle Scholar
  7. Clark JS, Gelfand AE (2006) Hierarchical modelling for the environmental sciences. Oxford University Press, OxfordGoogle Scholar
  8. Fulton EA, Link JS, Kaplan IC, Savina-Rolland M, Johnson P, Ainsworth C, Horne P, Gorton R, Gamble RJ, Smith ADM, Smith DC (2011) Lessons in modelling and management of marine ecosystems: the Atlantis experience. Fish Fish 12:171–188CrossRefGoogle Scholar
  9. Garcia SM, Kolding J, Rice J, Rochet MJ, Zhou S, Arimoto T, Beyer JE, Borges L, Bundy A, Dunn D, Fulton EA, Hall M, Heino M, Law R, Makino M, Rijnsdorp AD, Simard F, Smith ADM (2012) Reconsidering the consequences of selective fisheries. Science 335:1045–1047CrossRefPubMedGoogle Scholar
  10. Gelman A, Carlin JB, Stern HS, Rubin DB (2003) Bayesian data analysis, 2nd edn. Chapman & Hall, New YorkGoogle Scholar
  11. Hilborn R, Hilborn U (2012) Overfishing: what everyone needs to know. Oxford University Press, OxfordGoogle Scholar
  12. Holling CS (1959) The components of predation as revealed by a study of small-mammal predation of the European pine sawfly. Can Entomol 91:293–320CrossRefGoogle Scholar
  13. Hosack GR, Peters GW, Ludsin SA (2014) Interspecific relationships and environmentally driven catchabilities estimated from fisheries data. Can J Fish Aquat Sci 71:447–463CrossRefGoogle Scholar
  14. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning: with applications in R. Springer, New YorkCrossRefGoogle Scholar
  15. Kawabata A, Honda S, Watanabe C, Okamura H, Ichinokawa M (2013a) Stock assessment and evaluation for the Pacific sardine stock (fiscal year 2013), pp 15–46. Marine fisheries stock assessment and evaluation for Japanese waters (year 2013), Fisheries Agency and Fisheries Research Agency of Japan (in Japanese)Google Scholar
  16. Kawabata A, Watanabe C, Honda S, Okamura H, Ichinokawa M (2013b) Stock assessment and evaluation for the Pacific chub mackerel stock (fiscal year 2013), pp 135–168. Marine fisheries stock assessment and evaluation for Japanese waters (year 2013), Fisheries Agency and Fisheries Research Agency of Japan (in Japanese)Google Scholar
  17. Kishi MJ, Ito S, Megrey BA, Rose KA, Werner FE (2011) A review of the NEMURO and NEMURO.FISH models and their application to marine ecosystem investigations. J Oceanogr 67:3–16CrossRefGoogle Scholar
  18. Koen-Alonso M (2007) A process-oriented approach to the multispecies functional response. In: Rooney N, KS MC, DLG N (eds) From energetics to ecosystems: the dynamics and structure of ecological systems. Springer, New York, pp 1–36Google Scholar
  19. Koen-Alonso M, Yodzis P (2005) Multispecies modelling of some components in the marine community of northern and central Patagonia, Argentina. Can J Fish Aquat Sci 62:1490–1512CrossRefGoogle Scholar
  20. Lassen H, Medley P (2000) Virtual population analysis: a practical manual for stock assessment, FAO fisheries technical paper 400. FAO, Rome, pp 1–129Google Scholar
  21. Magnússon KG (1995) An overview of the multispecies VPA – theory and applications. Rev Fish Biol Fish 5:195–212CrossRefGoogle Scholar
  22. Matsuda H, Wada T, Takeuchi Y, Matsumiya Y (1992) Model analysis of the effect of environmental fluctuation on the species replacement pattern of pelagic fishes under interspecific competition. Res Popul Ecol 34:309–319CrossRefGoogle Scholar
  23. Meyer R, Millar RB (1999) BUGS in Bayesian stock assessments. Can J Fish Aquat Sci 56:1078–1086CrossRefGoogle Scholar
  24. Mori M, Butterworth DS (2006) A first step towards modelling the krill–predator dynamics of the Antarctic ecosystem. CCAMLR Sci 13:217–277Google Scholar
  25. Okamura H, Yatsu A, Hiramatsu K (2002) Fisheries management based on ecosystem models—a case study using Ecopath and Ecosim. Fish Sci 68(Suppl I):154–157CrossRefGoogle Scholar
  26. Pauly D, Christensen V, Walters C (2000) Ecopath, Ecosim, and Ecospace as tools for evaluating ecosystem impact of fisheries. ICES J Mar Sci 57:697–706CrossRefGoogle Scholar
  27. Pauly D, Christensen V, Guénette S, Pitcher TJ, Sumaila R, Walters CJ, Watson R, Zeller D (2002) Towards sustainability in world fisheries. Nature 418:689–695CrossRefPubMedGoogle Scholar
  28. Plagányi EE (2007) Models for an ecosystem approach to fisheries, FAO fisheries technical paper 477. FAO, Rome, pp 1–126Google Scholar
  29. Plagányi EE, Punt AE, Hillary R, Morello EB, Théband O, Hutton T, Pillans RD, Thorson JT, Fulton EA, Smith ADM, Smith F, Bayliss P, Haywood M, Lyne V, Rothlisberg PC (2014) Multispecies fisheries management and conservation: tactical applications using models of intermediate complexity. Fish Fish 15:1–22CrossRefGoogle Scholar
  30. Punt AE, Butterworth DS (1995) The effects of future consumption by the Cape fur seal on catches and catch rates of the Cape hakes. 4. Modelling the biological interaction between Cape fur seals Arctocephalus pusillus and the Cape hakes Merluccius capensis and M. paradoxus. S Afr J Marine Sci 16:255–285CrossRefGoogle Scholar
  31. Punt AE, Donovan G (2007) Developing management procedures that are robust to uncertainty: lessons from the International Whaling Commission. ICES J Mar Sci 64:603–612CrossRefGoogle Scholar
  32. Punt AE, Smith ADM, Smith DC, Tuck GN, Klaer NL (2013) Selecting relative abundance proxies for BMSY and BMEY. ICES J Mar Sci.
  33. Ricard D, Minto C, Jensen OP, Baum JK (2012) Examining the knowledge base and status of commercially exploited marine species with the RAM legacy stock assessment database. Fish Fish 13:380–398CrossRefGoogle Scholar
  34. Spencer PD, Ianelli JN (2005) Application of a Kalman filter to a multispecies stock complex. In: Kruse GH, Gallucci VF, Hay DE, Perry RI, Peterman RM, Shirley TC, Spencer PD, Wilson B, Woodby D (eds) Fisheries assessment and management in data-limited situations. Alaska Sea Grant College Program, University of Alaska Fairbanks, Fairbanks, pp 613–634CrossRefGoogle Scholar
  35. Sugihara G, May R, Ye H, Hsieh C, Deyle E, Fogarty M, Munch S (2012) Detecting causality in complex ecosystems. Science 338:496–500CrossRefPubMedGoogle Scholar
  36. Thorson JT, Cope JM, Branch TA, Jensen OP (2012) Spawning biomass reference points for exploited marine fishes, incorporating taxonomic and body size information. Can J Aquat Fish Sci 69:1556–1568CrossRefGoogle Scholar
  37. Walters C, Kitchell JF (2001) Cultivation/depensation effects on juvenile survival and recruitment: implications for the theory of fishing. Can J Fish Aquat Sci 58:39–50CrossRefGoogle Scholar
  38. Walters CJ, Martell SJD (2004) Fisheries ecology and management. Princeton University Press, PrincetonGoogle Scholar
  39. Watanabe C, Mito K, Okamura H, Ichinokawa M, Kawabata A, Honda S (2013) Stock assessment and evaluation for the Pacific anchovy stock (fiscal year 2013), pp 720–751. Marine fisheries stock assessment and evaluation for Japanese waters (year 2013), Fisheries Agency and Fisheries Research Agency of Japan (in Japanese)Google Scholar
  40. Woodward G, Ebenman B, Emmerson M, Montoya JM, Olesen JM, Valido A, Warren PH (2005) Body size in ecological networks. TREE 20(7):402–409PubMedGoogle Scholar
  41. Worm B, Barbier EB, Beaumont N, Duffy JE, Folke C, Halpern BS, Jackson JBC, Lotze HK, Micheli F, Palumbi SR, Sala E, Selkoe KA, Stachowicz JJ, Watson R (2006) Impacts of biodiversity loss on ocean ecosystem services. Science 314:787–790CrossRefPubMedGoogle Scholar
  42. Yodzis P (1998) Local trophodynamics and the interaction of marine mammals and fisheries in the Benguela ecosystem. J Anim Ecol 67:635–658CrossRefGoogle Scholar

Copyright information

© Springer Japan KK and the Japanese Society of Fisheries Science 2018

Authors and Affiliations

  • Hiroshi Okamura
    • 1
    Email author
  • Momoko Ichinokawa
    • 1
  • Osamu Komori
    • 2
  1. 1.National Research Institute for Fisheries Science, Japan Fisheries Research and Education AgencyKanazawa, YokohamaJapan
  2. 2.Department of Electrical, Electronic and Computer EngineeringUniversity of FukuiFukuiJapan

Personalised recommendations