Ecotoxicology

, Volume 22, Issue 3, pp 574–583 | Cite as

Extrapolating ecotoxicological effects from individuals to populations: a generic approach based on Dynamic Energy Budget theory and individual-based modeling

  • Benjamin T. Martin
  • Tjalling Jager
  • Roger M. Nisbet
  • Thomas G. Preuss
  • Monika Hammers-Wirtz
  • Volker Grimm
Article

Abstract

Individual-based models (IBMs) predict how dynamics at higher levels of biological organization emerge from individual-level processes. This makes them a particularly useful tool for ecotoxicology, where the effects of toxicants are measured at the individual level but protection goals are often aimed at the population level or higher. However, one drawback of IBMs is that they require significant effort and data to design for each species. A solution would be to develop IBMs for chemical risk assessment that are based on generic individual-level models and theory. Here we show how one generic theory, Dynamic Energy Budget (DEB) theory, can be used to extrapolate the effect of toxicants measured at the individual level to effects on population dynamics. DEB is based on first principles in bioenergetics and uses a common model structure to model all species. Parameterization for a certain species is done at the individual level and allows to predict population-level effects of toxicants for a wide range of environmental conditions and toxicant concentrations. We present the general approach, which in principle can be used for all animal species, and give an example using Daphnia magna exposed to 3,4-dichloroaniline. We conclude that our generic approach holds great potential for standardized ecological risk assessment based on ecological models. Currently, available data from standard tests can directly be used for parameterization under certain circumstances, but with limited extra effort standard tests at the individual would deliver data that could considerably improve the applicability and precision of extrapolation to the population level. Specifically, the measurement of a toxicant’s effect on growth in addition to reproduction, and presenting data over time as opposed to reporting a single EC50 or dose response curve at one time point.

Keywords

Population Dynamic Energy Budget Individual-based model Sub-lethal effects Physiological mode of action Effect model 

Supplementary material

10646_2013_1049_MOESM1_ESM.doc (173 kb)
Supplementary material 1 (DOC 173 kb)

References

  1. Álvarez OA, Jager T, Colao BN, Kammenga JE (2006) Temporal dynamics of effect concentrations. Environ Sci Technol 40(7):2478–2484CrossRefGoogle Scholar
  2. Ashauer R, Agatz A, Albert C, Ducrot V, Galic N, Hendriks J, Jager T, Kretschmann A, O’Conner I, Rubach MN, Nyman A, Schmitt W, Stadnicka J, van den Brink PJ, Preuss TG (2011) Toxicokinetic-toxicodynamic modeling of quantal and graded sublethal endpoints: a brief discussion of concepts. Environ Toxicol Chem 30(11):2519–2524CrossRefGoogle Scholar
  3. Baas J, Jager T, Kooijman SALM (2010) Understanding toxicity as processes in time. Sci Total Environ 408(18):3735–3739CrossRefGoogle Scholar
  4. de Roos AM, Persson L (2013) Population and community ecology of ontogenetic development. Princeton University PressGoogle Scholar
  5. Elendt BP (1990a) Influence of water composition on the chronic toxicity of 3,4-dichloroaniline to Daphnia magna. Water Res 24(9):1169–1172CrossRefGoogle Scholar
  6. Elendt BP (1990b) Influence of water composition on the chronic toxicity of 3,4-dichloroaniline to Daphnia magna. Water Res 24(9):1169–1172CrossRefGoogle Scholar
  7. Forbes VE, Hommen U, Thorbek P, Heimbach F, van den Brink PJ, Wogram J, Thulke HH, Grimm V (2009) Ecological models in support of regulatory risk assessments of pesticides: developing a strategy for the future. Integr Environ Assess Manag 5(1):167–172CrossRefGoogle Scholar
  8. Forbes VE, Olsen M, Palmqvist A, Calow P (2010) Environmentally sensitive life cycle traits have low elasticity: implications for theory and practice. Ecol Appl 20(5):1449–1455CrossRefGoogle Scholar
  9. Grimm V, Railsback SF (2012) Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology. Philos Trans R Soc B Biol Sci 367(1586):298–310CrossRefGoogle Scholar
  10. Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, Thulke HH, Weiner J, Wiegand T, DeAngelis DL (2005) Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310(5750):987–991Google Scholar
  11. Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, Goss-Custard J, Grand T, Heinz SK, Huse G, Huth A, Jepsen JU, Jørgensen C, Mooij WM, Müller B, Pe’er G, Piou C, Railsback S, Robbins AM, Robbins MM, Rossmanith E, Rüger N, Strand E, Souissi S, Stillman R, Vabø R, Visser U, DeAngelis DL (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198(1):115–126CrossRefGoogle Scholar
  12. Grimm V, Ashauer R, Forbes V, Hommen U, Preuss TG, Schmidt A, van den Brink PJ, Wogram J, Thorbek P (2009) CREAM: a European project on mechanistic effect models for ecological risk assessment of chemicals. Environ Sci Pollut Res 16(6):614–617CrossRefGoogle Scholar
  13. Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF (2010) The ODD protocol: a review and first update. Ecol Model 221(23):2760–2768CrossRefGoogle Scholar
  14. Gurney WSC, Middleton DAJ, Nisbet RM, McCauley E, Murdoch W, de Roos AM (1996) Individual energetics and the equilibrium demography of structured populations. Theor Popul Biol 49(3):344–368CrossRefGoogle Scholar
  15. Heckmann LH, Baas J, Jager T (2010) Time is of the essence. Environ Toxicol Chem 29(6):1396–1398Google Scholar
  16. Jager T, Heugens EH, Kooijman SA (2006) Making sense of ecotoxicological test results: towards application of process-based models. Ecotoxicology 15(3):305–314CrossRefGoogle Scholar
  17. Jager T, Vandenbrouck T, Baas J, de Coen WM, Kooijman SALM (2010) A biology-based approach for mixture toxicity of multiple endpoints over the life cycle. Ecotoxicology 19(2):351–361CrossRefGoogle Scholar
  18. Kendall RJ, Lacher TE (1994) Wildlife toxicology and population modeling, SETAC special publication. Lewis Publishers, Boca RatonGoogle Scholar
  19. Kooijman SALM, Bedaux JJM (1996) Analysis of toxicity tests on Daphnia survival and reproduction. Water Res 30(7):1711–1723CrossRefGoogle Scholar
  20. Kooijman SALM, Metz JAJ (1984) On the dynamics of chemically stressed populations: the deduction of population consequences from effects on individuals. Ecotoxicol Environ Saf 8:254–274CrossRefGoogle Scholar
  21. Kooijman SALM, Sousa T, Pecquerie L, Van der Meer J, Jager T (2008) From food-dependent statistics to metabolic parameters, a practical guide to the use of dynamic energy budget theory. Biol Rev 83(4):533–552CrossRefGoogle Scholar
  22. Kooiman SALM (2010) Dynamic energy budget theory for metabolic organisation. Cambridge University PressGoogle Scholar
  23. Lika K, Kearney MR, Freitas V, van der Veer HW, van der Meer J, Wijsman JW, Pecquerie L, Kooijman SA (2011) The “covariation method” for estimating the parameters of the standard Dynamic Energy Budget model I: philosophy and approach. J Sea Res 66(4):270–277CrossRefGoogle Scholar
  24. Martin BT, Zimmer EI, Grimm V, Jager T (2012) Dynamic Energy Budget theory meets individual-based modelling: a generic and accessible implementation. Methods Ecol Evol 3(2):445–449CrossRefGoogle Scholar
  25. Martin BT, Jager T, Nisbet RM, Pruess TG, Grimm V (in press) Predicting population dynamics from the properties of individuals: a cross-level test of Dynamic Energy Budget theory. Am Nat. doi:10.1086/669904
  26. Muller EB, Nisbet RM, Berkley HA (2010) Sublethal effects of toxic compounds on dynamic energy budgets; model formulation. Ecotoxicology 19:48–60CrossRefGoogle Scholar
  27. Munns WR Jr (2006) Assessing risks to wildlife populations from multiple stressors: overview of the problem and research needs. Ecol Soc 11(1):23Google Scholar
  28. Nisbet RM, Muller EB, Lika K, Kooijman SALM (2000) From molecules to ecosystems through Dynamic Energy Budget models. J Anim Ecol 69:913–926CrossRefGoogle Scholar
  29. Pastorok RA, Bartell SM, Ferson S, Ginzburg LR (eds) (2001) Ecological modeling in risk assessment: chemical effects on populations, ecosystems, and landscapes. CRC Press, Boca RatonGoogle Scholar
  30. Preuss TG, Hommen U, Alix A, Ashauer R, van den Brink PJ, Chapman P, Ducrot V, Forbes V, Grimm V, Schäfer D, Streissl F, Thorbek P (2009) Mechanistic effect models for ecological risk assessment of chemicals (MEMoRisk)—a new SETAC Europe Advisory Group. Environ Sci Pollut Res 16:250–252CrossRefGoogle Scholar
  31. Preuss TG, Hammers-Wirtz M, Ratte HT (2010) The potential of individual based population models to extrapolate effects measured at standardized test conditions to relevant environmental conditions—an example for 3,4-dichloroaniline on Daphnia magna. J Environ Monit 12(11):2070–2079CrossRefGoogle Scholar
  32. Railsback SF, Grimm V (2011) Individual-based modeling: a practical introduction. Princeton University PressGoogle Scholar
  33. Railsback SF, Harvey BC (2002) Analysis of habitat-selection rules using an individual-based model. Ecology 83(7):1817–1830Google Scholar
  34. Sokull-Kluettgen B (1998) Die kombinierte Wirkung von Nahrungsangebot und 3,4-Dichloranilin auf die Lebensdaten von zwei nahverwandten Cladocerenarten, Daphnia magna und Ceriodaphnia quadrangula. Shaker, Aachen Google Scholar
  35. Sousa T, Domingos T, Kooijman SALM (2008) From empirical patterns to theory: a formal metabolic theory of life. Philos Trans R Soc B: Biol Sci 363(1502):2453–2464CrossRefGoogle Scholar
  36. Stillman RA, Goss-Custard JD (2010) Individual-based ecology of coastal birds. Biol Rev 85(3):413–434CrossRefGoogle Scholar
  37. Thorbek P, Forbes VE, Heimback F, Hommen U, Thulke HH, Van den Brink PJ, Wogram J, Grimm V (2010) Ecological models for regulatory risk assessments of pesticides: Developing a strategy for the future. CRC press, Boca RatonGoogle Scholar
  38. Wiegand T, Jeltsch F, Hanski I, Grimm V (2003) Using pattern-oriented modeling for revealing hidden information: a key for reconciling ecological theory and application. Oikos 100(2):209–222CrossRefGoogle Scholar
  39. Wilensky U (1999) NetLogo. http://ccl.northwestern.edu/netlogo/. Center for connected learning and computer-based modeling. Northwestern University, Evanston

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Benjamin T. Martin
    • 1
  • Tjalling Jager
    • 2
  • Roger M. Nisbet
    • 3
  • Thomas G. Preuss
    • 4
  • Monika Hammers-Wirtz
    • 5
  • Volker Grimm
    • 1
    • 6
  1. 1.Department of Ecological ModellingHelmholtz Centre for Environmental Research—UFZLeipzigGermany
  2. 2.FALW/Department of Theoretical BiologyVU University AmsterdamAmsterdamThe Netherlands
  3. 3.Department of Ecology, Evolution, and Marine BiologyUniversity of California, Santa BarbaraSanta BarbaraUSA
  4. 4.Institute for Environmental ResearchRWTH Aachen UniversityAachenGermany
  5. 5.Gaiac—Research Institute for Ecosystem Analysis and AssessmentAachenGermany
  6. 6.University of Potsdam, Institute for Biochemistry and BiologyPotsdamGermany

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