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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Álvarez OA, Jager T, Colao BN, Kammenga JE (2006) Temporal dynamics of effect concentrations. Environ Sci Technol 40(7):2478–2484
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):2519–2524
Baas J, Jager T, Kooijman SALM (2010) Understanding toxicity as processes in time. Sci Total Environ 408(18):3735–3739
de Roos AM, Persson L (2013) Population and community ecology of ontogenetic development. Princeton University Press
Elendt BP (1990a) Influence of water composition on the chronic toxicity of 3,4-dichloroaniline to Daphnia magna. Water Res 24(9):1169–1172
Elendt BP (1990b) Influence of water composition on the chronic toxicity of 3,4-dichloroaniline to Daphnia magna. Water Res 24(9):1169–1172
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):167–172
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):1449–1455
Grimm V, Railsback SF (2012) Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology. Philos Trans R Soc B Biol Sci 367(1586):298–310
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):987–991
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):115–126
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):614–617
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):2760–2768
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):344–368
Heckmann LH, Baas J, Jager T (2010) Time is of the essence. Environ Toxicol Chem 29(6):1396–1398
Jager T, Heugens EH, Kooijman SA (2006) Making sense of ecotoxicological test results: towards application of process-based models. Ecotoxicology 15(3):305–314
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):351–361
Kendall RJ, Lacher TE (1994) Wildlife toxicology and population modeling, SETAC special publication. Lewis Publishers, Boca Raton
Kooijman SALM, Bedaux JJM (1996) Analysis of toxicity tests on Daphnia survival and reproduction. Water Res 30(7):1711–1723
Kooijman SALM, Metz JAJ (1984) On the dynamics of chemically stressed populations: the deduction of population consequences from effects on individuals. Ecotoxicol Environ Saf 8:254–274
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):533–552
Kooiman SALM (2010) Dynamic energy budget theory for metabolic organisation. Cambridge University Press
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):270–277
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):445–449
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
Muller EB, Nisbet RM, Berkley HA (2010) Sublethal effects of toxic compounds on dynamic energy budgets; model formulation. Ecotoxicology 19:48–60
Munns WR Jr (2006) Assessing risks to wildlife populations from multiple stressors: overview of the problem and research needs. Ecol Soc 11(1):23
Nisbet RM, Muller EB, Lika K, Kooijman SALM (2000) From molecules to ecosystems through Dynamic Energy Budget models. J Anim Ecol 69:913–926
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 Raton
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 16:250–252
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):2070–2079
Railsback SF, Grimm V (2011) Individual-based modeling: a practical introduction. Princeton University Press
Railsback SF, Harvey BC (2002) Analysis of habitat-selection rules using an individual-based model. Ecology 83(7):1817–1830
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
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):2453–2464
Stillman RA, Goss-Custard JD (2010) Individual-based ecology of coastal birds. Biol Rev 85(3):413–434
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 Raton
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):209–222
Wilensky U (1999) NetLogo. http://ccl.northwestern.edu/netlogo/. Center for connected learning and computer-based modeling. Northwestern University, Evanston
Acknowledgments
We thank two anonymous reviewers for comments that improved the quality of the manuscript. BTM, TJ, TP, and VG acknowledge support from by the European Union under the 7th Framework Programme (project acronym CREAM, contract number PITN-GA-2009-238148). RMN acknowledges support from US National Science Foundation under grant EF-0742521, and from the US National Science Foundation and the US Environmental Protection Agency under Cooperative Agreement Number EF 0830117.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Martin, B.T., Jager, T., Nisbet, R.M. et al. Extrapolating ecotoxicological effects from individuals to populations: a generic approach based on Dynamic Energy Budget theory and individual-based modeling. Ecotoxicology 22, 574–583 (2013). https://doi.org/10.1007/s10646-013-1049-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10646-013-1049-x