Environmental Science and Pollution Research

, Volume 16, Issue 6, pp 614–617 | Cite as

CREAM: a European project on mechanistic effect models for ecological risk assessment of chemicals

  • Volker GrimmEmail author
  • Roman Ashauer
  • Valery Forbes
  • Udo Hommen
  • Thomas G. Preuss
  • Annette Schmidt
  • Paul J. van den Brink
  • Jörn Wogram
  • Pernille Thorbek

Aims and scope of CREAM

Current risk assessments are mainly based on ecotoxicological endpoints at the level of individual organisms, but according to the EU directives, the protection goal aims at achieving sustainable populations (European Commission 2002a, b; Forbes et al. 2009; Preuss et al. 2009a; Thorbek et al. 2009). Population-level effects depend not only on exposure and toxicity, but also on important ecological factors that are impossible to fully address empirically. At present, a number of testing approaches exist that provide endpoints on the community and the population level, respectively (nontarget arthropod and earthworm field tests, aquatic and terrestrial model ecosystem tests). However, not all fields and regulatory questions can be covered by these approaches. To fill these gaps and to enhance the scientific quality of ecological risk assessments, we suggest implementing mechanistic effect models (MEMs), as these also enable the integration of the relevant...


Ecological modeling Good modeling practice Joint research project Pesticides Training 


  1. Ankley GT, Erickson RJ, Phipps GL, Mattson VR, Kosian PA, Sheedy BR, Cox JS (1995) Effects of light-intensity on the phototoxicity of fluoranthene to a benthic macroinvertebrate. Environ Sci Technol 29:2828–2833CrossRefGoogle Scholar
  2. Ashauer R, Boxall ABA, Brown CD (2007) New ecotoxicological model to simulate survival of aquatic invertebrates after exposure to fluctuating and sequential pulses of pesticides. Environ Sci Technol 41:1480–1486CrossRefGoogle Scholar
  3. Bartell SM, Pastorok RA, Akçakaya HR, Regan H, Ferson S, Mackay C (2003) Realism and relevance of ecological models used in chemical risk assessment. Hum Ecol Risk Assess 9:07–938CrossRefGoogle Scholar
  4. European Commission (2002a) Guidance document on aquatic ecotoxicology in the context of the directive 91/414/EEC, Rep. No. Sanco/3268/2001 rev.4 (final). Brussels, Belgium. Available at
  5. European Commission (2002b) Guidance document on terrestrial ecotoxicology under council directive 91/414/EEC. SANCO/10329/2002 rev 2 final. Brussels, Belgium. Available at
  6. Forbes VE, Hommen U, Thorbek T, 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 5:167–172CrossRefGoogle Scholar
  7. Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, Goss-Custard J, Grand T, Heinz S, Huse G, Huth A, Jepsen JU, Jørgensen C, Mooij WM, Müller B, Pe’er G, Piou C, Railsback SF, Robbins AM, Robbins MM, Rossmanith E, Rüger N, Strand E, Souissi S, Stillman RA, Vabø R, Visser U, DeAngelis DL (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198:115–126CrossRefGoogle Scholar
  8. Jager T, Kooijman S (2005) Modeling receptor kinetics in the analysis of survival data for organophosphorus pesticides. Environ Sci Technol 39:8307–8314CrossRefGoogle Scholar
  9. Pastorok RA, Bartell SM, Ferson S, Ginzburg LR (eds) (2002) Ecological modelling in risk assessment: chemical effects on populations, ecosystems, and landscapes. Lewis, Boca RatonGoogle Scholar
  10. Pastorok RA, Akcakaya HR, Regan H, Ferson S, Bartell SM (2003) Role of ecological modeling in risk assessment. Hum Ecol Risk Assess 9:939–972CrossRefGoogle Scholar
  11. 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 (2009a) Mechanistic effect models for ecological risk assessment of chemicals (MEMoRisk)—a new SETAC Europe Advisory Group. Environ Sci Pollut Res Int 16(3):250–252CrossRefGoogle Scholar
  12. Preuss TG, Hammers-Wirtz M, Hommen U, Rubach MN, Ratte HT (2009b) An individual-based population model of Daphnia magna for analysis and extrapolation of population dynamics under laboratory conditions. Ecol Model 220:310–329CrossRefGoogle Scholar
  13. Thorbek P, Forbes V, Heimbach F, Hommen U, Thulke HH, van den Brink PJ, Wogram J, Grimm V (eds) (2009) Ecological models for regulatory risk assessments of pesticides: developing a strategy for the future. CRC, Boca Raton (in press)Google Scholar
  14. Van den Brink PJ, Verboom J, Baveco JM, Heimbach F (2007) An individual-based approach to model spatial population dynamics of invertebrates in aquatic ecosystems after pesticide contamination. Environ Toxicol Chem 26:2226–2236CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Volker Grimm
    • 1
    Email author
  • Roman Ashauer
    • 2
  • Valery Forbes
    • 3
  • Udo Hommen
    • 4
  • Thomas G. Preuss
    • 5
  • Annette Schmidt
    • 6
  • Paul J. van den Brink
    • 7
  • Jörn Wogram
    • 8
  • Pernille Thorbek
    • 9
  1. 1.Department of Ecological ModellingUFZ, Helmholtz Centre for Environmental Research—UFZLeipzigGermany
  2. 2.EawagDübendorfSwitzerland
  3. 3.Center for Integrated Population EcologyRoskilde UniversityRoskildeDenmark
  4. 4.Fraunhofer IMESchmallenbergGermany
  5. 5.Institute for Environmental ResearchRWTH Aachen UniversityAachenGermany
  6. 6.Department of Scientific-Administrative Project SupervisionUFZ, Helmholtz Centre for Environmental Research—UFZLeipzigGermany
  7. 7.Wageningen University and Research CentreAlterra and Wageningen UniversityWageningenThe Netherlands
  8. 8.Department IV 1.3—Plant Protection ProductsFederal Environment Agency (UBA)DessauGermany
  9. 9.Syngenta, Environmental Safety, Jealott’s Hill, International Research CentreBracknellUK

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