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pp 1-28 | Cite as

Extrapolation of Laboratory-Measured Effects to Fish Populations in the Field

  • Charles R. E. HazleriggEmail author
Protocol
Part of the Methods in Pharmacology and Toxicology book series

Abstract

The aim of prospective environmental risk assessments of chemicals is to protect the environment from unwanted negative effects following the use of a given chemical product. The protection goal of relevant chemical regulations is primarily at the population level; however most toxicity data is often only available at the individual level from laboratory studies. Population modelling is one method used to link the effects of chemical exposure observed in the laboratory on individuals, to predicted effects on natural populations and the relevant protection goals. This chapter presents a method to develop a fish population model and is based on the following steps as outlined in the “modelling cycle”: (a) define the purpose of the model, (b) conceptualize the model, (c) formalize the model, (d) implement the model, (e) verify the model, (f) calibrate the model, (g) analyze the model (including sensitivity, uncertainty, and validation) and (h) communicate the model to all interested stakeholders (e.g., scientific community, regulatory authorities and the public). Though much of this chapter is based on the use of population models in chemical risk assessment, population models may be developed for a wide range of purposes (e.g., conservation of endangered species or fisheries stock assessments). While it is impractical to cover all these variations within this chapter, the general strategies employed to develop models within the modelling cycle are broadly applicable and examples are provided where necessary to illustrate each point. This chapter focuses on the development of individual-based population models (IBMs), though other model types (e.g., matrix models, TKTD models) are discussed and much of what is presented in this chapter for IBMs is equally applicable to these other modelling options.

You should use this chapter to develop an individual-based fish population model, though it should be noted that the nuances related to each model due to differences in research question between model developers will lead to models with different structure and outputs. Population modelling overcomes many of the challenges that may beset empirical studies performed to answer the same question at the same spatial and temporal scale (e.g., cost, time, interpretation of results) and as such is a powerful tool in further developing our ecological understanding. Adherence to good modelling practice when developing future models (such as using the approach outlined in this chapter) will ensure that population modelling becomes more widely accepted in the scientific and regulatory communities and population modelling may take its rightful place as a unique tool providing novel insights into population ecology and their application in the risk assessment of chemicals.

Keywords

Population modelling Individual-based modelling Agent-based modelling Fish Chemicals Population dynamics Risk assessment 

References

  1. 1.
    EC (2009) Regulation (EC) No 1107/2009 of the European Parliament and of the Council of 21 October 2009 concerning the placing of plant protection products on the market and repealing Council Directives 79/117/EEC and 91/414/EEC. Off J Eur Union. http://data.europa.eu/eli/reg/2009/1107/oj
  2. 2.
    EC (2012) Regulation (EU) No 528/2012 of the European Parliament and of the Council of 22 May 2012 concerning the making available on the market and use of biocidal products. Off J Eur Union. http://data.europa.eu/eli/reg/2012/528/oj
  3. 3.
    EC (2006) Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC. Off J Eur Union. http://data.europa.eu/eli/reg/2006/1907/2014-04-10
  4. 4.
    OECD (1992) Test no. 203: fish, acute toxicity test. OECD, Paris.  https://doi.org/10.1787/9789264069961-enGoogle Scholar
  5. 5.
    FOCUS (2015) Generic guidance for FOCUS surface water Scenarios version 1.4: update document based on the SANCO/4802/2011 rev.2. FOCUS Surface water Scenarios workgroup. May 2015Google Scholar
  6. 6.
    Schindler DW (1998) Whole-ecosystem experiments: replication versus realism: the need for ecosystem-scale experiments. Ecosystems 1(4):323–334Google Scholar
  7. 7.
    Forbes VE, Calow P, Sibly RM (2008) The extrapolation problem and how population modelling can help. Environ Toxicol Chem 27(10):1987–1994Google Scholar
  8. 8.
    National Research Council (2013) Assessing risks to endangered and threatened species from pesticides. National Academies Press, WashingtonGoogle Scholar
  9. 9.
    EFSA (2013) Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters. EFSA J 11(7):3290Google Scholar
  10. 10.
    EFSA (2014) Scientific opinion on good modelling practice in the context of mechanistic effect models for risk assessment of plant protection products. EFSA J 12(3):3589Google Scholar
  11. 11.
    Grimm V, Railsback SF (2005) Individual-based modelling and ecology. Princeton series in theoretical and computational biology. Princeton University Press, PrincetonGoogle Scholar
  12. 12.
    Jaworska JS, Rose KA, Barnthouse LW (1997) General response patterns of fish populations to stress: an evaluation using an individual-based simulation model. J Aquat Ecosyst Stress Recovery 6:15–31Google Scholar
  13. 13.
    Letcher BH, Rice JA, Crowder LB, Rose KA (1995) Variability in survival of larval fish: disentangling components with a generalised individual-based model. Can J Aquat Sci 53:787–801Google Scholar
  14. 14.
    EFSA (2010) Scientific opinion on the development of specific protection goal options for environmental risk assessment of pesticides, in particular in relation to the revision of the Guidance Documents on Aquatic and Terrestrial Ecotoxicology (SANCO/3268/2001 and SANCO/10329/2002). EFSA J 8(10):1821Google Scholar
  15. 15.
    EFSA (2016) Guidance to develop specific protection goals options for environmental risk assessment at EFSA, in relation to biodiversity and ecosystem services. EFSA Scientific Committee. EFSA J 14(6):4499Google Scholar
  16. 16.
    Ibrahim L, Preuss TG, Ratte HT, Hommen U (2013) A list of fish species that are potentially exposed to pesticides in edge-of-field water bodies in the European Union—a first step towards identifying vulnerable representatives for risk assessment. Environ Sci Pollut R 20(4):2679–2687Google Scholar
  17. 17.
    Ibrahim L, Preuss TG, Schaeffer A, Hommen U (2014) A contribution to the identification of representative vulnerable fish species for pesticide risk assessment in Europe—a comparison of population resilience using matrix models. Ecol Model 280:65–75Google Scholar
  18. 18.
    Dearden JC, Cronin MTD, Kaiser KLE (2009) How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR), SAR and QSAR. Environ Res 20(3–4):241–266Google Scholar
  19. 19.
    Beaudouin R, Goussen B, Piccini B, Augustine S, Devillers J, Brion F, Péry ARR (2015) An individual-based model of zebrafish population dynamics accounting for energy dynamics. PLoS One 10(5):e0125841Google Scholar
  20. 20.
    Castellani M, Heino M, Gilbey J, Araki H, Svasand T, Glover KA (2015) IBSEM: an individual-based Atlantic salmon population model. PLoS One 10(9):e0138444Google Scholar
  21. 21.
    Forbes VE, Galic N, Schmolke A, Vavra J, Pastorok R, Thorbek P (2016) Assessing the risks of pesticides to threatened and endangered species using population modelling: a critical review and recommendations for future work. Environ Toxicol Chem 35(8):1904–1913Google Scholar
  22. 22.
    Grimm V, Frank K, Jeltsch F, Brandl R, Uchmanski J, Wissel C (1996) Pattern-oriented modelling in population ecology. Sci Total Environ 183(1–2):151–166Google Scholar
  23. 23.
    Ankley GT, Bennett RS, Erickson RJ, Hoff DJ, Hornung MW, Johnson RD, Mount DR, Nichols JW, Russom CL, Schmieder PK, Serrano JA, Tietge JE, Villeneuve DL (2010) Adverse outcome pathway: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem 29(3):730–741Google Scholar
  24. 24.
    Wittwehr C, Aladjov H, Ankley G, Byrne HJ, de Knecht J, Heinzle E, Klambauer G, Landesmann B, Luijten M, MacKay C, Maxwell G, Meek ME, Paini A, Perkins E, Sobanski T, Villeneuve D, Waters KM, Whelan M (2017) How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology. Toxicol Sci 155(2):326–336Google Scholar
  25. 25.
    Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, Goss-Custard J et al (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198:115–126Google Scholar
  26. 26.
    Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF (2010) The ODD protocol: a review and first update. Ecol Model 221:2760–2768Google Scholar
  27. 27.
    Schmolke A, Thorbek P, DeAngelis DL, Grimm V (2010) Ecological models supporting environmental decision making: a strategy for the future. Trends Ecol Evol 25:479–486Google Scholar
  28. 28.
    Hazlerigg CRE, Tyler CR, Lorenzen K, Wheeler JR, Thorbek P (2014) Population relevance of toxicant mediated changes in sex ratio in fish: an assessment using an individual-based zebrafish (Danio rerio) model. Ecol Model 280:76–88Google Scholar
  29. 29.
    Pethybridge H, Roos D, Loizeau V, Pecquerie L, Bacher C (2013) Responses of European anchovy vital rates and population growth to environmental fluctuations: an individual-based modelling approach. Ecol Model 250:370–383Google Scholar
  30. 30.
    Kooijman SALM (2010) Dynamic energy budgets theory for metabolic organisations, 3rd edn. Cambridge University Press, CambridgeGoogle Scholar
  31. 31.
    EFSA (2011) Submission of scientific peer-reviewed open literature under regulation (EC) no. 1107/2009. EFSA J 9(2):2092Google Scholar
  32. 32.
    Klimisch HJ, Andrea M, Tillmann U (1997) A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. Regul Toxicol Pharmacol 25(1):1–5Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2019

Authors and Affiliations

  1. 1.Enviresearch Ltd.Newcastle-upon-TyneUK

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