Scientific Opinion on good modelling practice in the context of mechanistic effect models for risk assessment of plant protection products
2014; Wiley; Volume: 12; Issue: 3 Linguagem: Inglês
10.2903/j.efsa.2014.3589
ISSN1831-4732
Tópico(s)Insect and Pesticide Research
ResumoEFSA JournalVolume 12, Issue 3 3589 OpinionOpen Access Scientific Opinion on good modelling practice in the context of mechanistic effect models for risk assessment of plant protection products EFSA Panel on Plant Protection Products and their Residues (PPR), EFSA Panel on Plant Protection Products and their Residues (PPR)Search for more papers by this author EFSA Panel on Plant Protection Products and their Residues (PPR), EFSA Panel on Plant Protection Products and their Residues (PPR)Search for more papers by this author First published: 07 March 2014 https://doi.org/10.2903/j.efsa.2014.3589Citations: 56 Panel members: Alf Aagaard, Theo Brock, Ettore Capri, Sabine Duquesne, Metka Filipic, Antonio F. Hernandez-Jerez, Karen I. Hirsch-Ernst, Susanne Hougaard Bennekou, Michael Klein, Thomas Kuhl, Ryszard Laskowski, Matthias Liess, Alberto Mantovani, Colin Ockleford, Bernadette Ossendorp, Daniel Pickford, Robert Smith, Paulo Sousa, Ingvar Sundh, Aaldrik Tiktak, Ton Van Der Linden Correspondence: [email protected] Acknowledgement: The Panel wishes to thank the members of the Working Group on good modelling practice: Virginie Ducrot, Sabine Duquesne, Mira Kattwinkel, Matthias Liess, Alberto Mantovani, Melissa Reed, Richard Sibly, Robert Smith, Aaldrik Tiktak, Christopher John Topping for the preparatory work on this scientific opinion and EFSA staff Franz Streissl for the support provided to this scientific opinion. Adoption date: 13 February 2014 Published date: 7 March 2014 Question number: EFSA-Q-2011-00989 On request from: EFSA AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract The Panel has interpreted the Terms of Reference as a stepwise analysis of issues relevant to both the development and the evaluation of models to assess ecological effects of pesticides. The regulatory model should be selected or developed to address the relevant specific protection goal. The basis of good modelling practice must be the knowledge of relevant processes and the availability of data of sufficient quality. The opinion identifies several critical steps in order to set models within risk assessment, namely: problem formulation, considering the specific protection goals for the taxa or functional groups of concern; model domain of applicability, which drives the species and scenarios to model; species (and life stage) selection, considering relevant life history traits and toxicological/toxicokinetics characteristics of the pesticide; selection of the environmental scenario, which is defined by a combination of abiotic, biotic and agronomic parameters to provide a realistic worst-case situation. Model development should follow the modelling cycle, in which every step has to be fully documented: (i) problem definition; (ii) model formulation, i.e. design of a conceptual model; (iii) model formalisation, in which variables and parameters are linked together into mathematical equations or algorithms; (iv) model implementation, in which a computer code is produced and verified; (v) model setup, including sensitivity analysis, uncertainty analysis and comparison with observed data, that delivers the regulatory model; (vi) prior to actual use in risk assessment, the regulatory model should be evaluated for relevance to the specific protection goals; (vii) feedback from risk assessor with possible recommendations for model improvement. Model evaluation by regulatory authorities should consider each step of the modelling cycle: the opinion identifies points of particular attention for the use of mechanistic effect models in pesticide risk assessment. It is recommended that models be documented in a complete and transparent way, that a feedback platform be established involving risk assessors and model developers, and that a set of agreed models be made available. References AIAA (American Institute of Aeronautics and Astronautics), 1998. Guide for the verification and validation of computational fluid dynamics simulations (G-077-1998e), AIAA Standards, Available online: www.aiaa.org/StandardsDetail.aspx?id=3853, 19 pp., ISBN: 978-1-56347-354-8. Agatz A, Hammers-Wirtz M, Gabsi F, Ratte H-T, Brown CD, and Preuss TG, 2012. Promoting effects on reproduction increase population vulnerability of Daphnia magna. Environmental Toxicology and Chemistry, 31, 1604– 1610. Ashauer R and Escher BI, 2010. Advantages of toxicokinetic and toxicodynamic modelling in aquatic ecotoxicology and risk assessment. Journal of Environmental Monitoring, 12, 2056– 2061. Ashauer R, Agatz A, Albert C, Ducrot V, Galic N, Hendriks J, Jager T, Kretschmann A, O'Connor I, Rubach MN, Nyman A-M, Schmitt W, Stadnicka J, van den Brink PJ and Preuss TG, 2011. Toxicokinetic-toxicodynamic modelling of quantal and graded sublethal endpoints: A brief discussion of concepts. Environmental Toxicology and Chemistry, 30, 2519– 2524. Augusiak J, van den Brink PJ and Grimm V, 2013. Merging validation and evaluation of ecological models to 'evaluation': A review of terminology and a practical approach. Ecological Modelling, in press. Begon M, Townsend CR and Harper JL, 2006. Ecology: From individuals to ecosystems, 4th edn, Blackwell Publishing, Malden, MA, USA. Beketov MA and Liess M, 2006. The influence of predation on the chronic response of Artemia sp. populations to a toxicant. Journal of Applied Ecology, 43, 1069– 1074. Bernhardt-Römermann M, Gray A, Vanbergen AJ, Berges L, Bohner A, Brooker RW, de Bruyn L, de Cinti B, Dirnbock T, Grandin U, Hester AJ, Kanka R, Klotz S, Loucougaray G, Lundin L, Matteucci G, Meszaros I, Olah V, Preda E, Prevosto B, Pykala J, Schmidt W, Taylor ME, Vadineanu A, Waldmann T and Stadler J, 2011. Functional traits and local environment predict vegetation responses to disturbance: a pan-European multi-site experiment. Journal of Ecology, 99, 777– 787. Boesten JJTI, 2000. Modeller subjectivity in estimating pesticide parameters for leaching models using the same laboratory dataset. Agricultural Water Management, 44, 389– 409. Boesten JJTI, Köpp H, Adriaanse PI, Brock TCM and Forbes VE, 2007. Conceptual model for improving the link between exposure and effects in the aquatic risk assessment of pesticides. Ecotoxicology and Environmental Safety, 66, 291– 308. Boutin C, Elmegaard N and Kjaer C, 2004. Toxicity testing of fifteen non-crop plant species with six herbicides in a greenhouse experiment: implications for risk assessment. Ecotoxicology, 13, 349– 369. Brown JH, Gillooly JF, Allen AP, Savage VM and West GB, 2004. Toward a metabolic theory of ecology. Ecology, 85, 1771– 1789. Bunzel K, Liess M and Kattwinkel M, 2014. Landscape parameters driving aquatic pesticide exposure and effects. Environmental Pollution, 186, 90– 97. Campbell PJ, Arnold DJS, Brock TCM, Grandy NJ, Heger W, Heimbach F, Maund SJ and Streloke M, 1999. Guidance document on higher-tier aquatic risk assessment for pesticides (HARAP), SETAC-Europe, Belgium, Brussels, 179 pp. MP Candolfi, KL Barrett, P Campbell, R Forster, N Grandy, M-C Huet, G Lewis, P A Oomen, R Schmuck and H Vogt, (Eds.), 2001. Guidance document on regulatory testing and risk assessment procedures for plant protection products with non-target arthropods. Report of the SETAC/ESCORT 2 Workshop, Wageningen, The Netherlands, SETAC-Europe, Belgium, Brussels. Carpenter, D and Boutin, C. 2010. Sublethal effects of the herbicide glufosinate ammonium on crops and wild plants: short-term effects compared to vegetative recovery and plant reproduction. Ecotoxicology 19, 1322– 1336. Caswell H, 2001. Matrix population models, 2nd edn, Sinauer Associates, Sunderland, MA, USA. Clark J, Ortego LS and Fairbrother A, 2004. Sources of variability in plant toxicity testing. Chemosphere, 57, 1599– 1612. Comas LH and Eissenstat DM, 2009. Patterns in root trait variation among 25 co-existing North American forest species. Phytologist, 182, 919– 928. Dalkvist T, Sibly R and Topping C, 2013. Landscape structure mediates the effects of a stressor on field vole populations. Landscape Ecology, 28, 1961– 1974. Damgaard C, Mathiassen SK, Kudsk P, 2008. Modelling effects of herbicide drift on the competitive interactions between weeds. Environmental Toxicology and Chemistry, 27(6), 1302– 1308. DG SANCO (Health and Consumer Protection Directorate General) SCENIHR (Scientific Committee on Emerging and Newly Identified Health Risks) SCHER (Scientific Committee on Health and Environmental Risks) and SCCS (Scientific Committee on Consumer Safety), 2012. Preliminary report on Addressing the New Challenges for Risk Assessment. ISBN 978-92-79-XX, 157 pp. Available online: ec.europa.eu/health/scientific_committees/emerging/docs/scenihr_o_037.pdf Dorrough J and Scroggie MP, 2008. Plant responses to agricultural intensification. Journal of Applied Ecology, 45, 1274– 1283. EC (European Commission), 2002. Guidance Document on aquatic ecotoxicology in the context of the Directive 91/414/EEC (SANCO/3268/2001) rev.4 final, 17.11.2002, 1– 62. EC (European Commission), 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. OJ L 309/1, 24.11.2009, p. 1– 50. EC (European Commission), 2013. Regulation (EU) No 283/2013 of 1 March setting out the data requirements for active substances, in accordance with Regulation (EC) No 1107/2009 of the European Parliament and of the Council concerning the placing of plant protection products on the market. OJ L 93, 3.4.2013, p. 1– 84. EC (European Commission), 2013. Regulation (EU) No 284/2013 of 1 March setting out the data requirements for plant protection products, in accordance with Regulation (EC) No 1107/2009 of the European Parliament and of the Council concerning the placing of plant protection products on the market. OJ L 93, 3.4.2013, p. 85– 152. EFSA (European Food Safety Authority), 2006. Opinion of the Scientific Panel on Plant Protection Products and their Residues (PPR Panel) on a request from EFSA related to the evaluation of pirimicarb. EFSA Journal 2006, 240, 21pp. doi:10.2903/j.efsa.2005.240 EFSA (European Food Safety Authority), 2007a. Opinion of the Scientific Panel on Plant Protection Products and their Residues (PPR Panel) on a request from the European Commission on acute dietary intake assessment of pesticide residues in fruit and vegetables. EFSA Journal 2007, 538, 88 pp. doi:10.2903/j.efsa.2007.538 EFSA (European Food Safety Authority), 2007b. Opinion of the Scientific Panel on Plant Protection Products and their Residues (PPR Panel) on a request from the European Commission on the risks associated with an increase of the MRL for dieldrin on courgettes. EFSA Journal 2007, 554, 48pp. doi:10.2903/j.efsa.2007.554 EFSA (European Food Safety Authority), 2008. Scientific Opinion of the Panel on Plant Protection Products and their Residues (PPR) on the science behind the Guidance Document on risk assessment for birds and mammals. EFSA Journal 2008, 734, 181 pp. doi:10.2903/j.efsa.2008.734 EFSA (European Food Safety Authority), 2009. Guidance Document on risk assessment for birds and mammals on request from the EFSA. EFSA Journal 2009; 7(12):1438, 358 pp. doi:10.2903/j.efsa.2009.1438 EFSA (European Food Safety Authority), 2013. Guidance on the risk assessment of plant protection products on bees (Apis mellifera, Bombus spp. and solitary bees). EFSA Journal 2013; 11(7): 3295, 266 pp. doi:10.2903/j.efsa.2013.3295 EFSA PPR Panel (Panel on Plant Protection Products and their Residues), 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 Journal 2010; 8(10):1821, 55 pp. doi:10.2903/j.efsa.2010.1821 EFSA PPR Panel (Panel on Plant Protection Products and their Residues), 2012. Scientific Opinion on the science behind the guidance for scenario selection and scenario parameterisation for predicting environmental concentrations of plant protection products in soil. EFSA Journal 2012; 10(2):2562, 76 pp. doi:10.2903/j.efsa.2012.2562 EFSA PPR Panel (EFSA Panel on Plant Protection Products and their Residues), 2013. Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters. EFSA Journal 2013; 11(7):3290, 268 pp. doi:10.2903/j.efsa.2013.3290 EPA (United States Environmental Protection Agency), 2009. Guidance on the development, evaluation, and application of environmental models, EPA/100/K-09/003, 99pp. Escher BI and Hermens JLM, 2002. Modes of action in ecotoxicology: Their role in body burdens, species sensitivity, QSARs, and mixture effects. Environmental Science and Technology, 36, 4201– 4217. Fletcher JS, Muhitch MJ, Vann DR, McFarlane JC and Benenati FE, 1985. Phytotox database evaluation of surrogate plant species recommended by the U.S. Environmental Protection Agency and the Organization for Economic Cooperation and Development. Environmental Toxicology and Chemistry, 4, 523– 532. Focks A, ter Horst MMS, van den Berg F, Baveco JM, van den Brink PJ, 2013. Integrating chemical fate and population-level effect models forpesticides at landscape scale: New options for risk assessment. Ecological Modelling, in press. FOCUS (Forum for the Co-ordination of Pesticide Fate Models and their Use), 2001. FOCUS surface water scenarios in EU evaluation process under 91/414/EEC. Report of the FOCUS Working Group on Surface Water Scenarios. EU Document Reference SANCO/4802/2001-rev2. 245 pp. FOCUS (Forum for the Co-ordination of Pesticide Fate Models and their Use), 2009. Assessing potential for movement of active substances and their metabolites to ground water in the EU. Report of the FOCUS Ground Water Work Group, EC Document Reference Sanco/13144/2010 version 1, 604 pp. Forbes VE, Sibly RMS and Calow P, 2001. Toxicant impacts on density-limited populations: A critical review of theory, practice, and results. Ecological Applications, 11, 1249– 1257. Galic N, Hommen U and Baveco JM, 2010. Potential application of population models in the European ecological risk assessment of chemicals II: Review of models and their potential to address environmental protection aims. Integrated Environmental Assessment and Management, 6, 338– 360. Giddings M, Arts G and Hommen U, 2013. The relative sensitivity of macrophyte and algal species to herbicides and fungicides: An analysis using species sensitivity distributions. Integrated Environmental Assessment and Management, 9, 308– 318. Grimm V and Martin BT, 2013. Mechanistic effect modeling for ecological risk assessment: Where to go from here? Integrated Environmental Assessment and Management, 9, 58– 63. Grimm V and Railsback SF, 2005. Individual-based modeling and ecology. Princeton University Press, Princeton, NJ, USA, and Oxford, UK. Grimm V and Railsback SF, 2011. Pattern-oriented modelling: A 'multi-scope' for predictive systems ecology. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences, 367, 298– 310. Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, Thulke HH, Weiner J, Wiegand T and DeAngelis DL, 2005. Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science, 310, 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 SF, Robbins AM, Robbins MM, Rossmanith E, Rüger N, Strand E, Souissi S, Stillman RA, Vabø R, Visser U and DeAngelis DL, 2006. A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 198, 115– 126. Grimm V, Berger U, DeAngelis DL, Polhill GJ, Giskee J and Railsback SF, 2010. The ODD protocol: A review and first update. Ecological Modelling, 221, 2760– 2768. Hartig F, Calabrese J M, Reineking B, Wiegand T and Huth A, 2011. Statistical inference for stochastic simulation models – theory and application. Ecology Letters 14: 816– 827. Hisschemöller M and Hoppe R, 1996. Coping with intractable controversies: the case for problem structuring in policy design and analysis. Knowledge and Policy: The International Journal of Knowledge Transfer and Utilization, 8, 40– 60. Holland EP, Aegerter JN, Dytham C and Smith GC, 2007. Landscape as a model: The importance of geometry. PLoS Computational Biology, 3, 1979– 1992. Ibrahim L, Preuss TG, Ratte HT and 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. Environmental Science and Pollution Research International, 20, 2679– 2687. Hunka A, Meli M, Thit A, Palmqvist A, Thorbek P and Forbes V, 2013. Stakeholders' perspective on ecological modelling in environmental risk assessment of pesticides: Challenges and opportunities. Risk Analysis, 33(1), 68– 79. ISO/IEC/IEEE, 2013. Software and systems engineering-Software testing-Part 1: Concepts and definitions. ISO 29119 series-1, 56 pp. Jager T, Crommentuijn T, van Gestel CAM and Kooijman SALM, 2004. Simultaneous modeling of multiple endpoints in life-cycle toxicity tests. Environmental Science and Technology, 38, 2894– 2900. Jager T, Albert C, Preuss TG and Ashauer R, 2011. General unified threshold model of survival - a toxicokinetic-toxicodynamic framework for ecotoxicology. Environmental Science and Technology, 45, 2529– 2540. Janssen, PHM and Heuberger PSC, 1995. Calibration of process-oriented models. Ecological Modelling, 83, 55– 66. Johnston ASA, Sibly RM, Hodson ME, Thorbek P and Alvarez T, 2013. Assessing chemical effects on earthworms: An agent based modelling approach. CREAM Open Conference, 10–14 June 2013, Leipzig, Germany. Knillmann S, Stampfli NC, Beketov MA and Liess M, 2012a. Intraspecific competition increases toxicant effects in outdoor microcosms. Ecotoxicology, 21, 1857– 1866. Knillmann S, Stampfli NC, Noskov YA, Beketov MA and Liess M, 2012b. Interspecific competition delays recovery of Daphnia spp. populations from pesticide stress. Ecotoxicology, 21, 1039– 1049. Liess M, 2002. Population response to toxicants is altered by intraspecific interaction. Environmental Toxicology and Chemistry, 21, 138– 142. Liess M and Beketov MA, 2011. Traits and stress: Keys to identify community effects of low levels of toxicants in test systems. Ecotoxicology, 20, 1328– 1340. Liess M and von der Ohe PC, 2005. Analyzing effects of pesticides on invertebrate communities in streams. Environmental Toxicology and Chemistry, 24, 954– 965. Liess M, Schäfer R and Schriever C, 2008. The footprint of pesticide stress in communities - species traits reveal community effects of toxicants. Science of the Total Environment, 406, 484– 490. Liess M, Foit K, Becker A, Hassold E, Dolciotti I, Kattwinkel M, Duquesne S. 2013. Culmination of low-dose pesticide effects. Environmental Science & Technology. 47 (15), 8862– 8868. Luttik R, Mineau P and Roelofs W, 2005. A review of interspecies toxicity extrapolation in birds and mammals and a proposal for long-term toxicity data. Ecotoxicology, 14, 817– 832. Madelin R, 2004. The importance of scientific advice in the Community decision-making process. Opening address to the Inaugural joint meeting of the members of the Non-Food Scientific Committees. Directorate General for Health and Consumer Protection, European Commission, Brussels. Marrs RH, Williams CT, Frost AJ and Plant RA, 1989. Assessment of the effects of herbicide spray drift on a range of plant species of conservation interest. Environmental Pollution, 59, 71– 86. MNP (Netherlands Environmental Agency), 2008. Stakeholder Participation Guidance for the Netherlands Environmental Assessment Agency: Main Document. MNP publication number 550032007, Netherlands Environmental Assessment Agency, Bilthoven, the Netherlands. Available online www.pbl.nl/sites/default/files/cms/publicaties/550032007.pdf Modelink workshop, 2013. Hommen U, Alix A, Auteri D, Carpentier P, Dohmen P, Ducrot V, Forbes VE, Preuss TG, Reed M, Schmitt W, Thorbek P and Wendt-Rash L. 2013. How to use ecological effect models to link ecotoxicological tests to protection goals. SETAC-EU workshop, Le Croisic (France): 22–5 October 2012 and Monschau (Germany): 22–25 April 2013. Moe JM, Kristoffersen AB, Smith RH and Stenseth NC, 2005. From patterns to processes and back: Analysing density-dependent responses to an abiotic stressor by statistical and mechanistic modelling. Proceedings of the Royal Society Series B-Biological Sciences, 272, 2133– 2142. Munns WR, Gervais J, Hoffman AA, Hommen U, Nacci DE, Nakamaru M, Sibly RM and Topping C, 2008. Modeling approaches to population-level ecological risk assessment. In: Population level ecological risk assessment. Eds LW Barnthouse, WR Munns and MT Sorensen. Taylor & Frances, Boca Raton, FL, USA, 179– 210. NRC (National Research Council), 2007. Models in environmental regulatory decision making. Committee on Models in the Regulatory Decision Process, National Research Council. The National Academies Press, Washington, USA, 286 pp. Parrish RS and Smith CN, 1990. A method for testing whether model predictions fall within a prescribed factor of true values, with an application to pesticide leaching. Ecological Modelling, 51, 59– 72. PBL Netherlands Environmental Assessment Agency, 2011. The PBL-checklist for assessing model quality. Questionnaire, explanation and vocabulary. PBL Netherlands Environmental Assessment Agency, PBL, Bilthoven, the Netherlands. Available at www.pbl.nl/publicaties/het-pbl-normenkader-voor-modellen (in Dutch). Perrenet J and Zwaneveld B, 2012. The many faces of the modelling cycle. Journal of Mathematical Modelling and Application, 1, 3– 21. Peters RH, 1983. The ecological implications of body size. Cambridge University Press, Cambridge, UK. Railsback SF and Grimm V, 2011. Agent-based and individual-based modeling: A practical introduction. Princeton University Press, Princeton, NJ, USA. 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. Official Journal L 309/1 24.11.2009, p. 1– 50. Regulation (EC) No 546/2011 of 10 June 2011 implementing Regulation (EC) No 1107/2009 of the European Parliament and of the Council as regards uniform principles for evaluation and authorisation of plant protection products. Refsgaard JC and Henriksen HJ, 2004. Modelling guidelines-terminology and guiding principles. Advances in Water Resources, 27, 71– 82. Saltelli A and Annoni P, 2010. How to avoid a perfunctory sensitivity analysis. Environmental Modelling and Software, 25, 1508– 1517. Schmolke A, Thorbek P, DeAngelis DL and Grimm V, 2010. Ecological models supporting environmental decision-making: A strategy for the future. Ecology and Evolution, 25, 479– 486. RM Sibly, JH Brown and A Kodric-Brown, (Eds.), 2012. Metabolic ecology: A scaling approach. Wiley-Blackwell, Oxford, UK. Sibly RM, Grimm V, Martin BT, Johnston ASA, Kułakowska K, Topping CJ, Calow P, Nabe-Nielsen J, Thorbek P and DeAngelis DL, 2013. Representing the acquisition and use of energy by individuals in agent-based models of animal populations. Methods in Ecology and Evolution, 4, 151– 161. KR Solomon, TCM Brock, D De Zwart, SD Dyer, L Posthuma, SM Richards, H Sanderson, PK Sibley and PJ Brink, (Eds.), 2008. Extrapolation practice for ecotoxicological effect characterisation of chemicals. SETAC Press & CRC Press, Boca Raton, FL, USA, 380 pp. Stark JD, Tanigoshi L, Bounfour M and Antonelli A, 1997. Reproductive potential: Its influence on the susceptibility of a species to pesticides. Ecotoxicology Environmental Safety, 37, 273– 279. Stark JD, Banks JE and Vargas R, 2004. How risky is risk assessment: The role that life history strategies play in susceptibility of species to stress. Proceedings of the National Academy of Sciences of the USA, 101, 732– 736. Sterling A, 2010. Keep it complex. Nature, 468, 1029– 1031. Strandberg B, Mathiassen SK, Damgaard C and Bruus M, 2007. Testing non-target effects of herbicide spray drift. Poster presented at SETAC Europe 17th Annual Meeting, Porto, Portugal. Strandberg B, Mathiassen SK, Bruus M, Kjaer C, Damgaard C, Andersen HV, Bossi R, Løfstrøm P, Larsen SE, Bak J and Kudsk P, 2012. Effects of herbicides on non-target plants: How do effects in standard plant tests relate to effects in natural habitats? Pesticide Research. Vol. 137, Danish Ministry of the Environment, EPA, 116 pp. Tiktak A, 2000. Application of pesticide leaching models to the Vredepeel dataset. II Pesticide fate. Agricultural Water Management, 44, 119– 134. Tiktak A, Leijnse A and Vissenberg H, 1999. Uncertainty in regional-scale assessment of cadmium accumulation in the Netherlands. Journal of Environmental Quality, 28, 461– 470. Tiktak A, Boesten JJTI, van der Linden AMA and Vanclooster M, 2006. Mapping ground water vulnerability to pesticide leaching with a process-based metamodel of EuroPEARL. Journal of Environmental Quality, 35, 1213– 1226. Tiktak A, Boesten JJTI, Egsmose M, Gardi C, Klein M and Vanderborght J, 2013. European scenarios for exposure of soil organisms to pesticides. Journal of Environmental Science and Health Part B, 48, 703– 716. Topping CJ, 2011. Evaluation of wildlife management through organic farming. Ecological Engineering, 37, 2009– 2017. Topping CJ and Lagisz M, 2012. Spatial dynamic factors affecting population-level risk assessment for a terrestrial arthropod: An agent-based modeling approach. Human and Ecological Risk Assessment, 18, 168– 180. Topping CJ, Hansen TS, Jensen TS, Jepsen JU, Nikolajsen F and Odderskaer P, 2003. ALMaSS, an agent-based model for animals in temperate European landscapes. Ecological Modelling, 167, 65– 82. Topping CJ, Sibly RM, Akcakaya HR, Smith GC and Crocker DR, 2005. Risk assessment of UK skylark populations using life-history and individual-based landscape models. Ecotoxicology, 14, 925– 936. Topping CJ, Høye TT and Olesen CR, 2010. Opening the black box - Development, testing and documentation of a mechanistically rich agent-based model. Ecological Modelling, 221, 245– 255. Topping CJ, Dalkvist T and Grimm V, 2012. Post-hoc pattern-oriented testing and tuning of an existing large model: Lessons from the field vole. PLoS ONE, 7(9), e45872. Topping CJ, Kjær LJ, Hommen U, Høye TT, Preuss TG, Sibly RM and van Vliet P, 2013. Recovery based on plot experiments is a poor predictor of landscape-level population impacts of agricultural pesticides. Environmental Toxicology and Chemistry, in press. Vaal M, van der Wal JT, Hermens J and Hoekstra J, 1997. Pattern analysis of the variation in the sensitivity of aquatic species to toxicants. Chemosphere, 35, 1291– 1309. Van den Berg F, Tiktak A, Heuvelink GBM, Burgers SLGE, Brus DJ, de Vries F, Stolte J and Kroes, JG, 2012. Propagation of uncertainties in soil and pesticide properties to pesticide. Journal of Environmental Quality, 41, 253– 261. Vanderborght J, Tiktak A, Boesten JJTI and Vereecken H, 2011. Effect of pesticide fate parameters and their uncertainty on the selection of 'worst-case' scenarios of pesticide leaching to groundwater. Pest Management Science, 67, 294– 306. Westoby M and Wright J, 2006. Land-plant ecology on the basis of functional traits. Trends in Evolutionary Ecology, 21, 261– 268. White AL and Boutin C, 2007. Herbicidal effects on non-target vegetation: Investigating the limitations of current pesticide registration guidelines. Environmental Toxicology and Chemistry, 26, 2634– 2643. 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