Advertisement

The Potential for the Use of Agent-Based Models in Ecotoxicology

  • Christopher J. Topping
  • Trine Dalkvist
  • Valery E. Forbes
  • Volker Grimm
  • Richard M. Sibly
Chapter
Part of the Emerging Topics in Ecotoxicology book series (ETEP, volume 2)

Abstract

This chapter introduces ABMs, their construction, and the pros and cons of their use. Although relatively new, agent-based models (ABMs) have great potential for use in ecotoxicological research – their primary advantage being the realistic simulations that can be constructed and particularly their explicit handling of space and time in simulations. Examples are provided of their use in ecotoxicology primarily exemplified by different implementations of the ALMaSS system. These examples presented demonstrate how multiple stressors, landscape structure, details regarding toxicology, animal behavior, and socioeconomic effects can and should be taken into account when constructing simulations for risk assessment. Like ecological systems, in ABMs the behavior at the system level is not simply the mean of the component responses, but the sum of the often nonlinear interactions between components in the system; hence this modeling approach opens the door to implementing and testing much more realistic and holistic ecotoxicological models than are currently used.

Keywords

Population-level risk assessment ALMaSS Pattern-oriented modeling ODD Multiple stressors 

References

  1. 1.
    Folcik VA, An GC, Orosz CG (2007) The basic immune simulator: An agent-based model to study the interactions between innate and adaptive immunity. Theor Biol Med Model 4: 39. Available at http://www.tbiomed.com/content/4/1/39
  2. 2.
    Fossett M, Senft R (2004) SIMSEG and generative models: A typology of model-generated segregation patterns. Proceedings of the Agent 2004 Conference on Social Dynamics: Interaction, Reflexivity and Emergence, Chicago, IL, 39–78. Available at http://www.agent2005.anl.gov/Agent2004.pdf
  3. 3.
  4. 4.
    Chen X, Zhan FB (2008) Agent-based modelling and simulation of urban evacuation: Relative effectiveness of simultaneous and staged evacuation strategies. J Oper Res Soc 59: 25–33CrossRefGoogle Scholar
  5. 5.
    Anwar SM, Jeanneret CA, Parrott L, Marceau DJ (2007) Conceptualization and implementation of a multi-agent model to simulate whale-watching tours in the St. Lawrence Estuary in Quebec, Canada. Environ Model Softw 22: 1775–1787CrossRefGoogle Scholar
  6. 6.
    Mikler AR, Venkatachalam S, Ramisetty-Mikler S (2007) Decisions under uncertainty: A computational framework for quantification of policies addressing infectious disease epidemics. Stoch Environ Res Risk A 21: 533–543CrossRefGoogle Scholar
  7. 7.
    Muller G, Grebaut P, Gouteux JP (2004) An agent-based model of sleeping sickness: Simulation trials of a forest focus in southern Cameroon. CR Biol 327: 1–11CrossRefGoogle Scholar
  8. 8.
    Brede M, Boschetti F, McDonald D (2008) Strategies for resource exploitation. Ecol Complex 5: 22–29CrossRefGoogle Scholar
  9. 9.
    Blaum N, Wichmann MC (2007) Short-term transformation of matrix into hospitable habitat facilitates gene flow and mitigates fragmentation. J Anim Ecol 76: 1116–1127CrossRefGoogle Scholar
  10. 10.
    Mathevet R, Bousquet F, Le Page C, Antona M (2003) Agent-based simulations of interact-ions between duck population, farming decisions and leasing of hunting rights in the Camargue (Southern France). Ecol Model 165: 107–126CrossRefGoogle Scholar
  11. 11.
    Satake A, Leslie HM, Iwasa Y, Levin SA (2007) Coupled ecological-social dynamics in a forested landscape: Spatial interactions and information flow. J Theor Biol 246: 695–707CrossRefGoogle Scholar
  12. 12.
    DeAngelis DL, Gross LJ (1992) Individual-based models and approaches in ecology: Populations, communities and ecosystems. Chapman and Hall, New YorkGoogle Scholar
  13. 13.
    Louzoun Y, Solomon S, Atlan H, Cohen IR (2001) Modeling complexity in biology. Phys A 297: 242–252CrossRefGoogle Scholar
  14. 14.
    Grimm V (2008) Individual-based models. In: Jørgensen SE, Fath BD (eds), Ecological Models, Vol. [3] of Encyclopedia of Ecology, Elsevier, Oxford, 5: 1959–1968Google Scholar
  15. 15.
    Grimm V (1999) Ten years of individual-based modelling in ecology: What have we learned and what could we learn in the future? Ecol Model 115: 129–148CrossRefGoogle Scholar
  16. 16.
    Grimm V, Railsback SF (2005) Individual-based modelling and ecology. Princeton University Press, Princeton, NJGoogle Scholar
  17. 17.
    DeAngelis DL, Mooij WM (2005) Individual-based modeling of ecological and evolutionary processes. Annu Rev Ecol Evol Syst 36: 147–168CrossRefGoogle Scholar
  18. 18.
    Van den Brink P, Baveco JM, Verboom J, 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
  19. 19.
    DeAngelis DL, Mooij WM (2003) In praise of mechanistically-rich models. In: Canham CD, Cole JJ, Lauenroth WK (eds) Models in ecosystem science. University Press, Princeton, NJGoogle Scholar
  20. 20.
    Holland EP, Aegerter JN, Dytham C, Smith GC (2007) Landscape as a model: The importance of geometry. PloS Comput Biol 3: 1979–1992Google Scholar
  21. 21.
    Jepsen JU, Baveco JM, Topping, CJ, Verboom J, Vos CC (2005) Evaluating the effect of corridors and landscape heterogeneity on dispersal probability: A comparison of three spatially explicit modelling approaches. Ecol Model 181: 445–459CrossRefGoogle Scholar
  22. 22.
    DeAngelis DL, Cox DK, Coutant CC (1980) Cannibalism and size dispersal in young-of-the-year largemouth bass – Experiment and model. Ecol Model 8: 133–148CrossRefGoogle Scholar
  23. 23.
    Topping CJ, Rehder MJ, Mayoh BH (1999) Viola: A new visual programming language designed for the rapid development of interacting agent systems. Acta Biotheor 47: 129–140CrossRefGoogle Scholar
  24. 24.
    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, Jorgensen C, Mooij WM, Muller B, Pe’er G, Piou C, Railsback SF, Robbins AM, Robbins MM, Rossmanith E, Ruger N, Strand E, Souissi S, Stillman RA, Vabo R, Visser U, DeAngelis DL (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198: 115–126CrossRefGoogle Scholar
  25. 25.
    Caron-Lormier G, Humphry RW, Bohan DA, Hawes C, Thorbek P (2008) Asynchronous and synchronous updating in individual-based models. Ecol Model 212: 522–527CrossRefGoogle Scholar
  26. 26.
    Crooks AT (2007) The repast simulation/modelling system for geospatial simulation. Centre for Advanced Spatial Analysis (University College London), London, UK. Working Paper 123. Available at http://www.casa.ucl.ac.uk/working\_papers/paper123.pdf
  27. 27.
    Wilensky U (1999) NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Available at http://ccl.northwestern.edu/netlogo
  28. 28.
    Swarm (2006) Swarm: A platform for agent-based models. Available at http://www.swarm.org/
  29. 29.
    Topping CJ, Hansen TS, Jensen TS, Jepsen JU, Nikolajsen F, Odderskær P (2003) ALMaSS, an agent-based model for animals in temperate European landscapes. Ecol Model 167: 65–82CrossRefGoogle Scholar
  30. 30.
    Topping CJ, Odderskær P (2004) Modeling the influence of temporal and spatial factors on the assessment of impacts of pesticides on skylarks. Environ Toxicol Chem 23: 509–520CrossRefGoogle Scholar
  31. 31.
    Topping CJ, Sibly RM, Akcakaya HR, Smith GC, Crocker DR (2005) Risk assessment of UK skylark populations using life-history and individual-based landscape models. Ecotoxicology 14: 925–936CrossRefGoogle Scholar
  32. 32.
    Bilde T, Topping C (2004) Life history traits interact with landscape composition to influence population dynamics of a terrestrial arthropod: A simulation study. Ecoscience 11: 64–73Google Scholar
  33. 33.
    Thorbek P, Topping CJ (2005) The influence of landscape diversity and heterogeneity on spatial dynamics of agrobiont linyphiid spiders: An individual-based model. Biocontrol 50: 1–33CrossRefGoogle Scholar
  34. 34.
    Jepsen JU, Topping CJ (2004) Modelling roe deer (Capreolus capreolus) in a gradient of forest fragmentation: Behavioural plasticity and choice of cover. Can J Zool 82: 1528–1541CrossRefGoogle Scholar
  35. 35.
    Fuller RJ, Gregory RD, Gibbons DW, Marchant JH, Wilson JD, Baillie SR, Carter N (1995) Population declines and range contractions among lowland farmland birds. Conserv Biol 9: 1425–1441CrossRefGoogle Scholar
  36. 36.
    Chamberlain DE, Fuller RJ, Bunce RGH, Duckworth JC, Shrubb M (2000) Changes in the abundance of farmland birds in relation to the timing of agricultural intensification in England and Wales. J Appl Ecol 37: 771–788CrossRefGoogle Scholar
  37. 37.
    Odderskær P, Topping CJ, Petersen MB, Rasmussen J, Dalgaard T, Erlandsen M (2006) Ukrudtsstriglingens effekter på dyr, planter og ressourceforbrug. Miljøstyrelsen, Bekæmpelsesmiddelforskning fra Miljøstyrelsen 105. Available at http://www2.mst.dk/Udgiv/publikationer/2006/87-7052-343-6/pdf/87-7052-344-4.pdf
  38. 38.
    Schläpfer A (1988) Populationsökologie der Feldlerche Alauda arvensis in der intensiv genutzten Agrarlandschaft. Der Ornitologische Beobachter 85: 309–371Google Scholar
  39. 39.
    Jenny M (1990) Populationsdynamic der Feldlerche Alauda arvensis in einer intensiv genutzten Agrarlandschaft des Sweizerischen Mittellandes. Der Ornitilogischer Beobachter 87: 153–163Google Scholar
  40. 40.
    Daunicht WD (1998) Zum Einfluss der Feinstruktur in der Vegatation auf die Habitatwahl, Habitatnutzung, Siedlungsdichte und Populationsdynamik von Feldlerchen (Alauda arvensis) in großparzelligem Ackerland. Phd Thesis. University of Bern, BernGoogle Scholar
  41. 41.
    Navntoft S, Petersen BS, Esbjerg P, Jensen A, Johnsen I, Kristensen K, Petersen PH, Ørum JE (2007) Effects of mechanical weed control in spring cereals – Flora, fauna and economy. Danish Environmental Protection Agency, Pesticides Research No. 114Google Scholar
  42. 42.
    Wiegand K, Saltz D, Ward D, Levin SA (2008) The role of size inequality in self-thinning: A pattern-oriented simulation model for and savannas. Ecol Model 210: 431–445CrossRefGoogle Scholar
  43. 43.
    Thorbek P, Bilde T (2004) Reduced numbers of generalist arthropod predators after crop management. J Appl Ecol 41: 526–538CrossRefGoogle Scholar
  44. 44.
    Den Boer PJ (1990) The survival value of dispersal in terrestrial arthropods. Biol Conserv 54: 175–192CrossRefGoogle Scholar
  45. 45.
    Errington PL (1934) Vulnerability of bob-white populations to predation. Ecology 15: 110–127CrossRefGoogle Scholar
  46. 46.
    Dalkvist T, Topping CJ, Forbes VE (2009) Population-level impacts of pesticide-induced chronic effects on individuals depend more on ecology than toxicology. Ecotoxicol Environ Safety, 10.1016/j.ecoenv.2008.10.002Google Scholar
  47. 47.
    Anway MD, Cupp AS, Uzumcu M, Skinner MK (2005) Epigenetic transgenerational actions of endocrine disruptors and mate fertility. Science 308: 1466–1469CrossRefGoogle Scholar
  48. 48.
    Anway MD, Leathers C, Skinner MK (2006) Endocrine disruptor vinclozolin induced epigenetic transgenerational adult-onset disease. Endocrinology 147: 5515–5523CrossRefGoogle Scholar
  49. 49.
    Hansson L (1977) Spatial dynamics of field voles Microtus agrestis in heterogeneous landscapes. Oikos 29: 593–644CrossRefGoogle Scholar
  50. 50.
    Evans DM, Redpath SM, Elston DA, Evans SA, Mitchell RJ, Dennis P (2006) To graze or not to graze? Sheep, voles, forestry and nature conservation in the British uplands. J Appl Ecol 43: 499–505CrossRefGoogle Scholar
  51. 51.
    Smidt NM, Olsen H, Bildsøe M, Sluydts V, Leirs H (2005) Effects of grazing intensity on small mammal population ecology in wet meadows. Basic Appl Ecol 6: 57–66CrossRefGoogle Scholar
  52. 52.
    Jensen TS, Hansen TS (2001) Effekten af husdyrgræsning på småpattedyr. In: Pedersen L B, Buttenschøn R, Jensen T S (eds) Græsning på ekstensivt drevne naturarealer – Effekter på stofkredsløb og naturindhold. Skov & Landskab, Hørsholm, Park- og Landskabsserien 34: 107–121Google Scholar
  53. 53.
    Bell G, Lechowicz MJ, Appenzeller A, Chandler M, DeBlois E, Jackson L, Mackenzie B, Preziosi R, Schallenberg M, Tinker N (1993) The spatial structure of the physical environment. Oecologia 96: 114–121CrossRefGoogle Scholar
  54. 54.
    Clifford PA, Barchers DE, Ludwig DF, Sielken RL, Klingensmith JS, Graham RV, Banton MI (1995) An approach to quantifying spatial components of exposure for ecological risk assessment. Environ Toxicol Chem 14: 895–906CrossRefGoogle Scholar
  55. 55.
    Purucker ST, Welsh CJE, Stewart RN, Starzec P (2007) Use of habitat-contamination spatial correlation to determine when to perform a spatially explicit ecological risk assessment. Ecol Model 204: 180–192CrossRefGoogle Scholar
  56. 56.
    Jacobsen LB, Frandsen SE (1999) Analyse af de sektor-og samfundsøkonomiske konsekvenser af en reduktion af forbruget af pesticider i dansk landbrug. The Ministry of Food, Agriculture and Fisheries, Danish Research Institute of Food Economics, Denmark, 104Google Scholar
  57. 57.
    Topping CJ (2005) The impact on skylark numbers of reductions in pesticide usage in Denmark. Predictions using a landscape-scale individual based model. National Environmental Research Institute, Denmark, NERI Technical Report 527: 33. Available at http://technical-reports.dmu.dk
  58. 58.
    Goss-Custard JD, Durell SEALD (1990) Bird behaviour and environmental planning: Approaches in the study of wader populations. Ibis 132: 273–282CrossRefGoogle Scholar
  59. 59.
    Goss-Custard JD, Caldow RWG, Clarke RT, Durell SEALD, Sutherland WJ (1995) Deriving population parameters from individual variations in foraging behaviour. I. Empirical game-theory distribution model of oystercatchers Haematopus ostralegus feeding on mussels Mytilus edulis. J Anim Ecol 64: 265–276CrossRefGoogle Scholar
  60. 60.
    Sutherland WJ (1996) From individual behaviour to population ecology. Oxford University Press, OxfordGoogle Scholar
  61. 61.
    Goss-Custard JD, Sutherland WJ (1997) Individual behaviour, populations and conservation. In: JR Krebs, NB Davies (eds) Behavioural ecology: An evolutionary approach. Blackwell Science, OxfordGoogle Scholar
  62. 62.
    Stillman RA, Goss-Custard JD, West AD, Durell S, Caldow RWG, McGrorty S, Clarke RT (2000) Predicting mortality in novel environments: Tests and sensitivity of a behaviour-based model. J Appl Ecol 37: 564–588CrossRefGoogle Scholar
  63. 63.
    Stillman RA, Goss-Custard JD, West AD, Durell S, McGrorty S, Caldow RWG, Norris KJ, Johnstone IG, Ens BJ, Van der Meer J, Triplet P (2001) Predicting shorebird mortality and population size under different regimes of shellfishery management. J Appl Ecol 38: 857–868CrossRefGoogle Scholar
  64. 64.
    Stillman RA, Caldow RWG, Durell SEALD, West AD, McGrorty S, Goss-Custard JD, Perez-Hurtado A, Castro M, Estrella SM, Masero JA, Rodríguez-Pascual FH, Triplet P, Loquet N, Desprez M, Fritz H, Clausen P, Ebbinge BS, Norris K, Mattison E (2005) Coast bird diversity – maintaining migratory coastal bird diversity: Management through individual-based predictive population modelling. Centre for Ecology and Hydrology for the Commission of the European Communities, UKGoogle Scholar
  65. 65.
    Stillman RA, West AD, Goss-Custard JD, McGrorty S, Frost NJ, Morrisey DJ, Kenny AJ, Drewitt A (2005) Predicting site quality for shorebird communities: A case study on the Humber estuary, UK. Mar Ecol Prog Series 305: 203–217CrossRefGoogle Scholar
  66. 66.
    Goss-Custard JD, Burton NHK, Clark NA, Ferns PN, McGrorty S, Reading CJ, Rehfisch MM, Stillman RA, Townend I, West AD, Worrall DH (2006) Test of a behavior-based individual-based model: Response of shorebird mortality to habitat loss. Ecol Appl 16: 2215–2222CrossRefGoogle Scholar
  67. 67.
    Railsback SF, Lamberson RH, Harvey BC, Duffy WE (1999) Movement rules for individual-based models of stream fish. Ecol Model 123: 73–89CrossRefGoogle Scholar
  68. 68.
    Railsback SF (2001) Getting “results”: the pattern-oriented approach to analyzing natural systems with individual-based models. Nat Resour Model 14: 465–474CrossRefGoogle Scholar
  69. 69.
    Railsback SF (2001) Concepts from complex adaptive systems as a framework for individual-based modelling. Ecol Model 139: 47–62CrossRefGoogle Scholar
  70. 70.
    Railsback SF, Harvey BC (2001) Individual-based model formulation for cutthroat trout, Little Jonas Creek, California. General Technical Report PSW-GTR-182. Pacific Southwest Research Station, Forest Service, U.S. Department of Agriculture, Albany, CAGoogle Scholar
  71. 71.
    Railsback SF, Harvey BC (2002) Analysis of habitat selection rules using an individual-based model. Ecology 83: 1817–1830Google Scholar
  72. 72.
    Railsback SF, Harvey BC, Lamberson RH, Lee DE, Claasen NJ, Yoshihara S (2002) Population-level analysis and validation of an individual-based cutthroat trout model. Nat Resour Model 14: 465–474CrossRefGoogle Scholar
  73. 73.
    Railsback SF, Stauffer HB, Harvey BC (2003) What can habitat preference models tell us? Tests using a virtual trout population. Ecol Appl 13: 1580Google Scholar
  74. 74.
    Van Winkle W, Jager HI, Railsback SF, Holcomb BD, Studley TK, Baldrige JE (1998) Individual-based model of sympatric populations of brown and rainbow trout for instream flow assessment: Model description and calibration. Ecol Model 110: 175–207CrossRefGoogle Scholar
  75. 75.
    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: 987–991CrossRefGoogle Scholar
  76. 76.
    Sibly RM, Nabe-Nielsen J, Forchhammer MC, Forbes VE, Topping CJ (2008) On population dynamics in heterogeneous landscapes. Ecol Lett 17: 10.1023/A:1027390600748Google Scholar
  77. 77.
    Dalgaard T, Kjeldsen T, Rasmussen BM, Fredshavn JR, Münier B, Schou JS, Dahl M, Wiborg IA, Nørmark P, Hansen JF (2004) ARLAS’ scenariesystem. Et grundlag for helhedsorienterede konsekvensvurdringer af ændringer i arealanvendelsen. In: Hansen JF (ed) Arealanvendelse og landskabsudvikling Fremtidsperspektiver for natur, jordbrug, miljø og arealforvaltning. Danmarks Jordbrugsforskning, Markbrug, 110, 97–128 (in Danish with English summary). Available at: http://web.agrsci.dk/djfpublikation/djfpdf/djfma110.pdf
  78. 78.
    Dortch MS, Gerald JA (2004) Recent advances in the army risk assessment modeling system. In: Whelan G (ed) Brownfields, multimedia modeling and assessment. WIT Press, Southampton, UK. Available at: http://el.erdc.usace.army.mil/arams/pdfs/arams04-advance.pdf
  79. 79.
    Thorbek P, Forbes V, Heimbach F, Hommen U, Thulke HH, van den Brink P, Wogram J, Grimm V (2008) Ecological models in support of regulatory risk assessments of pesticides: Developing a strategy for the future. Integr Environ Assess Manag 5: 1Google Scholar
  80. 80.
    Caswell H (2001) Matrix population models: Construction, analysis, and interpretation. Sinauer Associates, Sunderland, MAGoogle Scholar
  81. 81.
    Levins R (1966) Strategy of model building in population biology. Am Sci 54: 421–431Google Scholar
  82. 82.
    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: 209–222CrossRefGoogle Scholar
  83. 83.
    Gutenkunst RN, Waterfall JJ, Casey FP, Brown KS, Myers CR, Sethna JP (2007) Universally sloppy parameter sensitivities in systems biology models. PloS Comput Biol 3: 1871–1878Google Scholar
  84. 84.
    Bak P (1997) How nature works: The science of self-organised criticality. University Press, OxfordGoogle Scholar
  85. 85.
    Frigg R (2003) Self-organised criticality – What it is and what it isn’t. Stud His Philos Sci 34A: 613–632CrossRefGoogle Scholar
  86. 86.
    Tarantola A (1987) Inverse problem theory: Methods for data fitting and model parameter estimation. Elsevier, New YorkGoogle Scholar
  87. 87.
    Lambert P, Rochard E (2007) Identification of the inland population dynamics of the European eel using pattern-oriented modelling. Ecol Model 206: 166–178CrossRefGoogle Scholar
  88. 88.
    Rossmanith E, Blaum N, Grimm V, Jeltsch F (2007) Pattern-oriented modelling for estimating unknown pre-breeding survival rates: The case of the lesser spotted woodpecker (Picoides minor). Biol Conserv 135: 555–564CrossRefGoogle Scholar
  89. 89.
    Kramer-Schadt S, Revilla E, Wiegand T, Grimm V (2007) Patterns for parameters in simulation models. Ecol Model 204: 553–556CrossRefGoogle Scholar
  90. 90.
    Polhill GJ, Parker DC, Brown DG, Grimm V (2008) Using the ODD protocol for describing three agent-based social simulation models of land use change. J Artif Soc Sci Sim 11(2/3) < http://jasss.soc.surrey.ac.uk/11/2/3.html > 
  91. 91.

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Christopher J. Topping
    • 1
  • Trine Dalkvist
    • 1
    • 2
  • Valery E. Forbes
    • 2
    • 3
  • Volker Grimm
    • 4
  • Richard M. Sibly
    • 2
    • 5
  1. 1.Department of Wildlife Ecology and Biodiversity, National Environmental Research InstituteUniversity of AarhusRøndeDenmark
  2. 2.Centre for Integrated Population EcologyRoskilde UniversityRoskildeDenmark
  3. 3.Department of Environmental, Social and Spatial ChangeRoskilde UniversityRoskildeDenmark
  4. 4.Department of Ecological ModellingHelmholtz Centre for Environmental Research – UFZLeipzigGermany
  5. 5.School of Biological SciencesUniversity of Reading, WhiteknightsReadingUK

Personalised recommendations