Agent-Based Models in Ecology: Patterns and Alternative Theories of Adaptive Behaviour

  • Volker Grimm
  • Steven F. Railsback
Part of the Contributions to Economics book series (CE)


Ecologists have used agent-based models for a long time, but refer to them as “individual-based models” (IBMs). Common characteristics of IBMs are discrete representation of unique individuals; local interactions; use of adaptive, fitness-seeking behaviour; explicit representation of how individuals and their environment affect each other; and representation of full life cycles.

Ecology has contributed to agent-based modelling in general by showing how to use agent-based techniques to explain real systems. Ecologists have used IBMs to understand how dynamics of many real systems arise from traits of individuals and their environment. Two modelling strategies have proven particularly useful.

The first strategy is “pattern-oriented modelling” (POM). POM starts with identifying a variety of observed patterns, at different scales and at both individual and system levels, that characterize the system’s dynamics and mechanisms. These patterns, along with the problem being addressed and conceptual models of the system, provide the basis for designing and testing an IBM. A model’s variables and mechanisms are chosen because they are essential for reproducing these characteristic patterns. After an IBM is assembled, alternative versions (different theories for individual behaviour; different parameterizations) can be tested by how well they reproduce the patterns.

The second strategy is developing general and reusable theory for the adaptive behaviour of individuals. A “theory” is a model of some specific individual behaviour from which system-level dynamics emerge. Theory can be developed by hypothesizing alternative models for the behaviour, then using the IBM to see which alternative best reproduces a variety of patterns that characterize the system dynamics of interest. Empirical observations are used to develop both theories and the patterns used to test and falsify them.

These two strategies are demonstrated with example models of schooling behaviour in fish, spatiotemporal dynamics in forests, and dispersal of brown bears.


Adaptive Behaviour Habitat Selection Ecological Modelling Alternative Theory Beech Forest 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    D.B. Botkin (1993) Forest dynamics: an ecological model. Oxford University Press, Oxford, New York.Google Scholar
  2. 2.
    D.B. Botkin, J.F. Janak, and J.R. Wallis (1972) Some ecological consequences of a computer model of forest growth. Journal of Ecology, 60:849–873.CrossRefGoogle Scholar
  3. 3.
    F. Bousquet and C. Le Page (2004) Multi-agent simulations and ecosystem management: a review. Ecological Modelling, 176:313–332.CrossRefGoogle Scholar
  4. 4.
    S. Camazine, J.-L. Deneubourg, N.R. Franks, J. Sneyd, G. Theraulaz, and E. Bonabeau (2001) Self-organization in biological systems. Princeton Studies in Complexity. Princeton University Press, Princeton, New Jersey.Google Scholar
  5. 5.
    D.L. DeAngelis and L.J. Gross (1992) Individual-based models and approaches in ecology: populations, communities and ecosystems. In: D.L. DeAngelis and L.J. Gross, editors, Individual-based models and approaches in ecology, pp. 523–525. Chapman and Hall, New York.Google Scholar
  6. 6.
    D.L. DeAngelis and W.M. Mooij (2003) In praise of mechanistically-rich models. In: C.D. Canham, J.J. Cole, and W.K. Lauenroth, editors, Models in ecosystem science, pp. 63–82. Princeton University Press, Princeton, New Jersey.Google Scholar
  7. 7.
    V. Grimm (1994) Mathematical models and understanding in ecology. Ecological Modelling, 75/76:641–651.CrossRefGoogle Scholar
  8. 8.
    V. Grimm (1999) Ten years of individual-based modelling in ecology: What have we learned, and what could we learn in the future? Ecological Modelling, 115:129–148.CrossRefGoogle Scholar
  9. 9.
    V. Grimm and U. Berger (2003) Seeing the forest for the trees, and vice versa: pattern-oriented ecological modelling. In: L. Seuront and P.G. Strutton, editors, Handbook of scaling methods in aquatic ecology: measurement, analysis, simulation, pp. 411–428. CRC Press, Boca Raton.CrossRefGoogle Scholar
  10. 10.
    V. Grimm, K. Frank, F. Jeltsch, R. Brandl, J. Uchmański, and C. Wissel (1996) Pattern-oriented modelling in population ecology. Science of the Total Environment, 183:151–166.CrossRefGoogle Scholar
  11. 11.
    V. Grimm and S.F. Railsback (2005) Individual-based modeling and ecology. Princeton University Press, Princeton, N.J.Google Scholar
  12. 12.
    B.C. Harvey and S.F. Railsback. Elevated turbidity reduces abundance and biomass of stream trout in an individual-based model. in prep.Google Scholar
  13. 13.
    M. Huston, D. DeAngelis, and W. Post (1988) New computer models unify ecological theory. BioScience, 38:682–691.CrossRefGoogle Scholar
  14. 14.
    A. Huth (1992) Ein Simulationsmodell zur Erklärung der kooperativen Bewegung von polarisierten Fischschwärmen. Phd, Universität Marburg.Google Scholar
  15. 15.
    A. Huth and C. Wissel (1992) The simulation of the movement of fish schools. Journal of Theoretical Biology, 156:365–385.CrossRefGoogle Scholar
  16. 16.
    J. Liu and P.S. Ashton (1995) Individual-based simulation models for forest succession and management. Forest Ecology and Management, 73:157–175.CrossRefGoogle Scholar
  17. 17.
    C. Neuert (1999) Die Dynamik räumlicher Strukturen in naturnahen Buchenwäldern Mitteleuropas. Phd, Universität Marburg.Google Scholar
  18. 18.
    C. Neuert, C. Rademacher, V. Grundmann, C. Wissel, and V. Grimm (2001) Struktur und Dynamik von Buchenurwäldern: Ergebnisse des regelbasierten Modells before. Naturschutz und Landschaftsplanung, 33:173–183.Google Scholar
  19. 19.
    J.R. Platt (1964) Strong inference. Science, 146(3642):347–352.ADSCrossRefGoogle Scholar
  20. 20.
    C. Rademacher, C. Neuert, V. Grundmann, C. Wissel, and V. Grimm (2004) Reconstructing spatiotemporal dynamics of central european beech forests: the rule-based model before. Forest Ecology and Management, (194: 349–368).CrossRefGoogle Scholar
  21. 21.
    C. Rademacher and S. Winter (2003) Totholz im Buchen-Urwald: generische Vorhersagen des Simulationsmodelles before-cwd zur Menge, räumlichen Verteilung und Verfügbarkeit. Forstwissenschaftliches Centralblatt, 122:337–357.CrossRefGoogle Scholar
  22. 22.
    S.F. Railsback (2001) Concepts from complex adaptive systems as a framework for individual-based modelling. Ecological Modelling, 139:47–62.CrossRefGoogle Scholar
  23. 23.
    S.F. Railsback (2001) Getting “results”: the pattern-oriented approach to analyzing natural systems with individual-based models. Natural Resource Modeling, 14:465–474.zbMATHCrossRefGoogle Scholar
  24. 24.
    S.F. Railsback and B.C. Harvey (2002) Analysis of habitat selection rules using an individual-based model. Ecology, 83:1817–1830.Google Scholar
  25. 25.
    S.F. Railsback, B.C. Harvey, R.H. Lamberson, D.E. Lee, N.J. Claasen, and S. Yoshihara (2002) Population-level analysis and validation of an individual-based cuthroat trout model. Natural Resource Modeling, 14:465–474.Google Scholar
  26. 26.
    S.F. Railsback, R.H. Lamberson, B.C. Harvey, and W.E. Duffy (1999) Movement rules for individual-based models of stream fish. Ecological Modelling, 123:73–89.CrossRefGoogle Scholar
  27. 27.
    S.F. Railsback, H.B. Stauffer, and B.C. Harvey (2003) What can habitat preference models tell us? tests using a virtual trout population. Ecological Applications, 13:1580–1594.CrossRefGoogle Scholar
  28. 28.
    H. Remmert (1991) The mosaic-cycle concept of ecosystems-an overview. In: H. Remmert, editor, The mosaic-cycle concept of ecosystems (Ecological Studies 85), pp. 1–21. Springer, Berlin Heidelberg New York.Google Scholar
  29. 29.
    C.W. Reynolds (1987) Flocks, herds, and schools: a distributed behavioral model. Computer Graphics, 21:25–36.CrossRefGoogle Scholar
  30. 30.
    S. Schadt, F. Knauer, P. Kaczensky, E. Revilla, T. Wiegand, and L. Trepl (2002) Rule-based assessment of suitable habitat and patch connectivity for the eurasian lynx. Ecological Applications, 12:1469–1483.CrossRefGoogle Scholar
  31. 31.
    H.H. Shugart (1984) A theory of forest dynamics: the ecological implications of forest succession models. Springer-Verlag, New York.Google Scholar
  32. 32.
    J. Uchmański and V. Grimm (1996) Individual-based modelling in ecology: what makes the difference? Trends in Ecology and Evolution, 11:437–441.CrossRefGoogle Scholar
  33. 33.
    J. Watson (1968) The double helix: a personal account of the discovery of the structure of DNA. Atheneum, New York.Google Scholar
  34. 34.
    T. Wiegand, F. Jeltsch, I. Hanski, and V. Grimm (2003) Using pattern-oriented modeling for revealing hidden information: a key for reconciling ecological theory and conservation practice. Oikos, 100:209–222.CrossRefGoogle Scholar
  35. 35.
    T. Wiegand, E. Revilla, and F. Knauer (2004) Dealing with uncertainty in spatially explicit population models. Biodiversity and Conservation, 13:53–78.CrossRefGoogle Scholar
  36. 36.
    W. Van Winkle, H.I. Jager, S.F. Railsback, B.D. Holcomb, T.K. Studley, and J.E. Baldrige (1998) Individual-based model of sympatric populations of brown and rainbow trout for instream flow assessment: model description and calibration. Ecological Modelling, 110:175–207.CrossRefGoogle Scholar
  37. 37.
    C. Wissel (1992) Modelling the mosaic-cycle of a Middle European beech forest. Ecological Modelling, 63:29–43.CrossRefGoogle Scholar

Copyright information

© Physica-Verlag Heidelberg 2006

Authors and Affiliations

  • Volker Grimm
    • 1
  • Steven F. Railsback
    • 2
  1. 1.UFZ Centre for Environmental Research Leipzig-HalleGermany
  2. 2.Lang, Railsback & AssociatesArcataUSA

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