Agent-Based Models in Ecology: Patterns and Alternative Theories of Adaptive Behaviour
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.
- D.B. Botkin (1993) Forest dynamics: an ecological model. Oxford University Press, Oxford, New York.
- 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. CrossRef
- F. Bousquet and C. Le Page (2004) Multi-agent simulations and ecosystem management: a review. Ecological Modelling, 176:313–332. CrossRef
- 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.
- 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.
- 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.
- V. Grimm (1994) Mathematical models and understanding in ecology. Ecological Modelling, 75/76:641–651. CrossRef
- 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. CrossRef
- 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. CrossRef
- 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. CrossRef
- V. Grimm and S.F. Railsback (2005) Individual-based modeling and ecology. Princeton University Press, Princeton, N.J.
- B.C. Harvey and S.F. Railsback. Elevated turbidity reduces abundance and biomass of stream trout in an individual-based model. in prep.
- M. Huston, D. DeAngelis, and W. Post (1988) New computer models unify ecological theory. BioScience, 38:682–691. CrossRef
- A. Huth (1992) Ein Simulationsmodell zur Erklärung der kooperativen Bewegung von polarisierten Fischschwärmen. Phd, Universität Marburg.
- A. Huth and C. Wissel (1992) The simulation of the movement of fish schools. Journal of Theoretical Biology, 156:365–385. CrossRef
- J. Liu and P.S. Ashton (1995) Individual-based simulation models for forest succession and management. Forest Ecology and Management, 73:157–175. CrossRef
- C. Neuert (1999) Die Dynamik räumlicher Strukturen in naturnahen Buchenwäldern Mitteleuropas. Phd, Universität Marburg.
- 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.
- J.R. Platt (1964) Strong inference. Science, 146(3642):347–352. CrossRef
- 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). CrossRef
- 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. CrossRef
- S.F. Railsback (2001) Concepts from complex adaptive systems as a framework for individual-based modelling. Ecological Modelling, 139:47–62. CrossRef
- S.F. Railsback (2001) Getting “results”: the pattern-oriented approach to analyzing natural systems with individual-based models. Natural Resource Modeling, 14:465–474. CrossRef
- S.F. Railsback and B.C. Harvey (2002) Analysis of habitat selection rules using an individual-based model. Ecology, 83:1817–1830.
- 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.
- 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. CrossRef
- 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. CrossRef
- 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.
- C.W. Reynolds (1987) Flocks, herds, and schools: a distributed behavioral model. Computer Graphics, 21:25–36. CrossRef
- 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. CrossRef
- H.H. Shugart (1984) A theory of forest dynamics: the ecological implications of forest succession models. Springer-Verlag, New York.
- J. Uchmański and V. Grimm (1996) Individual-based modelling in ecology: what makes the difference? Trends in Ecology and Evolution, 11:437–441. CrossRef
- J. Watson (1968) The double helix: a personal account of the discovery of the structure of DNA. Atheneum, New York.
- 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. CrossRef
- T. Wiegand, E. Revilla, and F. Knauer (2004) Dealing with uncertainty in spatially explicit population models. Biodiversity and Conservation, 13:53–78. CrossRef
- 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. CrossRef
- C. Wissel (1992) Modelling the mosaic-cycle of a Middle European beech forest. Ecological Modelling, 63:29–43. CrossRef
- Agent-Based Models in Ecology: Patterns and Alternative Theories of Adaptive Behaviour
- Book Title
- Agent-Based Computational Modelling
- Book Subtitle
- Applications in Demography, Social, Economic and Environmental Sciences
- pp 139-152
- Print ISBN
- Online ISBN
- Series Title
- Contributions to Economics
- Series ISSN
- Physica-Verlag HD
- Copyright Holder
- Physica-Verlag Heidelberg
- Additional Links
- Industry Sectors
- eBook Packages
- Editor Affiliations
- 1. Instituto di Metodi Quantitativi, Università Bocconi & IGIER
- 2. Vienna Institute of Demography
- 3. ACDIS, University of Illinois
- Author Affiliations
- 4. UFZ Centre for Environmental Research Leipzig-Halle, Germany
- 5. Lang, Railsback & Associates, Arcata, California, USA
To view the rest of this content please follow the download PDF link above.