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Individual-Based Modeling of Ecological and Evolutionary Processes

DeAngelis, Donald L., Mooij, Wolf M.·2005·Annual Review of Ecology, Evolution, and Systematics

Abstract

▪ Abstract Individual-based models (IBMs) allow the explicit inclusion of individual variation in greater detail than do classical differential-equation and difference-equation models. Inclusion of such variation is important for continued progress in ecological and evolutionary theory. We provide a conceptual basis for IBMs by describing five major types of individual variation in IBMs: spatial, ontogenetic, phenotypic, cognitive, and genetic. IBMs are now used in almost all subfields of ecology and evolutionary biology. We map those subfields and look more closely at selected key papers on fish recruitment, forest dynamics, sympatric speciation, metapopulation dynamics, maintenance of diversity, and species conservation. Theorists are currently divided on whether IBMs represent only a practical tool for extending classical theory to more complex situations, or whether individual-based theory represents a radically new research program. We feel that the tension between these two poles of thinking can be a source of creativity in ecology and evolutionary theory.

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Metadata

Authors
DeAngelis, D. L., & Mooij, W. M.
Venue
Annual Review of Ecology, Evolution, and Systematics
Published
2005-12-01
Volume
36
Issue
1
Pages
147-168
ISSN
  • 1543-592X (ISSN-L)
  • 1545-2069
OpenAlex
W2143797502
Cited by
634
References
0
FWCI
4.39
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Individual-Based Modeling of Ecological and Evolutionary Processes. https://app.cochat.ai/papers/10.1146/annurev.ecolsys.36.102003.152644
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DeAngelis, D. L., & Mooij, W. M. (2005). Individual-Based Modeling of Ecological and Evolutionary Processes. Annual Review of Ecology, Evolution, and Systematics. https://doi.org/10.1146/annurev.ecolsys.36.102003.152644

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.