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Cited by 1,226

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Complexity, Pattern, and Evolutionary Trade-Offs in Animal Aggregation

Parrish, Julia K., Edelstein-Keshet, Leah·1999·Science

Abstract

One of the most striking patterns in biology is the formation of animal aggregations. Classically, aggregation has been viewed as an evolutionarily advantageous state, in which members derive the benefits of protection, mate choice, and centralized information, balanced by the costs of limiting resources. Consisting of individual members, aggregations nevertheless function as an integrated whole, displaying a complex set of behaviors not possible at the level of the individual organism. Complexity theory indicates that large populations of units can self-organize into aggregations that generate pattern, store information, and engage in collective decision-making. This begs the question, are all emergent properties of animal aggregations functional or are some simply pattern? Solutions to this dilemma will necessitate a closer marriage of theoretical and modeling studies linked to empirical work addressing the choices, and trajectories, of individuals constrained by membership in the group.

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Metadata

Authors
Parrish, J. K., & Edelstein-Keshet, L.
Venue
Science
Published
1999-04-02
Volume
284
Issue
5411
Pages
99-101
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2044680442
Cited by
1,226
References
2
FWCI
21.33
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Complexity, Pattern, and Evolutionary Trade-Offs in Animal Aggregation. https://app.cochat.ai/papers/10.1126/science.284.5411.99
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Parrish, J. K., & Edelstein-Keshet, L. (1999). Complexity, Pattern, and Evolutionary Trade-Offs in Animal Aggregation. Science. https://doi.org/10.1126/science.284.5411.99

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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.