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Local Orientation and the Evolution of Foraging: Changes in Decision Making Can Eliminate Evolutionary Trade-offs

Overview of attention for article published in PLoS Computational Biology, October 2011
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Title
Local Orientation and the Evolution of Foraging: Changes in Decision Making Can Eliminate Evolutionary Trade-offs
Published in
PLoS Computational Biology, October 2011
DOI 10.1371/journal.pcbi.1002186
Pubmed ID
Authors

Daniel J. van der Post, Dirk Semmann

Abstract

Information processing is a major aspect of the evolution of animal behavior. In foraging, responsiveness to local feeding opportunities can generate patterns of behavior which reflect or "recognize patterns" in the environment beyond the perception of individuals. Theory on the evolution of behavior generally neglects such opportunity-based adaptation. Using a spatial individual-based model we study the role of opportunity-based adaptation in the evolution of foraging, and how it depends on local decision making. We compare two model variants which differ in the individual decision making that can evolve (restricted and extended model), and study the evolution of simple foraging behavior in environments where food is distributed either uniformly or in patches. We find that opportunity-based adaptation and the pattern recognition it generates, plays an important role in foraging success, particularly in patchy environments where one of the main challenges is "staying in patches". In the restricted model this is achieved by genetic adaptation of move and search behavior, in light of a trade-off on within- and between-patch behavior. In the extended model this trade-off does not arise because decision making capabilities allow for differentiated behavioral patterns. As a consequence, it becomes possible for properties of movement to be specialized for detection of patches with more food, a larger scale information processing not present in the restricted model. Our results show that changes in decision making abilities can alter what kinds of pattern recognition are possible, eliminate an evolutionary trade-off and change the adaptive landscape.

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The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 2%
United Kingdom 1 2%
South Africa 1 2%
Unknown 56 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 27%
Researcher 15 25%
Student > Master 10 17%
Student > Bachelor 3 5%
Student > Doctoral Student 2 3%
Other 6 10%
Unknown 7 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 53%
Environmental Science 6 10%
Biochemistry, Genetics and Molecular Biology 4 7%
Computer Science 2 3%
Medicine and Dentistry 2 3%
Other 6 10%
Unknown 8 14%