Local Orientation and the Evolution of Foraging: Changes in Decision Making Can Eliminate Evolutionary Trade-offs
Authored by der Post Daniel J van, Dirk Semmann
Date Published: 2011
DOI: 10.1371/journal.pcbi.1002186
Sponsors:
German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)
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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.
Tags
behavior
models
movements
ecology
Distributions
Efficiency
Environments
Landscapes
Walk
Search strategies