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Variation between self- and mutual assessment in animal contests
Mike Mesterton-Gibbons1, Stephen M Heap
1Department of Mathematics, Florida State University, Tallahassee, Florida 32306.
The American Naturalist
|January 28, 2014
Summary
Animal contest theory needs refinement. This study models fighting ability differences, revealing trade-offs in self-assessment versus mutual assessment strategies, crucial for understanding animal conflict resolution.
Area of Science:
- Behavioral Ecology
- Game Theory
- Evolutionary Biology
Background:
- Animal contests over resources are common and resolved by withdrawal.
- Current theories propose self-assessment or mutual assessment strategies for withdrawal decisions.
- Observed variations in these strategies do not align with existing theory.
Purpose of the Study:
- To develop theoretical models explaining the functional significance of different animal assessment strategies.
- To investigate the role of fighting ability differences in shaping assessment strategies.
- To identify key factors influencing the choice between self-assessment and mutual assessment.
Main Methods:
- Utilized a hawk-dove game model with two distinct fighting ability classes.
- Analyzed strategic decisions regarding investment in mutual assessment.
- Examined trade-offs between information acquisition and information disclosure.
- Investigated the balance between mutual assessment and contest persistence.
Main Results:
- Identified simultaneous trade-offs in assessment strategy decisions.
- Weak individuals face a trade-off between gaining information and revealing their own state.
- All individuals must balance investment in mutual assessment against contest persistence.
- Model suggests errors and information-sharing consequences are key to strategy variation.
Conclusions:
- Theoretical advancements are needed to explain the diversity of animal assessment strategies.
- Fighting ability, information costs, and contest dynamics significantly influence assessment strategy evolution.
- Potential for errors and differential information consequences offer fundamental explanations for observed variations.
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