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The optimal sampling strategy for unfamiliar prey.

Thomas N Sherratt¹

¹Department of Biology, Carleton University, Ottawa, Ontario K1S 5B6, Canada. tom_sherratt@.carleton.ca

Evolution; International Journal of Organic Evolution

|July 7, 2011

Summary

This summary is machine-generated.

Predators balance learning about new prey with avoiding harm. This study models how predators decide when to stop sampling unprofitable prey, considering prey commonness and chemical defenses.

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Area of Science:

Ecology
Evolutionary Biology
Behavioral Ecology

Background:

Understanding predator foraging is key to antipredator defense evolution.
Predators face a trade-off between sampling novel prey for information and avoiding unprofitable encounters.

Purpose of the Study:

To model predator decision-making when encountering unfamiliar prey types.
To explore the exploration-exploitation trade-off in predator foraging.

Main Methods:

The problem was framed as a "two-armed bandit" problem.
Bayesian inference was used to update predator beliefs about prey profitability and future encounter rates.
Dynamic programming identified critical states for ceasing prey sampling.

Main Results:

The model explains why predators sample more unprofitable prey when the type is common.
It also explains predator neophobia towards rare, unfamiliar prey types.

Conclusions:

Predator sampling strategies are influenced by prey profitability, perceived rarity, and chemical defenses.
This framework illuminates the evolution of antipredator mechanisms like mimicry and polymorphism.