The survival-rate-maximizing policy for Bayesian foragers: wait for good news

• DOI: 10.1093/beheco/9.4.345
• Publication Date: 2007-01-13T16:47:48Z
• Authors: Ola Olsson; Noél M. A. Holmgren
• Keywords: Optimal foraging theory

We present a model of the survival-maximizing foraging behavior an animal searching in patches for hidden prey with clumped distribution. assume forager to be Bayesian: it updates its statistical estimate number current patch while foraging. When arrives at parch, has expectation patch's quality, which equals average quality environment While foraging, uses information about time spent and how many been caught during this time. It can both instantaneous intake rate potential rest parch visit. distribution is clumped, may increase if near future. Being optimal, Bayesian should therefore base patch-leaving decision on estimated value, not value. value measured survival mortality occur either as starvation or predation, abandoned when estimates that dining visit long term This means rate, left, nor constant but increasing function patch. Therefore, giving-up densities will also higher longer search times. The are expected increasing, humped, initial densities. These properties included previous empirical studies tests.