Abstract
According to optimal foraging theory, foraging decisions are based on the forager's current estimate of the quality of its environment. However, in a novel environment, a forager does not possess information regarding the quality of the environment, and may make a decision based on a biased estimate. We show, using a simple simulation model, that when facing uncertainty in heterogeneous environments it is better to overestimate the quality of the environment (to be an "optimist") than underestimate it, as optimistic animals learn the true value of the environment faster due to higher exploration rate. Moreover, we show that when the animal has the capacity to remember the location and quality of resource patches, having a positively biased estimate of the environment leads to higher fitness gains than having an unbiased estimate, due to the benefits of exploration. Our study demonstrates how a simple model of foraging with incomplete information, derived directly from optimal foraging theory, can produce well documented complex space-use patterns of exploring animals.
Original language | American English |
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Article number | e34578 |
Journal | PLoS ONE |
Volume | 7 |
Issue number | 4 |
DOIs | |
State | Published - 3 Apr 2012 |
All Science Journal Classification (ASJC) codes
- General
- General Biochemistry,Genetics and Molecular Biology
- General Agricultural and Biological Sciences