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A Field Test of Risk‐Sensitive Foraging in Black‐Capped Chickadees (Parus Atricapillus)
Author(s) -
Barkan Christopher P. L.
Publication year - 1990
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.2307/1940276
Subject(s) - foraging , parus , predation , ecology , optimal foraging theory , biology , statistics , mathematics
The combination of a nonlinear functional relationship between energy intake and fitness, along with stochasticity in foraging success, has led to the development of risk—sensitive foraging models. These models are significant because they can lead to predictions different from those of the traditional deterministic models based upon a premise of simple energy intake rate maximization. The risk—sensitive foraging models suggest that the energy state of the forager could affect its sensitivity to the variance in foraging success associated with a particular strategy. Empirical tests conducted on several species of small, captive animals have generally supported the risk—sensitivity models; however, no experiments on free—living animals had been done. I conducted the first field test of a risk—sensitive foraging model. The test was conducted on Black—capped Chickadees (Parus atricapillus), using an experimental feeder that mimicked their natural foraging substrate and allowed me to manipulate the expected value and the variances of prey. I estimated chickadee daily energy expenditure and their rate of food intake and determined them to be on a positive energy budget while using the feeder. The birds were given a choice of two prey types with equal expected values but different variances. Consistent with a risk—sensitive foraging model, the chickadees showed a clear, significant preference for the lower variance prey. Analysis of the foraging rates of the birds revealed no significant difference related to prey type other than the difference in variance. The results suggest the importance of the incorporation of environmental stochasticity into models of animal foraging.