Experiments and a model examining learning in the area-restricted search behavior of ferrets (<i>Mustela putorius furo</i>)

Abstract

Area-restricted searches have been described as important components of the foraging behavior of many organisms. It is unclear, however, whether individual foragers can use learning to fine-tune their searches, or even whether these searches are efficiently performed. I used a simulation model to make qualitative predictions about search behavior in a laboratory system. The simulation model indicates that the sinuosity and path length of searches strongly affect search efficiency. The model predicts that, for a rate-maximizing forager, path length should increase and search sinuosity should decrease as prey become less clumped. Foraging animals may therefore be selected to learn the path length and sinuosity of searches in response to changing degrees of dumping of prey. These predictions were tested in a laboratory system involving ferrets (Mustela putorius furo) foraging for oil-drop “prey items.” Search paths changed in a graded manner to experimental manipulations of the dumping of prey. As predicted by the model, ferrets learned to perform longer and less sinuous search paths as prey became less clumped. This study provides the first evidence that area-restricted search behavior is learned and can be fine-tuned to efficiently exploit different spatial distributions of food.