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

Area-restricted searches have been described as important componentsof the foraging behavior of many organisms. It is unclear, however,whether individual foragers can use learning to fine-tune theirsearches, or even whether these searches are efficiently performed.I used a simulation model to make qualitative predictions aboutsearch behavior in a laboratory system. The simulation modelindicates that the sinuosity and path length of searches stronglyaffect search efficiency. The model predicts that, for a rate-maximizingforager, path length should increase and search sinuosity shoulddecrease as prey become less clumped. Foraging animals may thereforebe selected to learn the path length and sinuosity of searchesin response to changing degrees of dumping of prey. These predictionswere tested in a laboratory system involving ferrets (Mustelaputorius furo) foraging for oil-drop "prey items." Search pathschanged in a graded manner to experimental manipulations ofthe dumping of prey. As predicted by the model, ferrets learnedto perform longer and less sinuous search paths as prey becameless clumped. This study provides the first evidence that area-restrictedsearch behavior is learned and can be fine-tuned to efficientlyexploit different spatial distributions of food.