Individual Decisions and the Distribution of Predators in a Patchy Environment

The theoretical distribution of non-omniscient predators in a heterogeneous multipatch environment is studied by numerical simulation and indicates that the IFD prediction is not dependent on assuming omniscient predation, but instead can be jointly derived from reasonable assumptions about learning and optimal foraging at individual level.

Abstract

SUMMARY (1) The theoretical distribution of non-omniscient predators in a heterogeneous multipatch environment is studied by numerical simulation. Although various relative densities are explored, we mostly attend to environments where prey density is approximately one order of magnitude higher than predator density. (2) Predators are assumed to follow the rule of abandoning their current patch when local capture rate is lower than estimated capture rate in the environment as a whole. This rule was chosen because it is known to maximize long-term capture rate in many foraging situations. For convenience, we assume that after abandoning a patch, predators arrive at random at any patch in the environment. (3) Predators detect precisely the capture rate in their current patch but 'learn' about the environmental average. Learning is simulated using a linear operator model which estimates global capture rate as a weighted average of past and current experienced capture rate. Following a common approach is psychological models of simple learning, the relative weight of past and present capture rate is controlled by a parameter denominated the 'memory factor'. (4) In the absence of depletion, the predators distribute themselves close to the predictions of the Ideal Free Distribution model. This result indicates that the IFD prediction is not dependent on assuming omniscient predation, but instead can be jointly derived from reasonable assumptions about learning and optimal foraging at individual level.