DENSITY-DEPENDENT PATCH EXPLOITATION AND ACQUISITION OF ENVIRONMENTALINFORMATION

We study density-dependent resource harvest patterns due to Bayesian f oraging for different distributions of resources. We first consider a forager with information about the stochastic properties of its enviro nment. In this case we show that when the number of food items per pat ch follows a distribution from the exponential family, the density dep endance is given by the ratio sigma(2)/mu of the distribution of numbe r of food items per patch, Bayesian foraging can therefore lead to pos itive (negative binomial distributional) or negative (binomial distrib ution) density dependent resource harvest and even to density independ ent (Poisson distribution) resource harvest, depending on the distribu tion of resources in the environment. In a second stage we incorporate learning about the distribution of resources ire the whole environmen t, The mean of the distribution of number of food items per patch of a given environment is learnt faster than the variance if the distribut ion. Learning occurs faster in poorer than richer environments. (C) 19 97 Academic Press.