Density-dependent patch exploitation and acquisition of environmental information

We study density-dependent resource harvest patterns due to Bayesian foraging for different distributions of resources. We first consider a forager with information about the stochastic properties of its environment. In this case we show that when the number of food items per patch follows a distribution from the exponential family, the density dependance is given by the ratio sigma(2)/mu of the distribution of number of food items per patch, Bayesian foraging can therefore lead to positive (negative binomial distributional) or negative (binomial distribution) density dependent resource harvest and even to density independent (Poisson distribution) resource harvest, depending on the distribution of resources in the environment. In a second stage we incorporate learning about the distribution of resources ire the whole environment, The mean of the distribution of number of food items per patch of a given environment is learnt faster than the variance if the distribution. Learning occurs faster in poorer than richer environments. (C) 1997 Academic Press.