The L\'evy flight foraging hypothesis: comparison between stationary distributions and anomalous diffusion

TL;DR

This paper analyzes optimal foraging strategies using Le9vy flights, showing how the best approach varies with region size and prey distribution, highlighting differences between one-dimensional and higher-dimensional cases.

Contribution

It provides a rigorous comparison of stationary, Gaussian, and Le9vy foraging strategies based on region size and prey distribution Fourier properties.

Findings

Large regions favor Gaussian random walk strategies.

Small regions favor Le9vy foragers with small fractional exponents.

Optimal strategies depend on the Fourier transform support of prey distribution.

Abstract

We consider a stationary prey in a given region of space and we aim at detecting optimal foraging strategies. On the one hand, when the prey is uniformly distributed, the best possible strategy for the forager is to be stationary and uniformly distributed in the same region. On the other hand, in several biological settings, foragers cannot be completely stationary, therefore we investigate the best seeking strategy for L\'evy foragers in terms of the corresponding L\'evy exponent. In this case, we show that the best strategy depends on the region size in which the prey is located: large regions exhibit optimal seeking strategies close to Gaussian random walks, while small regions favor L\'evy foragers with small fractional exponent. We also consider optimal strategies in view of the Fourier transform of the distribution of a stationary prey. When this distribution is supported in a suitable volume, then the foraging efficiency functional is monotone increasing with respect to the L\'evy exponent and accordingly the optimal strategy is given by the Gaussian dispersal. If instead the Fourier transform of the distribution of a stationary prey is supported in the complement of a suitable volume, then the foraging efficiency functional is monotone decreasing with respect to the L\'evy exponent and therefore the optimal strategy is given by a null fractional exponent (which in turn corresponds, from a biological standpoint, to a strategy of ``ambush'' type). We will devote a rigorous quantitative analysis also to emphasize some specific differences between the one-dimensional and the higher-dimensional cases.