Levy movements and a slowly decaying memory allow efficient collective learning in groups of interacting foragers

Abstract

Many animal species benefit from spatial learning to adapt their foraging movements to the distribution of resources. Learning involves the collection, storage and retrieval of information, and depends on both the random search strategies employed and the memory capacities of the individual. For animals living in social groups, spatial learning can be further enhanced by information transfer among group members. However, how individual behavior affects the emergence of collective states of learning is still poorly understood. Here, with the help of a spatially explicit agent-based model where individuals transfer information to their peers, we analyze the effects on the use of resources of varying memory capacities in combination with different exploration strategies, such as ordinary random walks and Levy flights. We find that individual Levy displacements associated with a slow memory decay lead to a very rapid collective response, a high group cohesion and to an optimal exploitation of the best resource patches in static but complex environments, even when the interaction rate among individuals is low. Author SummaryHow groups of social animals collectively learn to find and exploit resources in complex environments is not well-understood. By means of a computational model where individuals are initially spread out across a landscape, we study the effects of individual exploratory behaviors and memory capacities on the emergence of spatial learning. Collective learning emerges spontaneously only if group members transfer information between each other at a sufficiently high rate, so that individual experiences can be used by others. In static but heterogeneous environments with many resource sites of varying attractiveness, we find that random displacements over many spatial scales combined with a slow memory decay lead to a rapid collective response and highly cohesive groups. Collective learning is noticeable through an optimal exploitation of the best resource sites, which far exceeds what individuals would achieve on their own. Our study sheds light on important mechanisms responsible for collective learning in ecology, with potential applications in other areas of science.