Modeling the mobility of living organisms in heterogeneous landscapes: Does memory improve foraging success?

TL;DR

This paper introduces a foraging model where a learning agent combines memory and random steps to optimize resource exploitation in complex environments, highlighting the benefits of memory in mobility patterns.

Contribution

It presents a novel foraging framework integrating memory and randomness, and offers tools to analyze non-random mobility behaviors in heterogeneous landscapes.

Findings

Memory enhances foraging efficiency in changing environments.

Optimal balance of deterministic and random steps maximizes resource exploitation.

Generated trajectories exhibit complex spatio-temporal patterns.

Abstract

Thanks to recent technological advances, it is now possible to track with an unprecedented precision and for long periods of time the movement patterns of many living organisms in their habitat. The increasing amount of data available on single trajectories offers the possibility of understanding how animals move and of testing basic movement models. Random walks have long represented the main description for micro-organisms and have also been useful to understand the foraging behaviour of large animals. Nevertheless, most vertebrates, in particular humans and other primates, rely on sophisticated cognitive tools such as spatial maps, episodic memory and travel cost discounting. These properties call for other modeling approaches of mobility patterns. We propose a foraging framework where a learning mobile agent uses a combination of memory-based and random steps. We investigate how advantageous it is to use memory for exploiting resources in heterogeneous and changing environments. An adequate balance of determinism and random exploration is found to maximize the foraging efficiency and to generate trajectories with an intricate spatio-temporal order. Based on this approach, we propose some tools for analysing the non-random nature of mobility patterns in general.