Visibility graphs of animal foraging trajectories

TL;DR

This paper applies visibility graph analysis to social insects' foraging trajectories, revealing how collective interactions influence movement complexity and how these effects vary with termite density.

Contribution

It introduces the use of visibility graphs to analyze animal movement data, uncovering collective effects in termite foraging behavior beyond simple jamming explanations.

Findings

Complexity peaks at intermediate termite densities.

Movement patterns differ qualitatively with increased density.

Collective effects influence foraging trajectories beyond jamming.

Abstract

The study of self-propelled particles is a fast-growing research topic where biologically inspired movement is increasingly becoming of much interest. A relevant example is the collective motion of social insects, whose variety and complexity offer fertile grounds for theoretical abstractions. It has been demonstrated that the collective motion involved in the searching behavior of termites is consistent with self-similarity, anomalous diffusion and L\'evy walks. In this work, we use visibility graphs -- a method that maps time series into graphs and quantifies the signal complexity via graph topological metrics -- in the context of social insects foraging trajectories extracted from experiments. Our analysis indicates that the patterns observed for isolated termites change qualitatively when the termite density is increased, and such change cannot be explained by jamming effects only, pointing to collective effects emerging due to non-trivial foraging interactions between insects as the cause. Moreover, we find that such an onset of complexity is maximized for intermediate termite densities.