Stochastic modeling of long-legged ant A. gracilipes locomotion in laboratory experiments

TL;DR

This paper develops a stochastic model combining active Brownian and run-and-tumble behaviors to accurately describe the movement patterns of long-legged ants, aiding understanding of their locomotion and sensory mechanisms.

Contribution

It introduces a combined stochastic model that reproduces ant movement trajectories and identifies key probabilistic distributions of movement parameters.

Findings

Model accurately reproduces trajectory statistics

Identifies consistent turn angle and run time distributions

Provides analytical predictions matching experimental data

Abstract

Stochastic modeling of movement behavior provides a valuable way to understand how complex motion can be generated from relatively simple building blocks. Ants demonstrate sophisticated social behavior ranging from foraging to nest relocation; while emphasis is often placed on the communication methods used to synchronize individuals, the movement paradigms of those individuals are of tantamount importance. Here, we apply a stochastic modeling approach to better understand the movement of isolated long-legged ant (A. gracilipes) specimens, informed by extensive laboratory tracking experiments. We find that a combination of active Brownian and run-and-tumble models reproduces the trajectory statistics observed in experiments, both qualitatively and quantitatively. We identify reproducible probability distributions for the turn angles, run times, and waiting times across specimens, and find good agreement between analytical predictions and quantities empirically measured from the trajectories. Having such a model allows for a better understanding and predictions of movement ecology from both simulations and analytics, and even can give insight into the underlying generative mechanisms of motion and the ants' sensory systems.