2026-08-03 沖縄科学技術大学院大学
© ジャック・フェザストーン
＜関連情報＞
- https://www.oist.jp/ja/news-center/news/2026/8/3/random-physics-helps-model-individual-ant-motion
- https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014069
実験室実験における長脚アリA. gracilipesの運動の確率モデル化 Stochastic modeling of long-legged ant A. gracilipes locomotion in laboratory experiments
Jack Featherstone,Anouk Béraud,Meta Virant-Doberlet,Antonio Celani,Mahesh M. Bandi
PLOS Computational Biology Published: August 6, 2026
DOI:https://doi.org/10.1371/journal.pcbi.1014069
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 ants (A. gracilipes), 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.
