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

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.Author summary: Being able to identify a simple model of motion for a potentially very complex organism is a tantalizing goal, and has lead to the growth of the field of movement ecology over the past few decades. Stochastic modeling has been widely applied to animal motion as a tool to this end, though many applications end up with an incomplete view of such a process by only considering things like step-size distributions in isolation. Here, we apply stochastic modeling to experimental ant (A. gracilipes) motion data to not only identify probability distributions of relevant quantities, but also to make comparisons to analytically-derived results. This gives a qualitative and quantitative understanding of the different phases that comprise motion, and offers a description of the individual behavior of ants. Further, the stochastic model can be leveraged to make predictions about the ants’ behavior, as well as to simulate individual ant trajectories. This ability to simulate motion is particularly useful for exploring behavior in new geometries and identifying whether features of the motion are intrinsic to the ant or a result of the external environment.