Collective behavior and predation success in a predator-prey model inspired by hunting bats

We establish an agent-based model to study the impact of prey behavior on the hunting success of predators. The predators and prey are modeled as self-propelled particles moving in a three-dimensional domain and subject to specific sensing abilities and behavioral rules inspired by bat hunting. The predators randomly search for prey. The prey either align velocity directions with peers, defined as "interacting" prey, or swarm "independently" of peer presence; both types of prey are subject to additive noise. In a simulation study, we find that interacting prey using low noise have the maximum predation avoidance because they form localized large groups, while they suffer high predation as noise increases due to the formation of broadly dispersed small groups. Independent prey, which are likely to be uniformly distributed in the domain, have higher predation risk under a low noise regime as they traverse larger spatial extents. These effects are enhanced in large prey populations, which exhibit more ordered collective behavior or more uniform spatial distribution as they are interacting or independent, respectively.