In this paper, we establish an agent-based model to study the impact of collective behavior of a prey species on the hunting success of predators inspired by insectivorous bats and swarming insects, called “bugs”. In the model, we consider bats preying on bugs in a three-dimensional space with periodic boundaries. The bugs follow one of the two regimes: either they swarm randomly without interacting with peers, or they seek to align their velocity directions, which results in collective behavior. Simultaneously, the bats sense their environment with a sensing space inspired by big brown bats (Eptesicus fuscus) and independently prey on bugs. We define order parameters to measure the alignment and cohesion of the bugs and relate these quantities to the cohesion and the hunting success of the bats. Comparing the results when the bugs swarm randomly or collectively, we find that collectively behaving bugs tend to align, which results in relatively more cohesive groups. In addition, cohesion among bats is induced since bats may be attracted to the same localized bug group. Due to the fact that bats need to hunt more widely for groups of bugs, collectively behaving bugs suffer less predation compared to their randomly swarming counterparts. These findings are supported by the biological literature which cites protection from predation as a primary motivator for social behavior.
- Dynamic Systems and Control Division
Bats Versus Bugs: Collective Behavior of Prey Decreases Predation in a Biologically-Inspired Multi-Agent System
Lin, Y, & Abaid, N. "Bats Versus Bugs: Collective Behavior of Prey Decreases Predation in a Biologically-Inspired Multi-Agent System." Proceedings of the ASME 2013 Dynamic Systems and Control Conference. Volume 1: Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications; Bio-Medical and Bio-Mechanical Systems; Biomedical Robots and Rehab; Bipeds and Locomotion; Control Design Methods for Adv. Powertrain Systems and Components; Control of Adv. Combustion Engines, Building Energy Systems, Mechanical Systems; Control, Monitoring, and Energy Harvesting of Vibratory Systems. Palo Alto, California, USA. October 21–23, 2013. V001T07A002. ASME. https://doi.org/10.1115/DSCC2013-3816
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