An agent-based algorithm resembles behaviour of tree-dwelling bats under fission-fusion dynamics

Area of Science:

• Animal behavior
• Computational modeling
• Ecology

Background:

• Tree-dwelling bats exhibit complex fission-fusion social dynamics.
• Roost switching involves swarming behavior for recruitment.
• Understanding these dynamics is crucial for bats in changing environments.

Purpose of the Study:

• To model and understand the roost switching behavior of tree-dwelling bats.
• To apply a computational swarm algorithm to simulate bat social dynamics.
• To validate the model against field data of Leisler's bats (Nyctalus leisleri).

Main Methods:

• Development of the SkyBat agent-based computational model.
• Simulation of bat group fission-fusion dynamics using swarm algorithms.
• Comparison of simulated spatiotemporal swarming patterns with field data.

Main Results:

• The SkyBat model successfully replicated natural fission-fusion dynamics.
• Simulated swarming activity patterns closely resembled those of bats.
• Key metrics like group formation, size, and roost height showed no significant difference from field data.

Conclusions:

• Swarm algorithms provide a foundational framework for understanding bat roost switching.
• The SkyBat model offers a valuable tool for studying bat behavior in dynamic environments.
• This research highlights interdisciplinary applications in animal behavior and computer science.