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We propose a novel approach for modelling bat motion dynamics and use it topredict roost locations using data from static acoustic detectors.Specifically, radio tracking studies of Greater Horseshoe bats demonstrate thatbat movement can be split into two phases: dispersion and return. Dispersion iseasily understood and can be modelled as simple random motion. The return phaseis much more complex, as it requires intelligent directed motion and results inall agents returning home in a stereotypical manner. Critically, combiningreaction-diffusion theory and domain shrinking we deterministically andstochastically model a ``leap-frogging'' motion, which fits favourably with theobserved tracking data.

Coupling Bayesian theory and static acoustic detector data to model bat motion and locate roosts