  We propose a novel approach for modelling bat motion dynamics and use it to
predict roost locations using data from static acoustic detectors.
Specifically, radio tracking studies of Greater Horseshoe bats demonstrate that
bat movement can be split into two phases: dispersion and return. Dispersion is
easily understood and can be modelled as simple random motion. The return phase
is much more complex, as it requires intelligent directed motion and results in
all agents returning home in a stereotypical manner. Critically, combining
reaction-diffusion theory and domain shrinking we deterministically and
stochastically model a ``leap-frogging'' motion, which fits favourably with the
observed tracking data.
