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

2021/06/22 by Lucy Henley, Owen D. Jones, Henley, Lucy +5 · 1 citation
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Environmental Science · #62C10 #62F15 #92D40 #Animal Vocal Communication and Behavior #Bat Biology and Ecology Studies #Dynamical Systems (math.DS) #FOS: Biological sciences #FOS: Mathematics #Marine animal studies overview #Populations and Evolution (q-bio.PE) #Quantitative Methods (q-bio.QM)

paper · pdf · doi:10.48550/arxiv.2106.11969

openalex publication_date 2021/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Tutorial on maximum likelihood estimation

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