Bayesian state-space models reveal unobserved off-shore nocturnal migration from Motus data

Abstract

Recent advances in wildlife tracking technology, including the Motus system, have allowed researchers to continuously track small organisms with lightweight radio transmitters over large spatial and temporal ranges. The quantity and format of data generated by the Motus system is unprecedented and requires novel statistical methods. Building from the bsam package in R, we propose new biologically informed Bayesian state-space models for animal movement in JAGS that include informed assumptions about behavior. To apply the models, we employed a localization routine on a Motus data set from migrating Red-eyed Vireos (Vireo olivaceus). This allowed us to apply the new models to estimate unobserved locations and behaviors. Directed migratory flights were detected at night and often over water (e.g. the Bay of Fundy, the Long Island Sound and the New York Bight). Migratory flights were not exclusively nocturnal.