We present dynamic spatial modeling and computational methods for the analysis of collections of birds/animals moving in a spatially inhomogeneous force field under the influence of environmental covariates. Interest lies in identifying the role of different covariates in guiding the motion, both in terms of the shape of their implied field, as well as their overall presence or absence of influence. Models are based on discrete-time, dynamic state-space models for locations and directional velocities of each of a set of animals, combined with a latent force-field over the temporal domain that drives changes in velocities. We extend models for the force fields using dynamic Bayesian radial basis function regression to define a potential surface varying in space but also in the space of covariates, with the force field given by the gradient of the potential in 3-D. Corresponding variable selection priors allow us to detect which covariates play a role in shaping the motion, and provide a basis for understanding their precise functional form. We exemplify the work with analysis of GPS tracking data from a set of toucans in central America, focusing on characterizing the birds' response and contribution to different temperature levels

host: Dr Janine Illian

refID: 1132

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host: Dr Janine Illian

refID: 1126

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