Abstract:
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Occupancy models are commonly used in ecology to model the presence/absence of species while accounting for imperfect detection. In this talk, we propose a multivariate spatio-temporal occupancy model to jointly model multiple species of interest while accounting for spatial, temporal, and cross-species dependencies. Data augmentation along with the specified form of the model permit all Gibbs updates in the Markov chain Monte Carlo algorithm, making the model computationally efficient and scalable with both the number of species and size of spatial lattice. We illustrate our model with a three species camera trap study on Thomson's gazelle, wildebeest, and zebra in the Serengeti National Park of Tanzania, Africa.
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