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Abstract:
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We propose a Bayesian hierarchical multi-population multistate Jolly-Seber model that incorporates covariates. The model is proposed using a state-space framework and has several distinct advantages. First, multiple populations within the same study area can be modeled simultaneously. Consequently, it is possible to improve parameter estimation by "borrowing strength" across different populations. In many cases, such as our motivating example involving endangered species, this borrowing of strength is crucial, as there is relatively less information for one of the populations under consideration. Second, in addition to accommodating covariate information, we develop a computationally efficient Markov chain Monte Carlo algorithm that requires no tuning. Importantly, the model we propose allows us to draw inference on each population as well as on multiple populations simultaneously. Finally, we demonstrate our method through a motivating example of estimating the spatial distribution and migration of hatchery and wild populations of the endangered pallid sturgeon (Scaphirhynchus albus), using data from the Pallid Sturgeon Population Assessment Program on the Lower Missouri River.
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