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Abstract:
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Monitoring fish and wildlife population abundances and estimating parameters which influence abundance, such as survival probabilities or fecundity rates, are ubiquitous objectives for ecological scientists and resource managers. Bayesian hierarchical models, particularly state space models, are a framework for combining mechanistic population dynamics models with animal abundance time series data as well as accounting for environmental, demographic, and sampling variation. Markov chain Monte Carlo and particle filters are two approaches to fitting such models. Results of a comparative study of the two fitting procedures as applied to grey seal and Chinook salmon data are presented.
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