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Bayesian analysis of mark-recapture data with travel time-dependent survival probabilities. (With discussion). (English) Zbl 1143.62356

Summary: The authors extend the classical Cormack-Jolly-Seber mark-recapture model to account for both temporal and spatial movements through a series of markers (e.g., dams). Survival rates are modeled as a function of (possibly) unobserved travel times. Because of the complex nature of the likelihood, they use a Bayesian approach based on the complete data likelihood, and integrate the posterior through Markov chain Monte Carlo methods. They test the model through simulations and apply it also to actual salmon data arising from the Columbia river system. The methodology was developed for use by the Pacific Ocean Shelf Tracking (POST) project.

MSC:

62P12 Applications of statistics to environmental and related topics
62F15 Bayesian inference
65C40 Numerical analysis or methods applied to Markov chains
62P10 Applications of statistics to biology and medical sciences; meta analysis

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References:

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