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Bayesian modelling and software development for the POST project

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Thesis type
(Project) M.Sc.
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The Pacific Ocean Shelf Tracking (POST) project is part of the Census of Marine Life Study. In this project, acoustic transmitters are surgically implanted into salmon and the salmon are tracked during their migration over a series of listing lines placed along the ocean floor. Estimating between locations survival probabilities of animals is a key component in mark-recapture studies. Detection probabilities at listening lines are nuisance parameters. They are also need to be estimated. We develop a Bayesian model for estimating detection probabilities and survival probabilities that is well suited for the POST project. Previous mark-recapture models do not make any adjustments in survival probabilities between listening lines for travel times of fish whereas our model treats survival probabilities as a function of travel times. The model is implemented via Markov chain Monto Carlo using WinBUGS. Simulation results indicate that the model is well behaved in estimating parameters.
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