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Activity Number:
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125
- A New Era of Mark-Recapture: Advances in Bayesian Methods for Modeling and Inference
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Type:
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Topic-Contributed
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Date/Time:
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Monday, August 9, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #317588
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Title:
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Incorporating Dead Recoveries into a Temporally-Stratified Mark-Recapture Model for Migratory Animals
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Author(s):
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James R Faulkner*
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Companies:
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National Oceanic and Atmospheric Administration
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Keywords:
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Bayesian;
ecology;
Hamiltonian Monte Carlo;
recapture-recovery;
spatiotemporal;
survival
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Abstract:
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Estimating survival probabilities and movement behavior of individually-marked animals traveling along a migration route with imperfect, spatiotemporally-varying detection requires complex and highly parameterized mark-recapture models. Implementation of such models has been made possible by recent advances using Bayesian methods. The tags of some animals that die in route are recovered with some probability and information from those tags can be used to improve estimation of other model parameters of interest. Methods that utilize recovered tags have been used in mark-recapture models for years, but have not been incorporated into temporally-stratified models of this kind. We develop models that allow tags to be recovered in locations and times different from the location and time of death. We use simulation to test the models and find that the additional information provided by recovered tags decreases bias and increases precision of parameters related to survival. We also apply the models to a population of juvenile salmon that are subject to avian predation during their seaward migration.
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Authors who are presenting talks have a * after their name.
