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Abstract:
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To estimate survival of migrating populations through migratory corridors (e.g. anadromous fish in rivers), “space-for-time" mark-recapture models employ discrete sampling locations in space to monitor marked populations as they move past monitoring sites rather than the standard practice of using fixed sampling points in time. Because these models focus on estimating survival over discrete spatial segments, model parameters are implicitly averaged over the temporal dimension and the effect of time-varying covariates on model parameters is complicated by unknown passage times for individuals that are not detected at monitoring sites. In response, we developed a set of models to estimate temporally stratified survival, capture, and state-transition probabilities by including a discretized arrival time process. Our models provide flexibility in model specification including temporally stratified covariates, random effects, and can account for finite tag life due to battery failure. We demonstrate two applications of our framework for federally-listed populations of Chinook salmon (Oncorhynchus tshawytscha) in the Columbia and Sacramento rivers fit in a Bayesian framework using Stan.
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