A multi-year hierarchical Bayesian mark-recapture model using recurring salmonid behavior to account for sparse or missing data

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Authors Bryce N. Oldemeyer, Timothy Copeland, Brian P. Kennedy
Journal/Conference Name Canadian Journal of Fisheries and Aquatic Sciences
Paper Category
Paper Abstract Mark-recapture studies using data collected at rotary screw traps (RSTs) are used to estimate abundances of migrating juvenile salmonids exiting natal rearing habitats. Frequently, environmental conditions and mechanical failures decrease RST efficiencies, or completely halt operations, leading to sparse and missing data. In this study, we show how a time-stratified hierarchical Bayesian model framework can incorporate prior information to increase the accuracy and precision of estimates made with sparse and missing data. To do this, we incorporated annually recurring salmonid emigration characteristics into the model using multiple years of data. We compared abundance estimates of the hierarchical multiyear model with 3 single-year Bayesian models, using simulated and real RST data. The hierarchical multiyear model was as accurate and precise as the best model when data were complete and abundant, but outperformed other models when data were sparse and missing for multiweek blocks. For species with low abundances or low detection efficiencies, the hierarchical multiyear model used data from all years and recurring emigration characteristics to increase the accuracy and precision of estimates. This model is a valuable tool for fish and wildlife biologists who repeat mark-recapture studies annually and encounter sparse and missing data.
Date of publication 2018
Code Programming Language R
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