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A temporally stratified extension of space‐for‐time Cormack–Jolly–Seber for migratory animals

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  • Dalton J. Hance
  • Russell W. Perry
  • John M. Plumb
  • Adam C. Pope

Abstract

Understanding drivers of temporal variation in demographic parameters is a central goal of mark‐recapture analysis. To estimate the survival of migrating animal populations in migration corridors, 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 integrated over the temporal dimension. Furthermore, modeling the effect of time‐varying covariates on model parameters is complicated by unknown passage times for individuals that are not detected at monitoring sites. To overcome these limitations, we extended the Cormack–Jolly–Seber (CJS) framework to estimate temporally stratified survival and capture probabilities by including a discretized arrival time process in a Bayesian framework. We allow for flexibility in the model form by including temporally stratified covariates and hierarchical structures. In addition, we provide tools for assessing model fit and comparing among alternative structural models for the parameters. We demonstrate our framework by fitting three competing models to estimate daily survival, capture, and arrival probabilities at four hydroelectric dams for over 200 000 individually tagged migratory juvenile salmon released into the Snake River, USA.

Suggested Citation

  • Dalton J. Hance & Russell W. Perry & John M. Plumb & Adam C. Pope, 2020. "A temporally stratified extension of space‐for‐time Cormack–Jolly–Seber for migratory animals," Biometrics, The International Biometric Society, vol. 76(3), pages 900-912, September.
    • Handle: RePEc:bla:biomet:v:76:y:2020:i:3:p:900-912
    DOI: 10.1111/biom.13171
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    References listed on IDEAS

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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. J. D. Lebreton & R. Pradel Cefe, 2002. "Multistate recapture models: Modelling incomplete individual histories," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 353-369.
    3. Simon J. Bonner & Carl J. Schwarz, 2011. "Smoothing Population Size Estimates for Time-Stratified Mark–Recapture Experiments Using Bayesian P-Splines," Biometrics, The International Biometric Society, vol. 67(4), pages 1498-1507, December.
    4. S. J. Bonner & C. J. Schwarz, 2006. "An Extension of the Cormack–Jolly–Seber Model for Continuous Covariates with Application to Microtus pennsylvanicus," Biometrics, The International Biometric Society, vol. 62(1), pages 142-149, March.
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