Current address: Department of Fisheries and Wildlife, Oregon State University, Corvallis, Oregon, USA
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Bayesian shared frailty models for regional inference about wildlife survival
Article first published online: 20 OCT 2011
DOI: 10.1111/j.1469-1795.2011.00495.x
© 2011 The Authors. Animal Conservation © 2011 The Zoological Society of London
Additional Information
How to Cite
Halstead, B. J., Wylie, G. D., Coates, P. S., Valcarcel, P., Casazza, M. L. (2012), Bayesian shared frailty models for regional inference about wildlife survival. Animal Conservation, 15: 117–124. doi: 10.1111/j.1469-1795.2011.00495.x
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Current address: Department of Fisheries and Wildlife, Oregon State University, Corvallis, Oregon, USA
Publication History
- Issue published online: 29 MAR 2012
- Article first published online: 20 OCT 2011
- Manuscript Accepted: 26 AUG 2011
- Manuscript Received: 27 JUL 2011
Funded by
- CALFED
- US Army Corps of Engineers
- US Fish and Wildlife Service
Keywords:
- Bayesian analysis;
- giant gartersnake;
- hierarchical model;
- proportional hazards;
- random effects;
- Thamnophis gigas
Abstract
The estimation of survival is an essential but difficult task important for developing rigorous conservation programs. Radio telemetry studies of wildlife survival are often characterized by small sample sizes and high rates of censoring. In cases where multiple radio telemetry studies of a species exist, shared frailty models of survival offer the ability to combine data from multiple studies and improve the precision of survival estimates. We used Bayesian analysis of shared frailty models to examine survival of adult females of the giant gartersnake (Thamnophis gigas) in the Sacramento Valley, California, USA, and to examine the effects of individual and habitat characteristics on daily risk of mortality. Posterior mean annual survival probability of adult females was 0.61 [95% credible interval (CI) = 0.41–0.79]. The daily risk of mortality for adult female giant gartersnakes while in terrestrial habitats was 0.38 (0.09–0.89) times as great as when they inhabited aquatic habitats. Although 95% CIs for hazard ratios of other covariates included one, sites varied substantially in the effect of linear habitats, which appear to have context-dependent effects on survival. Assessing survival with shared frailty models allows the prediction of survival probabilities at novel sites and identifies regional and context-specific mortality risks that can be targeted for conservation action.