Autoregressive Models for Capture‐Recapture Data: A Bayesian Approach

• Published in: Biometrics
• Main Authors: Johnson, Devin S., Hoeting, Jennifer A.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Oxford University Press (OUP) 2003
• Subjects: Applied Mathematics; General Agricultural and Biological Sciences; General Immunology and Microbiology; General Biochemistry, Genetics and Molecular Biology; General Medicine; Statistics and Probability; Anas acuta
• Online Access: http://dx.doi.org/10.1111/1541-0420.00041; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F1541-0420.00041; https://onlinelibrary.wiley.com/doi/pdf/10.1111/1541-0420.00041

Summary In this article, we incorporate an autoregressive time‐series framework into models for animal survival using capture‐recapture data. Researchers modeling animal survival probabilities as the realization of a random process have typically considered survival to be independent from one time period to the next. This may not be realistic for some populations. Using a Gibbs sampling approach, we can estimate covariate coefficients and autoregressive parameters for survival models. The procedure is illustrated with a waterfowl band recovery dataset for northern pintails ( Anas acuta ). The analysis shows that the second lag autoregressive coefficient is significantly less than 0, suggesting that there is a triennial relationship between survival probabilities and emphasizing that modeling survival rates as independent random variables may be unrealistic in some cases. Software to implement the methodology is available at no charge on the Internet.