Autoregressive Models for Capture‐Recapture Data: A Bayesian Approach
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 p...
| Published in: | Biometrics |
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| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Oxford University Press (OUP)
2003
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| Subjects: | |
| 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: | 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. |
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