Autoregressive models for capture–recapture data: A Bayesian approach
In this paper, we incorporate an autoregressive time-series framework into models for animal survival using capture-recapture data. Researchers modelling animal survival probabilities as the realization of a random process have typically considered survival to be independent from one time period to...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
| Published: |
2003
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| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.324.1564 http://www.stat.colostate.edu/~nsu/starmap/jah.art2.pdf |
| Summary: | In this paper, we incorporate an autoregressive time-series framework into models for animal survival using capture-recapture data. Researchers modelling 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 on 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 modelling 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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