期刊
JOURNAL OF WILDLIFE MANAGEMENT
卷 65, 期 4, 页码 726-737出版社
WILEY
DOI: 10.2307/3803023
关键词
band-recovery models; capture-recapture; Markov chain Monte Carlo; random effects; spatial modeling; spatial statistics; waterfowl banding; waterfowl harvest rate
Historically, the statistical complexity of modeling spatial relationships in band-recovery data has limited the use of spatial models in the management of waterfowl populations. Consequently, managers have assumed simplified spatial relationships (e.g., by stratification and pooling data over large geographic areas) to obtain spatially explicit estimates of vital rates. As an alternative. we used a binomial random effects approach it) modeling spatial variation in band-recovery, data. The model accommodates spatial correlation and heterogeneity in recovery rates and facilitates spatially explicit estimation of recovery, rates with sparse data and at arbitrary levels of spatial resolution. Although the model is structurally simple, estimation using conventional likelihood techniques is complex. Instead, we rely on a technique known as Markov chain Monte Carlo (MCMC) simulation. We used this model to construct a map of mallard (Anas platyrhynchos) recovery rates on a relatively fine-grained grid and for estimation of recovery rates within predefined geographic strata. The results show a strong gradient in recovery rate. with lower values in the western United States and higher values in the eastern United States. The spatial correlation in the model allows useful stratum-level estimates to be produced for strata with small sample sizes.
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