期刊
BIOMETRICS
卷 65, 期 1, 页码 267-274出版社
WILEY-BLACKWELL
DOI: 10.1111/j.1541-0420.2008.01038.x
关键词
Abundance estimation; Bayesian analysis; Capture-recapture; Cluster size; Data augmentation; Heterogeneity; Individual covariates; Markov chain Monte Carlo; Nonignorable missing data; Population size; WinBUGS
I consider the analysis of capture-recapture models with individual covariates that influence detection probability. Bayesian analysis of the joint likelihood is carried out using a flexible data augmentation scheme that facilitates analysis by Markov chain Monte Carlo methods, and a simple and straightforward implementation in freely available software. This approach is applied to a study of meadow voles (Microtus pennsylvanicus) in which auxiliary data on a continuous covariate (body mass) are recorded, and it is thought that detection probability is related to body mass. In a second example, the model is applied to an aerial waterfowl survey in which a double-observer protocol is used. The fundamental unit of observation is the cluster of individual birds, and the size of the cluster (a discrete covariate) is used as a covariate on detection probability.
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