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Capture-recapture abundance estimation using a semi-complete data likelihood approach

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Abstract

Capture-recapture data are often collected when abundance estimation is of interest. In the presence of unobserved individual heterogeneity, specified on a continuous scale for the capture probabilities, the likelihood is not generally available in closed form, but expressible only as an analytically intractable integral. Model-fitting algorithms to estimate abundance most notably include a numerical approximation for the likelihood or use of a Bayesian data augmentation technique considering the complete data likelihood. We consider a Bayesian hybrid approach, defining a "semi-complete" data likelihood, composed of the product of a complete data likelihood component for individuals seen at least once within the study and a marginal data likelihood component for the individuals not seen within the study, approximated using numerical integration. This approach combines the advantages of the two different approaches, with the semi-complete likelihood component specified as a single integral (over the dimension of the individual heterogeneity component). In addition, the models can be fitted within BUGS/JAGS (commonly used for the Bayesian complete data likelihood approach) but with significantly improved computational efficiency compared to the commonly used super-population data augmentation approaches (between about 10 and 77 times more efficient in the two examples we consider). The semi-complete likelihood approach is flexible and applicable to a range of models, including spatially explicit capture-recapture models. The model-fitting approach is applied to two different datasets corresponding to the closed population model Mh for snowshoe hare data and a spatially explicit capture-recapture model applied to gibbon data.
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Original languageEnglish
Pages (from-to)264-285
JournalAnnals of Applied Statistics
Volume10
Issue number1
DOIs
Publication statusPublished - Mar 2016

    Research areas

  • BUGS, Capture-recapture, Closed populations, Individual heterogeneity, JAGS, Spatially explicit

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