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

TL;DR

This paper introduces a semi-complete data likelihood approach for capture-recapture abundance estimation that improves computational efficiency and flexibility, especially in models with unobserved individual heterogeneity.

Contribution

It proposes a novel semi-complete likelihood method combining advantages of existing approaches, enhancing efficiency and applicability in Bayesian capture-recapture models.

Findings

Significantly faster computation (10-77 times) compared to traditional methods.

Applicable to spatially explicit capture-recapture models.

Successfully applied to snowshoe hare and gibbon datasets.

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 $M_h$ for snowshoe hare data and a spatially explicit capture-recapture model applied to gibbon data.