EURING Proceedings

Journal of Ornithology

, Volume 152, Supplement 2, pp 521-537

First online:

Parameter-expanded data augmentation for Bayesian analysis of capture–recapture models

  • J. Andrew RoyleAffiliated withUSGS Patuxent Wildlife Research Center Email author 
  • , Robert M. DorazioAffiliated withUSGS Southeast Ecological Science CenterDepartment of Statistics, University of Florida

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Data augmentation (DA) is a flexible tool for analyzing closed and open population models of capture–recapture data, especially models which include sources of hetereogeneity among individuals. The essential concept underlying DA, as we use the term, is based on adding “observations” to create a dataset composed of a known number of individuals. This new (augmented) dataset, which includes the unknown number of individuals N in the population, is then analyzed using a new model that includes a reformulation of the parameter N in the conventional model of the observed (unaugmented) data. In the context of capture–recapture models, we add a set of “all zero” encounter histories which are not, in practice, observable. The model of the augmented dataset is a zero-inflated version of either a binomial or a multinomial base model. Thus, our use of DA provides a general approach for analyzing both closed and open population models of all types. In doing so, this approach provides a unified framework for the analysis of a huge range of models that are treated as unrelated “black boxes” and named procedures in the classical literature. As a practical matter, analysis of the augmented dataset by MCMC is greatly simplified compared to other methods that require specialized algorithms. For example, complex capture–recapture models of an augmented dataset can be fitted with popular MCMC software packages (WinBUGS or JAGS) by providing a concise statement of the model’s assumptions that usually involves only a few lines of pseudocode. In this paper, we review the basic technical concepts of data augmentation, and we provide examples of analyses of closed-population models (M 0, M h , distance sampling, and spatial capture–recapture models) and open-population models (Jolly–Seber) with individual effects.


Hierarchical models Individual covariates Individual heterogeneity Markov chain Monte Carlo Occupancy models