WinBUGS for Population Ecologists: Bayesian Modeling Using Markov Chain Monte Carlo Methods

Abstract

The statistical analysis of mark-recapture-recovery (MRR) data dates back to the 1960s, when the foundation was laid for stochastic models, fitted to data by the method of maximum likelihood. There have been a number of developments which have proved to be extremely influential. Two of these are: the extension of MRR data and modelling to multi-site inference, and the integrated modelling of single-site MRR and census data. The aim of this study is to unite these two independent research programs, in order to enable effective integrated analysis of multi-site MRR data and multi-site census data. Census data can be described by a state-space model, and the likelihood is formed using the Kalman filter. By making use of movement information provided by MRR data, it is possible to avoid flat likelihood surfaces, thus allowing estimation of site-dependent parameters. This increases the precision of dispersal parameters and allows estimation of parameters inestimable from MRR studies alone. This paper extends research within the area of integrated population analysis by developing methods for analysing multi-site census data coupled with multi-site capture recapture data. The methodology is explored using a simulated data set, the structure of which is motivated by a dataset of Great cormorants (Phalacrocorax carbo sinensis).