A Bayesian approach to combining animal abundance and demographic data

• Main Authors: Brooks, S. P., King, R., Morgan, B. J. T.
• Format: Article
• Language: English
• Published: Museu de Ciències Naturals de Barcelona 2004-06-01
• Series: Animal Biodiversity and Conservation
• Subjects: one frequently encounters both count and mark-recapture-recovery data. Here; we consider an integrated Bayesian analysis of ring¿recovery and count data using a state-space model. We then impose a Leslie-matrix-based model on the true population counts describing the natural birth-death and age transition processes. We focus upon the analysis of both count and recovery data collected on British lapwings (Vanellus vanellus) combined with records of the number of frost days each winter. We demonstrate how the combined analysis of these data provides a more robust inferential framework and discuss how the Bayesian approach using MCMC allows us to remove the potentially restrictive normality assumptions commonly assumed for analyses of this sort. It is shown how WinBUGS may be used to perform the Bayesian analysis. WinBUGS code is provided and its performance is critically discussed.; Census data; Integrated analysis; Kalman filter; Logistic regression; Ring-recovery data; State-space model; WinBUGS
• Online Access: http://abc.museucienciesjournals.cat/files/ABC-27-1-pp-515-529.pdf

In studies of wild animals, one frequently encounters both count and mark-recapture-recovery data. Here, we consider an integrated Bayesian analysis of ring¿recovery and count data using a state-space model. We then impose a Leslie-matrix-based model on the true population counts describing the natural birth-death and age transition processes. We focus upon the analysis of both count and recovery data collected on British lapwings (Vanellus vanellus) combined with records of the number of frost days each winter. We demonstrate how the combined analysis of these data provides a more robust inferential framework and discuss how the Bayesian approach using MCMC allows us to remove the potentially restrictive normality assumptions commonly assumed for analyses of this sort. It is shown how WinBUGS may be used to perform the Bayesian analysis. WinBUGS code is provided and its performance is critically discussed.