Hierarchical Bayes for Capture-recapture Data

• Activity Number: 179
• Type: Topic Contributed
• Date/Time: Tuesday, August 10, 2004 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): James S. Clark*+
• Companies: Duke University
• Keywords: MCMC ; demography ; capture-recapture ; stage structure

Abstract

Understanding population dynamics requires inference that admits the complexity of natural populations and the data ecologists obtain from them. Populations possess structure, which may be defined as "fixed" stages through which individuals pass, and superimposed variability among individuals and groups. Data contain missing values and inaccurate censuses. I extend the "missing value" framework for Bayesian analysis of structured populations to admit the heterogeneity in demography and in data that is typical of ecological populations. This hierarchical treatment of capture-recapture data allows inference on demographic rates and variability, together with simultaneous inference on population structure. Predictive distributions demonstrate profound impacts of population and data complexity on inference pertaining to life history schedules and growth. I demonstrate the method in simulation and apply it to field datasets from natural populations.