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The authors present findings from a Bayesian analysis of Scotland's four primary capture-recapture data sources for 2000 that was carried out to estimate numbers of current injecting drug users by region (Greater Glasgow vs. elsewhere in Scotland), sex (male vs. female), and age group (15-34 years vs. > or =35 years). A secondary goal of the analysis was to obtain Bayesian estimates and credible intervals for the demographic influences on Scotland's drug-related death rate per 100 current injectors. Incorporation of informative priors altered the models with highest posterior probability. Expert opinion on how demography influenced Scottish drug injectors' propensity to be listed in different data sources was taken into account, along with external information about European injectors' drug-related death rates and male:female ratios. Higher drug-related mortality was confirmed in older drug injectors and those outside of Greater Glasgow. Female injectors' lower drug-related death rate was not sustained beyond 34 years of age. The authors recommend that demographic influences be accommodated in behavioral capture-recapture estimation, especially when it is a prelude to secondary analysis, such as the analysis of drug-related death rates presented here.

We develop a Bayesian capture-recapture model that provides estimates of abundance as well as time-varying and heterogeneous survival and capture probability distributions. The model uses a state-space approach by incorporating an underlying population model and an observation model, and here is applied to photo-identification data to estimate trends in the abundance and survival of a population of bottlenose dolphins (Tursiops truncatus) in northeast Scotland. Novel features of the model include simultaneous estimation of time-varying survival and capture probability distributions, estimation of heterogeneity effects for survival and capture, use of separate data to inflate the number of identified animals to the total abundance, and integration of separate observations of the same animals from right and left side photographs. A Bayesian approach using Markov chain Monte Carlo methods allows for uncertainty in measurement and parameters, and simulations confirm the model's validity.

Phylogeographic methods have attracted a lot of attention in recent years, stressing the need to provide a solid statistical framework for many existing methodologies so as to draw statistically reliable inferences. Here, we take a flexible fully Bayesian approach by reducing the problem to a clustering framework, whereby the population distribution can be explained by a set of migrations, forming geographically stable population clusters. These clusters are such that they are consistent with a fixed number of migrations on the corresponding (unknown) subdivided coalescent tree. Our methods rely upon a clustered population distribution, and allow for inclusion of various covariates (such as phenotype or climate information) at little additional computational cost. We illustrate our methods with an example from weevil mitochondrial DNA sequences from the Iberian peninsula.