Estimating Demographic Parameters from Complex Data Sets: A Comparison of Bayesian Hierarchical and Maximum-Likelihood Methods for Estimating Survival Probabilities of Tawny Owls, Strix aluco in Finland

Abstract

We compared a method of moments approach using estimates from a maximum likelihood framework, ultrastructural models within a maximum likelihood framework, and hierarchical models estimated using Markov chain Monte Carlo within a Bayesian framework for estimating survival and recapture probabilities and their variance components for a large, complex 20 year data set consisting of both live recaptures and recoveries. Estimates of mean age-specific survival and recapture probabilities for four age classes (young, second-year, third-year and adult) were similar with all approaches, but the maximum likelihood approach with year-specific parameters estimated some recovery and recapture probabilities on boundaries, leading to overestimates of some individual adult survival probabilities and hence overestimates of adult variance components. All approaches estimated similar coefficients for the relationships between winter temperature and survival probabilities, but the maximum likelihood approaches appeared to exaggerate variation in relation to prey abundance. Annual estimates from the Bayesian hierarchical models were sensitive to the choice of the hierarchical structure; modelling the difference between second-year, third-year and adults in survival and recapture probabilities as random effects better estimated the patterns of annual variation than treating all age classes as independent. Our comparisons suggest that Bayesian hierarchical models may be more likely to produce reliable estimates than maximum likelihood methods, even for large data sets, especially if there are many parameters and considerable annual variation in sample sizes.