Prior choice and data requirements of Bayesian multivariate hierarchical models fit to tag-recovery data: The need for power analyses.

ABSTRACT: Recent empirical studies have quantified correlation between survival and recovery by estimating these parameters as correlated random effects with hierarchical Bayesian multivariate models fit to tag-recovery data. In these applications, increasingly negative correlation between survival and recovery has been interpreted as evidence for increasingly additive harvest mortality. The power of these hierarchal models to detect nonzero correlations has rarely been evaluated, and these few studies have not focused on tag-recovery data, which is a common data type. We assessed the power of multivariate hierarchical models to detect negative correlation between annual survival and recovery. Using three priors for multivariate normal distributions, we fit hierarchical effects models to a mallard (

SUBMITTER: Deane CE 

PROVIDER: S-EPMC10041078 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature