Bayesian hierarchical mixture models for otolith microchemistry analysis

The field of fisheries research commonly uses classical statistical classification methods to estimate the proportion of fish that return to natal spawning grounds to spawn. With the advent of otolith microchemical analysis, researchers are able to extract information from fish ear stones (otoliths) about the chemical composition of water in which fish have spent distinct periods of their lives. Here we present a method of analysis set in the Bayesian statistical paradigm which enables explicit incorporation of habitat information into the analysis. The ecological system is seen as arising from a mixture of disparate fish populations and information from the biological relationships inherent in otolith formation is exploited through the hierarchical model structure. We present the model and motivation, demonstrate the validity of the model through simulation studies, and conclude with an analysis of a data set from Lake Erie.