My expertise is in the development and application of statistical methodology for the modelling of real data, often specializing in challenging problems such as those encountered in ecology and fisheries research. I have published extensively under both the frequentist and Bayesian paradigms, the choice of paradigm being chosen by the pragmatic need to get the job done rather than any philosophical idealism.
My earlier research included estimating the mixing proportions of salmon in mixed stock fisheries, determining growth curves, modeling the size-selectivity of fishing gear, and estimating population abundance. I have also published extensively on the effect of marine reserves, including the use of generalized linear mixed models to estimate the abundance of snapper in and around the Leigh Marine Reserve. More recently, I pioneered the use of Bayesian methodology to implement nonlinear state-space models for non-Gaussian data.
More recently, I have published on local robustness of Bayesian inference, and this in turn has lead to exciting new research in model comparison using information criterion. These are excellent areas of PhD research for well prepared students with a passion for making sense of real data, and they provide skills that will always be in demand by academia, industry, conservation, business and government agencies throughout the world.