Major goals of environmental data analysis are the assessment of the condition of the environment and the relationship between condition and potential stressors.
Environmental stress occurs at different temporal and spatial scales.
Methods used to relate measures of environmental health and environmental information are typically based on regression or correlation analysis and include methods such as multiple regression analysis, canonical correlation analysis and canonical correspondence analysis. Typical data sets are large in terms of the number of variables used in the modeling process and it may be quite difficult to select amongst different competing models. This research will investigate the use of Bayesian analysis as a set of methods for interpreting the importance of relationships between stressors and environmental variables and for prediction under this model uncertainty. The approaches that will be used is based on Bayesian model averaging and Bayesian hierarchical modeling. The research will use multiple regression analysis to evaluate relationships between biological measures of health and chemical, physical, habitat and land use variables.