Detecting pattern in biological stressor response relationships using model based cluster analysis
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Environmental monitoring of aquatic systems is needed to estimate the quality of the systems, to evaluate standards and to study stressor–response relationships. Monitoring programs often focus on the collection of biological, chemical and physical measures of the system. An important concern is the effect of chemical and physical stressors on the biological community. Evaluation of relationships may be difficult as the extent of the relationship is not known. From a management perspective, interest is on what factors affect the biological community and where these factors have an influence. The focus of this paper is on the use of regression based cluster analysis as a tool for finding relationships between a single biological response and a suite of environmental stressors. The approach to cluster analysis uses a penalized regression classification likelihood and Markov Chain Model Composition Monte Carlo. This approach allows for simultaneous development of regression models and clustering of the regression models. The method is applied to the analysis of a data set describing stressors/response relationship in Ohio.
KeywordsBayesian methods Cluster analysis Markov Chain MonteCarlo (MCMC) simulation Regression Water quality
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