Application of rough sets analysis to identify polluted aquatic sites based on a battery of biomarkers: a comparison with classical methods

The evaluation of toxicological effects at the cellular and molecular levels in organisms are often used to determine sites subjected to contamination problems that pose a threat to the long-term survival of organisms. However, the integration of multiple measurements on the health status of organisms into a model for site discrimination is challenging. This study compares two discrimination methods which are based on rule inference: rough sets (RS) analysis and classification trees (CT) with classical multivariate discriminant analysis (DA). Site classification was attempted with six biomarkers of effects: metallothionein levels, lipid peroxidation, DNA damage, levels of lipophosphoproteins (i.e., vitellins), phagocytosis activity and haemocyte cell viability on clam (Mya arenaria) populations from the Saguenay River fjord (Quebec, Canada). Rule based methods have the advantage of complete independence from data distribution constraints in contrast to the classification methods from multivariate analysis that are more commonly used in ecotoxicology. Results show that RS and CT gave better classifications than DA because they do not require strong distributional assumptions. Moreover, RS provided classification rules that could identify the most important biomarker(s) for site discrimination. RS and CT were shown to be simple and efficient methods for classifying multivariable ecotoxicological data. This methodology would be especially useful when freedom from distributional assumptions is required.