Numerical Analyses of Soft Bottom Macroinvertebrates to Diagnose the Pollution in Tropical Coastal Waters

Soft bottom benthic organisms especially polychaetes are known to adapt as r or k selected strategies to different gradients of pollution. This will result in changes of benthic community structure from that of normal structure. There are a number of techniques to assess the impact of pollution on benthic community structure. Hence, to test this hypotheses some of the univariate and multivariate techniques were applied to soft bottom macro-invertebrates data of coastal waters of Mangalore, central west coast of India, a hot-spot area. Univariate techniques such as Pearson Rosenberg Model (PRM), abundance biomass comparison curve (ABC), geometrical class distribution, dominance-diversity curve, benthic community structure indices and multivariate techniques such as cluster classification, multidimensional scaling (MDS) and principal component analysis (PCA) were used to discriminate and diagnose the disturbance among the sites. Effectiveness and applicability of some of the above techniques are highlighted and discussed with the present set of data for environmental impact assessment studies.