Abstract
Principal component analysis and cluster analysis, applied to experimental data obtained by colonization of artificial substrates revealed clusters of ecological stations which, in the plane of the first 2 principal components, are localized successively on a non-linear structure correlated to a water quality gradient. Non linearity of interrelationships between species abundances produces distortions in the principal components, which prevent the complete description of a theoretical model, but the variability between samples is so large, that a technique using correlations between species is the only 1 which can mask the random noise. Principal component analysis is in fact a good tool for the classification of ecological stations in a reduced space. Biological and physico-chemical profiles of the ecological stations and their sequence on this non-linear structure, permit rediscovery of the water quality gradient and its effects on the biocenose. The results are compared with 2 classical biological indices.