Chemometric discrimination between streams based on chemical, limnological and biological data taken from freshwater fishes and their interrelationships

The VALIMAR project aims at identifying biomarkers in fish that are suitable to detect and predict environmental stress from chemical pollution or from limnological parameters in the field. For two small streams in Southern Germany, concentration values of 31 contaminants in water and sediment and 12 limnological parameters as well as 27 biomarkers measured in brown trout and stone loach were collected. All these physicochemical and biological parameters have been analysed for patterns that discriminate between the streams, using discriminant analysis (DA), analysis of variance (ANOVA) and of covariance (ANCOVA), and principal component analysis (PCA) as multivariate statistical techniques. Moreover, the biological data were analyzed with respect to species-specific patterns, and the partial least-squares regression method (PLS) was used to study the impact of chemical and limnological data on the health status of the target species as characterized by the biomarker data. Abiotic as well as biotic data yielded good separations between the streams, with the ultrastructure of gill (US-gill) being the strongest discriminator variable among all 27 biomarkers tested. With regard to the two fish species, the biomarker data from brown trout show significantly greater differences between the two streams than the biological responses in stone loach. Application of PLS yields significant regression models for only few biomarkers including US-Gill, which can be partly traced back to significant noise levels in the data set as quantified by permutation tests.