Multivariate discriminant analysis distinguishes metal- from non metal-related biomarker responses in the clam Chamaelea gallina.

Molecular biomarkers are among the most sensitive and earliest responses to pollutants. However, lack of detailed knowledge on variability of responses and their possible seasonal variation limit their use. In addition, the seasonality of biological processes modulates the response of organisms to pollutant stressors. Using multivariate statistics, we have studied the influence of environmental and biological factors on the response of a battery of molecular biomarkers in the clam Chamaelea gallina collected along the South-Spanish littoral. Multivariate discriminant analysis clearly distinguished biomarker response between clean and polluted areas, using heavy metals as indicator of pollution. Such differences disappeared when the dataset was normalised for metal content, thus indicating that pollution was the main significant cause of the changes observed between clean and polluted sites. In conclusion, this work shows that, when applying a complete biomarker panel, multivariate statistical tools can be used to discern pollutant- from non pollutant-related responses.