Multivariate statistical approach to the temporal and spatial patterns of selected bioindicators observed in the North Sea during the years 1995-1997

A comprehensive database, containing biological and chemical information, collected in the framework of the bilateral interdisciplinary MARS project ("biological indicators of natural and man-made changes in marine and coastal waters") during the years 1995-1997 in the coastal environment of the North Sea, was subjected to a multivariate statistical evaluation. The MARS project was designated to combine a variety of approaches and to develop a set of methods for the employment of biological indicators in pollution monitoring and environmental quality assessment. In total, nine ship cruises to four coastal sampling sites were conducted; 765 fish and 384 mussel samples were analysed for biological and chemical parameters. Additional information on the chemical background at the sampling sites was derived from sediment samples, collected at each of the four sampling sites. Based on the available chemical data in sediments and black mussel (Mytilus edulis) a pollution gradient between the selected sites, was established. The chemical body burden of flounder (Platichthys flesus) from these sites, though, did not reflect this gradient equally clear. In contrast, the biological information derived from measurements in fish samples displayed significant a regional as well as a temporal pattern. A multivariate bioindicator data matrix was evaluated employing a factor analysis model to identify relations between selected biological indicators, and to improve the understanding of a regional and temporal component in the parameter response. In a second approach, applying the k-means algorithm on the data matrix, two significantly different clusters of samples, characterised by the current health status of the fish, were extracted. Using this classification a temporal, and in the second order, a less pronounced spatial effect was evident. In particular, during July 1996, a clear sign of deteriorating environmental conditions was extracted from the biological data matrix.