| Summary: | The section of the lowland Narew River within Polish borders (432km long) flowing between two big reservoirs, and its tributaries were selected for the study. At 321 sites a total of 49,675 fish and lamprey specimens, representing 36 taxa, were collected. The sites were classified using the Kohonen self-organizing map (SOM) on the basis of fish and lamprey relative biomass data. The trained SOM (lattice 6×4) showed three main clusters of samples assigned to the neurons: (1) A1–B4, (2) C1–D4, and (3) E1–F4, differing not only in fish fauna composition but also in some environmental variables not presented to the SOM. Generally, sites from small, regulated streams with few trees along banks were dominant in cluster AB, while sites from natural larger rivers with many trees along banks were assigned to cluster EF. Cluster CD contained sites of intermediate character. In AB we distinguished an assemblage with five species present in each neuron (gudgeon, loach, stickleback, ten-spined stickleback and pike), and in EF an assemblage with seven ones (stickleback, ide, perch, roach, pike, burbot and bleak), but 100% occurrence stability in each neuron was recorded only for roach in EF. A significantly lowest species richness and values of the Shannon index of biodiversity were recorded in AB, that is for the smallest streams. Additionally, many environmental, population and assemblage variables also showed more subtle gradients within each cluster. The clear differences between clusters and gradients within them, recorded even for variables indirectly analysed with SOM, prove that the obtained classification was very effective, which additionally testifies to the reliability of the distinguished fish assemblages. SOM provides more detailed information on the mutual relations between species through component planes than the detrended correspondence analysis through points in a multivariate space, thus being much useful for coenological studies. Moreover, such rich data as in this study diminish the legibility of ...
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