Abstract
Benthic macroinvertebrate communities serve as an efficient indicator group for assessing biological water quality. Communities, however, are difficult to analyze since the data consist of diverse taxa in a non-linear fashion. We implemented the self-organizing map (SOM) to classification of benthic macroinvertebrate communities collected across different levels of disturbances in streams in a large-scale. The trained SOM was feasible in providing a comprehensive view on community patterns, and the clustering by the SOM showed the gradient of pollution accordingly. New data sets sampled regularly for monitoring were further tested for tracing temporal changes in community states based on the trained SOM. Physico-chemical and biological indices were correspondingly evaluated according to the trained SOM, and biological water quality indices were differentiated in the clustered communities.
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