Use of unsupervised neural networks for ecoregional zoning of hydrosystems through diatom communities: case study of Adour-Garonne watershed (France)

Knowing that diatoms are good indicators of stream ecological conditions, the aim of our research program was to test on a pilot data-set the interest and efficiency of using a Self-Organizing Map (SOM) as an ordination technique to determine and to classify types of river ecosystems. Such neural networks have already been successfully used for other aquatic communities patterning. Diatoms, waterchemistry and stream morpho-dynamical parameters were characterised for 49 non impacted sampling stations spread over the Adour-Garonne watershed (South-Western France). Combining the SOM to the Structuring Index we selected in a second step the most relevant species (called "structuring species") influencing this typology. In this way, three main homogeneous regions were characterised, as regards to diatom communities and abiotic parameters, allowing us to meet the Water Framework Directive requirements concerning stream ecoregional classification.