Predicting local fish species richness in the Garonne River basin

The aim of this work was to predict local fish species richness in the Garonne river basin using three environmental variables (distance from the source, elevation and catchment area). Commonly, patterns of fish species richness have been investigated using simple or multi-linear statistical models. Here, we used backpropagation of artificial neural networks (ANNs) to develop stochastic models of local fish diversity. Two independent data collections were used, the first one to build and test the model; the second one to validate the model. Correlation coefficients between observed values and predicted values both in the testing and the validation procedures were highly significant (r = 0.904, P < 0.001 and r = 0.822, P < 0.001, respectively). The ANN model obtained using only three environmental variables succeeded in explaining ca 70 % of the total variation in local fish species richness. Through these findings, ANNs can be seen as a powerful predictive tool compared to traditional modelling approaches. ((C) Academie des sciences/Elsevier, Paris.)