Predicting the species richness of aquatic insects in streams using a limited number of environmental variables

Abstract

Artificial neural networks were used to predict the species richness of 4 major orders of aquatic insects (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e., EPTC) at a site, using a limited number of environmental variables. EPTC richness was recorded in the Adour–Garonne stream system (France), at 155 unstressed sampling sites, which were characterized using 4 environmental variables: elevation, stream order, distance from the source, and maximum water temperature. The EPTC and environmental data were first computed with the Self-Organizing Map (SOM) algorithm. Then, using the k-means algorithm, clusters were detected on the map and the sampling sites were classified separately for each variable and for EPTC richness. Four clusters could be identified on the SOM map, according to the 4 environmental variables, and this classification was chiefly related to stream order and elevation (i.e., the longitudinal location of sampling sites within a stream system). Similarly, 4 subsets were derived from the SOM according to a gradient of EPTC richness. There was also a high coincidence between observed (field data) and calculated (predicted from the output neurons of the SOM) species richness in each taxonomic group. Species richness relationships between Ephemeroptera, Trichoptera, and Coleoptera for both observed and predicted data were highly significant. However, correlation coefficients among species richness of Plecoptera and the other groups were low. Last, a multilayer perceptron neural network, trained using the backpropagation algorithm, was used to predict EPTC richness (output) using the 4 above-mentioned environmental variables (input). The model showed high predictability (r = 0.91 and r = 0.61 for training and test data sets, respectively), and a sensitivity analysis revealed that elevation and stream order contributed the most among the 4 input variables. Prediction of species richness using a limited number of environmental variables is, thus, a valuable tool for the assessment of disturbance in a given area. The degree to which human activities have altered EPTC richness can be determined by knowing what the EPTC richness should be under undisturbed conditions in a given area.