The influence of environmental variables on the abundance of aquatic insects: a comparison of ordination and artificial neural networks

Two methods to predict the abundance of the mayflies Baetis rhodani and Baetis vernus (Insecta, Ephemeroptera) in the Breitenbach (Central Germany), based on a long-term data set of species and environmental variables were compared. Statistic methods and canonical correspondence analysis (CCA) attributed abundance of emerged insects to a specific discharge pattern during their larval development. However, prediction (specimens per year) is limited to magnitudes of thousands of specimens (which is outside 25% of the mean). The application of artificial neural networks (ANN) with various methods of variable pre-selection increased the precision of the prediction. Although more than one appropriate pre-processing method or artificial neural networks was found, R-2 for the best abundance prediction was 0.62 for B. rhodani and 0.71 for B. vernus.