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

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons56984

Wagner,  Rüdiger
Limnological River Station Schlitz, Max Planck Institute for Limnology, Max Planck Institute for Evolutionary Biology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons56915

Schmidt,  Hans-Heinrich
Limnological River Station Schlitz, Max Planck Institute for Limnology, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Wagner, R., Dapper, T., & Schmidt, H.-H. (2000). The influence of environmental variables on the abundance of aquatic insects: a comparison of ordination and artificial neural networks. Hydrobiologia, 422/423, 143-152.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-C86D-D
Abstract
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, R2 for the best abundance prediction was 0.62 for B. rhodani and 0.71 for B. vernus.