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Modelling population dynamics of aquatic insects with artificial neural networks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons56848

Obach,  Michael
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/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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Zitation

Obach, M., Wagner, R., Werner, H., & Schmidt, H.-H. (2001). Modelling population dynamics of aquatic insects with artificial neural networks. Ecological Modelling, 146(1-3), 207-217.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-C826-9
Zusammenfassung
We modelled the total number of individuals of selected water insects based on a 30-year data set of population dynamics and environmental variables (discharge, temperature, precipitation, abundance of parental generation) in a small stream in central Germany. For data exploration, visualisation of data, outlier detection, hypothesis generation, and to detect basic patterns in the data, we used Kohonen's self organizing maps (SOM). They are comparable to statistical cluster analysis by ordinating data into groups. Based on annual abundance patterns of Ephemeroptera, Plecoptera and Trichoptera (EPT), species groups with similar ecological requirements were distinguished. Furthermore, we applied linear neural networks, general regression neural networks, modified multi-layer perceptrons, and radial basis function networks combined with a SOM (RBFSOM) and successfully predicted the annual abundance of selected species from environmental variables. Results were visualised in three-dimensional plots. Relevance detection methods were sensitivity analysis, stepwise method and Genetic Algorithms. Instead of a sliding windows approach we computed the in- and output data of fixed periods for two caddis flies. In order to assess the quality of the models we applied several reliability measures and compared the generalisation error with the long- term mean of the target variable. RBFSOMs were used to denominate and visualise local and general model accuracy. Results were interpreted on the basis of known species traits. We conclude that it is possible to predict the abundance of aquatic insects based on relevant environmental factors using artificial neural networks.