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  Modelling population dynamics of aquatic insects with artificial neural networks

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.

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 Urheber:
Obach, Michael1, Autor           
Wagner, Rüdiger1, Autor           
Werner, Heinrich, Autor
Schmidt, Hans-Heinrich1, Autor           
Affiliations:
1Limnological River Station Schlitz, Max Planck Institute for Limnology, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_976546              

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Schlagwörter: radial basis function neural network; self-organizing maps; hybrid training; general regression neural network; visualisation of multidimensional data; reliability measure
 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.

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Sprache(n): eng - English
 Datum: 2001-12-01
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: eDoc: 27674
ISI: 000172947900017
Anderer: 0993
 Art des Abschluß: -

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Titel: Ecological Modelling
  Alternativer Titel : Ecol. Model.
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 146 (1-3) Artikelnummer: - Start- / Endseite: 207 - 217 Identifikator: ISSN: 0304-3800