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Patternising phytoplankton dynamics of two shallow lakes in response to restoration measures by applying non-supervised artificial neural networks

Author

Listed:
  • A. Talib

    (University of Adelaide)

  • F. Recknagel

    (University of Adelaide)

  • D. Molen

    (Institute of Inland Water Management)

Abstract

Long-term time-series data sets of two shallow Dutch lakes, Lake Veluwemeer and Lake Wolderwijd were subjected to ordination and clustering by means of non-supervised artificial neural networks (ANN). Splitting of the data sets into sub-series corresponding with three different management periods have allowed a comparative analysis of both the short-term seasonal and long-term phytoplankton dynamics in relation to the restoration measures. The lakes were considered as hyper-eutrophic and have been managed both with bottom-up and top-down management approaches. Results of the study have demonstrated that non-supervised ANN allow to elucidate causal relationships of complex ecological processes (1) within the specific genus, Oscillatoria and Scenedesmus and (2) the combination of external nutrient control and in-lake food web manipulation of the two lakes achieved to control eutrophication.

Suggested Citation

  • A. Talib & F. Recknagel & D. Molen, 2007. "Patternising phytoplankton dynamics of two shallow lakes in response to restoration measures by applying non-supervised artificial neural networks," Environment Systems and Decisions, Springer, vol. 27(1), pages 195-205, March.
    • Handle: RePEc:spr:envsyd:v:27:y:2007:i:1:d:10.1007_s10669-007-9023-x
    DOI: 10.1007/s10669-007-9023-x
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