Patternising phytoplankton dynamics of two shallow lakes in response to restoration measures by applying non-supervised artificial neural networks |
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Authors: | A. Talib F. Recknagel D. van der Molen |
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Affiliation: | (1) School of Earth and Environmental Sciences, University of Adelaide, Adelaide, 5005, Australia;(2) Present address: Universiti Sains Malaysia,USM, 11800 Penang, Malaysia;(3) Institute of Inland Water Management, 8200 AA Lelystad, The Netherlands |
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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. |
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Keywords: | Bottom-up management Eutrophication Non-supervised ANN Oscillatoria Scenedesmus Shallow lakes Top-down management |
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