Pattern Recognition Of Grazing Dynamics In
Response To Fish Removal (Lake Wolderwijd,
Netherlands) Using Non-supervised Artificial
Neural Networks
Author(s)
A. Talib, Y. Abu Hasan, F. Recknagel & D. T. van der Molen
Abstract
Limnological time-series data sets of the eutrophic Dutch lake Wolderwijd were
modelled by means of non-supervised artificial neural networks (NSANN) for
pattern recognition. This lake has been subjected to various eutrophication
control measures for the past 3 decades, including the top-down approach of
planktivorous fish removal or biomanipulation. NSANN was applied for
patternising the effects of pre- and post-fish removal on the phyto- and
zooplankton dynamics. Results of the study have demonstrated that NSANN can:
(1) elucidate short-term shifts and long-term causal relationships of complex
ecological dynamics, associated with grazing, taking into account the ongoing
impacts of phosphorus removal in the lake, (2) illustrate the potential impact of
zooplankton grazing during the clear-water phase in spring and (3) the complex
dynamics involving water quality, phyto- and zooplankton changes that take
place when Daphnia peaks
Keywords: non-supervised ANN, biomanipulation, phytoplankton, zooplankton,
grazing, planktivorous fish.
1 Introduction
Exploiting Daphnia to control eutrophication is dependent on increased
predation on planktivorous fishes by increasing piscivorous fish-stocks. This is a
top-down approach based on the trophic cascades (Carpenter et al. [1]) for
Keywords
non-supervised ANN, biomanipulation, phytoplankton, zooplankton,
grazing, planktivorous fish.
Related Book
Other papers in this volume
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