Pattern recognition of grazing dynamics in response to fish removal (Lake Wolderwijd, Netherlands) using non-supervised artificial neural networks

摘要: Limnological time-series data sets of the eutrophic Dutch lake Wolderwijd were modelled by means non-supervised artificial neural networks (NSANN) for pattern recognition. This has been subjected to various eutrophication control measures past 3 decades, including top-down approach planktivorous fish removal or biomanipulation. NSANN was applied patternising effects preand post-fish on phytoand zooplankton dynamics. Results study have demonstrated that can: (1) elucidate short-term shifts and long-term causal relationships complex ecological dynamics, associated with grazing, taking into account ongoing impacts phosphorus in lake, (2) illustrate potential impact grazing during clear-water phase spring (3) dynamics involving water quality, changes take place when Daphnia peaks