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Modelling water quality, bioindication and population dynamics in lotic ecosystems using neural networks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons56984

Wagner,  Rüdiger
Limnological River Station Schlitz, Max Planck Institute for Limnology, Max Planck Institute for Evolutionary Biology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons56915

Schmidt,  Hans-Heinrich
Limnological River Station Schlitz, Max Planck Institute for Limnology, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Zitation

Schleiter, I. M., Borchardt, D., Wagner, R., Dapper, T., Schmidt, K.-D., Schmidt, H.-H., et al. (1999). Modelling water quality, bioindication and population dynamics in lotic ecosystems using neural networks. Ecological Modelling, 120(2-3), 271-286. doi:10.1016/S0304-3800(99)00108-8.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-C88F-1
Zusammenfassung
The assessment of properties and processes of running waters is a major issue in aquatic environmental management. Because system analysis and prediction with deterministic and stochastic models is often limited by the complexity and dynamic nature of these ecosystems, supplementary or alternative methods have to be developed. We tested the suitability of various types of artificial neural networks for system analysis and impact assessment in different fields: (1) temporal dynamics of water quality based on weather, urban storm-water run-off and waste-water effluents; (2) bioindication of chemical and hydromorphological properties using benthic macroinvertebrates; and (3) long-term population dynamics of aquatic insects. Specific pre-processing methods and neural models were developed to assess relations among complex variables with high levels of significance. For example, the diurnal variation of oxygen concentration (modelled from precipitation and oxygen of the preceding day; R2 = 0.79), population dynamics of emerging aquatic insects (modelled from discharge, water temperature and abundance of the parental generation; R2 = 0.93), and water quality and habitat characteristics as indicated by selected sensitive benthic organisms (e.g. R2 = 0.83 for pH and R2 = 0.82 for diversity of substrate, using five out of 248 species). Our results demonstrate that neural networks and modelling techniques can conveniently be applied to the above mentioned fields because of their specific features compared with classical methods. Particularly, they can be used to reduce the complexity of data sets by identifying important (functional) inter-relationships and key variables. Thus, complex systems can be reasonably simplified in clear models with low measuring and computing effort. This allows new insights about functional relationships of ecosystems with the potential to improve the assessment of complex impact factors and ecological predictions.