Modelling Community Structure in Freshwater Ecosystems

General introduction....Pages 1-5
Using bioindicators to assess rivers in Europe: An overview....Pages 7-19
Review of modelling techniques....Pages 21-40
Introduction....Pages 41-42
Patterning riverine fish assemblages using an unsupervised neural network....Pages 43-53
Predicting fish assemblages in France and evaluating the influence of their environmental variables....Pages 54-63
Fish diversity conservation and river restoration in southwest France: a review....Pages 64-75
Modelling of freshwater fish and macro-crustacean assemblages for biological assessment in New Zealand....Pages 76-89
A Comparison of various fitting techniques for predicting fish yield in Ubolratana reservoir (Thailand) from a time series data....Pages 90-99
Patterning spatial variations in fish assemblage structures and diversity in the Pilica River system....Pages 100-113
Optimisation of artificial neural networks for predicting fish assemblages in rivers....Pages 114-129
Introduction....Pages 131-132
Sensitivity and robustness of a stream model based on artificial neural networks for the simulation of different management scenarios....Pages 133-146
A neural network approach to the prediction of benthic macroinvertebrate fauna composition in rivers....Pages 147-157
Predicting Dutch macroinvertebrate species richness and functional feeding groups using five modelling techniques....Pages 158-166
Comparison of clustering and ordination methods implemented to the full and partial data of benthic macroinvertebrate communities in streams and channels....Pages 167-188
Prediction of macroinvertebrate diversity of freshwater bodies by adaptive learning algorithms....Pages 189-205
Hierarchical patterning of benthic macroinvertebrate communities using unsupervised artificial neural networks....Pages 206-220
Species spatial distribution and richness of stream insects in south-western France using artificial neural networks with potential use for biosurveillance....Pages 221-238
Patterning community changes in benthic macroinvertebrates in a polluted stream by using artificial neural networks....Pages 239-251
Patterning, predicting stream macroinvertebrate assemblages in Victoria (Australia) using artificial neural networks and genetic algorithms....Pages 252-260
Introduction....Pages 261-262
Applying case-based reasoning to explore freshwater phytoplankton dynamics....Pages 263-272
Modelling community changes of cyanobacteria in a flow regulated river (the lower Nakdong River, S. Korea) by means of a Self-Organizing Map (SOM)....Pages 273-287
Use of artificial intelligence (MIR-max) and chemical index to define type diatom assemblages in Rhône basin and Mediterranean region....Pages 288-303
Classification of stream diatom communities using a self-organizing map....Pages 304-316
Diatom typology of low-impacted conditions at a multi-regional scale: combined results of multivariate analyses and SOM....Pages 317-342
Prediction with artificial neural networks of diatom assemblages in headwater streams of Luxembourg....Pages 343-354
Use of neural network models to predict diatom assemblages in the Loire-Bretagne basin (France)....Pages 355-365
Introduction....Pages 367-368
Evaluation of relevant species in communities: development of structuring indices for the classification of communities using a self-organizing map....Pages 369-380
Projection pursuit with robust indices for the analysis of ecological data....Pages 381-389
A framework for computer-based data analysis and visualisation by pattern recognition....Pages 340-400
A rule-based vs. a set-covering implementation of the knowledge system LIMPACT and its significance for maintenance and discovery of ecological knowledge....Pages 401-410
Predicting macro-fauna community types from environmental variables by means of support vector machines....Pages 411-434
User interface tool....Pages 435-450
General conclusions and perspectives....Pages 451-454