1;Foreword;5 2;Contents;11 3;General introduction;13 4;1 Using bioindicators to assess rivers in Europe: An overview;18 4.1;1.1 Introduction;18 4.2;1.2 Stream typology;18 4.3;1.3 Diatom ecology and use for river quality assessment;20 4.4;1.4 Typologies, assessment systems and prediction techniques based on macroinvertebrates;23 4.5;1.5 Advantages of using fish as an indicator taxon;27 4.6;1.6 Conclusions;29 5;2 Review of modelling techniques;31 5.1;2.1 Introduction;31 5.2;2.2 Conventional statistical models;31 5.3;2.3 Artificial neural networks (ANNs);36 5.4;2.4 Bayesian and Mixture models;45 5.5;2.5 Support vector machines (SVMs);47 5.6;2.6 Genetic algorithms (GAs);48 5.7;2.7 Mutual information and regression maximisation (MIR-max);49 5.8;2.8 Structural dynamic models;49 6;3 Fish community assemblages;51 6.1;3.1 Introduction;51 6.2;3.2 Patterning riverine fish assemblages using an unsupervised neural network;53 6.3;3.3 Predicting fish assemblages in France and evaluating the influence of their environmental variables;64 6.4;3.4 Fish diversity conservation and river restoration in southwest France: a review;74 6.5;3.5 Modelling of freshwater fish and macro-crustacean assemblages for biological assessment in New Zealand;86 6.6;3.6 A Comparison of various fitting techniques for predicting fish yield in Ubolratana reservoir ( Thailand) from a time series data;100 6.7;3.7 Patterning spatial variations in fish assemblage structures and diversity in the Pilica River system;110 6.8;3.8 Optimisation of artificial neural networks for predicting fish assemblages in rivers;124 7;4 Macroinvertebrate community assemblages;140 7.1;4.1 Introduction;140 7.2;4.2 Sensitivity and robustness of a stream model based on artificial neural networks for the simulation of different management scenarios;142 7.3;4.3 A neural network approach to the prediction of benthic macroinvertebrate fauna composition in rivers;156 7.4;4.4 Predicting Dutch macroinvertebrate species richness and functional feeding groups using five modelling techniques;167 7.5;4.5 Comparison of clustering and ordination methods implemented to the full and partial data of benthic macroinvertebrate communities in streams and channels;176 7.6;4.6 Prediction of macroinvertebrate diversity of freshwater bodies by adaptive learning algorithms;198 7.7;4.7 Hierarchical patterning of benthic macroinvertebrate communities using unsupervised artificial neural networks;215 7.8;4.8 Species spatial distribution and richness of stream insects in south- western France using artificial neural networks with potential use for biosurveillance;230 7.9;4.9 Patterning community changes in benthic macroinvertebrates in a polluted stream by using artificial neural networks;248 7.10;4.10 Patterning, predicting stream macroinvertebrate assemblages in Victoria ( Australia) using artificial neural networks and genetic algorithms;261 8;5 Diatom and other algal assemblages;270 8.1;5.1 Introduction;270 8.2;5.2 Applying case-based reasoning to explore freshwater phytoplankton dynamics;272 8.3;5.3 Modelling community changes of cyanobacteria in a flow regulated river ( the lower Nakdong River, S. Korea) by means of a Self- Organizing Map ( SOM);282 8.4;5.4 Use of artificial intelligence (MIR-max) and chemical index to define type diatom assemblages in Rhône basin and Mediterranean region;297 8.5;5.5 Classification of stream diatom communities using a self- organizing map;313 8.6;5.6 Diatom typology of low-impacted conditions at a multi- regional scale: combined results of multivariate analyses and SOM;326 8.7;5.7 Prediction with artificial neural networks of diatom assemblages in headwater streams of Luxembourg;352 8.8;5.8 Use of neural network models to predict diatom assemblages in the Loire- Bretagne basin ( France);364 9;6 Development of community assessment techniques;375 9.1;6.1 Introduction;375 9.2;6.2 Evaluation of relevant species in communities: development of structuring indices for the classification of co