Lek / Scardi / Verdonschot Modelling Community Structure in Freshwater Ecosystems

1. Auflage 2005
ISBN: 978-3-540-26894-9
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 518 Seiten

This volume presents approaches and methodologies for predicting the structure and diversity of key aquatic communities (namely, diatoms, benthic macroinvertebrates and fish), under natural conditions and under man-made disturbance. The intent is to offer an organized means for modeling, evaluating and restoring freshwater ecosystems.

Autoren/Hrsg.

Lek, Sovan

Scardi, Michele

Descy, Jean-Pierre

Park, Young-Seuk

Verdonschot, P. F. M

Descy, J. -P.

Inhaltsverzeichnis

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 communities using a self- organizing map;377
9.3;6.3 Projection pursuit with robust indices for the analysis of ecological data;389
9.4;6.4 A framework for computer-based data analysis and visualisation by pattern recognition;398
9.5;6.5 A rule-based vs. a set-covering implementation of the knowledge system LIMPACT and its significance for maintenance and discovery of ecological knowledge;409
9.6;6.6 Predicting macro-fauna community types from environmental variables by means of support vector machines;419
10;7 User interface tool;443
10.1;7.1 Introduction;443
10.2;7.2 Software aims;444
10.3;7.3 System requirements;444
10.4;7.4 Installing/Uninstalling;444
10.5;7.5 Models implemented in the tool;444
10.6;7.6 How to use the software;447
10.7;7.7 Organisms used in the PAEQANN software;455
11;8 General conclusions and perspectives;459
12;References;463
13;Subject index;521