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
Modelling Community Structure in Freshwater Ecosystems
β Scribed by S Lek (auth.), Professor Dr. Sovan Lek, Professor Dr. Michele Scardi, Dr. ir. Piet F.M. Verdonschot, Professor Dr. Jean-Pierre Descy, Dr. Young-Seuk Park (eds.)
- Publisher
- Springer-Verlag Berlin Heidelberg
- Year
- 2005
- Tongue
- English
- Leaves
- 525
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
The landmass on which we live is an integral part of our water catchment. Any human - tivity will inevitably have some consequences on the availability and composition of fresh waters. These consequences are becoming increasingly important and detectable as the - man population grows. The problem is to be addressed at the global scale, as frequently, decisions made have inter-regional and international impacts, and must therefore be coor- nated. In a number of European Member States, for example, the availability of water - sources depends on the activities of other upstream countries. The demand for fresh water in Europe, as well as in the world, is increasing. There is an upward pressure on European water demand for public supplies (drinking water, recreation, etc. ), for industry, and for - rigated agriculture. The ecological impacts of different uses are complex, and currently not always predictable. This book should help planners in their decisions on different water management options for human use. Water, of course, is not only relevant as a resource, exploited for human activities, but it is also relevant to aquatic ecosystems and to their quality. Preservation and restoration of the ecological quality of these ecosystems have a major social impact, as it has been stressed in several European Community actions.
β¦ Table of Contents
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 RhΓ΄ne 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
β¦ Subjects
Ecotoxicology; Ecosystems; Geoecology/Natural Processes; Applied Ecology; Math. Appl. in Environmental Science; Computer Appl. in Life Sciences
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