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Modelling Community Structure in Freshwater Ecosystems
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2005.
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| Edición: | 1st ed. 2005. |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- Fish community assemblages
- Patterning riverine fish assemblages using an unsupervised neural network
- Predicting fish assemblages in France and evaluating the influence of their environmental variables
- Fish diversity conservation and river restoration in southwest France: a review
- Modelling of freshwater fish and macro-crustacean assemblages for biological assessment in New Zealand
- A Comparison of various fitting techniques for predicting fish yield in Ubolratana reservoir (Thailand) from a time series data
- Patterning spatial variations in fish assemblage structures and diversity in the Pilica River system
- Optimisation of artificial neural networks for predicting fish assemblages in rivers
- General introduction
- Macroinvertebrate community assemblages
- Sensitivity and robustness of a stream model based on artificial neural networks for the simulation of different management scenarios
- A neural network approach to the prediction of benthic macroinvertebrate fauna composition in rivers
- Predicting Dutch macroinvertebrate species richness and functional feeding groups using five modelling techniques
- Comparison of clustering and ordination methods implemented to the full and partial data of benthic macroinvertebrate communities in streams and channels
- Prediction of macroinvertebrate diversity of freshwater bodies by adaptive learning algorithms
- Hierarchical patterning of benthic macroinvertebrate communities using unsupervised artificial neural networks
- Species spatial distribution and richness of stream insects in south-western France using artificial neural networks with potential use for biosurveillance
- Patterning community changes in benthic macroinvertebrates in a polluted stream by using artificial neural networks
- Patterning, predicting stream macroinvertebrate assemblages in Victoria (Australia) using artificial neural networks and genetic algorithms
- Using bioindicators to assess rivers in Europe: An overview
- Diatom and other algal assemblages
- Applying case-based reasoning to explore freshwater phytoplankton dynamics
- Modelling community changes of cyanobacteria in a flow regulated river (the lower Nakdong River, S. Korea) by means of a Self-Organizing Map (SOM)
- Use of artificial intelligence (MIR-max) and chemical index to define type diatom assemblages in Rhône basin and Mediterranean region
- Classification of stream diatom communities using a self-organizing map
- Diatom typology of low-impacted conditions at a multi-regional scale: combined results of multivariate analyses and SOM
- Prediction with artificial neural networks of diatom assemblages in headwater streams of Luxembourg
- Use of neural network models to predict diatom assemblages in the Loire-Bretagne basin (France)
- Review of modelling techniques
- Development of community assessment techniques
- Evaluation of relevant species in communities: development of structuring indices for the classification of communities using a self-organizing map
- Projection pursuit with robust indices for the analysis of ecological data
- A framework for computer-based data analysis and visualisation by pattern recognition
- A rule-based vs. a set-covering implementation of the knowledge system LIMPACT and its significance for maintenance and discovery of ecological knowledge
- Predicting macro-fauna community types from environmental variables by means of support vector machines
- User interface tool
- General conclusions and perspectives.