Modelling Community Structure in Freshwater Ecosystems

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Lek, Sovan (Επιμελητής έκδοσης), Scardi, Michele (Επιμελητής έκδοσης), Verdonschot, Piet F.M (Επιμελητής έκδοσης), Descy, Jean-Pierre (Επιμελητής έκδοσης), Park, Young-Seuk (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.
• Θέματα: Environment.; Bioinformatics.; Computational biology.; Applied ecology.; Ecosystems.; Geoecology.; Environmental geology.; Environmental sciences.; Ecotoxicology.; Geoecology/Natural Processes.; Applied Ecology.; Math. Appl. in Environmental Science.; Computer Appl. in Life Sciences.

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...

Πίνακας περιεχομένων:

• 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.