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05149nam a22004695i 4500 |
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978-3-540-26894-9 |
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20191028091542.0 |
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cr nn 008mamaa |
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100301s2005 gw | s |||| 0|eng d |
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|a 9783540268949
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| 024 |
7 |
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|a 10.1007/b138251
|2 doi
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| 040 |
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|a Sistema de Bibliotecas del Tecnológico de Costa Rica
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| 245 |
1 |
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|a Modelling Community Structure in Freshwater Ecosystems
|c edited by Sovan Lek, Michele Scardi, P.F.M Verdonschot, J.-P. Descy, Young-Seuk Park.
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| 250 |
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|a 1st ed. 2005.
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| 260 |
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2005.
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| 300 |
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|a XII, 518 p.
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 505 |
0 |
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|a 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.
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| 650 |
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0 |
|a Ecotoxicology.
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| 650 |
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0 |
|a Ecosystems.
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| 650 |
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0 |
|a Geoecology.
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| 650 |
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0 |
|a Environmental geology.
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| 650 |
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0 |
|a Applied ecology.
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| 650 |
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0 |
|a Environmental sciences.
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| 650 |
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0 |
|a Bioinformatics .
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| 650 |
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0 |
|a Computational biology .
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| 650 |
1 |
4 |
|a Ecotoxicology.
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| 650 |
2 |
4 |
|a Ecosystems.
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| 650 |
2 |
4 |
|a Geoecology/Natural Processes.
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| 650 |
2 |
4 |
|a Applied Ecology.
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| 650 |
2 |
4 |
|a Math. Appl. in Environmental Science.
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| 650 |
2 |
4 |
|a Computer Appl. in Life Sciences.
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| 700 |
1 |
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|a Lek, Sovan.
|e editor.
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| 700 |
1 |
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|a Scardi, Michele.
|e editor.
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| 700 |
1 |
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|a Verdonschot, P.F.M.
|e editor.
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| 700 |
1 |
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|a Descy, J.-P.
|e editor.
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| 700 |
1 |
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|a Park, Young-Seuk.
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/b138251
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