Artificial Neuronal Networks

1. Front Matter

   Pages I-XXVI

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2. Introduction

   1. Front Matter

      Pages 1-1

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   2. Neuronal Networks: Algorithms and Architectures for Ecologists and Evolutionary Ecologists

      • S. Lek, J. L. Giraudel, J. F. Guégan

      Pages 3-27

3. Artificial Neuronal Networks in Landscape Ecology and Remote Sensing

   1. Front Matter

      Pages 29-29

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   2. Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neuronal Networks

      • D. S. Kimes, R. F. Nelson, S. T. Fifer

      Pages 31-44

   3. Soft Mapping of Coastal Vegetation from Remotely Sensed Imagery with a Feed-Forward Neuronal Network

      • G. M. Foody

      Pages 45-56

   4. Ultrafast Estimation of Neotropical Forest DBH Distributions from Ground Based Photographs Using a Neuronal Network

      • M. A. Dubois, L. Cournac, J. Chave, B. Riera

      Pages 57-68

   5. Normalized Difference Vegetation Index Estimation in Grasslands of Patagonia by ANN Analysis of Satellite and Climatic Data

      • F. G. Tomasel, J. M. Paruelo

      Pages 69-79

   6. On the Probabilistic Interpretation of Area Based Fuzzy Land Cover Mixing Proportions

      • J. Manslow, M. Brown, M. Nixon

      Pages 81-95

4. Artificial Neuronal Networks in Population, Community and Ecosystem Ecology

   1. Front Matter

      Pages 97-97

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   2. Patterning of Community Changes in Benthic Macroinvertebrates Collected from Urbanized Streams for the Short Time Prediction by Temporal Artificial Neuronal Networks

      • T.-S. Chon, Y.-S. Park, E. Y. Cha

      Pages 99-114

   3. Neuronal Network Models of Phytoplankton Primary Production

      • M. Scardi

      Pages 115-129

   4. Predicting Presence of Fish Species in the Seine River Basin Using Artificial Neuronal Networks

      • P. Boët, T. Fuhs

      Pages 131-142

   5. Elucidation and Prediction of Aquatic Ecosystems by Artificial Neuronal Networks

      • F. Recknagel, H. Wilson

      Pages 143-155

   6. Performance Comparison between Regression and Neuronal Network Models for Forecasting Pacific Sardine (Sardinops caeruleus) Biomass

      • M. A. Cisneros-Mata, T. Brey, A. Jarre-Teichmann

      Pages 157-164

   7. A Comparison of Artificial Neuronal Network and Conventional Statistical Techniques for Analysing Environmental Data

      • G. R. Ball, D. Palmer-Brown, G. E. Mills

      Pages 165-183

5. Artificial Neuronal Networks in Genetics and Evolutionary Ecology

   1. Front Matter

      Pages 185-185

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   2. Application of the Self-Organizing Mapping and Fuzzy Clustering to Microsatellite Data: How to Detect Genetic Structure in Brown Trout (Salmo trutta) Populations

      • J. L. Giraudel, D. Aurelle, P. Berrebi, S. Lek

      Pages 187-202

   3. The Macroepidemiology of Parasitic and Infectious Diseases: A Comparative Study Using Artificial Neuronal Nets and Logistic Regressions

      • J. F. Guégan, F. Thomas, T. de Meeüs, S. Lek, F. Renaud

      Pages 203-224

   4. Evolutionarily Optimal Networks for Controlling Energy Allocation to Growth, Reproduction and Repair in Men and Women

      • A. T. Teriokhin, E. V. Budilova

      Pages 225-237

In this book, an easily understandable account of modelling methods with artificial neuronal networks for practical applications in ecology and evolution is provided. Special features include examples of applications using both supervised and unsupervised training, comparative analysis of artificial neural networks and conventional statistical methods, and proposals to deal with poor datasets. Extensive references and a large range of topics make this book a useful guide for ecologists, evolutionary ecologists and population geneticists.

• Tempo
• algorithms
• artificial neural network
• ecology
• ecosystem
• ecosystem ecology
• evolution
• fuzzy
• genetics
• modeling
• neural networks
• performance
• phytoplankton
• plankton
• vegetation

• C.E.S.A.C., U.M.R. C.N.R.S. 5576 C Bâtiment IVR3, Université Paul Sabatier Toulouse III, Toulouse cedex 04, France

  Sovan Lek

• Centre d’Etudes sur le Polymorphisme des Micro-organismes C.E.P.M/U.M.R C.N.R.S.-I.R.D. 9926, ORSTOM, Montpellier cedex 01, France

  Jean-François Guégan

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