Ecological Informatics

1. Front Matter

   Pages I-XXVIII

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2. Introduction

   1. Front Matter

      Pages 1-1

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   2. Ecological Applications of Fuzzy Logic

      • A. Salski

      Pages 3-14

   3. Ecological Applications of Unsupervised Artificial Neural Networks

      • J. L. Giraudel, S. Lek

      Pages 15-33

   4. Ecological Applications of Genetic Algorithms

      • D. Morrall

      Pages 35-48

   5. Ecological Applications of Evolutionary Computation

      • P. A. Whigham, G. B. Fogel

      Pages 49-71

   6. Ecological Applications of Adaptive Agents

      • F. Recknagel

      Pages 73-88

3. Prediction and Elucidation of Stream Ecosystems

   1. Front Matter

      Pages 89-89

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   2. Development and Application of Predictive River Ecosystem Models Based on Classification Trees and Artificial Neural Networks

      • P. Goethals, A. Dedecker, W. Gabriels, N. De Pauw

      Pages 91-107

   3. Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks

      • I. M. Schleiter, M. Obach, R. Wagner, H. Werner, H.-H. Schmidt, D. Borchardt

      Pages 109-126

   4. Non-linear Approach to Grouping, Dynamics and Organizational Informatics of Benthic Macroinvertebrate Communities in Streams by Artificial Neural Networks

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

      Pages 127-178

   5. Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblages in Freshwater Streams by Artificial Neural Networks

      • H. Hoang, F. Recknagel, J. Marshall, S. Choy

      Pages 179-192

4. Prediction and Elucidation of River Ecosystems

   1. Front Matter

      Pages 193-193

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   2. Prediction and Elucidation of Population Dynamics of the Blue-green Algae Microcystis aeruginosa and the Diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System (South Korea) by a Recurrent Artificial Neural Network

      • K.-S. Jeong, F. Recknagel, G.-J. Joo

      Pages 195-213

   3. An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model

      • G. J. Bowden, G. C. Dandy, H. R. Maier

      Pages 215-232

   4. Utility of Sensitivity Analysis by Artificial Neural Network Models to Study Patterns of Endemic Fish Species

      • M. Gevrey, S. Lek, T. Oberdorff

      Pages 233-246

5. Prediction and Elucidation of Lake and Marine Ecosystems

   1. Front Matter

      Pages 247-247

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   2. A Comparison between Neural Network Based and Multiple Regression Models for Chlorophyll-a Estimation

      • C. Karul, S. Soyupak

      Pages 249-263

   3. A Generic Artificial Neural Network Model for Short Term Predictions of Algal Blooms in Lakes and Reservoirs

      • H. Wilson, F. Recknagel

      Pages 265-289

   4. Predictive Rules for Phytoplankton Dynamics in Freshwater Lakes Discovered by Evolutionary Algorithms

      • J. Bobbin, F. Recknagel

      Pages 291-311

Ecological Informatics is defined as the design and application of computational techniques for ecological analysis, synthesis, forecasting and management. The book provides an introduction to the scope, concepts and techniques of this newly emerging discipline. It illustrates numerous applications of Ecological Informatics for stream systems, river systems, freshwater lakes and marine systems as well as image recognition at micro and macro scale. Case studies focus on applications of artificial neural networks, genetic algorithms, fuzzy logic and adaptive agents to current ecological management issues such as toxic algal blooms, eutrophication, habitat degradation, conservation of biodiversity and sustainable fishery

• Artificial Intelligence
• Computational Ecology
• Environmental Informatics
• System Sciences
• algorithms
• biodiversity
• data mining
• ecology
• ecosystem
• environment
• lake
• marine
• phytoplankton
• plankton
• population dynamics

• Department of Soil and Water, Adelaide University, Glen Osmond, Australia

  Friedrich Recknagel

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