Artificial Neuronal Networks: Application To Ecology And Evolution
Sovan Lek, Jean‐François Guégan · Medical Entomology and Zoology · 2012
I Introduction.- 1 Neuronal Networks: Algorithms and Architectures for Ecologists and Evolutionary Ecologists.- 1.1 Introduction.- 1.2 Back Propagation Neuronal Network (BPN).- 1.2.1 Structure of BPN.- 1.2.2 BPN Algorithm.- 1.2.3 Training the Network.- 1.2.4 Testing the Network.- 1.2.5 Overtraining or Overfitting the Network.- 1.2.6 Use Aspects.- 1.2.7 BPN versus MLR.- 1.3 Kohonen Self-Organizing Mapping (SOM).- 1.3.1 Algorithm.- 1.3.2 Missing Data.- 1.3.3 Outliers.- 1.3.4 Use of Different Metrics.- 1.3.5 Aspects of Use.- 1.4 Conclusion.- Acknowledgements.- References.- II Artificial Neuronal Networks in Landscape Ecology and Remote Sensing.- 2 Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neuronal Networks.- 2.1 Introduction.- 2.2 Traditional Extraction Techniques.- 2.3 Neuronal Networks.- 2.4 Uses of Neuronal Networks and Remote Sensing Data.- 2.4.1 Neuronal Networks as Initial Models.- 2.4.2 Neuronal Networks as Baseline Control.- 2.4.3 Neuronal Networks for Inverting Physically-Based Models.- 2.4.4 Neuronal Networks for Defining Relevant Variables.- 2.4.5 Neuronal Networks as Adaptable Systems.- 2.5 Disadvantages of Using Neuronal Networks with Remote Sensing Data.- 2.6 Conclusions and Implications.- References.- 3 Soft Mapping of Coastal Vegetation from Remotely Sensed Imagery with a Feed-Forward Neuronal Network.- 3.1 Introduction.- 3.2 Test Site and Data.- 3.3 Methods.- 3.4 Results and Discussion.- 3.5 Summary and Conclusions.- Acknowledgements.- References.- 4 Ultrafast Estimation of Neotropical Forest DBH Distributions from Ground Based Photographs Using a Neuronal Network.- 4.1 Introduction.- 4.2 Methods.- 4.2.1 Study Site.- 4.2.2 Tree Inventory.- 4.2.3 Photograph Sampling.- 4.2.4 Image Processing.- 4.2.5 Extraction of the Input Vector.- 4.2.6 Neuronal Network Design.- 4.3 Results.- 4.4 Discussion.- 4.5 Conclusion.- Acknowledgements.- References.- 5 Normalized Difference Vegetation Index Estimation in Grasslands of Patagonia by ANN Analysis of Satellite and Climatic Data.- 5.1 Introduction.- 5.2 Methodology.- 5.2.1 Artificial Neuronal Networks.- 5.2.2 The Data Set.- 5.3 Results and Discussion.- Acknowledgements.- References.- 6 On the Probabilistic Interpretation of Area Based Fuzzy Land Cover Mixing Proportions.- 6.1 Introduction.- 6.2 Conceptual Classification.- 6.2.1 The Probabilistic Interpretation of Sub-Pixel Area Proportions.- 6.2.2 Implications of the Probabilistic Interpretation.- 6.2.3 Summary.- 6.3 Sub-Pixel Area Proportion Estimation on the FLIERS Project.- 6.3.1 The Data.- 6.3.2 The Neuronal Networks.- 6.3.3 The Experiments.- 6.3.4 Results.- 6.4 Conclusion.- References.- III Artificial Neuronal Networks in Population, Community and Ecosystem Ecology.- 7 Patterning of Community Changes in Benthic Macroinvertebrates Collected from Urbanized Streams for the Short Time Prediction by Temporal Artificial Neuronal Networks.- 7.1 Introduction.- 7.2 Methods.- 7.2.1 Multilayer Perceptron with Time Delay.- 7.2.2 Recurrent Neuronal Network.- 7.2.3 Field Data.- 7.3 Training and Recognition.- 7.3.1 Multilayer Perceptron with Time Delay.- 7.3.2 Recurrent Neuronal Network.- 7.4 Discussion and Conclusion.- Acknowledgements.- References.- 8 Neuronal Network Models of Phytoplankton Primary Production.- 8.1 Introduction.- 8.2 Materials and Methods.- 8.3 Results.- 8.3.1 Basic Primary Production Modelling: Neuronal Networks vs. Linear Regressions.- 8.3.2 A Depth-Resolved Primary Production Model.- 8.3.3 Modelling Primary Production in the Oceans.- 8.3.4 Neuronal Network Models vs. Conventional Models.- 8.3.5 Sensitivity Analysis.- 8.4 Discussion.- Acknowledgements.- References.- 9 Predicting Presence of Fish Species in the Seine River Basin Using Artificial Neuronal Networks.- 9.1 Introduction.- 9.2 Description and Selection of Data.- 9.3 Methodology.- 9.3.1 Choice of Implementation and Tuning of Parameters.- 9.3.2 Architecture of the Network and Error Criterion.- 9.3.3 Weighting of Data.- 9.3.4 Prediction Error Assessment.- 9.4 Results.- 9.5 Discussion.- 9.5.1 Ecological Soundness.- 9.5.2 On Methodology.- References.- 10 Elucidation and Prediction of Aquatic Ecosystems by Artificial Neuronal Networks.- 10.1 Introduction.- 10.2 Phytoplankton Abundance in Lakes and Rivers.- 10.2.1 Prediction.- 10.2.2 Elucidation.- 10.3 Prediction of Density of Brown Trout Redds in Streams.- 10.4 Conclusions.- Acknowledgements.- References.- 11 Performance Comparison between Regression and Neuronal Network Models for Forecasting Pacific Sardine (Sardinops caeruleus) Biomass.- 11.1 Introduction.- 11.2 Materials.- 11.3 Methods.- 11.4 Results and Discussion.- Acknowledgements.- References.- 12 A Comparison of Artificial Neuronal Network and Conventional Statistical Techniques for Analyzing Environmental Data.- 12.1 Introduction.- 12.2 Methods.- 12.2.1 Database Development.- 12.2.2 Development and Analysis of ANN Models.- 12.2.3 Conventional Statistical Methods.- 12.2.4 Combination of PCA with Other Techniques.- 12.3 Results.- 12.3.1 ANN Analysis and Testing.- 12.3.2 Conventional Statistical Analysis.- 12.3.3 Combination of PCA with Least Squares Regression (LSR) and ANNs.- 12.3.4 Summary of Results.- 12.4 Discussion.- 12.4.1 Comparison of Modelling Performance.- 12.4.2 Determination of the Importance of Input Variables.- 12.5 Conclusion.- References.- IV Artificial Neuronal Networks in Genetics and Evolutionary Ecology.- 13 Application of the Self-Organizing Mapping and Fuzzy Clustering to Microsatellite Data: How to Detect Genetic Structure in Brown Trout (Salmo trutta) Populations.- 13.1 Introduction.- 13.2 Material and Methods.- 13.2.1 Biological Samples.- 13.2.2 Microsatellites.- 13.2.3 Artificial Neuronal Networks.- 13.3 Results and Discussion.- 13.3.1 The Self-Organizing Map.- 13.3.2 The Fuzzy Clustering.- 13.4 Conclusion.- Acknowledgements.- References.- 14 The Macroepidemiology of Parasitic and Infectious Diseases: A Comparative Study Using Artificial Neuronal Nets and Logistic Regressions.- 14.1 Introduction.- 14.2 Materials and Methods.- 14.2.1 Materials.- 14.2.2 Methods.- 14.3 Results.- 14.3.1 Logistic Regressions and Artificial Neuronal Networks Face to Face.- 14.3.2 Occurrence and Threshold Effects.- 14.4 Discussion.- 14.5 Conclusion.- Acknowledgements.- References.- 15 Evolutionarily Optimal Networks for Controlling Energy Allocation to Growth, Reproduction and Repair in Men and Women.- 15.1 Introduction.- 15.2 Optimal Control Model.- 15.3 Computing Optimal Strategies.- 15.4 Fitting the Optimal Control Model.- 15.5 Network Control Model.- 15.6 Optimizing the Network Model.- 15.7 Discussion.- Acknowledgements.- References.- V Perspectives.- 16 Can Neuronal Networks be Used in Data-Poor Situations?.- 16.1 Introduction.- 16.2 Neuronal Network Training and its Limitations.- 16.3 The Geochemical Data Problem.- 16.4 Preprocessing Data.- 16.5 Results.- 16.6 Discussion and Conclusion.- References.