Habitat suitability and distribution models: with applications in R / Antoine Guisan, Wilfried Thuiller, Niklaus E. Zimmermann ; with contributions from Valeria di Cola, Damien Georges, Achilleas Psomas

Incluye bibliografía: páginas 417-457 e índice: páginas 458-462

Glosario: páginas 411-416

Foreword page.. Preface.. Acknowledgments.. Authors' Contributions.. Introduction.. 1 General Content of the Book.. 1.1 What Is This Book About?.. 1.2 How Is the Book Structured?.. 1.3 Why Write a Textbook with R Examples?.. 1.4 What Is This Book Not About?.. 1.5 Why Was This Book Needed?.. 1.6 Who Is This Book For?.. 1.7 Where Can I Find Supporting Material?.. 1.8 What Are Readers Assumed to Know Already?.. 1.9 How Does This Book Difer From Previous Ones?.. 1.10 What Terminology Is Used in This Book?.. Part I Overview, Principles, Theory, and Assumptions Behind Habitat Suitability Modeling.. 2 Overview of the Habitat Suitability Modeling Procedure.. 2.1 The Diferent Methodological Steps of Habitat Suitability Modeling.. 2.2 The Initial Conceptual Step.. 3 What Drives Species Distributions?.. 3.1 The Overall Context: Dispersal, Habitat, and Biotic Filtering.. 3.2 Speciation, Dispersal, Species Pools, and Neutral Theory.. 3.3 The Abiotic Environment: Habitats and Fundamental Niches.. 3.4 The Biotic Environment: Species Interactions, Community Assembly, and Realized Niches.. 3.5 Further Discussion of the Realized Environmental Niche and Other Related Niche Concepts.. 4 From Niche to Distribution: Basic Modeling Principles and Applications.. 4.1 From Geographical Distribution to Niche Quantiication.. 4.2 From the Quantiied Niche to Spatial Predictions.. 4.3 From Individual Species Predictions to Communities.. 4.4 Main Fields of Application.. 5 Assumptions Behind Habitat Suitability Models.. 5.1 Theoretical Assumptions.. 5.2 Methodological Assumptions.. Part II Data Acquisition, Sampling Design, and Spatial Scales.. 6 Environmental Predictors: Issues of Processing and Selection.. 6.1 Existing Environmental Databases.. 6.2 Performing Simple GIS Analyses in R.. 6.3 RS- Based Predictors.. 6.4 Properties and Selection of Variables.. 7 Species Data: Issues of Acquisition and Design.. 7.1 Existing Data and Databases

7.2 Spatial Autocorrelation and Pseudo- Replicates.. 7.3 Sample Size, Prevalence, and Sample Accuracy.. 7.4 Sampling Design and Data Collection.. 7.5 Presence- Absence vs. Presence- Only Data.. 8 Ecological Scales: Issues of Resolution and Extent.. 8.1 Issues of Resolution.. 8.2 Issues of Extent.. Part III Modeling Approaches and Model Calibration.. 9 Envelopes and Distance- Based Approaches.. 9.1 Concepts.. 9.2 Envelope Approaches.. 9.3 Distance- Based Methods.. 10 Regression- Based Approaches.. 10.1 Concepts.. 10.2 Generalized Linear Models.. 10.3 Generalized Additive Models.. 10.4 Multivariate Adaptive Regression Splines.. 11 Classiication Approaches and Machine- Learning Systems.. 11.1 Concepts.. 11.2 Recursive Partitioning.. 11.3 Linear Discriminant Analysis and Extensions.. 11.4 Artiicial Neural Networks.. 12 Boosting and Bagging Approaches.. 12.1 Concepts.. 12.2 Random Forests.. 12.3 Boosted Regression Trees.. 13 Maximum Entropy.. 13.1 Concepts.. 13.2 Maxent in R.. 14 Ensemble Modeling and Model Averaging.. Part IV Evaluating Models: Errors and Uncertainty.. 15 Measuring Model Accuracy: Which Metrics to Use?.. 15.1 Comparing Predicted Probabilities of Presence to Presence- Absence Observations.. 15.2 Comparing Probabilistic Predictions to Presence- Only Observations.. 16 Assessing Model Performance: Which Data to Use?.. 16.1 Assessment of Model Fit Using Resubstitution and Randomization.. 16.2 Internal Evaluation by Resampling.. 16.3 External Evaluation (Fully Independent Data.. Part V Predictions in Space and Time.. 17 Projecting Models in Space and Time.. 17.1 Additional Considerations and Assumptions When Projecting Models: Analog Environment, Niche Completeness, and Niche Stability.. 17.2 Projecting Models in Space.. 17.3 Projecting Models in Time.. 17.4 Ensemble Projections.. Part VI Data and Tools Used in this Book, with Developed Case Studies

18 Datasets and Tools Used for the Examples in this Book.. 19 The Biomod2 Modeling Package Examples.. 19.1 Example 1: Habitat Suitability Modeling of Protea laurifolia in South Africa.. 19.2 Example 2: Creating Diversity Maps for the Laurus Species.. Part VII Conclusions and Future Perspectives.. 20 Conclusions and Future Perspectives in Habitat Suitability Modeling.. 20.1 Further Progress in HSMs through Metagenomics and Remote Sensing.. 20.2 Point- Process Models for Presence- Only Data.. 20.3 Hierarchical Bayesian Approaches to Integrate Models at Diferent Scales.. 20.4 Ensemble of Small Models for Rarer Species.. 20.5 Improving the Modeling Techniques to Fit Simple and Ensemble HSMs.. 20.6 Multi- Species Modeling and Joint- Species Distribution Modeling.. 20.7 Use of Artiicial Data.. Glossary and Deinitions of Terms and Concepts.. Methods, Approaches, Models, Techniques, Algorithms.. ENM, SDM, HSM, etc.: Diferent Names and Acronyms for the Same Models!.. Environment, Habitat, Niche, Niche-Biotope Duality, and Distribution.. Technical Acronyms for the Most Commonly Used Modeling Techniques.. References.. Index.. Color plates can be found between pages 238 and 239

This book introduces the key stages of niche-based habitat suitability model building, evaluation and prediction required for understanding and predicting future patterns of species and biodiversity. Beginning with the main theory behind ecological niches and species distributions, the book proceeds through all major steps of model building, from conceptualization and model training to model evaluation and spatio-temporal predictions. Extensive examples using R support graduate students and researchers in quantifying ecological niches and predicting species distributions with their own data, and help to address key environmental and conservation problems. Reflecting this highly active field of research, the book incorporates the latest developments from informatics and statistics, as well as using data from remote sources such as satellite imagery. A website at www.unil.ch/hsdm contains the codes and supporting material required to run the examples and teach courses. eng