.

5 (hodnocen1 x )

(14) Půjčeno:14x 

BK

First published

Cambridge : Cambridge University Press, 2017

xiii, 462 stran, 32 nečíslovaných stran obrazových příloh : ilustrace (některé barevné), mapy ; 23 cm

ISBN 978-0-521-75836-9 (brožováno)

Ecology, biodiversity and conservation

Obsahuje bibliografii na stranách 417-457 a rejstřík

001460992

Contents // Foreword page xii // Preface xv // Acknowledgments xvi // Authors’ Contributions xvii // INTRODUCTION 1 // 1 General Content of the Book 3 // 1.1 What Is This Book About? 3 // 1.2 How Is the Book Structured? 3 // 1.3 Why Write a Textbook with R Examples? 4 // 1.4 What Is This Book Not About? 5 // 1.5 Why Was This Book Needed? 5 // 1.6 Who Is This Book For? 6 // 1.7 Where Can I Find Supporting Material? 6 // 1.8 What Are Readers Assumed to Know Already? 7 // 1.9 How Does This Book Differ From Previous Ones? 7 // 1.10 What Terminology Is Used in This Book? 8 // PART I OVERVIEW, PRINCIPLES, THEORY, AND // ASSUMPTIONS BEHIND HABITAT SUITABILITY MODELING 9 // 2 Overview of the Habitat Suitability Modeling // Procedure 11 // 2.1 The Different Methodological Steps of Habitat // Suitability Modeling 11 // 2.2 The Initial Conceptual Step 18 // 3 What Drives Species Distributions? 21 // 3.1 The Overall Context: Dispersal, Habitat, and // Biotic Filtering 22 // 3.2 Speciatiori, Dispersal, Species Pools, and Neutral // Theory 26 // 3.3 The Abiotic Environment: Habitats and // Fundamental Niches 29 // 3.4 The Biotic Environment: Species Interactions, // Community Assembly, and Realized Niches 33 // 3.5 Further Discussion of the Realized // Environmental Niche and Other Related Niche Concepts 37 // 4 From Niche to Distribution: Basic Modeling // Principles and Applications 41 // 4.1 From Geographical Distribution to Niche // Quantification 41 // 4.2 From the Quantified Niche to

Spatial Predictions 46 // 4.3 From Individual Species Predictions to // Communities 48 // 4.4 Main Fields of Application 49 // 5 Assumptions Behind Habitat Suitability Models 52 // 5.1 Theoretical Assumptions 52 // 5.2 Methodological Assumptions 55 // PART II DATA ACQUISITION, SAMPLING DESIGN, AND // SPATIAL SCALES 59 // 6 Environmental Predictors: Issues of Processing // and Selection 61 // 6.1 Existing Environmental Databases 61 // 6.2 Performing Simple GIS Analyses in R 70 // 6.3 RS-Based Predictors 92 // 6.4 Properties and Selection of Variables 102 // 7 Species Data: Issues of Acquisition and Design 110 // 7.1 Existing Data and Databases 110 // 7.2 Spatial Autocorrelation and Pseudo-Replicates 112 // 7.3 Sample Size, Prevalence, and Sample Accuracy 116 // 7.4 Sampling Design and Data Collection 120 // 7.5 Presence-Absence vs. Presence-Only Data 129 // 8 Ecological Scales: Issues of Resolution // and Extent 135 // 8.1 Issues of Resolution 136 // 8.2 Issues of Extent 147 // PART III MODELING APPROACHES AND MODEL // CALIBRATION 151 // 9 Envelopes and Distance-Based Approaches 155 // 9.1 Concepts 155 // 9.2 Envelope Approaches 155 // 9.3 Distance-Based Methods 159 // 10 Regression-Based Approaches 165 // 10.1 Concepts 165 // 10.2 Generalized Linear Models 167 // 10.3 Generalized Additive Models 175 // 10.4 Multivariate Adaptive Regression Splines 182 // 11 Classification Approaches and Machine-Learning // Systems 188 // 11.1 Concepts 188 // 11.2 Recursive Partitioning 189

11.3 Linear Discriminant Analysis and Extensions 194 // 11.4 Artificial Neural Networks 197 // 12 Boosting and Bagging Approaches 202 // 12.1 Concepts 202 // 12.2 Random Forests 203 // 12.3 Boosted Regression Trees 209 // 13 Maximum Entropy 217 // 13.1 Concepts 217 // 13.2 Maxent in R 219 // 14 Ensemble Modeling and Model Averaging 224 // PART IV EVALUATING MODELS: ERRORS AND // UNCERTAINTY 237 // 15 Measuring Model Accuracy: Which Metrics to Use? // 241 // 15.1 Comparing Predicted Probabilities of Presence // to Presence—Absence Observations 243 // 15.2 Comparing Probabilistic Predictions to // Presence-Only Observations 264 // 16 Assessing Model Performance: Which Data // to Use? 270 // 16.1 Assessment of Model Fit Using Resubstitution // and Randomization 272 // 16.2 Internal Evaluation by Resampling 275 // 16.3 External Evaluation (Fully Independent Data) 296 // PART V PREDICTIONS IN SPACE AND TIME 301 // 17 Projecting Models in Space and Time 303 // 17.1 Additional Considerations and Assumptions When Projecting Models: Analog Environment, // Niche Completeness, and Niche Stability 303 // 17.2 Projecting Models in Space 316 // 17.3 Projecting Models in Time 325 // 17.4 Ensemble Projections 335 // PART VI DATA AND TOOLS USED IN THIS BOOK, // WITH DEVELOPED CASE STUDIES 349 // 18 Datasets and Tools Used for the Examples in // this Book 351 // 19 The Biomod2 Modeling Package Examples 357 // 19.1 Example 1: Habitat Suitability Modeling of // Protea laurifolia in South Africa 357

// 19.2 Example 2: Creating Diversity Maps for the // Laurus Species 385 // PART VII CONCLUSIONS AND FUTURE PERSPECTIVES 401 // 20 Conclusions and Future Perspectives in Habitat // Suitability Modeling 403 // 20.1 Further Progress in HSMs through // Metagenomics and Remote Sensing 403 // 20.2 Point-Process Models for Presence-Only Data 404 // 20.3 Hierarchical Bayesian Approaches to Integrate // Models at Different Scales 405 // 20.4 Ensemble of Small Models for Rarer Species 406 // 20.5 Improving the Modeling Techniques to Fit // Simple and Ensemble HSMs 407 // 20.6 Multi-Species Modeling and Joint-Species // Distribution Modeling 408 // 20.7 Use of Artificial Data 410 // Glossary and Definitions of Terms and Concepts 411 // Methods, Approaches, Models, Techniques, // Algorithms 411 // ENM, SDM, HSM, etc.: Different Names and Acronyms for the Same Models! 412 // Environment, Habitat, Niche, Niche-Biotope Duality, and Distribution 413 // Technical Acronyms for the Most Commonly Used Modeling Techniques 415 // References 417 // Index 458 // Color plates can be found between pages 238 and 239