Habitat Suitability and Distribution Models

Foreword; Preface; Acknowledgements; Authors' contributions; Introduction; 1. General content of the book; Part I. Overview, Principles, Theory and Assumptions behind Habitat Suitability Modeling: 2. Overview of the HSM modeling procedure; 3. What drives species distributions?; 4. From niche to distribution: basic modeling principles and applications; 5. Assumptions behind HSMs; Part II. Data Acquisition, Sampling Design and Spatial Scales: 6. Environmental predictors – issues of processing and selection; 7. Species data – issues of acquisition and design; 8. Ecological scales – issues of resolution and extent; Part III. Modeling Approaches and Model Calibration: 9. Envelopes and distance-based approaches; 10. Regression-based approaches; 11. Classification approaches and machine learning systems; 12. Boosting and bagging approaches; 13. Maximum Entropy; 14. Ensemble modeling and modeling averaging; Part IV. Evaluating Models: Errors and Uncertainty: 15. Measuring model accuracy: which metrics to use?; 16. Assessing model performance: which data to use?; Part V. Predictions in Space and Time: 17. Projecting models in space and time; 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; Part VII. Conclusions and Future Perspectives: 20. Conclusions and future perspectives in habitat suitability modeling; Glossary and definitions of terms and concepts; References; Index.