Antoine Guisan is Professor at the Université de Lausanne, Switzerland, where he leads the ECOSPAT Spatial Ecology group. Besides being a specialist in habitat suitability and distribution models, his interests also include ecological niche dynamics in space and time, community and multitrophic modeling, very high resolution spatial modeling in mountain environments, and applications of models to environmental decision-making and transfer of scientific knowledge to society.
Inhaltsangabe
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.
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.
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