Predictive Species and Habitat Modeling in Landscape Ecology [electronic resource] : Concepts and Applications /
Drew, C. Ashton.editor.
Wiersma, Yolanda F.editor.
Huettmann, Falk.editor.
Much research in landscape ecology makes use of spatial models to define species-habitat associations. The early years of landscape ecology necessarily focused on the evolution of effective data sources, metrics, and statistical approaches that could truly capture the spatial and temporal patterns and processes of interest. Now that these tools are well established, we reflect in this volume on the ecological theories that underpin the assumptions commonly made during species distribution modeling and mapping. This is crucial for applying models to questions of global sustainability. This book will offer a unique perspective on modeling within the discipline of landscape ecology, which complements that of other recent publications. Through chapters that focus on particular aspects of modeling, illustrative case studies, and surveys of the field of modeling, this book illustrates that we can (and need to) pay attention to the foundational ecological theories and assumptions which support model development. We show how this can be done in modeling through theory, traditional inference, and predictions. Chapter authors have attempted to critically identify, evaluate, and even formally test these ecological theories and have also written thoughtful reflections on the state of landscape-scale species/habitat modeling. Predictive Species and Habitat Modeling in Landscape Ecology: Concepts and Applications is intended to be useful to researchers in landscape ecology, as well as those in conservation biology, wildlife management, population and community ecology, and general ecology. The book will be a valuable resource for graduate students incorporating landscape ecology and/or species modeling in their degree programs. About the Editors C. Ashton Drew is a postdoctoral researcher in the USGS North Carolina Cooperative Fish and Wildlife Research Unit, North Carolina State University. Her research interests focus on how models can support adaptive monitoring and management. Yolanda F. Wiersma is Assistant Professor in Biology at Memorial University (Canada). She conducts research in Boreal Landscape Ecology, with a focus on models of wildlife-habitat interactions, forestry and protected areas. Falk Huettmann is Associate Professor in the Biology and Wildlife Department, Institute of Arctic Biology, University of Alaska-Fairbanks. His research interests are in wildlife/habitat modeling, GIS and remote sensing, and data management worldwide.
Description based upon print version of record.
Includes bibliographical references and index.
Acknowledgements -- Foreword by Jack Liu -- List of Contributors -- List of abbreviations -- Introduction -- Chapter 1. Landscape modeling of species and their habitats: history, uncertainty, and complexity (by Yolanda Wiersma, Ashton Drew, Falk Huettmann) -- Section 1. Current State of Knowledge -- Chapter 2. Integrating Theory and Predictive Modeling for Conservation Research (by Jeremy T. Kerr, Manisha Kulkarni, and Adam Algar) -- Chapter 3. The State of Spatial and Spatio-Temporal Statistical Modeling (by Mevin B. Hooten) -- Section 2. Integration of Ecological Theory into Modeling Practice -- Chapter 4. Proper Data Management as a Scientific Foundation for Reliable Species Distribution Modeling (by Benjamin Zuckerberg, Falk Huettmann, and Jacqueline Frair) -- Chapter 5. The Role of Assumptions in Predictions of Habitat Availability and Quality (by Edward J. Laurent, C. Ashton Drew, Wayne E. Thogmartin) -- Chapter 6. Insights from Ecological Theory on Temporal Dynamics and Species Distribution Modeling (by Robert J. Fletcher, Jr., Jock S. Young, Richard L. Hutto, Anna Noson, and Christopher T. Rota) -- Section 3. Simplicity, Complexity, and Uncertainty in Applied Models -- Chapter 7. Focused Assessment of Scale-Dependent Vegetation Pattern (by Todd R. Lookingbill, Monique E. Rocca, and Dean L. Urban) -- Chapter 8. Modeling Species Distribution and Change Using Random Forest (by Jeffrey S. Evans, Melanie A. Murphy, Zachary A. Holden, and Samuel A. Cushman) -- Chapter 9. Genetic Patterns as a Function of Landscape Process: Applications of Neutral Genetic Markers for Predictive Modeling in Landscape Ecology (by Melanie A. Murphy and Jeffrey S. Evans) -- Chapter 10. Simplicity, Model Fit, Complexity and Uncertainty in Spatial Prediction Models Applied Over Time: We Are Quite Sure, Aren’t We? (by Falk Huettmann and Thomas Gottschalk) -- Chapter 11. Variation, Use, and Mis-use of Statistical Models: a Review of the Effects on the Interpretation of Research Results (by Yolanda F. Wiersma) -- Chapter 12. Expert Knowledge as a Basis for Landscape Ecological Predictive Models (by C. Ashton Drew and Ajith H. Perera) -- Section 4. Designing Models for Increased Utility -- Chapter 13. Choices and Strategies for Using a Resource Inventory Database to Support Local Wildlife Habitat Monitoring (by L. Jay Roberts, Brian A. Maurer, Michael Donovan) -- Chapter 14. Using Species Distribution Models for Conservation Planning and Ecological Forecasting (by Josh J. Lawler, Yolanda F. Wiersma and Falk Huettmann) -- Conclusion -- Chapter 15. The state of habitat and species modeling today (by Ashton Drew, Yolanda Wiersma, Falk Huettmann).
Much research in landscape ecology makes use of spatial models to define species-habitat associations. The early years of landscape ecology necessarily focused on the evolution of effective data sources, metrics, and statistical approaches that could truly capture the spatial and temporal patterns and processes of interest. Now that these tools are well established, we reflect in this volume on the ecological theories that underpin the assumptions commonly made during species distribution modeling and mapping. This is crucial for applying models to questions of global sustainability. This book will offer a unique perspective on modeling within the discipline of landscape ecology, which complements that of other recent publications. Through chapters that focus on particular aspects of modeling, illustrative case studies, and surveys of the field of modeling, this book illustrates that we can (and need to) pay attention to the foundational ecological theories and assumptions which support model development. We show how this can be done in modeling through theory, traditional inference, and predictions. Chapter authors have attempted to critically identify, evaluate, and even formally test these ecological theories and have also written thoughtful reflections on the state of landscape-scale species/habitat modeling. Predictive Species and Habitat Modeling in Landscape Ecology: Concepts and Applications is intended to be useful to researchers in landscape ecology, as well as those in conservation biology, wildlife management, population and community ecology, and general ecology. The book will be a valuable resource for graduate students incorporating landscape ecology and/or species modeling in their degree programs. About the Editors C. Ashton Drew is a postdoctoral researcher in the USGS North Carolina Cooperative Fish and Wildlife Research Unit, North Carolina State University. Her research interests focus on how models can support adaptive monitoring and management. Yolanda F. Wiersma is Assistant Professor in Biology at Memorial University (Canada). She conducts research in Boreal Landscape Ecology, with a focus on models of wildlife-habitat interactions, forestry and protected areas. Falk Huettmann is Associate Professor in the Biology and Wildlife Department, Institute of Arctic Biology, University of Alaska-Fairbanks. His research interests are in wildlife/habitat modeling, GIS and remote sensing, and data management worldwide.
Landscape ecology.
Ecology .
Wildlife.
Fish.
Conservation biology.
Nature conservation.
Community ecology, Biotic.
Landscape Ecology.
Theoretical Ecology/Statistics.
Fish & Wildlife Biology & Management.
Conservation Biology/Ecology.
Nature Conservation.
Community & Population Ecology.