Note: Intro -- Predictive Species and Habitat Modelingin Landscape Ecology -- Foreword -- Acknowledgments -- Contents -- Abbreviations -- Chapter 1: Introduction. Landscape Modeling of Species and Their Habitats: History, Uncertainty, and Complexity -- Part I Current State of Knowledge -- Part II Integration of Ecological Theory into Modeling Practice -- Part III Simplicity, Complexity, and Uncertainty in Applied Models -- Part IV Designing Models for Increased Utility -- Author Bios -- Index.

Note: Intro -- Title Page -- Copyright -- Preface -- Contents -- Part I Theory and Practice -- Chapter 1 Introduction: Ecological Knowledge, Theory and Information in Space and Time -- Chapter 2 Space and Time in Ecology: Noise or Fundamental Driver? -- Chapter 3 The Problem of Ecological Scaling in Spatially Complex, Nonequilibrium Ecological Systems -- Chapter 4 Landscape Ecology: Past, Present, and Future -- Chapter 5 The Gradient Paradigm: A Conceptual and Analytical Framework for Landscape Ecology -- Part II Method and Data -- Chapter 6 Data on Distribution and Abundance: Monitoring for Research and Management -- Chapter 7 Animal Movement Data: GPS Telemetry, Autocorrelation and the Need for Path-Level Analysis -- Chapter 8 Using a Random Forest Model and Public Data to Predict the Distribution of Prey for Marine Wildlife Management -- Chapter 9 Landscape Genomics: A Brief Perspective -- Chapter 10 Spatial Information Management in Wildlife Ecology: Adding Spatially Explicit Behaviour Data to the Equation? -- Chapter 11 Critical Remote Sensing Contributions to Spatial Wildlife Ecological Knowledge and Management -- Chapter 12 Spatial Data Management Through Metadata: Global Concepts, Formats, Tools and Requirements -- Chapter 13 Free Database Availability, Metadata and the Internet: An Example of Two High Latitude Components of the Census of Marine Life -- Chapter 14 Components of Spatial Information Management in Wildlife Ecology: Software for Statistical and Modeling Analysis -- Chapter 15 Spatial Analysis of Wildlife Distribution and Disease Spread -- Chapter 16 Current State of the Art for Statistical Modelling of Species Distributions -- Appendix - Freely Available Software Resources -- Chapter 17 Landscape Genetics -- Part III Examples. , Chapter 18 Using Geographical Mapping and Occupancy Modeling to Study the Distribution of theCritically Endangered Leopard (Panthera pardus) Population in Armenia -- Chapter 19 Mapping Landscape Resistance to Identify Corridors and Barriers for Elephant Movement in Southern Africa -- Chapter 20 Habitat Fragmentation Effects Depend on Complex Interactions Between Population Size and Dispersal Ability: Modeling Influences of Roads, Agriculture and Residential Development Across a Range of Life-History Characteristics -- Chapter 21 Linking Cetaceans to Their Environment: Spatial Data Acquisition, Digital Processing and Predictive Modeling for Marine Spatial Planning in the Northwest Atlantic -- Chapter 22 Multi-spectral Satellite-Airborne Management of Ice Form Marine Mammals and Their Habitat in the Presence of Climate Change Using a "Hot Spots" Approach -- Chapter 23 How Spatial Information Contributes to the Conservation and Management of Biodiversity -- Chapter 24 Future and Outlook: Where Are We, and Where Will the Spatial Information Management in Wildlife Ecology Be in 50 Years from Now? -- Index.

Note: Intro -- Foreword -- Foreword by Dr Johannes Refisch -- Devotion -- About the Book -- Acknowledgements -- Contents -- Contributors -- Chapter-1 -- A Short Introduction to Tropical Land- and Seascapes and Their Wildlife Conservation Management -- 1.1 Introduction -- 1.2 A Quick Definition of the Tropics -- 1.3 Some Aspects of the Tropics -- 1.4 The Tropics and Their Economy -- 1.5 Some Basic Metrics: Abiotic and Biotic Descriptions of the Tropics -- 1.6 The Tropics, Human Society, and Climate Change -- 1.7 A Focus on the Neotropics and Central America -- 1.8 Wildlife and Management in the Tropics: An Adaptive Neotropical Management Failure -- 1.9 Outlook for the Tropics: If We Continue to Fail with Wildlife Conservation Management Further -- References -- Chapter-2 -- If Leaders Like Alexander Von Humboldt, Ronald Reagan, and Xi Jinping Would Have Been Environmental Justice Radicals Using Public Internet and Drones: A Short Political Ecology of Central America with Regard to Global Agenda Setting in 2014 -- 2.1 Introduction -- 2.2 Central America and the Governance of One of the Wealthiest Economies in the World -- 2.3 Central America and Some of Its Wicked and Complex Problems -- 2.4 Status of Democracy in Central America: What Is at Stake? -- 2.5 Central America and Its (Good?) Leaders -- 2.6 More Conflicts, Policies, Treaties, and Politics in Central America -- 2.7 Central American Realities Due to Neoliberalism and Globalization: Another Look at Telecoupling -- 2.8 Many Central American Performance Metrics Now Driven by China -- 2.9 Nature Sets Us the Limits in Central America and Elsewhere -- 2.10 Specific Problems of Nature and Imperialism in Central America: Avatar Revisited -- 2.11 Poverty Eradication and the Modern Church: Liberation Theology and Rastafari to the Rescue. , 2.12 Environmentalism, NGOs, Foreign Aid, and "Modern" Conservation Issues in Central America -- 2.13 Globally Driven Science-Based Conservation Management in Central America as the Future? Visions and Ethics Matter -- References -- Chapter-3 -- Bird Conservation Status and Meaningful Socioeconomic Correlates in Central America: Results from an Open Access Data-Mining Approach for Parrots Using Machine Learning Indicate Serious Economic Problems -- 3.1 Introduction -- 3.1.1 Economic Growth in a World of Finite Natural Resources -- 3.1.2 The Region of Central America -- 3.1.3 Banana Republics and Deforestation in Central America -- 3.1.4 Illegal Trapping for Pet Trade and Other Overexploitation Forms -- 3.2 Methods -- 3.2.1 Data Collection -- 3.2.2 Modeling Using Machine Learning Software -- 3.3 Results -- 3.3.1 Critically Endangered Birds -- 3.3.2 Extinct Birds -- 3.3.3 Critically Endangered Parrots -- 3.3.4 Extinct Parrots -- 3.4 Discussion -- 3.5 Conclusion -- Appendix: Response and ROC Curves of Models -- Critically Endangered Birds Model -- Extinct Birds Model -- Critically Endangered Parrots Model -- Extinct Parrots Model -- References -- Chapter-4 -- Reversed Kuznets Curves in the Caribbean: Linking Marine Biodiversity Endangerment with the National Gross Domestic Product and Assessed with Human Metrics (Life Expectancy, Infant Mortality, Population Growth Rate) -- 4.1 Introduction -- 4.1.1 Known Status of the Caribbean Marine Biodiversity and Its Coral Reefs -- 4.1.2 Coral Reefs, Economy, and Protection -- 4.2 Methodology -- 4.3 Results -- 4.4 Discussion -- 4.4.1 Current Study and Assessment of the Caribbean -- 4.4.2 Failure of the Traditional-Style "Law" in the Caribbean -- 4.4.3 Linking Biodiversity Endangerment and GDP with Human Metric Assessments (Life Expectancy, Infancy Mortality, and Population Growth Rate). , 4.4.4 What Does the Future Hold for the Marine Biodiversity of the Caribbean? -- 4.4.5 So What Needs to Be Done: Adding the Impact of Economic Growth to Biodiversity Science and with More Holistic Metrics -- 4.4.6 Call for a Better Economy and for (Much) Better Institutions -- References -- Chapter-5 -- An Oceanography Conservation View of Central America: Caribbean Wealth, Pacific Wilderness, Plunder and Mismanagement Meets Seafloor Mining, Deep-Sea Drilling, Climate Change, and Human Population Explosion in the EEZs and Beyond -- 5.1 Central American Oceans in a Wider Context -- 5.2 The Central American Pacific: A Closer Look -- 5.3 The Caribbean in More Detail -- 5.4 A Very Brief History of Central American Ocean Waters and Their Biological Status -- 5.5 Conservation and Management of Central American Oceans -- 5.6 An Outlook of Central American Marine Resources: "Tyranny of the Locals" and of the Markets Keep Showing Their Destructive Powers and Invisible Hands -- References -- Chapter-6 -- Coral Reefs of Central America, Dimethylsulphide Seascapes, Open Access Data, GIS, and Machine Learning Predictions: Another Application of the New Megascience for Global Sustainability -- 6.1 Introduction -- 6.2 Methods -- 6.2.1 Coral Reef at Risk Data -- 6.2.2 DMS Data -- 6.2.3 GIS Overlays, Data Extraction, Correlations, and Machine Learning Modeling -- 6.3 Results -- 6.3.1 Model 1 -- 6.3.2 Model 2 -- 6.4 Discussion -- 6.4.1 Model 1 Coral Reef Locations -- 6.4.2 Model 2 Risk Factors of Coral Reefs -- 6.4.3 Megascience and Applicability -- References -- Chapter-7 -- Field Schools and Research Stations in a Global Context: La Suerte (Costa Rica) and Ometepe (Nicaragua) in a Wider Perspective -- 7.1 Introduction -- 7.2 Central American Field Schools and Research Stations (a Selection) -- 7.2.1 Barro Colorado Island -- 7.2.2 La Selva -- 7.2.3 Santa Rosa. , 7.2.4 Monteverde -- 7.2.5 La Suerte -- 7.2.6 Ometepe -- 7.2.7 Field Schools and Research Sites Elsewhere -- 7.3 Connections, Problems, and Opportunities of Field Sites -- References -- Chapter-8 -- A Brief History of the Molina Family, and the Birth of Maderas Rainforest Conservancy at the La Suerte and Ometepe Field Stations-A Narrative -- 8.1 Introduction -- 8.2 La Suerte Biological Station, Costa Rica -- 8.3 Ometepe Biological Field Station, Nicaragua -- 8.4 Conservation Choices due to Contrasts -- 8.5 Projects -- 8.5.1 Nicaragua -- 8.5.2 Costa Rica -- 8.5.3 Sierra Leone -- 8.6 Future Plans -- Chapter-9 -- Teaching (Tropical) Biodiversity with International Field Schools: A Flexible Success Model in a Time of "Wireless" Globalization -- 9.1 What Is It Like to Be Teaching in a Field School? -- 9.2 Global Biodiversity: What Is Really to Be Taught? -- 9.3 What Teaching Style to Use in a Field School Emphasizing Global Biodiversity? -- 9.4 The Use of Digital Tools, Toys, and Opportunities to Teach Biodiversity in the Field Are in Line with the Biodiversity Convention -- 9.5 The Good, the Bad, the Ugly, and the Very Good Field School: High-Quality Teaching -- 9.6 Flexibility and Applying Field Schools Elsewhere: High Schools, e-Learning, Blended Learning, and MOOCS in the Arctic and at Sea -- 9.7 Great Field School Opportunities: Beyond "Just" Biology and Conservation -- 9.8 Why You Should Take a Field School Class and Teach One -- References -- Appendix 1: Web References for Field School Teachers and Students (Selection) -- Course Materials -- Other References of Interest -- Objectives -- Lecture Topics -- Independent Projects -- Exam -- General Daily Schedule -- Course Schedule -- Grades -- Course Goals -- Chapter-10. , A Documented Plant and Tree Species Narrative of La Suerte (Costa Rica) and Ometepe (Nicaragua): Overcoming Ancient Taxonomy Demons for a More Relevant and Valid Conservation Research Effort in the Tropics -- 10.1 Introduction -- 10.2 The Central American Botany -- 10.3 The Classic Plant Taxonomy Justification to be Revised -- 10.4 Plants as a Resource of Primarily Intrinsic Value, of Course -- 10.5 Human Behavior and Curiosity for Plant and Tree Species Versus Destructive Science -- 10.6 On the Human-Nature Relationship: Towards a Betterment -- 10.7 Basic Plant and Tree Identification Resources -- 10.7.1 Plants of La Suerte -- 10.7.2 Trees of La Suerte -- 10.7.3 Plants of Ometepe -- 10.7.4 Trees of Ometepe -- 10.8 Redefining Plant Identification and Taxonomic Goals for Conservation -- References -- Chapter-11 -- The Incredible World of Insects in Central America: A Virtual Interview with Prof. Dr. Bugs at La S -- Chapter-12 -- Amphibians and Reptiles at the Ometepe and La Suerte Field Sites: Toward an Overview of the Evolution, Diversity, and Natural History of Central America's Herpetofauna -- 12.1 Introduction -- 12.2 A Brief Overview: The Evolution of Terrestrial Vertebrates in the Tropical Environment -- 12.2.1 Early Transitional Amphibian Fossils of the Devonian -- 12.2.2 Dominant Amphibians and Early Reptiles of the Carboniferous -- 12.2.3 Climate Change and Mass Extinction Usher in the Permian -- 12.3 Diversity and Natural History of the Central American Herpetofauna -- 12.3.1 Amphibians of Central America -- 12.3.2 Reptiles of Central America -- 12.4 Conclusion -- References -- Chapter-13 -- Exciting Findings and Insights from the Caiman Study at La Suerte: First Lessons from the Scientific Frontline -- Chapter-14. , Conservation of a Neotropical Herpetofauna: An Introduction to the Crisis of Amphibians and Reptiles in Central America and Beyond.

Note: Intro -- Dedication -- Foreword -- Preface -- Acknowledgments -- Contents -- Contributors -- About the Editors -- Part I: Introduction -- Chapter 1: Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective -- 1.1 Introduction -- 1.2 Some Terminology -- 1.3 A Few Paragraphs on the History of Machine Learning -- 1.4 Machine Learning in Ecology and Wildife Biology to Date -- 1.5 Algorithms as a Bottleneck for Wildlife Conservation -- 1.6 Data Issues and Availability Related to Data Mining and Machine Learning -- 1.7 Workflows -- 1.8 Citizen Science -- 1.9 A Great Future could be around the Corner, Waiting for you Online, and in the Wilderness of this World -- References -- Chapter 2: Use of Machine Learning (ML) for Predicting and Analyzing Ecological and 'Presence Only' Data: An Overview of Applications and a Good Outlook -- 2.1 Introduction -- 2.2 Popular and Widely Available Machine Learning Techniques -- 2.3 Applications of Machine Learning in Wildlife Biology -- 2.4 Strengths and Some Described Weaknesses of Machine Learning -- 2.5 A Case Example -- 2.6 Machine Learning in Climate Change Models and Other Complex Applications -- 2.7 Conclusions: Future Outlook and Topics Awaiting Research and Application for Machine Learning (ML) -- References -- Chapter 3: Boosting, Bagging and Ensembles in the Real World: An Overview, some Explanations and a Practical Synthesis for Holistic Global Wildlife Conservation Applications Based on Machine Learning with Decision Trees -- 3.1 Introduction -- 3.2 A Quick Refresher on Linear Models (LMs), Parsimony and Classification and Regression Trees (CARTs) -- 3.3 Boosting -- 3.3.1 What Boosting is in a Nutshell -- 3.3.2 Short History of 'Boosting' -- 3.3.3 Why is Boosting so Powerful? -- 3.4 Bagging. , 3.4.1 What Bagging is in a Nutshell -- 3.4.2 Short History of Bagging -- 3.4.3 Why Bagging is so Powerful -- 3.5 Ensemble Models -- 3.5.1 What is an Ensemble Model? -- 3.5.2 History of Ensemble Models -- 3.5.3 Why Ensemble Models are so Powerful -- 3.6 Model Applications and Inference -- 3.6.1 Boosting Experiences and Applications -- 3.6.2 Bagging Experiences and Applications -- 3.6.3 Ensembles -- 3.6.4 Precautionary, Pro-Active, and Predictive Models for Better Resource Conservation Management -- 3.7 A Commonly Heard Criticism and Misunderstanding of Machine Learning, and Characteristics of Man-Made Science and Conservation Driven by Reductionism -- 3.8 Synthesis and Outlook -- References -- Part II: Predicting Patterns -- Chapter 4: From Data Mining with Machine Learning to Inference in Diverse and Highly Complex Data: Some Shared Experiences, Intellectual Reasoning and Analysis Steps for the Real World of Science Applications -- 4.1 Introduction -- 4.2 Model Selection with Many Predictors as an Analysis Scheme and as a Major Platform for Statistical Testing, Prediction and Inference -- 4.3 Confront Models with Data: Moving towards an Evidence-Based Analysis -- 4.4 A Real-World Data and Analysis Workflow Example -- 4.4.1 Pre-Assumption (as Experienced in Real World Examples) -- 4.5 Real World Tools and Minimum Approaches to Start Data Mining and Predictions -- 4.6 A Set of Commonly Heard Criticisms and Comments for these Data Mining Steps and How to Answer them from a Machine Learning Aspect for Best inferences -- 4.7 On 'Best Professional Practices', Professional Bias, Ignorance, Misconduct, Professional Societies, Education and Culture: What is a Lie and Punishable Intent when underlying Methods Problems are well-known but are ignored? -- 4.8 Conclusion -- References. , Chapter 5: Ensembles of Ensembles: Combining the Predictions from Multiple Machine Learning Methods -- 5.1 Introduction -- 5.2 Methods -- 5.2.1 Data Set -- 5.2.2 Accuracy Assessment -- 5.2.3 Modeling Algorithms -- 5.3 Results and Discussion -- References -- Chapter 6: Machine Learning for Macroscale Ecological Niche Modeling - a Multi-Model, Multi-Response Ensemble Technique for Tree Species Management Under Climate Change -- 6.1 Introduction -- 6.2 Controlling Bias and Variance -- 6.3 Ensemble Learning Via Decision Trees -- 6.4 Ensemble Models -- 6.4.1 Bagging, Random Forest and Extreme Random Forests -- 6.4.2 Boosting Decision Trees -- 6.4.3 RuleFit -- 6.5 Multiple Abundances - Habitat Suitability -- 6.6 Explanatory Variables (Predictors) -- 6.7 Multi-Model Ensemble Approach -- 6.8 Results and Interpretation -- 6.8.1 Model Reliability -- 6.8.2 Prediction Confidence -- 6.8.3 Combined Habitat Quality and Prediction Confidence -- 6.8.4 Predictor Importance -- 6.9 Discussion -- 6.10 Conclusion -- References -- Chapter 7: Mapping Aboveground Biomass of Trees Using Forest Inventory Data and Public Environmental Variables within the Alaskan Boreal Forest -- 7.1 Introduction -- 7.2 Methods and Materials -- 7.2.1 Biomass Data -- 7.2.2 Environmental Factors -- 7.2.3 The Calibration and Validation Datasets -- 7.2.4 Statistical Methods -- 7.2.5 Predictive Maps -- 7.3 Results -- 7.3.1 Variable Selection and Importance -- 7.3.2 Random Forests Analysis Model Assessment -- 7.3.3 Influence of the Environmental Factors on Aboveground Forest Biomass -- 7.3.4 Regression Tree Analysis Model Assessment -- 7.3.5 Spatial Dependency of Aboveground Forest Biomass -- 7.3.6 Predicted Aboveground Forest Biomass Patterns -- 7.4 Discussion -- References -- Part III: Data Exploration and Hypothesis Generation with Machine Learning. , Chapter 8: 'Batteries' in Machine Learning: A First Experimental Assessment of Inference for Siberian Crane Breeding Grounds in the Russian High Arctic Based on 'Shaving' 74 Predictors -- 8.1 Introduction -- 8.2 Methods -- 8.2.1 Presence and Absence Points for Siberian Crane -- 8.2.2 GIS Predictors -- 8.2.3 'Battery' Runs in Salford System's Predictive Modeler (SPM7) -- 8.3 Results -- 8.3.1 Predictive Performance Metrics -- 8.3.2 Visual Assessment of Prediction Maps -- 8.4 Discussion -- Appendix 1: Details of 74 GIS Environmental layers Used in the Model Prediction (+ 3 Additional Internal Columns) -- Appendix 2 -- List of Top 20 Predictors, as identified by TreeNet ranking -- Appendix 3 -- Prediction Model Details for the Best Performing Model (the 'Kitchen sink model' with 74 predictors) -- Appendix 4 -- Prediction Map 3 for the 'BIO14 model' -- Prediction Map 4 for the 'TMax12BIO14 model' -- Prediction Map 5 for the 'Top5 model' -- Prediction Map 6 for the 'Top10 model' -- Prediction Map 7 for the 'Top29 model' -- Prediction Map 8 for the 'Top35 model' -- Prediction Map 9 for the 'Bottom 44 model' -- Prediction map 10 for the 'Leaving out top 3 interacting predictors model' -- References -- Chapter 9: Landscape Applications of Machine Learning: Comparing Random Forests and Logistic Regression in Multi-Scale Optimized Predictive Modeling of American Marten Occurrence in Northern Idaho, USA -- 9.1 Introduction -- 9.2 Methods -- 9.2.1 Study Area -- 9.2.2 Occurrence Data and Logistic Regression Model -- 9.2.3 Predictor Variables for Analysis -- 9.2.4 Modeling Approaches -- 9.2.5 Model Assessment -- 9.3 Results -- 9.3.1 Random Forests Univariate Scaling -- 9.3.2 Random Forests Multivariate Model -- 9.3.3 Model Comparison -- 9.3.4 Model Performance -- 9.4 Discussion -- 9.5 Conclusion -- References. , Chapter 10: Using Interactions among Species, Landscapes, and Climate to Inform Ecological Niche Models: A Case Study of American Marten (Martes americana) Distribution in Alaska -- 10.1 Introduction -- 10.1.1 Stochastic Gradient Boosting -- 10.1.2 Variable Interactions -- 10.1.3 Ecological Niche Models -- 10.2 Methods -- 10.2.1 Training Dataset -- 10.2.2 Model Iterations -- 10.2.3 Interaction Network Graphs -- 10.2.4 Landscape Predictions -- 10.3 Results -- 10.3.1 Varclus Analysis -- 10.3.2 Full Model -- 10.3.3 Top Sub-Models -- 10.3.4 Other Sub-Models -- 10.3.5 Spatial Models -- 10.4 Discussion -- 10.4.1 Parsimonious Versus Highly Interactive Models -- 10.4.2 High Level Categorical (HLC) Predictors -- 10.4.3 Interaction Effects -- 10.4.4 Predicted Marten distribution in Alaska -- 10.4.5 Conclusions and Suggested Practices -- References -- Chapter 11: Advanced Data Mining (Cloning) of Predicted Climate-Scapes and Their Variances Assessed with Machine Learning: An Example from Southern Alaska Shows Topographical Biases and Strong Differences -- 11.1 Introduction -- 11.2 Methods -- 11.2.1 GIS Data and Operations -- 11.2.2 Data Mining -- 11.3 Results -- 11.4 Discussion -- Appendix A Example Map of Data Sets Used for this Machine Learning Assessment of Climate Models: Adaptwest in July. Raw Climate Surface and All GIS Maps are available from the Authors on Request -- Appendix B Remaining Details of the TreeNet Model not shown in the text: (a) gains curve, and partial dependence plots for (b) proximity to road, (c) proximity to river -- References -- Chapter 12: Using TreeNet, a Machine Learning Approach to Better Understand Factors that Influence Elevated Blood Lead Levels in Wintering Golden Eagles in the Western United States -- 12.1 Methods -- 12.2 Results -- 12.3 Discussion -- References. , Part IV: Novel Applications of Machine Learning Beyond Species Distribution Models.

Note: Intro -- Protection of the Three Poles -- Foreword -- Preface -- A Prefatory Note from the Editor -- Acknowledgments -- Contents -- Part I: Introduction -- Part II: Polar Overview -- Part III: The Antarctic -- Part IV: The Hindu Kush-Himalayas -- Part V: The Arctic -- Part VI: Synthesis -- Epilogue -- Index.

Note: Intro -- Foreword by the Editors -- Preface: The Third Pole - Lessons for 'Barbarians' Everywhere? -- Acknowledgements -- Contents -- About the Editors -- Contributors -- Part I: Landscapes -- Chapter 1: Mountain Landscapes and Watersheds of the Hindu Kush-Himalaya (HKH) and Their Biogeography: A Descriptive Overview and Introduction for 18 Nations in the Anthropocene -- References -- Chapter 2: The Hindu Kush-Himalaya (HKH) Region in the Modern Global and Climate Context: Major Weather Systems, Monsoon, Asian Brown Clouds (ABCs), Digital Data/Models and Global Linkages of Telecoupling and Teleconnection all Affecting Global Human Well -- References -- Chapter 3: From the Mountains and Glaciers Down to the Rivers to the Estuaries and Oceans: Another Sad Tale of 18 or so Rivers -- 3.1 Yarlung-Tsangpo River -- 3.2 Ganges River -- 3.3 Yamun River (Jumna) -- 3.4 Padma River -- 3.5 Brahmaputra River -- 3.6 Irrawaddy River (Ayeyarwady) -- 3.7 Mekong River -- 3.8 Yellow River (Huanghe) -- 3.9 Yangtze/Jangtsekiang River -- 3.10 Heilongyang/Amur River -- 3.11 Indus River -- 3.12 Jhelum River -- 3.13 Koshi River -- 3.14 Gandaki River -- 3.15 Tarim River -- 3.16 Amu Darya River (Amo or Oxus) -- 3.17 Illi River -- 3.18 Onon River -- 3.19 A Needed Unifying Watershed View for Rivers of HKH -- 3.20 Aqua quo vadis (During Climate Warming, Human Consumption Rise, Human Population Explosion, Lack of Water, and all Synergies Combined)? -- References -- Chapter 4: The Hindu Kush-Himalaya (HKH) in the Global and Marine Context: Major Estuaries, Coast-Scapes, Ocean Coupling, Seawalls, over 2 Billion People and Global (Food) Security -- References -- Chapter 5: A View from Space on Poyang Lake: What We Can Already See and What It Means -- 5.1 Introduction -- 5.2 In Situ Observation and Satellite Technology -- 5.2.1 The In Situ Observation Network. , 5.2.2 Known Satellites and Their Environmental Applications -- 5.2.2.1 Inundation Extent Delineation from Remote Sensing Imagery -- 5.2.2.2 Water Level Estimation from Remote Sensing Data -- 5.3 Spatio-Temporal Variations of Poyang Lake -- 5.3.1 Change in Lake Water Surface -- 5.3.1.1 Total Lake Area -- 5.3.1.2 Inundation -- 5.3.2 Lake Stage -- 5.3.3 Landscape Pattern -- 5.3.4 Lake Droughts -- 5.4 Concluding Remarks -- References -- Chapter 6: Effective Poyang Lake Conservation? A Local Ecology View from Downstream Involving Internationally Migratory Birds When Trying to Buffer and Manage Water from HKH with 'Modern' Concepts -- 6.1 Waterflow and Changes of the Poyang Lake -- 6.2 Modern Changes of Poyang Lake -- 6.3 A Relevant Future in the Anthropocene for Poyang Lake as We Know It -- References -- Chapter 7: The Future of Biodiversity in the Changing Watersheds of Kashmir Himalaya, Pakistan: Conservation Challenges and Opportunities -- 7.1 Introduction -- 7.2 The Human Population and Belief System in the Azad Jammu and Kashmir Watersheds -- 7.3 Languages in the Azad Jammu and Kashmir Watersheds -- 7.4 Climate of the Azad Jammu and Kashmir Watersheds -- 7.5 Forest Biodiversity in the Azad Jammu and Kashmir Watersheds -- 7.6 Some Important Wildlife Species in the Azad Jammu and Kashmir Watersheds -- 7.7 Rivers and Wetlands in the Azad Jammu and Kashmir Watersheds -- 7.8 Protected Areas Systems and Conservation in the Azad Jammu and Kashmir Watersheds -- 7.9 Destruction and Threats to Biodiversity in Kashmir, Hindu Kush-Himalaya -- 7.10 Rights and Concessions for Natural Resources in the Azad Jammu and Kashmir Watersheds -- 7.11 Commercial Uses of Forests in the Azad Jammu and Kashmir Watersheds -- 7.12 Grazing Issues in the Azad Jammu and Kashmir Watersheds -- 7.13 Hunting and Poaching in the Azad Jammu and Kashmir Watersheds. , 7.14 How Many Species Are Gone Extinct in Pakistan, Thus Far? -- 7.15 Example: Guchhi or Black-Mushroom Harvest -- 7.16 Forest Fires in the Azad Jammu and Kashmir Watersheds -- 7.17 Construction of Dams and Hydro Power Projects in the Azad Jammu and Kashmir Watersheds -- 7.18 Generic and Wide Lack of Capacity in Climate Changes Mitigation and Adaptation -- 7.19 Conservation Challenges and Opportunities for the the Azad Jammu and Kashmir Watersheds -- Appendix: List of Hydropower Projects in the Azad Jammu and Kashmir Watersheds in Various Stages of Development and Under Different Agency Jurisdictions -- References -- Chapter 8: Towards a Landscape Perspective of Diseases in Plants: An Overview and Review of a Critical but Overlooked Ecology Issue in the Hindu Kush-Himalayan Region -- 8.1 Introduction -- 8.2 Landscape Concept of Plant Diseases -- 8.3 The Impact of Landscape Properties on Viral Diseases -- 8.4 The Impact of Landscape Properties on Fungal Diseases -- 8.5 The Impact of Landscape Properties on Bacterial Diseases -- 8.6 The Impact of Landscape Properties on Arthropod-Borne Diseases -- 8.7 Conclusions and Research and Management Recommendations -- Appendix: Various Diseases of Different Hosts, Their Etiologic Agents, and Their Characteristics in the Nepal Himalaya -- References -- Chapter 9: Showing True Change of the Hindu Kush Himalaya Region Through the Power of Photo Monitoring as a Visual Memory of Change -- 9.1 Introduction -- 9.2 Background, Context and Bio Data of the Photographer -- Chapter 10: A First-Hand Narrative Account on Tibet's Paper Parks: How China's Greenwashing in Tibet Flies Under the Radar -- 10.1 Nature Reserves and Nomad Resettlement -- 10.2 Hoodwinking the UN World Heritage Committee -- 10.3 When Miners Happen to Venture into Nature Reserves -- 10.4 Eco-Babble -- References. , Chapter 11: Forestry Management in Nepal: An Example and a Review of Growth & -- Yield -- 11.1 Introduction -- 11.2 Classification of Forest -- 11.2.1 Ecological Classification of Forests -- 11.2.2 Classification of Forests for Management (Table 11.2) -- 11.3 Forest Management in Nepal -- 11.4 History of Forest Management -- 11.4.1 Before 1926 A.D. -- 11.4.2 From 1927 to 1960 A.D. (Table 11.3) -- 11.4.3 From 1961 to 1977 A.D. (Table 11.4) -- 11.4.4 From 1978 to 1990 A.D. (Table 11.5) -- 11.4.5 From 1991 to Now (Table 11.6) -- 11.5 Community Forestry -- 11.6 Collaborative Forest Management -- 11.7 Scientific Approach to Forest Management -- 11.8 REDD in Nepal -- 11.8.1 Benefits from REDD -- 11.9 Fire Management -- 11.10 Forest Resources for People's Livelihood -- 11.11 Conclusion -- References -- Part II: Concepts, Cultures, Religions and the Mind -- Chapter 12: What It Is Like to Be a Land-Locked Nation: Some Discriminatory Examples and Details from National Watersheds of the Hindu Kush-Himalaya Under the Neoliberal 'Free Market' Participation Paradigm -- 12.1 Introduction -- 12.2 Conclusion and Outlook for Land-Locked Nations in the HKH Region -- References -- Chapter 13: Ethnobiology and Indigenous Regimes in the Conservation of Species, Watersheds, and Landscapes: Experiences and Evidences from the Hindu Kush-Himalayan Nations for a Global Application -- 13.1 Introduction -- 13.2 Ethnobiology in Nepal -- 13.3 A Short Hindu Kush-Himalayan (HKH) Political History, Its Ethnic Groups, and Related Conservation Output on a Landscape Scale -- 13.4 Associated Religions and Practices of Ethnobiology by the HKH People -- 13.5 Ethnobiology, Traditional Healers, and the Conservation of Resources, Landscapes, and Watersheds -- 13.6 Ethnobiology, Musical, Esthetic and Recreational Tools, and Conservation of Landscapes. , 13.7 Ethnic Knowledge: At Least Two Sides of a Coin in Conservation -- 13.8 Conclusions and Recommendations -- References -- Chapter 14: Spirituality Beats It All: A Quick Conservation Overview, Self-Organization and the Great Value of (Indigenous) Religions for Hindu Kush-Himalaya Landscapes, Its Geo-Parks, Species, Ecological Processes and Watersheds -- References -- Chapter 15: Settling the Terror of Your Mind with the Deities: About Fear, Anxiety, Inherent Chaos and Self-Doubt in Hind Kush-Himalaya Expeditions and Associated Research -- 15.1 Mind Games -- 15.2 Beyond Deaths and Evil Spirits: In the Field, Finally -- 15.3 Aftermaths -- References -- Part III: Real-World Policy, Conservation Management of Wildlife, Habitat, and Biodiversity Data -- Chapter 16: The Relevance and Significant Role of Mid-elevation for the Watersheds and Biodiversity Conservation in the Hindu Kush-Himalaya: The Case of Nepal in the Anthropocene -- 16.1 Introduction -- 16.2 The Mid-Elevation Region in Nepal: An Introduction -- 16.3 Flora and Fauna in Nepal's Mid-elevation -- 16.4 Land-Use Systems in Nepal's Mid-elevation -- 16.5 Water-Sources in Nepal's Mid-elevation -- 16.6 The Significant Role of Mid-hills in Biodiversity Conservation -- 16.7 Anthropogenic Impacts in Mid-hill Ecosystems -- 16.7.1 Aspects of Human History -- 16.7.2 Fire -- 16.7.3 Landslides -- 16.7.4 A Generic Ineffectiveness of Policy -- 16.8 Conclusion -- References -- Chapter 17: Nature and Landscape Governance in Royal Times: Experiences from the Shah and Rana Regimes in Nepal Re-assembled from Literature and Interview Data -- 17.1 Introduction -- 17.1.1 Shah Regime: A Quick Overview -- 17.1.2 Rana Regime -- 17.1.3 Biodiversity in Nepal: A Quick Overview -- 17.2 Biodiversity Conservation and Governance Efforts in Nepal -- 17.3 Biodiversity and Wildlife Conservation Status in Rana and Shah Regime. , 17.4 Challenges for Biodiversity and Wildlife Conservation in Recent Years.