ALBERT

Note: Contents 1 Introduction to Spatial Ecology and Its Relevance for Conservation 1.1 What Is Spatial Ecology? 1.2 The Importance of Space in Ecology 1.3 The Importance of Space in Conservation 1.4 The Growth of Frameworks for Spatial Modeling 1.5 The Path Ahead References Part I Quantifying Spatial Pattern in Ecological Data 2 Scale 2.1 Introduction 2.2 Key Concepts and Approaches 2.2.1 Scale Defined and Clarified 2.2.2 Why Is Spatial Scale Important? 2.2.3 Multiscale and Multilevel Quantitative Problems 2.2.4 Spatial Scale and Study Design 2.3 Examples in R 2.3.1 Packages in R 2.3.2 The Data 2.3.3 A Simple Simulated Example 2.3.4 Multiscale Species Response to Land Cover 2.4 Next Steps and Advanced Issues 2.4.1 Identifying Characteristic Scales Beyond Species–Environment Relationships 2.4.2 Sampling and Scale 2.5 Conclusions References 3 Land-Cover Pattern and Change 3.1 Introduction 3.2 Key Concepts 3.2.1 Land Use Versus Land Cover 3.2.2 Conceptual Models for Land Cover and Habitat Change 3.2.3 Habitat Loss and Fragmentation 3.2.4 Quantifying Land-Cover Pattern 3.3 Examples in R 3.3.1 Packages in R 3.3.2 The Data 3.3.3 Quantifying Land-Cover Variation at Different Scales 3.3.4 Simulating Land Cover: Neutral Landscapes 3.4 Next Steps and Advanced Issues 3.4.1 Testing for Pattern Differences Between Landscapes 3.4.2 Land-Cover Quantification via Image Processing 3.4.3 Categorical Versus Continuous Metrics 3.5 Conclusions References 4 Spatial Dispersion and Point Data 4.1 Introduction 4.2 Key Concepts and Approaches 4.2.1 Characteristics of Point Patterns 4.2.2 Summary Statistics for Point Patterns 4.2.3 Common Statistical Models for Point Patterns 4.3 Examples in R 4.3.1 Packages in R 4.3.2 The Data 4.3.3 Creating Point Pattern Data and Visualizing It 4.3.4 Univariate Point Patterns 4.3.5 Marked Point Patterns 4.3.6 Inhomogeneous Point Processes and Point Process Models 4.3.7 Alternative Null Models 4.3.8 Simulating Point Processes 4.4 Next Steps and Advanced Issues 4.4.1 Space-Time Analysis 4.4.2 Replicated Point Patterns 4.5 Conclusions References 5 Spatial Dependence and Autocorrelation 5.1 Introduction 5.2 Key Concepts and Approaches 5.2.1 The Causes of Spatial Dependence 5.2.2 Why Spatial Dependence Matters 5.2.3 Quantifying Spatial Dependence 5.3 Examples in R 5.3.1 Packages in R 5.3.2 The Data 5.3.3 Correlograms 5.3.4 Variograms 5.3.5 Kriging 5.3.6 Simulating Spatially Autocorrelated Data 5.3.7 Multiscale Analysis 5.4 Next Steps and Advanced Issues 5.4.1 Local Spatial Dependence 5.4.2 Multivariate Spatial Dependence 5.5 Conclusions References 6 Accounting for Spatial Dependence in Ecological Data 6.1 Introduction 6.2 Key Concepts and Approaches 6.2.1 The Problem of Spatial Dependence in Ecology and Conservation 6.2.2 The Generalized Linear Model and Its Extensions 6.2.3 General Types of Spatial Models 6.2.4 Common Models that Account for Spatial Dependence 6.2.5 Inference Versus Prediction 6.3 Examples in R 6.3.1 Packages in R 6.3.2 The Data 6.3.3 Models that Ignore Spatial Dependence 6.3.4 Models that Account for Spatial Dependence 6.4 Next Steps and Advanced Issues 6.4.1 General Bayesian Models for Spatial Dependence 6.4.2 Detection Errors and Spatial Dependence 6.5 Conclusions References Part II Ecological Responses to Spatial Pattern and Conservation 7 Species Distributions 7.1 Introduction 7.2 Key Concepts and Approaches 7.2.1 The Niche Concept 7.2.2 Predicting Distributions or Niches? 7.2.3 Mechanistic Versus Correlative Distribution Models 7.2.4 Data for Correlative Distribution Models 7.2.5 Common Types of Distribution Modeling Techniques 7.2.6 Combining Models: Ensembles 7.2.7 Model Evaluation 7.3 Examples in R 7.3.1 Packages in R 7.3.2 The Data 7.3.3 Prepping the Data for Modeling 7.3.4 Contrasting Models 7.3.5 Interpreting Environmental Relationships 7.3.6 Model Evaluation 7.3.7 Combining Models: Ensembles 7.4 Next Steps and Advanced Issues 7.4.1 Incorporating Dispersal 7.4.2 Integrating Multiple Data Sources 7.4.3 Dynamic Models 7.4.4 Multi-species Models 7.4.5 Sampling Error and Distribution Models 7.5 Conclusions References 8 Space Use and Resource Selection 8.1 Introduction 8.2 Key Concepts and Approaches 8.2.1 Distinguishing Among the Diversity of Habitat-Related Concepts and Terms 8.2.2 Habitat Selection Theory 8.2.3 General Types of Habitat Use and Selection Data 8.2.4 Home Range and Space Use Approaches 8.2.5 Resource Selection Functions at Different Scales 8.3 Examples in R 8.3.1 Packages in R 8.3.2 The Data 8.3.3 Prepping the Data for Modeling 8.3.4 Home Range Analysis 8.3.5 Resource Selection Functions 8.4 Next Steps and Advanced Issues 8.4.1 Mechanistic Models and the Identification of Hidden States 8.4.2 Biotic Interactions 8.4.3 Sampling Error and Resource Selection Models 8.5 Conclusions References 9 Connectivity 9.1 Introduction 9.2 Key Concepts and Approaches 9.2.1 The Multiple Meanings of Connectivity 9.2.2 The Connectivity Concept 9.2.3 Factors Limiting Connectivity 9.2.4 Three Common Perspectives on Quantifying Connectivity 9.3 Examples in R 9.3.1 Packages in R 9.3.2 The Data 9.3.3 Functional Connectivity Among Protected Areas for Florida Panthers 9.3.4 Patch-Based Networks and Graph Theory 9.3.5 Combining Connectivity Mapping with Graph Theory 9.4 Next Steps and Advanced Issues 9.4.1 Connectivity in Space and Time 9.4.2 Individual-Based Models 9.4.3 Diffusion Models 9.4.4 Spatial Capture–Recapture 9.5 Conclusions References 10 Population Dynamics in Space 10.1 Introduction 10.2 Key Concepts and Approaches 10.2.1 Foundational Population Concepts 10.2.2 Spatial Population Concepts 10.2.3 Population Viability Analysis 10.2.4 Common Types of Spatial Population Models 10.3 Examples in R 10.3.1 Packages in R 10.3.2 The Data 10.3.3 Spatial Correlation and Synchrony 10.3.4 Metapopulation Metrics 10.3.5 Estimating Colonization–Extinction Dynamics 10.3.6 Projecting Dynamics 10.3.7 Metapopulation Viability and Environmental Change 10.4 Next Steps and Advanced Issues 10.4.1 Spatial Population Matrix Models 10.4.2 Diffusion and Spatial Dynamics 10.4.3 Agent-Based Models 10.4.4 Integrated Population Models 10.5 Conclusions References 11 Spatially Structured Communities 11.1 Introduction 11.2 Key Concepts and Approaches 11.2.1 Spatial Community Concepts 11.2.2 Common Approaches to Understanding Community–Environment Relationships 11.2.3 Spatial Models for Communities 11.3 Examples in R 11.3.1 Packages in R 11.3.2 The Data 11.3.3 Modeling Communities and Extrapolating in Space 11.3.4 Spatial Dependence in Communities 11.3.5 Community Models with Explicit Accounting for Space 11.4 Next Steps and Advanced Issues 11.4.1 Decomposition of Space–Environment Effects 11.4.2 Accounting for Dependence Among Species 11.4.3 Spatial Networks 11.5 Conclusions References 12 What Have We Learned? Looking Back and Pressing Forward 12.1 The Impact of Spatial Ecology and Conservation 12.2 Looking Forward: Frontiers for Spatial Ecology and Conservation 12.3 Where to Go from Here for Advanced Spatial Modeling? 12.4 Beyond R 12.5 Conclusions References Appendix A: An Introduction to R Index