Landscape Genetics: Concepts, Methods, Applications -- Contents -- List of Contributors -- Website -- Acknowledgments -- Glossary -- Chapter 1: Introduction to Landscape Genetics - Concepts, Methods, Applications -- 1.1 Introduction -- 1.2 Defining Landscape Genetics -- 1.3 The Three Analytical Steps of Landscape Genetics -- 1.4 The Interdisciplinary Challenge of Landscape Genetics -- 1.4.1 The Two Scopes of Landscape Genetic Research -- 1.5 Structure of This Book - Concepts, Methods, Applications -- 1.5.1 Limitations and Potential of This Book -- References -- Part 1: Concepts -- Chapter 2: Basics of Landscape Ecology: An Introduction to Landscapes and Population Processes for Landscape Geneticists -- 2.1 Introduction -- 2.2 How Landscapes Affect Population Genetic Processes -- 2.2.1 Area Effects -- 2.2.2 Edge Effects -- 2.2.3 Isolation Effects -- 2.3 Defining the Landscape for Landscape Genetic Research -- 2.3.1 What is a Landscape? -- 2.3.2 Thematic Content -- 2.3.3 Thematic Resolution -- 2.3.4 Spatial Extent and Grain -- 2.3.5 A Priori Hypotheses should Guide Landscape Definition -- 2.4 Defining Populations and Characterizing Dispersal Processes -- 2.4.1 Panmictic Populations -- 2.4.2 Metapopulations -- 2.4.3 Gradient Populations -- 2.5 Putting It Together: Combinations of Landscape and Population Models -- 2.6 Frameworks for Delineating Landscapes and Populations for Landscape Genetics -- 2.6.1 Step 1: Establish Analysis Objectives -- 2.6.2 Step 2: Define the Landscape -- Define the Extent of the Landscape -- Establish a Model of the Landscape Structure -- Establish a Relevant Grain of Analysis -- 2.6.3 Step 3: Define the Population and Design the Sampling Scheme -- 2.6.4 Step 4: Characterize the Landscape Relative to Analysis Objectives -- 2.6.5 Step 5: Conduct Analysis -- 2.7 Current Challenges and Future Opportunities -- References.

Chapter 3: Basics of Population Genetics: Quantifying Neutral and Adaptive Genetic Variation for Landscape Genetic Studies -- 3.1 Introduction -- 3.2 Overview of Landscape Influences on Genetic Variation -- 3.3 Overview of Dna Types and Molecular Methods -- 3.3.1 Types of DNA -- 3.3.2 Adaptive versus Neutral Loci -- 3.3.3 Molecular Methods -- 3.3.4 Unit of Analysis -- 3.4 Important Population Genetic Models -- 3.4.1 Hardy-Weinberg Equilibrium -- 3.4.2 Linkage Equilibrium -- 3.4.3 Effective Population Size and Genetic Drift -- 3.4.4 Mutation -- 3.4.5 Migration (Gene Flow) -- 3.4.6 Isolation-by-Distance and Landscape -- 3.5 Measuring Genetic Diversity -- 3.5.1 Population Level -- 3.5.2 Individual Level -- 3.6 Evaluating Genetic Structure and Detecting Barriers -- 3.6.1 Population-Based Measures -- 3.6.2 Individual-Based Genetic Distance Metrics -- 3.6.3 Bayesian Clustering Methods -- 3.6.4 Barrier Detection Methods -- 3.7 Estimating Gene Flow Using Indirect and Direct Methods -- 3.7.1 Indirect Measures of Gene Flow - Coalescent Approaches -- 3.7.2 Direct Measures - Assignment Tests -- 3.7.3 Parentage Analysis -- 3.8 Conclusion and Future Directions -- References -- Chapter 4: Basics of Study Design: Sampling Landscape Heterogeneity and Genetic Variation for Landscape Genetic Studies -- 4.1 Introduction -- 4.2 Study Design Terminology Used in This Chapter -- 4.2.1 Sampling Level -- 4.2.2 Sampling Intensity -- 4.2.3 Spatial Sampling Scheme -- 4.2.4 Temporal Sampling Scheme -- 4.3 General Study Design Considerations -- 4.4 Considerations for Landscape Genetic Study Design -- 4.4.1 Considerations for Sampling Landscape Data -- 4.4.2 Considerations for Sampling Genetic Data -- 4.4.3 Matching Landscape and Genetic Data -- 4.5 Current Knowledge About Study Design Effects in Landscape Genetics -- 4.5.1 Sampling of Landscape Heterogeneity.

4.5.2 Individual- versus Population-Based Sampling -- 4.5.3 Spatial Sampling Design versus Sampling Intensity -- 4.5.4 Sampling Intensity -- 4.5.5 Matching Sampling and Statistical Methods -- 4.6 Recommendations for Optimal Sampling Strategies in Landscape Genetics -- 4.7 Conclusions and Future Directions -- References -- Chapter 5: Basics of Spatial Data Analysis: Linking Landscape and Genetic Data for Landscape Genetic Studies -- 5.1 Introduction -- 5.2 How to Model Landscape Effects on Genetic Variation -- 5.2.1 Type of Landscape Data -- 5.2.2 Type of Genetic Data -- 5.2.3 Type of Statistical Model -- 5.2.4 Model Selection -- 5.2.5 How to Put Space into the Multivariate Regression Model -- 5.2.6 Multivariate Linear Regression with OLS -- 5.2.7 Spatial Weights Matrix W -- 5.2.8 Spatial Regression -- 5.2.9 Spatial Eigenvectors -- 5.2.10 Multivariate Moran's I -- 5.2.11 Spatial Filtering -- 5.3 How to Model Isolation-By-Distance -- 5.3.1 IBD and Spatial Regression with CAR -- 5.3.2 IBD and Spatial Filtering with MEM -- 5.3.3 IBD and Multiple Regression of Distance Matrices (MRM) -- 5.4 Future Directions -- Acknowledgments -- References -- Part 2: Methods -- Chapter 6. Simulation Modeling in Landscape Genetics -- 6.1 Introduction -- 6.2 A Brief Overview of Models and Simulations -- 6.3 General Benefits of Simulation Modeling -- 6.4 Landscape Genetic Simulation Modeling -- 6.5 Examples of Simulation Modeling in Landscape Genetics -- 6.5.1 Analytical Evaluations: (When) Do Methods Work? How can we Best Quantify Landscape-Genetic Relationships? -- 6.5.2 Theoretical Developments: How/Why Does Landscape Heterogeneity Influence Genetics? -- 6.5.3 Empirical Applications: Using Simulation to Elucidate, Evaluate, and Explain Empirical Observations -- 6.6 Designing and Choosing Landscape Genetic Simulation Models.

6.6.1 Software for Landscape Genetics Simulations -- 6.6.2 Practical Guidelines for Conducting Landscape Genetic Simulations -- 6.7 The Future of Landscape Genetic Simulation Modeling -- References -- Chapter 7: Clustering and Assignment Methods in Landscape Genetics -- 7.1 Introduction -- 7.2 Exploratory Data Analysis and Model-Based Clustering for Population Structure Analysis -- 7.2.1 Exploratory Data Analysis -- 7.2.2 Model-Based Clustering Approaches -- 7.2.3 Visualization of PCA and STRUCTURE Results -- 7.2.4 Simulated Examples -- 7.3 Spatially-Explicit Methods in Landscape Genetics -- 7.4 Spatial Eda Methods: Spatial Pca and Spatial Factor Analysis -- 7.5 Spatial Mbc Methods -- 7.6 Habitat and Environmental Heterogeneity Models -- 7.6.1 Going Beyond Geography -- 7.6.2 Canonical Correspondence Analysis and Redundancy Analysis -- 7.6.3 Bayesian MBC Algorithms using Environmental Data -- 7.6.4 Ancestry Distribution Models -- 7.7 Discussion -- 7.7.1 From Landscape Ecology and Population Genetics to Landscape Genetic Methods -- 7.7.2 Interpretations of EDA and MBC Outputs -- 7.7.3 Ancient Events -- 7.7.4 Continuous Variation -- 7.7.5 Strengths and Weaknesses of EDA and MBC Methods -- 7.7.6 Divergent Selection and Population Structure -- References -- Chapter 8: Resistance Surface Modeling in Landscape Genetics -- 8.1 Introduction -- 8.1.1 What is a Resistance Surface? -- 8.1.2 Using Resistance Surfaces: A Framework -- 8.1.3 Selecting Variables for Resistance Surfaces: Initial Questions and Assumptions -- 8.1.4 What Factors Dictate the Utility of Variables for Resistance Surfaces? -- 8.2 Techniques for Parameterizing Resistance Surfaces -- Expert Opinion -- Empirical Parameterization -- 8.3 Estimating Connectivity From Resistance Surfaces -- 8.4 Statistical Validation of Resistance Surfaces.

8.4.1 Applications of Resistance Surfaces in Landscape Genetics -- 8.4.2 Concise Considerations for Effective Uses of Resistance Surfaces -- 8.5 The Future of the Resistance Surface in Landscape Genetics -- 8.5.1 Advances in Remote Sensing -- 8.5.2 Development of Model Selection and Optimization Methodologies -- 8.5.3 Resistance Surfaces in Adaptive Landscape Genomics -- 8.6 Conclusions -- References -- Chapter 9: Genomic Approaches in Landscape Genetics -- 9.1 Introduction -- 9.2 Current Landscape Genomics Methods -- 9.2.1 Population Genomics -- 9.2.2 QTL to Genome-Wide Association Studies -- 9.2.3 Candidate Gene Approaches -- 9.2.4 Exomes and Transcriptomes -- 9.3 General Challenges in Landscape Genomics -- 9.3.1 Spatial Data Collection -- 9.4 Spatial Autocorrelation -- 9.4.1 Isolation-by-Adaptation -- 9.5 Applications of Landscape Genomics to Climate Change -- References -- Chapter 10: Graph Theory and Network Models in Landscape Genetics -- 10.1 Introduction -- 10.2 Background on Graph Theory -- 10.2.1 What is a Graph? -- 10.2.2 What are the Assumptions of Graph Theoretic Approaches? -- 10.2.3 What Edges are Relevant? -- 10.3 Landscape Genetic Applications -- 10.3.1 Describing Population Structure -- Theoretical Background -- Conceptual Framework -- Data Requirements -- Software -- Case Study -- 10.3.2 Hypothesis of Connectivity -- Theoretical Background -- Implementation -- Case Studies -- 10.3.3 Functional Connectivity -- Theoretical Background -- Modeling Framework -- Data Requirements and Assumptions -- Software Implementation -- Case Study -- 10.4 Recommendations for Using Graph Approaches in Landscape Genetics -- 10.4.1 Recommendation 1: Clearly Identify Research Questions -- 10.4.2 Recommendation 2: Choosing an Adequate Study Design -- 10.4.3 Recommendation 3: Testing Underlying Assumptions -- 10.5 Current Research Needs.

10.6 Conclusion - Potential for Application of Graphs for Conservation.