Intro; Preface; Contents; About the Authors; Supplementary Material; Chapter 1: Introduction; 1.1 Why Numerical Ecology?; 1.2 Why R?; 1.3 Readership and Structure of the Book; 1.4 How to Use This Book; 1.5 The Data Sets; 1.5.1 The Doubs Fish Data; 1.5.2 The Oribatid Mite Data; 1.6 A Quick Reminder About Help Sources; 1.7 Now It Is Time; Chapter 2: Exploratory Data Analysis; 2.1 Objectives; 2.2 Data Exploration; 2.2.1 Data Extraction; 2.2.2 Species Data: First Contact; 2.2.3 Species Data: A Closer Look; 2.2.4 Ecological Data Transformation; 2.2.5 Environmental Data; 2.3 Conclusion

Chapter 3: Association Measures and Matrices3.1 Objectives; 3.2 The Main Categories of Association Measures (Short Overview); 3.2.1 Q Mode and R Mode; 3.2.2 Symmetrical or Asymmetrical Coefficients in Q Mode: The Double-Zero Problem; 3.2.3 Association Measures for Qualitative or Quantitative Data; 3.2.4 To Summarize; 3.3 Q Mode: Computing Dissimilarity Matrices Among Objects; 3.3.1 Q Mode: Quantitative Species Data; 3.3.2 Q Mode: Binary (Presence-Absence) Species Data; 3.3.3 Q Mode: Quantitative Data (Excluding Species Abundances)

3.3.4 Q Mode: Binary Data (Excluding Species Presence-Absence Data)3.3.5 Q Mode: Mixed Types Including Categorical (Qualitative Multiclass) Variables; 3.4 R Mode: Computing Dependence Matrices Among Variables; 3.4.1 R Mode: Species Abundance Data; 3.4.2 R Mode: Species Presence-Absence Data; 3.4.3 R Mode: Quantitative and Ordinal Data (Other than Species Abundances); 3.4.4 R Mode: Binary Data (Other than Species Abundance Data); 3.5 Pre-transformations for Species Data; 3.6 Conclusion; Chapter 4: Cluster Analysis; 4.1 Objectives; 4.2 Clustering Overview

4.3 Hierarchical Clustering Based on Links4.3.1 Single Linkage Agglomerative Clustering; 4.3.2 Complete Linkage Agglomerative Clustering; 4.4 Average Agglomerative Clustering; 4.5 Wardś Minimum Variance Clustering; 4.6 Flexible Clustering; 4.7 Interpreting and Comparing Hierarchical Clustering Results; 4.7.1 Introduction; 4.7.2 Cophenetic Correlation; 4.7.3 Looking for Interpretable Clusters; 4.7.3.1 Graph of the Fusion Level Values; 4.7.3.2 Compare Two Dendrograms to Highlight Common Subtrees; 4.7.3.3 Multiscale Bootstrap Resampling; 4.7.3.4 Average Silhouette Widths

4.7.3.5 Comparison Between the Dissimilarity Matrix and Binary Matrices Representing Partitions4.7.3.6 Species Fidelity Analysis; 4.7.3.7 Silhouette Plot of the Final Partition; 4.7.3.8 Final Dendrogram with Graphical Options; 4.7.3.9 Spatial Plot of the Clustering Result; 4.7.3.10 Heat Map and Ordered Community Table; 4.8 Non-hierarchical Clustering; 4.8.1 k-means Partitioning; 4.8.1.1 k-means with Random Starts; 4.8.1.2 Use of k-means Partitioning to Optimize an Independently Obtained Classification; 4.8.2 Partitioning Around Medoids (PAM); 4.9 Comparison with Environmental Data