Functional and Phylogenetic Ecology in R

Description

Functional and Phylogenetic Ecology in R is designed to teach readers to use R for phylogenetic and functional trait analyses. Over the past decade, a dizzying array of tools and methods were generated to incorporate phylogenetic and functional information into traditional ecological analyses. Increasingly these tools are implemented in R, thus greatly expanding their impact. Researchers getting started in R can use this volume as a step-by-step entryway into phylogenetic and functional analyses for ecology in R. More advanced users will be able to use this volume as a quick reference to understand particular analyses. The volume begins with an introduction to the R environment and handling relevant data in R. Chapters then cover phylogenetic and functional metrics of biodiversity; null modeling and randomizations for phylogenetic and functional trait analyses; integrating phylogenetic and functional trait information; and interfacing the R environment with a popular C-based program. This book presents a unique approach through its focus on ecological analyses and not macroevolutionary analyses. The author provides his own code, so that the reader is guided through the computational steps to calculate the desired metrics. This guided approach simplifies the work of determining which package to use for any given analysis. Example datasets are shared to help readers practice, and readers can then quickly turn to their own datasets. Dedication Preface Contents Chapter 1: Introduction 1.1 Why Phylogenetics and Functional Traits in Ecology? 1.2 Why R? 1.3 Structure and How to Use This Book? 1.4 Setting Working Directories and Package Installation Chapter 2: Phylogenetic Data in R 2.1 Objectives 2.2 Loading Phylogenies into R and the Structure of the “Phylo” Class 2.3 Plotting Phylogenetic Trees in R 2.4 Manipulating and Calculating Additional Information from Phylogenetic Trees in R 2.5 Simulating Phylogenies in R 2.6 Conclusions 2.7 Exercises Chapter 3: Phylogenetic Diversity 3.1 Objectives 3.2 Background 3.3 “Community” Datasets 3.4 Tree-Based Measures of Phylogenetic Diversity 3.5 Distance-Based Measures of Phylogenetic Diversity 3.5.1 Pairwise Measures 3.5.2 Nearest Neighbor Measures 3.6 Comparing Metrics 3.7 Conclusions 3.8 Exercises Chapter 4: Functional Diversity 4.1 Objectives 4.2 Background 4.3 Quantifying the Functional Composition of Communities Using the Moments of Trait Distributions 4.4 Dendrogram-Based Versus Euclidean Distance-Based Measures of Functional Diversity 4.4.1 Generating Trait Distance Matrices 4.4.2 Generating Trait Dendrograms 4.4.3 Pairwise and Nearest Neighbor Measures 4.4.4 Ranges and Convex Hulls 4.4.5 Other Measures 4.5 Comparing Metrics of Functional Diversity 4.6 Conclusions 4.7 Exercises Chapter 5: Phylogenetic and Functional Beta Diversity 5.1 Objectives 5.2 Background 5.3 Tree-Based Measures of Phylogenetic Beta Diversity 5.3.1 UniFrac 5.3.2 Phylogenetic Sorenson’s Index 5.4 Distance-Based Measures of Phylogenetic and Functional Beta Diversity 5.4.1 Pairwise Measures 5.4.2 Nearest Neighbor Measures 5.5 Other Metrics 5.6 Comparing Metrics 5.7 Conclusions 5.8 Exercises Chapter 6: Null Models 6.1 Objectives 6.2 Background 6.2.1 Why Use Null Models for Phylogenetic and Functional Analyses? 6.2.2 Calculating Standardized Effect Sizes, Quantiles, and P -Values 6.3 Classes of Null Models in Phylogenetic and Functional Analyses of Species Assemblages? 6.4 Randomizing Community Data Matrices in R 6.4.1 Unconstrained Randomizations 6.4.2 Constrained Randomizations 6.5 Randomizing Phylogenetic Data 6.5.1 Unconstrained Randomizations 6.5.2 Constrained Randomizations 6.6 Randomizing Functional Trait Data 6.6.1 Unconstrained Randomizations 6.6.2 Constrained Randomizations 6.7 Null Models for Phylogenetic and Functional Alpha Diversity 6.8 Null Models for Phylogenetic and Functional Beta Diversity 6.9 Conclusions 6.10 Exercises Chapter 7: Comparative Methods and Phylogenetic Signal 7.1 Objectives 7.2 Trait Correlations 7.2.1 Independent Contrasts 7.2.2 Phylogenetic Generalized Least Squares 7.2.3 Phylogenetic Eigenvector Regression 7.3 Quantifying Phylogenetic Signal 7.3.1 Mantel Test 7.3.2 Blomberg’s K and Significance Tests 7.3.3 Pagel’s Lambda 7.3.4 Standardized Contrast Variance, Unstandardized Contrast Means, and Randomization Tests 7.3.5 Phylogenetic Eigenvectors 7.4 Quantifying the Timing and Magnitude of Trait Divergences 7.5 Conclusions 7.6 Exercises Chapter 8: Partitioning the Phylogenetic, Functional, Environmental, and Spatial Components of Community Diversity 8.1 Objectives 8.2 Background 8.3 Partitioning Variation in Community Functional Alpha Diversity by the Environment, Space, and the Community Phylogenetic Alpha Diversity 8.3.1 Partitioning FD Using Multiple Regression on Distance Matrices 8.3.2 Partitioning FD Using Principal Coordinates of Neighbor Matrices (PCNM) and Forward Selection 8.4 Variance Partitioning of Phylogenetic or Functional Beta Diversity Along Environmental and Spatial Gradients 8.4.1 Beta Diversity and Multiple Regression on Distance Matrices 8.4.2 Partitioning Beta Diversity Using Principal Coordinates of Neighbor Matrices (PCNM) and Forward Selection 8.5 Integrating Phylogenetic, Trait, Environmental and Spatial Information to Quantify the Role of Abiotic Filtering During Community Assembly 8.6 Conclusions 8.7 Exercises Chapter 9: Integrating R with Other Phylogenetic and Functional Trait Analytical Software 9.1 Objectives 9.2 Background: The Development of Eco-Informatics Tools for Phylogenetic- and Functional Trait-Based Ecology 9.3 Phylocom 9.3.1 Quantifying Phylogenetic and Functional Diversity and Dispersion in Phylocom 9.3.2 Comparative Analyses in Phylocom 9.3.3 Interfacing R and Phylocom for Null Modeling 9.4 Conclusions 9.5 Exercises References Index