Phylogenetic Comparative Methods in R

✍ Scribed by Liam J. Revell, Luke J. Harmon

• Publisher: Princeton University Press
• Year: 2022
• Tongue: English
• Leaves: 441
• Category: Library

✦ Synopsis

An authoritative introduction to the latest comparative methods in evolutionary biology

Phylogenetic comparative methods are a suite of statistical approaches that enable biologists to analyze and better understand the evolutionary tree of life, and shed vital new light on patterns of divergence and common ancestry among all species on Earth. This textbook shows how to carry out phylogenetic comparative analyses in the R statistical computing environment. Liam Revell and Luke Harmon provide an incisive conceptual overview of each method along with worked examples using real data and challenge problems that encourage students to learn by doing. By working through this book, students will gain a solid foundation in these methods and develop the skills they need to interpret patterns in the tree of life.

• Covers every major method of modern phylogenetic comparative analysis in R
• Explains the basics of R and discusses topics such as trait evolution, diversification, trait-dependent diversification, biogeography, and visualization
• Features a wealth of exercises and challenge problems
• Serves as an invaluable resource for students and researchers, with applications in ecology, evolution, anthropology, disease transmission, conservation biology, and a host of other areas
• Written by two of today’s leading developers of phylogenetic comparative methods

✦ Table of Contents

Cover
Contents
1. A brief introduction to phylogenetics in R
1.1 Introduction
1.2 Preliminaries
1.3 R phylogenetics
1.4 ape and the "phylo" object in R
1.5 The internal structure of a tree in R
1.6 Reading and writing phylogenetic trees
1.7 Plotting and manipulating trees
1.8 Multiple trees in a single object
1.9 Managing trees and comparative data
1.10 A simple comparative analysis: Phylogenetic principal components analysis
1.11 Practice problems
2. Phylogenetically independent contrasts
2.1 Introduction
2.2 Phylogenetic nonindependence
2.3 Phylogenetically independent contrasts
2.4 What happens if we ignore the tree?
2.5 Practice problems
3. Phylogenetic generalized least squares
3.1 Introduction
3.2 Statistical nonindependence of phylogenetic data
3.3 Equivalence of contrasts regression and PGLS
3.4 Assumptions of PGLS
3.5 Phylogenetic ANOVA and ANCOVA
3.6 Practice problems
4. Modeling continuous character evolution on a phylogeny
4.1 Introduction
4.2 The Brownian motion model
4.3 Brownian motion on a phylogeny
4.4 Properties of Brownian motion
4.5 Fitting a Brownian model to data
4.6 Phylogenetic signal
4.7 Other models of continuous character evolution on phylogenies
4.8 Fitting and comparing alternative continuous character models
4.9 Practice problems
5. Multi-rate, multi-regime, and multivariate models for continuous traits
5.1 Multi-rate Brownian evolution
5.2 Multi-optimum Ornstein–Uhlenbeck evolution
5.3 Multivariate Brownian evolution
5.4 Exploring evolutionary heterogeneity
5.5 Practice problems
6. Modeling discrete character evolution on a phylogeny
6.1 Introduction
6.2 The Mk model
6.3 Fitting the Mk model to data
6.4 Comparing alternative discrete character models
6.5 Practice problems
7. Other models of discrete character evolution
7.1 Introduction
7.2 Correlated binary traits
7.3 Modeling heterogeneity in the evolutionary rate for a discrete trait
7.4 Modeling rate variation using the hidden-rates model
7.5 A polymorphic trait model
7.6 The threshold model for studying discrete and continuous character traits
7.7 Practice problems
8. Reconstructing ancestral states
8.1 Introduction
8.2 Ancestral states for continuous characters
8.3 Properties of ancestral state estimation for continuous traits
8.4 Discrete characters
8.5 Joint ancestral state reconstruction
8.6 Marginal ancestral state reconstruction
8.7 Stochastic character mapping
8.8 What about parsimony?
8.9 Practice problems
9. Analysis of diversification with phylogenies
9.1 Introduction
9.2 Lineage-through-time plots and the γ statistic
9.3 Estimating speciation and extinction rates from a reconstructed phylogeny
9.4 The effect of incomplete sampling on diversification rates
9.5 Likelihood surface of a birth-death model
9.6 Analyzing diversification using diversitree
9.7 Practice problems
10. Time-and density-dependent diversification
10.1 Introduction
10.2 Time-varying diversification
10.3 Fitting time-variable diversification models to data
10.4 Diversity-dependent diversification
10.5 Testing for variation in diversification rates among clades
10.6 Practice problems
11. Character-dependent diversification
11.1 Introduction
11.2 Binary-state speciation and extinction (BiSSE) model
11.3 Multi-state speciation and extinction (MuSSE) model
11.4 Hidden-state speciation and extinction (HiSSE) model
11.5 Quantitative-trait speciation and extinction (QuaSSE) model
11.6 Practice problems
12. Biogeography and phylogenetic community ecology
12.1 Introduction
12.2 Ancestral area reconstruction
12.3 Phylogenetic community ecology
12.4 Practice problems
13. Plotting phylogenies and comparative data
13.1 Introduction
13.2 Phylogenies in the R plotting environment
13.3 Plotting phylogenies without actually plotting them
13.4 Algorithms for drawing trees
13.5 Practice problems
References
Index