Phylogenetic Comparative Methods In The Era Of Big Data

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

Phylogenetic comparative approaches are powerful analytical tools for making evolutionary inferences from interspecific data and phylogenies. The phylogenetic toolkit available to evolutionary biologists is currently growing at an incredible speed, but most methodological papers are published in the specialized statistical literature and many are incomprehensible for the user community. This textbook provides an overview of several newly developed phylogenetic comparative methods that allow to investigate a broad array of questions on how phenotypic characters evolve along the branches of phylogeny and how such mechanisms shape complex animal communities and interspecific interactions. The individual chapters were written by the leading experts in the field and using a language that is accessible for practicing evolutionary biologists. The authors carefully explain the philosophy behind different methodologies and provide pointers – mostly using a dynamically developing online interface – on how these methods can be implemented in practice. These “conceptual” and “practical” materials are essential for expanding the qualification of both students and scientists, but also offer a valuable resource for educators. Another value of the book are the accompanying online resources (available at: http://www.mpcm-evolution.com), where the authors post and permanently update practical materials to help embed methods into practice.

Phylogenetic comparative methods seek to untangle the complex web of selective pressures driving biological evolution. These methods seek to identify associations between different biological traits over evolutionary history. Statistical models of phenotypic evolution need to account for the shared evolutionary history between different species, and accounting for this non-independence poses computational challenges. These challenges are compounded by missing observations, high-dimensional traits and highly-structured data. Here, I develop computational and modeling approaches that dramatically improve the computational efficiency and scalability of these models to enable Bayesian phylogenetic comparative analysis of unprecedentedly large data sets. First, I develop an algorithm that analytically marginalizes missing observations in a (relatively) simple model of phenotypic evolution. This algorithm is broadly applicable beyond this simple model and allows scalable inference under a variety of model extensions. These extensions include models that accommodate residual variance, allowing measurement of phylogenetic heritability, and linear dimension reduction, allowing phylogenetic comparative analyses for high-dimensional traits. I combine this work into a generalizable modeling framework that allows researchers to build flexible, highly structured models that remain scalable for both large number of taxa and many observations per taxon. This work achieves increases in computation speed by more than two orders of magnitude across several contexts, bringing computation time down from weeks or months to minutes or hours in multiple real-world applications.

This book integrates a wide variety of data analysis methods into a single and flexible interface: the R language. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters.

An introduction to statistical analyses of phylogenetic trees using comparative methods.

The increasing availability of molecular and genetic databases coupled with the growing power of computers gives biologists opportunities to address new issues, such as the patterns of molecular evolution, and re-assess old ones, such as the role of adaptation in species diversification. In the second edition, the book continues to integrate a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments. The second edition is completed updated, covering the full gamut of R packages for this area that have been introduced to the market since its previous publication five years ago. There is also a new chapter on the simulation of evolutionary data. Graduate students and researchers in evolutionary biology can use this book as a reference for data analyses, whereas researchers in bioinformatics interested in evolutionary analyses will learn how to implement these methods in R. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered: manipulation of phylogenetic data, phylogeny estimation, tree drawing, phylogenetic comparative methods, and estimation of ancestral characters. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters.

Researchers interested in the history of the Indo-European family of languages have used a variety of methods to estimate the phylogeny of the family, and have obtained widely differing results. In this paper we explore the reconstructions of the Indo-European phylogeny obtained by using the major phylogeny estimation procedures on an existing database of 336 characters (including lexical, phonological, and morphological characters) for 24 Indo-European languages. Our study finds that the different methods agree in part, but that there are also several striking differences. We discuss the reasons for these differences, and make proposals with respect to phylogenetic reconstruction in historical linguistics.