Analysis of Phylogenetics and Evolution with R

Analysis of Phylogenetics and Evolution with R

Analysis of Phylogenetics and Evolution with R

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.

Analysis of Phylogenetics and Evolution with R

Analysis of Phylogenetics and Evolution with R

Analysis of Phylogenetics and Evolution with R

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.

Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology

Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology

Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology

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.

Population Genomics with R

Population Genomics with R

Population Genomics with R

Population Genomics With R presents a multidisciplinary approach to the analysis of population genomics. The methods treated cover a large number of topics from traditional population genetics to large-scale genomics with high-throughput sequencing data. Several dozen R packages are examined and integrated to provide a coherent software environment with a wide range of computational, statistical, and graphical tools. Small examples are used to illustrate the basics and published data are used as case studies. Readers are expected to have a basic knowledge of biology, genetics, and statistical inference methods. Graduate students and post-doctorate researchers will find resources to analyze their population genetic and genomic data as well as help them design new studies. The first four chapters review the basics of population genomics, data acquisition, and the use of R to store and manipulate genomic data. Chapter 5 treats the exploration of genomic data, an important issue when analysing large data sets. The other five chapters cover linkage disequilibrium, population genomic structure, geographical structure, past demographic events, and natural selection. These chapters include supervised and unsupervised methods, admixture analysis, an in-depth treatment of multivariate methods, and advice on how to handle GIS data. The analysis of natural selection, a traditional issue in evolutionary biology, has known a revival with modern population genomic data. All chapters include exercises. Supplemental materials are available on-line (http://ape-package.ird.fr/PGR.html).

Phylogenetic Diversity

Phylogenetic Diversity

Phylogenetic Diversity

“Biodiversity” refers to the variety of life. It is now agreed that there is a “biodiversity crisis”, corresponding to extinction rates of species that may be 1000 times what is thought to be “normal”. Biodiversity science has a higher profile than ever, with the new Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services involving more than 120 countries and 1000s of scientists. At the same time, the discipline is re-evaluating its foundations – including its philosophy and even core definitions. The value of biodiversity is being debated. In this context, the tree of life (“phylogeny”) is emerging as an important way to look at biodiversity, with relevance cutting across current areas of concern – from the question of resilience within ecosystems, to conservation priorities for globally threatened species – while capturing the values of biodiversity that have been hard to quantify, including resilience and maintaining options for future generations. This increased appreciation of the importance of conserving “phylogenetic diversity”, from microbial communities in the human gut to global threatened species, has inevitably resulted in an explosion of new indices, methods, and case studies. This book recognizes and responds to the timely opportunity for synthesis and sharing experiences in practical applications. The book recognizes that the challenge of finding a synthesis, and building shared concepts and a shared toolbox, requires both an appreciation of the past and a look into the future. Thus, the book is organized as a flow from history, concepts and philosophy, through to methods and tools, and followed by selected case studies. A positive vision and plan of action emerges from these chapters, that includes coping with inevitable uncertainties, effectively communicating the importance of this “evolutionary heritage” to the public and to policy-makers, and ultimately contributing to biodiversity conservation policy from local to global scales.

Assumptions Inhibiting Progress in Comparative Biology

Assumptions Inhibiting Progress in Comparative Biology

Assumptions Inhibiting Progress in Comparative Biology

This book is a thought-provoking assessment of assumptions inhibiting progress in comparative biology. The volume is inspired by a list generated years earlier by Donn Rosen, one of the most influential, innovative and productive comparative biologists of the latter 20th century. His list has assumed almost legendary status among comparative evolutionary biologists. Surprisingly many of the obstructing assumptions implicated by Rosen remain relevant today. Any comparative biologist hoping to avoid such assumptions in their own research will benefit from this introspective volume.

Biotic Evolution and Environmental Change in Southeast Asia

Biotic Evolution and Environmental Change in Southeast Asia

Biotic Evolution and Environmental Change in Southeast Asia

Authoritative reviews and focused case studies on the history and future of the fauna and flora of Southeast Asia.

Multivariate Analysis of Ecological Data with ade4

Multivariate Analysis of Ecological Data with ade4

Multivariate Analysis of Ecological Data with ade4

This book introduces the ade4 package for R which provides multivariate methods for the analysis of ecological data. It is implemented around the mathematical concept of the duality diagram, and provides a unified framework for multivariate analysis. The authors offer a detailed presentation of the theoretical framework of the duality diagram and also of its application to real-world ecological problems. These two goals may seem contradictory, as they concern two separate groups of scientists, namely statisticians and ecologists. However, statistical ecology has become a scientific discipline of its own, and the good use of multivariate data analysis methods by ecologists implies a fair knowledge of the mathematical properties of these methods. The organization of the book is based on ecological questions, but these questions correspond to particular classes of data analysis methods. The first chapters present both usual and multiway data analysis methods. Further chapters are dedicated for example to the analysis of spatial data, of phylogenetic structures, and of biodiversity patterns. One chapter deals with multivariate data analysis graphs. In each chapter, the basic mathematical definitions of the methods and the outputs of the R functions available in ade4 are detailed in two different boxes. The text of the book itself can be read independently from these boxes. Thus the book offers the opportunity to find information about the ecological situation from which a question raises alongside the mathematical properties of methods that can be applied to answer this question, as well as the details of software outputs. Each example and all the graphs in this book come with executable R code.

Computer Simulation and Data Analysis in Molecular Biology and Biophysics

Computer Simulation and Data Analysis in Molecular Biology and Biophysics

Computer Simulation and Data Analysis in Molecular Biology and Biophysics

This book provides an introduction to two important aspects of modern bioch- istry, molecular biology, and biophysics: computer simulation and data analysis. My aim is to introduce the tools that will enable students to learn and use some f- damental methods to construct quantitative models of biological mechanisms, both deterministicandwithsomeelementsofrandomness;tolearnhowconceptsofpr- ability can help to understand important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data. The availability of very capable but inexpensive personal computers and software makes it possible to do such work at a much higher level, but in a much easier way, than ever before. TheExecutiveSummaryofthein?uential2003reportfromtheNationalAcademy of Sciences, “BIO 2010: Transforming Undergraduate Education for Future - search Biologists” [12], begins The interplay of the recombinant DNA, instrumentation, and digital revolutions has p- foundly transformed biological research. The con?uence of these three innovations has led to important discoveries, such as the mapping of the human genome. How biologists design, perform, and analyze experiments is changing swiftly. Biological concepts and models are becoming more quantitative, and biological research has become critically dependent on concepts and methods drawn from other scienti?c disciplines. The connections between the biological sciences and the physical sciences, mathematics, and computer science are rapidly becoming deeper and more extensive.