Evolutionary Analysis

Enhanced by the most up-to-date information available, including a text-specific web-site, this book provides coverage of both microevolution and macroevolution through a variety of taxonomic groups. It focuses throughout on phylogenetic trees.

For undergraduate courses in Evolution. By presenting evolutionary biology as an ongoing research effort, this best-selling text aims to help students think like scientists. The authors convey the excitement and logic of evolutionary science by introducing principles through recent and classical studies, and by emphasizing 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.

Covers theory, practice and programming in Bayesian phylogenetics with BEAST. The why, how and what of BEAST 2.

Traditional doctrines of economic policy have largely failed to address the fact that human economies are evolving, complex adaptive systems rather than equilibrating mechanisms. This volume represents a step towards a more evolutionary approach to economic policy.

Evolutionary genetics is the study of how genetic variation leads to evolutionary change. With the recent explosion in the availability of whole genome sequence data, vast quantities of genetic data are being generated at an ever-increasing pace with the result that programming has become an essential tool for researchers. Most importantly, a thorough understanding of evolutionary principles is essential for making sense of this genetic data. This up-to-date textbook covers all the major components of modern evolutionary genetics, carefully explaining fundamental processes such as mutation, natural selection, genetic drift, and speciation, together with their consequences. The book also draws on a rich literature of exciting and inspiring examples to demonstrate the diversity of evolutionary research, including an emphasis on how evolution and selection has shaped our own species. Furthermore, at the end of each chapter, study questions are provided to motivate the reader to think and reflect on the concepts introduced. Practical experience is essential when it comes to developing an understanding of how to use genetic and genomic data to analyze and address interesting questions in the life sciences and how to interpret results in meaningful ways. In addition to the main text, a series of online tutorials using the R language serves as an introduction to programming, statistics, and the analysis of evolutionary genetic data. The R environment stands out as an ideal all-purpose, open source platform to handle and analyze such data. The book and its online materials take full advantage of the authors' own experience in working in a post-genomic revolution world, and introduce readers to the plethora of molecular and analytical methods that have only recently become available.

There are many different types of organisms in the world: they differ in size, physiology, appearance, and life history. The challenge for evolutionary biology is to explain how such diversity arises. The Evolution of Life Histories does this by showing that natural selection is the principal underlying force molding life history variation. The book describes in particular the ways in which variation can be analyzed and predicted. It covers both the genetic and optimization approaches to life history analysis and gives an overview of the general framework of life history theory and the mathematical tools by which predictions can be made and tested. Factors affecting the age schedule of birth and death and the costs of reproduction are discussed. The Evolution of Life Histories concentrates on those theoretical developments that have been tested experimentally. It will interest both students and professionals in evolution, evolutionary ecology, mathematical and theoretical biology, and zoology and entomology.

Networks provide a very useful way to describe a wide range of different data types in biology, physics and elsewhere. Apart from providing a convenient tool to visualize highly dependent data, networks allow stringent mathematical and statistical analysis. In recent years, much progress has been achieved to interpret various types of biological network data such as transcriptomic, metabolomic and protein interaction data as well as epidemiological data. Of particular interest is to understand the organization, complexity and dynamics of biological networks and how these are influenced by network evolution and functionality. This book reviews and explores statistical, mathematical and evolutionary theory and tools in the understanding of biological networks. The book is divided into comprehensive and self-contained chapters, each of which focuses on an important biological network type, explains concepts and theory and illustrates how these can be used to obtain insight into biologically relevant processes and questions. There are chapters covering metabolic, transcriptomic, protein interaction and epidemiological networks as well as chapters that deal with theoretical and conceptual material. The authors, who contribute to the book, are active, highly regarded and well-known in the network community. Sample Chapter(s). Chapter 1: A Network Analysis Primer (350 KB). Contents: A Network Analysis Primer (M P H Stumpf & C Wiuf); Evolutionary Analysis of Protein Interaction Networks (C Wiuf & O Ratmann); Motifs in Biological Networks (F Schreiber & H SchwAbbermeyer); Bayesian Analysis of Biological Networks: Clusters, Motifs, Cross-Species Correlations (J Berg & M Lnssig); Network Concepts and Epidemiological Models (R R Kao & I Z Kiss); Evolutionary Origin and Consequences of Design Properties of Metabolic Networks (T Pfeiffer & S Bonhoeffer); Protein Interactions from an Evolutionary Perspective (F Pazos & A Valencia); Statistical Null Models for Biological Network Analysis (W P Kelly et al.). Readership: Academics, researchers, postgraduates and advanced undergraduates in bioinformatics. Biologists, mathematicians/statisticians, physicists and computer scientists.