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Mathematical and statistical approaches to evolutionary theory are numerous. The NATO Advanced Study Institute (ASI) held at the Universite de Montreal, Montreal, August 3-21, 1987, was an opportunity to review most of the classical approaches and to study the more recent developments. The participation of theoretical biologists and geneticists as well as applied mathematicians and statisticians made possible exchanges of ideas between students and scholars having different views on the subject. These Proceedings contain the lecture notes of seven (7) of the eleven (11) series of lectures that were given. ESS (Evolutionarily Stable Stragety) theory is considered from many perspectives, from a game-theoretic approach to understanding behavior and evolution (W.G.S. Hines), and a systematic classification of properties and patterns of ESS's (C. Cannings) to particular applications of the differential geometry of the Shahshahani metric (E. Akin). Extensions of ESS theory to sexual populations and finite populations, not to mention games between relatives, are presented (W.G.S. Hines). Special attention is given to the classical game called the War of Attrition but with n players and random rewards (C. Cannings). The Shahshahani metric is also used to show the occurrence of cycling in the two-locus, two-allele model (E. Akin). Various inference problems in population genetics are adressed. Procedures to detect and measure selection components and polymorphism (in particular, the Wahlund effect) at one or several loci from mother-offspring combinations in natural populations are discussed at length (F.B. Christiansen).
Studies of evolution at the molecular level have experienced phenomenal growth in the last few decades, due to rapid accumulation of genetic sequence data, improved computer hardware and software, and the development of sophisticated analytical methods. The flood of genomic data has generated an acute need for powerful statistical methods and efficient computational algorithms to enable their effective analysis and interpretation. Molecular Evolution: a statistical approach presents and explains modern statistical methods and computational algorithms for the comparative analysis of genetic sequence data in the fields of molecular evolution, molecular phylogenetics, statistical phylogeography, and comparative genomics. Written by an expert in the field, the book emphasizes conceptual understanding rather than mathematical proofs. The text is enlivened with numerous examples of real data analysis and numerical calculations to illustrate the theory, in addition to the working problems at the end of each chapter. The coverage of maximum likelihood and Bayesian methods are in particular up-to-date, comprehensive, and authoritative. This advanced textbook is aimed at graduate level students and professional researchers (both empiricists and theoreticians) in the fields of bioinformatics and computational biology, statistical genomics, evolutionary biology, molecular systematics, and population genetics. It will also be of relevance and use to a wider audience of applied statisticians, mathematicians, and computer scientists working in computational biology.
Over the last several decades, mathematical models have become central to the study of social evolution, both in biology and the social sciences. But students in these disciplines often seriously lack the tools to understand them. A primer on behavioral modeling that includes both mathematics and evolutionary theory, Mathematical Models of Social Evolution aims to make the student and professional researcher in biology and the social sciences fully conversant in the language of the field. Teaching biological concepts from which models can be developed, Richard McElreath and Robert Boyd introduce readers to many of the typical mathematical tools that are used to analyze evolutionary models and end each chapter with a set of problems that draw upon these techniques. Mathematical Models of Social Evolution equips behaviorists and evolutionary biologists with the mathematical knowledge to truly understand the models on which their research depends. Ultimately, McElreath and Boyd’s goal is to impart the fundamental concepts that underlie modern biological understandings of the evolution of behavior so that readers will be able to more fully appreciate journal articles and scientific literature, and start building models of their own.
Thirty years ago, biologists could get by with a rudimentary grasp of mathematics and modeling. Not so today. In seeking to answer fundamental questions about how biological systems function and change over time, the modern biologist is as likely to rely on sophisticated mathematical and computer-based models as traditional fieldwork. In this book, Sarah Otto and Troy Day provide biology students with the tools necessary to both interpret models and to build their own. The book starts at an elementary level of mathematical modeling, assuming that the reader has had high school mathematics and first-year calculus. Otto and Day then gradually build in depth and complexity, from classic models in ecology and evolution to more intricate class-structured and probabilistic models. The authors provide primers with instructive exercises to introduce readers to the more advanced subjects of linear algebra and probability theory. Through examples, they describe how models have been used to understand such topics as the spread of HIV, chaos, the age structure of a country, speciation, and extinction. Ecologists and evolutionary biologists today need enough mathematical training to be able to assess the power and limits of biological models and to develop theories and models themselves. This innovative book will be an indispensable guide to the world of mathematical models for the next generation of biologists. A how-to guide for developing new mathematical models in biology Provides step-by-step recipes for constructing and analyzing models Interesting biological applications Explores classical models in ecology and evolution Questions at the end of every chapter Primers cover important mathematical topics Exercises with answers Appendixes summarize useful rules Labs and advanced material available
The natural world is infinitely complex and hierarchically structured, with smaller units forming the components of progressively larger systems: molecules make up cells, cells comprise tissues and organs that are, in turn, parts of individual organisms, which are united into populations and integrated into yet more encompassing ecosystems. In the face of such awe-inspiring complexity, there is a need for a comprehensive, non-reductionist evolutionary theory. Having emerged at the crossroads of paleobiology, genetics, and developmental biology, the hierarchical approach to evolution provides a unifying perspective on the natural world and offers an operational framework for scientists seeking to understand the way complex biological systems work and evolve. Coedited by one of the founders of hierarchy theory and featuring a diverse and renowned group of contributors, this volume provides an integrated, comprehensive, cutting-edge introduction to the hierarchy theory of evolution. From sweeping historical reviews to philosophical pieces, theoretical essays, and strictly empirical chapters, it reveals hierarchy theory as a vibrant field of scientific enterprise that holds promise for unification across the life sciences and offers new venues of empirical and theoretical research. Stretching from molecules to the biosphere, hierarchy theory aims to provide an all-encompassing understanding of evolution and—with this first collection devoted entirely to the concept—will help make transparent the fundamental patterns that propel living systems.
In this accessible and illuminating study of how the science of mathematics developed, a veteran math researcher and educator looks at the ways in which our evolutionary makeup is both a help and a hindrance to the study of math. Artstein chronicles the discovery of important mathematical connections between mathematics and the real world from ancient times to the present. The author then describes some of the contemporary applications of mathematics—in probability theory, in the study of human behavior, and in combination with computers, which give mathematics unprecedented power. The author concludes with an insightful discussion of why mathematics, for most people, is so frustrating. He argues that the rigorous logical structure of math goes against the grain of our predisposed ways of thinking as shaped by evolution, presumably because the talent needed to cope with logical mathematics gave the human race as a whole no evolutionary advantage. With this in mind, he offers ways to overcome these innate impediments in the teaching of math.
The rise of the neutral theory of molecular evolution seems to have aroused a renewed interest in mathematical population genetics among biologists, who are primarily experimenters rather than theoreticians. This has encouraged me to set out the mathematics of the evolutionary process in a manner that, I hope, will be comprehensible to those with only a basic knowledge of calculus and matrix algebra. I must acknowledge from the start my great debt to my students. Equipped initially with rather limited mathematics, they have pursued the subject with much enthusiasm and success. This has enabled me to try a number of different approaches over the years. I was particularly grateful to Dr L. J. Eaves and Professor W. E. Nance for the opportunity to give a one-semester course at the Medical College of Virginia, and I would like to thank them, their colleagues and their students for the many kindnesses shown to me during my visit. I have concentrated almost entirely on stochastic topics, since these cause the greatest problems for non-mathematicians. The latter are particularly concerned with the range of validity of formulae. A sense of confidence in applying these formulae is, almost certainly, best gained by following their derivation. I have set out proofs in fair detail, since, in my experience, minor points of algebraic manipulation occasionally cause problems. To avoid loss of continuity, I have sometimes put material in notes at the end of chapters.
This highly interdisciplinary book discusses the phenomenon of life, including its origin and evolution, against the background of thermodynamics, statistical mechanics, and information theory. Among the central themes is the seeming contradiction between the second law of thermodynamics and the high degree of order and complexity produced by living systems. As the author shows, this paradox has its resolution in the information content of the Gibbs free energy that enters the biosphere from outside sources. Another focus of the book is the role of information in human cultural evolution, which is also discussed with the origin of human linguistic abilities. One of the final chapters addresses the merging of information technology and biotechnology into a new discipline — bioinformation technology.This third edition has been updated to reflect the latest scientific and technological advances. Professor Avery makes use of the perspectives of famous scholars such as Professor Noam Chomsky and Nobel Laureates John O'Keefe, May-Britt Moser and Edward Moser to cast light on the evolution of human languages. The mechanism of cell differentiation, and the rapid acceleration of information technology in the 21st century are also discussed.With various research disciplines becoming increasingly interrelated today, Information Theory and Evolution provides nuance to the conversation between bioinformatics, information technology, and pertinent social-political issues. This book is a welcome voice in working on the future challenges that humanity will face as a result of scientific and technological progress.
When Charles Darwin finished The Origin of Species, he thought that he had explained every clue, but one. Though his theory could explain many facts, Darwin knew that there was a significant event in the history of life that his theory did not explain. During this event, the “Cambrian explosion,” many animals suddenly appeared in the fossil record without apparent ancestors in earlier layers of rock. In Darwin’s Doubt, Stephen C. Meyer tells the story of the mystery surrounding this explosion of animal life—a mystery that has intensified, not only because the expected ancestors of these animals have not been found, but because scientists have learned more about what it takes to construct an animal. During the last half century, biologists have come to appreciate the central importance of biological information—stored in DNA and elsewhere in cells—to building animal forms. Expanding on the compelling case he presented in his last book, Signature in the Cell, Meyer argues that the origin of this information, as well as other mysterious features of the Cambrian event, are best explained by intelligent design, rather than purely undirected evolutionary processes.