Computer modeling is now an integral part of research in evolutionary biology. This book outlines how evolutionary questions are formulated and how, in practice, they can be resolved by analytical and numerical methods.

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

Evolution by natural selection explains the tree of life and the complex adaptations found throughout nature. The power and versatility of evolutionary explanations have proved tempting to scientists outside of biology, but adapting evolutionary concepts to new domains has been challenging. Even within biology, there are many difficult questions and problem cases that face evolutionary theory. Modelling Evolution offers a new, general account of evolution by natural selection that identifies the essential features of evolutionary models that transcend any particular discipline. Evolution by natural selection in its broad sense is the systemic advantage of a type, in contrast to the narrow definition using heritable variation in fitness. This account is explained, contextualised and applied to a variety of questions in both biology and the social sciences. Offering an accessible and comprehensive account of evolution that is applicable both to biology and the broader social sciences, Modelling Evolution will appeal to students and researchers interested in fields such as biology, economics, sociology, history, and psychology.

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.

Landscapes are characterized by a wide variation, both spatially and temporally, of tolerance and response to natural processes and anthropogenic stress. These tolerances and responses can be analyzed through individual landscape parameters, such as soils, vegetation, water, etc., or holistically through ecosystem or watershed studies. However, such approaches are both time consuming and costly. Soil erosion and landscape evolution modeling provide a simulation environment in which both the short- and long-term consequences of land-use activities and alternative land use strategies can be compared and evaluated. Such models provide the foundation for the development of land management decision support systems. Landscape Erosion and Evolution Modeling is a state-of-the-art, interdisciplinary volume addressing the broad theme of soil erosion and landscape evolution modeling from different philosophical and technical approaches, ranging from those developed from considerations of first-principle soil/water physics and mechanics to those developed empirically according to sets of behavioral or empirical rules deriving from field observations and measurements. The validation and calibration of models through field studies is also included. This volume will be essential reading for researchers in earth, environmental and ecosystem sciences, hydrology, civil engineering, forestry, soil science, agriculture and climate change studies. In addition, it will have direct relevance to the public and private land management communities.

"What underlying forces are responsible for the observed patterns of variability, given a collection of DNA sequences?" In approaching this question a number of probability models are introduced and anyalyzed.Throughout the book, the theory is developed in close connection with data from more than 60 experimental studies that illustrate the use of these results.

This authoritative text/reference presents a review of the history, current status, and potential future directions of computational biology in molecular evolution. Gathering together the unique insights of an international selection of prestigious researchers, this must-read volume examines the latest developments in the field, the challenges that remain, and the new avenues emerging from the growing influx of sequence data. These viewpoints build upon the pioneering work of David Sankoff, one of the founding fathers of computational biology, and mark the 50th anniversary of his first scientific article. The broad spectrum of rich contributions in this essential collection will appeal to all computer scientists, mathematicians and biologists involved in comparative genomics, phylogenetics and related areas.

The central concern of this book is with the "prediction problem" in biomedical research. In particular, the authors examine the use of animal models to predict human responses in drug and disease research. The arguments discussed are drawn from both biological and biomedical theory (with numerous examples and case studies drawn from evolutionary biology, complex systems theory, oncology, teratology, and AIDS research), and analyses of empirical evidence (concerning, for example, data on intra- and inter-species differences revealed by recent results from genome analyses of various species, human population studies, and statistical studies of the predictive utility of animal models). This book comes to the unique conclusion that while animals can be successfully used for many endeavors in science such as basic and comparative research, they cannot be used to predict drug and disease response in humans. The arguments presented are rooted in the history, philosophy, and methodology of biomedical research. This book will be of interest to anyone involved, directly or indirectly, in biomedical research (including physicians, veterinarians and scientists), and anyone interested in the history, philosophy and methodology of science. In contrast to books written by and for the animal rights movement and books written by and for the animal-based research industry, this book honestly examines all sides of the scientific arguments for using animals in science and concludes that each group in turn exaggerates the flaws or strengths of using animals. There are areas in science where animals can be viably used but there are also areas where they cannot be so used. REVIEWS See Philosophies, Ethics, and Humanities in Medicine 17 August 2010

Individual-Based Models of Cultural Evolution shows readers how to create individual-based models of cultural evolution using the programming language R. The field of cultural evolution has emerged in the last few decades as a thriving, interdisciplinary effort to understand cultural change and cultural diversity within an evolutionary framework and using evolutionary tools, concepts, and methods. Given its roots in evolutionary biology, much of cultural evolution is grounded in, or inspired by, formal models. Yet many researchers interested in cultural evolution come from backgrounds that lack training in formal modelling, such as psychology, anthropology or archaeology. This book addresses that gap. It provides example code in R for readers to run their own models, moving from very simple models of the basic processes of cultural evolution, such as biased transmission and cultural mutation, to more advanced topics such as the evolution of social learning, demographic effects, and social network analysis. Features of this book: Recreates existing models in the literature to show how these were created and to enable readers to have a better understanding of their significance and how to apply them to their own research questions Provides full R code to realize models and analyse and plot outputs, with line-by-line analysis Requires no previous knowledge of the field of cultural evolution, and only very basic programming knowledge This is an essential resource for researchers and students interested in cultural evolution, including disciplines such as psychology, anthropology, archaeology, and biology as well as sociology and digital humanities.

Experts examine new modeling strategies for the interpretation of biological data and their integration into the conceptual framework of theoretical biology, detailing approaches that focus on morphology, development, behavior, or evolution. Abstract and conceptual models have become an indispensable tool for analyzing the flood of highly detailed empirical data generated in recent years by advanced techniques in the biosciences. Scientists are developing new modeling strategies for analyzing data, integrating results into the conceptual framework of theoretical biology, and formulating new hypotheses. In Modeling Biology, leading scholars investigate new modeling strategies in the domains of morphology, development, behavior, and evolution. The emphasis on models in the biological sciences has been accompanied by a new focus on conceptual issues and a more complex understanding of epistemological concepts. Contributors to Modeling Biology discuss models and modeling strategies from the perspectives of philosophy, history, and applied mathematics. Individual chapters discuss specific approaches to modeling in such domains as biological form, development, and behavior. Finally, the book addresses the modeling of these properties in the context of evolution, with a particular emphasis on the emerging field of evolutionary developmental biology (or evo-devo). Contributors Giorgio A. Ascoli, Chandrajit Bajaj, James P. Collins, Luciano da Fontoura Costa, Kerstin Dautenhahn, Nigel R. Franks, Scott Gilbert, Marta Ibañes Miguez, Juan Carlos Izpisúa-Belmonte, Alexander S. Klyubin, Thomas J. Koehnle, Manfred D. Laubichler, Sabina Leonelli, James A. R. Marshall, George R. McGhee Jr., Gerd B. Müller, Chrystopher L. Nehaniv, Karl J. Niklas, Lars Olsson, Eirikur Palsson, Daniel Polani, Diego Rasskin Gutman, Hans-Jörg Rheinberger, Alexei V. Samsonovich, Jeffrey C. Schank, Harry B. M. Uylings, Jaap van Pelt, Iain Werry