
- 281 pages
- English
- PDF
- Available on iOS & Android
Genetic Structure and Selection in Subdivided Populations
About this book
Various approaches have been developed to evaluate the consequences of spatial structure on evolution in subdivided populations. This book is both a review and new synthesis of several of these approaches, based on the theory of spatial genetic structure. François Rousset examines Sewall Wright's methods of analysis based on F-statistics, effective size, and diffusion approximation; coalescent arguments; William Hamilton's inclusive fitness theory; and approaches rooted in game theory and adaptive dynamics. Setting these in a framework that reveals their common features, he demonstrates how efficient tools developed within one approach can be applied to the others.
Rousset not only revisits classical models but also presents new analyses of more recent topics, such as effective size in metapopulations. The book, most of which does not require fluency in advanced mathematics, includes a self-contained exposition of less easily accessible results. It is intended for advanced graduate students and researchers in evolutionary ecology and population genetics, and will also interest applied mathematicians working in probability theory as well as statisticians.
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Information
Table of contents
- Cover
- Title
- Contents
- List of Figures and Tables
- Acknowledgments
- Preface
- 1. Introduction
- 2. Selection and Drift
- 3. Spatially Homogeneous Dispersal: The Island Model and Isolation by Distance
- 4. Interpretations of Inbreeding and Relatedness Coefficients in Subdivided Populations
- 5. Evolutionary Dynamics
- 6. Convergence Stability in a Spatially Homogeneous Population
- 7. Inclusive Fitness, Cooperation, and Altruism
- 8. Diploidy (and Sex)
- 9. Effective Size: Concepts and Applications to Stable Populations
- 10. Fluctuating Demography: Neutral Models
- 11. Selection in Class-Structured Populations
- 12. Overview and Perspectives
- Appendix A. Mathematical Appendix
- References
- Index
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