Evolutionary Quantitative Genetics (PDF)
(Sprache: Englisch)
Evolutionary quantitative genetics (EQG) provides a formal theoretical foundation for quantitatively linking natural selection and genetic variation to the rate and expanse of adaptive evolution. It has become the dominant conceptual framework for...
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Evolutionary quantitative genetics (EQG) provides a formal theoretical foundation for quantitatively linking natural selection and genetic variation to the rate and expanse of adaptive evolution. It has become the dominant conceptual framework for interpreting the evolution of quantitative traits in terms of elementary forces (mutation, inheritance, selection, and drift). Despite this success, the relevance of EQG to many biological scenarios remains relatively
unappreciated, with numerous fields yet to fully embrace its approach. Part of the reason for this lag is that conceptual advances in EQG have not yet been fully synthesized and made accessible to a wider academic audience. A comprehensive, accessible overview is therefore now timely, and Evolutionary
Quantitative Genetics provides this much-needed synthesis.
The central argument of the book is that an adaptive landscape concept can be used to understand both evolutionary process within lineages and the pattern of adaptive radiations. In particular, it provides a convincing argument that models with a moving adaptive peak carry us further than any other conceptual approach yet devised. Although additive theory holds center stage, the book mentions and references departures from additivity including non-Gaussian distributions of allelic effects,
dominance, epistasis, maternal effects and phenotypic plasticity.
This accessible, advanced textbook is aimed principally at students (from senior undergraduate to postgraduate) as well as practising scientists in the fields of evolutionary biology, ecology, physiology, functional morphology, developmental biology, comparative biology, paleontology, and beyond who are interested in how adaptive radiations are produced by evolutionary and ecological processes.
unappreciated, with numerous fields yet to fully embrace its approach. Part of the reason for this lag is that conceptual advances in EQG have not yet been fully synthesized and made accessible to a wider academic audience. A comprehensive, accessible overview is therefore now timely, and Evolutionary
Quantitative Genetics provides this much-needed synthesis.
The central argument of the book is that an adaptive landscape concept can be used to understand both evolutionary process within lineages and the pattern of adaptive radiations. In particular, it provides a convincing argument that models with a moving adaptive peak carry us further than any other conceptual approach yet devised. Although additive theory holds center stage, the book mentions and references departures from additivity including non-Gaussian distributions of allelic effects,
dominance, epistasis, maternal effects and phenotypic plasticity.
This accessible, advanced textbook is aimed principally at students (from senior undergraduate to postgraduate) as well as practising scientists in the fields of evolutionary biology, ecology, physiology, functional morphology, developmental biology, comparative biology, paleontology, and beyond who are interested in how adaptive radiations are produced by evolutionary and ecological processes.
Autoren-Porträt von Stevan J. Arnold
Stevan J. Arnold is Professor Emeritus, Department of Integrative Biology, Oregon State University, USA. He was made a Fellow of the AAA&S (American Academy of Arts and Sciences) in 2009, and has held the presidency of both the Society for the Study of Evolution (1998) and the American Society of Naturalists (2012). He is one of the top experts in the field and has published large numbers of highly cited academic articles in the realm ofquantitative genetics.
Bibliographische Angaben
- Autor: Stevan J. Arnold
- 2023, 464 Seiten, Englisch
- Verlag: Oxford University Press
- ISBN-10: 0192675532
- ISBN-13: 9780192675538
- Erscheinungsdatum: 19.06.2023
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