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The Adaptive Landscape in Evolutionary Biology$
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Erik Svensson and Ryan Calsbeek

Print publication date: 2013

Print ISBN-13: 9780199595372

Published to Oxford Scholarship Online: December 2013

DOI: 10.1093/acprof:oso/9780199595372.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 29 November 2021

Analyzing and Comparing the Geometry of Individual Fitness Surfaces

Analyzing and Comparing the Geometry of Individual Fitness Surfaces

Chapter:
(p.126) Chapter 9 Analyzing and Comparing the Geometry of Individual Fitness Surfaces
Source:
The Adaptive Landscape in Evolutionary Biology
Author(s):

Stephen F. Chenoweth

John Hunt

Howard D. Rundle

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199595372.003.0009

For almost 30 years, Lande and Arnold's approximation of individual fitness surfaces through multiple regression has provided a common framework for comparing the strength and form of phenotypic selection across traits, fitness components and sexes. This chapter provides an overview of the statistical and geometric approaches available for the multivariate analysis of phenotypic selection that build upon the Lande and Arnold approach. First, it details least squares based approaches for the estimation of multivariate selection in a single population. Second, it shows how these approaches can be extended for the statistical comparison of individual fitness surfaces among groups such as populations or experimental treatments, addressing the inferential differences between analyses of randomly chosen groups versus situations in which groups are experimentally fixed. In each case, it points out known issues and caveats associated with the approaches. Finally, using case studies, the chapter shows how these estimates of multivariate selection can be integrated with quantitative genetic analyses to better understand issues such as the maintenance of genetic variance under selection and how genetic constraints can bias evolutionary responses to selection.

Keywords:   selection gradient, fitness surface, response surface, random regression, model misspecification

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