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On the Limits of Quantitative Genetics for the Study of Phenotypic Evolution

Content Provider	Springer Nature Link
Author	Pigliucci, Massimo Schlichting, Carl D.
Copyright Year	1997
Abstract	During the last two decades the role of quantitative genetics in evolutionary theory has expanded considerably. Quantitative genetic-based models addressing long term phenotypic evolution, evolution in multiple environments (phenotypic plasticity) and evolution of ontogenies (developmental trajectories) have been proposed. Yet, the mathematical foundations of quantitative genetics were laid with a very different set of problems in mind (mostly the prediction of short term responses to artificial selection), and at a time in which any details of the genetic machinery were virtually unknown. In this paper we discuss what a model is in population biology, and what kind of model we need in order to address the complexities of phenotypic evolution. We review the assumptions of quantitative genetics and its most recent accomplishments, together with the limitations that such assumptions impose on the modelling of some aspects of phenotypic evolution. We also discuss three alternative appr oaches to the theoretical description of evolutionary trajectories (nonlinear dynamics, complexity theory and optimization theory), and their respective advantages and limitations. We conclude by calling for a new theoretical synthesis, including quantitative genetics and not necessarily limited to the other approaches here discussed.
Starting Page	143
Ending Page	160
Page Count	18
File Format	PDF
ISSN	00015342
Journal	Acta Biotheoretica
Volume Number	45
Issue Number	2
e-ISSN	15728358
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	1997-01-01
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Philosophy of Biology Evolutionary Biology
Content Type	Text
Resource Type	Article
Subject	Philosophy Applied Mathematics Medicine Biochemistry, Genetics and Molecular Biology Environmental Science Agricultural and Biological Sciences

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