A tale of two matrices: Multivariate approaches in evolutionary biology

Blows, M. W. (2007) A tale of two matrices: Multivariate approaches in evolutionary biology. Journal of Evolutionary Biology, 20 1: 1-8. doi:10.1111/j.1420-9101.2006.01164.x

Author Blows, M. W.
Title A tale of two matrices: Multivariate approaches in evolutionary biology
Journal name Journal of Evolutionary Biology   Check publisher's open access policy
ISSN 1010-061X
Publication date 2007-01-01
Year available 2006
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1111/j.1420-9101.2006.01164.x
Open Access Status Not yet assessed
Volume 20
Issue 1
Start page 1
End page 8
Total pages 8
Editor Fairbairn, D. J.
Merila, J.
Place of publication Oxford
Publisher Wiley-Blackwell Publishing
Language eng
Subject C1
270207 Quantitative Genetics
270799 Ecology and Evolution not elsewhere classified
780105 Biological sciences
Abstract Two symmetric matrices underlie our understanding of microevolutionary change. The first is the matrix of nonlinear selection gradients (gamma) which describes the individual fitness surface. The second is the genetic variance-covariance matrix (G) that influences the multivariate response to selection. A common approach to the empirical analysis of these matrices is the element-by-element testing of significance, and subsequent biological interpretation of pattern based on these univariate and bivariate parameters. Here, I show why this approach is likely to misrepresent the genetic basis of quantitative traits, and the selection acting on them in many cases. Diagonalization of square matrices is a fundamental aspect of many of the multivariate statistical techniques used by biologists. Applying this, and other related approaches, to the analysis of the structure of gamma and G matrices, gives greater insight into the form and strength of nonlinear selection, and the availability of genetic variance for multiple traits.
Keyword Ecology
Evolutionary Biology
Genetics & Heredity
genetic constraints
genetic variance-covariance matrix
nonlinear selection
stabilizing selection
Variance-covariance Matrix
Sexually Selected Traits
Genetic Variance
Principal Components
Lek Paradox
Quantitative Genetics
Stopping Rules
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes This document is a journal review.

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collections: Excellence in Research Australia (ERA) - Collection
2008 Higher Education Research Data Collection
School of Biological Sciences Publications
School of Biological Sciences Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 197 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 192 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 19 Feb 2008, 01:56:28 EST