VISUALIZING MULTIVARIATE SELECTION

TLDR

Natural selection can be viewed as the multivariate relationship between fitness and phenotype, as shown by recent quantitative‑genetic theory. The study examines how a fitness surface relates to phenotypic selection coefficients estimated by multiple regression and uses canonical analysis to clarify multivariate selection, while also proposing a two‑mode classification of selection. The authors model fitness as a multidimensional surface over phenotypic traits and relate it to selection coefficients via multiple regression and canonical analysis. The analysis enables visualization of the fitness surface implied by selection coefficients, offering a compact summary that facilitates comparison of surfaces and hypothesis generation about trait adaptive significance.

Abstract

Recent developments in quantitative-genetic theory have shown that natural selection can be viewed as the multivariate relationship between fitness and phenotype. This relationship can be described by a multidimensional surface depicting fitness as a function of phenotypic traits. We examine the connection between this surface and the coefficients of phenotypic selection that can be estimated by multiple regression and show how the interpretation of multivariate selection can be facilitated through the use of the method of canonical analysis. The results from this analysis can be used to visualize the surface implied by a set of selection coefficients. Such a visualization provides a compact summary of selection coefficients, can aid in the comparison of selection surfaces, and can help generate testable hypotheses as to the adaptive significance of the traits under study. Further, we discuss traditional definitions of directional, stabilizing, and disruptive selection and conclude that selection may be more usefully classified into two general modes, directional and nonlinear selection, with stabilizing and disruptive selection as special cases of nonlinear selection.