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. 2018 Jun 21;2(4):272-280.
doi: 10.1002/evl3.63. eCollection 2018 Aug.

A note on measuring natural selection on principal component scores

Veronica K Chong  1 , Hannah F Fung  1 , John R Stinchcombe  1   2
Affiliations

A note on measuring natural selection on principal component scores

Veronica K Chong et al. Evol Lett. .

Abstract

Measuring natural selection through the use of multiple regression has transformed our understanding of selection, although the methods used remain sensitive to the effects of multicollinearity due to highly correlated traits. While measuring selection on principal component (PC) scores is an apparent solution to this challenge, this approach has been heavily criticized due to difficulties in interpretation and relating PC axes back to the original traits. We describe and illustrate how to transform selection gradients for PC scores back into selection gradients for the original traits, addressing issues of multicollinearity and biological interpretation. In addition to reducing multicollinearity, we suggest that this method may have promise for measuring selection on high-dimensional data such as volatiles or gene expression traits. We demonstrate this approach with empirical data and examples from the literature, highlighting how selection estimates for PC scores can be interpreted while reducing the consequences of multicollinearity.

Keywords: Flowering time; life history; multicollinearity; principal component analysis; principal component regression; selection gradients; trade‐offs.

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Figures

Figure 1
Figure 1
Line mean correlation between flowering time and flowering duration in the Arabidopsis chamber experiment. The inbred line mean correlation is –0.95.
Figure 2
Figure 2
Selection differentials for (A) flowering time and (B) flowering duration, (C) Rosette leaf number, and (D) branch number in the Arabidopsis experiment. Differentials are portrayed in the original trait units; plotted points are line means.
See this image and copyright information in PMC

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