A note on measuring natural selection on principal component scores

• Published in: Evolution letters Vol. 2; no. 4; pp. 272 - 280
• Main Authors: Chong, Veronica K., Fung, Hannah F., Stinchcombe, John R.
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.08.2018; John Wiley and Sons Inc
• Subjects: Animal behavior; Comment and Opinion; Comments and Opinions; Estimates; Flowering time; Gene expression; Hypotheses; Hypothesis testing; life history; multicollinearity; Natural selection; principal component analysis; principal component regression; selection gradients; trade‐offs; Volatiles; trade‐offs; multicollinearity; principal component regression; life history; selection gradients; Flowering time; principal component analysis
• ISSN: 2056-3744; 2056-3744
• DOI: 10.1002/evl3.63

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