Data from: A note on measuring natural selection on principal component scores

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 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