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借助多元回归开展自然选择量化研究，已革新了学界对选择作用的认知，但现有方法仍易受高度相关性状引发的多重共线性（multicollinearity）干扰。尽管以主成分得分（principal component scores）量化选择看似是解决该难题的有效途径，但该方法因解释难度较高，且难以将主成分轴还原至原始性状，而饱受学界批评。本文详细阐述并演示了如何将主成分得分对应的选择梯度（selection gradients）转换为原始性状的选择梯度，以此解决多重共线性问题并优化生物学解释效果。除缓解多重共线性外，本文还提出该方法或可用于量化高维数据下的选择作用，例如挥发物或基因表达性状（gene expression traits）。研究通过实证数据与文献案例验证了该方法的可行性，并阐明了如何在降低多重共线性负面影响的同时，对主成分得分的选择估计结果进行合理解读。