ArticleEvolution letters2018
A note on measuring natural selection on principal component scores.
Article in Evolution letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Measuring natural selection on the transcriptome.The New phytologist · 2025Review
- Predicting Fitness-Related Traits Using Gene Expression and Machine Learning.Genome biology and evolution · 2025Article
- Natural selection on floral volatiles and other traits can change with snowmelt timing and summer precipitation.The New phytologist · 2025Article
- Regularized regression can improve estimates of multivariate selection in the face of multicollinearity and limited data.Evolution letters · 2024Article
- A multidimensional selective landscape drives adaptive divergence between and within closely related Phlox species.Nature communications · 2024Article
- The quantitative genetics of gene expression in Mimulus guttatus.PLoS genetics · 2024Article
- Body Length and Craniometrics of Non-Native Raccoons in Two Regions in Middle Japan during Early Invasion Stages.Animals : an open access journal from MDPI · 2022Article
- Measuring, comparing and interpreting phenotypic selection on floral scent.Journal of evolutionary biology · 2022Article
- Host diversity positively affects the temporal stability of foliar fungal diseases in a Tibetan alpine meadow.Annals of botany · 2022Article
- On the evolution of trophic position.Ecology letters · 2021Article
- The strength and form of natural selection on transcript abundance in the wild.Molecular ecology · 2021Article
- How general is cognitive ability in non-human animals? A meta-analytical and multi-level reanalysis approach.Proceedings. Biological sciences · 2020Article
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Authors and funding
3 authors.
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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.
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