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Once upon multivariate analyses: when they tell several stories about biological evolution

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Hardouin,  Emilie
Department Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Citation

Renaud, S., Dufour, A.-B., Hardouin, E., Ledevin, R., & Auffray, J.-C. (2015). Once upon multivariate analyses: when they tell several stories about biological evolution. PLoS One, 10(7): e0132801. doi:10.1371/journal.pone.0132801.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0028-4BA7-D
Abstract
Geometric morphometrics aims to characterize of the geometry of complex traits. It is therefore by essence multivariate. The most popular methods to investigate patterns of differentiation in this context are (1) the Principal Component Analysis (PCA), which is an eigenvalue decomposition of the total variance-covariance matrix among all specimens; (2) the Canonical Variate Analysis (CVA, a.k.a. linear discriminant analysis (LDA) for more than two groups), which aims at separating the groups by maximizing the between-group to withingroup variance ratio; (3) the between-group PCA (bgPCA) which investigates patterns of between-group variation, without standardizing by the within-group variance. Standardizing within-group variance, as performed in the CVA, distorts the relationships among groups, an effect that is particularly strong if the variance is similarly oriented in a comparable way in all groups. Such shared direction of main morphological variance may occur and have a biological meaning, for instance corresponding to the most frequent standing genetic variation in a population. Here we undertake a case study of the evolution of house mouse molar shape across various islands, based on the real dataset and simulations.We investigated how patterns of main variance influence the depiction of among-group differentiation according to the interpretation of the PCA, bgPCA and CVA. Without arguing about a method performing ‘better’ than another, it rather emerges that working on the total or between-group variance (PCA and bgPCA) will tend to put the focus on the role of direction of main variance as line of least resistance to evolution. Standardizing by the within-group variance (CVA), by dampening the expression of this line of least resistance, has the potential to reveal other relevant patterns of differentiation that may otherwise be blurred.