@article{PAR00013607,
  title = {{O}nce upon multivariate analyses : when they tell several stories about biological evolution},
  author = {{R}enaud, {S}. and {D}ufour, {A}. {B}. and {H}ardouin, {E}. {A}. and {L}edevin, {R}. and {A}uffray, {J}ean-{C}hristophe},
  editor = {},
  language = {{ENG}},
  abstract = {{G}eometric morphometrics aims to characterize of the geometry of complex traits. {I}t is therefore by essence multivariate. {T}he most popular methods to investigate patterns of differentiation in this context are (1) the {P}rincipal {C}omponent {A}nalysis ({PCA}), which is an eigenvalue decomposition of the total variance-covariance matrix among all specimens; (2) the {C}anonical {V}ariate {A}nalysis ({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} (bg{PCA}) which investigates patterns of between-group variation, without standardizing by the within-group variance. {S}tandardizing 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. {S}uch 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. {H}ere we undertake a case study of the evolution of house mouse molar shape across various islands, based on the real dataset and simulations. {W}e investigated how patterns of main variance influence the depiction of among-group differentiation according to the interpretation of the {PCA}, bg{PCA} and {CVA}. {W}ithout arguing about a method performing ` better' than another, it rather emerges that working on the total or between-group variance ({PCA} and bg{PCA}) will tend to put the focus on the role of direction of main variance as line of least resistance to evolution. {S}tandardizing 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.},
  keywords = {{EUROPE}},
  booktitle = {},
  journal = {{P}los {O}ne},
  volume = {10},
  numero = {7},
  pages = {e0132801},
  ISSN = {1932-6203},
  year = {2015},
  DOI = {10.1371/journal.pone.0132801},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00013607},
}