Benchmarking the Mantel test and derived methods for testing association between distance matrices.
distance matrices
mantel test
pairwise distances
spatial autocorrelation
statistical power
type I error
Journal
Molecular ecology resources
ISSN: 1755-0998
Titre abrégé: Mol Ecol Resour
Pays: England
ID NLM: 101465604
Informations de publication
Date de publication:
02 Dec 2023
02 Dec 2023
Historique:
revised:
12
10
2023
received:
21
05
2023
accepted:
30
10
2023
medline:
2
12
2023
pubmed:
2
12
2023
entrez:
2
12
2023
Statut:
aheadofprint
Résumé
Testing the association between objects is central in ecology, evolution, and quantitative sciences in general. Two types of variables can describe the relationships between objects: point variables (measured on individual objects), and distance variables (measured between pairs of objects). The Mantel test and derived methods have been extensively used for distance variables. Yet, these methods have been criticized due to low statistical power and inflated type I error when spatial autocorrelation is present. Here, we assessed the statistical power between different types of tested variables and the type I error rate over a wider range of autocorrelation intensities than previously assessed, both on univariate and multivariate data. We also illustrated the performance of distance matrix statistics through computational simulations of genetic diversity. We show that the Mantel test and derived methods are not affected by inflated type I error when spatial autocorrelation affects only one variable when investigating correlations, or when either the response or the explanatory variable(s) is affected by spatial autocorrelation while investigating causal relationships. As previously noted, with autocorrelation affecting more variables, inflated type I error could be reduced by modifying the significance threshold. Additionally, the Mantel test has no problem of statistical power when the hypothesis is formulated in terms of distance variables. We highlight that transformation of variable types should be avoided because of the potential information loss and modification of the tested hypothesis. We propose a set of guidelines to help choose the appropriate method according to the type of variables and defined hypothesis.
Identifiants
pubmed: 38041538
doi: 10.1111/1755-0998.13898
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
ID : 310030_185327
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
ID : 31003A_182577
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
ID : P5R5PB_203169
Informations de copyright
© 2023 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd.
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