Legendre20151239

Référence

Legendre, P., Fortin, M.-J. and Borcard, D. (2015) Should the Mantel test be used in spatial analysis? Methods in Ecology and Evolution, 6(11):1239-1247. (Scopus )

Résumé

Summary: The Mantel test is widely used in biology, including landscape ecology and genetics, to detect spatial structures in data or control for spatial correlation in the relationship between two data sets, for example community composition and environment. The study demonstrates that this is an incorrect use of that test. The null hypothesis of the Mantel test differs from that of correlation analysis; the statistics computed in the two types of analyses differ. We examined the basic assumptions of the Mantel test in spatial analysis and showed that they are not verified in most studies. We showed the consequences, in terms of power, of the mismatch between these assumptions and the Mantel testing procedure. The Mantel test H0 is the absence of relationship between values in two dissimilarity matrices, not the independence between two random variables or data tables. The Mantel R2 differs from the R2 of correlation, regression and canonical analysis; these two statistics cannot be reduced to one another. Using simulated data, we show that in spatial analysis, the assumptions of linearity and homoscedasticity of the Mantel test (H1: small values of D1 correspond to small values of D2 and large values of D1 to large values of D2) do not hold in most cases, except when spatial correlation extends over the whole study area. Using extensive simulations of spatially correlated data involving different representations of geographic relationships, we show that the power of the Mantel test is always lower than that of distance-based Moran's eigenvector map (dbMEM) analysis and that the Mantel R2 is always smaller than in dbMEM analysis, and uninterpretable. These simulation results are novel contributions to the Mantel debate. We also show that regression on a geographic distance matrix does not remove the spatial structure from response data and does not produce spatially uncorrelated residuals. Our main conclusion is that Mantel tests should be restricted to questions that, in the domain of application, only concern dissimilarity matrices, and are not derived from questions that can be formulated as the analysis of the vectors and matrices from which one can compute dissimilarity matrices. © 2015 British Ecological Society.

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@ARTICLE { Legendre20151239,
    AUTHOR = { Legendre, P. and Fortin, M.-J. and Borcard, D. },
    TITLE = { Should the Mantel test be used in spatial analysis? },
    JOURNAL = { Methods in Ecology and Evolution },
    YEAR = { 2015 },
    VOLUME = { 6 },
    NUMBER = { 11 },
    PAGES = { 1239-1247 },
    NOTE = { cited By 34 },
    ABSTRACT = { Summary: The Mantel test is widely used in biology, including landscape ecology and genetics, to detect spatial structures in data or control for spatial correlation in the relationship between two data sets, for example community composition and environment. The study demonstrates that this is an incorrect use of that test. The null hypothesis of the Mantel test differs from that of correlation analysis; the statistics computed in the two types of analyses differ. We examined the basic assumptions of the Mantel test in spatial analysis and showed that they are not verified in most studies. We showed the consequences, in terms of power, of the mismatch between these assumptions and the Mantel testing procedure. The Mantel test H0 is the absence of relationship between values in two dissimilarity matrices, not the independence between two random variables or data tables. The Mantel R2 differs from the R2 of correlation, regression and canonical analysis; these two statistics cannot be reduced to one another. Using simulated data, we show that in spatial analysis, the assumptions of linearity and homoscedasticity of the Mantel test (H1: small values of D1 correspond to small values of D2 and large values of D1 to large values of D2) do not hold in most cases, except when spatial correlation extends over the whole study area. Using extensive simulations of spatially correlated data involving different representations of geographic relationships, we show that the power of the Mantel test is always lower than that of distance-based Moran's eigenvector map (dbMEM) analysis and that the Mantel R2 is always smaller than in dbMEM analysis, and uninterpretable. These simulation results are novel contributions to the Mantel debate. We also show that regression on a geographic distance matrix does not remove the spatial structure from response data and does not produce spatially uncorrelated residuals. Our main conclusion is that Mantel tests should be restricted to questions that, in the domain of application, only concern dissimilarity matrices, and are not derived from questions that can be formulated as the analysis of the vectors and matrices from which one can compute dissimilarity matrices. © 2015 British Ecological Society. },
    AFFILIATION = { Département de Sciences Biologiques, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, QC, Canada; Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada },
    AUTHOR_KEYWORDS = { Landscape ecology; Landscape genetics; Mantel test; Moran's eigenvector maps (MEM); Network analysis; Numerical simulations; Redundancy analysis; Spatially structured data },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1111/2041-210X.12425 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954076908&doi=10.1111%2f2041-210X.12425&partnerID=40&md5=fef8f116ed2ed8fd23c630762bc94f2b },
}

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