ASSESSING PHYLOGENETIC SIGNAL WITH MEASUREMENT ERROR: A COMPARISON OF MANTEL TESTS, BLOMBERG ET AL.'S K, AND PHYLOGENETIC DISTOGRAMS

Authors

  • Olivier J. Hardy,

    1. Evolutionary Biology and Ecology Unit, Faculté des Sciences, Université Libre de Bruxelles CP160/12, 50 av. F. Roosevelt, 1050 Brussels, Belgium
    2.  E-mail: ohardy@ulb.ac.be
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  • Sandrine Pavoine

    1. Muséum national d’Histoire naturelle, Département Ecologie et Gestion de la Biodiversité, UMR 7204 CNRS UPMC, 61 rue Buffon, 75005 Paris, France
    2. Mathematical Ecology Research Group, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, United Kingdom
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Abstract

In macroevolutionary studies, different approaches are commonly used to measure phylogenetic signal—the tendency of related taxa to resemble one another—including the K statistic and the Mantel test. The latter was recently criticized for lacking statistical power. Using new simulations, we show that the power of the Mantel test depends on the metrics used to define trait distances and phylogenetic distances between species. Increasing power is obtained by lowering variance and increasing negative skewness in interspecific distances, as obtained using Euclidean trait distances and the complement of Abouheif proximity as a phylogenetic distance. We show realistic situations involving “measurement error” due to intraspecific variability where the Mantel test is more powerful to detect a phylogenetic signal than a permutation test based on the K statistic. We highlight limitations of the K-statistic (univariate measure) and show that its application should take into account measurement errors using repeated measures per species to avoid estimation bias. Finally, we argue that phylogenetic distograms representing Euclidean trait distance as a function of the square root of patristic distance provide an insightful representation of the phylogenetic signal that can be used to assess both the impact of measurement error and the departure from a Brownian evolution model.

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