12
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Evaluating modularity in morphometric data: challenges with the RV coefficient and a new test measure

      Methods in Ecology and Evolution

      Wiley-Blackwell

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references 47

          • Record: found
          • Abstract: not found
          • Article: not found

          Perspective: Complex Adaptations and the Evolution of Evolvability

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Morphological Integration and Developmental Modularity

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data.

               Dean C Adams (2014)
              Phylogenetic signal is the tendency for closely related species to display similar trait values due to their common ancestry. Several methods have been developed for quantifying phylogenetic signal in univariate traits and for sets of traits treated simultaneously, and the statistical properties of these approaches have been extensively studied. However, methods for assessing phylogenetic signal in high-dimensional multivariate traits like shape are less well developed, and their statistical performance is not well characterized. In this article, I describe a generalization of the K statistic of Blomberg et al. that is useful for quantifying and evaluating phylogenetic signal in highly dimensional multivariate data. The method (K(mult)) is found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices. Using computer simulations based on Brownian motion, I demonstrate that the expected value of K(mult) remains at 1.0 as trait variation among species is increased or decreased, and as the number of trait dimensions is increased. By contrast, estimates of phylogenetic signal found with a squared-change parsimony procedure for multivariate data change with increasing trait variation among species and with increasing numbers of trait dimensions, confounding biological interpretations. I also evaluate the statistical performance of hypothesis testing procedures based on K(mult) and find that the method displays appropriate Type I error and high statistical power for detecting phylogenetic signal in high-dimensional data. Statistical properties of K(mult) were consistent for simulations using bifurcating and random phylogenies, for simulations using different numbers of species, for simulations that varied the number of trait dimensions, and for different underlying models of trait covariance structure. Overall these findings demonstrate that K(mult) provides a useful means of evaluating phylogenetic signal in high-dimensional multivariate traits. Finally, I illustrate the utility of the new approach by evaluating the strength of phylogenetic signal for head shape in a lineage of Plethodon salamanders.
                Bookmark

                Author and article information

                Journal
                Methods in Ecology and Evolution
                Methods Ecol Evol
                Wiley-Blackwell
                2041210X
                May 2016
                May 06 2016
                : 7
                : 5
                : 565-572
                Article
                10.1111/2041-210X.12511
                © 2016
                Product

                Comments

                Comment on this article