Systematic Biology· 2014Q1
A Generalized K Statistic for Estimating Phylogenetic Signal from Shape and Other High-Dimensional Multivariate Data
- 657citations
- Q1SCImago
- 2014year
Short summary
A generalized K statistic (K(mult)) is introduced to accurately quantify phylogenetic signal in high-dimensional multivariate traits, such as shape, overcoming limitations of existing methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces K(mult), a generalized K statistic for high-dimensional multivariate phylogenetic signal.
- K(mult) shows stable expected values (1.0) across trait variation and trait dimensions in simulations.
- Existing squared-change parsimony methods produce confounded interpretations with increasing trait variation and dimensions.
- K(mult) demonstrates appropriate Type I error rates and high statistical power for hypothesis testing.
- The method's statistical properties are consistent across various phylogenetic structures and covariance models.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Systematic Biology, 2014 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Geometry and Topology
Geometry and TopologyMathematics