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Quantifying and comparing phylogenetic evolutionary rates for shape and other high-dimensional phenotypic data.

Dean C Adams1

  • 1Department of Ecology, Evolution, and Organismal Biology; and Department of Statistics, Iowa State University, Ames, IA 50011, USA.

Systematic Biology
|December 17, 2013
PubMed
Summary

A new method quantifies evolutionary rates for high-dimensional traits (σ2 mult), outperforming traditional methods (R) for complex data like shape. This advance enables better evolutionary comparisons for traits previously difficult to analyze.

Keywords:
Evolutionary ratesgeometric morphometricsmacroevolutionmorphological evolutionphylogenetic comparative method

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Area of Science:

  • Evolutionary Biology
  • Phylogenetics
  • Quantitative Genetics

Background:

  • Quantifying and comparing rates of phenotypic evolution is crucial in evolutionary biology.
  • Existing phylogenetic comparative methods effectively analyze single traits (σ(2)) or rate matrices (R) for multiple traits.
  • High-dimensional traits, such as biological shape, have lacked adequate analytical frameworks for evolutionary rate comparison.

Purpose of the Study:

  • To introduce a novel method for quantifying phylogenetic evolutionary rates in high-dimensional multivariate data (σ2 mult).
  • To evaluate the statistical performance of hypothesis-testing procedures comparing σ2 mult across different species groups.
  • To demonstrate the utility of the new method for analyzing complex phenotypic traits like shape.

Main Methods:

  • Developed a method to quantify phylogenetic evolutionary rates for high-dimensional multivariate data (σ2 mult) using equivalencies between covariance and distance matrices (R-mode and Q-mode).
  • Employed simulations to assess the statistical performance (Type I error and power) of hypothesis tests comparing σ2 mult.
  • Applied the method to analyze head shape evolution in Plethodon salamanders.

Main Results:

  • The proposed σ2 mult method demonstrated appropriate Type I error rates and high statistical power across various conditions and trait dimensions.
  • Likelihood tests based on the evolutionary rate matrix (R) showed inflated Type I error rates with increasing trait dimensions (p).
  • Tests based on R become computationally infeasible when the number of trait dimensions equals or exceeds the number of taxa (p ≥ N).

Conclusions:

  • Tests based on σ2 mult provide a robust and accessible means for comparing evolutionary rates of high-dimensional phenotypic data.
  • This method expands the phylogenetic comparative toolkit, enabling analysis of traits previously inaccessible to R-based methods.
  • The approach is valuable for understanding the evolution of complex traits like shape, as exemplified by the Plethodon salamander head shape analysis.