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Exploring Phylogenetic Signal in Multivariate Phenotypes by Maximizing Blomberg's K.

Philipp Mitteroecker1,2, Michael L Collyer3, Dean C Adams4

  • 1Department of Evolutionary Biology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria.

Systematic Biology
|July 6, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to measure phylogenetic signal in complex, multivariate data. The new statistics, KA and KG, offer improved power for detecting evolutionary patterns in traits.

Keywords:
Cranial shapemultivariate evolutionphylogenetic generalized least squaresphylogenyrelative eigenanalysis

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Area of Science:

  • Evolutionary Biology
  • Phylogenetics
  • Quantitative Genetics

Background:

  • Phylogenetic signal describes the tendency of related species to share similar traits.
  • Measuring phylogenetic signal for single traits is established, but challenging for multivariate data.
  • Modern biological studies frequently involve complex, multivariate phenotypic data.

Purpose of the Study:

  • To develop a novel method for exploring phylogenetic signal in multivariate phenotypes.
  • To introduce interpretable components and summary statistics for multivariate phylogenetic signal.
  • To assess the performance of new statistics against existing methods.

Main Methods:

  • Decomposition of multivariate data into linear combinations (K-components) maximizing/minimizing phylogenetic signal (Blomberg's K).
  • Development and algebraic/statistical characterization of new summary statistics KA and KG.
  • Simulation studies comparing KA and KG with the statistic Kmult.
  • Empirical application to vertebrate cranial shape data (crocodyliforms and papionins).

Main Results:

  • The new method allows for biological interpretation of components reflecting phylogenetic signal.
  • KA and KG demonstrated higher statistical power than Kmult, particularly for low or concentrated signal.
  • Significant phylogenetic signal was detected in specific dimensions of vertebrate cranial shape.

Conclusions:

  • The proposed method effectively quantifies multivariate phylogenetic signal.
  • New statistics KA and KG provide a powerful tool for evolutionary analyses.
  • Phylogenetic signal can be unevenly distributed across trait dimensions, impacting phylogenetic inference.