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Phylogenetic comparative methods may favor complex models. Our study shows improved algorithms avoid bias against simpler evolutionary models like Brownian motion, even with large phylogenies.

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Area of Science:

  • Evolutionary Biology
  • Phylogenetics
  • Quantitative Methods

Background:

  • Multivariate Gaussian phylogenetic comparative methods are debated for model complexity bias.
  • Rotation invariance is a potential issue in numerical estimation approaches for phylogenetic methods.

Purpose of the Study:

  • To dissect the concept of rotation invariance in phylogenetic comparative methods.
  • To investigate potential bias against simpler evolutionary models (e.g., Brownian motion) in phylogenetic analyses.
  • To evaluate the performance of an improved likelihood evaluation algorithm in handling complex phylogenetic models.

Main Methods:

  • Dissection of the concept of rotation invariance.
  • Simulations using an improved likelihood evaluation algorithm within the mvSLOUCH software.
  • Comparison of results with previous findings regarding model bias.

Main Results:

  • Rotation invariance can be an issue with numerical, but not necessarily analytical, estimation methods.
  • No observed bias against the Brownian motion model in simulations with the improved algorithm.
  • The new algorithm successfully handles larger phylogenies and more complex evolutionary models.

Conclusions:

  • Improved likelihood evaluation algorithms can mitigate issues of model complexity bias in phylogenetic comparative methods.
  • The mvSLOUCH algorithm demonstrates robustness in analyzing complex phylogenetic scenarios without favoring overly complex models.