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A General and Efficient Algorithm for the Likelihood of Diversification and Discrete-Trait Evolutionary Models.

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A new algorithm significantly speeds up macroevolutionary analyses by reducing computational redundancy in phylogenetic tree calculations. This innovation enables the analysis of massive datasets, advancing our understanding of evolutionary processes.

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Area of Science:

  • Computational Biology
  • Macroevolutionary Theory
  • Phylogenetics

Background:

  • Macroevolutionary analyses face computational limitations due to increasing phylogenetic tree and data sizes.
  • Complex models are needed to investigate evolutionary processes, further straining computational resources.

Purpose of the Study:

  • To introduce a novel algorithm for efficiently computing macroevolutionary model likelihoods.
  • To reduce computational redundancy in likelihood calculations along phylogenetic tree edges.

Main Methods:

  • Developed a new algorithm based on the 'flow' of differential equations in backward time.
  • Applied the algorithm to birth-death models, state- or time-dependent rate models, and discrete-trait evolution models.
  • Implemented the algorithm for state-dependent diversification models (BiSSE, MuSSE) in the R package `castor`.

Main Results:

  • The algorithm efficiently computes likelihoods for various macroevolutionary models.
  • The `castor` package implementation is orders of magnitude faster than existing methods for large phylogenies.
  • Enables fitting of state-dependent diversification models to phylogenies with millions of tips.

Conclusions:

  • The novel algorithm significantly enhances computational efficiency for macroevolutionary analyses.
  • Facilitates the application of complex models to massive phylogenetic datasets.
  • Promises substantial computational improvements for a wide range of macroevolutionary models.