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Modeling the Evolution of Rates of Continuous Trait Evolution.

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This study introduces a new Bayesian method, evolving rates (evorates), to model gradual and stochastic changes in phenotypic evolution rates. The method reveals varying evolutionary rates across lineages and time, improving our understanding of macroevolutionary dynamics.

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Area of Science:

  • Evolutionary biology
  • Macroevolutionary dynamics
  • Comparative genomics

Background:

  • Phenotypic evolution rates vary significantly across taxa, from rapid adaptive radiations to stasis in "living fossils."
  • Existing models often assume deterministic or infrequent rate changes, potentially leading to underfitting and misleading conclusions.
  • Understanding rate variation is crucial for explaining phenotypic diversity across space, time, and taxa.

Purpose of the Study:

  • To develop a novel trait evolution model allowing gradual, stochastic rate variation across clades.
  • To extend the model for decreasing or increasing rates over time, accommodating "early/late burst" scenarios.
  • To implement an efficient Bayesian method (evorates) for fitting this flexible model to comparative data.

Main Methods:

  • Development of a new trait evolution model with gradually and stochastically varying rates.
  • Extension to model time-dependent rate changes (decreasing/increasing).
  • Implementation of a Bayesian inference framework named "evolving rates" (evorates).

Main Results:

  • Simulations confirm evorates reliably infers lineage-specific and time-varying evolutionary rates.
  • Application to cetacean body size evolution shows an overall slowdown over time.
  • Identified recent bursts in oceanic dolphins and stasis in Mesoplodon beaked whales.

Conclusions:

  • The evorates method provides a powerful tool for analyzing complex patterns of phenotypic evolution.
  • Demonstrates empirical utility by revealing nuanced body size evolution in cetaceans.
  • Offers a more flexible and accurate approach to modeling macroevolutionary rate variation.