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Estimating Correlated Rates of Trait Evolution with Uncertainty.

D S Caetano1,2, L J Harmon1

  • 1Department of Biological Sciences, Institute for Bioinformatics and Evolutionary Studies (IBEST), University of Idaho, Moscow, ID 83843, USA.

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Summary
This summary is machine-generated.

Correlated trait evolution shapes evolutionary trajectories. This study introduces a new phylogenetic method to detect shifts in evolutionary integration, revealing novel insights into phenotypic evolution across diverse clades.

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Area of Science:

  • Evolutionary biology
  • Quantitative genetics
  • Phylogenetics

Background:

  • Correlated evolution of traits influences phenotypic evolution trajectories.
  • Shifts in evolutionary integration can unlock novel morphospace exploration.
  • Understanding these patterns is crucial for evolutionary biology.

Purpose of the Study:

  • To develop and validate a phylogenetic method for detecting shifts in evolutionary integration.
  • To analyze the pace and pattern of trait evolution and correlation across clades.
  • To incorporate uncertainty in trait evolution and ancestral states using Bayesian methods.

Main Methods:

  • Phylogenetic comparative methods using Bayesian Markov chain Monte Carlo (MCMC).
  • Joint estimation of model parameters, including trait evolution and ancestral regimes.
  • Extension of Felsenstein's pruning algorithm for multivariate Brownian motion models with multiple rate regimes.
  • Use of summary statistics to test for regime shifts.

Main Results:

  • The developed method successfully detects shifts in evolutionary integration across phylogenetic trees.
  • Simulations confirm the method's performance under various scenarios.
  • Case studies on Centrarchidae fishes and Anolis lizards illustrate shifts in evolutionary trajectories.

Conclusions:

  • The new method provides a robust framework for studying evolutionary integration and its shifts.
  • This approach enhances our understanding of how correlated trait evolution impacts macroevolutionary patterns.
  • The findings have implications for studying phenotypic evolution, modularity, and diversification.