Model Selection Performance in Phylogenetic Comparative Methods Under Multivariate Ornstein-Uhlenbeck Models of Trait...

Area of Science:

• Evolutionary Biology
• Quantitative Genetics
• Computational Biology

Background:

• Phylogenetic comparative methods (PCMs) are crucial for understanding trait evolution.
• Distinguishing between evolutionary models of trait co-adaptation is challenging.
• Advancements in computational algorithms facilitate complex model testing.

Purpose of the Study:

• To demonstrate the application of mvSLOUCH for testing multiple co-adaptation hypotheses.
• To assess the power of model selection criteria in distinguishing between evolutionary models.
• To investigate factors influencing model identifiability.

Main Methods:

• Utilized mvSLOUCH software for multivariate phylogenetic comparative analyses.
• Analyzed empirical datasets on ungulate feeding/morphology and Ferula fruit evolution.
• Conducted simulations to evaluate model distinguishability.
• Employed Akaike's Information Criterion corrected for small sample size (AICc) for model selection.

Main Results:

• AICc effectively distinguished between most competing evolutionary models.
• Simulations indicated potential bias towards simpler models like Brownian motion or Ornstein-Uhlenbeck.
• Measurement error and constraints on the drift matrix significantly impacted model identifiability.

Conclusions:

• mvSLOUCH provides a robust framework for testing complex evolutionary hypotheses.
• AICc is a valuable tool for model selection in phylogenetic comparative studies.
• Understanding model limitations, including measurement error, is essential for accurate evolutionary inference.