A Comparative Test for Divergent Adaptation: Inferring Speciation Drivers from Functional Trait Divergence.

Area of Science:

• Evolutionary biology
• Speciation research
• Quantitative genetics

Background:

• Ecological differentiation is a proposed driver of speciation, but evidence is mainly from model species.
• Nonmodel taxa suggest speciation can occur with limited ecological or ecomorphological differentiation.
• Distinguishing divergent adaptation from other evolutionary processes is crucial for understanding species richness.

Purpose of the Study:

• To develop statistical models for detecting divergent ecological adaptation in paired lineages.
• To quantify the extent of divergent selection based on phenotypic optima.
• To provide a framework for analyzing trait divergence in a comparative manner.

Main Methods:

• Introduction of new statistical models for divergent adaptation in continuous traits.
• Encoding models in the R package 'diverge'.
• Testing model performance via simulation and application to empirical bird and mammal trait data.

Main Results:

• The models quantify divergent selection by measuring the distance between phenotypic optima.
• The 'diverge' package allows variation in optima distance across different variables.
• Demonstrated application to real-world trait divergence data.

Conclusions:

• This framework offers the first explicit statistical test for divergent selection signatures in trait divergence data.
• Enables broader empirical research into the dynamics and prevalence of divergent adaptation.
• Facilitates understanding of speciation mechanisms beyond model systems.