Inferring the Effect of Species Interactions on Trait Evolution

Area of Science:

• Macroevolutionary biology
• Theoretical ecology
• Phylogenetics

Background:

• Classic trait evolution models (Brownian motion, Ornstein-Uhlenbeck) assume independent species evolution.
• Recent models incorporate species interactions but often lack a basis in population dynamics and trait variance dynamics.

Purpose of the Study:

• Develop a general trait evolution framework grounded in population dynamics.
• Accommodate various species interactions, including abundance-dependent competition.
• Provide a tool for inferring species interactions from trait and abundance data.

Main Methods:

• Developed a novel trait evolution framework integrating population dynamics.
• Utilized Approximate Bayesian Computation (ABC) for parameter inference.
• Applied the model to simulated data and empirical data of baleen whale body lengths.

Main Results:

• Inference performs well when predicted diversity matches observed species numbers.
• A model with competition weighted by metabolic rate showed slightly better fit to whale body length data.
• Different models produced substantially different abundance distribution predictions.

Conclusions:

• The proposed framework offers a conceptual approach to uncover species interactions driving trait evolution.
• Abundance distribution data are crucial for distinguishing between evolutionary models and inferring interaction types.
• Identifies necessary data for practical application in macroevolutionary studies.