Adaptive Trait Evolution in Random Environment

TL;DR

This paper introduces a novel stochastic model for trait evolution that extends the Ornstein-Uhlenbeck process, allowing for dynamic optima, and demonstrates its improved fit across numerous biological datasets.

Contribution

The paper develops a new model incorporating stochastic dynamics for trait optima, enhancing the analysis of trait evolution in diverse species.

Findings

The new model better fits most datasets compared to traditional methods.

It captures stochastic fluctuations in trait optima.

Improves understanding of trait evolution dynamics.

Abstract

Current phylogenetic comparative methods generally employ the Ornstein-Uhlenbeck(OU) process for modeling trait evolution. Being able of tracking the optimum of a trait within a group of related species, the OU process provides information about the stabilizing selection where the population mean adopts a particular trait value. The optima of a trait may follow certain stochastic dynamics along the evolutionary history. In this paper, we extend the current framework by adopting a rate of evolution which behave according to pertinent stochastic dynamics. The novel model is applied to analyze about 225 datasets collected from the existing literature. Results validate that the new framework provides a better fit for the majority of these datasets.