Inferring ancestral states without assuming neutrality or gradualism using a stable model of continuous character evolution

TL;DR

This paper introduces a stable model of continuous character evolution on phylogenetic trees that relaxes neutrality and gradualism assumptions, enabling better inference of ancestral states in volatile evolutionary scenarios.

Contribution

It generalizes the Brownian motion model by incorporating heavy-tailed stable distributions and develops MCMC methods for improved ancestral state reconstruction.

Findings

Model performs well on simulated data and mammalian body mass data

Better captures large evolutionary jumps than traditional models

Supports hypothesis testing and model selection in evolutionary studies

Abstract

The value of a continuous character evolving on a phylogenetic tree is commonly modelled as the location of a particle moving under one-dimensional Brownian motion with constant rate. The Brownian motion model is best suited to characters evolving under neutral drift or tracking an optimum that drifts neutrally. We present a generalization of the Brownian motion model which relaxes assumptions of neutrality and gradualism by considering increments to evolving characters to be drawn from a heavy-tailed stable distribution (of which the normal distribution is a specialized form). We describe Markov chain Monte Carlo methods for fitting the model to biological data paying special attention to ancestral state reconstruction, and study the performance of the model in comparison with a selection of existing comparative methods, using both simulated data and a database of body mass in 1,679 mammalian species. We discuss hypothesis testing and model selection. The new model is well suited to a stochastic process with a volatile rate of change in which biological characters undergo a mixture of neutral drift and occasional evolutionary events of large magnitude.