Phylogenetic accuracy under non-stationary and non-homogeneous conditions: A simulation study

Phylogenetic inference typically assumes that the data have evolved under
Stationary, Reversible, and Homogeneous (SRH) conditions. Many empirical
and simulation studies have shown that assuming SRH conditions can lead to
significant errors in phylogenetic inference when the data violate these
assumptions. Yet, many simulation studies focused on extreme non-SRH
conditions that represent worst-case scenarios and not the average
empirical dataset. In this study, we simulate datasets under
various degrees of non-SRH conditions using empirically derived
parameters to mimic real data and examine the effects
of incorrectly assuming SRH conditions on inferring phylogenies.
Our results show that maximum likelihood inference is generally
quite robust to a wide range of SRH model violations but is
inaccurate under extreme convergent evolution.