A Nonparametric Approach to Estimating Divergence Times in the Absence of Rate Constancy

Abstract

A new method for estimating divergence times when evolutionary rates are variable across lineages is proposed. The method, called nonparametric rate smoothing (NPRS), relies on minimization of ancestor-descendant local rate changes and is motivated by the likelihood that evolutionary rates are autocorrelated in time. Fossil information pertaining to minimum and/or maximum ages of nodes in a phylogeny is incorporated into the algorithms by constrained optimization techniques. The accuracy of NPRS was examined by comparison to a clock-based maximum-likelihood method in computer simulations. NPRS provides more accurate estimates of divergence times when (1) sequence lengths are sufficiently long, (2) rates are truly nonclocklike, and (3) rates are moderately to highly autocorrelated in time. The algorithms were applied to estimate divergence times in seed plants based on data from the chloroplast rbcL gene. Both constrained and unconstrained NPRS methods tended to produce divergence time estimates more consistent with paleobotanical evidence than did clock-based estimates.