Abstract
Phylogenetic methods can use the sampling times of molecular sequence data to calibrate the molecular clock, enabling the estimation of substitution rates and time scales for rapidly evolving pathogens and data sets containing ancient DNA samples. A key aspect of such calibrations is whether a sufficient amount of molecular evolution has occurred over the sampling time window, that is, whether the data can be treated as being from a measurably evolving population. Here we investigate the performance of a fully Bayesian evaluation of temporal signal (BETS) in molecular sequence data. The method involves comparing the fit of two models: a model in which the data are accompanied by the actual (heterochronous) sampling times, and a model in which the samples are constrained to be contemporaneous (isochronous). We conduct extensive simulations under a range of conditions to demonstrate that BETS accurately classifies data sets according to whether they contain temporal signal or not, even when there is substantial among-lineage rate variation. We explore the behaviour of this classification in analyses of five data sets: modern samples of A/H1N1 influenza virus, the bacterium Bordetella pertussis, and coronaviruses from mammalian hosts, and ancient DNA data sets of Hepatitis B virus and of mitochondrial genomes of dog species. Our results indicate that BETS is an effective alternative to other measures of temporal signal. In particular, this method has the key advantage of allowing a coherent assessment of the entire model, including the molecular clock and tree prior which are essential aspects of Bayesian phylodynamic analyses.