Bayesian MCMC (Markov Chain Monte Carlo) deployed in beast (version 1.8.0) (Drummond & Rambaut, 2007) was then used to infer the evolutionary dynamics of the aforementioned dataset, with a particular focus on Bhutanese S. sonnei. To determine the best-fit priors to the data at hand, multiple beast runs were conducted using the TVM or GTR nucleotide substitution model with constant, exponential growth or Bayesian skyline demographic history priors, in combination with either a strict or a relaxed molecular clock (uncorrelated lognormal distribution) (Drummond & Rambaut, 2007). For each beast analysis, marginal likelihood estimation was conducted using both path sampling and stepping-stone sampling approaches to facilitate model selection (Baele et al., 2012, 2013). Bayes factor comparison indicated the TVM+Γ4 substitution model with a relaxed log-normal molecular clock and a piecewise Bayesian skyline demographic model as the best-fit model to the data. Analyses using these priors were then performed in triplicate using a continuous 100 million generation MCMC chain with samples taken every 10 000 generations and parameter convergence (indicated by effective sample size values >200) was assessed in Tracer (version 1.5). LogCombiner (version 1.8) was used to combine triplicate runs, with removal of 20 % burn-in.