We employed both strict and relaxed lognormal molecular clocks, the latter allowing rate variation among branches [37] . The coefficient of variation (σ r ) was used as a quantification of the rate variation among branches (σ r > 0.2 was considered as significant rate variation among branches) ( Table 1) . A constant size demographic model was used as coalescent prior. Each alignment of both data sets was analyzed using Markov Chain Monte-Carlo (MCMC) computations run over a sufficient time to achieve convergence of the chains, which was analyzed by inspection of the MCMC samples using TRACER 1.4 [38] . The 95% highest posterior density (HPD) interval is the shortest credible interval that contains 95% of the samples values. Statistical uncertainties of the substitution rates and the TMRCA were summarized as the lower 95%, median, and upper 95% values of the HPD. Out of the tested models (GTR + Γ, both with strict and relaxed lognormal molecular clocks), the clock model that performed better was the lognormal molecular clock, which yielded the highest marginal likelihood. Clock models were also compared in terms of Bayes Factors (BF, Table  2 ). The relaxed model clock following a lognormal distribution was also supported by the highest log 10 BF as suggested [39] .