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The GTR + G substitution model was selected as the best DNA evolution model for genomic sequences, according to the Akaike Information Criterion AICC) and Bayesian Information Criterion (BIC).

Using the DNA evolution model chosen by MODELTEST and the NJ tree as a starting tree, two independent runs were performed, each with four chains for 5,000,000 7,500,000 generations (where the convergence diagnostics for each gene hit a stop value of 0.01), in which trees were sampled every 100 generations.

We estimated the DNA sequence evolution model that best fit the data using jModelTest 0.1.1.

As in DNA sequence evolution models, we use a continuous time Markov chain to describe this dependency structure.

Second, while maximum likelihood can be a very accurate phylogeny estimator when the sequences evolve under the model assumed in the ML software, true biological datasets do not evolve under the idealized conditions reflected in even the most complex DNA sequence evolution models used in this experiment.

In the commonly used Markov model of DNA evolution, substitution rates are modelled with parameters denoting the probability of particular kinds of substitution over an infinitesimal time interval – the higher the probability of a substitution, the more frequently it occurs.

For ML analyses, the best model of DNA evolution for the concatenated dataset was chosen among 56 nested models with Modeltest 3.7 [ 32].

Using the final alignment, GTR + Gamma + Proportion Invariant (GTR+G+I) model of DNA evolution was determined by the hierarchical likelihood ratio test implemented in MrModeltest [65] as the best model for the data.

As a simple model of DNA evolution, the Jukes-Cantor formula [33] is applied to estimate a probability of change for each base pair, with a customizable transition rate α (0.001 by default) and time t based on the edge weights.

The substitution model of DNA evolution was selected based on AIC using Modeltest ver. 3.06 [ 69].

Under an ideal model of DNA evolution, a random Markov process would produce randomly distributed analogies.