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In MrBayes we analysed four independent Markov Chain Monte Carlo (MCMC) runs with a default temperature of 0.2.
For Bayesian tree reconstructions in MrBayes, we conducted four Markov Chain Monte Carlo (MCMC) runs with a default temperature of 0.2 and the TrN + I + G model as selected as best-fit model in jModeltest 2.1 [ 72] under the Bayesian information criterion (BIC) and the Decision Theory Performance-based Selection (DT).
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MRBAYES was run with two sets of four simultaneous Markov chains with a default temperature string and for 2 million generations and trees were sampled every 100 generations.
For Bayesian analyses, four Monte Carlo Markov Chains with the default temperature of 0.1 were used.
For Bayesian reconstruction, the dataset was partitioned into codon positions and four Markov Chain Monte Carlo (MCMC) chains were used with the default temperature of 0.1.
We used four Markov Chain Monte Carlo (MCMC) chains with the default temperature of 0.1.
We used four independent Markov Chain Monte Carlo (MCMC) runs with the default temperature of 0.1.
In all analyses we used four Monte Carlo markov chains (MCMC) with the default temperature of 0.2.
We used four Monte Carlo Markov Chains (MCMC) with the default temperature of 0.1.
For Bayesian analyses, we applied four independent Markov Chain Monte Carlo (MCMC) runs with the default temperature of 0.2.
In all analyses, we used two runs with four Monte Carlo Markov Chains (MCMC), the default temperature of 0.2, 10.000.000 generations and a sampling frequency of 1000.
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