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Five runs of four incrementally heated chains were run in parallel (temperature increment = 0.5).
Bayesian analyses were started from random trees, sampling one tree every 1000th generation, with four incrementally heated chains.
Each analysis included four simultaneous and incrementally heated Markov chains; each replicate used default heating values.
Two parallel runs were performed in MrBayes, each consisting of four chains, one "cold" and three incrementally heated.
Each analysis contained 4 chains (1 cold and 3 incrementally heated) and trees were sampled every 1,000 generations.
MrBayes was run for 106 generations, with four incrementally heated Markov chains, a sampling frequency of 103 generations and the temperature set at 0.5.
For each analysis, four incrementally heated chains were used per run, with samples taken from the cold chain every 100 generations.
Two independent analyses were run with Metropolis-coupled MCMC using four incrementally heated Markov chains for 1,000,000 generations until the standard deviation of the split frequencies was <0.01.
Bayesian analyses were initiated with random starting trees, run with four incrementally heated chains (Metropolis-coupled Markov chain Monte Carlo; [54] for 10 million generations, and sampled at intervals of 1000 generations. Two independent Bayesian analyses were run to avoid entrapment on local optima.
MrBayes was run for 3 million generations with four incrementally heated chains.
Four separate MrBayes runs, each including four incrementally heated chains, were run for 20 million generations.
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