Bayesian species delimitation can be robust to guide tree inference errors

The Bayesian method of species delimitation (Yang and Rannala, 2010) uses a so-called guide tree to reduce the number of models to be evaluated in the reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm (Green, 1995). It has been pointed out that the method tends to over-split if a random population tree is used as the guide tree (Fujita and Leaché, 2011). Here we conduct a simulation study to examine the performance of the method under more realistic scenarios, that is, when the guide tree is inferred from the sequence data. We found that Bayesian species delimitation is in general robust to errors in the inferred guide tree.