The paper presents a Bayesian method for species delimitation using multilocus sequence data. The authors address the challenges of gene tree incongruence and ancestral lineage sorting, which can lead to incorrect species assignments. They propose a Bayesian modeling approach that accounts for uncertainties in gene trees and the ancestral coalescent process. The method relies on a user-specified guide tree to reduce the space of possible species delimitations and species trees. The statistical performance of the method is evaluated through simulations, and its application is illustrated with real datasets from rotifers, fence lizards, and human populations. The results show that the method can accurately identify species, even in the presence of gene tree incongruence and hybridization. The authors also discuss the impact of the guide tree on the inference and the potential limitations of the method.The paper presents a Bayesian method for species delimitation using multilocus sequence data. The authors address the challenges of gene tree incongruence and ancestral lineage sorting, which can lead to incorrect species assignments. They propose a Bayesian modeling approach that accounts for uncertainties in gene trees and the ancestral coalescent process. The method relies on a user-specified guide tree to reduce the space of possible species delimitations and species trees. The statistical performance of the method is evaluated through simulations, and its application is illustrated with real datasets from rotifers, fence lizards, and human populations. The results show that the method can accurately identify species, even in the presence of gene tree incongruence and hybridization. The authors also discuss the impact of the guide tree on the inference and the potential limitations of the method.