In many cases, the small sample size makes the use of Bootstrap not suited for the proper assessment of the uncertainty. A Bayesian model has been developed at this purpose. It takes account of sampling variability and gives the posterior distribution of population parameters, e.g. the range. It may also replace the need of a Bootstrap distribution to assess the precision of inferences. Moreover, it allows for the Bayesian interpretation of the conditional distributions for phylogenetic parameters, at least if a suitable constraint is set on the Bayesian posterior distribution.
Trees are unrooted.
I thank the Center for Computational Genetics and Biological Modeling, at Stanford University, where part of this research has been performed partially supported by a grant for young scientists from the University of Florence.