Data from: Species delimitation using genome-wide SNP data

The multispecies coalescent has provided important progress for
evolutionary inferences, including increasing the statistical rigor and
objectivity of comparisons among competing species delimitation models.
However, Bayesian species delimitation methods typically require brute
force integration over gene trees via Markov chain Monte Carlo (MCMC),
which introduces a large computation burden and precludes their
application to genomic-scale data. Here we combine a recently introduced
dynamic programming algorithm for estimating species trees that bypasses
MCMC integration over gene trees with sophisticated methods for estimating
marginal likelihoods, needed for Bayesian model selection, to provide a
rigorous and computationally tractable technique for genome-wide species
delimitation. We provide a critical yet simple correction that brings the
likelihoods of different species trees, and more importantly their
corresponding marginal likelihoods, to the same common denominator, which
enables direct and accurate comparisons of competing species delimitation
models using Bayes factors. We test this approach, which we call Bayes
factor delimitation (*with genomic data; BFD*), using common species
delimitation scenarios with computer simulations. Varying the numbers of
loci and the number of samples suggest that the approach can distinguish
the true model even with few loci and limited samples per species.
Misspecification of the prior for population size θ has little impact on
support for the true model. We apply the approach to West African forest
geckos (Hemidactylus fasciatus complex) using genome-wide SNP data. This
new Bayesian method for species delimitation builds on a growing trend for
objective species delimitation methods with explicit model assumptions
that are easily tested.