Data from: Species delimitation using Bayes factors: simulations and application to the Sceloporus scalaris species group (Squamata: Phrynosomatidae)

Current molecular methods of species delimitation are limited by the types
of species delimitation models and scenarios that can be tested. Bayes
factors allow for more flexibility in testing non-nested species
delimitation models and hypotheses of individual assignment to alternative
lineages. Here, we examined the efficacy of Bayes factors in delimiting
species through simulations and empirical data from the Sceloporus
scalaris species group. Marginal likelihood scores of competing species
delimitation models, from which Bayes factor values were compared, were
estimated with four different methods: harmonic mean estimation, smoothed
harmonic mean estimation, path-sampling/thermodynamic integration, and
stepping-stone analysis. We also performed model selection using a
posterior simulation-based analog of the Akaike information criterion
through Markov chain Monte Carlo analysis (AICM). Bayes factor species
delimitation results from the empirical data were then compared with
results from the reversible-jump MCMC (rjMCMC) coalescent-based species
delimitation method Bayesian Phylogenetics and Phylogeography
(BP&P). Simulation results show that harmonic and smoothed
harmonic mean estimators perform poorly compared to path sampling and
stepping stone marginal likelihood estimators when identifying the true
species delimitation model. Furthermore, Bayes factor species delimitation
showed improved performance when species limits are tested by reassigning
individuals between species, as opposed to either lumping or splitting
lineages. In the empirical data, Bayes factor species delimitation through
path sampling and stepping-stone analyses, as well as the rjMCMC method,
each provide support for the recognition of all scalaris group taxa as
independent evolutionary lineages. Bayes factor species delimitation and
BP&P also support the recognition of three previously undescribed
lineages. In both simulated and empirical datasets, harmonic and smoothed
harmonic mean marginal likelihood estimators provided much higher marginal
likelihood estimates than path sampling and stepping-stone estimators. The
AICM displayed poor repeatability in both simulated and empirical
datasets, and produced inconsistent model rankings across replicate runs
with the empirical data. Our results suggest that species delimitation
through the use of Bayes factors with marginal likelihood estimates via
path-sampling or stepping-stone analyses provide a useful and
complementary alternative to existing species delimitation methods.