DOI: 10.5061/dryad.c7s77
title: Data from: Species delimitation using Bayes factors: simulations and application to the Sceloporus scalaris species group (Squamata: Phrynosomatidae)

description: 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.
description: BACH1.nexAlignment of BACH1 nuclear gene region.EXPH5.nexAlignment of EXPH5 nuclear gene region.KIF24.nexAlignment of KIF24 nuclear gene region.NKTR.nexAlignment of NKTR nuclear gene region.NOS1.nexAlignment of NOS1 nuclear gene region.R35.nexAlignment of R35 nuclear gene region.mtDNA.nexAlignment of three mtDNA gene regions.A_AICM.xml*BEAST input file for calculating AICM estimation at the end of the run.B_PS.xml*BEAST input file for Model B for performing path-sampling estimation at the end of the run.concat.nexAlignment of 6 nuclear DNA gene regions concatenated.mtDNAScalaris.bpp.ctlBP&P control file for running mtDNA groupings.mtDNAScalaris.ImapBP&P input file showing which individuals belong to which species.mtDNAScalaris.txtBP&P sequence data file.nDNA1.bpp.ctlBP&P control file for nDNA groupings.nDNA1.ImapBP&P input file showing which individuals belong to which species.nDNA1BP&P input sequence data file.Supplemental InformationSupplemental information (tables and figures) for this manuscript.