DOI: 10.5061/dryad.h6s2k
title: Data from: Impact of model violations on the inference of species boundaries under the multispecies coalescent

description: The use of genetic data for identifying species-level lineages across the
 tree of life has received increasing attention in the field of systematics
 over the past decade. The multispecies coalescent model provides a
 framework for understanding the process of lineage divergence, and has
 become widely adopted for delimiting species. However, because these
 studies lack an explicit assessment of model fit, in many cases, the
 accuracy of the inferred species boundaries are unknown. This is
 concerning given the large amount of empirical data and theory that
 highlight the complexity of the speciation process. Here, we seek to fill
 this gap by using simulation to characterize the sensitivity of inference
 under the multispecies coalescent to several violations of model
 assumptions thought to be common in empirical data. We also assess the fit
 of the multispecies coalescent model to empirical data in the context of
 species delimitation. Our results show substantial variation in model fit
 across datasets. Posterior predictive tests find the poorest model
 performance in datasets that were hypothesized to be impacted by model
 violations. We also show that while the inferences assuming the
 multispecies coalescent are robust to minor model violations, such
 inferences can be biased under some biologically plausible scenarios.
 Taken together, these results suggest that researchers can identify
 individual datasets in which species delimitation under the multispecies
 coalescent is likely to be problematic, thereby highlighting the cases
 where additional lines of evidence to identify species boundaries are
 particularly important to collect. Our study supports a growing body of
 work highlighting the importance of model checking in phylogenetics, and
 the usefulness of tailoring tests of model fit to assess the reliability
 of particular inferences.
description: Simulation in CoMuS for
 BPPThis python script can
 be used to simulate datasets under the multispecies coalescent using CoMuS
 and to setup input files for analyzing these datasets in BPP. Use
 instructions are contained in the header of the file.Comus_BPP.pySimulation in CoMuS for STACEYThis python script can be used to simulate datasets under the multispecies coalescent using CoMuS and to setup input files for analyzing these datasets in STACEY. Use instructions are contained in the header of the file.Comus_stacey.pySimulation in ms for BPPThis python script can be used to simulate coalescent genealogies using ms and sequence datasets using ms, and then to setup input files for analyzing these datasets in BPP. Use instructions are contained in the header of the file.msSimulations_BPP.pySimulation in ms for STACEYThis python script can be used to simulate coalescent genealogies using ms and sequence datasets using ms, and then to setup input files for analyzing these datasets in STACEY. Use instructions are contained in the header of the file.msSimulations_stacey.pyPosterior prediction in BPPThis python script can be used to perform posterior prediction by sampling the posterior of a BPP analysis, simulating new datasets using McCoal, and setting up input files for analyzing the simulated datasets in BPP. Use instructions are contained in the header of the file.posteriorprediction_BPP.pyPosterior predictive test statisticsThis python script can be used to calculate several posterior predictive test statistics using the output from a BPP empirical analysis and the associated posterior predictive BPP analyses. Use instructions are contained in the header of the file.PPDTestStatistics.pyIbdSim SettingsThis text file contains the simulation parameters used for the data simulations done in IBDSim.IbdSettings_file.txtSupplementary Table 1This table shows summaries of the dataset characteristics for eight empirical datasets analyzed using posterior prediction.TableS1.pdfSupplementary Table 2This table shows summaries of the numbers of species, divergence time, and population size estimates for the empirical data (posterior) and across all posterior predictive datasets (PPD) for the seven datasets analyzed in BPP.TableS2.pdf