No abstract

Genealogical data are an important source of evidence for delimiting species, yet few statistical methods are available for calculating the probabilities associated with different species delimitations. Bayesian species delimitation uses reversible-jump Markov chain Monte Carlo (rjMCMC) in conjunction with a user-specified guide tree to estimate the posterior distribution for species delimitation models containing different numbers of species. We apply Bayesian species delimitation to investigate the speciation history of forest geckos (Hemidactylus fasciatus) from tropical West Africa using five nuclear loci (and mtDNA) for 51 specimens representing 10 populations. We find that species diversity in H. fasciatus is currently underestimated, and describe three new species to reflect the most conservative estimate for the number of species in this complex. We examine the impact of the guide tree, and the prior distributions on ancestral population sizes (u) and root age (t 0 ), on the posterior probabilities for species delimitation. Mis-specification of the guide tree or the prior distribution for u can result in strong support for models containing more species. We describe a new statistic for summarizing the posterior distribution of species delimitation models, called speciation probabilities, which summarize the posterior support for each speciation event on the starting guide tree.

In recent articles published in Molecular Phylogenetics and Evolution, Mark Springer and John Gatesy (S&G) present numerous criticisms of recent implementations and testing of the multispecies coalescent (MSC) model in phylogenomics, popularly known as "species tree" methods. After pointing out errors in alignments and gene tree rooting in recent phylogenomic data sets, particularly in Song et al. (2012) on mammals and Xi et al. (2014) on plants, they suggest that these errors seriously compromise the conclusions of these studies. Additionally, S&G enumerate numerous perceived violated assumptions and deficiencies in the application of the MSC model in phylogenomics, such as its assumption of neutrality and in particular the use of transcriptomes, which are deemed inappropriate for the MSC because the constituent exons often subtend large regions of chromosomes within which recombination is substantial. We acknowledge these previously reported errors in recent phylogenomic data sets, but disapprove of S&G's excessively combative and taunting tone. We show that these errors, as well as two nucleotide sorting methods used in the analysis of Amborella, have little impact on the conclusions of those papers. Moreover, several concepts introduced by S&G and an appeal to "first principles" of phylogenetics in an attempt to discredit MSC models are invalid and reveal numerous misunderstandings of the MSC. Contrary to the claims of S&G we show that recent computer simulations used to test the robustness of MSC models are not circular and do not unfairly favor MSC models over concatenation. In fact, although both concatenation and MSC models clearly perform well in regions of tree space with long branches and little incomplete lineage sorting (ILS), simulations reveal the erratic behavior of concatenation when subjected to data subsampling and its tendency to produce spuriously confident yet conflicting results in regions of parameter space where MSC models still perform well. S&G's claims that MSC models explain little or none (0-15%) of the observed gene tree heterogeneity observed in a mammal data set and that MSC models assume ILS as the only source of gene tree variation are flawed. Overall many of their criticisms of MSC models are invalidated when concatenation is appropriately viewed as a special case of the MSC, which in turn is a special case of emerging network models in phylogenomics. We reiterate that there is enormous promise and value in recent implementations and tests of the MSC and look forward to its increased use and refinement in phylogenomics.

Phylogenetic analysis of large datasets using complex nucleotide substitution models under a maximum likelihood framework can be computationally infeasible, especially when attempting to infer confidence values by way of nonparametric bootstrapping. Recent developments in phylogenetics suggest the computational burden can be reduced by using Bayesian methods of phylogenetic inference. However, few empirical phylogenetic studies exist that explore the efficiency of Bayesian analysis of large datasets. To this end, we conducted an extensive phylogenetic analysis of the wide-ranging and geographically variable Eastern Fence Lizard (Sceloporus undulatus). Maximum parsimony, maximum likelihood, and Bayesian phylogenetic analyses were performed on a combined mitochondrial DNA dataset (12S and 16S rRNA, ND1 protein-coding gene, and associated tRNA; 3,688 bp total) for 56 populations of S. undulatus (78 total terminals including other S. undulatus group species and outgroups). Maximum parsimony analysis resulted in numerous equally parsimonious trees (82,646 from equally weighted parsimony and 335 from weighted parsimony). The majority rule consensus tree derived from the Bayesian analysis was topologically identical to the single best phylogeny inferred from the maximum likelihood analysis, but required approximately 80% less computational time. The mtDNA data provide strong support for the monophyly of the S. undulatus group and the paraphyly of "S. undulatus" with respect to S. belli, S. cautus, and S. woodi. Parallel evolution of ecomorphs within "S. undulatus" has masked the actual number of species within this group. This evidence, along with convincing patterns of phylogeographic differentiation suggests "S. undulatus" represents at least four lineages that should be recognized as evolutionary species.

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. [Bayes factor; model testing; phylogeography; RADseq; simulation; speciation.].

Gene flow among populations or species and incomplete lineage sorting (ILS) are two evolutionary processes responsible for generating gene tree discordance and therefore hindering species tree estimation. Numerous studies have evaluated the impacts of ILS on species tree inference, yet the ramifications of gene flow on species trees remain less studied. Here, we simulate and analyse multilocus sequence data generated with ILS and gene flow to quantify their impacts on species tree inference. We characterize species tree estimation errors under various models of gene flow, such as the isolation-migration model, the n-island model, and gene flow between non-sister species or involving ancestral species, and species boundaries crossed by a single gene copy (allelic introgression) or by a single migrant individual. These patterns of gene flow are explored on species trees of different sizes (4 vs. 10 species), at different time scales (shallow vs. deep), and with different migration rates. Species trees are estimated with the multispecies coalescent model using Bayesian methods (BEST and *BEAST) and with a summary statistic approach (MPEST) that facilitates phylogenomic-scale analysis. Even in cases where the topology of the species tree is estimated with high accuracy, we find that gene flow can result in overestimates of population sizes (species tree dilation) and underestimates of species divergence times (species tree compression). Signatures of migration events remain present in the distribution of coalescent times for gene trees, and with sufficient data it is possible to identify those loci that have crossed species boundaries. These results highlight the need for careful sampling design in phylogeographic and species delimitation studies as gene flow, introgression, or incorrect sample assignments can bias the estimation of the species tree topology and of parameter estimates such as population sizes and divergence times.

The accumulation of biodiversity in tropical forests can occur through multiple allopatric and parapatric models of diversification, including forest refugia, riverine barriers and ecological gradients. Considerable debate surrounds the major diversification process, particularly in the West African Lower Guinea forests, which contain a complex geographic arrangement of topographic features and historical refugia. We used genomic data to investigate alternative mechanisms of diversification in the Gaboon forest frog, Scotobleps gabonicus, by first identifying population structure and then performing demographic model selection and spatially explicit analyses. We found that a majority of population divergences are best explained by allopatric models consistent with the forest refugia hypothesis and involve divergence in isolation with subsequent expansion and gene flow. These population divergences occurred simultaneously and conform to predictions based on climatically stable regions inferred through ecological niche modelling. Although forest refugia played a prominent role in the intraspecific diversification of S. gabonicus, we also find evidence for potential interactions between landscape features and historical refugia, including major rivers and elevational barriers such as the Cameroonian Volcanic Line. We outline the advantages of using genomewide variation in a model-testing framework to distinguish between alternative allopatric hypotheses, and the pitfalls of limited geographic and molecular sampling. Although phylogeographic patterns are often species-specific and related to life-history traits, additional comparative studies incorporating genomic data are necessary for separating shared historical processes from idiosyncratic responses to environmental, climatic and geological influences on diversification.

Single nucleotide polymorphisms (SNPs) are useful markers for phylogenetic studies owing in part to their ubiquity throughout the genome and ease of collection. Restriction site associated DNA sequencing (RADseq) methods are becoming increasingly popular for SNP data collection, but an assessment of the best practises for using these data in phylogenetics is lacking. We use computer simulations, and new double digest RADseq (ddRADseq) data for the lizard family Phrynosomatidae, to investigate the accuracy of RAD loci for phylogenetic inference. We compare the two primary ways RAD loci are used during phylogenetic analysis, including the analysis of full sequences (i.e., SNPs together with invariant sites), or the analysis of SNPs on their own after excluding invariant sites. We find that using full sequences rather than just SNPs is preferable from the perspectives of branch length and topological accuracy, but not of computational time. We introduce two new acquisition bias corrections for dealing with alignments composed exclusively of SNPs, a conditional likelihood method and a reconstituted DNA approach. The conditional likelihood method conditions on the presence of variable characters only (the number of invariant sites that are unsampled but known to exist is not considered), while the reconstituted DNA approach requires the user to specify the exact number of unsampled invariant sites prior to the analysis. Under simulation, branch length biases increase with the amount of missing data for both acquisition bias correction methods, but branch length accuracy is much improved in the reconstituted DNA approach compared to the conditional likelihood approach. Phylogenetic analyses of the empirical data using concatenation or a coalescent-based species tree approach provide strong support for many of the accepted relationships among phrynosomatid lizards, suggesting that RAD loci contain useful phylogenetic signal across a range of divergence times despite the presence of missing data. Phylogenetic analysis of RAD loci requires careful attention to model assumptions, especially if downstream analyses depend on branch lengths.