The evolutionary implications and frequency of hybridization and introgression are increasingly being recognized across the tree of life. To detect hybridization from multi-locus and genome-wide sequence data, a popular class of methods are based on summary statistics from subsets of 3 or 4 taxa. However, these methods often carry the assumption of a constant substitution rate across lineages and genes, which is commonly violated in many groups. In this work, we quantify the effects of rate variation on the D test (also known as ABBA-BABA test), the D3 test, and HyDe. All three tests are used widely across a range of taxonomic groups, in part because they are very fast to compute. We consider rate variation across species lineages, across genes, their lineage-by-gene interaction, and rate variation across gene-tree edges. We simulated species networks according to a birth-death-hybridization process so as to capture a range of realistic species phylogenies. For all three methods tested, we found a marked increase in the false discovery of reticulation (type-1 error rate) when there is rate variation across species lineages. The D3 test was the most sensitive, with around 80% type-1 error, such that D3 appears to more sensitive to a departure from the clock than to the presence of reticulation. For all three tests, the power to detect hybridization events decreased as the number of hybridization events increased, indicating that multiple hybridization events can obscure one another if they occur within a small subset of taxa. Our study highlights the need to consider rate variation when using site-based summary statistics, and points to the advantages of methods that do not require assumptions on evolutionary rates across lineages or across genes.

The popularity of relaxed clock Bayesian inference of clade origin timings has generated several recent publications with focal results considerably older than the fossils of the clades in question. Here we critically examine two such clades: the animals (with focus on the bilaterians); and the mammals (with focus on the placentals). Each example displays a set of characteristic pathologies which, although much commented on, are rarely corrected for. We conclude that in neither case does the molecular clock analysis provide any evidence for an origin of the clade deeper than what is suggested by the fossil record. In addition, both these clades have other features (including, in the case of the placental mammals, proximity to a large mass extinction) that allow us to generate precise expectations of the timings of their origins. Thus, in these instances the fossil record can provide a powerful test of molecular clock methodology, and why it goes astray; and we have every reason to think these problems are general.

Determining the link between genomic and phenotypic change is a fundamental goal in evolutionary biology. Insights into this link can be gained by using a phylogenetic approach to test for correlations between rates of molecular and morphological evolution. However, there has been persistent uncertainty about the relationship between these rates, partly because conflicting results have been obtained using various methods that have not been examined in detail. We carried out a simulation study to evaluate the performance of five statistical methods for detecting correlated rates of evolution. Our simulations explored the evolution of molecular sequences and morphological characters under a range of conditions. Of the methods tested, Bayesian relaxed-clock estimation of branch rates was able to detect correlated rates of evolution correctly in the largest number of cases. This was followed by correlations of root-to-tip distances, Bayesian model selection, independent sister-pairs contrasts, and likelihood-based model selection. As expected, the power to detect correlated rates increased with the amount of data, both in terms of tree size and number of morphological characters. Likewise, greater among-lineage rate variation in the data led to improved performance of all five methods, particularly for Bayesian relaxed-clock analysis when the rate model was mismatched. We then applied these methods to a data set from flowering plants and did not find evidence of a correlation in evolutionary rates between genomic data and morphological characters. The results of our study have practical implications for phylogenetic analyses of combined molecular and morphological data sets, and highlight the conditions under which the links between genomic and phenotypic rates of evolution can be evaluated quantitatively.

Allopolyploid plants have long been regarded as possessing genetic advantages under certain circumstances due to the combined effects of their hybrid origins and duplicated genomes. However, the evolutionary consequences of allopolyploidy in lineage diversification remain to be fully understood. Here, we investigate the evolutionary consequences of allopolyploidy using 138 transcriptomic sequences of Gesneriaceae, including 124 newly sequenced, focusing particularly on the largest subtribe Didymocarpinae. We estimated the phylogeny of Gesneriaceae using concatenated and coalescent-based methods based on five different nuclear matrices and 27 plastid genes, focusing on relationships among major clades. To better understand the evolutionary affinities in this family, we applied a range of approaches to characterize the extent and cause of phylogenetic incongruence. We found that extensive conflicts between nuclear and chloroplast genomes and among nuclear genes were caused by both incomplete lineage sorting (ILS) and reticulation, and we found evidence of widespread ancient hybridization and introgression. Using the most highly supported phylogenomic framework, we revealed multiple bursts of gene duplication throughout the evolutionary history of Gesneriaceae. By incorporating molecular dating and analyses of diversification dynamics, our study shows that an ancient allopolyploidization event occurred around the Oligocene-Miocene boundary, which may have driven the rapid radiation of core Didymocarpinae.

Resolving phylogenetic relationships among taxa remains a challenge in the era of big data due to the presence of genetic admixture in a wide range of organisms. Rapidly developing sequencing technologies and statistical tests enable evolutionary relationships to be disentangled at a genome-wide level, yet many of these tests are computationally intensive and rely on phased genotypes, large sample sizes, restricted phylogenetic topologies, or hypothesis testing. To overcome these difficulties, we developed a deep learning-based approach, named ERICA, for inferring genome-wide evolutionary relationships and local introgressed regions from sequence data. ERICA accepts sequence alignments of both population genomic data and multiple genome assemblies, and efficiently identifies discordant genealogy patterns and exchanged regions across genomes when compared with other methods. We further tested ERICA using real population genomic data from Heliconius butterflies that have undergone adaptive radiation and frequent hybridization. Finally, we applied ERICA to characterize hybridization and introgression in wild and cultivated rice, revealing the important role of introgression in rice domestication and adaptation. Taken together, our findings demonstrate that ERICA provides an effective method for teasing apart evolutionary relationships using whole genome data, which can ultimately facilitate evolutionary studies on hybridization and introgression.

We consider the evolution of phylogenetic gene trees along phylogenetic species networks, according to the network multispecies coalescent process, and introduce a new network coalescent model with correlated inheritance of gene flow. This model generalizes two traditional versions of the network coalescent: with independent or common inheritance. At each reticulation, multiple lineages of a given locus are inherited from parental populations chosen at random, either independently across lineages, or with positive correlation according to a Dirichlet process. This process may account for locus-specific probabilities of inheritance, for example. We implemented the simulation of gene trees under these network coalescent models in the Julia package PhyloCoalSimulations, which depends on PhyloNetworks and its powerful network manipulation tools. Input species phylogenies can be read in extended Newick format, either in numbers of generations or in coalescent units. Simulated gene trees can be written in Newick format, and in a way that preserves information about their embedding within the species network. This embedding can be used for downstream purposes, such as to simulate species-specific processes like rate variation across species, or for other scenarios as illustrated in this note. This package should be useful for simulation studies and simulation-based inference methods. The software is available open source with documentation and a tutorial at https://github.com/cecileane/PhyloCoalSimulations.jl.

Evolutionary dynamics operating across deep time leave footprints in the shapes of phylogenetic trees. For the last several decades, researchers have used increasingly large and robust phylogenies to study the evolutionary history of individual clades and to investigate the causes of the glaring disparities in diversity among groups. While typically not the focal point of individual clade-level studies, many researchers have remarked on recurrent patterns that have been observed across many different groups and at many different time scales. While previous studies have documented various such regularities in topology and branch length distributions, they have typically focused on a single pattern and used a disparate collection (oftentimes, of quite variable reliability) of trees to assess it. Here we take advantage of modern megaphylogenies and unify previous disparate observations about the shapes embedded in the Tree of Life to create a catalog of the "major features of macroevolution". By characterizing such a large swath of subtrees in a consistent way, we hope to provide a set of phenomena that process-based macroevolutionary models of diversification ought to seek to explain.

The use of next-generation sequencing technology has enabled phylogenetic studies with hundreds of thousands of taxa. Such large-scale phylogenies have become a critical component in genomic epidemiology in pathogens such as SARS-CoV-2 and influenza A virus. However, detailed phenotypic characterization of pathogens or generating a computationally tractable dataset for detailed phylogenetic analyses requires objective subsampling of taxa. To address this need, we propose parnas, an objective and flexible algorithm to sample and select taxa that best represent observed diversity by solving a generalized k-medoids problem on a phylogenetic tree. parnas solves this problem efficiently and exactly by novel optimizations and adapting algorithms from operations research. For more nuanced selections, taxa can be weighted with metadata or genetic sequence parameters, and the pool of potential representatives can be user-constrained. Motivated by influenza A virus genomic surveillance and vaccine design, parnas can be applied to identify representative taxa that optimally cover the diversity in a phylogeny within a specified distance radius. We demonstrated that parnas is more efficient and flexible than existing approaches. To demonstrate its utility, we applied parnas to (i) quantify SARS-CoV-2 genetic diversity over time, (ii) select representative influenza A virus in swine genes derived from over 5 years of genomic surveillance data, and (iii) identify gaps in H3N2 human influenza A virus vaccine coverage. We suggest that our method, through the objective selection of representatives in a phylogeny, provides criteria for quantifying genetic diversity that has application in the the rational design of multivalent vaccines and genomic epidemiology. PARNAS is available at https://github.com/flu-crew/parnas.

Bayesian phylogenetic inference requires a tree prior, which models the underlying diversification process which gives rise to the phylogeny. Existing birth-death diversification models include a wide range of features, for instance lineage-specific variations in speciation and extinction rates. While across-lineage variation in speciation and extinction rates is widespread in empirical datasets, few heterogeneous rate models have been implemented as tree priors for Bayesian phylogenetic inference. As a consequence, rate heterogeneity is typically ignored when reconstructing phylogenies, and rate heterogeneity is usually investigated on fixed trees. In this paper, we present a new BEAST2 package implementing the cladogenetic diversification rate shift (ClaDS) model as a tree prior. ClaDS is a birth-death diversification model designed to capture small progressive variations in birth and death rates along a phylogeny. Unlike previous implementations of ClaDS, which were designed to be used with fixed, user-chosen phylogenies, our package is implemented in the BEAST2 framework and thus allows full phylogenetic inference, where the phylogeny and model parameters are co-estimated from a molecular alignment. Our package provides all necessary components of the inference, including a new tree object and operators to propose moves to the MCMC. It also includes a graphical interface through BEAUti. We validate our implementation of the package by comparing the produced distributions to simulated data, and show an empirical example of the full inference, using a dataset of cetaceans.

Inference of deep phylogenies has almost exclusively used protein rather than DNA sequences, based on the perception that protein sequences are less prone to homoplasy and saturation or to issues of compositional heterogeneity than DNA sequences. Here we analyze a model of codon evolution under an idealized genetic code and demonstrate that those perceptions may be misconceptions. We conduct a simulation study to assess the utility of protein versus DNA sequences for inferring deep phylogenies, with protein-coding data generated under models of heterogeneous substitution processes across sites in the sequence and among lineages on the tree, and then analyzed using nucleotide, amino acid, and codon models. Analysis of DNA sequences under nucleotide-substitution models (possibly with the third codon positions excluded) recovered the correct tree at least as often as analysis of the corresponding protein sequences under modern amino acid models. We also applied the different data-analysis strategies to an empirical dataset to infer the metazoan phylogeny. Our results from both simulated and real data suggest that DNA sequences may be as useful as proteins for inferring deep phylogenies and should not be excluded from such analyses. Analysis of DNA data under nucleotide models has a major computational advantage over protein-data analysis, potentially making it feasible to use advanced models that account for among-site and among-lineage heterogeneity in the nucleotide-substitution process in inference of deep phylogenies.

Despite the obstacles facing marine colonists, most lineages of aquatic organisms have colonized and diversified in freshwaters repeatedly. These transitions can trigger rapid morphological or physiological change and, on longer timescales, lead to increased rates of speciation and extinction. Diatoms are a lineage of ancestrally marine microalgae that have diversified throughout freshwater habitats worldwide. We generated a phylogenomic dataset of genomes and transcriptomes for 59 diatom taxa to resolve freshwater transitions in one lineage, the Thalassiosirales. Although most parts of the species tree were consistently resolved with strong support, we had difficulties resolving a Paleocene radiation, which affected the placement of one freshwater lineage. This and other parts of the tree were characterized by high levels of gene tree discordance caused by incomplete lineage sorting and low phylogenetic signal. Despite differences in species trees inferred from concatenation versus summary methods and codons versus amino acids, traditional methods of ancestral state reconstruction supported six transitions into freshwaters, two of which led to subsequent species diversification. Evidence from gene trees, protein alignments, and diatom life history together suggest that habitat transitions were largely the product of homoplasy rather than hemiplasy, a condition where transitions occur on branches in gene trees not shared with the species tree. Nevertheless, we identified a set of putatively hemiplasious genes, many of which have been associated with shifts to low salinity, indicating that hemiplasy played a small but potentially important role in freshwater adaptation. Accounting for differences in evolutionary outcomes, in which some taxa became locked into freshwaters while others were able to return to the ocean or become salinity generalists, might help further distinguish different sources of adaptive mutation in freshwater diatoms.

The Lowest Radial Distance (LoRaD) method is a modification of the recently introduced Partition-Weighted Kernel method for estimating the marginal likelihood of a model, a quantity important for Bayesian model selection. For analyses involving a fixed tree topology, LoRaD improves upon the Steppingstone or Thermodynamic Integration (Path Sampling) approaches now in common use in phylogenetics because it requires sampling only from the posterior distribution, avoiding the need to sample from a series of ad hoc power posterior distributions, and yet is more accurate than other fast methods such as the Generalized Harmonic Mean (GHM) method. We show that the method performs well in comparison to the Generalized Steppingstone method on an empirical fixed-topology example from molecular phylogenetics involving 180 parameters. The LoRaD method can also be used to obtain the marginal likelihood in the variable-topology case if at least one tree topology occurs with sufficient frequency in the posterior sample to allow accurate estimation of the marginal likelihood conditional on that topology. [Bayesian; marginal likelihood; phylogenetics.].

Prokaryotic genomes are often considered to be mosaics of genes that do not necessarily share the same evolutionary history due to widespread horizontal gene transfers (HGTs). Consequently, representing evolutionary relationships of prokaryotes as bifurcating trees has long been controversial. However, studies reporting conflicts among gene trees derived from phylogenomic data sets have shown that these conflicts can be the result of artifacts or evolutionary processes other than HGT, such as incomplete lineage sorting, low phylogenetic signal, and systematic errors due to substitution model misspecification. Here, we present the results of an extensive exploration of phylogenetic conflicts in the cyanobacterial order Nostocales, for which previous studies have inferred strongly supported conflicting relationships when using different concatenated phylogenomic data sets. We found that most of these conflicts are concentrated in deep clusters of short internodes of the Nostocales phylogeny, where the great majority of individual genes have low resolving power. We then inferred phylogenetic networks to detect HGT events while also accounting for incomplete lineage sorting. Our results indicate that most conflicts among gene trees are likely due to incomplete lineage sorting linked to an ancient rapid radiation, rather than to HGTs. Moreover, the short internodes of this radiation fit the expectations of the anomaly zone, i.e., a region of the tree parameter space where a species tree is discordant with its most likely gene tree. We demonstrated that concatenation of different sets of loci can recover up to 17 distinct and well-supported relationships within the putative anomaly zone of Nostocales, corresponding to the observed conflicts among well-supported trees based on concatenated data sets from previous studies. Our findings highlight the important role of rapid radiations as a potential cause of strongly conflicting phylogenetic relationships when using phylogenomic data sets of bacteria. We propose that polytomies may be the most appropriate phylogenetic representation of these rapid radiations that are part of anomaly zones, especially when all possible genomic markers have been considered to infer these phylogenies. [Anomaly zone; bacteria; horizontal gene transfer; incomplete lineage sorting; Nostocales; phylogenomic conflict; rapid radiation; Rhizonema.].

Time-calibrated phylogenetic trees are a tremendously powerful tool for studying evolutionary, ecological, and epidemiological phenomena. Such trees are predominantly inferred in a Bayesian framework, with the phylogeny itself treated as a parameter with a prior distribution (a "tree prior"). However, we show that the tree "parameter" consists, in part, of data, in the form of taxon samples. Treating the tree as a parameter fails to account for these data and compromises our ability to compare among models using standard techniques (e.g., marginal likelihoods estimated using path-sampling and stepping-stone sampling algorithms). Since accuracy of the inferred phylogeny strongly depends on how well the tree prior approximates the true diversification process that gave rise to the tree, the inability to accurately compare competing tree priors has broad implications for applications based on time-calibrated trees. We outline potential remedies to this problem, and provide guidance for researchers interested in assessing the fit of tree models. [Bayes factors; Bayesian model comparison; birth-death models; divergence-time estimation; lineage diversification].

There is still no consensus as to how to select models in Bayesian phylogenetics, and more generally in applied Bayesian statistics. Bayes factors are often presented as the method of choice, yet other approaches have been proposed, such as cross-validation or information criteria. Each of these paradigms raises specific computational challenges, but they also differ in their statistical meaning, being motivated by different objectives: either testing hypotheses or finding the best-approximating model. These alternative goals entail different compromises, and as a result, Bayes factors, cross-validation, and information criteria may be valid for addressing different questions. Here, the question of Bayesian model selection is revisited, with a focus on the problem of finding the best-approximating model. Several model selection approaches were re-implemented, numerically assessed and compared: Bayes factors, cross-validation (CV), in its different forms (k-fold or leave-one-out), and the widely applicable information criterion (wAIC), which is asymptotically equivalent to leave-one-out cross-validation (LOO-CV). Using a combination of analytical results and empirical and simulation analyses, it is shown that Bayes factors are unduly conservative. In contrast, CV represents a more adequate formalism for selecting the model returning the best approximation of the data-generating process and the most accurate estimates of the parameters of interest. Among alternative CV schemes, LOO-CV and its asymptotic equivalent represented by the wAIC, stand out as the best choices, conceptually and computationally, given that both can be simultaneously computed based on standard Markov chain Monte Carlo runs under the posterior distribution. [Bayes factor; cross-validation; marginal likelihood; model comparison; wAIC.].

Phylogenetic analysis of polyploid hybrid species has long posed a formidable challenge as it requires the ability to distinguish between alleles of different ancestral origins in order to disentangle their individual evolutionary history. This problem has been previously addressed by conceiving phylogenies as reticulate networks, using a two-step phasing strategy that first identifies and segregates homoeologous loci and then, during a second phasing step, assigns each gene copy to one of the subgenomes of an allopolyploid species. Here, we propose an alternative approach, one that preserves the core idea behind phasing-to produce separate nucleotide sequences that capture the reticulate evolutionary history of a polyploid-while vastly simplifying its implementation by reducing a complex multistage procedure to a single phasing step. While most current methods used for phylogenetic reconstruction of polyploid species require sequencing reads to be pre-phased using experimental or computational methods-usually an expensive, complex, and/or time-consuming endeavor-phasing executed using our algorithm is performed directly on the multiple-sequence alignment (MSA), a key change that allows for the simultaneous segregation and sorting of gene copies. We introduce the concept of genomic polarization that, when applied to an allopolyploid species, produces nucleotide sequences that capture the fraction of a polyploid genome that deviates from that of a reference sequence, usually one of the other species present in the MSA. We show that if the reference sequence is one of the parental species, the polarized polyploid sequence has a close resemblance (high pairwise sequence identity) to the second parental species. This knowledge is harnessed to build a new heuristic algorithm where, by replacing the allopolyploid genomic sequence in the MSA by its polarized version, it is possible to identify the phylogenetic position of the polyploid's ancestral parents in an iterative process. The proposed methodology can be used with long-read and short-read high-throughput sequencing data and requires only one representative individual for each species to be included in the phylogenetic analysis. In its current form, it can be used in the analysis of phylogenies containing tetraploid and diploid species. We test the newly developed method extensively using simulated data in order to evaluate its accuracy. We show empirically that the use of polarized genomic sequences allows for the correct identification of both parental species of an allotetraploid with up to 97% certainty in phylogenies with moderate levels of incomplete lineage sorting (ILS) and 87% in phylogenies containing high levels of ILS. We then apply the polarization protocol to reconstruct the reticulate histories of Arabidopsis kamchatica and Arabidopsis suecica, two allopolyploids whose ancestry has been well documented. [Allopolyploidy; Arabidopsis; genomic polarization; homoeologs; incomplete lineage sorting; phasing; polyploid phylogenetics; reticulate evolution.].

In the age of genome sequencing, whole genome data is readily and frequently generated, leading to a wealth of new information that can be used to advance various fields of research. New approaches, such as alignment-free phylogenetic methods that utilize k-mer-based distance scoring, are becoming increasingly popular given their ability to rapidly generate phylogenetic information from whole genome data. However, these methods have not yet been tested using environmental data, which often tends to be highly fragmented and incomplete. Here we compare the results of one alignment-free approach (which utilizes the D 2 statistic) to traditional multi-gene maximum likelihood trees in three algal groups that have high quality genome data available. In addition, we simulate lower-quality, fragmented genome data using these algae to test method robustness to genome quality and completeness. Finally, we apply the alignment-free approach to environmental metagenome assembled genome data of unclassified Saccharibacteria and Trebouxiophyte algae, and single-cell amplified data from uncultured marine stramenopiles to demonstrate its utility with real datasets. We find that in all instances, the alignment-free method produces phylogenies that are comparable, and often more informative, than those created using the traditional multi-gene approach. The k-mer-based method performs well even when there is significant missing data, that includes marker genes traditionally used for tree reconstruction. Our results demonstrate the value of alignment-free approaches for classifying novel, often cryptic or rare, species, that may not be culturable or are difficult to access using single-cell methods, but fill important gaps in the tree of life.

Past sea level changes and geological instability along watershed boundaries have largely influenced fish distribution across coastal basins, either by dispersal via palaeodrainages now submerged or by headwater captures, respectively. Accordingly, the South American Atlantic coast encompasses several small and isolated drainages that share a similar species composition, representing a suitable model to infer historical processes. Leporinus bahiensis is a freshwater fish species widespread along adjacent coastal basins over narrow continental shelf with no evidence of palaeodrainage connections at low sea level periods. Therefore, this study aimed to reconstruct its evolutionary history to infer the role of headwater captures in the dispersal process. To accomplish this, we employed molecular-level phylogenetic and population structure analyses based on Sanger sequences (five genes) and genome-wide SNP data. Phylogenetic trees based on Sanger data were inconclusive, but SNPs data did support the monophyletic status of L. bahiensis. Both COI and SNP data revealed structured populations according to each hydrographic basin. Species delimitation analyses revealed from three (COI) to five (multilocus approach) MOTUs, corresponding to the sampled basins. An intricate biogeographic scenario was inferred and supported by ABC (Approximate Bayesian Computation) analysis. Specifically, a staggered pattern was revealed and characterized by sequential headwater captures from basins adjacent to upland drainages into small coastal basins at different periods. These headwater captures resulted in dispersal throughout contiguous coastal basins, followed by deep genetic divergence among lineages. To decipher such recent divergences, as herein represented by L. bahiensis populations, we used genome-wide SNPs data. Indeed, the combined use of genome-wide SNPs data and ABC method allowed us to reconstruct the evolutionary history and speciation of L. bahiensis. This framework might be useful in disentangling the diversification process in other neotropical fishes subject to a reticulate geological history.

Bayesian phylogenetics is now facing a critical point. Over the last 20 years, Bayesian methods have reshaped phylogenetic inference and gained widespread popularity due to their high accuracy, the ability to quantify the uncertainty of inferences and the possibility of accommodating multiple aspects of evolutionary processes in the models that are used. Unfortunately, Bayesian methods are computationally expensive, and typical applications involve at most a few hundred sequences. This is problematic in the age of rapidly expanding genomic data and increasing scope of evolutionary analyses, forcing researchers to resort to less accurate but faster methods, such as maximum parsimony and maximum likelihood. Does this spell doom for Bayesian methods? Not necessarily. Here, we discuss some recently proposed approaches that could help scale up Bayesian analyses of evolutionary problems considerably. We focus on two particular aspects: online phylogenetics, where new data sequences are added to existing analyses, and alternatives to Markov chain Monte Carlo (MCMC) for scalable Bayesian inference. We identify five specific challenges and discuss how they might be overcome. We believe that online phylogenetic approaches and Sequential Monte Carlo hold great promise and could potentially speed up tree inference by orders of magnitude. We call for collaborative efforts to speed up the development of methods for real-time tree expansion through online phylogenetics.

Mangrove pit vipers of the Trimeresurus purpureomaculatus-erythrurus complex are the only species of viper known to inhabit mangroves. Despite serving integral ecological functions in mangrove ecosystems, the evolutionary history, distribution, and species boundaries of mangrove pit vipers remain poorly understood, partly due to overlapping distributions, confusing phenotypic variations, and the lack of focused studies. Here, we present the first genomic study on mangrove pit vipers and introduce a robust hypothesis-driven species delimitation framework that considers gene flow and phylogenetic uncertainty in conjunction with a novel application of a new class of speciation-based delimitation model implemented through the program Delineate.Our results showed that gene flow produced phylogenetic conflict in our focal species and substantiates the artefactual branch effect where highly admixed populations appear as divergent non-monophyletic lineages arranged in a stepwise manner at the basal position of clades. Despite the confounding effects of gene flow, we were able to obtain unequivocal support for the recognition of a new species based on the intersection and congruence of multiple lines of evidence. This study demonstrates that an integrative hypothesis-driven approach predicated on the consideration of multiple plausible evolutionary histories, population structure/differentiation, gene flow, and the implementation of a speciation-based delimitation model can effectively delimit species in the presence of gene flow and phylogenetic conflict.