Species are fundamental units in biological research and can be defined on the basis of various operational criteria. There has been growing use of molecular approaches for species delimitation. Among the most widely used methods, the generalized mixed Yule-coalescent (GMYC) and Poisson tree processes (PTP) were designed for the analysis of single-locus data but are often applied to concatenations of multilocus data. In contrast, the Bayesian multispecies coalescent approach in the software Bayesian Phylogenetics and Phylogeography (BPP) explicitly models the evolution of multilocus data. In this study, we compare the performance of GMYC, PTP, and BPP using synthetic data generated by simulation under various speciation scenarios. We show that in the absence of gene flow, the main factor influencing the performance of these methods is the ratio of population size to divergence time, while number of loci and sample size per species have smaller effects. Given appropriate priors and correct guide trees, BPP shows lower rates of species overestimation and underestimation, and is generally robust to various potential confounding factors except high levels of gene flow. The single-threshold GMYC and the best strategy that we identified in PTP generally perform well for scenarios involving more than a single putative species when gene flow is absent, but PTP outperforms GMYC when fewer species are involved. Both methods are more sensitive than BPP to the effects of gene flow and potential confounding factors. Case studies of bears and bees further validate some of the findings from our simulation study, and reveal the importance of using an informed starting point for molecular species delimitation. Our results highlight the key factors affecting the performance of molecular species delimitation, with potential benefits for using these methods within an integrative taxonomic framework.

Background: Explicit evolutionary models are required in maximum-likelihood and Bayesian inference, the two methods that are overwhelmingly used in phylogenetic studies of DNA sequence data. Appropriate selection of nucleotide substitution models is important because the use of incorrect models can mislead phylogenetic inference. To better understand the performance of different model-selection criteria, we used 33,600 simulated data sets to analyse the accuracy, precision, dissimilarity, and biases of the hierarchical likelihood-ratio test, Akaike information criterion, Bayesian information criterion, and decision theory. Results: We demonstrate that the Bayesian information criterion and decision theory are the most appropriate model-selection criteria because of their high accuracy and precision. Our results also indicate that in some situations different models are selected by different criteria for the same dataset. Such dissimilarity was the highest between the hierarchical likelihood-ratio test and Akaike information criterion, and lowest between the Bayesian information criterion and decision theory. The hierarchical likelihood-ratio test performed poorly when the true model included a proportion of invariable sites, while the Bayesian information criterion and decision theory generally exhibited similar performance to each other. Conclusions: Our results indicate that the Bayesian information criterion and decision theory should be preferred for model selection. Together with model-adequacy tests, accurate model selection will serve to improve the reliability of phylogenetic inference and related analyses.

Background: A well-informed choice of genetic locus is central to the efficacy of DNA barcoding. Current DNA barcoding in animals involves the use of the 5' half of the mitochondrial cytochrome oxidase 1 gene (CO1) to diagnose and delimit species. However, there is no compellinga priorireason for the exclusive focus on this region, and it has been shown that it performs poorly for certain animal groups. To explore alternative mitochondrial barcoding regions, we compared the efficacy of the universalCO1barcoding region with the other mitochondrial protein-coding genes in eutherian mammals. Four criteria were used for this comparison: the number of recovered species, sequence variability within and between species, resolution to taxonomic levels above that of species, and the degree of mutational saturation.Results: Based on 1,179 mitochondrial genomes of eutherians, we found that the universalCO1barcoding region is a good representative of mitochondrial genes as a whole because the high species-recovery rate (> 90%) was similar to that of other mitochondrial genes, and there were no significant differences in intra- or interspecific variability among genes. However, an overlap between intra- and interspecific variability was still problematic for all mitochondrial genes. Our results also demonstrated that any choice of mitochondrial gene for DNA barcoding failed to offer significant resolution at higher taxonomic levels.Conclusions: We suggest that theCO1barcoding region, the universal DNA barcode, is preferred among the mitochondrial protein-coding genes as a molecular diagnostic at least for eutherian species identification. Nevertheless, DNA barcoding with this marker may still be problematic for certain eutherian taxa and our approach can be used to test potential barcoding loci for such groups.

Background: Since its emergence in March 2009, the pandemic 2009 H1N1 influenza A virus has posed a serious threat to public health. To trace the evolutionary path of these new pathogens, we performed a selection-pressure analysis of a large number of hemagglutinin (HA) and neuraminidase (NA) gene sequences of H1N1 influenza viruses from different hosts.Results: Phylogenetic analysis revealed that both HA and NA genes have evolved into five distinct clusters, with further analyses indicating that the pandemic 2009 strains have experienced the strongest positive selection. We also found evidence of strong selection acting on the seasonal human H1N1 isolates. However, swine viruses from North America and Eurasia were under weak positive selection, while there was no significant evidence of positive selection acting on the avian isolates. A site-by-site analysis revealed that the positively selected sites were located in both of the cleaved products of HA (HA1 and HA2), as well as NA. In addition, the pandemic 2009 strains were subject to differential selection pressures compared to seasonal human, North American swine and Eurasian swine H1N1 viruses.Conclusions: Most of these positively and/or differentially selected sites were situated in the B-cell and/or T-cell antigenic regions, suggesting that selection at these sites might be responsible for the antigenic variation of the viruses. Moreover, some sites were also associated with glycosylation and receptor-binding ability. Thus, selection at these positions might have helped the pandemic 2009 H1N1 viruses to adapt to the new hosts after they were introduced from pigs to humans. Positive selection on position 274 of NA protein, associated with drug resistance, might account for the prevalence of drug-resistant variants of seasonal human H1N1 influenza viruses, but there was no evidence that positive selection was responsible for the spread of the drug resistance of the pandemic H1N1 strains.

Bayesian molecular dating is widely used to study evolutionary timescales. This procedure usually involves phylogenetic analysis of nucleotide sequence data, with fossil-based calibrations applied as age constraints on internal nodes of the tree. An alternative approach is tip-dating, which explicitly includes fossil data in the analysis. This can be done, for example, through the joint analysis of molecular data from present-day taxa and morphological data from both extant and fossil taxa. In the context of tip-dating, an important development has been the fossilized birth–death process, which allows non-contemporaneous tips and sampled ancestors while providing a model of lineage diversification for the prior on the tree topology and internal node times. However, tip-dating with fossils faces a number of considerable challenges, especially, those associated with fossil sampling and evolutionary models for morphological characters. We conducted a simulation study to evaluate the performance of tip-dating using the fossilized birth–death model. We simulated fossil occurrences and the evolution of nucleotide sequences and morphological characters under a wide range of conditions. Our analyses of these data show that the number and the maximum age of fossil occurrences have a greater influence than the degree of among-lineage rate variation or the number of morphological characters on estimates of node times and the tree topology. Tip-dating with the fossilized birth–death model generally performs well in recovering the relationships among extant taxa but has difficulties in correctly placing fossil taxa in the tree and identifying the number of sampled ancestors. The method yields accurate estimates of the ages of the root and crown group, although the precision of these estimates varies with the probability of fossil occurrence. The exclusion of morphological characters results in a slight overestimation of node times, whereas the exclusion of nucleotide sequences has a negative impact on inference of the tree topology. Our results provide an overview of the performance of tip-dating using the fossilized birth–death model, which will inform further development of the method and its application to key questions in evolutionary biology.