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Abstract：
Most single-locus molecular approaches to species delimitation available to date have been designed and tested on data sets comprising at least tens of species, whereas the opposite case (species-poor data sets for which the hypothesis that all individuals are conspecific cannot by rejected beforehand) has rarely been the focus of such attempts. Here we compare the performance of barcode gap detection, haplowebs and generalized mixed Yule-coalescent (GMYC) models to delineate chimpanzees and bonobos using nuclear sequence markers, then apply these single-locus species delimitation methods to data sets of one, three, or six species simulated under a wide range of population sizes, speciation rates, mutation rates and sampling efforts. Our results show that barcode gap detection and GMYC models are unable to delineate species properly in data sets composed of one or two species, two situations in which haplowebs outperform them. For data sets composed of three or six species, bGMYC and haplowebs outperform the single-threshold and multiple-threshold versions of GMYC, whereas a clear barcode gap is only observed when population sizes and speciation rates are both small. The latter conditions represent a \"sweet spot\" for molecular taxonomy where all the single-locus approaches tested work well; however, the performance of these methods decreases strongly when population sizes and speciation rates are high, suggesting that multilocus approaches may be necessary to tackle such cases.
摘要：
迄今为止，大多数用于物种划界的单基因座分子方法都是在至少包含数十个物种的数据集上设计和测试的，而相反的情况（物种贫乏的数据集，所有个体都是同种的假设不能被事先拒绝）很少成为此类尝试的重点。在这里，我们比较了条形码间隙检测的性能，haplowebs和广义混合Yule合并（GMYC）模型使用核序列标记来描绘黑猩猩和黑猩猩，然后将这些单基因座物种定界方法应用于一个数据集，三,或在广泛的种群规模下模拟的六个物种，物种形成率，突变率和抽样工作。我们的结果表明，条形码缺口检测和GMYC模型无法在由一个或两个物种组成的数据集中正确地描绘物种，haplowebs胜过他们的两种情况。对于由三个或六个物种组成的数据集，bGMYC和haplowebs优于单阈值和多阈值版本的GMYC，而只有当种群规模和物种形成率均较小时，才会观察到清晰的条形码间隙。后一种条件代表了分子分类学的“最佳点”，其中所有测试的单基因座方法都能很好地工作；但是，当种群规模和物种形成率高时，这些方法的性能会大大降低，这表明多位点的方法可能是必要的，以解决这种情况。
