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Evaluation of a Bayesian Coalescent Method of Species Delimitation

机译:贝叶斯合并物种划界方法的评估

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A Bayesian coalescent-based method has recently been proposed to delimit species using multilocus genetic sequence data. Posterior probabilities of different species delimitation models are calculated using reversible-jump Markov chain Monte Carlo algorithms. The method accounts for species phylogenies and coalescent events in both extant and extinct species and accommodates lineage sorting and uncertainties in the gene trees. Although the method is theoretically appealing, its utility in practical data analysis is yet to be rigorously examined. In particular, the analysis may be sensitive to priors on ancestral population sizes and on species divergence times and to gene flow between species. Here we conduct a computer simulation to evaluate the statistical performance of the method, such as the false negatives (the error of lumping multiple species into one) and false positives (the error of splitting one species into several). We found that the correct species model was inferred with high posterior probability with only one or two loci when 5 or 10 sequences were sampled from each population, or with 50 loci when only one sequence was sampled. We also simulated data allowing migration under a two-species model, a mainland-island model and a stepping-stone model to assess the impact of gene flow (hybridization or introgression). The behavior of the method was diametrically different depending on the migration rate. Low rates at < 0.1 migrants per generation had virtually no effect, so that the method, while assuming no hybridization between species, identified distinct species despite small amounts of gene flow. This behavior appears to be consistent with biologists' practice. In contrast, higher migration rates at >= 10 migrants per generation caused the method to infer one species. At intermediate levels of migration, the method is indecisive. Our results suggest that Bayesian analysis under the multispecies coalescent model may provide important insights into population divergences, and may be useful for generating hypotheses of species delimitation, to be assessed with independent information from anatomical, behavioral, and ecological data.
机译:最近已经提出了一种基于贝叶斯联盟的方法,该方法使用多基因座遗传序列数据对物种进行划界。使用可逆跳跃马尔可夫链蒙特卡洛算法计算不同物种定界模型的后验概率。该方法考虑了既有物种和绝种物种的物种系统发育和合并事件,并适应了谱系中的谱系排序和不确定性。尽管该方法在理论上很吸引人,但在实际数据分析中的效用尚待严格检验。特别地,该分析可能对先祖种群大小和物种发散时间以及物种之间的基因流先验敏感。在这里,我们进行计算机仿真以评估该方法的统计性能,例如假阴性(将多个物种归为一个物种的错误)和假阳性(将一个物种分为多个物种的错误)。我们发现,从每个种群中采样5个或10个序列时,只有一个或两个基因座,才有较高的后验概率,而当仅采样一个序列时,则推断出了正确的物种模型。我们还模拟了允许在两种物种模型,大陆-岛屿模型和垫脚石模型下迁移的数据,以评估基因流的影响(杂交或渗入)。该方法的行为截然不同,具体取决于迁移率。每代<0.1个移民的低比率实际上没有影响,因此,该方法在假设物种之间没有杂交的情况下,尽管有少量的基因流,但仍能识别出不同的物种。这种行为似乎与生物学家的做法一致。相比之下,每代> = 10个移民的更高迁移率导致该方法推断出一个物种。在迁移的中间水平,该方法是举棋不定的。我们的结果表明,在多物种合并模型下的贝叶斯分析可能会提供有关种群差异的重要见解,并且可能有助于生成物种定界假设,并使用来自解剖,行为和生态数据的独立信息进行评估。

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