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The Spectre of Too Many Species

机译:太多物种的幽灵

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摘要

Recent simulation studies examining the performance of Bayesian species delimitation as implemented in the bpp program have suggested that bpp may detect population splits but not species divergences and that it tends to over-split when data of many loci are analyzed. Here, we confirm these results and provide the mathematical justifications. We point out that the distinction between population and species splits made in the protracted speciation model (PSM) has no influence on the generation of gene trees and sequence data, which explains why no method can use such data to distinguish between population splits and speciation. We suggest that the PSM is unrealistic as its mechanism for assigning species status assumes instantaneous speciation, contradicting prevailing taxonomic practice. We confirm the suggestion, based on simulation, that in the case of speciation with gene flow, Bayesian model selection as implemented in bpp tends to detect population splits when the amount of data (the number of loci) increases. We discuss the use of a recently proposed empirical genealogical divergence index (gdi) for species delimitation and illustrate that parameter estimates produced by a full likelihood analysis as implemented in bpp provide much more reliable inference under the gdi than the approximate method phrapl. We distinguish between Bayesian model selection and parameter estimation and suggest that the model selection approach is useful for identifying sympatric cryptic species, while the parameter estimation approach may be used to implement empirical criteria for determining species status among allopatric populations.
机译:最近的仿真研究检查了bpp程序中实施的贝叶斯物种划界的性能,已表明bpp可以检测到种群分裂,但不能检测物种差异,并且在分析许多基因座的数据时,它倾向于过度分裂。在这里,我们确认这些结果并提供数学依据。我们指出,长期物种形成模型(PSM)中进行的种群和物种分裂之间的区别对基因树和序列数据的生成没有影响,这解释了为什么没有方法可以使用此类数据来区分种群分裂和物种形成。我们认为,PSM是不切实际的,因为其分配物种状态的机制假定是瞬时物种形成,与现行的分类学实践相矛盾。我们基于模拟确认了这样的建议,即在利用基因流进行物种形成的情况下,以bpp实施的贝叶斯模型选择倾向于在数据量(基因座数量)增加时检测种群分裂。我们讨论了使用最近提出的经验家谱差异指数(gdi)进行物种定界,并说明了在bpp中实施的全似然分析所产生的参数估计值在gdi下比近似方法phrapl可靠得多。我们区分贝叶斯模型选择和参数估计,并建议该模型选择方法可用于识别同伴隐性物种,而参数估计方法可用于实现确定异种种群中物种状态的经验标准。

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