The spectre of too many species

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 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 protracted speciation model 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.

近期针对BPP程序所实现的贝叶斯物种界定（Bayesian species delimitation）方法性能开展的模拟研究表明，BPP或可检测种群分化事件，却无法识别物种分化过程，且在分析多基因座（loci）数据时易出现过度划分的问题。本研究证实了上述结论，并给出了相应的数学依据。我们指出，渐进式物种形成模型（protracted speciation model）中对种群分化与物种分化的区分，并不会对基因树与序列数据的生成产生影响，这也解释了为何现有方法无法利用此类数据区分种群分化与物种形成过程。我们认为该渐进式物种形成模型并不符合实际，因其在赋予物种地位时假设物种形成是瞬时完成的，这与当前主流的分类学实践相悖。我们通过模拟研究证实了此前的相关结论：在存在基因流（gene flow）的物种形成场景中，BPP程序所实现的贝叶斯模型选择方法，会随着数据量（即基因座数量）的增加，更倾向于检测到种群分化事件。我们探讨了近期提出的经验基因分化指数（empirical genealogical divergence index, gdi）在物种界定中的应用，并通过实例说明，相较于近似方法PHRAPL，BPP程序所实现的全似然分析所得到的参数估计值，在基于gdi进行推断时能提供更为可靠的结果。我们对贝叶斯模型选择与参数估计进行了区分，并提出：模型选择方法可用于识别同域隐存物种（sympatric cryptic species），而参数估计方法则可用于基于经验标准，对异域种群（allopatric populations）的物种地位进行判定。