Data from: Impact of model violations on the inference of species boundaries under the multispecies coalescent.

The use of genetic data for identifying species-level lineages across the tree of life has received increasing attention in the field of systematics over the past decade. The multispecies coalescent model provides a framework for understanding the process of lineage divergence, and has become widely adopted for delimiting species. However, because these studies lack an explicit assessment of model fit, in many cases, the accuracy of the inferred species boundaries are unknown. This is concerning given the large amount of empirical data and theory that highlight the complexity of the speciation process. Here, we seek to fill this gap by using simulation to characterize the sensitivity of inference under the multispecies coalescent to several violations of model assumptions thought to be common in empirical data. We also assess the fit of the multispecies coalescent model to empirical data in the context of species delimitation. Our results show substantial variation in model fit across datasets. Posterior predictive tests find the poorest model performance in datasets that were hypothesized to be impacted by model violations. We also show that while the inferences assuming the multispecies coalescent are robust to minor model violations, such inferences can be biased under some biologically plausible scenarios. Taken together, these results suggest that researchers can identify individual datasets in which species delimitation under the multispecies coalescent is likely to be problematic, thereby highlighting the cases where additional lines of evidence to identify species boundaries are particularly important to collect. Our study supports a growing body of work highlighting the importance of model checking in phylogenetics, and the usefulness of tailoring tests of model fit to assess the reliability of particular inferences.过去十年间，借助遗传数据识别生命之树各分支的物种水平谱系的研究方向，在系统分类学（systematics）领域愈发受到关注。多物种溯祖模型（multispecies coalescent model）为理解谱系分化过程提供了理论框架，并已被广泛应用于物种界定研究。然而，此类研究普遍缺乏对模型拟合（model fit）的显性评估，因此在多数场景下，推断得到的物种边界的准确性尚未可知。 考虑到已有大量实证数据与理论研究均揭示了物种形成过程的复杂性，这一现状令人担忧。为此，本研究旨在填补这一空白：我们通过模拟实验，分析了多物种溯祖模型下的推断过程，对若干种在实证数据中较为常见的模型假设违背情形的敏感性。同时，我们还在物种界定研究的语境下，评估了多物种溯祖模型对实证数据的拟合程度。 研究结果显示，不同数据集间的模型拟合程度存在显著差异。后验预测检验（posterior predictive tests）结果表明，被推测受到模型假设违背影响的数据集，其模型表现最差。我们还发现，尽管基于多物种溯祖模型的推断过程对轻微的模型假设违背具有稳健性，但在某些符合生物学合理性的场景下，此类推断仍可能存在偏倚。 综合来看，这些研究结果表明，研究者可以识别出那些基于多物种溯祖模型的物种界定研究大概率存在问题的数据集，从而凸显出在这些案例中，收集额外证据以确定物种边界的重要性尤为突出。本研究佐证了日益增多的相关研究成果，这些研究均强调了系统发育学（phylogenetics）领域中模型检验的重要性，以及定制化的模型拟合检验在评估特定推断可靠性方面的实用价值。