The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data from closely related species to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for one species versus MSC for two species) and compared using Bayesian model selection. This approach, implemented in the program bpp, has been found to be prone to over-splitting. Alternatively heuristic criteria based on population parameters (such as popula- tion split times, population sizes, and migration rates) estimated from genomic data may be used to delimit species. Here we develop hierarchical merge and split algorithms for heuristic species delimitation based on the genealogical divergence index (𝑔𝑑𝑖) and implement them in a python pipeline called hhsd. We characterize the behavior of the 𝑔𝑑𝑖 under a few simple scenarios of gene flow. We apply the new approaches to a dataset simulated under a model of isolation by distance as well as three empirical datasets. Our tests suggest that the new approaches produced sensible results and were less prone to over-splitting. We discuss possible strategies for accommodating paraphyletic species in the hierarchical algorithm, as well as the challenges of species delimitation based on heuristic criteria.

中文翻译：

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多物种聚结模型与迁移下的分层启发式物种划定


多物种合并 （MSC） 模型适应了整个基因组的谱系波动，并为对来自密切相关物种的基因组序列数据进行比较分析提供了一个自然框架，以推断物种分化和基因流动的历史。给定一组种群，物种界定（和物种系统发育）的假设可以表述为 MSC 模型的实例（例如，一个物种的 MSC 与两个物种的 MSC），并使用贝叶斯模型选择进行比较。这种方法在 bpp 程序中实现，已被发现容易过度分裂。或者，基于从基因组数据估计的种群参数（例如种群分裂时间、种群规模和迁移率）的启发式标准可用于界定物种。在这里，我们开发了基于谱系分歧指数 （gdi） 的启发式物种划定的分层合并和拆分算法，并在名为 hhsd 的 python 管道中实现它们。我们描述了基因流动的几个简单情景下 gdi 的行为。我们将新方法应用于在距离隔离模型以及三个经验数据集下模拟的数据集。我们的测试表明，新方法产生了合理的结果，并且不易出现过度拆分。我们讨论了在分层算法中容纳并系物种的可能策略，以及基于启发式标准进行物种划定的挑战。