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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具有迁移的多物种合并模型下的层次启发式物种界定


多物种合并（MSC）模型适应整个基因组的谱系波动，并为比较分析密切相关物种的基因组序列数据提供自然框架，以推断物种分化和基因流的历史。给定一组种群，物种界定（和物种系统发育）的假设可以制定为 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。这种在程序 bpp 中实现的方法被发现容易出现过度分割。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，我们开发基于谱系分歧指数 (𝑔𝑑𝑖) 的启发式物种界定的分层合并和分割算法，并在名为 hhsd 的 Python 管道中实现它们。我们在几个简单的基因流场景下描述了𝑔𝑑𝑖的行为。我们将新方法应用于在距离隔离模型下模拟的数据集以及三个经验数据集。我们的测试表明，新方法产生了合理的结果，并且不太容易出现过度分裂。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的物种界定的挑战。