Species delimitation, the process of deciding how to group a set of organisms into units called species, is one of the most challenging problems in evolutionary computational biology. While many methods exist for species delimitation, most based on the coalescent theory, few are scalable to very large datasets and those methods that scale tend to be not very accurate. Species delimitation is closely related to species tree inference from discordant gene trees, a problem that has enjoyed rapid advances in recent years. A major advance has been the surprising accuracy and scalability of methods that rely on breaking gene trees into quartets of leaves. In this paper, we build on this success and propose a new method called SODA for species delimitation. We test SODA in extensive simulations and show that it can easily scale to very large datasets while maintaining high accuracy.

中文翻译：

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SODA：使用四重奏频率进行多位点物种定界

物种划界是决定如何将一组生物划分为称为物种的单元的过程，是进化计算生物学中最具挑战性的问题之一。尽管存在许多用于物种划界的方法，但是大多数方法基于合并理论，但很少有可扩展到非常大的数据集的方法，而那些缩放方法却往往不太准确。物种划分与从不一致的基因树推断物种树密切相关，这一问题近年来得到了迅速发展。主要的进步是依靠将基因树分解成叶子四重奏的方法的惊人的准确性和可扩展性。在本文中，我们以成功为基础，提出了一种新的称为SODA的物种划界方法。