Symbiosis is a key driver of evolutionary novelty and ecological diversity, but our understanding of how macroevolutionary processes originate extant symbiotic associations is still very incomplete. Cophylogenetic tools are used to assess the congruence between the phylogenies of two groups of organisms related by extant associations. If phylogenetic congruence is higher than expected by chance, we conclude that there is cophylogenetic signal in the system under study. However, how to quantify cophylogenetic signal is still an open issue. We present a novel approach, Random Tanglegram Partitions (Random TaPas) that applies a given global-fit method to random partial tanglegrams of a fixed size to identify the associations, terminals and nodes that maximize phylogenetic congruence. By means of simulations, we show that the output value produced is inversely proportional to the number and proportion of cospeciation events employed to build simulated tanglegrams. In addition, with time-calibrated trees, Random TaPas can also distinguish cospeciation from pseudocospeciation. Random TaPas can handle large tanglegrams in affordable computational time and incorporates phylogenetic uncertainty in the analyses. We demonstrate its application with two real examples: Passerine birds and their feather mites, and orchids and bee pollinators. In both systems, Random TaPas revealed low cophylogenetic signal, but mapping its variation onto the tanglegram pointed to two different coevolutionary processes. We suggest that the recursive partitioning of the tanglegram buffers the effect of phylogenetic nonindependence occurring in current global-fit methods and therefore Random TaPas is more reliable than regular global-fit methods to identify host-symbiont associations that contribute most to cophylogenetic signal. Random TaPas can be implemented in the public-domain statistical software R with scripts provided herein. A User's Guide is also available at GitHub.

中文翻译：

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随机 Tanglegram 分区（随机 TaPas）：一种亚历山大式方法来研究 Cophylogenetic Gordian Knot

共生是进化新颖性和生态多样性的关键驱动因素，但我们对宏观进化过程如何产生现存共生关联的理解仍然非常不完整。Cophylogenetic 工具用于评估通过现存关联相关的两组生物的系统发育之间的一致性。如果系统发育一致性高于预期，我们得出结论，在研究的系统中存在共同系统发育信号。然而，如何量化共系发生信号仍然是一个悬而未决的问题。我们提出了一种新方法，随机缠结图分区（Random TaPas），该方法将给定的全局拟合方法应用于固定大小的随机部分缠结图，以识别最大化系统发育一致性的关联、终端和节点。通过模拟，我们表明，产生的输出值与用于构建模拟缠结图的协同物种形成事件的数量和比例成反比。此外，通过时间校准树，Random TaPas 还可以区分协同物种形成和伪协同物种形成。随机 TaPas 可以在负担得起的计算时间内处理大型缠结图，并在分析中加​​入系统发育的不确定性。我们用两个真实的例子展示了它的应用：雀鸟和它们的羽毛螨，以及兰花和蜜蜂传粉者。在这两个系统中，Random TaPas 显示出低的共系统发育信号，但将其变异映射到缠结图上指向两个不同的共同进化过程。我们建议缠结图的递归分区缓冲了当前全局拟合方法中发生的系统发育非独立性的影响，因此随机 TaPas 比常规全局拟合方法更可靠，以识别对共系统发育信号贡献最大的宿主-共生体关联。可以使用此处提供的脚本在公共域统计软件 R 中实现随机 TaPa。GitHub 上还提供了用户指南。