Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia, Official P.O. Box 22085, 46071 Valencia, Spain.
Identification and Naming Department, Royal Botanic Gardens, Kew, Richmond TW9 3AB, UK.
Syst Biol. 2020 Nov 1;69(6):1212-1230. doi: 10.1093/sysbio/syaa033.
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.[Codiversification; coevolution; cophylogenetic signal; Symbiosis.].
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系的理解还非常不完整。共进化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的机会水平,我们得出结论,在研究系统中存在共进化信号。然而,如何量化共进化信号仍然是一个悬而未决的问题。我们提出了一种新方法,即随机纠缠图分区(Random TaPas),它将给定的全局拟合方法应用于固定大小的随机部分纠缠图,以识别最大程度提高系统发育一致性的关联、终端和节点。通过模拟,我们表明输出值与用于构建模拟纠缠图的同源共进化事件的数量和比例成反比。此外,使用时间校准的树,Random TaPas 还可以区分同源共进化和假同源共进化。Random TaPas 可以在可承受的计算时间内处理大型纠缠图,并在分析中纳入系统发育不确定性。我们通过两个实际示例演示了其应用:雀形目鸟类及其羽毛螨,以及兰花和蜜蜂传粉者。在这两个系统中,Random TaPas 显示出低共进化信号,但将其变化映射到纠缠图上,揭示了两种不同的协同进化过程。我们建议,纠缠图的递归分区缓冲了当前全局拟合方法中出现的系统发育非独立性的影响,因此,与常规全局拟合方法相比,Random TaPas 更可靠地识别对共进化信号贡献最大的宿主-共生体关联。Random TaPas 可以使用提供的脚本在公共领域统计软件 R 中实现。用户指南也可在 GitHub 上获得。[共适应;协同进化;共进化信号;共生。]。