Macroevolutionary Rates of Species Interactions: Approximate Bayesian Inference from Cophylogenies

Understanding the macroevolutionary dynamics of species interactions such as parasitisms, commensalisms, and mutualisms is an important goal in evolutionary ecology. To this end, statistical inference from extant cophylogenetic systems holds immense potential. However, such inference cannot yet quantify the rates of different types of speciation that occur in the host and symbiont clades on the same timeline. Here we present an Approximate Bayesian Computation (ABC) approach that, while taking into account host and symbiont extinction, infers rates of four types of speciation from a cophylogenetic system: (i) host speciation, (ii) symbiont speciation without host-switching, (iii) symbiont speciation with host-switching, and (iv) cospeciation. The new ABC approach relies on a novel design of summary statistics based on the Branch Length Difference (BLenD) curves of the cophylogeny. The posterior distribution of the speciation rates have lower errors than the prior distribution, showing that the ABC can successfully update prior beliefs about the speciation rates in a cophylogeny. This informative update is particularly powerful when the cophylogeny of interest is sufficiently large. This new approach is demonstrated with an application to a cophylogenetic dataset of Batesian mimicry in beetles, for which the results suggest that host speciation is the fastest among all four speciation processes considered. We discuss potential improvements for the use of the BLenD curves as summary statistics for simulation-based inference, including potential applications in machine learning approaches. Understanding speciation rate variation within and between cophylogenetic systems, enabled by this approach and an increasing availability of time-calibrated cophylogenies, have potential implications for various areas in ecology and evolution such as host conservatism, trait-driven diversification, pathogen spillover risk, and parasite extinction risk.

解析寄生、共栖、互利共生等物种相互作用的宏观演化动力学，是进化生态学领域的重要研究目标。为此，基于现存共演化系统（cophylogenetic system）的统计推断拥有巨大应用潜力，然而当前此类方法尚无法在同一时间轴上量化宿主与共生类群的各类物种形成速率。本文提出一种近似贝叶斯计算（Approximate Bayesian Computation，ABC）方法，该方法在纳入宿主与共生体灭绝因素的同时，可从共演化系统中推断四类物种形成事件的速率：(i) 宿主物种形成；(ii) 无宿主转换的共生体物种形成；(iii) 伴随宿主转换的共生体物种形成；(iv) 协同物种形成。该新型ABC方法依托基于共演化树分支长度差异（Branch Length Difference，BLenD）曲线构建的全新摘要统计量设计。其得到的物种形成速率后验分布相较于先验分布误差更低，表明该ABC方法可成功更新关于共演化系统物种形成速率的先验认知，且当目标共演化系统规模足够大时，这种信息丰富的更新效果尤为显著。我们通过将该方法应用于甲虫贝茨拟态（Batesian mimicry）的共演化数据集验证了其有效性，结果显示在所考量的四类物种形成过程中，宿主物种形成速率最快。本文还讨论了将BLenD曲线作为基于模拟的推断的摘要统计量的潜在改进方向，包括其在机器学习方法中的潜在应用。通过该方法结合日益增多的经时间校准的共演化系统，理解共演化系统内部及系统间的物种形成速率变异，可为进化生态学与生态学的诸多领域带来潜在启示，例如宿主保守性、性状驱动的物种分化、病原体溢出风险以及寄生虫灭绝风险等。