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.