Rtapas: An R Package to Assess Cophylogenetic Signal between Two Evolutionary Histories

Abstract

Cophylogeny represents a framework to understand how ecological and evolutionary process influence lineage diversification. The recently developed algorithm Random Tanglegram Partitions provides a directly interpretable statistic to quantify the strength of cophylogenetic signal and incorporates phylogenetic uncertainty into its estimation, and maps onto a tanglegram the contribution to cophylogenetic signal of individual host-symbiont associations. We introduce Rtapas, an R package to perform Random Tanglegram Partitions. Rtapas applies a given global-fit method to random partial tanglegrams of a fixed size to identify the associations, terminals, and internal nodes that maximize phylogenetic congruence. This new package extends the original implementation with a new algorithm that examines the contribution to phylogenetic incongruence of each host-symbiont association and adds ParaFit, a method designed to test for topological congruence between two phylogenies, to the list of global-fit methods than can be applied. Rtapas facilitates and speeds up cophylogenetic analysis, as it can handle large phylogenies (100+ terminals) in affordable computational time as illustrated with two real-world examples. Rtapas can particularly cater for the need for causal inference in cophylogeny in two domains: (i) Analysis of complex and intricate host-symbiont evolutionary histories and (ii) assessment of topological (in)congruence between phylogenies produced with different DNA markers and specifically identify subsets of loci for phylogenetic analysis that are most likely to reflect gene-tree evolutionary histories.

Evoregions of fleas and their small mammalian hosts: Do they coincide?

“…However, the opposite is definitely true (Traub, 1980;Whiting et al, 2008). This, however, does not mean that flea diversification strictly follows that of hosts because, otherwise, the phylogenies of fleas and hosts, across all of the trees, would be highly congruent, whereas this is generally not the case (Krasnov and Shenbrot, 2002;Lu and Wu, 2005;Llaberia-Robledillo et al, 2023). Moreover, Llaberia-Robledillo et al ( 2023) analysed cophylogenetic congruence and incongruence between 130 small mammalian species and 202 flea species and found that the degree of cophylogenetic congruence was concentrated in some (e.g.…”

“…Moreover, at the large taxonomic scale, when a variety of flea and host species were considered in the analyses, the phylogenetic interaction effect appeared to be substantial and indicated that related flea species were more likely to be found on related host species (Hadfield et al, 2014; Krasnov et al, 2016). This was the case despite the fact that the degree of phylogenetic incongruence in hosts was concentrated in some, but not other, phylogenetic clades, whereas it was characteristic for the entire phylogenetic tree of fleas (Llaberia-Robledillo et al, 2023). These contradictions could arise due to the substantial influence of ecological and geographic factors on flea-host common history (Krasnov and Shenbrot, 2002), with the effect of these factors varying between biogeographic realms (Krasnov et al, 2022 a ).…”

Horizontal gene transfer in plant microbiomes: integrons as hotspots for cross-species gene exchange

“…LG was determined to be the best fit out of FastTree2’s available models (WAG, LG, JTT) using ModelFinder [parameter: -m TESTONLY -mset WAG, LG, JTT]. To visually assess incongruence between IntI, attC and host trees, we constructed pairwise tanglegrams between each of the trees using the R package Rtapas v1.1.1 ( Llaberia-Robledillo et al, 2023 ). To quantitatively assess the level of incongruence between trees, we calculated the distance between tree topologies using the nPH85 metric ( Geoghegan et al, 2017 ).…”

Functional similarity affects similarity in partner composition in flea-mammal networks

“…Multiple methods for testing phylogenetic signal in the interaction networks have been proposed (e.g. Cattin et al 2004 ; Ives and Godfray 2006 ; Hadfield et al 2014 ; Balbuena et al 2013 ; Minoarivelo et al 2014 ; Li et al 2020 ; Llaberia-Robledillo et al 2023 ). Phylogenetic bipartite linear model (PBLM, Ives and Godfray 2006 ) is one of the most popular of these methods.…”