How well do multispecies coalescent methods perform with mitochondrial genomic data? A case study of butterflies and moths (Insecta: Lepidoptera)

Abstract

AbstractDespite the broad adoption of multispecies coalescent (MSC) methods for nuclear phylogenomics, they have yet to be applied to mitochondrial (mt) genomic data. As the potential sources of phylogenomic bias that MSC methods can address, such as incomplete lineage sorting, horizontal gene transfer and gene tree heterogeneity, have been found in mt genomic data, these approaches may improve the accuracy of phylogenetic inference with these data. In the present study, we examined the behaviour of MSC methods in reconstructing the phylogeny of Lepidoptera (butterflies and moths), a group for which mt genomic data are known to have strong resolving power. Traditional concatenation methods of analysing mt genomes for Lepidoptera infer topologies highly congruent with those generated from independent nuclear datasets. Individual mt gene trees performed poorly in recovering consensus relationships at deep levels (i.e. superfamily monophyly and inter‐relationships) and only moderately well for shallow relationships (i.e. within Papilionoidea). In contrast, MSC analyses with ASTRAL performed strongly with almost complete concordance to both concatenated mt genome analyses and independent nuclear analyses at both deep and shallow phylogenetic scales. Outgroup choice had a limited impact on tree accuracy, with even phylogenetically distant outgroups still resulting in topologies highly congruent with results from nuclear datasets, although MSC analyses appeared to be marginally more affected by outgroup choice than concatenation analyses. In general, discordance between concatenation and MSC analyses was found at nodes whose resolution varied between previous nuclear phylogenomic studies. The sensitivity of individual relationships to analysis with MSC vs concatenation can thus be used to test the robustness of phylogenetic hypotheses. For insect phylogenetics, MSC is a reliable inference method for mt genomic data and is thus a useful complement to the already widely used concatenation approaches.