Oral Paper

         Phylogenomics

Strong phylogenetic signal despite high phylogenomic complexity in an Andean plant radiation (Freziera, Pentaphylacaceae)

Presenting Author
Laura Frost
Description
The Andes mountains of western South America are a globally important biodiversity hotspot, yet there is a paucity of resolved phylogenies for plant clades from this region. Filling an important gap to our understanding of the World’s richest flora, we present the first phylogeny of Freziera (Pentaphylacaceae), an Andean-centered, cloud forest radiation. Our dataset was obtained via hybrid-enriched target sequence capture of Angiosperms353 universal loci for 50 of the ca. 75 spp., obtained almost entirely from herbarium specimens. We identify high phylogenomic complexity in Freziera, including a significant proportion of paralogous loci and a high degree of gene tree discordance. Via gene tree filtering, by-eye observation of gene trees, and detailed examination of warnings from recently improved assembly pipelines, we identified that cryptic paralogs (i.e., the presence of only one copy of a multi-copy gene due to assembly errors) were a major source of gene tree heterogeneity that had a negative impact on phylogenetic inference and support. These cryptic paralogs likely result from limitations in data collection that are common in museomics, combined with a history of genome duplication; they may be common in plant phylogenomic datasets. After accounting for cryptic paralogs as source of gene tree error, we identified a significant, but non-specific signal of introgression using Patterson’s D and f4 statistics. Despite phylogenomic complexity, we were able to resolve Freziera into nine well-supported subclades whose histories have been shaped by myriad evolutionary processes, including incomplete lineage sorting, historical gene flow, and gene duplication. Our results highlight the complexities of plant phylogenomics, and point to the need to test for multiple sources of gene tree discordance via careful examination of empirical datasets.