Scientific Area
Abstract Detail
Nº613/706 - Towards a comprehensive phylogeny of the largest flowering plant genus: A herbarium-based study of Astragalus (Fabaceae)
Format: ORAL
Authors
Daniele Buono1, Diego F. Morales-Briones 1, Shahin Zarre2, Aaron Liston3, Gudrun Kadereit1
Affiliations
1 Ludwig-Maximilians-Universität München, Munich, Germany
2 University of Tehran, Tehran, Iran
3 Oregon State University, Corvallis, USA
Abstract
Astragalus (Fabaceae, Papilionoideae), with about 3,000 species, is considered the largest genus of flowering plants and represents a striking example of recent rapid radiation. Our understanding of the evolutionary drivers and adaptive traits responsible for this mega-radiation is fragmentary due to insufficient sampling and poorly resolved phylogenetic trees. It has been suggested that edaphic specialisation and colonisation of extreme microhabitats led to the divergence of lineages in Astragalus. To assess the evolutionary radiation and its drivers in Astragalus, we are using a target enrichment approach to obtain a robust phylogeny. We designed a large bait set based on 686 orthologous genes (819 exons) specific for the Astragalean clade, which can potentially resolve complex relationships in Astragalus. Our sampling is focused on main centres of diversity (Iran, Turkey, and Afghanistan) and relies on the rich Astragalus collection at the Botanical State Collection Munich (BSM-SNSB - about 25,000 Astragalus specimens from all around the world). We constructed a preliminary phylogeny including representatives of major sections based on 101 species (85 Astragalus plus 13 Astragalean clade species and three outgroups). A well-supported phylogeny at the subgenus level was recovered, mainly in agreement with previous phylogenies. Yet, several differences between previous phylogenies and the current sections were recognised, and clear signals of discordance along the backbone were found. We also established a fast and reliable workflow for digitising the Astragalus herbarium collection at SNSB. Using manual and automated machine learning techniques allows us to extract morphological traits from specimens and voucher data from labels. Mined data, such as geographic origin and habitat description, can complement genomic data and allow for various studies (e.g., niche modelling, evolution of morphological traits, and much more) to be performed on the extensive herbarium collection, gaining more insights into the drivers of diversification in Astragalus.