A data-driven approach to integrative taxonomy

Central to understanding biodiversity is the systematic classification of biological taxa where concepts on integrating omics data have not yet been comprehensively executed. The data-driven approach is aiming at integrating phylogenetic data from three or more scales aiming at enriching the robustness of species delimitations while also revealing evolutionary drivers of diversification and speciation. This study investigates complex-thallose liverworts as reference and combines the analysis of DNA marker sequencing, morphometrics utilizing bioimaging measurements and chemometrics using liquid chromatography high-resolution mass-spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition of tandem mass-spectra (DDA-MS), with character variation evaluated with dendrograms and data mining. The comparative analysis revealed similar tree topologies albeit with diverging positions of taxa that were attributed to flavonoid-glycosides, auronidins and phenanthrenes resulting from evolutionary radiation and adaptations to stressful bioclimatic conditions, whereas unique fatty acyls in Riccia link to ecological causes of speciation. Characteristic amino acid motifs confirm the intermediary position of liverworts between algae and land plants. By functionally attributing and mapping chemometric markers to the taxonomy, the data-driven approach integrating omics into integrative taxonomy is opening opportunities for plant systematics to identify evolutionary drivers and form new hypotheses on the diversification and speciation of species.