Metabolomic evidence for biogeographic segregation in Espeletiinae (Asteraceae): A new perspective on an Andean adaptive radiation in sky islands (Interspecific chemical variability assay)

ABSTRACT:

The biogeography of tropical Andean plants and the effects of altitudinal barriers as evolutionary constraints have been the focus of great debate. Although genetic markers have provided evidence for the geographic isolation of Andean-endemic groups, even highly variable molecular markers commonly used to assess biogeography do not have the resolving power needed for a fine geographic discrimination in some Andean taxa. We present a metabolomics approach based on ultrahigh-performance liquid chromatography-mass spectrometry combined with multivariate statistical methods and machine learning algorithms of multiple species and populations of Espeletiinae (Asteraceae) to provide metabolomic evidence for biogeographic segregation in this Andean-endemic subtribe. Our approach allows the discrimination of Espeletiinae taxa at different geographic scales with characteristic metabolic fingerprints related to their country of origin on a global scale, to their páramo massifs on a regional scale and to their páramo complexes on a local scale, revealing inter- and intraspecific metabolic variations. Our results, together with previous studies, suggest that Andean geography and Pleistocene glacial cycles not only shaped the evolutionary history of Espeletiinae but also its metabolic fingerprints, demonstrating the potential of metabolomics for understanding biogeographic patterns in recently diversified plant groups where genetic divergence is still at an early stage.

Interspecific chemical variability assay protocols and data are reported in the current study MTBLS944.

Intraspecific chemical variability assay protocols and data are reported in study MTBLS943.

INSTRUMENT(S): Liquid Chromatography MS - Negative (LC-MS (Negative))

SUBMITTER: Guillermo Padilla-Gonzalez 

PROVIDER: MTBLS944 | MetaboLights | 2020-03-24

REPOSITORIES: MetaboLights