Phenolic compounds as unambiguous chemical markers for the identification of keystone plant species in the bale mountains, ethiopia

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Bruk Lemma - , Martin Luther University Halle-Wittenberg, Forest and Rangeland Biodiversity Directorate (Author)
  • Claudius Grehl - , Martin Luther University Halle-Wittenberg (Author)
  • Michael Zech - , Institute of Geography, Heisenberg Chair of Physical Geography with a Focus on Paleoenvironmental Research, Martin Luther University Halle-Wittenberg, TUD Dresden University of Technology (Author)
  • Betelhem Mekonnen - , Martin Luther University Halle-Wittenberg, Misrak Polytechnic College (Author)
  • Wolfgang Zech - , University of Bayreuth (Author)
  • Sileshi Nemomissa - , Addis Ababa University (Author)
  • Tamrat Bekele - , Addis Ababa University (Author)
  • Bruno Glaser - , Martin Luther University Halle-Wittenberg (Author)

Abstract

Despite the fact that the vegetation pattern and history of the Bale Mountains in Ethiopia were reconstructed using pollen, little is known about the former extent of Erica species. The main objective of the present study is to identify unambiguous chemical proxies from plant-derived phenolic compounds to characterize Erica and other keystone species. Mild alkaline CuO oxidation has been used to extract sixteen phenolic compounds. After removal of undesired impurities, individual phenols were separated by gas chromatography and were detected by mass spectrometry. While conventional phenol ratios such as syringyl vs. vanillyl and cinnamyl vs. vanillyl and hierarchical cluster analysis of phenols failed for unambiguous Erica identification, the relative abundance of coumaryl phenols (>0.20) and benzoic acids (0.05—0.12) can be used as a proxy to distinguish Erica from other plant species. Moreover, a Random Forest decision tree based on syringyl phenols, benzoic acids (>0.06), coumaryl phenols (<0.21), hydroxybenzoic acids, and vanillyl phenols (>0.3) could be established for unambiguous Erica identification. In conclusion, serious caution should be given before interpreting this calibration study in paleovegetation reconstruction in respect of degradation and underground inputs of soil organic matter.

Details

Original languageEnglish
Article number228
JournalPlants
Volume8
Issue number7
Publication statusPublished - Jul 2019
Peer-reviewedYes

Keywords

Keywords

  • Biomarkers, Erica, Machine learning, Oxidation, Paleoclimate, Paleovegetation, Phenols, Pollen