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Ionomic variation in leaves of 819 plant species growing in the botanical garden of Hokkaido University, Japan.

Toshihiro WatanabeTakayuki Azuma
Published in: Journal of plant research (2021)
Ionomics is the measurement of total metal, metalloid, and nonmetal accumulation in living organisms. Plant ionomics has been applied to various types of research in the last decade. It has been reported that the ionome of a plant is strongly affected by its evolution and by environmental factors. In this study, we analyzed the concentration of 23 elements in leaves of 819 plant species (175 families) growing in the Botanic Garden of Hokkaido University, Japan. Relative variation estimated by the coefficient of variation in foliar concentrations of essential elements among various plant species tended to be low, whereas nickel concentration showed exceptionally large relative variation. By contrast, the relative variation in nonessential elements was high, particularly in sodium, aluminum, and arsenic. The higher relative variations in these element concentrations can be explained by the occurrence of plants that are hyperaccumulators for these elements. Differences in life forms such as herbaceous/woody species, deciduous/evergreen woody species and annual/perennial herbaceous species affected the concentration of several elements in the leaves. These differences were considered to be due to the combined factors including differences in lifespan, growth rate, and cell wall thickness of the leaves. Results of principal component analyses (based on concentration data of essential and nonessential elements in leaf samples) indicated phylogenetic influences on plant ionomes at the family level in Polypodiales, Pinales, Poales, and Ericales. Furthermore, when analyzing correlations among concentrations of all elements in each order and comparing among different orders, the results also suggested that Polypodiales, Pinales, and Poales each had a specific ion homeostasis network.
Keyphrases
  • cell wall
  • magnetic resonance
  • risk assessment
  • machine learning
  • big data
  • essential oil
  • multidrug resistant
  • diffusion weighted imaging
  • gram negative