Harnessing photosynthetic C18O16O discrimination dynamics under leaf water nonsteady state to estimate mesophyll conductance: a new, regression-based method.

Abstract

Mesophyll conductance (gm) is a crucial plant trait that can significantly limit photosynthesis. Measurement of photosynthetic C18O16O discrimination (Δ18O) has proved to be the only viable means of resolving gm in both C3 and C4 plants. However, the currently available methods to exploit Δ18O for gm estimation are error prone due to their inadequacy in constraining the degree of oxygen isotope exchange (θ) during mesophyll CO2 hydration. Here, we capitalized on experimental manipulation of leaf water isotopic dynamics to establish a novel, nonsteady state, regression-based approach for simultaneous determination of gm and θ from online Δ18O measurements. We demonstrated the methodological and theoretical robustness of this new Δ18O-gm estimation approach and showed through measurements on several C3 and C4 species that thisapproach can serve as a benchmark method against which to identify previously-unrecognized biases of the existing Δ18O-gm methods. Our results highlight the unique value of this nonsteady state-based approach for contributing to ongoing efforts toward quantitative understanding of mesophyll conductance for crop yield improvement and carbon cycle modeling.