Harnessing photosynthetic C 18 O 16 O discrimination dynamics under leaf water nonsteady state to estimate mesophyll conductance: a new, regression-based method.

Mesophyll conductance (g m ) is a crucial plant trait that can significantly limit photosynthesis. Measurement of photosynthetic C 18 O 16 O discrimination (Δ 18 O) has proved to be the only viable means of resolving g m in both C 3 and C 4 plants. However, the currently available methods to exploit Δ 18 O for g m estimation are error prone due to their inadequacy in constraining the degree of oxygen isotope exchange (θ) during mesophyll CO 2 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 g m and θ from online Δ 18 O measurements. We demonstrated the methodological and theoretical robustness of this new Δ 18 O-g m estimation approach and showed through measurements on several C 3 and C 4 species that this approach can serve as a benchmark method against which to identify previously-unrecognized biases of the existing Δ 18 O-g m 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.