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

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

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