Consistent responses of vegetation gas exchange to elevated atmospheric CO2 emerge from heuristic and optimization models

Journal Article (Journal Article)

Elevated atmospheric CO2 concentration is expected to increase leaf CO2 assimilation rates, thus promoting plant growth and increasing leaf area. It also decreases stomatal conductance, allowing water savings, which have been hypothesized to drive large-scale greening, in particular in arid and semiarid climates. However, the increase in leaf area could reduce the benefits of elevated CO2 concentration through soil water depletion. The net effect of elevated CO2 on leaf- and canopy-level gas exchange remains uncertain. To address this question, we compare the outcomes of a heuristic model based on the Partitioning of Equilibrium Transpiration and Assimilation (PETA) hypothesis and three model variants based on stomatal optimization theory. Predicted relative changes in leaf- and canopy-level gas exchange rates are used as a metric of plant responses to changes in atmospheric CO2 concentration. Both model approaches predict reductions in leaf-level transpiration rate due to decreased stomatal conductance under elevated CO2, but negligible (PETA) or no (optimization) changes in canopy-level transpiration due to the compensatory effect of increased leaf area. Leaf- and canopy-level CO2 assimilation is predicted to increase, with an amplification of the CO2 fertilization effect at the canopy level due to the enhanced leaf area. The expected increase in vapour pressure deficit (VPD) under warmer conditions is generally predicted to decrease the sensitivity of gas exchange to atmospheric CO2 concentration in both models. The consistent predictions by different models that canopy-level transpiration varies little under elevated CO2 due to combined stomatal conductance reduction and leaf area increase highlight the coordination of physiological and morphological characteristics in vegetation to maximize resource use (here water) under altered climatic conditions.

Full Text

Duke Authors

Cited Authors

  • Manzoni, S; Fatichi, S; Feng, X; Katul, GG; Way, D; Vico, G

Published Date

  • September 14, 2022

Published In

Volume / Issue

  • 19 / 17

Start / End Page

  • 4387 - 4414

Electronic International Standard Serial Number (EISSN)

  • 1726-4189

International Standard Serial Number (ISSN)

  • 1726-4170

Digital Object Identifier (DOI)

  • 10.5194/bg-19-4387-2022

Citation Source

  • Scopus