Optimal stomatal theory predicts CO2 responses of stomatal conductance in both gymnosperm and angiosperm treesShow others and affiliations
2023 (English)In: New Phytologist, ISSN 0028-646X, E-ISSN 1469-8137, Vol. 237, no 4, p. 1229-1241Article in journal (Refereed) Published
Abstract [en]
Optimal stomatal theory predicts that stomata operate to maximise photosynthesis (A(net)) and minimise transpirational water loss to achieve optimal intrinsic water-use efficiency (iWUE). We tested whether this theory can predict stomatal responses to elevated atmospheric CO2 (eCO(2)), and whether it can capture differences in responsiveness among woody plant functional types (PFTs). We conducted a meta-analysis of tree studies of the effect of eCO(2) on iWUE and its components A(net) and stomatal conductance (g(s)). We compared three PFTs, using the unified stomatal optimisation (USO) model to account for confounding effects of leaf-air vapour pressure difference (D). We expected smaller g(s), but greater A(net), responses to eCO(2) in gymnosperms compared with angiosperm PFTs. We found that iWUE increased in proportion to increasing eCO(2) in all PFTs, and that increases in A(net) had stronger effects than reductions in g(s). The USO model correctly captured stomatal behaviour with eCO(2) across most datasets. The chief difference among PFTs was a lower stomatal slope parameter (g(1)) for the gymnosperm, compared with angiosperm, species. Land surface models can use the USO model to describe stomatal behaviour under changing atmospheric CO2 conditions.
Place, publisher, year, edition, pages
John Wiley & Sons, 2023. Vol. 237, no 4, p. 1229-1241
Keywords [en]
climate change, deciduous, evergreen, free-air CO2 enrichment, photosynthesis, water-use efficiency
National Category
Forest Science
Research subject
Technology (byts ev till Engineering), Forestry and Wood Technology
Identifiers
URN: urn:nbn:se:lnu:diva-118182DOI: 10.1111/nph.18618ISI: 000899223100001PubMedID: 36373000Scopus ID: 2-s2.0-85144100878OAI: oai:DiVA.org:lnu-118182DiVA, id: diva2:1724679
2023-01-092023-01-092023-02-16Bibliographically approved