A meta‐analysis of responses of C3 plants to atmospheric CO2: dose–response curves for 85 traits ranging from the molecular to the whole‐plant level

Summary Generalised dose–response curves are essential to understand how plants acclimate to atmospheric CO2. We carried out a meta‐analysis of 630 experiments in which C3 plants were experimentally grown at different [CO2] under relatively benign conditions, and derived dose–response curves for 85...

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Veröffentlicht in:The New phytologist 2022-02, Vol.233 (4), p.1560-1596
Hauptverfasser: Poorter, Hendrik, Knopf, Oliver, Wright, Ian J., Temme, Andries A., Hogewoning, Sander W., Graf, Alexander, Cernusak, Lucas A., Pons, Thijs L.
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Sprache:eng
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Zusammenfassung:Summary Generalised dose–response curves are essential to understand how plants acclimate to atmospheric CO2. We carried out a meta‐analysis of 630 experiments in which C3 plants were experimentally grown at different [CO2] under relatively benign conditions, and derived dose–response curves for 85 phenotypic traits. These curves were characterised by form, plasticity, consistency and reliability. Considered over a range of 200–1200 µmol mol−1 CO2, some traits more than doubled (e.g. area‐based photosynthesis; intrinsic water‐use efficiency), whereas others more than halved (area‐based transpiration). At current atmospheric [CO2], 64% of the total stimulation in biomass over the 200–1200 µmol mol−1 range has already been realised. We also mapped the trait responses of plants to [CO2] against those we have quantified before for light intensity. For most traits, CO2 and light responses were of similar direction. However, some traits (such as reproductive effort) only responded to light, others (such as plant height) only to [CO2], and some traits (such as area‐based transpiration) responded in opposite directions. This synthesis provides a comprehensive picture of plant responses to [CO2] at different integration levels and offers the quantitative dose–response curves that can be used to improve global change simulation models.
ISSN:0028-646X
1469-8137
DOI:10.1111/nph.17802