4.8 Article

Global photosynthetic capacity is optimized to the environment

Journal

ECOLOGY LETTERS
Volume 22, Issue 3, Pages 506-517

Publisher

WILEY
DOI: 10.1111/ele.13210

Keywords

Carbon cycle; Carboxylation; coordination; ecophysiology; electron transport; Jmax; light availability; nitrogen availability; temperature; V-cmax

Categories

Funding

  1. Laboratory Directed Research and Development (LDRD) fund under the DOE, BER Office of Science at Lawrence Berkeley National Laboratory
  2. National Natural Science Foundation of China [31600388]
  3. The Fonds de recherche du Quebec - Nature et Technologies [FRQNT-2017-NC-198009]
  4. Natural Sciences and Engineering Research Council of Canada (NSERC) [2016-05716]
  5. Next-Generation Ecosystem Experiments (NGEE Arctic) project - Office of Biological and Environmental Research in the Department of Energy, Office of Science
  6. United States Department of Energy [DE-SC0012704]
  7. NASA [NNX10AJ94G, NNX08AN31G]
  8. USDA Hatch/McIntire-Stennis awards [WIS01809, WIS02010]
  9. Newton International fellowship [NF082365]
  10. MSCA fellowship [705432]
  11. NERC [NE/N012526/1] Funding Source: UKRI
  12. NASA [131028, NNX08AN31G, NNX10AJ94G, 97764] Funding Source: Federal RePORTER
  13. Royal Society [NF082365] Funding Source: Royal Society

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Earth system models (ESMs) use photosynthetic capacity, indexed by the maximum Rubisco carboxylation rate (V-cmax), to simulate carbon assimilation and typically rely on empirical estimates, including an assumed dependence on leaf nitrogen determined from soil fertility. In contrast, new theory, based on biochemical coordination and co-optimization of carboxylation and water costs for photosynthesis, suggests that optimal V-cmax can be predicted from climate alone, irrespective of soil fertility. Here, we develop this theory and find it captures 64% of observed variability in a global, field-measured V-cmax dataset for C-3 plants. Soil fertility indices explained substantially less variation (32%). These results indicate that environmentally regulated biophysical constraints and light availability are the first-order drivers of global photosynthetic capacity. Through acclimation and adaptation, plants efficiently utilize resources at the leaf level, thus maximizing potential resource use for growth and reproduction. Our theory offers a robust strategy for dynamically predicting photosynthetic capacity in ESMs.

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