File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.fmre.2023.12.011
- Scopus: eid_2-s2.0-85186414854
- Find via
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Article: Global photosynthetic capacity jointly determined by enzyme kinetics and eco-evo-environmental drivers
Title | Global photosynthetic capacity jointly determined by enzyme kinetics and eco-evo-environmental drivers |
---|---|
Authors | |
Keywords | Belowground resource constraint Climate Eco-evolutionary optimality Ecophysiology Enzyme kinetics Global carbon cycling Leaf photosynthetic capacity Leaf traits |
Issue Date | 6-Feb-2024 |
Publisher | Elsevier B.V. on behalf of KeAi Communications Co. Ltd. |
Citation | Fundamental Research, 2024 How to Cite? |
Abstract | Accurate understanding of global photosynthetic capacity (i.e. maximum RuBisCO carboxylation rate, Vc, max) variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cycles with climate change, but a holistic understanding and assessment remains lacking. Here we hypothesized that Vc, max was dictated by both factors of temperature-associated enzyme kinetics (capturing instantaneous ecophysiological responses) and the amount of activated RuBisCO (indexed by Vc, max standardized at 25 ℃, Vc, max25), and compiled a comprehensive global dataset (n = 7339 observations from 428 sites) for hypothesis testing. The photosynthesis data were derived from leaf gas exchange measurements using portable gas exchange systems. We found that a semi-empirical statistical model considering both factors explained 78% of global Vc, max variability, followed by 55% explained by enzyme kinetics alone. This statistical model outperformed the current theoretical optimality model for predicting global Vc, max variability (67%), primarily due to its poor characterization on global Vc, max25 variability (3%). Further, we demonstrated that, in addition to climatic variables, belowground resource constraint on photosynthetic machinery built-up that directly structures the biogeography of Vc, max25 was a key missing mechanism for improving the theoretical modelling of global Vc, max variability. These findings improve the mechanistic understanding of global Vc, max variability and provide an important basis to benchmark process-based models of terrestrial photosynthesis and carbon cycling under climate change. |
Persistent Identifier | http://hdl.handle.net/10722/347263 |
ISSN | 2023 Impact Factor: 5.7 2023 SCImago Journal Rankings: 0.849 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yan, Z | - |
dc.contributor.author | Detto, M | - |
dc.contributor.author | Guo, Z | - |
dc.contributor.author | Smith, NG | - |
dc.contributor.author | Wang, H | - |
dc.contributor.author | Albert, LP | - |
dc.contributor.author | Xu, X | - |
dc.contributor.author | Lin, Z | - |
dc.contributor.author | Liu, S | - |
dc.contributor.author | Zhao, Y | - |
dc.contributor.author | Chen, S | - |
dc.contributor.author | Bonebrake, TC | - |
dc.contributor.author | Wu, J | - |
dc.date.accessioned | 2024-09-20T00:31:03Z | - |
dc.date.available | 2024-09-20T00:31:03Z | - |
dc.date.issued | 2024-02-06 | - |
dc.identifier.citation | Fundamental Research, 2024 | - |
dc.identifier.issn | 2096-9457 | - |
dc.identifier.uri | http://hdl.handle.net/10722/347263 | - |
dc.description.abstract | <p>Accurate understanding of global photosynthetic capacity (i.e. maximum RuBisCO carboxylation rate, Vc, max) variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cycles with climate change, but a holistic understanding and assessment remains lacking. Here we hypothesized that Vc, max was dictated by both factors of temperature-associated enzyme kinetics (capturing instantaneous ecophysiological responses) and the amount of activated RuBisCO (indexed by Vc, max standardized at 25 ℃, Vc, max25), and compiled a comprehensive global dataset (n = 7339 observations from 428 sites) for hypothesis testing. The photosynthesis data were derived from leaf gas exchange measurements using portable gas exchange systems. We found that a semi-empirical statistical model considering both factors explained 78% of global Vc, max variability, followed by 55% explained by enzyme kinetics alone. This statistical model outperformed the current theoretical optimality model for predicting global Vc, max variability (67%), primarily due to its poor characterization on global Vc, max25 variability (3%). Further, we demonstrated that, in addition to climatic variables, belowground resource constraint on photosynthetic machinery built-up that directly structures the biogeography of Vc, max25 was a key missing mechanism for improving the theoretical modelling of global Vc, max variability. These findings improve the mechanistic understanding of global Vc, max variability and provide an important basis to benchmark process-based models of terrestrial photosynthesis and carbon cycling under climate change.</p> | - |
dc.language | eng | - |
dc.publisher | Elsevier B.V. on behalf of KeAi Communications Co. Ltd. | - |
dc.relation.ispartof | Fundamental Research | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Belowground resource constraint | - |
dc.subject | Climate | - |
dc.subject | Eco-evolutionary optimality | - |
dc.subject | Ecophysiology | - |
dc.subject | Enzyme kinetics | - |
dc.subject | Global carbon cycling | - |
dc.subject | Leaf photosynthetic capacity | - |
dc.subject | Leaf traits | - |
dc.title | Global photosynthetic capacity jointly determined by enzyme kinetics and eco-evo-environmental drivers | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.fmre.2023.12.011 | - |
dc.identifier.scopus | eid_2-s2.0-85186414854 | - |
dc.identifier.eissn | 2667-3258 | - |
dc.identifier.issnl | 2667-3258 | - |