TY - JOUR
T1 - Global photosynthetic capacity jointly determined by enzyme kinetics and eco-evo-environmental drivers
AU - Yan, Zhengbing
AU - Detto, Matteo
AU - Guo, Zhengfei
AU - Smith, Nicholas G.
AU - Wang, Han
AU - Albert, Loren P.
AU - Xu, Xiangtao
AU - Lin, Ziyu
AU - Liu, Shuwen
AU - Zhao, Yingyi
AU - Chen, Shuli
AU - Bonebrake, Timothy C.
AU - Wu, Jin
N1 - Publisher Copyright:
© 2024
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Belowground resource constraint
KW - Climate
KW - Eco-evolutionary optimality
KW - Ecophysiology
KW - Enzyme kinetics
KW - Global carbon cycling
KW - Leaf photosynthetic capacity
KW - Leaf traits
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U2 - 10.1016/j.fmre.2023.12.011
DO - 10.1016/j.fmre.2023.12.011
M3 - Article
AN - SCOPUS:85186414854
SN - 2096-9457
JO - Fundamental Research
JF - Fundamental Research
ER -