Global photosynthetic capacity jointly determined by enzyme kinetics and eco-evo-environmental drivers

Accurate understanding of global photosynthetic capacity (i.e. maximum RuBisCO carboxylation rate, V_{c, 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 V_{c, max} was dictated by both factors of temperature-associated enzyme kinetics (capturing instantaneous ecophysiological responses) and the amount of activated RuBisCO (indexed by V_{c, max} standardized at 25 ℃, V_{c, 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 V_{c, max} variability, followed by 55% explained by enzyme kinetics alone. This statistical model outperformed the current theoretical optimality model for predicting global V_{c, max} variability (67%), primarily due to its poor characterization on global V_{c, 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 V_{c, max25} was a key missing mechanism for improving the theoretical modelling of global V_{c, max} variability. These findings improve the mechanistic understanding of global V_{c, max} variability and provide an important basis to benchmark process-based models of terrestrial photosynthesis and carbon cycling under climate change.