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Abstract Plants respond to increasing atmospheric CO2 concentrations by reducing leaf nitrogen content and photosynthetic capacity—patterns that correspond with increased net photosynthesis and growth. Despite the longstanding notion that nitrogen availability regulates these responses, eco-evolutionary optimality theory posits that leaf-level responses to elevated CO2 are driven by leaf nitrogen demand for building and maintaining photosynthetic enzymes and are independent of nitrogen availability. In this study, we examined leaf and whole-plant responses of Glycine max L. (Merr) subjected to full-factorial combinations of two CO2, two inoculation, and nine nitrogen fertilization treatments. Nitrogen fertilization and inoculation did not alter leaf photosynthetic responses to elevated CO2. Instead, elevated CO2 decreased the maximum rate of ribulose-1,5-bisophosphate oxygenase/carboxylase (Rubisco) carboxylation more strongly than it decreased the maximum rate of electron transport for ribulose-1,5-bisphosphate (RuBP) regeneration, increasing net photosynthesis by allowing rate-limiting steps to approach optimal coordination. Increasing fertilization enhanced positive whole-plant responses to elevated CO2 due to increased below-ground carbon allocation and nitrogen uptake. Inoculation with nitrogen-fixing bacteria did not influence plant responses to elevated CO2. These results reconcile the role of nitrogen availability in plant responses to elevated CO2, showing that leaf photosynthetic responses are regulated by leaf nitrogen demand while whole-plant responses are constrained by nitrogen availability.
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Sensitivity analysis and estimation using a hierarchical Bayesian method for the parameters of the FvCB biochemical photosynthetic model
Photosynthesis is a major process included in land surface models. Accurately estimating the parameters of the photosynthetic sub-models can greatly improve the ability of these models to accurately simulate the carbon cycle of terrestrial ecosystems. Here, we used a hierarchical Bayesian approach to fit the Farquhar–von Caemmerer–Berry model, which is based on the biochemistry of photosynthesis using 236 curves for the relationship between net CO2 assimilation and changes in the intercellular CO2 concentration. An advantage of the hierarchical Bayesian algorithm is that parameters can be estimated at multiple levels (plant, species, plant functional type, and population level) simultaneously. The parameters of the hierarchical strategy were based on the results of a sensitivity analysis. The Michaelis–Menten constant (Kc25), enthalpies of activation (EJ and EV), and two optical parameters (θ and α) demonstrated considerable variation at different levels, which suggests that this variation cannot be ignored. The maximum electron transport rate (Jmax25), maximum rate of Rubisco activity (Vcmax25), and dark respiration in the light (Rd25) were higher for broad-leaved plants than for needle-leaved plants. Comparison of the model’s simulated outputs with observed data showed strong and significant positive correlations, particularly when the model was parameterized at the plant level. In summary, our study is the first effort to combine sensitivity analysis and hierarchical Bayesian parameter estimation. The resulting realistic parameter distributions for the four levels provide a reference for current and future land surface models. Furthermore, the observed variation in the parameters will require attention when using photosynthetic parameters in future models.
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- PAR ID:
- 10123832
- Date Published:
- Journal Name:
- Photosynthesis research
- ISSN:
- 1573-5079
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11120-019-00684-z
