Estimating photosynthetic capacity from optimized Rubisco–chlorophyll relationships among vegetation types and under global change
Bibliographic record
Abstract
Abstract The maximum rate of carboxylation (Vcmax), a key parameter indicating photosynthetic capacity, is commonly fixed as a constant by vegetation types and/or varies according to empirical scaling functions in Earth system models (ESMs). As such, the setting of Vcmax results in uncertainties of estimated carbon assimilation. It is known that the coupling between leaf chlorophyll and Rubisco (ribulose-1,5-biphosphate carboxylase-oxygenase) contents can be applied to estimate Vcmax. However, how this coupling is affected by environmental changes and varies among plant functional types (PFTs) has not been well investigated yet. The effect of varying coupling between chlorophyll and Rubisco contents on the estimation of Vcmax is still not clear. In this study, we compiled data from 76 previous studies to investigate the coupling between Chlorophyll (Chl) and Rubisco (Rub), in different PFTs and under different environmental conditions. We also assessed the ability of a Rub-based semi-mechanistic model to estimate Vcmax normalized to 25 °C (Vcmax25) based on the Rub–Chl relationship. Our results revealed strong, linear Rub-Chl relationships for different PFTs (R 2 = 0.73, 0.67, 0.54 and 0.72 for forest, crop, grass and shrub, and C4 plants, respectively). The Rub–Chl slope of natural C3 PFTs was consistent and significantly different from those of crops and C4 plants. A meta-analysis indicated that reduced light intensity, elevated CO2, and nitrogen addition strongly altered Rub/Chl. A semi-mechanistic model based on PFT-specific Rub–Chl relationships was able to estimate Vcmax25 with high confidence. Our findings have important implications for improving global carbon cycle modeling by ESMs through the improved parameterization of Vcmax25 using remotely sensed Chl content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".