Seasonal variation in the relationship between leaf chlorophyll content and photosynthetic capacity
Bibliographic record
Abstract
Abstract Accurate estimation of photosynthesis is crucial for ecosystem carbon cycle modelling. Previous studies have established an empirical relationship between photosynthetic capacity (maximum carboxylation rate, Vcmax; maximum electron transport rate, Jmax) and leaf chlorophyll (Chl) content to infer global photosynthetic capacity. However, the basis for the Chl‐Vcmax relationship remains unclear, which is further evidenced by the temporal variations in the Chl‐Vcmax relationship. Using multiple years of observations of four deciduous tree species, we found that Vcmax and Jmax acclimate to photosynthetically active radiation faster (4–8 weeks) than Chl (10–12 weeks). This mismatch in temporal scales causes seasonality in the Vcmax‐Chl relationship. To account for the mismatch, we used a Chl fluorescence parameter (quantum yield of Photosystem II, Φ(II)) to tighten the relationship and found Φ(II) × Chl correlated with Vcmax and Jmax (r2 = 0.74 and 0.72 respectively) better than only Chl (r2 = 0.7 and 0.6 respectively). It indicates that Φ(II) accounts for the short‐term adjustment of leaf photosynthetic capacity to light, which was not captured by Chl. Our study advances our understanding of the ecophysiological basis for the empirical Vcmax‐Chl relationship and how to better infer Vcmax from Chl and fluorescence, which guides large‐scale photosynthesis simulations using remote sensing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".