Species and stand-age driven differences in photochemical reflectance index and light use efficiency across four temperate forests
Bibliographic record
Abstract
Photosynthetic light use efficiency (LUE) determines the ability of a plant to assimilate atmospheric carbon dioxide to biomass and is known to be controlled by environmental conditions, light regimes and forest age. The photochemical reflectance index (PRI), derived from leaf or canopy remotely sensed spectra, has been shown to be an effective and accurate estimator of LUE. In this study, we propose a new LUE estimation method that separates the PRI into daily maximal PRI (PRI0) for indicating daily maximal light use efficiency (LUEmax) and ΔPRI, defined as the difference between PRI0 and instantaneous PRI, for estimating the diurnal physiological stress (fstress). We develop and apply the method across three temperate pine stands and a deciduous stand of different ages, in Southern Ontario, Canada. Half hourly canopy level spectra were acquired from a tower-based spectro-radiometer system (AMSPEC-III) over the growing season at the four stands. Results show that the PRI0 predicted well LUEmax (R2 > 0.6, p < 0.05) in both coniferous and deciduous stands and was able to track seasonal changes in pigment pools sizes. The ΔPRI was sensitive to short-term meteorological conditions, specifically temperature, vapor pressure deficit (VPD), and light variations resulting in strong correlations (p < 0.05) with fstress and half hourly LUE. This new method significantly improves the estimation accuracy (R2 increases from 0.1 to around 0.7) for PRI-based LUE estimation across all four stands of varying age and species composition and suggests that PRI-based LUE estimation has the ability to inform on both the effects of seasonal and diurnal change in photosynthetic efficiency under different meteorological conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".