Sunflecks in trees and forests: from photosynthetic physiology to global change biology
Bibliographic record
Abstract
Sunflecks are brief, intermittent periods of high photon flux density (PFD) that can significantly improve carbon gain in shaded forest understories and lower canopies of trees. In this review, we discuss the physiological basis of leaf-level responses to sunflecks and the mechanisms plants use to tolerate sudden changes in PFD and leaf temperature induced by sunflecks. We also examine the potential effects of climate change stresses (including elevated temperatures, rising CO(2) concentrations and drought) on the ability of tree species to use sunflecks, and advocate more research to improve our predictions of seedling and tree carbon gain in future climates. Lastly, while we have the ability to model realistic responses of photosynthesis to fluctuating PFD, dynamic responses of photosynthesis to sunflecks are not accounted for in current models of canopy carbon uptake, which can lead to substantial overestimates of forest carbon fixation. Since sunflecks are a critical component of seasonal carbon gain for shaded leaves, sunfleck regimes and physiological responses to sunflecks should be incorporated into models to more accurately capture forest carbon dynamics.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".