On the relation between tree foliage clumping at the branch scale and light and water limitations
Bibliographic record
Abstract
Foliage clumping is known to have a significant effect on the radiative transfer and mass and energy exchanges in forests. It is an important component of canopy structure to consider for the estimation of photosynthesis rates and the interpretation of observed solar induced fluorescence (SIF). Yet, relatively little is known about the drivers of foliage clumping, and few observations of foliage clumping are available at the branch scale. Here, we report on a study using laser light to estimate foliage clumping at the tree branch scale in eight broadleaf species, at different heights above ground, from four sites located in two climatic zones: one water limited, and one light limited. We also integrate our results with published foliage clumping estimates from two sites (one in each climatic zone). We find that foliage arrangement on branches exposed to high solar irradiance tend to be random at the dry sites, but are very clumped at humid sites where competition for light is high. Branches sampled at the top of tall canopies at humid sites showed that foliage clumping increased with tree height, suggesting that higher competition for light results in the production of larger numbers of leaves grouped together which reduces the light interception efficiency on a per leaf area basis. Comparison with landscape clumping values suggests that the spatial availability of a limiting resource is a major driver of foliage clumping in forests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".