Vertical structure heterogeneity in broadleaf forests: Effects on light interception and canopy photosynthesis
Bibliographic record
Abstract
Foliage clumping refers to the aggregation of leaves around branches and tree crowns. It has been shown to influence considerably light interception in forests and is increasingly being used in terrestrial biosphere models (TBMs) to estimate canopy photosynthesis, represented by a single value encompassing all spatial scales (shoot, branch, crown and plot): the clumping factor (Ω). Several studies have pointed to possible vertical variations in foliage clumping, and a recent study confirmed very low clumping factor values (high leaf aggregation) at the top of deciduous broadleaf forests. However complete profiles of clumping factors have never been described in tall forest canopies. As TBMs are currently moving towards multilayer schemes for the computation of photosynthesis, we investigated whether vertical profiles of foliage clumping should be considered within those schemes. We first used ground lidar combined with hemispherical photos to characterise branch level clumping factor (Ωv) at different heights in a deciduous broadleaf forest. We then applied the branch level clumping factor profile to four forests plots where complete 3D structure derived from ground lidar was available to produce vertical profiles (30 cm resolution) for foliage clumping (Ωl) and leaf area index (LAI). We ran radiative transfer simulations using different scenarios for representing vertical structure to assess its effect on canopy photosynthesis (without considering energy balance, temperature, kinetics or water balance). We found that when considering branch level clumping, the plot level clumping factor values were considerably lower (leaves more clumped together) than the average values commonly used for broadleaf forests. Modeling light fluxes using this lower value increased canopy photosynthesis because of a combination of greater light penetration to lower layers and non-linearity in the light-response curve. Considering the vertical profiles of foliage clumping further increased canopy photosynthesis through a greater contribution from shaded leaves in the lower and upper canopy levels. Further, we find that there is an optimal range of value for clumping factor which maximises photosynthesis, and the four sites surveyed (LAIs above 4) display clumping factors values close to this optimum. Solely on the basis of the effect of radiative transfer, we did not find notable differences in canopy photosynthesis when the LAI profile was considered. We find that it may be of interest to consider foliage clumping vertical profiles within TBM multilayer schemes, and we present a generic profile equation for use in dense deciduous forests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".