Contributions of leaf photosynthetic capacity, leaf angle and self-shading to the maximization of net photosynthesis in Acer saccharum: a modelling assessment
Bibliographic record
Abstract
BACKGROUND AND AIMS: Plants are expected to maximize their net photosynthetic gains and efficiently use available resources, but the fundamental principles governing trade-offs in suites of traits related to resource-use optimization remain uncertain. This study investigated whether Acer saccharum (sugar maple) saplings could maximize their net photosynthetic gains through a combination of crown structure and foliar characteristics that let all leaves maximize their photosynthetic light-use efficiency (ε). METHODS: A functional-structural model, LIGNUM, was used to simulate individuals of different leaf area index (LAI(ind)) together with a genetic algorithm to find distributions of leaf angle (L(A)) and leaf photosynthetic capacity (A(max)) that maximized net carbon gain at the whole-plant level. Saplings grown in either the open or in a forest gap were simulated with A(max) either unconstrained or constrained to an upper value consistent with reported values for A(max) in A. saccharum. KEY RESULTS: It was found that total net photosynthetic gain was highest when whole-plant PPFD absorption and leaf ε were simultaneously maximized. Maximization of ε required simultaneous adjustments in L(A) and A(max) along gradients of PPFD in the plants. When A(max) was constrained to a maximum, plants growing in the open maximized their PPFD absorption but not ε because PPFD incident on leaves was higher than the PPFD at which ε(max) was attainable. Average leaf ε in constrained plants nonetheless improved with increasing LAI(ind) because of an increase in self-shading. CONCLUSIONS: It is concluded that there are selective pressures for plants to simultaneously maximize both PPFD absorption at the scale of the whole individual and ε at the scale of leaves, which requires a highly integrated response between L(A), A(max) and LAI(ind). The results also suggest that to maximize ε plants have evolved mechanisms that co-ordinate the L(A) and A(max) of individual leaves with PPFD availability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".