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 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 | 0.001 | 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".