Physiological, morphological and allocational plasticity in understory deciduous trees: importance of plant size and light availability
Bibliographic record
Abstract
In a 4-year study, we investigated changes in leaf physiology, crown morphology and whole-tree biomass allocation in seedlings and saplings of shade-tolerant sugar maple (Acer saccharum Marsh.) and intermediate shade-tolerant yellow birch (Betula alleghaniensis Britt.) growing in natural understory light (0.5 to 35% of full sunlight) or in understory light reduced by 50% with shade nets to simulate the effect of gap closure. Leaf physiological parameters were mainly influenced by the light gradient, whereas crown morphological and whole-tree allocational parameters were mainly influenced by tree size. No single physiological, morphological or allocational trait was identified that could explain the difference in shade tolerance between the species. Yellow birch had higher growth rates, biomass allocation to branches and leaf physiological plasticity and lower crown morphological plasticity in unmodified understory light than sugar maple. Sugar maple did not display significant physiological plasticity, but showed variation with tree size in both crown morphology and whole-tree biomass allocation. When sugar maple was small, a greater proportion of whole-tree biomass was allocated to roots. However, physiological differences between the species decreased with decreasing light and most morphological and allocational differences tended to disappear with increasing tree size, suggesting that many species differences in shade-tolerance are expressed mainly during the seedling stage. Understory trees of both species survived for 4 years under shade nets, possibly because of higher plasticity when small and the use of stored reserves when taller.
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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 | 0.000 |
| 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 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".