Living long by staying small: stem layering as an adaptive life-history trait in shade-tolerant tree seedlings
Bibliographic record
Abstract
Suppressed seedling banks are important in replacement dynamics in late-successional forests. However, demographic properties of seedling populations are poorly known, and there has been little attention to traits that might affect fitness in suppressed seedlings. Acer saccharum Marsh., a shade-tolerant dominant in eastern North American forests, frequently develops adventitious roots along prostrate portions of stems (“layering”). Measurements of Acer seedlings in old-growth forests in Michigan indicate that layered seedlings proportionally reduce structural allocations to older layered stem tissues, retain leaf area / height ratios of younger unlayered seedlings, and tend to survive longer. In tree seedlings, allometric consequences of normal stem growth lead to declining ratios of photosynthetic to nonphotosynthetic biomass, which potentially reduces shade tolerance and limiting age. The layering habit may defer this penalty by changing the allometry of growth. Resulting increases in life expectancy should increase chance of access to increased light and of reaching the canopy. Thus, because flowering is generally restricted to canopy trees, the tendency to layer may increase fitness. Properties of individuals in suppressed seedling banks may be selectively and ecologically important, shaping life histories and population dynamics.
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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) | 0.001 | 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".