A long-term study of tree seedling recruitment in southern Appalachian forests: the effects of canopy gaps and shrub understories
Bibliographic record
Abstract
We examined the importance of intermediate-sized gaps and a dense shrub layer on tree seedling recruitment in a southern Appalachian deciduous forest. We created 12 canopy gaps under two contrasting understory conditions: 6 gaps were dominated by the dense, shade-producing shrub, Rhododendron maximum L., while the remaining gaps were relatively open. Density of first-year and >first-year seedlings was monitored for 5 years in transects extending from adjacent undisturbed forest through the experimental gaps. We concurrently measured the understory light environment, soil moisture, litter biomass, and seed rain. Neither species diversity nor richness consistently increased following gap formation. Acer rubrum L. responded consistently to canopy gaps with increased seedling densities while most other species, including both shade-tolerant and shade-intolerant species, did not. Seedling densities were especially low and unresponsive to gap formation in areas dominated by R. maximum. Understory light levels were consistently low beneath R. maximum and did not increase with canopy gap formation. Our results suggest that dense shrub cover can neutralize recruitment opportunities in canopy gaps, that seed rain often limits recruitment in gaps, and that canopy gaps that are larger or include understory disturbance are needed to maintain diversity in these 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 | 0.001 | 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 | 0.001 |
| 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".