Growth and mortality response of forest regeneration to partial harvesting varies by species’ shade tolerance
Bibliographic record
Abstract
Does species’ shade tolerance regulate natural regeneration abundance and composition when partial harvestings (≤80% of basal area removal) are operated on a landscape scale? We examined this question using 835 permanent plots located across forested landscapes of Maine, USA. These plots were surveyed for regeneration growth, mortality, and recruitment before and after treatment application (i.e., partially harvested and unharvested). Our results showed that relative to unharvested stands, high-intensity partial harvesting (41%–80% of basal area removal) increased the number of seedlings (diameter at breast height (DBH) < 2.5 cm) recruited to saplings (DBH of 2.5–12.69 cm) and sapling diameter growth irrespective of species’ shade tolerance over a 15-year period after treatment. However, high-intensity partial harvesting increased sapling mortality during the initial 5 years since harvesting, whereas low-intensity partial harvesting (5%–40% of basal area removal) maintained the natural regeneration dynamics (growth, recruitment, and mortality) of unharvested stands. We found that harvesting intensity, basal area, and seedling density by shade-tolerance group before harvesting are more important attributes than species’ shade tolerance for determining the responses of natural regeneration to partial harvesting. The greater importance of preharvest stand attributes on postharvest regeneration may suggest an integrated overstory and understory manipulation approach for attaining the desired regeneration composition.
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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".