Long-term (17-year) dynamics of herbaceous plant communities after shelterwood regeneration harvests in southern Appalachian cove- and upland hardwood forests
Bibliographic record
Abstract
The southern Appalachians are a “hotspot” of plant diversity. Herbaceous communities are especially rich in mesic cove hardwood forests, compared to drier upland hardwood forests. We evaluated changes in forest structure and herbaceous plant communities over 17 years in mature cove- (CHM) and upland hardwood (UHM) forests, and young 2-age cove- (CHSW) and upland hardwood (UHSW) stands created by shelterwood-with-reserves regeneration harvests (SW). Structure of mature forests was relatively static. In contrast, reduced canopy cover after harvests initiated rapid increases in small tree stem density and blackberry ( Rubus) cover, followed by dense shade as young trees gained height. We identified 201 herbaceous species including 156 forbs. Species richness was about double in CHM and CHSW than in UHM and UHSW; composition changed little over time within treatments, even as forest structure changed in SW. Among the 79 herbaceous species analyzed, relative abundance of 26 showed a response; most were more abundant in CHM, CHSW, or both compared to UHM, UHSW, or both. Our results indicated that shelterwood harvests had a neutral or positive effect on herbaceous plant richness, diversity, and abundance of most species, and suggested that environmental gradients associated with forest type influenced herbaceous communities much more than SW alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".