Understory plant responses to uneven-aged forestry alternatives in northern hardwood–conifer forests
Bibliographic record
Abstract
The understory layer encompasses the majority of plant species diversity in forested ecosystems and may be sensitive to timber harvest disturbance. We hypothesize that (i) uneven-aged, low-intensity silvicultural systems can maintain understory plant diversity and support late-successional species following harvest disturbance; (ii) retaining and enhancing stand structural complexity can increase understory plant diversity in northern hardwood–conifer forests; and (iii) plant responses are influenced by interactions among canopy structure, soils, and climate processes. Experimental treatments include single-tree selection and group selection, both modified to increase structural retention, and a third technique designed to promote late-successional forest structure and function, structural complexity enhancement. Four replications of each treatment were applied to 2 ha units in Vermont and New York, USA. Understory vegetation was monitored 2 years pre- and 4 years post-treatment. Results show that over time, understory responses were strongly affected by overstory treatment and less influenced by soils and drought. All treatments succeeded at maintaining overall composition and diversity. However, late-successional diversity increased significantly in structural complexity enhancement units compared with group selection units. These results indicate that while conventional uneven-aged systems can maintain understory plant diversity, variations that retain or enhance structural complexity may be more effective at retaining late-successional species.
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 | 0.000 | 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".