Influence of invasive shrubs and deer browsing on regeneration in temperate deciduous forests
Bibliographic record
Abstract
While both chronic white-tailed deer (Odocoileus virginianus Zimmermann) overbrowsing and invasive shrubs have been linked to native regeneration failure in temperate hardwood forests, little is known of their relative importance and possible synergistic effects. Therefore, we established three study areas in Connecticut to examine the interaction of three levels of invasive shrub control and absence or presence of deer herbivory on forest regeneration over 9 years. Five observations suggest that obtaining forest regeneration requires a comprehensive management strategy. First, competitive interference by invasive shrubs in intact thickets continued to impede forest regeneration, especially taller seedlings, after deer were excluded. Second, density of small seedlings increased following removal of the competitive interference by invasive shrubs. Third, deer browsing depressed growth of small seedlings into taller height classes. Fourth, preferential browsing reduced species richness, especially in taller seedling classes, by favoring growth of low palatability species such as beech. Lastly, if a disturbance (e.g., cutting) does not kill the root system of the invasive shrubs, the window of forest regeneration recruitment may be narrow because invasive shrubs can quickly recover. In stands with both established shrub thickets and high deer densities, invasive shrub control and multiyear reduction of deer densities are both recommended to obtain adequate forest regeneration.
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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 | 0.001 | 0.001 |
| 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".