Complex interactions of deer herbivory, soil chemistry, and competing vegetation explain oak–hickory forest tree regeneration in central Pennsylvania, USA
Bibliographic record
Abstract
The root causes of forest tree regeneration failure are difficult to resolve, although numerous studies show ungulate herbivory, soil conditions, and competition from undesirable vegetation as likely contributors. To better understand the relative importance of each issue, we conducted a 7-year manipulative experiment to assess the interactive effects of white-tailed deer ( Odocoileus virginianus) herbivory, soil acidity, and competing vegetation on tree regeneration in oak–hickory forests of central Pennsylvania, USA. Outcomes depended on initial tree seedling abundance, and all three factors had significant interactions. At low initial seedling abundance, fencing resulted in the greatest increase, but all treatments had a positive effect on seedling growth and abundance. At higher initial seedling abundance, abundance failed to recover 7 years after herbicide treatment and soil pH was an important predictor. When soil pH was >4.6 from lime application, seedling growth and abundance in unfenced controls with high initial abundance was comparable to the fenced-only treatment. Competing vegetation, assumed to be a symptom of excessive, long-term deer herbivory, does not seem to be the primary factor limiting tree regeneration in our study area. Ameliorating acid deposition warrants greater consideration as a management action because it could provide long-lasting benefits compared to short-term fence installations.
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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) | 0.000 | 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".