Deer and invasive plants in suburban forests: assessing variation in deer pressure and herbivory
Bibliographic record
Abstract
Fragmented suburban forests of the northeastern US are challenged by abundant white-tailed deer and nonindigenous plant invasions. Deer browsing/grazing pressure varies among sites, potentially affecting herbivory on nonindigenous plants and their invasion success. We aimed to identify a useful deer pressure indicator for suburban forests and then use it to relate deer pressure to grazing on and abundance of two herbaceous invaders, Microstegum vimineum and Alliaria petiolata. We compared three indicators: fecal pellet accumulation rate, deer browse on indigenous woody plants, and indigenous shrub layer cover. The pellet method produced estimates generally far below the region’s known deer density. Browse rates and shrub layer cover were negatively correlated, and correlations of the three indicators with evidence of deer pressure from a subsequent 6.5-year exclosure experiment supported shrub layer cover as the better choice. Using that measure in 10 forests, we detected a weak pattern of more grazed stands under greater deer pressure, but few plants per stand were grazed; any negative influence of deer on these species was limited to individuals, without population effects. Alliaria petiolata abundance was unrelated to deer pressure, but M. vimineum abundance was greater in forests with more deer pressure, suggesting facilitation of its invasion.
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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 | 0.001 | 0.001 |
| 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".