The legacy of deer overabundance: long-term delays in herbaceous understory recovery
Bibliographic record
Abstract
Decades of white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)) overpopulation have dramatically homogenized forests across much of the eastern United States, creating depauperate forest understory communities. The rate at which these communities recover once deer browsing has been reduced remains an open question. We evaluate overbrowsing legacy effects by examining how forest herbaceous layers respond in terms of biodiversity, density, and community composition over 11 years using exclosures and control plots within a mature beech–maple forest. Although little recovery occurred in the first 5 years, total density and preferred browse density rebounded substantially during the final years of the study. Although community composition began to diverge between exclosure and control plots after 5 years, diversity failed to recover even after 11 years of excluding browsers. Our findings show that vulnerable species can increase after excluding browsers but only if those species were initially present. Biodiversity recovery may be extremely slow because preferred browse species have been nearly extirpated from many forests and thus are unable to recruit into refugia. We empirically demonstrate the extent of the ghost of herbivory past or legacy effect of browsing, i.e., the substantial time delay between herbivore abatement and community response after decades of high deer densities.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".