Understory plant communities fail to recover species diversity after excluding deer for nearly 20 years
Bibliographic record
Abstract
White-tailed deer ( Odocoileus virginianus) have been overabundant in eastern North America for more than five decades, resulting in depauperate understories and ricocheting effects on higher trophic levels. Even after deer populations are reduced, understory plant communities may fail to recover for an unknown length of time due to persistent legacy effects. We surveyed understory plant communities in six deer exclosures and paired reference plots in northwestern Pennsylvania to determine the degree to which 19 years of deer exclusion was sufficient for recovery of species richness, diversity, percent cover, and understory structural complexity. We observed a 2.3-fold increase in tree cover and a 60% reduction in fern cover in the ground layer, as well as a 114-fold increase in foliage density between 80 and 200 cm above ground level, in exclosures compared to reference plots. However, the exclosures did not permanently support higher overall percent cover, species richness, or diversity in the ground layer, nor did we detect any meaningful divergence in community composition between exclosures and reference plots. We conclude that 19 years of release from chronic over-browsing are sufficient to restore understory structural complexity, but recovery of diversity in the ground layer will require more time or direct intervention.
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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".