Less is more: vegetation changes coincide with white‐tailed deer suppression over thirty years
Bibliographic record
Abstract
Abstract Although ecological impacts of overabundant white‐tailed deer ( Odocoileus virginianus ) are well documented in eastern North America, few studies have evaluated the long‐term effects of adaptive deer population suppression after a period of overabundance. We examined vegetation community changes over a period of 30 years (1992–2021) on the Long Point Peninsula, Ontario, Canada following a >85% reduction of a previously overabundant white‐tailed deer population. We documented a significant increase in species diversity and shifts in the species composition of understory plants and woody vegetation. We then evaluated several hypotheses to explain these patterns. Our results provide support for the all‐you‐can‐browse hypothesis, in which the abundance of woody stems above the browse layer did not increase within the first 3 years of sampling but, consistent within an expected period of recruitment, increased by >1,500% from 1995–2021. We also found support for both the lawn maintenance hypothesis, with a significant decline in the proportional abundance of non‐preferred species relative to preferred species, and for the seed bank hypothesis, with native species accounting for nearly 80% of new species observed over the sampling period. We conclude that the effective, long‐term management and continued suppression of an previously overabundant white‐tailed deer population can lead to increased vegetation community heterogeneity and diversity, which is likely one of the most important steps for the regeneration of woody stems and native vegetation communities.
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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 | 0.001 |
| 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) | 0.001 | 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".