Structuring Effects of Deer in Boreal Forest Ecosystems
Bibliographic record
Abstract
Many deer populations have recently increased worldwide leading to strong direct and indirect ecological and socioeconomical impacts on the composition, dynamic, and functions of forest ecosystems. Deer directly modify the composition and structure of vegetation communities, but they also indirectly affect other species of the ecosystem by modifying the structure of the vegetation. Here we review the results of a research program on overabundant white-tailed deer (Odocoileus virginianus) in the boreal forest of Anticosti Island (Québec, Canada) aimed at identifying deer densities compatible with forest regeneration. Various silvicultural systems and treatments failed to regenerate deer habitat at high deer densities, but planting size-adapted seedlings could be effective at moderate densities. Using a controlled deer density experiment, we found vegetation recovery at deer densities ≤ 15 deer/km2. The same experiment revealed that other groups of organisms such as insects and birds responded favorably to a reduction of deer density. We also found that alternative successional trajectories may occur after a certain period of heavy browsing during early succession. We conclude that one of the most important remaining research gaps is the need to identify habitat-specific threshold densities at which deer impacts occur and then to design effective wildlife and forest management strategies to limit deer impacts and sustain ecosystem integrity.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".