An experimental study of how variation in deer density affects vegetation and songbird assemblages of recently harvested boreal forests
Bibliographic record
Abstract
Intense browsing by abundant large herbivores can threaten the ecological integrity of ecosystems by inducing modifications in the structure and composition of vegetation that trigger trophic cascades affecting plant and animal communities. We investigated the relationships between density of white-tailed deer ( Odocoileus virginianus (Zimmermann, 1780)), forest succession after clear-cut, and songbird communities on Anticosti Island, Quebec, Canada. We hypothesized that lower deer densities would alter the trajectory of forest succession after clear-cutting and lead to a rapid recovery of habitat attributes favorable to songbirds associated with a dense complex shrub layer. Six years after establishing a controlled browsing experiment (0, 7.5, 15, and >27 deer·km–2) in recent clearcuts, reducing deer densities ≤7.5 deer·km–2initiated the restoration of balsam fir ( Abies balsamea (L.) Mill.) forests and increased the regeneration of paper birch ( Betula papyrifera Marshall). Increasing birch ground cover from 10% to 20% increased songbird total abundance, species richness, and diversity by 17%, 39%, and 31%, respectively. Alder Flycatcher (Empidonax alnorum Brewster, 1895) was only present at ≤7.5 deer·km–2and strongly associated with birch regeneration. The regeneration of browse-resistant plants such as white spruce ( Picea glauca (Moench) Voss) in some areas at high deer density favored the maintenance of many shrub-dependent songbirds but also species usually associated with forest canopy. Active management of deer populations in Canadian harvested boreal forests will mitigate losses in vegetation and songbirds caused by over-browsing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".