Density‐related effects of deer browsing on the regeneration dynamics of boreal forests
Bibliographic record
Abstract
Summary The density of large herbivores is a major driver of forest ecosystem structure and function in conjunction with episodic disturbances, especially in forests with a regeneration strategy based on shade‐tolerant seedlings capable of re‐establishing canopy dominance (advance regeneration). Yet, uncertainty about the relationships between forest regeneration, herbivore density and other disturbances makes it difficult to set population goals. Using an innovative controlled browsing experiment, we investigated the relationships between the regeneration dynamics of balsam firAbies balsamea, the density of white‐tailed deerOdocoileus virginianusand timber harvesting. We hypothesize that advance tree regeneration either: (i) recovers approximately linearly as deer density is reduced; (ii) recovers exponentially; or (iii) does not recover because factors other than browsing control advance regeneration. We tested these alternatives through manipulation of deer densities (0, 7·5, 15 deer km−2andin situlocal densities) and forest cover (clearcut and uncut forest). Balsam fir seedling mortality decreased exponentially with decreasing deer density in clearcut and approximately linearly in uncut forest. Independently of deer density, the recruitment of seedlings in clearcut dropped from 56 ± 5% to 7 ± 1% within 3 years. Seedling growth increased exponentially with decreasing deer density in clearcut whereas no height growth was observed in uncut forest. Overall, the abundance of fir saplings recovered exponentially in clearcut but remained low and independent of deer density in uncut forest. The abundance of sprucePiceaspp. saplings was unrelated to deer density and increased with time. Synthesis and applications. Forest disturbance from selective browsing at high deer densities over an extended period of time leads to recruitment failure following a canopy disturbance such as a clearcut. Indirect competitive advantage given to species resistant to browsing can shift forest composition. Nonlinear relationships between fir regeneration and deer densities imply that the level of culling required to reach herbivore densities compatible with natural regeneration of native forest is larger than expected if tree regeneration was proportional to deer density. In the boreal forest of Anticosti Island, local densities < 15 deer km−2achieved within 3 years following clearcut are compatible with the maintenance of native forest.
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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 | 0.000 | 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".