Successful forest restoration using plantation at high deer density: How neighboring vegetation drives browsing pressure and tree growth
Bibliographic record
Abstract
Browsing can be an environmental stress to forest ecosystems, where the composition and structure of plant communities depend on the balance between ungulate browsing and the ability of plants to tolerate and acclimate to this stressor. Tree planting is a management tool for restoring forest whose integrity has been compromised by the intensity and repetition of browsing. This tool may be insufficient when the stress leading to forest degradation is ongoing. Balsam fir (Abies balsamea (L.) Mill.) plantations are used on Anticosti Island (Quebec, Canada) to restore winter habitat for white-tailed deer (Odocoileus virginianus Zimmermann, 1780). To promote natural regeneration and growth of planted firs that are the staple winter forage of deer, enclosures were temporarily erected and deer densities reduced to decrease browsing pressure. The stress induced by heavy browsing at the time of fence removal and the density of competitors in regenerating cutblocks, however, could compromise fir growth and forest recruitment. Our objective was to identify factors influencing browsing intensity and balsam fir’s growth following fence removal. We aimed to identify factors influencing lateral browsing intensity and number of trunk reiterations on fir at the landscape scale, including distance and size of the surrounding forests, and at the local scale through intraspecific density-dependence and interspecific associative effects. We then sought to identify the stressors most influencing balsam fir growth among browsing variables and a neighborhood crowding index (NCI). We measured browsing intensity and growth of 114 balsam firs 8 years following planting and again 2 and 4 years after dismantling two management enclosures. At the landscape scale, our results showed that when balsam fir was more than 150 m from a forest edge, the probability of browsing was close to zero and consistently a low percentage of shelter area surrounding a focal fir led to a decrease in browsing pressure. At the local scale, intensity of lateral browsing increased with the NCI of white spruce (Picea glauca (Moench) Voss). Fir growth was negatively influenced by the additive effects of browsing and NCI. Finally, our results showed that 19.7% (95 %CI: 4.5%, 42.6%) of the effect of the surrounding vegetation on growth was mediated by browsing. The success of forest ecosystem restoration through planting depends on density of neighboring tree species that can generate environmental stress by influencing growth indirectly through browsing as well as directly through competition for resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".