Moose Browsing Tends Spruce Plantations More Efficiently Than a Single Mechanical Release
Bibliographic record
Abstract
Forest vegetation management can improve planted seedling survival and growth and is thus widely used in plantation silviculture. In some jurisdictions, mechanical release using brushsaws has replaced the traditional use of chemical herbicides for forest vegetation management purposes. However, its associated costs and the increasing difficulty of finding qualified labor represent a challenge. The browsing of competition by large herbivores may represent an alternative to mechanical release when planted seedlings are resistant to browsing. Here, we compare the efficacy of moose browsing relative to mechanical release in controlling competing vegetation and in promoting white spruce growth in plantations. In a high moose density region, we used an experimental design consisting of four pairs of moose exclosures and unfenced plots; fifty percent of both the access-restricted and unrestricted study areas received a mechanical release treatment. Moose browsing was more efficient than mechanical release in diminishing the sapling density and basal area of competing species. Mechanical release only reduced the sapling density of taller competitors (height > 201 cm), whereas browsing reduced the sapling densities of competitors across a greater size range (height > 130 cm). These effects of moose browsing on competition translated into a greater positive effect of moose browsing on the basal area of planted spruces. We attribute the higher effectiveness of moose browsing relative to mechanical release to its chronic nature. Moose browsed continuously throughout the year and for multiple years, whereas mechanical release was applied only one time between the second and fourth years after planting. Our results suggest that pairing wildlife management and silviculture decisions could be in the best interest of both the hunting and forestry industries in regions where plantations are frequent and use browse-resistant crop trees. Favouring browsers in controlling the density of competing species could increase the hunting experience and income, while providing an effective, cost-free, and socially acceptable forest vegetation management service.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.001 |
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 teacher head, 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".