Identifying strategies to manage boreal forests: simulating moose and timber management scenarios at a landscape scale in the face of changing environmental conditions
Bibliographic record
Abstract
Abstract There are ongoing debates among different stakeholders about which forest and ungulate management strategies will sustain high levels of timber and animal harvest and maintain important ecosystem functions under climate change. Ungulate-forest interactions are complex, including periods where forest regeneration is sensitive to browsing pressure, making it difficult to predict the consequences of a given strategy over time. To aid decision-making, we simulated the impacts of moose browsing on forest succession under 18 different combinations of moose (Alces alces) harvest rate levels and forest management scenarios in a boreal forest landscape in southern Sweden given projected changes in forest growth due to climate change. We found that the current management practices are important for sustaining a moose-forest system. Increasing moose harvest rates led to slightly smaller moose populations, larger estimates of landscape carrying capacity, and less biomass removal of Scots pine (Pinus sylvestris), a commercially valuable species. However, minor changes in the moose harvest were hardly affecting timber production. Increasing the timber harvest rotation time led to the highest estimates of Scots pine biomass, while thinning younger cohorts lead to the highest estimates of Norway spruce (Picea abies) biomass. These changes came without much effect to moose population dynamics. However, the increased broadleaf production scenario had a very large positive effect on total aboveground live biomass of deciduous species and on landscape carrying capacity and moose density. This scenario subsequently resulted in the greatest estimates of biomass removal of Scots pine, highlighting the tradeoffs associated with increased moose production.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".