Balancing conflicting goals in ungulate management and forestry in the light of climate change in hemiboreal and boreal forests: insights from Europe and Northern America
Bibliographic record
Abstract
Forests produce a vast diversity of ecosystem services. To safeguard management goals such as timber production, forest management needs to consider risks for damage caused by different agents, such as ungulates. The selective foraging behaviour of ungulates can limit forest regeneration, generating conflicts with forestry objectives and challenging both forest and ungulate management. In hemiboreal and boreal forests, both timber production and ungulate numbers have increased considerably during the past 50 years. Climate change will affect forestry and ungulates, possibly generating novel ungulate–forest interactions (e.g., changed forest systems, new herbivore assemblages). To support a framework for future management of ungulate–forest systems in hemiboreal and boreal forests in Europe and North America in the light of climate change, we provide an overview of the literature on current management strategies that seek to balance the conflict between timber production and maintaining ungulate densities at a level that satisfies various stakeholder groups. Derived from current literature, we suggest that future mitigations enhancing forest resilience and simultaneously reducing browsing damage should include the following overarching strategies: (1) Both ungulate and forest management require a large-scale and context-specific planning to ensure a suitable forage landscape for ungulates diluting browsing pressure on economic valuable trees. This might be particularly needed in places where much of the forest is privately owned. (2) Anthropogenic ungulate–forest systems require continuous regulation of ungulate numbers (i.e., by predation, hunting, or a combination of both) to enable “windows of opportunities” for forest regeneration and to counteract positive feedback loops of forage-enriching activities for ungulates by forestry. (3) Given increasing system complexity, adaptive ecosystem-based management plans for ungulates should consider multispecies approaches to match management with other interests in multifunctional forest landscapes. Given the large diversity across northern temperate and boreal ungulate–forest systems (e.g., centralized versus de-centralized management, access to land, game meat trade, ecological and social complexity), there are differences in preconditions across the Northern hemisphere to balance timber production and ungulate densities.
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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 | 0.000 |
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 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".