Stakeholder‐driven management strategies for recovering large herbivores
Bibliographic record
Abstract
Abstract In modern landscapes, the sustainable coexistence of humans and wildlife depends on involving stakeholders in the development and implementation of management strategies. This is particularly important for species like the European bison ( Bison bonasus ) and Eurasian moose ( Alces alces ), which are reoccupying regions between Germany and Poland after a prolonged absence. The return of these species generates mixed emotions, as interactions with these species are associated with both costs and benefits to people. Addressing the apparent unpreparedness in managing these trade‐offs, we implemented a digital participatory impact assessment in two steps. First, we engaged bison and moose experts to develop management scenarios and assessment criteria. Then, in a subsequent virtual workshop, stakeholders evaluated four scenarios along economic, social, and ecological dimensions. Quantitative and qualitative analyses revealed divergent perspectives and priorities, yet consensus emerged on the necessary future steps: formulating a comprehensive management strategy with guidelines and protocols for managing specific conflict scenarios, such as the incursion of large herbivores onto highways. Our approach underscores the importance of early stakeholder engagement in fostering a more equitable and sustainable management of human‐wildlife interactions. Moreover, demonstrating the feasibility of remote stakeholder involvement, our study presents a robust model for enhancing coexistence, adaptable even where in‐person meetings are challenging.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".