Collaborative conservation for snow leopards: Lessons learned from successful community‐based interventions
Bibliographic record
Abstract
Abstract Collaborative conservation interventions based on engagement with local communities are increasingly common, especially for large carnivores that negatively impact people's livelihoods and well‐being. However, evaluating the effectiveness of large‐scale community‐based conservation interventions is rarely done, making it problematic to assess or justify their impact. In our study focused on snow leopards ( Panthera uncia ) in five countries, we show that bespoke and well‐implemented community‐based and conflict management intervention efforts can lead to more sustainable conservation outcomes. Collaborative interventions, spread over about 88,000 km 2 of snow leopard habitat, reduced livestock depredation and disease and associated economic costs. Additionally, they generated conservation‐linked livelihoods and enhanced community decision‐making, leading to more positive behavioral intent toward snow leopards and improved communities’ cooperation, economic security, and confidence. Our results provide lessons learned and recommendations for practitioners and governments to alleviate conflicts and foster coexistence with snow leopards and large carnivores more broadly. These include prioritizing locally led tailored solutions based on the PARTNERS principles, recognizing local community rights in conservation decision‐making, and recognizing the role of social norms in ensuring accountability.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".