Care, conflict, and coexistence: Human–wildlife relations in community forests
Bibliographic record
Abstract
Abstract Human–wildlife conflict (HWC) presents a persistent challenge for global biodiversity conservation. Yet, focusing on conflict alone may obscure the complex drivers of positive and negative interactions between people and wildlife coinhabiting the same geographies. In India's Uttarakhand Himalayan region, van panchayat (VP) community forests support agro‐pastoralist livelihoods and forest protection. While the governance and livelihood dimensions of the VP are well documented, their engagement with wildlife is sparsely investigated, despite that community forests are important spaces of human–wildlife interaction in shared landscapes. Enabling community forests to contribute effectively to wildlife management requires understanding what local factors drive stewardship while reducing conflict. Informed by interviews conducted in 2019–2020 and household surveys collected in 2021 in 15 villages in Pithoragarh District, Uttarakhand, we explore the nature of human–wildlife relations in VPs. We report on qualitative and quantitative analyses to consider community forest users' perceptions of living with wildlife—mediated by cultural norms, livelihood demands, and everyday encounters—and investigate beliefs about (1) lethal control as a response to conflict and (2) responsibilities for managing and protecting wildlife. Our findings indicate high prevalence of HWC and associated hardships (mentioned by 71% of survey participants), alongside high expressed ethics of care, tolerance, and responsibility for wild animals (60%). Most participants rejected killing wildlife in circumstances of conflict as acceptable based on moral prohibitions and the availability of alternative options while adopting significant responsibility for their protection. Characterizing community forests as important sites of interaction and coexistence, residents identified community‐led forest conservation as a primary strategy for mitigating HWC. These multifaceted human–wildlife relationships, shaped by encounters in a shared landscape, inform communities' decisions and coping strategies for coexisting with their wild neighbours. Approaches to mitigating conflict that prioritize separating people from wildlife and emphasize the state's responsibilities for wildlife management may undermine communities' roles as conservation actors. Conversely, legal recognition and support for the role of community forests in wildlife management could enhance the legitimacy and effectiveness of management decisions. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".