Livelihood vulnerability and human wildlife conflict in Nepal’s lowland protected areas
Bibliographic record
Abstract
Human wildlife conflict (HWC) has emerged as a serious challenge, particularly in low-income countries where protected areas and human settlements overlap, affecting both biodiversity and human well-being. Although HWC generally cause economic losses, disrupt food security and exert socio-psychological pressures on local people, it also negatively affects wildlife populations by increasing retaliatory killings and threatening the survival of endangered species. Nevertheless, the ways in which HWC shapes the local livelihoods of already vulnerable communities remain underexplored. To address this gap, we analysed HWC incident data and vulnerability indicators from 92 municipalities surrounding protected areas (PAs) in Nepal’s lowland Tarai region. Using both the Livelihood Vulnerability Index (LVI) and an IPCC-based vulnerability framework, we assessed the extent to which HWC contributes to overall livelihood vulnerability. We found that while municipalities adjacent to PAs indeed experience higher livelihood vulnerability, the direct contribution of HWC—though significant at the local level—is relatively modest compared to socio-economic and environmental drivers. These findings highlight HWC as an integral part of broader socio-ecological processes, closely linked with livelihood insecurity, poverty, limited access to services and climate stresses. Addressing HWC, therefore, requires a holistic, multi-sectoral approach that goes beyond reactive, species-specific mitigation to incorporate livelihood diversification, strengthen social safety nets, improved access to basic services, and climate-resilient development pathways. We recommend that future conservation and adaptation programs should adopt integrated socio-ecological frameworks that prioritize community-based conflict mitigation, improve compensation mechanisms, and align wildlife management with national livelihood resilience goals such as Sustainable Development Goals (SDGs) and global biodiversity conservation targets under the Kunming–Montreal Global Biodiversity Framework (GBF).
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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".