Spatio-Temporal Patterns of Livestock Predation by Leopards in Bardia National Park, Nepal
Bibliographic record
Abstract
Human–wildlife conflict is a challenging issue that requires the attention of conservationists worldwide. Habitat fragmentation and encroachment reduce the abundance of prey species, and an increase in the number of predators leads to a higher risk of conflict with large cats such as leopards, jeopardizing conservation efforts. This study explored the spatio-temporal pattern of the human–leopard conflict in Bardia National Park, Nepal, from 2000 to 2020. To analyze the conflict with leopards, we used data (compensation cases filed in the park) from the buffer zone management office, the National Trust for Nature Conservation (NTNC), and the Department of National Park and Wildlife Conservation (DNPWC). Leopard attacks on livestock are increasing exponentially, with 3335 livestock killed in 2652 attacks occurring during the study period. Although livestock depredation by leopards occurred all over the park, the southern cluster has most documented livestock damage (64.01%). The eastern and northern clusters reported fluctuating and dispersed predation events, respectively. Our spatial analysis indicated no effect of topography (slope) on livestock depredation by leopards. We recorded the highest number of leopard attacks and predation during the dry winter season when the nights are longer and livestock remain in their sheds. This carnivore mostly limited its prey to small-sized livestock (95.77%) such as goats, sheep, and pigs, whereas attacks on large-sized (cow and buffalo) livestock were least frequent. Among small-sized livestock, goats are the most predated (66.92%), followed by pigs (20.30%), in all seasons. The escalating human–leopard conflict in BNP is thus a severe threat to conservation efforts as the park has already invested a substantial amount of money (approx. USD 80,000) compensating for livestock lost in leopard attacks over the last two decades. Improving habitat conditions to reduce competition inside the park, developing an insurance scheme for livestock and humans, providing support for upgraded sheds, and the development of practical and feasible strategies that focus on specific animals and clusters of the national park are needed to reduce conflicts to maintain the co-existence between wildlife and human beings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".