An Emerging Issue of Human-Leopard Conflict in the Human-Dominated Landscape of Mid-Hills: A Case Study from Tanahun District of Nepal
Bibliographic record
Abstract
Information on the spatial and temporal patterns of losses caused by leopard (Panthera pardus) in terms of human attacks and livestock depredation in the human-dominated landscape of the mid-hills of Nepal is essential in formulating and implementing effective mitigation measures. This study aimed to assess the spatial and temporal patterns of leopard attacks on humans and livestock and the economic losses incurred by livestock depredation between 2015 and 2019 in the Bhanu municipality of Tanahun District. We adopted a household survey (N = 110), key informant (N = 10), and focus group discussion (N = 4) for this study. We purposively chose two conflict wards: 2 and 4, based on the severity of the attacks by the leopard. Within each ward, we selected the households randomly and conducted a semistructured questionnaire survey in September 2020. A total of 8 incidents of human attacks and 142 incidents of livestock depredation were recorded, with six human casualties in ward 2 and 1.45 incidents of livestock depredation per household in ward 4. The maximum attack was observed during 2019 both on humans (n = 6) and livestock (n = 67). Leopards mostly attacked children below 9 years, living within 200 m of the nearest forest edge, with the highest attack during the autumn months (62.5%). During the five years, leopard killed goats that represent 83.1% of total livestock loss categories. A significant difference was found in the frequency of attacks on livestock over the years (χ2 = 87.60, df = 4, and <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>P</a:mi> <a:mo>≤</a:mo> <a:mn>0.01</a:mn> </a:math> ), months (χ2 = 16.53, df = 11, and <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>P</c:mi> <c:mo>=</c:mo> <c:mn>0.12</c:mn> </c:math> ), and time of day (χ2 = 48.47, df = 3, and <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>P</e:mi> <e:mo>≤</e:mo> <e:mn>0.001</e:mn> </e:math> ) with the highest attack during the year 2019 (47.18%), July (14.08%), and daytime (42.96%), respectively. Households living nearer to the forest edge (<200 m) lost more livestock (72.54%). The monetary value of a total of 8142 USD (74 USD per household) was lost due to livestock depredation, with major monetary loss at a distance >400 m from the forest edge. We suggest adopting mitigation measures like predator-proof livestock corals while stall feeding and strengthening conscientious livestock herding practices during grazing, encouraging livestock insurance schemes, educating local communities about leopard behavior, caring for and protecting children intensively in the leopard attack sites, improving the prey base in the wild, and timely management of man-eater leopard to reduce the conflict in the study area and the country.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| 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 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".