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Silwal T, Neupane B, Raut N, Dhami B, Adhikari B, Adhikari A, Paudel A, Kandel SR, Miya MS. Identifying risk zones and landscape features that affect common leopard depredation. PeerJ 2024; 12:e17497. [PMID: 38832039 PMCID: PMC11146323 DOI: 10.7717/peerj.17497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 05/09/2024] [Indexed: 06/05/2024] Open
Abstract
Human-wildlife conflict (HWC) is a pressing issue worldwide but varies by species over time and place. One of the most prevalent forms of HWC in the mid-hills of Nepal is human-common-leopard conflict (HLC). Leopard attacks, especially in forested areas, can severely impact villagers and their livestock. Information on HLC in the Gorkha district was scarce, thus making it an ideal location to identify high-risk zones and landscape variables associated with such events. Registered cases were collected and reviewed from the Division Forest Office (DFO) during 2019-2021. Claims from DFO records were confirmed with herders and villagers via eight focus group discussions. To enhance modeling success, researchers identified a total of 163 leopard attack locations on livestock, ensuring a minimum distance of at least 100 meters between locations. Using maximum entropy (MaxEnt) and considering 13 environmental variables, we mapped common leopard attack risk zones. True Skill Statistics (TSS) and area under receiver-operator curve (AUC) were used to evaluate and validate the Output. Furthermore, 10 replications, 1,000 maximum iterations, and 1000 background points were employed during modeling. The average AUC value for the model, which was 0.726 ± 0.021, revealed good accuracy. The model performed well, as indicated by a TSS value of 0.61 ± 0.03. Of the total research area (27.92 km2), about 74% was designated as a low-risk area, 19% as a medium-risk area, and 7% as a high-risk area. Of the 13 environmental variables, distance to water (25.2%) was the most significant predictor of risk, followed by distance to road (16.2%) and elevation (10.7%). According to response curves, the risk of common leopard is highest in the areas between 1.5 to 2 km distances from the water sources, followed by the closest distance from a road and an elevation of 700 to 800 m. Results suggest that managers and local governments should employ intervention strategies immediately to safeguard rural livelihoods in high-risk areas. Improvements include better design of livestock corrals, insurance, and total compensation of livestock losses. Settlements near roads and water sources should improve the design and construction of pens and cages to prevent livestock loss. More studies on the characteristics of victims are suggested to enhance understanding of common leopard attacks, in addition to landscape variables. Such information can be helpful in formulating the best management practices.
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Affiliation(s)
- Thakur Silwal
- Tribhuvan University, Institute of Forestry, Kathmandu, Nepal
- Tribhuvan University, Institute of Forestry, Pokhara, Nepal
| | - Bijaya Neupane
- Tribhuvan University, Institute of Forestry, Pokhara, Nepal
- Department of Forest Sciences, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland
| | - Nirjala Raut
- Tribhuvan University, Institute of Forestry, Pokhara, Nepal
| | - Bijaya Dhami
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
| | - Binaya Adhikari
- Department of Biology, University of Kentucky, Lexington, KY, United States of America
| | - Amit Adhikari
- Tribhuvan University, Institute of Forestry, Pokhara, Nepal
| | - Aakash Paudel
- Tribhuvan University, Institute of Forestry, Pokhara, Nepal
| | | | - Mahamad Sayab Miya
- Department of Biology, Western Kentucky University, Bowling Green, KY, United States of America
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Dhami B, Chhetri NB, Neupane B, Adhikari B, Bashyal B, Maraseni T, Thapamagar T, Dhakal Y, Tripathi A, Koju NP. Predicting the current habitat refugia of Himalayan Musk deer ( Moschus chrysogaster) across Nepal. Ecol Evol 2024; 14:e10949. [PMID: 38371859 PMCID: PMC10870248 DOI: 10.1002/ece3.10949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/20/2023] [Accepted: 01/02/2024] [Indexed: 02/20/2024] Open
Abstract
Himalayan Musk deer, Moschus chrysogaster is widely distributed but one of the least studied species in Nepal. In this study, we compiled a total of 429 current presence points of direct observation of the species, pellets droppings, and hoofmarks based on field-based surveys during 2018-2021 and periodic data held by the Department of National Park and Wildlife Conservation. We developed the species distribution model using an ensemble modeling approach. We used a combination of bioclimatic, anthropogenic, topographic, and vegetation-related variables to predict the current suitable habitat for Himalayan Musk deer in Nepal. A total of 16 predictor variables were used for habitat suitability modeling after the multicollinearity test. The study shows that the 6973.76 km2 (5%) area of Nepal is highly suitable and 8387.11 km2 (6%) is moderately suitable for HMD. The distribution of HMD shows mainly by precipitation seasonality, precipitation of the warmest quarter, temperature ranges, distance to water bodies, anthropogenic variables, and land use and land cover change (LULC). The probability of occurrence is less in habitats with low forest cover. The response curves indicate that the probability of occurrence of HMD decreases with an increase in precipitation seasonality and remains constant with an increase in precipitation of the warmest quarter. Thus, the fortune of the species distribution will be limited by anthropogenic factors like poaching, hunting, habitat fragmentation and habitat degradation, and long-term forces of climate change.
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Affiliation(s)
- Bijaya Dhami
- Department of Biological SciencesUniversity of AlbertaEdmontonAlbertaCanada
- IUCN/SSC Deer Specialist GroupGlandSwitzerland
| | | | - Bijaya Neupane
- Institute of Forestry, Pokhara CampusTribhuvan UniversityPokharaNepal
- Department of Forest Sciences, Faculty of Agriculture and ForestryUniversity of HelsinkiHelsinkiFinland
| | - Binaya Adhikari
- Department of BiologyUniversity of KentuckyLexingtonKentuckyUSA
| | - Bijay Bashyal
- IUCN/SSC Deer Specialist GroupGlandSwitzerland
- Central Department of Environmental ScienceTribhuvan UniversityKathmanduNepal
| | - Tek Maraseni
- University of Southern QueenslandToowoombaQueenslandAustralia
| | | | | | | | - Narayan Prasad Koju
- Center for Post Graduate Studies, Nepal Engineering CollegePokhara UniversityBhaktapurNepal
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