Data-Driven Insights into Human–Gaur Conflicts: Spatiotemporal Trends and Risk Mapping Across Tamil Nadu, India DOI
Thekke Thumbath Shameer,

Priyambada Routray,

A. Udhayan

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Май 15, 2025

Abstract Human–wildlife conflict (HWC) is one of the most pressing conservation challenges, particularly in shared landscapes where humans and wildlife are adversely affected. Despite various mitigation efforts globally, frequency HWC continues to rise. Among conflict-prone species, Indian gaur (Bos gaurus) has increasingly been involved such interactions across southern India. To support development long-term strategies for Human–Gaur Conflict (HGC), we conducted a comprehensive study using data collected from compensation records 48 forest divisions Tamil Nadu between 2016 2024. We analyzed spatial temporal trends, predicted risk zones ensemble modeling, identified key drivers influencing HGC. Our findings reveal that intensity was highest Nilgiri division, followed by Dharmapuri Kodaikanal. Crop damage predominant type, human injuries, with incident peaks observed during December March. Elevation emerged as influential predictor models, clear positive correlation showing increased rising elevation. The model also 18,335 km² state falls under zones, accounting approximately 14.1% Nadu's total geographical area. This provides critical insights into ecology HGC highlights utility predictive modeling identifying high-risk zones. outcomes can inform conservationists managers designing implementing proactive measures, especially areas have high likelihood future conflict.

Язык: Английский

Habitat Mapping of Bos gaurus in Parsa National Park, Nepal: Ensemble Modeling Approach DOI Creative Commons
Anish Dhakal, Dinesh Neupane, Sunjeep Pun

и другие.

Ecology and Evolution, Год журнала: 2025, Номер 15(3)

Опубликована: Март 1, 2025

ABSTRACT Bos gaurus , a globally vulnerable and protected priority species in Nepal, has experienced habitat loss fragmentation, poaching, diseases. Consequently, their population is isolated significantly Parsa National Park Chitwan Nepal. However, distribution even these areas limited to topographical features. This study focuses on suitability modeling of B. (PNP) utilizing the ensemble approach identify key ecogeographical climatic variables influencing suitable estimate around Park, After multicollinearity test, potential were integrated with ground presence points for modeling. The model revealed that distance from waterholes settlements, slope, bioclimatic highly influenced 's suitability. found only 31.29% (285.55 km 2 ) area as PNP. eastern part park (newly extended Halkhoriya Lake) south‐central section show . wildlife‐friendly infrastructure East–West Highway (that fragments park) within can facilitate movement among crucial patches. Future projections under SSP1‐2.6 climate scenario indicate gradual reduction habitat, indicating marginal impact change gaur area. These changes highlight vulnerability risk potentially leading declines. conservation strategies including maintaining water sources, restoring degraded habitats, particularly northern park, enhancing connectivity through wildlife corridors could ensure long‐term survival.

Язык: Английский

Процитировано

1
Data-Driven Insights into Human–Gaur Conflicts: Spatiotemporal Trends and Risk Mapping Across Tamil Nadu, India DOI
Thekke Thumbath Shameer,

Priyambada Routray,

A. Udhayan

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Май 15, 2025

Abstract Human–wildlife conflict (HWC) is one of the most pressing conservation challenges, particularly in shared landscapes where humans and wildlife are adversely affected. Despite various mitigation efforts globally, frequency HWC continues to rise. Among conflict-prone species, Indian gaur (Bos gaurus) has increasingly been involved such interactions across southern India. To support development long-term strategies for Human–Gaur Conflict (HGC), we conducted a comprehensive study using data collected from compensation records 48 forest divisions Tamil Nadu between 2016 2024. We analyzed spatial temporal trends, predicted risk zones ensemble modeling, identified key drivers influencing HGC. Our findings reveal that intensity was highest Nilgiri division, followed by Dharmapuri Kodaikanal. Crop damage predominant type, human injuries, with incident peaks observed during December March. Elevation emerged as influential predictor models, clear positive correlation showing increased rising elevation. The model also 18,335 km² state falls under zones, accounting approximately 14.1% Nadu's total geographical area. This provides critical insights into ecology HGC highlights utility predictive modeling identifying high-risk zones. outcomes can inform conservationists managers designing implementing proactive measures, especially areas have high likelihood future conflict.

Язык: Английский

Процитировано

0