Population status and density estimate of Leopard Panthera pardus fusca in dry thorn forests of southern India

Q4 Environmental Science
Krishnakumar Nagarajan, D. Ramesh, D. Boominathan, Sanket Bhale, P. Chanchani, R. Balasundaram
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Abstract

The study aims to assess the population status and density of the Leopard (Panthera pardus fusca) in the Erode Forest Division, Tamil Nadu. The Erode Forest division has numerous villages within its boundaries and is exposed to associated biotic pressures. In order to improve the management practices of the wildlife in the area and to enhance its conservation, it is relevant to assess its status. In 2018 and 2020, we estimated the population density of the leopard in the entire forest division using 2 km2 sample grids and the camera trapping method. In each grid, we deployed a pair of camera traps, the cumulative number of trap-nights being 22983 (10732 trap nights in 2018 and 12251 in 2020). We analysed the obtained data using spatially explicit capture-recapture models (SECR). The surveys yielded 198 images of the leopard in 2018 and 272 images in 2020, of which 48 and 44 leopard individuals were identified in the respective years based on the rosette pattern. The density estimates were 5.16 (SE = ±0.89) and 4.00 (SE ± 0.72) individuals/100 km2 in 2018 and 2020, respectively. We found that in some regions overlapping with the human-use area, leopard densities were high, indicating that the species successfully exploits areas near human habitation and highlighting its high potential for interaction with humans. This baseline estimate and insights will help prioritize management actions, strengthen large mammal conservation beyond the boundaries of protected areas (PA), and plan human-wildlife conflict mitigation measures to enable the persistence of large carnivores in multi-use forests.
印度南部干刺林中豹的种群现状和密度估计
该研究旨在评估泰米尔纳德邦侵蚀森林区豹(Panthera pardus fusca)的种群状况和密度。侵蚀森林区在其边界内有许多村庄,并面临相关的生物压力。为了改善该地区野生动物的管理措施,加强对其的保护,有必要对其现状进行评估。在2018年和2020年,我们采用2 km2的样本网格和相机诱捕法估算了整个森林分区的豹种群密度。在每个网格中,我们部署了一对相机陷阱,陷阱夜的累计数量为22983(2018年为10732个陷阱夜,2020年为12251个陷阱夜)。我们使用空间显式捕获-再捕获模型(SECR)分析了获得的数据。这些调查在2018年和2020年分别获得了198张和272张豹子的图像,其中48张和44张是根据玫瑰图案在各自的年份识别出来的。2018年和2020年的密度预测值分别为5.16 (SE =±0.89)和4.00 (SE±0.72)只/100 km2。我们发现,在一些与人类使用区域重叠的区域,豹子的密度很高,这表明该物种成功地利用了人类居住区域附近的区域,并突出了其与人类互动的高潜力。这一基线估计和见解将有助于确定管理行动的优先顺序,加强保护区边界以外的大型哺乳动物保护,并规划人类与野生动物冲突缓解措施,使大型食肉动物能够在多用途森林中持续生存。
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来源期刊
Zoology and Ecology
Zoology and Ecology Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
1.00
自引率
0.00%
发文量
1
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