Wildlife Institute of India, Chandrabani, Dehradun, Uttarakhand, India.
PLoS One. 2020 Feb 19;15(2):e0228374. doi: 10.1371/journal.pone.0228374. eCollection 2020.
Asiatic lions (Panthera leo persica) are an icon of conservation success, yet their status is inferred from total counts that cannot account for detection bias and double counts. With an effort of 4,797 km in 725 km2 of western Gir Protected Area, India, we used polygon search based spatially explicit capture recapture framework to estimate lion density. Using vibrissae patterns and permanent body marks we identified 67 lions from 368 lion sightings. We conducted distance sampling on 35 transects with an effort of 101.5 km to estimate spatial prey density using generalized additive modeling (GAM). Subsequently, we modeled lion spatial density with prey, habitat characteristics, anthropogenic factors and distance to baiting sites. Lion density (>1-year-old lions) was estimated at 8.53 (SE 1.05) /100 km2 with lionesses having smaller movement parameter (σ = 2.55 km; SE 0.12) compared to males (σ = 5.32 km; SE 0.33). Detection corrected sex ratio (female:male lions) was 1.14 (SE 0.02). Chital (Axis axis) was the most abundant ungulate with a density of 63.29 (SE 10.14) as determined by conventional distance sampling (CDS) and 58.17 (SE 22.17)/km2 with density surface modeling (DSM), followed by sambar (Rusa unicolor) at 3.84 (SE 1.07) and 4.73 (SE 1.48)/km2 estimated by CDS and DSM respectively. Spatial lion density was best explained by proximity to baiting sites and flat valley habitat but not as much by prey density. We demonstrate a scientifically robust approach to estimate lion abundance, that due to its spatial context, can be useful for management of habitat and human-lion interface. We recommend this method for lion population assessment across their range. High lion densities in western Gir were correlated with baiting. The management practice of attracting lions for tourism can perturb natural lion densities, disrupt behavior, lion social dynamics and have detrimental effects on local prey densities.
亚洲狮（ Panthera leo persica ）是保护成功的象征，但它们的现状是根据无法解释检测偏差和重复计数的总数来推断的。在印度西部吉尔保护区的 725 平方公里范围内进行了 4797 公里的努力，我们使用基于多边形搜索的空间显式捕获再捕获框架来估计狮子的密度。我们使用触须图案和永久身体标记，从 368 次狮子目击事件中识别出 67 只狮子。我们在 35 条截距上进行了距离抽样，使用广义加性模型（GAM）来估计空间猎物密度。随后，我们使用猎物、栖息地特征、人为因素和与诱饵点的距离来模拟狮子的空间密度。狮子的密度（> 1 岁的狮子）估计为 8.53（SE 1.05）/ 100 km 2 ，与雄性相比，母狮的运动参数较小（σ= 2.55 km；SE 0.12），而雄性的运动参数较大（σ= 5.32 km；SE 0.33）。经检测校正的雌雄比（雌狮：雄狮）为 1.14（SE 0.02）。印度黑羚（ Axis axis ）是最丰富的有蹄类动物，其密度为 63.29（SE 10.14），通过传统距离抽样（CDS）确定，密度表面建模（DSM）确定的密度为 58.17（SE 22.17）/ km 2 ，其次是水鹿（ Rusa unicolor ），分别为 CDS 和 DSM 估计的 3.84（SE 1.07）和 4.73（SE 1.48）/ km 2 。狮子的空间密度与诱饵点的接近度和平坦的山谷栖息地关系最密切，但与猎物密度的关系不大。我们展示了一种估计狮子数量的科学可靠方法，由于其空间背景，对于管理栖息地和人与狮子的接口可能很有用。我们建议在其范围内使用这种方法来评估狮子种群。西部吉尔的高狮子密度与诱饵有关。吸引狮子进行旅游的管理实践可能会扰乱自然狮子的密度，破坏行为、狮子的社会动态，并对当地猎物密度产生不利影响。
