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首页> 外文期刊>Journal of Zoology >Spatial partial identity model reveals low densities of leopard and spotted hyaena in a miombo woodland
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Spatial partial identity model reveals low densities of leopard and spotted hyaena in a miombo woodland

机译:空间部分身份模型揭示了Miombo林地中的豹纹和斑点揭发的低密度

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Decline in global carnivore populations has led to increased demand for assessment of carnivore densities in understudied habitats. Spatial capture-recapture (SCR) is used increasingly to estimate species densities, where individuals are often identified from their unique pelage patterns. However, uncertainty in bilateral individual identification can lead to the omission of capture data and reduce the precision of results. The recent development of the two-flank spatial partial identity model (SPIM) offers a cost-effective approach, which can reduce uncertainty in individual identity assignment and provide robust density estimates. We conducted camera trap surveys annually between 2016 and 2018 in Kasungu National Park, Malawi, a primary miombo woodland and a habitat lacking baseline data on carnivore densities. We usedSPIMto estimate density for leopard (Panthera pardus) and spotted hyaena (Crocuta crocuta) and compared estimates with conventional SCR methods. Density estimates were low across survey years, when compared to estimates from sub-Saharan Africa, for both leopard (1.9 +/- 0.19sdadults/100 km(2)) and spotted hyaena (1.15 +/- 0.42sdadults/100 km(2)). Estimates fromSPIMimproved precision compared with analytical alternatives. Lion (Panthera leo) and wild dog (Lycaon pictus) were absent from the 2016 survey, but lone dispersers were recorded in 2017 and 2018, and both species appear limited to transient individuals from within the wider transfrontier conservation area. Low densities may reflect low carrying capacity in miombo woodlands or be a result of reduced prey availability from intensive poaching. We provide the first leopard density estimates from Malawi and a miombo woodland habitat, whilst demonstrating thatSPIMis beneficial for density estimation in surveys where only one camera trap per location is deployed. The low density of large carnivores requires urgent management to reduce the loss of the carnivore guild in Kasungu National Park and across the wider transfrontier landscape.
机译:全球食肉动物数量的下降导致对未研究栖息地食肉动物密度评估的需求增加。空间捕获-再捕获(SCR)被越来越多地用于估计物种密度,个体通常根据其独特的毛皮模式进行识别。然而,双边个体识别中的不确定性会导致捕获数据的遗漏,并降低结果的精度。最近发展的双翼空间部分身份模型(SPIM)提供了一种经济高效的方法,可以减少个人身份分配中的不确定性,并提供稳健的密度估计。2016年至2018年间,我们每年在马拉维卡桑古国家公园(Kasungu National Park,马拉维)进行相机陷阱调查,这是一片主要的米翁博林地,也是一个缺乏食肉动物密度基线数据的栖息地。我们使用DSPIM估算豹子(Panthera pardus)和斑点鬣狗(Crocuta Crocuta)的密度,并将估算值与传统的SCR方法进行比较。与撒哈拉以南非洲的估计值相比,调查年份的豹子(1.9+/-0.19sdadults/100km(2))和斑点鬣狗(1.15+/-0.42sdadults/100km(2))的密度估计值较低。与分析替代方案相比,SPIM的估计提高了精度。2016年的调查中没有狮子(Panthera leo)和野狗(Lycaon pictus),但2017年和2018年记录到了单独的散布者,而且这两个物种似乎仅限于更广泛的跨国界保护区内的短暂个体。低密度可能反映了米奥姆博林地的承载能力较低,或者是密集偷猎导致猎物可用性降低的结果。我们提供了马拉维和miombo林地栖息地的第一批豹子密度估计值,同时证明SPIMIS在每个位置仅部署一个摄像头的调查中有利于密度估计。大型食肉动物的低密度需要紧急管理,以减少卡松古国家公园和更广阔的跨国界景观中食肉动物公会的损失。

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