Geostatistical models using remotely-sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania.
Geostatistical models using remotely-sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania.
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DOI：
10.1111/1365-2664.13091
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发表时间：
2018-07
期刊：
影响因子：
--
通讯作者：
Stanton MC
中科院分区：
文献类型：
--
作者：
Lord JS;Torr SJ;Auty HK;Brock PM;Byamungu M;Hargrove JW;Morrison LJ;Mramba F;Vale GA;Stanton MC
监测丰度对于病媒管理至关重要，但通常只能在一小部分管理区域实现。对于病媒控制计划，通常在当地规模（10 平方公里）内进行采样来估计丰度，而干预措施通常会扩展到 100 平方公里。地统计模型已用于在可用数据的点之间进行插值，但这仍然需要在整个感兴趣区域进行昂贵的采样。相反，我们使用地统计模型来预测坦桑尼亚塞伦盖蒂采采蝇（人类和动物非洲锥虫载体）丰度的局部尺度空间变化，超出了模型所拟合的数据的空间范围。我们在塞伦盖蒂国家公园 (SNP) 内 10 公里范围内以及延伸至人类和牲畜居住区的四个样带上对斯氏舌藻 (Glossina swynnertoni) 和 pallidipes 进行了采样。我们将地质统计模型与 SNP 内部 10 公里以上的数据进行拟合，以生成整个区域（包括未受保护区域）的丰度地图。在 SNP 内，茂密林地中每个陷阱每天捕获的 G. pallidipes 平均数量为 166 (± 24 SE)，而草地为 3 (±1)。 Glossina swynnertoni 更均匀，平均值分别为 15 (±3) 和 15 (±8)。一般来说，模型预测从保护区到未保护区的丰度下降，这与人为植被变化有关，这一点在实地调查中得到了证实。 合成与应用。我们的方法使病媒控制管理者能够识别预计采采蝇丰度相对较高的地点，从而设计和实施改进的监测策略。在东部和南部非洲，锥虫病与荒野地区有关。我们的研究确定了塞伦盖蒂国家公园外农业区可以维持采采蝇种群的植被。我们的方法将协助各国识别、监测，并在必要时控制锥虫病疫源地中的采采蝇。这对于采采蝇有特定的应用，但该方法也可以用于其他病原体的载体。我们的方法使病媒控制管理者能够识别预计采采蝇丰度相对较高的地点，从而设计和实施改进的监测策略。在东部和南部非洲，锥虫病与荒野地区有关。我们的研究确定了塞伦盖蒂国家公园外农业区可以维持采采蝇种群的植被。我们的方法将协助各国识别、监测，并在必要时控制锥虫病疫源地中的采采蝇。这对于采采蝇有特定的应用，但该方法也可以用于其他病原体的载体。
Monitoring abundance is essential for vector management, but it is often only possible in a fraction of managed areas. For vector control programmes, sampling to estimate abundance is usually carried out at a local‐scale (10s km2), while interventions often extend across 100s km2. Geostatistical models have been used to interpolate between points where data are available, but this still requires costly sampling across the entire area of interest. Instead, we used geostatistical models to predict local‐scale spatial variation in the abundance of tsetse—vectors of human and animal African trypanosomes—beyond the spatial extent of data to which models were fitted, in Serengeti, Tanzania. We sampled Glossina swynnertoni and Glossina pallidipes >10 km inside the Serengeti National Park (SNP) and along four transects extending into areas where humans and livestock live. We fitted geostatistical models to data >10 km inside the SNP to produce maps of abundance for the entire region, including unprotected areas. Inside the SNP, the mean number of G. pallidipes caught per trap per day in dense woodland was 166 (± 24 SE), compared to 3 (±1) in grassland. Glossina swynnertoni was more homogenous with respective means of 15 (±3) and 15 (±8). In general, models predicted a decline in abundance from protected to unprotected areas, related to anthropogenic changes to vegetation, which was confirmed during field survey. Synthesis and applications. Our approach allows vector control managers to identify sites predicted to have relatively high tsetse abundance, and therefore to design and implement improved surveillance strategies. In East and Southern Africa, trypanosomiasis is associated with wilderness areas. Our study identified pockets of vegetation which could sustain tsetse populations in farming areas outside the Serengeti National Park. Our method will assist countries in identifying, monitoring and, if necessary, controlling tsetse in trypanosomiasis foci. This has specific application to tsetse, but the approach could also be developed for vectors of other pathogens. Our approach allows vector control managers to identify sites predicted to have relatively high tsetse abundance, and therefore to design and implement improved surveillance strategies. In East and Southern Africa, trypanosomiasis is associated with wilderness areas. Our study identified pockets of vegetation which could sustain tsetse populations in farming areas outside the Serengeti National Park. Our method will assist countries in identifying, monitoring and, if necessary, controlling tsetse in trypanosomiasis foci. This has specific application to tsetse, but the approach could also be developed for vectors of other pathogens.
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影响因子：
2.6
作者：
Ducheyne, E.;Mweempwa, C.;De Pus, C.;Vernieuwe, H.;De Deken, R.;Hendrickx, G.;Van den Bossche, P.
通讯作者：
Van den Bossche, P.
影响因子：
3.2
作者：
Cecchi, Giuliano;Paone, Massimo;Mattioli, Raffaele C.
通讯作者：
Mattioli, Raffaele C.
影响因子：
16.8
作者：
Hassell, James M.;Begon, Michael;Ward, Melissa J.;Fevre, Eric M.
通讯作者：
Fevre, Eric M.
影响因子：
2.5
作者：
Gondwe, Nkwachi;Marcotty, Tanguy;Van den Bossche, Peter
通讯作者：
Van den Bossche, Peter
影响因子：
1.9
作者：
Hendrickx, G;Napala, A;Slingenbergh, JHW
通讯作者：
Slingenbergh, JHW