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首页> 外文期刊>The Journal of Applied Ecology >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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Geostatistical models using remotely-sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania

机译:地质统计模型使用遥感数据预测稀树大草原采采蝇在下降接口之间的受保护的和不受保护的地区在坦桑尼亚的塞伦盖蒂,

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摘要

1. 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 km(2)), 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.
机译:1. 管理,但它通常是唯一的可能部分区域管理。计划,抽样估计丰富通常在一个局部范围(10公里(2)),虽然干预经常扩展到100平方公里。地质统计模型被用来插入点之间数据的地方可用,但这仍然需要昂贵抽样整个感兴趣的领域。相反,我们利用地质统计学模型预测局部范围空间变化丰富tsetse-vectors人类和动物的非洲人trypanosomes-beyond空间数据的程度模型拟合,在坦桑尼亚的塞伦盖蒂。

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