Modelling the distribution of domestic ducks in Monsoon Asia.
Modelling the distribution of domestic ducks in Monsoon Asia.
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DOI：
10.1016/j.agee.2011.04.013
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发表时间：
2011-05-01
影响因子：
6.6
通讯作者：
Gilbert, Marius
中科院分区：
文献类型：
--
作者：
Van Boeckel, Thomas P.;Prosser, Diann;Franceschini, Gianluca;Biradar, Chandra;Wint, William;Robinson, Tim;Gilbert, Marius
多项地理空间研究表明，家鸭被认为是高致病性禽流感 (HPAI) 的重要宿主，这些研究已将家鸭确定为与疾病存在相关的重要风险因素。尽管它们在高致病性禽流感流行病学中很重要，但人们对它们在季风亚洲的大规模分布却知之甚少。在本研究中，我们创建了亚洲家鸭普查数据的空间数据库，并用它来训练空间分辨率为1公里的家鸭分布统计分布模型。该方法基于联合国粮食及农业组织用于生成世界网格牲畜（GLW）数据库的建模框架，并依赖于家鸭密度和一组农业生态解释变量之间的分层回归模型。我们评估了分层分析和组合预测的不同方法，以优化预测的拟合优度。我们发现，使用基于畜牧生产系统的分层，可以以合理的精度预测家鸭密度（平均 RMSE 以及对数转换的观测密度和预测密度之间的相关系数分别为 0.58 和 0.80）。我们测试了使用泰国和越南鸭子分布的人工退化数据作为训练数据，并将建模输出与原始高分辨率数据进行比较。这表明，至少对于这两个国家来说，这些方法可用于准确分解省级（行政一级）统计数据，以提供高分辨率模型分布。
Domestic ducks are considered to be an important reservoir of highly pathogenic avian influenza (HPAI), as shown by a number of geospatial studies in which they have been identified as a significant risk factor associated with disease presence. Despite their importance in HPAI epidemiology, their large-scale distribution in monsoon Asia is poorly understood. In this study, we created a spatial database of domestic duck census data in Asia and used it to train statistical distribution models for domestic duck distributions at a spatial resolution of 1 km. The method was based on a modelling framework used by the Food and Agriculture Organisation to produce the Gridded Livestock of the World (GLW) database, and relies on stratified regression models between domestic duck densities and a set of agro-ecological explanatory variables. We evaluated different ways of stratifying the analysis and of combining the prediction to optimize the goodness of fit of the predictions. We found that domestic duck density could be predicted with reasonable accuracy (mean RMSE and correlation coefficient between log-transformed observed and predicted densities being 0.58 and 0.80, respectively), using a stratification based on livestock production systems. We tested the use of artificially degraded data on duck distributions in Thailand and Vietnam as training data, and compared the modelled outputs with the original high-resolution data. This showed, for these two countries at least, that these approaches could be used to accurately disaggregate provincial level (administrative level 1) statistical data to provide high resolution model distributions.
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影响因子：
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作者：
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