Many unreported crop pests and pathogens are probably already present
Many unreported crop pests and pathogens are probably already present
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DOI：
10.1111/gcb.14698
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发表时间：
2019-08-01
影响因子：
11.6
通讯作者：
Gurr, Sarah J.
中科院分区：
文献类型：
--
作者：
Bebber, Daniel P.;Field, Elsa;Gurr, Sarah J.
入侵物种威胁全球生物多样性、粮食安全和生态系统功能。这种入侵给农业带来了挑战，入侵物种对作物造成重大破坏，需要进行重大经济投资才能控制生产损失。病虫害风险分析(PRA)是确定农业生物安全工作优先顺序的关键，但由于对当前作物病虫害和病原体分布的不完全了解而受到阻碍。在这里，我们开发了当前害虫分布的预测模型，并使用国家以下分辨率的新观测数据对这些模型进行了测试。我们应用广义线性模型(GLM)估计了CABI害虫分布数据库中1,739种作物害虫的存在概率。我们检验了对中华人民共和国中国(PRC)100次未观测到的害虫发生的模型预测，并与从中国文献中提取的这些害虫的观测结果进行了对比。到目前为止，关于全球害虫分布的数据库中没有这一资源。最后，我们预测了所有未观察到的有害生物在全球范围内的发生。存在概率随着宿主存在、在邻近区域的存在、人均国内生产总值和全球流行率而增加。出现概率随着距海岸的平均距离和每头害虫的已知寄主数量而降低。该模型能较好地预测我国各省区虫害的发生情况，其ROC曲线下面积(AUC)为0.75~0.76。在中国、印度、巴西南部和前苏联的一些国家，预计会有大量目前未被观察到但可能存在的害虫(这里定义为未报告的害虫，预测的存在概率为>；0.75)。我们表明，GLMS可以在国家以下分辨率下预测假缺失害虫的存在。西方学术界在很大程度上无法接触到中国文献，但其中包含了支持PRA的重要信息。以前的研究通常认为，全球分布数据库中未报告的有害生物代表真正的缺失。我们的分析提供了一种量化假缺席的方法，以改进PRA和物种分布模型。
Invasive species threaten global biodiversity, food security and ecosystem function. Such incursions present challenges to agriculture where invasive species cause significant crop damage and require major economic investment to control production losses. Pest risk analysis (PRA) is key to prioritize agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here, we develop predictive models of current pest distributions and test these models using new observations at subnational resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1,739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People's Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models are good predictors of pest presence in provinces of the PRC, with area under the ROC curve ( AUC) values of 0.75-0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability >0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. We show that GLMs can predict presences of pseudoabsent pests at subnational resolution. The Chinese literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represent a true absence. Our analysis provides a method for quantifying pseudoabsences to enable improved PRA and species distribution modelling.