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Using mock surveillance to quantify pest detectability prior to establishment of exotic leafminers
Crop Protection ( IF 2.8 ) Pub Date : 2021-05-28 , DOI: 10.1016/j.cropro.2021.105713
Elia I. Pirtle , Paul A. Umina , Cindy E. Hauser , James L. Maino

Quantifying detectability of exotic pests in local contexts is crucial for designing efficient and regionally relevant surveillance guidelines. Ironically, for areas most in need of preparedness advice, the study of detectability is hampered by the pest's absence. Here, we present a mock surveillance method whereby pest symptoms are simulated on plants, and the detection probability of visual inspection by practitioners is measured across a range of high priority regions at risk of incursions. Detectability of simulated leaf mining damage was found to be no different to real damage in surveillance trials conducted on the vegetable leafminer, Liriomyza sativae, a recently established pest in Australia. Additional trials confirmed that reducing the natural search speed of survey participants by half increased the odds of detecting a leaf mine by an estimated 3.0 times. However, as movement speed mediates a trade-off between the total area covered (the number of pest encounters) and sensitivity (detection probability for each encounter), we found that a survey effort of 10 s per transect meter enhanced field-level detection of L. sativae at a variety of abundances and available times for surveillance. This study demonstrates that mock surveillance trials, through their use of visual effects to mimic plant pest symptoms, can produce locally tested recommendations to enhance preparedness for exotic pests.



中文翻译:

在建立外来潜叶虫之前,使用模拟监测来量化害虫的可检测性

量化本地环境中外来有害生物的可检测性对于设计有效且与区域相关的监测指南至关重要。具有讽刺意味的是,对于最需要防范建议的地区,可检测性研究因害虫的缺失而受到阻碍。在这里,我们提出了一种模拟监测方法,该方法在植物上模拟害虫症状,并在一系列具有入侵风险的高优先级区域中测量从业者目视检查的检测概率。在对蔬菜潜叶虫 Liriomyza sativae进行的监测试验中,发现模拟潜叶损害的可检测性与实际损害没有区别最近在澳大利亚发现的一种害虫。其他试验证实,将调查参与者的自然搜索速度降低一半会使检测到叶雷的几率增加约 3.0 倍。然而,由于移动速度介导了覆盖的总面积(遇到害虫的数量)和灵敏度(每次遇到的检测概率)之间的权衡,我们发现每样带米 10 秒的调查工作增强了现场水平检测L. sativae在各种丰度和可用时间进行监测。这项研究表明,模拟监测试验通过使用视觉效果来模拟植物害虫症状,可以产生经过当地测试的建议,以加强对外来害虫的防范。

更新日期:2021-06-04
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