Management of agricultural pests requires an understanding of pest species diversity, their interactions with beneficial insects and spatial-temporal patterns of pest abundance. Invasive and agriculturally important insect pests can build up very high populations, especially in cropping landscapes. Traditionally, sampling effort for species identification involves small sample sizes and is labour intensive. Here, we describe a multi-primer high throughput sequencing (HTS) metabarcoding method and associated analytical workflow for a rapid, intensive, high-volume survey of pest species compositions. We demonstrate our method using the taxonomically challenging Bemisia pest cryptic species complex as examples. The whiteflies Bemisia including the ‘tabaci’ species are agriculturally important capable of vectoring diverse plant viruses that cause diseases and crop losses. Our multi-primer metabarcoding HTS amplicon approach simultaneously process high volumes of whitefly individuals, with efficiency to detect rare (i.e., 1%) test-species, while our improved whitefly primers for metabarcoding also detected beneficial hymenopteran parasitoid species from whitefly nymphs. Field-testing our redesigned Bemisia metabarcoding primer sets across the Tanzania, Uganda and Malawi cassava cultivation landscapes, we identified the sub-Saharan Africa 1 Bemisia putative species as the dominant pest species, with other cryptic Bemisia species being detected at various abundances. We also provide evidence that Bemisia species compositions can be affected by host crops and sampling techniques that target either nymphs or adults. Our multi-primer HTS metabarcoding method incorporated two over-lapping amplicons of 472bp and 518bp that spanned the entire 657bp 3’ barcoding region for Bemisia, and is particularly suitable to molecular diagnostic surveys of this highly cryptic insect pest species complex that also typically exhibited high population densities in heavy crop infestation episodes. Our approach can be adopted to understand species biodiversity across landscapes, with broad implications for improving trans-boundary biosecurity preparedness, thus contributing to molecular ecological knowledge and the development of control strategies for high-density, cryptic, pest-species complexes.

中文翻译：

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一种用于全种群物种多样性和组成调查的高通量扩增子测序方法

农业害虫管理需要了解害虫物种多样性、它们与益虫的相互作用以及害虫丰度的时空模式。侵入性和农业上重要的害虫可以建立非常高的种群，特别是在种植景观中。传统上，物种鉴定的抽样工作涉及小样本且劳动密集型。在这里，我们描述了一种多引物高通量测序 (HTS) 元条形码方法和相关的分析工作流程，用于对害虫物种组成进行快速、密集、高容量的调查。我们使用在分类学上具有挑战性的Bemisia害虫隐性物种复合体作为示例来演示我们的方法。粉虱Bemisia包括'tabaci'物种在农业上具有重要意义，能够传播导致疾病和作物损失的多种植物病毒。我们的多引物宏条形码 HTS 扩增子方法同时处理大量粉虱个体，有效检测稀有（即 1%）测试物种，而我们改进的用于宏条形码的粉虱引物还检测到来自粉虱若虫的有益膜翅目寄生物种。在坦桑尼亚、乌干达和马拉维木薯种植景观中对我们重新设计的Bemisia 元条形码引物组进行现场测试，我们确定了撒哈拉以南非洲 1 Bemisia假定物种为主要害虫物种，并在不同丰度下检测到其他神秘的Bemisia物种。我们还提供证据表明Bemisia物种组成可能会受到寄主作物和针对若虫或成虫的采样技术的影响。我们的多引物HTS metabarcoding方法并入两个472bp和518bp，横跨整个657bp 3'条形编码区域过度研磨扩增子烟，并特别适合于这种高隐蔽昆虫害虫物种的分子诊断调查复杂的，通常也表现出高的重度作物侵染事件中的人口密度。我们的方法可用于了解跨景观的物种生物多样性，对改善跨境生物安全准备具有广泛的影响，从而有助于分子生态知识和高密度、隐蔽、害虫物种复合体的控制策略的发展。