Department of Life Sciences, Natural History Museum, London, UK.
Department of Life Sciences, Imperial College London, Ascot, UK.
Mol Ecol Resour. 2020 Jan;20(1):40-53. doi: 10.1111/1755-0998.13056. Epub 2019 Oct 8.
Improved taxonomic methods are needed to quantify declining populations of insect pollinators. This study devises a high-throughput DNA barcoding protocol for a regional fauna (United Kingdom) of bees (Apiformes), consisting of reference library construction, a proof-of-concept monitoring scheme, and the deep barcoding of individuals to assess potential artefacts and organismal associations. A reference database of cytochrome oxidase c subunit 1 (cox1) sequences including 92.4% of 278 bee species known from the UK showed high congruence with morphological taxon concepts, but molecular species delimitations resulted in numerous split and (fewer) lumped entities within the Linnaean species. Double tagging permitted deep Illumina sequencing of 762 separate individuals of bees from a UK-wide survey. Extracting the target barcode from the amplicon mix required a new protocol employing read abundance and phylogenetic position, which revealed 180 molecular entities of Apiformes identifiable to species. An additional 72 entities were ascribed to nuclear pseudogenes based on patterns of read abundance and phylogenetic relatedness to the reference set. Clustering of reads revealed a range of secondary operational taxonomic units (OTUs) in almost all samples, resulting from traces of insect species caught in the same traps, organisms associated with the insects including a known mite parasite of bees, and the common detection of human DNA, besides evidence for low-level cross-contamination in pan traps and laboratory procedures. Custom scripts were generated to conduct critical steps of the bioinformatics protocol. The resources built here will greatly aid DNA-based monitoring to inform management and conservation policies for the protection of pollinators.
需要改进分类方法来量化昆虫传粉媒介数量的下降。本研究设计了一种针对英国蜜蜂(膜翅目)区域动物群的高通量 DNA 条形码协议,包括参考文库构建、概念验证监测计划以及个体的深度条形码评估,以评估潜在的人为因素和生物关联。包含英国已知的 278 种蜜蜂的细胞色素氧化酶 c 亚基 1(cox1)序列参考数据库与形态分类概念高度一致,但分子物种界限导致在林奈物种内出现了许多分裂和(较少)聚集体。双标记允许对来自英国范围内调查的 762 只蜜蜂个体进行深度 Illumina 测序。从混合扩增物中提取目标条形码需要采用新的协议,该协议采用读长丰度和系统发育位置,从而揭示了 180 种可识别为 Apiformes 物种的分子实体。另外,基于读长丰度和与参考集的系统发育关系,将 72 个实体归因于核假基因。读长聚类揭示了几乎所有样本中的一系列次要分类操作单元(OTUs),这些 OTUs 是由同一陷阱中捕获的昆虫物种的痕迹、与昆虫有关的生物(包括一种已知的蜜蜂寄生螨)以及人类 DNA 的常见检测结果引起的,此外还证明了陷阱和实验室程序中存在低水平的交叉污染。生成了自定义脚本来执行生物信息学协议的关键步骤。这里构建的资源将极大地帮助基于 DNA 的监测,为保护传粉媒介的管理和保护政策提供信息。