1. Bee populations and other pollinators face multiple, synergistically acting threats, which have led to population declines, loss of local species richness and pollination services, and extinctions. However, our understanding of the degree, distribution and causes of declines is patchy, in part due to inadequate monitoring systems, with the challenge of taxonomic identification posing a major logistical barrier. Pollinator conservation would benefit from a high‐throughput identification pipeline.
2. We show that the metagenomic mining and resequencing of mitochondrial genomes (mitogenomics) can be applied successfully to bulk samples of wild bees. We assembled the mitogenomes of 48 UK bee species and then shotgun‐sequenced total DNA extracted from 204 whole bees that had been collected in 10 pan‐trap samples from farms in England and been identified morphologically to 33 species. Each sample data set was mapped against the 48 reference mitogenomes.
3. The morphological and mitogenomic data sets were highly congruent. Out of 63 total species detections in the morphological data set, the mitogenomic data set made 59 correct detections (93·7% detection rate) and detected six more species (putative false positives). Direct inspection and an analysis with species‐specific primers suggested that these putative false positives were most likely due to incorrect morphological IDs. Read frequency significantly predicted species biomass frequency (R² = 24·9%). Species lists, biomass frequencies, extrapolated species richness and community structure were recovered with less error than in a metabarcoding pipeline.
4. Mitogenomics automates the onerous task of taxonomic identification, even for cryptic species, allowing the tracking of changes in species richness and distributions. A mitogenomic pipeline should thus be able to contain costs, maintain consistently high‐quality data over long time series, incorporate retrospective taxonomic revisions and provide an auditable evidence trail. Mitogenomic data sets also provide estimates of species counts within samples and thus have potential for tracking population trajectories.

中文翻译：

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通过有丝分裂基因组学对野生蜜蜂多样性和丰度进行高通量监测。

1. 
   蜜蜂种群和其他授粉媒介面临多重协同作用的威胁，导致种群数量下降、当地物种丰富度和授粉服务丧失以及灭绝。然而，我们对下降的程度、分布和原因的了解并不完整，部分原因是监测系统不足，而分类识别的挑战构成了主要的后勤障碍。传粉媒介保护将受益于高通量识别管道。
2. 
   我们表明，线粒体基因组的宏基因组挖掘和重测序（丝裂基因组学）可以成功应用于野生蜜蜂的大量样本。我们组装了 48 个英国蜜蜂物种的线粒体基因组，然后对从英格兰农场的 10 个泛陷阱样本中收集的 204 只蜜蜂提取的总 DNA 进行了鸟枪法测序，并从形态学上鉴定出 33 个物种。每个样本数据集均针对 48 个参考线粒体基因组进行映射。
3. 
   形态学和线粒体基因组数据集高度一致。在形态学数据集中检测到的 63 个物种中，线粒体基因组数据集做出了 59 个正确检测（检测率为 93·7%），并检测到了另外 6 个物种（假定的误报）。直接检查和物种特异性引物分析表明，这些假定的假阳性很可能是由于形态学 ID 不正确造成的。读取频率显着预测物种生物量频率（ R ² = 24·9%）。物种列表、生物量频率、推断的物种丰富度和群落结构的恢复误差比元条形码管道中的误差更小。
4. 
   丝裂基因组学使繁重的分类识别任务自动化，甚至对于神秘物种来说也是如此，从而可以跟踪物种丰富度和分布的变化。因此，线粒体基因组管道应该能够控制成本，在长时间序列中保持一致的高质量数据，纳入回顾性分类学修订并提供可审计的证据线索。线粒体基因组数据集还提供样本内物种计数的估计，因此具有跟踪种群轨迹的潜力。