Data from: Metabarcoding of freshwater invertebrates to detect the effects of a pesticide spill

Biomonitoring underpins the environmental assessment of freshwater ecosystems and guides management and conservation. Current methodology for surveys of (macro)invertebrates uses coarse taxonomic identification where species-level resolution is difficult to obtain. Next-generation sequencing of entire assemblages (metabarcoding) provides a new approach for species detection, but requires further validation. We used metabarcoding of invertebrate assemblages with two fragments of the cox1 "barcode" and partial nuclear ribosomal (SSU) genes, to assess the effects of a pesticide spill in the River Kennet (Southern England). Operational Taxonomic Unit (OTU) recovery was tested under72 parameters (read denoising, filtering, pair merging and clustering). Similar taxonomic profiles were obtained under a broad range of parameters. The SSU marker recovered Platyhelminthes and Nematoda, missed by cox1,while Rotifera were only amplified with cox1. A reference set was created from all available barcode entries for Arthropoda in the BOLD database and clustered into OTUs. The River Kennet metabarcoding produced matches to 207 of these reference OTUs, five times the number of species recognised with morphological monitoring. The increase was due to: greater taxonomic resolution (e.g. splitting a single morphotaxon ‘Chironomidae’ into 55 named OTUs); splitting of binomial species names into multiple molecular OTUs in species complexes; and the use of a filtration-flotation protocol for extraction of minute specimens (meiofauna). Community analyses revealed strong differences between "impacted" vs. "control" samples, detectable with each gene marker, for each major taxonomic group, and for meio- and macro-faunal samples separately. Thus, highly resolved taxonomic data can be extracted at a fraction of the time and cost of traditional non-molecular methods, opening new avenues for freshwater invertebrate biodiversity monitoring and molecular ecology.生物监测（Biomonitoring）是淡水生态系统环境评估的核心支撑，亦为环境管理与保护工作提供科学指引。当前针对（大型）无脊椎动物的调查方法多采用粗分类学鉴定手段，难以获取物种级分辨率的分类数据。对全部群落进行下一代测序的宏条形码（metabarcoding）技术为物种检测提供了全新途径，但仍需进一步验证。本研究采用cox1“条形码”基因的两个片段以及核核糖体小亚基（SSU）基因的部分序列，对无脊椎动物群落进行宏条形码测序，以评估农药泄漏对英格兰南部肯尼特河的影响。本研究在72种参数设置（包括读段去噪、序列过滤、配对拼接与聚类）下对操作分类单元（OTU）的回收效果进行了测试。在宽泛的参数取值范围内，均可获得相似的分类学特征图谱。SSU标记可检测到cox1标记遗漏的扁形动物门（Platyhelminthes）与线虫动物门（Nematoda），而轮虫动物门（Rotifera）仅能通过cox1标记实现扩增。研究人员从生命条形码数据系统（BOLD）中调取节肢动物门（Arthropoda）的全部可用条形码条目，构建参考数据集并聚类为操作分类单元。肯尼特河的宏条形码测序结果可匹配其中207个参考OTU，数量是形态学监测鉴定物种数的5倍。这一数量提升主要源于三方面：更高的分类学分辨率（例如将单一形态分类单元‘摇蚊科（Chironomidae）’划分为55个带有命名的OTU）；在物种复合群中，将双名法命名的物种拆分为多个分子OTU；以及采用过滤-浮选实验方案提取微型标本（小型底栖生物（meiofauna））。群落分析结果显示，‘受影响’与‘对照’样本间存在显著差异，该差异可通过每种基因标记、针对各主要分类类群，以及分别针对小型底栖生物与大型无脊椎动物样本得以检测。因此，相较于传统非分子监测方法，本研究仅需极小一部分时间与成本即可获取高分辨率的分类学数据，为淡水无脊椎动物生物多样性监测与分子生态学研究开辟了全新路径。