Data from: DNA metabarcoding for high-throughput monitoring of estuarine macrobenthic communities

Morphology-based profiling of benthic communities has been extensively applied to aquatic ecosystems' health assessment. However, it remains a low-throughput, and sometimes ambiguous, procedure. Despite DNA metabarcoding has been applied to marine benthos, a comprehensive approach providing species-level identifications for estuarine macrobenthos is still lacking. Here we report a combination of experimental and field studies to assess the aptitude of COI metabarcoding to provide robust species-level identifications for high-throughput monitoring of estuarine macrobenthos. To investigate the ability of metabarcoding to detect all species present in bulk DNA extracts, we contrived three phylogenetically diverse communities, and applied four different primer pairs to generate PCR products within the COI barcode region. Between 78-83% of the species in the contrived communities were recovered through HTS. Subsequently, we compared morphology and metabarcoding-based approaches to determine the species composition from four distinct estuarine sites. Our results indicate that species richness would be considerably underestimated if only morphological methods were used: globally 27 species identified through morphology versus 61 detected by metabarcoding. Although further refinement is required to improve efficiency and output of this approach, here we show the great aptitude of COI metabarcoding to provide high quality and auditable species identifications in estuarine macrobenthos monitoring.

基于形态学的底栖生物群落特征分析已被广泛应用于水生生态系统健康评估工作中。但该方法仍存在通量较低、且部分情况下结果存在歧义的局限。尽管DNA元条形码（DNA metabarcoding）技术已应用于海洋底栖生物研究，但目前仍缺乏能够为河口大型底栖生物提供物种级鉴定的完整方案。本研究通过实验与野外调查相结合的方式，旨在评估COI元条形码（COI metabarcoding）技术能否为河口大型底栖生物的高通量监测提供可靠的物种级鉴定结果。为探究元条形码技术对混合DNA提取物中所有物种的检出能力，我们构建了3个系统发育多样性各异的人工群落，并采用4对不同引物在COI条形码区域内扩增得到PCR产物。通过高通量测序（High-throughput sequencing, HTS）共检出人工群落中78%~83%的物种。随后，我们针对4个不同河口站点的样品，对比了形态学与元条形码两种分析方法的物种组成鉴定效果。研究结果显示，若仅采用形态学方法，物种丰富度将被显著低估：形态学方法仅鉴定出27个物种，而元条形码技术则检出61个物种。尽管仍需对该技术进一步优化以提升其效率与产出，但本研究已证实，COI元条形码技术可为河口大型底栖生物监测提供高质量且可溯源的物种鉴定结果。