Data from: Taxonomy-free molecular diatom index for high-throughput eDNA biomonitoring

Current biodiversity assessment and biomonitoring are largely based on the morphological identification of selected bioindicator taxa. Recently, several attempts have been made to use eDNA metabarcoding as an alternative tool. However, until now, most applied metabarcoding studies have been based on the taxonomic assignment of sequences that provides reference to morphospecies ecology. Usually, only a small portion of metabarcoding data can be used due to a limited reference database and a lack of phylogenetic resolution. Here, we investigate the possibility to overcome these limitations using a taxonomy-free approach that allows the computing of a molecular index directly from eDNA data without any reference to morphotaxonomy. As a case study, we use the benthic diatoms index, commonly used for monitoring the biological quality of rivers and streams. We analysed 87 epilithic samples from Swiss rivers, the ecological status of which was established based on the microscopic identification of diatom species. We compared the diatom index derived from eDNA data obtained with or without taxonomic assignment. Our taxonomy-free approach yields promising results by providing a correct assessment for 77% of examined sites. The main advantage of this method is that almost 95% of OTUs could be used for index calculation, compared to 35% in the case of the taxonomic assignment approach. Its main limitations are under-sampling and the need to calibrate the index based on the microscopic assessment of diatoms communities. However, once calibrated, the taxonomy-free molecular index can be easily standardized and applied in routine biomonitoring, as a complementary tool allowing fast and cost-effective assessment of the biological quality of watercourses.

当前生物多样性评估与生物监测工作，主要依托选定生物指示类群的形态学鉴定手段开展。近年来，已有多项研究尝试将环境DNA元条形码（eDNA metabarcoding）作为替代监测工具。然而截至目前，绝大多数应用元条形码技术的研究均基于序列的分类学赋值，该方法需参照形态物种的生态学特征。受限于参考数据库规模不足与系统发育分辨率欠缺，通常仅有极小比例的元条形码数据可被有效利用。本研究旨在探索采用无分类学方法突破上述局限的可行路径：该方法可直接从环境DNA数据中计算得到分子指数，无需参照形态分类学体系。本研究以常用于监测河流与溪流生物质量的底栖硅藻指数为案例载体，分析了采自瑞士河流的87份附石样本——这些样本的生态状态此前已通过硅藻物种的显微镜鉴定得以确定，并对比了基于经分类学赋值与未经分类学赋值的环境DNA数据所得到的硅藻指数结果。我们的无分类学方法表现出良好的应用前景：对77%的受试位点可给出准确的生态评估结果。该方法的核心优势在于，近95%的操作分类单元（Operational Taxonomic Units，OTUs）可用于指数计算，而采用分类学赋值方法时这一比例仅为35%。其主要局限则为采样量不足，以及需基于硅藻群落的显微镜评估结果对指数进行校准。不过，完成校准后，该无分类学分子指数可轻松实现标准化，并作为辅助工具应用于常规生物监测，从而实现快速且低成本的水道生物质量评估。