Macroinvertebrate assemblages are the most common bioindicators used for stream biomonitoring, yet the standard approach exhibits several time-consuming steps, including the sorting and identification of organisms based on morphological criteria. In this study, we examined if DNA metabarcoding could be used as an efficient molecular-based alternative to the morphology-based monitoring of streams using macroinvertebrates. We compared results achieved with the standard morphological identification of organisms sampled in 18 sites located on 15 French wadeable streams to results obtained with the DNA metabarcoding identification of sorted bulk material of the same macroinvertebrate samples, using read numbers (expressed as relative frequencies) as a proxy for abundances. In particular, we evaluated how combining and filtering metabarcoding data obtained from three different markers (COI: BF1-BR2, 18S: Euka02 and 16S: Inse01) could improve the efficiency of bioassessment. In total, 140 taxa were identified based on morphological criteria, and 127 were identified based on DNA metabarcoding using the three markers, with an overlap of 99 taxa. The threshold values used for sequence filtering based on the “best identity” criterion and the number of reads had an effect on the assessment efficiency of data obtained with each marker. Compared to single marker results, combining data from different markers allowed us to improve the match between biotic index values obtained with the bulk DNA versus morphology-based approaches. Both approaches assigned the same ecological quality class to a majority (86%) of the site sampling events, highlighting both the efficiency of metabarcoding as a biomonitoring tool but also the need for further research to improve this efficiency.

中文翻译：

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用于评估底栖无脊椎动物河流生态状况的形态学与 DNA 元条形码方法：测试不同的标记组合和数据过滤策略

大型无脊椎动物组合是用于河流生物监测的最常见的生物指标，但标准方法展示了几个耗时的步骤，包括基于形态学标准对生物进行分类和识别。在这项研究中，我们检查了 DNA 元条形码是否可以作为一种有效的基于分子的替代方法来替代使用大型无脊椎动物的基于形态的流监测。我们将在位于 15 条法国可涉水流的 18 个地点采样的生物体的标准形态学鉴定结果与相同大型无脊椎动物样本的分选散装材料的 DNA 元条形码鉴定获得的结果进行比较，使用读数（表示为相对频率）作为丰度的代名词。特别是，我们评估了组合和过滤从三种不同标记（COI：BF1-BR2、18S：Euka02 和 16S：Inse01）获得的元条形码数据如何提高生物评估的效率。总共根据形态学标准确定了 140 个分类群，使用三个标记基于 DNA 元条形码确定了 127 个分类群，重叠了 99 个分类群。用于基于“最佳同一性”标准和读数数量的序列过滤的阈值对每个标记获得的数据的评估效率有影响。与单一标记的结果相比，结合来自不同标记的数据使我们能够改善使用散装 DNA 与基于形态学的方法获得的生物指数值之间的匹配。两种方法都将相同的生态质量等级分配给大多数 (86%) 的现场采样事件，