Environmental DNA (eDNA) metabarcoding is raising expectations for biomonitoring of organisms that have hitherto been neglected. To bypass current limitations in taxonomic assignments due to incomplete or erroneous reference databases, taxonomy-free approaches are proposed for biomonitoring at the level of operational taxonomic units (OTUs). This is challenging, because OTUs cannot be annotated and directly compared against classically derived taxonomic data. The application of good stringency treatments to infer the validity of OTUs and clear understanding of the consequences of such treatments is especially relevant for biodiversity assessments. We investigated how common practices of stringency filtering affect eDNA diversity estimates in the statistical framework of Hill numbers. We collected water eDNA samples at 61 sites across a 740-km² river catchment, reflecting a spatially realistic scenario in biomonitoring. After bioinformatic processing of the data, we studied how different stringency treatments affect conclusions with respect to biodiversity at the catchment and site levels. The applied stringency treatments were based on the consistent appearance of OTUs across filter replicates, a relative abundance cut-off and rarefaction. We detected large differences in diversity estimates when accounting for presence/absence only, such that detected diversity at the catchment scale differed by an order of magnitude between the treatments. These differences disappeared when using stringency treatments with increasing weighting of the OTU abundances. Our study demonstrated the usefulness of Hill numbers for biodiversity analyses and comparisons of eDNA data sets that strongly differ in diversity. We recommend best practice for data stringency filtering for biomonitoring using eDNA.

中文翻译：

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使用希尔数进行生物监测中无分类环境 DNA 元条形码的决策和最佳实践

环境 DNA (eDNA) 元条形码正在提高人们对迄今为止被忽视的生物体进行生物监测的期望。为了绕过由于不完整或错误的参考数据库而导致的分类分配的当前限制，提出了在操作分类单元 (OTU) 级别进行生物监测的无分类方法。这是具有挑战性的，因为 OTU 不能被注释，也不能直接与经典派生的分类数据进行比较。应用良好的严格处理来推断 OTU 的有效性并清楚了解此类处理的后果对于生物多样性评估尤其重要。我们研究了严格过滤的常见做法如何影响希尔数统计框架中的 eDNA 多样性估计。我们在 740 公里的 61 个地点收集了水 eDNA 样本²河流集水区，反映了生物监测中的空间现实场景。在对数据进行生物信息处理后，我们研究了不同的严格处理如何影响流域和地点级别的生物多样性结论。应用的严格处理基于过滤器重复中 OTU 的一致外观、相对丰度截止值和稀疏性。我们在仅考虑存在/不存在时检测到多样性估计的巨大差异，因此在流域尺度上检测到的多样性在处理之间存在一个数量级的差异。当使用严格处理并增加 OTU 丰度的权重时，这些差异消失了。我们的研究证明了希尔数对于生物多样性分析和比较差异很大的 eDNA 数据集的有用性。我们推荐使用 eDNA 进行生物监测的数据严格性过滤的最佳实践。