ArticleEnvironmental science and ecotechnology2024
Evaluating eDNA and eRNA metabarcoding for aquatic biodiversity assessment: From bacteria to vertebrates.
Article in Environmental science and ecotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Biomonitoring 3.0: Beyond Taxa Lists to Evidence-Tiered Monitoring of Ecosystem Dynamics and Interactions.Ecology and evolution · 2026Review
- Environmental RNA reflects seasonal changes in the metabolic activity of stream salamanders.Scientific reports · 2026Article
- Residual eDNA in eRNA Extracts Skews eRNA-Based Biodiversity Assessment: Call for Optimised DNase Treatment.Molecular ecology resources · 2026Article
- Article
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Authors and funding
7 authors.
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Abstract
The monitoring and management of aquatic ecosystems depend on precise estimates of biodiversity. Metabarcoding analyses of environmental nucleic acids (eNAs), including environmental DNA (eDNA) and environmental RNA (eRNA), have garnered attention for their cost-effective and non-invasive biomonitoring capabilities. However, the accuracy of biodiversity estimates obtained through eNAs can vary among different organismal groups. Here we evaluate the performance of eDNA and eRNA metabarcoding across nine organismal groups, ranging from bacteria to terrestrial vertebrates, in three cross-sections of the Yangtze River, China. We observe robust complementarity between eDNA and eRNA data. The relative detectability of eNAs was notably influenced by major taxonomic groups and organismal sizes, with eDNA providing more robust signals for larger organisms. Both eDNA and eRNA exhibited similar cross-sectional and longitudinal patterns. However, the detectability of larger organisms declined in eRNA metabarcoding, possibly due to differential RNA release and decay among different organismal groups or sizes. While underscoring the potential of eDNA and eRNA in large river biomonitoring, we emphasize the need for differential interpretation of eDNA versus eRNA data. This highlights the importance of careful method selection and interpretation in biomonitoring studies.
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