eDNA-Based Monitoring of Riverine Biodiversity Hotspots

Authors

  • Lukas Rossi Department of Machine Learning, Baltic AI Research University, Tallinn, Estonia Author
  • Oscar Petrov Department of Artificial Intelligence, Swiss Institute of Machine Intelligence, Zurich, Switzerland Author

Keywords:

environmental DNA, eDNA metabarcoding, riverine biodiversity, fish diversity, macroinvertebrates, 12S rRNA, COI barcoding, biodiversity hotspots, Water Framework Directive, electrofishing comparison, conservation monitoring

Abstract

Environmental DNA (eDNA) metabarcoding has emerged as a transformative tool for non-invasive, high-throughput assessment of aquatic biodiversity, offering detection sensitivity superior to conventional electrofishing and netting methods for rare and cryptic taxa. This study applied eDNA metabarcoding targeting the mitochondrial 12S rRNA and COI gene regions to characterise fish and macroinvertebrate diversity across 28 riverine sampling sites spanning six river systems in Estonia, Switzerland, and Spain (n = 336 water samples; 12 samples per site collected monthly across March–August 2021). Sequencing on Illumina MiSeq (2 × 250 bp) generated 48.4 million high-quality reads assigned to 284 fish amplicon sequence variants (ASVs) and 1,642 macroinvertebrate ASVs representing 186 families. eDNA detected significantly more species per site than concurrent electrofishing (mean 28.4 ± 4.2 vs. 18.6 ± 3.8 fish species; paired t-test: t = 8.42, p < 0.001). Eight species of conservation concern undetected by electrofishing were confirmed by eDNA, including Romanogobio uranoscopus and Zingel streber at sites with no prior records. eDNA read abundance correlated significantly with electrofishing catch-per-unit-effort for seven of ten target species (Spearman rs range: 0.64–0.88, all p < 0.01). Biodiversity hotspot sites (top quartile of ASV richness) were characterised by higher dissolved oxygen (mean 9.8 ± 0.6 mg/L), lower turbidity (mean 4.2 ± 1.1 NTU), and greater substrate heterogeneity index (SHI: 0.74 ± 0.08) relative to non-hotspot sites. These results validate eDNA metabarcoding as a cost-effective and sensitive tool for riverine biodiversity surveillance and hotspot delineation under the EU Water Framework Directive.

Author Biographies

  • Lukas Rossi, Department of Machine Learning, Baltic AI Research University, Tallinn, Estonia

    Lukas Rossi,
    Professor, Department of Machine Learning, Baltic AI Research University, Tallinn, Estonia. Email:
    lukas.rossi535@gmail.com | ORCID: 0000-4920-6644-8260-6785

  • Oscar Petrov, Department of Artificial Intelligence, Swiss Institute of Machine Intelligence, Zurich, Switzerland

    Oscar Petrov

    Professor, Department of Artificial Intelligence, Swiss Institute of Machine Intelligence, Zurich, Switzerland. Email:
    oscar.petrov954@gmail.com | ORCID: 0000-5849-9042-6497-1563

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Published

2026-07-28 — Updated on 2022-04-26

How to Cite

eDNA-Based Monitoring of Riverine Biodiversity Hotspots. (2022). International Journal of Animal Biodiversity, Conservation and Systematics ( IJABC), 2(1), 10-18. https://stanfordgroup.org/index.php/IJABC/article/view/220

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