Applying environmental DNA metabarcoding to calculate an index of biotic integrity for freshwater fish

preprint OA: closed
📄 Open PDF Full text JSON View at publisher
Full text 2,018 characters · extracted from oa-doi-fallback · click to expand
Abstract Multi-metric indices like the Index of Biotic Integrity (IBI) are important biomonitoring tools for Clean Water Act compliance in the United States (US) but have also been implemented worldwide across many taxonomic groups. Environmental DNA (eDNA) metabarcoding could complement IBIs by increasing detection sensitivity for rare taxa while also reducing monitoring costs. To date, no studies have examined the efficacy of using eDNA metabarcoding to calculate IBIs in the US. Here, we used eDNA metabarcoding to calculate a fish-based IBI for streams and rivers of the Tennessee River Basin in northern Alabama, US. We collected water samples from 50 stream and river sites across a gradient of land use intensity, extracted eDNA from these samples, and sequenced the eDNA using vertebrate-specific primers. We compared our eDNA-IBI to a previous fish-IBI implemented by the state of Alabama using conventional sampling, as well as predicted biological condition of these streams from the US Environmental Protection Agency (EPA) based on a benthic macroinvertebrate multi-metric index (BMMI). We found a highly significant, positive relationship between the eDNA-IBI and fish-IBI for the same streams but a weaker, non-significant positive relationship between the eDNA-IBI and BMMI. Notably, the former was recovered despite eDNA-IBI sampling being conducted nine years after the conventional fish sampling at slightly different locations, the fish-IBI having used greater sampling effort throughout the year (including spring and autumn rather than only summer sampling), and a lack of reference DNA sequences that prevented eDNA detection for some species detected by conventional fish sampling. Accordingly, our study provides a baseline for how an eDNA-IBI may work relative to multi-metric indices calculated from conventional sampling, which can be improved through future directions identified and discussed in our paper. Competing Interest Statement The authors have declared no competing interest.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00