# Residual eDNA in eRNA Extracts Skews eRNA‐Based Biodiversity Assessment: Call for Optimised DNase Treatment

## TL;DR

Residual DNA in environmental RNA samples can falsely inflate biodiversity estimates, and better DNase treatment is needed to improve accuracy.

## Contribution

Demonstrates that residual eDNA in eRNA extracts leads to false positives and recommends optimized DNase treatment protocols.

## Key findings

- Untreated eRNA samples showed over 25% more taxa per site compared to DNase-treated samples.
- Residual eDNA caused over 10-fold increases in some taxon abundances.
- Community composition shifted significantly between treated and untreated samples due to residual eDNA.

## Abstract

Environmental RNA (eRNA) metabarcoding has rapidly emerged as a powerful tool for assessing contemporary biodiversity patterns across diverse ecosystems. However, the potential for false positive detections caused by co‐extracted environmental DNA (eDNA) remains unquantified. Distinguishing true signals from false positives caused by residual eDNA is a technical challenge in eRNA‐based metabarcoding. To address this issue, we employed a freshwater river receiving treated effluent from a wastewater treatment plant as a model system. In such settings, eDNA in the treated effluent can lead to the detection of non‐local species (e.g., marine taxa). Treated effluent typically contains minimal or no eRNA, making it well‐suited for evaluating the influence of eDNA carryover. By comparing DNase‐treated and untreated eRNA samples, we assessed the impact of residual eDNA on fish species richness and community composition. Our results showed that omitting DNase treatment significantly inflated taxonomic richness, with untreated samples detecting a conservative estimate of over 25% more taxa per site. Fold‐change analysis revealed that residual eDNA inflated taxon abundances in both high‐ and low‐abundance taxa, with some showing over 10‐fold increases. Community composition analyses revealed clear clustering between treated and untreated samples, highlighting substantial shifts driven by residual eDNA. These findings demonstrate that co‐extracted eDNA can severely distort eRNA‐based biodiversity estimates, leading to false positives and misrepresented contemporary community profiles. We recommend further evaluation of DNase treatment parameters, including enzyme concentration, incubation time and treatment times, and the adoption of optimised protocols to standardise and improve the accuracy of eRNA‐based biodiversity monitoring.