Design, Validation, and Implementation Considerations for Species‐Targeted Environmental RNA Assays to Detect Presence and Physiological State

ABSTRACT Environmental RNA (eRNA) is emerging as a promising tool for detecting species presence and/or physiological condition in biodiversity monitoring, ecotoxicology, and conservation. However, compared to environmental DNA (eDNA), optimization of eRNA analysis protocols is relatively undeveloped, and consensus among researchers is limited. While eRNA assay design and implementation have many commonalities with eDNA assays, there are additional key considerations needed for specific and sensitive eRNA assays while mitigating false positives and negatives. We critically examine these in the current perspective for targeted real‐time polymerase chain reaction (qPCR). Selection of RNA targets should be guided by the specific objectives of the eRNA assay and should account for genomic DNA (gDNA) removal and RNA‐to‐cDNA conversion before qPCR analysis. This includes consideration of RNA type and origin; if the measured RNA is acting as a physiological indicator reflecting organismal health, stress, or demographic patterns; if the RNA is acting as an indicator of presence; or if the RNA is a normalizer for the physiological indicators. Primer–probe design should, when possible, span exon–exon junctions to minimize gDNA amplification. If that is not possible, minus reverse transcriptase (RT–) reactions are required for every sample. Rigorous assay validation begins with in silico screens against target and non‐target taxa transcript sequences, followed by in vitro testing using cDNA generated from RNA and gDNA extracts from voucher specimens. Benchmarking sensitivity performance through measures such as limit of detection (LOD), limit of quantification (LOQ), and amplification efficiency strengthens assay confidence. Field validation of eRNA assays using environmental samples where target species are present and absent and/or in a desired physiological state (e.g., exposed to contaminants) helps estimate false‐positive and false‐negative rates. We critically examine current eRNA practices and provide guidance and suggestions for robust eRNA assay design and implementation.