This evaluation reveals optimized primer lengths improve biodiversity recovery and reproducibility in eRNA metabarcoding, suggesting standardized methods for assessments.
Environmental RNA (eRNA)‐based techniques have rapidly emerged as powerful tools for biodiversity assessment across a range of theoretical and applied disciplines. Reverse transcription, the process of converting eRNA into complementary DNA (cDNA), is a crucial step in eRNA‐based assessments. However, primer length optimization for reverse transcription is often overlooked. Here we evaluated how varying primer lengths affected two key parameters—biodiversity recovery and reproducibility—across the two best‐performing reverse transcription strategies: random priming and oligo(dT) priming for eukaryotic eRNA‐metabarcoding. Using fish eRNA collected from a coastal zone, we conducted technical replicates to assess the performance of primer lengths ranging from 4‐mer to 30‐mer for both strategies. Our results showed that primer length significantly affected biodiversity recovery, reproducibility, and community structure of replicates. Specifically, shorter primers, particularly N 4 and N 5 , in random priming and medium‐length primers (T 15 ‐ 24 ), particularly T 20 , in oligo(dT) priming demonstrated superior performance, especially in detecting biodiversity in the rare biosphere. A comparative analysis of random and oligo(dT) primers highlighted the advantage of shorter random primers in improving biodiversity detection sensitivity and consistency. To explicitly reduce false negatives arising from reliance on a single reverse transcription strategy, we recommend the combined use of short random primers (e.g., N 4 and N 5 ) alongside medium‐length primers (e.g., T 20 ) to enhance comprehensive biodiversity recovery. Our findings provide valuable insights for improving the accuracy and reliability of eRNA‐metabarcoding in biodiversity assessments, thus facilitating the development of standardized methods to enable cross‐laboratory comparisons and ensure consistent, robust outcomes in large‐scale meta‐analyses.
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