DNA metabarcoding captures different macroinvertebrate biodiversity than morphological identification approaches across a continental scale

Abstract DNA‐based aquatic biomonitoring methods show promise to provide rapid, standardized, and efficient biodiversity assessment to supplement and in some cases replace current morphology‐based approaches that are often less efficient and can produce inconsistent results. Despite this potential, broad‐scale adoption of DNA‐based approaches by end‐users remains limited, and studies on how these two approaches differ in detecting aquatic biodiversity across large spatial scales are lacking. Here, we present a comparison of DNA metabarcoding and morphological identification, leveraging national‐scale, open‐source, ecological datasets from the National Ecological Observatory Network (NEON). Across 24 wadeable streams in North America with 179 paired sample comparisons, we found that DNA metabarcoding detected twice as many unique taxa than morphological identification overall. The two approaches showed poor congruence in detecting the same taxa, averaging 59%, 35%, and 23% of shared taxa detected at the order, family, and genus levels, respectively. Importantly, the two approaches detected different proportions of indicator taxa like %EPT and %Chironomidae. DNA metabarcoding detected far fewer Chironomid and Trichopteran taxa than morphological identification, but more Ephemeropteran and Plecopteran taxa, a result likely due to primer choice. Overall, our results showed that DNA metabarcoding and morphological identification detected different benthic macroinvertebrate communities. Despite these differences, we found that the same environmental variables were correlated with invertebrate community structure, suggesting that both approaches can accurately detect biodiversity patterns across environmental gradients. Further refinement of DNA metabarcoding protocols, primers, and reference libraries–as well as more standardized, large‐scale comparative studies–may improve our understanding of the taxonomic agreement and data linkages between DNA metabarcoding and morphological approaches.