Separating faces in ARMS metabarcoding improves marine biodiversity monitoring: a comparison across protocols, experimental designs, and photographic surveys

Abstract Monitoring marine biodiversity requires approaches that capture its full complexity through space and time. DNA metabarcoding coupled with Autonomous Reef Monitoring Structures (ARMS) is increasingly used for this purpose, yet most applications still pool all sessile fractions and rarely benchmark molecular ouputs against photographic observations. Here, we combined photographic analysis with cytochrome c oxidase I (COI) metabarcoding across ten north-western Mediterranean sites to test, compare, and refine ARMS-based monitoring protocols. We first optimized laboratory procedures (DNA extraction and polymerase choice) and applied the control-driven, replicate-aware VTAM pipeline to minimize false positives and ensure full traceability. We then conducted the first face-by-face comparison of α- and β-diversity between imaging and eDNA in which each individual ARMS face was metabarcoded separately rather than pooled. Metabarcoding detected ∼15× higher site-level richness and revealed stronger correlations with geographic distance and environmental gradients, whereas photography provided complementary information on macro-taxa and surface cover. For metabarcoding, processing each face separately yielded much higher richness and markedly stronger β-diversity–distance correlations than with the NOAA pooling protocol, demonstrating that pooling inflates sampling variance resulting in a loss of the ecological signal. Grouping faces into five structural categories offered a more operational alternative while further increasing α-diversity and strengthening β-diversity correlations. Overall, our results show that retaining ARMS microhabitat structure is critical for maximizing metabarcoding performance. Using five structural sessile fractions per ARMS combined with a control-driven bioinformatic workflow provides a reproducible, scalable framework for long-term eDNA monitoring and early detection of biodiversity change.