Separating Faces in ARMS Metabarcoding Improves Marine Biodiversity Monitoring: A Comparison Across Protocols, Experimental Designs and Photographic Surveys

Monitoring marine biodiversity requires approaches capable of capturing its spatial and temporal complexity. 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 outputs against photographic observations. Here, we combined photographic analysis with cytochrome c oxidase I (COI) metabarcoding across 10 north-western Mediterranean sites to 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 traceability. We then conducted the first face-by-face comparison of α- and β-diversity between imaging and eDNA, metabarcoding each ARMS face separately rather than pooling samples. Metabarcoding detected ~15× higher site-level richness and revealed stronger correlations with geographic distance and environmental gradients-which stemmed from its finer taxonomic resolution-whereas photography provided complementary information on macro-taxa and surface cover. For metabarcoding, processing each face separately yielded much higher richness and stronger β-diversity-distance correlations than with the NOAA pooling protocol, demonstrating that pooling inflates sampling variance, weakening ecological signal. Grouping the 17 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.