Molecular biosecurity surveillance programs increasingly use environmental DNA (eDNA) for detecting marine non-indigenous species (NIS). However, the current molecular detection workflow is cumbersome, prone to errors and delays, and is limited in providing knowledge about eDNA beyond the spatial and temporal extent of the sampling. These limitations can hinder management efforts and restrict the “opportunity window” for a rapid response to new marine NIS incursions. Emerging innovative field-deployable digital droplet PCR (ddPCR) systems offer improved workflow efficiency by autonomously analyzing targeted free-floating extra-cellular eDNA (free-eDNA) signals. Despite their potential, these systems have not been tested in marine environments. Thus, an aquarium study was conducted with three distinct marine NIS: the Mediterranean fanworm Sabella spallanzanii, the ascidian clubbed tunicate Styela clava, and the brown bryozoan Bugula neritina to evaluate the detectability of free-eDNA in seawater. The detectability of targeted free-eDNA was assessed by directly analyzing aquarium water samples using an optimized species-specific ddPCR assay, without filtration or DNA extraction, so-called, “direct-ddPCR”. The results demonstrated the consistent detection of Sabella spallanzanii and Bugula neritina free-eDNA when these organisms were present in high abundance. Once organisms were removed, the free-eDNA signal exponentially declined, noting that free-eDNA persisted between 24-72 hours. Results indicate that organism biomass, specimen characteristics (e.g., stress and viability), and species-specific biological differences may influence free-eDNA detectability. These results are critical for implementing in-situ nucleic acid automated continuous sensing systems for marine biosurveillance, enabling point-of-need detection and rapid management response to biosecurity threats.

Environmental DNA (eDNA) analyses are powerful for describing marine biodiversity but must be optimized for their effective use in routine monitoring. To maximize eDNA detection probabilities of sparsely distributed populations, water samples are usually concentrated from larger volumes and filtered using fine-pore membranes, often a significant cost-time bottleneck in the workflow. This study aimed to streamline eDNA sampling by investigating plankton net versus bucket sampling, direct versus sequential filtration including self-preserving filters. Biodiversity was assessed using metabarcoding of the small ribosomal subunit (18S rRNA) and mitochondrial cytochrome c oxidase I (COI) genes. Multi-species detection probabilities were estimated for each workflow using a probabilistic occupancy modelling approach. Significant workflow-related differences in biodiversity metrics were reported. Highest amplicon sequence variant (ASV) richness was attained by the bucket sampling combined with self-preserving filters, comprising a large portion of micro-plankton. Less diversity but more metazoan taxa were captured in the net samples combined with 5 µm pore size filters. Pre-filtered 1.2 µm samples yielded few or no unique ASVs. The highest average (~32%) metazoan detection probabilities in the 5 µm pore size net samples confirmed the effectiveness of pre-concentrating plankton for biodiversity screening. These results contribute to streamlining eDNA sampling protocols for uptake and implementation in marine biodiversity research and surveillance.

Characterization of microbial assemblages via environmental DNA metabarcoding is increasingly being used in routine monitoring programs due to its sensitivity and cost-effectiveness. Several programs have been developed recently which infer functional profiles from 16S rRNA gene data using hidden-state prediction (HSP) algorithms. These might offer an economic and scalable alter-native to shotgun metagenomics. To date, HSP-based methods have seen limited use for benthic marine surveys and their performance in these environments remains unevaluated. In this study, 16S rRNA metabarcoding was applied to sediment samples collected at 0 and ≥ 1200 m from Norwegian salmon farms, and three metabolic inference approaches (PAPRICA, PICRUSt2 and TAX4FUN2) evaluated against metagenomics and environmental data. While metabarcoding and metagenomics recovered a comparable functional diversity, the taxonomic composition differed be-tween approaches, with genera richness up to 20× higher for metabarcoding. Comparisons between the sensitivity (highest true positive rates) and specificity (lowest true negative rates) of HSP-based programs in detecting functions found in metagenomics data ranged, respectively, from 0.52 and 0.60 to 0.76 and 0.79. However, little correlation was observed between the relative abundance of their specific functions. Functional beta-diversity of HSP-based data was strongly associated with that of metagenomics (r ≥ 0.86 for PAPRICA and TAX4FUN2) and responded similarly to the impact of fish farm activities. Our results demonstrate that although HSP-based metabarcoding approaches provide a slightly different functional profile than metagenomics, partly due to recovering a distinct community, they represent a cost-effective and valuable tool for characterizing and assessing the effects of fish farming on benthic ecosystems.