Towards a Standardized Operating Procedure for eDNA-Based Biomonitoring in Coastal Marine Salmon Aquaculture

Abstract

The marine aquaculture industry and regulators are in the process of implementing environmental DNA (eDNA) metabarcoding of microbial communities for compliance monitoring. This requires standardization of sampling, laboratory, and data analysis protocols. Towards this goal, we in this study completed two further milestones using samples collected from two Scottish salmon farms: (i) We tested the effect of using two different PCR protocols (i.e., different DNA polymerases, master mixes, and annealing temperatures), which are frequently being used in eDNA biomonitoring of aquaculture installations, for the amplification of the taxonomic marker gene (V3-V4 hypervariable region of the bacterial 16S rRNA gene). (ii) We quantified sampling background noise obtained from eDNA samples and statistically compared results with the sampling bias observed in macrofaunal samples from the same source sediments. We detected differences in bacterial community structures resulting from the performance of different PCR protocols, profoundly influencing the interpretation of biomonitoring results. Furthermore, we found that sampling-induced errors for eDNA samples were similar to errors for macrofaunal samples collected according to compliance monitoring protocol (~25% variability in both cases). Finally, we showed that within-grab variances of microbial community structures were in the same order of magnitude (less than 10× difference in all cases) as the one obtained from replicate grabs collected from the same locale (impact category). Based on our findings, we suggest using a consistent PCR protocol for biomonitoring efforts to improve the comparability of results, especially when different service providers are conducting the biomonitoring. We propose a sampling scheme to be considered in eDNA biomonitoring that includes taking three replicate grabs at each locale, with one replicate sample from each grab. This minimizes sampling-induced errors and makes upcoming eDNA-based monitoring results comparable with previous compliance monitoring results obtained from macrofaunal data.