Development and validation of a multi-trophic metabarcoding biotic index for benthic organic enrichment biomonitoring using a salmon farm case-study.

Sampling

Salmon farms are often associated with strong benthic enrichment gradients. Routine monitoring is usually required by regulation, and has traditionally been based on benthic macrofaunal communities. This is reliant on taxonomic expertise, excludes analsyis of micro-organisms and turn-around times can be slow, limiting opportunities for adaptive management. Environmental metabarcoding is a powerful high-throughput sequencing-based technique that is promising for identifying and quantifying assemblages of benthic organisms for biomonitoring. Previous studies have demonstrated relationships between specific taxonomic groups (e.g., bacteria, foraminifera or other eukaryotes) and anthropogenic effects. However, there is uncertainty around applicability at larger spatial and temporal scales, and the absence of fixed categorical scales makes the use of data challenging for routine biomonitoring. In this study, we analysed 105 sediment samples collected over three years from three salmon farms spanning two separate bioregions. Environmental DNA and RNA (eDNA/eRNA) metabarcoding of three taxonomic groups (foraminifera [For], bacteria [Bac], and general eukaryotes [Euk]) was undertaken in parallel with traditional macrofaunal and biochemical analysis (which were used to calculate an Enrichment Stage [ES] index for each sample). The most abundant 200 to 250 Operational Taxonomic Units in each taxonomic group were assigned to Eco-Groups (EG) using a quantitative, quality-based method. These were then used to develop a Metabarcoding Biotic Index (MBI) for each group individually (For-MBI, Bac-MBI, and Euk-MBI) and in combination (multi-trophic MBI; mt-MBI). Based on our assessment criteria we robustly allocated ca. 500 EG to a wide range of known and unknown taxa. Using both the development (year 1 [Y1] and 2 [Y2]) and independent validation (year 3) datasets the weakest correlation was between For-MBI and ES (eDNA/RNA, R2 = 0.731 to 0.850), whereas strong correlations were obtained between the mt-MBI and, or when just Bac and Euk data (mt[Bac+Euk]-MBI) were combined (eDNA/RNA, R2 > 0.900). Consequently, we suggest that foraminifera be excluded from the mt-MBI, which will reduce analytical time and costs. The strong correlations obtained between the mt-MBI and ES confirms that it has the potential to complement or even replace current fish farm biomonitoring techniques in the near future.

Method steps

1. Pipeline used: https://www.ebi.ac.uk/metagenomics/pipelines/4.1