Using eDNA metabarcoding as an alternative to macrobenthic assessments

Fish-farmers are required to undertake benthic monitoring to meet regulatory compliance assessment.  Until recently, benthic monitoring required 7 grab-samples to be taken along a single transect per production cycle but, under a new regulatory framework, fish-farmers are now required to collect and analyse 28 benthic samples across three to four transects.  From each sample macrobenthic taxonomic assessments are made and used to generate the ‘infaunal quality index’ (IQI) from which the benthic status and compliance is assessed.  The four-fold increase in sampling effort (from 7 to 28 samples) is very expensive to deliver and is beyond the capacity of current service providers.  In addition, macrobenthic faunal analysis is time-consuming (3 – 6 months) and the time-gap between sampling and data interpretation prevents active, near-real-time management of farm sites. 

Metabarcoding is the process of identifying organisms via their unique DNA sequences, present in the environment (eDNA) and can be applied to sediment samples.  Metabarcoding involves four steps: 1. eDNA extraction, 2. eDNA amplification of specific markers, 3. eDNA sequencing followed by 4. Sequence annotation.  In previous, SAIC and Salmon Scotland supported, proof-of-concept research, the project team subjected grab samples taken around fish-cages to both standard taxonomic- and metabarcoding (eDNA)-based analysis.  The team demonstrated that bacterial-based eDNA characterisation was precise and, using Random forest (RF) machine learning, that patterns of bacterial eDNA could be used to predict macrobenthic IQI.

This project (BactMetBar) is a partnership between SAMS/RLI (UHI), The University of Kaiserslautern (Germany), Salmon Scotland, MOWI, SSF and SEPA.  In BactMetBar, these organisations are innovating together to deliver the following key objectives:

• Refine, standardise and publish standard protocols in relation to eDNA extraction and amplification for Scottish benthic samples taken for eDNA-based analysis over a diverse range of habitats
• Develop data-processing and RF algorithms that best predict the IQI from bacterial eDNA sequence data by accounting for spatial and temporal variability in the bacterial response to fish-farm associated change
• Collate the statistical routines into a single, user-friendly ‘R-package’
• Publicise the standard operating procedures (SOPs) and demonstrate the BactMetBar R-package and embed the innovative alternative approach to macrobenthic-based monitoring in the sector (knowledge exchange).

BactMetBar will help ensure that farm assimilative capacity is sustainably utilised throughout the production cycle and within regulatory limits, whilst enabling the sector to cost-effectively, and in near real-time, demonstrate compliance with regulations.

This project and its outputs have the potential to create a shift change in how the Scottish salmon farmers assess the impacts of their farms.  The benefits stretch beyond regulatory compliance assessment, supporting improved production efficiencies and the development of new farms through improved modelling.