Development of DNA-based metagenomic methodologies for seabed monitoring and aquaculture management
Salmon farms are required to demonstrate that they meet environmental standards underneath and around their fish-cages as part of their SEPA licence conditions. The major part of compliance monitoring is achieved by taking sediment samples from the seabed, along four or five transects (factor: Distance) radiating from the farm, and assessing which sediment-dwelling animals dominate and which have been eliminated. This approach requires considerable taxonomic expertise, is time consuming and, therefore, expensive. Another problem with this approach is that the results take several months to produce preventing active farm management. To address these deficiencies we are developing an alternative monitoring method based on identifying sediment-dwelling organisms (bacteria or eukaryotes) using next generation sequencers (NGS) to identify species via their DNA.
The MeioMetBar project met its two main objectives. Firstly, we demonstrated that bacterial assemblages showed a high level of consistency and repeatability and gave a very high level of discrimination between different Distances (Fig 1). We also showed that bacteria such as Cytophagia, Nitrospira, Bacteriodetes and various sulphate reducers responded very strongly to organic enrichment around fish-farms. Of the eukaryotes, we identified key impact indicators such as Capitella sp and Malacoceros sp via their DNA.
This collaboration, which maintains and expands existing partnerships, links industry (MOWI, Scotland) and the regulator (SEPA) with academics that specialise in aquaculture management (SAMS) and genomics (The Rivers and Loch Institute). This multi-sector, cross-disciplinary team is uniquely placed to further develop and apply this NGS technology.
Figure 1: MDS plot confirming that samples taken from the same grab are very similar (same coloured dots are closer together) and that Distances are very clearly discriminated on the basis of their NGS bacterial community.
We are planning further research to determine how NGS-assessed assemblages change over time and at various spatial scales for example between grabs taken at the same place. We are also optimizing machine learning algorithms so that we can best predict animal communities based on NGS data.
Role of SAMS
Academic lead
