Pens at a fish farm in Scotland. The use of eDNA will speed up analysis of samples taken from the seabed at farm sites.

Environment watchdog evaluates faster and cheaper seabed testing technique

E-DNA monitoring set to replace labour-intensive microscope method of checking health of sediment-dwelling animals under and around Scotland's fish farms

Published

A new environmental-DNA (eDNA) approach for monitoring the seabed conditions around marine fish farms could significantly speed up the assessment of sediment samples, enabling salmon producers and the Scottish Environment Protection Agency (SEPA) to gather timely, accurate information to demonstrate environmental impact and recovery.

Marine benthic invertebrates are essential for maintaining the natural ecosystem, helping to process biomass and protect the health of farmed and wild fish. These sediment-dwelling animals can also act as bioindicators of the effects of human activity on seabed health. Because of this, they are monitored by SEPA and operators of marine fish farms to ensure that farms are operating sustainably.

Previously, benthic monitoring relied on scientists painstakingly picking out all the invertebrate animals from samples of seabed sediments and then identifying each animal with the aid of microscopes. However, the process can take up to three days for a single sample and estimates suggest that it is costing the aquaculture sector up to £1 million per year.

Metabarcoding

An alternative, faster and more cost-effective process is now available, drawing upon DNA techniques first used in human forensics to identify the organisms present in sediment samples. Scientists have used metabarcoding – a technique that cross-references samples against a sequence database to identify different species - to identify thousands of bacterial species simultaneously.

How it works

The DNA analysis step involves:

  • Using high-throughput sequencing techniques to read the sequence of DNA (or barcode) for a specific, short region of bacterial DNA extracted from samples of seabed sediment.
  • Cross-referencing the identified barcodes from the sample against a database of known barcodes for thousands of bacterial species to determine the bacterial species present in the sample.

The Infaunal Quality Index (IQI) is a metric used to assess the ecological quality of marine benthic habitats, particularly focusing on the communities of infaunal organisms - aquatic animals that live in the substrate of a body of water and which are especially common in soft sediments.

After gathering a sample, the bacteria present in the sediment is first characterised using DNA, with a machine learning model then applied to predict the health of invertebrate community based on the bacteria. The Infaunal Quality Index (IQI) – a well established ecological quality benchmark – is then used to classify the health of the invertebrate community.

Following an extensive six-year project, samples analysed using the new method are now being presented to SEPA for validation, with an open-source toolkit and standard operating procedures also being created for anyone in the sector to use.

The research was supported by the Sustainable Aquaculture Innovation Centre (SAIC), Institute of Biodiversity and Freshwater Conservation at UHI Inverness, SEPA, University of Kaiserslautern in Germany, salmon farmers Mowi Scotland and Scottish Sea Farms, sector trade body Salmon Scotland and lead research partner, the Scottish Association for Marine Science (SAMS).

A critical task

Stephen Macintyre, head of environment at Mowi Scotland, said: “Demonstrating good environmental performance at our sites is critical, both for our customers and for compliance reasons. At the moment, we sample the seabed followed by sieving and sorting sediment to identify species, but it is a time-consuming, labour-intensive process that hasn’t been updated for 30 years or so.

“As an alternative, the DNA-based approach will enable us to understand our environmental performance much quicker, almost in real time, and take action where required to improve the environmental picture. Environmental DNA is already widely used elsewhere for nature-based assessments and also has the potential to be applied to assess the wider marine biodiversity that exists around our fish farms. The practical outputs from this project are very promising, and we are now in talks with SEPA about integrating DNA-based compliance assessments into our site monitoring programme.”

Stephen Macintyre: "The DNA-based approach will enable us to understand our environmental performance much quicker, almost in real time."

Technical components of the research, including DNA sequencing, data analysis and statistics, and the development of a machine-learning algorithm to predict the IQI of samples based on their bacterial characterisation, were independently reviewed by Biostatistics Scotland.

Years in the making

Sarah Riddle, director of innovation and engagement at SAIC, said: “This project has been years in the making and it is great to see the results of a long-term collaboration between the sector, academia, and regulators having the potential to transform a key aspect of aquaculture monitoring. E-DNA sampling could provide widespread benefits to both the aquaculture sector and its regulators, with potential for this approach to be adopted across the globe by seafood producing nations. Armed with data, producers can be better informed to make decisions around key environmental and fish health factors influenced by the seabed.”

Peter Pollard, head of ecology at SEPA, said: “The MeioMetBar Project has been an important and successful collaboration. It is truly the beginning of a step change in our ability and that of fish farm operators to cost-effectively assess, manage and regulate the effects on seabed life of fish farm discharges and so help protect the health and biodiversity of Scotland’s seas.

“The research is an example of the rapid innovation now taking place in more efficient and effective ways of monitoring the environment. Work is already under way to expand and enhance the capabilities of the method developed by the project, with the next-generation method expected to be available in 2025.”