Project aims to come up with a better sea lice model

A consortium of researchers from Scotland, Norway and the Faroe Islands are working on a project aimed at creating a better model for the dispersion of sea lice in water.

The project, known as SAVED – Sustainable Aquaculture: Validating Ectoparasite Dispersal (Models) – recently received a funding boost from the Sustainable Aquaculture Innovation Centre (SAIC). The aim is to create a new system to validate the results of existing dispersion models, used by producers, academics and regulatory bodies.

Sea lice are a naturally occurring parasite for salmonids, but when their population grows to large numbers it can a negative impact on the health, not only of farmed fish but also migrating wild salmon and trout.

Sea lice modelling has become a key element in planning consent for fish farms in jurisdictions like Norway and, more recently, Scotland. If the modelling shows that a proposed farm site could lead to unacceptable sea lice numbers affecting wild salmon, then consent will not be granted. There is no universal consensus, however, regarding whether the models currently used to predict sea lice dispersion reflect reality in the marine environment.

Project partners include the University of Strathclyde; Mowi Scotland; Scottish Sea Farms; Bakkafrost Scotland; the Scottish Government’s Marine Directorate; the Norwegian Institute of Marine Research; Firum, Aquaculture Research Station of the Faroes, The NW Edge, and Scottish Environment Protection Agency (SEPA), as an observer.

A variety of dispersal modelling tools are already available to help the sector manage the challenge of sea lice and inform decisions about future aquaculture sites. However, each model works with a different set of underlying assumptions, meaning they tend to return different results. A new, universally accepted tool for cross-comparison between models and data could lead to a more robust, standardised approach to model evaluation, leading to more accurate predictions of potential risk to wild fish populations from sea lice.

Salmon farm net cages (photo: SAIC)

Combining international data

The free online tool will be informed by several existing physical and behavioural models, which include elements such as winds and tides, the way sea lice move in the water and how they react to light exposure. Researchers will also combine data from Scotland, Norway and the Faroe Islands to gain a detailed understanding of the uncertainties produced in each nation’s results.

With a new standardised approach, academics, producers and regulators using any of the models currently available on the market will be able to use the online benchmark tool to provide an additional level of validation and have assurance that the output is as reliable as possible.

Dr Meadhbh Moriarty, senior aquatic epidemiological modeller for the Scottish Government’s Marine Directorate, said: “Different sea lice dispersal models use varying complex mathematical techniques, but it is important to ensure that the same set of input data returns a valid result, no matter which product is used. To reduce the variability, we are creating a bespoke Python script that can be applied to each model and ensure it is fit for purpose.

“Another important aspect is the development of a ‘data dictionary’ which can help to guarantee that everyone using these models is interpreting the figures in the same way. Having input from so many partners across three of the major salmon-producing nations, each with its own governance system, is a big bonus for the project. We hope that the end result will be adopted by the aquaculture sector at scale, helping to better manage the threat of sea lice.”

Heather Jones, CEO of SAIC, added: “In recent years we have seen growing demand for data-driven practices to mitigate fish health concerns, including sea lice modelling. However, valuable insight can only be based on quality data, so the tools must return dependable results that can be interpreted consistently. This project is a fantastic example of international collaboration for the greater good. The benchmark could have significant benefits in terms of helping bring about proportionate regulation and enabling the future growth and development of farming.”

Philip Gillibrand, oceanographer and hydrodynamic modeller at MOWI, added: “We hope that this project will provide a tool to make the cross-comparison of different sea lice dispersal models, and their evaluation against observations, as consistent, rigorous, transparent and streamlined as possible.”