A consortium of researchers from Scotland, Norway and the Faroe Islands are working on a project aimed at developing a better model for the spread 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). It aims to create a new system to validate the results of existing dispersal models used by producers, academics and regulators.

Sea lice are a naturally occurring parasite of salmonids. However, when their population increases in large numbers, it can have a negative effect on the health of not only farmed fish, but also on the health of migrating wild salmon and trout.

Sea lice modelling has become a key element in planning consents for fish farms in jurisdictions such as Norway and, more recently, Scotland. If the modelling shows that a proposed farm site is likely to lead to unacceptable levels of sea lice affecting wild salmon, then consent will not be granted. However, there is no universal consensus on whether the models currently used to predict sea lice distribution reflect the reality in the marine environment.

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

Several distribution modelling tools are already available to help the sector address the sea lice challenge and make decisions about future aquaculture locations. However, each model operates with a different set of underlying assumptions, meaning they tend to produce different results. A new, universally accepted tool for cross-comparison between models and data could provide a more robust, standardised approach to model evaluation, leading to more accurate predictions of potential risks to wild fish populations from sea lice.

Net cages for salmon farms (photo: SAIC)

Combining international data

The free online tool is informed by several existing physical and behavioural models, which include elements such as wind and tides, the way sea lice move through the water and how they respond 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 country’s results.

A new standardised approach means that academics, manufacturers and regulators using models currently on the market can use the online benchmarking tool to provide an additional level of validation and assurance that the outcome is as reliable as possible.

Dr Meadhbh Moriarty, Senior Aquatic Epidemiological Modeller for the Scottish Government’s Marine Directorate, said: “Different sea lice distribution models use different complex mathematical techniques, but it is important to ensure that the same set of input data produces a valid result, regardless of which product is used. To reduce variability, we are creating a bespoke Python script that can be applied to each model, ensuring it is fit for purpose.

“Another important aspect is the development of a ‘data dictionary’, which can help ensure that everyone using these models interprets the figures in the same way. Input from so many partners in three of the largest salmon producing countries, each with its own governance system, is a great asset to the project. We hope that the end result will be widely adopted by the aquaculture sector, helping to better manage the sea lice threat.”

Heather Jones, CEO of SAIC, added: “In recent years we have seen a growing demand for data-driven practices to reduce fish health concerns, including sea lice modelling. However, valuable insights can only be based on quality data, so the tools need to provide reliable 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 to deliver proportionate regulation and enabling the future growth and development of agriculture.”

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 distribution models and their evaluation against observations as consistent, rigorous, transparent and streamlined as possible.”