Salmon lice from salmon farms pose an important threat to wild and farmed salmonids in Norwegian waters as they produce large amounts of planktonic larvae that spread with ocean currents. The Reglice project focuses on developing existing hydrodynamic modelling tools through validation of both the physical environment and lice dispersion.
Validate and improve the dispersion model
The salmon lice dispersion model developed at the Institute of Marine Research (IMR) can be used to map areas with elevated infection pressure. The modelled distribution of salmon lice compares well with observations of salmon lice on smolt in sentinel cages. During the observation period 2012-2015 the model matched within the same infestation class in 78% of the cases. By taking advantage of data from previous years the model can be used to predict areas of elevated infestation pressure.
The salmon lice dispersion model is used to monitor the salmon lice situation along the entire Norwegian coast. The Institute of Marine Research visits specific locations and count the lice on wild fish, however it expensive and time consuming to cover the entire coast. Therefore, are the observations combined with the salmon lice dispersion model to provide a comprehensive picture of the infection pressure all along the coast to ensure high quality and low costs.
Cooperation with Meteorological Institute
IMR cooperates with the Meteorological Institute (MET) on the operational system that provides updated maps of the salmon lice infestation pressure in near real-time. The operational system relies on output from the hydrodynamic model covering the coast with 800m resolution (NorKyst800), calculating current, temperature and salinity all along the coast. The data is then transferred from MET to IMR, before the salmon lice dispersion model is updated with the latest reports on number of lice from the farms. The results are published on www.lakselus.no and updated weekly.
The lice avoid low salinities
The salmon lice move up and down in the water column depending on light and salinity. They swim towards the light during the day, however if the surface salinity is low, they start swimming downwards. Previously, the salmon lice model has used a salinity of 20 as the cue for downwards swimming. However, Crosbie et al (2019) has performed new laboratory experiments that show a more gradual avoidance of low salinities. The updated knowledge on swimming behavior was implemented into the model and the results show improved forecast skill.
Sampling of lice
Large spatial and temporal variability can create difficult conditions to monitor planktonic salmon lice. Identifications have traditionally been achieved with light microscopy, however, for many studies this method is the bottleneck in the number of samples that can be processed. In this project we have tested five methods and compared against light microscopy for accuracy, precision and associated cost and labor. The methods tested were visual-based (fluorescence microscopy and automated imaging using a FlowCam VS) and molecular-based (ddPCR, qfPCR, qPCR). Only one molecular method (ddPCR) showed acceptable accuracy (85%) and precision, followed by fluorescence microscopy and qfPCR with moderate precision. The suitability of the methods tested here will depend on the research question and resources available, and their improvement potential for accuracy and precision.
The project has developed network models of lice connectivity between farms for the entire Norwegian coastline. This analysis provides a tool that can be used to assess the impact of management strategies on the dispersion of lice and study seasonal patterns in lice connectivity. This network model has been utilized to identify firebreaks (areas of no production) to disrupt the transport ways of disease. This management strategy will lower the infection pressure at farms, slow down the evolution of drug resistance and ultimately reduce the spread of parasites to wild salmon populations. At least one firebreak that fragmented the network into two large unconnected groups of farms was identified for all seasons. During spring, when wild salmon migrate out into the ocean, and the louse levels per fish at farms must be minimized, two effective firebreaks were created by removing 13 and 21 farms.
In the newly developed management system for growth in Norwegian aquaculture (Traffic light system), salmon lice on wild fish is the controlling factor giving rise to either growth or reduction in the production. We have in this project developed and quality assured the salmon lice dispersion model to ensure knowledge-based advice to the authorities and the managers and thereby contributed to the foundation of the traffic light system.
Overvåkningssystemet, inkludert modell og observasjoner, er viktig kunnskapsleverandør til vurderingen av bærekraft i forvaltningssystemet for vekst, det såkalte trafikklyssystemet. Gjennom prosjektet har disse resultatene blitt kvalitetssikret og dermed bidratt til at trafikklyssystemet har blitt etablert. Prosjektet har bidratt til en bærekraftig vekst i norsk akvakultur. Samtidig brukes resultatene videre til å gi råd om optimal fordeling av oppdrettsanlegg innenfor produksjonsområdene.
Prosjektet har styrket samarbeidet med universitetet i Melbourne (Australia) gjennom utveksling av studenter og forskere. Gjennom prosjektet har MET fått prioritert arbeidet med et varslingssystem som er skreddersydd for HIs forvaltningsoppgaver. Slik har prosjektet bidratt til å videreutvikle et eksisterende samarbeide mellom to offentlige etater, med mål om å opprette produkter som svarer til kravene i en dynamisk samfunnsutvikling der et stadig økende fokus rettes mot oppdrettsnæringen.
Salmon lice from salmon farms pose an important threat to wild and farmed salmonids in Norwegian waters as they produce large amounts of planktonic larvae that spread with ocean currents. High resolution model systems realistically represent currents, hydrography, and the response of lice to environmental variables, and are therefore valuable in simulating lice dispersion and infestation pressure for risk management. The project will focus on further developing existing hydrodynamic modelling tools through validation of both the physical environment and lice dispersion. New methods for direct measurements (qPCR and FISH-CS) of lice in plankton samples will be developed and tested, and provide direct data for model validation. The improved model system will be used to test various scenarios of spatial distribution of fish farms along the coastline. In this way, the dispersion pattern of lice can reveal suitable production zones, infection fire gates, possible stocking zones and best locations and fallowing strategies within stocking zones, and the effects of farm-based control and prevention strategies. Outcomes from the different scenarios will be optimized and disseminated to government and industry.