Principal Investigator: Gregor McEwan
Co-Investigators: Crawford Revie & Mark Fast
Sea lice infestations are a major threat in salmon farming, the largest marine aquaculture industry in the world. The parasites cause stress, reduced growth, and sometimes mortality, and reduce farm production. Infestations are most often controlled with chemical treatments. However, chemical controls can cause resistance to evolve, and resistance to common chemical treatments has been observed in most major salmon producing areas.
A variety of strategies for mitigating resistance evolution have been developed in terrestrial systems, but such methods are lacking in aquatic systems. In aquatic systems, environmental drivers (e.g. hydrodynamics, temperature, salinity) and types of treatment (bath and in-feed) may influence evolutionary processes. Additionally, there are both chemical and non-chemical control methods (e.g. cleaner fish, mechanical) specific to aquaculture. Consequently, resistance management strategies developed for terrestrial settings cannot simply be transferred to salmon farms.
This project included work in two primary areas. First, we extended our existing agent-based model to better reflect the complexities of salmon aquaculture, such that our model can now simulate real farm scenarios – including a variety of stocking and harvest dates, environmental readings, and multiple treatment types – using data from Norwegian farms.
Second, we used the extended model to compare treatment strategies for control of sea lice to minimise the number of required treatments and slow the evolution of resistance. We found that strategies that provided the best control of numbers in the short term led to high resistance in the long term. For long-term mitigation of resistance, and treatment effectiveness, we found combining treatment types to be the most effective strategy.