Predicting the effectiveness of depth-based technologies to prevent salmon lice infection using a dispersal model

- Robust empirical data used to validate a salmon lice dispersal model.
- Model with best fit had larvae with shorter development times and temperature-controlled behaviour.
- Vertical distributions predicted by the dispersal model had a high correlation with vertically distributed infection levels.
- Effectiveness of lice preventive strategies depends on the environmental conditions at each site.

Salmon lice is one of the major parasitic problems affecting wild and farmed salmonid species. The planktonic larval stages of these marine parasites can survive for extended periods without a host and are transported long distances by water masses. Salmon lice larvae have limited swimming capacity, but can influence their horizontal transport by vertical positioning. Here, we adapted a coupled biological-physical model to calculate the distribution of farm-produced salmon lice (Lepeophtheirus salmonis) during winter in the southwest coast of Norway. We tested 4 model simulations to see which best represented empirical data from two sources: (1) observed lice infection levels reported by farms; and (2) experimental data from a vertical exposure experiment where fish were forced to swim at different depths with a lice-barrier technology. Model simulations tested were different development time to the infective stage (35 or 50°-days), with or without the presence of temperature-controlled vertical behaviour of lice early planktonic stages (naupliar stages). The best model fit occurred with a 35°-day development time to the infective stage, and temperature-controlled vertical behaviour. We applied this model to predict the effectiveness of depth-based preventive lice-barrier technologies. Both simulated and experimental data revealed that hindering fish from swimming close to the surface efficiently reduced lice infection. Moreover, while our model simulation predicted that this preventive technology is widely applicable, its effectiveness will depend on environmental conditions. Low salinity surface waters reduce the effectiveness of this technology because salmon lice avoid these conditions, and can encounter the fish as they sink deeper in the water column. Correctly parameterized and validated salmon lice dispersal models can predict the impact of preventive approaches to control this parasite and become an essential tool in lice management strategies.