Large scale modelling of salmon lice (Lepeophtheirus salmonis) infection pressure based on lice monitoring data from Norwegian salmonid farms

Abstract

Infection by parasitic sea lice is a substantial problem in industrial scale salmon farming. To control the problem, Norwegian salmonid farms are not permitted to exceed a threshold level of infection on their fish, and farms are required to monitor and report lice levels on a weekly basis to ensure compliance with the regulation. In the present study, we combine the monitoring data with a deterministic model for salmon lice population dynamics to estimate farm production of infectious lice stages. Furthermore, we use an empirical estimate of the relative risk of salmon lice transmission between farms, that depend on inter-farm distances, to estimate the external infection pressure at a farm site, i.e. the infection pressure from infective salmon lice of neighbouring farm origin. Finally, we test whether our estimates of infection pressure from neighbouring farms as well as internal within farm infection pressure, predicts subsequent development of infection in cohorts of farmed salmonids in their initial phase of marine production. We find that estimated external infection pressure is a main predictor of salmon lice population dynamics in newly stocked cohorts of salmonids. Our results emphasize the importance of keeping the production of infectious lice stages at low levels within local networks of salmon farms. Our model can easily be implemented for real time estimation of infection pressure at the national scale, utilizing the masses of data generated through the compulsory lice monitoring in salmon farms. The implementation of such a system should give the salmon industry greater predictability with respect to salmon lice infection levels, and aid the decision making process when the development of new farm sites are planned.

Statistical and ecological challenges of monitoring parasitic salmon lice infestations in wild salmonid fish stocks

“…In fact, it is rather surprising that the binary and proportional models were able to incorporate such a poor variable successfully. Our measure of infestation pressure from farms has a good spatiotemporal resolution, and Kristoffersen et al (2014) recently evaluated this measure and confirmed that it can be used to predict infestation levels of farmed fish over spatial scales. However, it is based on the assumption that salmon lice larvae are spread evenly in all directions away from the fish farm, without taking into account currents or variations in temperature and salinity.…”

Toward a model-based prediction system for salmon lice infestation pressure

“…The national salmon lice monitoring program ) is based on sampling and assessment of salmon lice intensity on wild salmonids at standard monitoring localities all along the coastline. The program is logistically demanding, methodologically difficult, costly and gives only information from a few single fjord systems along a long coastline scattered with salmon farms and important wild stocks of salmonids (Kristoffersen et al 2014. Consequently, a change in the monitoring, advisory and management system for lice has been proposed (Taranger et al 2012), partly initiated since 2013 (Svåsand et al 2015 and recently also adopted and further developed by the Norwegian government and Parliament (Anonymous 2015).…”

Connected networks of sea lice populations: dynamics and implications for control

“…For prediction of sea lice abundances over time, simpler statistical approaches directly dealing with empirical data may be more effective (e.g. Aldrin et al 2013, Rogers et al 2013, Kristoffersen et al 2014) and allow for the integration of a broad range of influencing factors. However, by building up from the physiological properties of sea lice, our approach allows a more fundamental look at how external infections, on-site reproduction, inter-site connectivity, and manage-ment activities affect lice abundances in connected networks.…”

“…Several authors have also investigated lice dynamics using spatiotemporal statistical models, where site lice abundances are explained by environmental conditions, chemical treatment dates, farm biomass, and distance-based proxies for connectivity. These studies have underlined the importance of external infection pressure (Kristoffersen et al , 2014 and self-infection (Aldrin et al 2013) in explaining juvenile lice abundances at farm sites. The possibility for posi tive feedback effects through high levels of self-infection has also been investigated (Krkosek et al 2010).…”