Consequences of reduced effectiveness of salmon lice treatments for lice control.

The effective control of ectoparasitic salmon lice, Lepeophtheirus salmonis, in fish farms is challenged by the salmon lice having developed resistance towards several antiparasitic drugs and by the effectiveness of non-medicinal treatments being limited by considerations of fish welfare. When new antiparasitics are introduced to the market, these should be used sparingly to slow resistance development. Using a population model for salmon lice parameterised for salmonid fish farms in Norway, we quantified how reduced treatment effectiveness influences treatment frequency and lice abundance. Furthermore, we investigated when in the production cycle a highly effective lice treatment leads to the largest reduction in the total number of treatments, mean lice abundance and lice larvae production. Results showed that reductions in treatment effectiveness to lower than 50% led to the steepest increases in treatment frequency and mean lice abundance, as well as to increased risk that lice abundance increased beyond control. The timing of the most effective treatment had only moderate effects on the total treatment need and the mean number of adult female lice through the production cycle, but large effect on the production of lice larvae in spring. These findings imply that farmers can optimise the timing of the most effective treatment to reduce the release of lice larvae in the period of year when wild salmonids are in coastal waters, without compromising total treatment need or mean lice levels.

Forecasting acidification effects using a Bayesian calibration and uncertainty propagation approach.

We present a statistical framework for model calibration and uncertainty estimation for complex deterministic models. A Bayesian approach is used to combine data from observations, the deterministic model, and prior parameter distributions to obtain forecast distributions. A case study is presented in which the statistical framework is applied using the hydrogeochemical model (MAGIC) for an assessment of recovery from acidification of soils and surface waters at a long-term study site in Norway under different future acid deposition conditions. The water quality parameters are coupled with a simple dose-response model for trout population health. Uncertainties in model output parameters are estimated and forecast results are presented as probability distributions for future water chemistry and as probability distributions of future healthy trout populations. The forecast results are examined for three different scenarios of future acid deposition corresponding to three different emissions control strategies for Europe. Despite the explicit consideration of uncertainties propagated into the future forecasts, there are clear differences among the scenarios. The case study illustrates how inclusion of uncertainties in model predictions can strengthen the inferences drawn from model results in support of decision making and assessments.