A stage-structured Bayesian hierarchical model for salmon lice populations at individual salmon farms – Estimated from multiple farm data sets

Salmon farming has become a prosperous international industry over the last decades. Along with growth
in the production farmed salmon, however, an increasing threat by pathogens has emerged. Of special
concern is the propagation and spread of the salmon louse, Lepeophtheirus salmonis. To gain insight into
this parasite’s population dynamics in large scale salmon farming system, we present a fully mechanistic
stage-structured population model for the salmon louse, also allowing for complexities involved in the
hierarchical structure of full scale salmon farming. The model estimates parameters controlling a wide
range of processes, including temperature dependent demographic rates, fish size and abundance effects
on louse transmission rates, effect sizes of various salmon louse control measures, and distance based
between farm transmission rates. Model parameters were estimated from data including 32 salmon
farms, except the last production months for five farms, which were used to evaluate model predictions.
We used a Bayesian estimation approach, combining the prior distributions and the data likelihood into
a joint posterior distribution for all model parameters. The model generated expected values that fitted
the observed infection levels of the chalimus, adult female and other mobile stages of salmon lice,
reasonably well. Predictions for the periods not used for fitting the model were also consistent with
the observational data. We argue that the present model for the population dynamics of the salmon
louse in aquaculture farm systems may contribute to resolve the complexity of processes that drive this
host-parasite relationship, and hence may improve strategies to control the parasite in this production
system.
Population model
Aquaculture
Stochastic model
Sea lice counts