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Aquaculture Environment Interactions

    AEI prepress abstract   -  DOI: https://doi.org/10.3354/aei00485

    Inferring time at sea from scale circuli: implications for the interpretation of salmon lice distributions on sea trout

    R. M. Serra-Llinares*, R. Nilsen, P. T. Fjeldheim, A. D. Sandvik, T. Haraldstad, Ø. Karlsen

    *Corresponding author:

    ABSTRACT: Infestations with the ectoparasite salmon lice Lepeophtheirus salmonis are considered one of the main stressors for wild salmonids in farm intensive areas. In Norway, lice infestations on sea trout Salmo trutta are monitored annually at fixed stations distributed along the coast, providing management authorities with an empirical basis to evaluate the potential parasite transmission between farmed and wild salmonids, and to evaluate the effect of different management strategies. However, the representativeness of these data is debated, as information on where and for how long the fish have been at sea before capture is rarely available. In this study, we used scale circulus counts to determine marine residency time for sea trout postsmolts caught in Hardangerfjord, a farm-intensive area in western Norway, and show how this information can be used for a better interpretation of lice infestations reported by surveillance programs. By analyzing scales from individuals of known marine residency time (n = 48), we established a periodicity of circulus deposition over the first summer at sea of approximately 8 d per circulus [95% CI: 7.6 – 8.4 d]. Next, we applied zero-altered (‘hurdle’) statistical models to show how marine residency time, inferred from scale circuli, significantly affected both the probability and intensity of infestation with salmon lice among wild sea trout postsmolts of unknown migration timing (n = 321). Importantly, incorporating circulus counts as a proxy for time at sea significantly improved model likelihood, highlighting the importance of incorporating supporting data on individual sea trout migration traits to salmon lice surveillance programs to improve our understanding of the observed distributions.