Toward a model-based prediction system for salmon lice infestation pressure
High salmon lice density is a threat to wild and farmed salmonid fish in Norway. To assess and identify areas for high salmon lice infestation pressure, continuous monitoring is necessary. The national Norwegian salmon lice monitoring program has until now been based on sampling and counting of salm...
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Inter-Research
2016
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/5fbae79156c944638a4dd45d67aabc7b |
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| Sumario: | High salmon lice density is a threat to wild and farmed salmonid fish in Norway. To assess and identify areas for high salmon lice infestation pressure, continuous monitoring is necessary. The national Norwegian salmon lice monitoring program has until now been based on sampling and counting of salmon lice on wild salmonids and smolts in sentinel cages. The number of lice eggs hatched into the water masses, the relatively long-lasting pelagic life stages and the high spatiotemporal variability of the ocean currents all have a major influence on the local infestation pressure. Thus, a new monitoring system including a numerical ocean model with high temporal and spatial resolution has been established. The plan is that the model will complement, direct or replace parts of the logistically demanding and costly field-based monitoring program. In this study, we evaluate the model’s ability to realistically simulate the spread and density of pelagic salmon lice. Results from a 4 yr model run are presented, and the simulated density compared to the mean abundance on smolts in sentinel cages. The comparison demonstrates that the modeled salmon lice density corresponds well with the observational data. Within a slight shift in space, the model matches the observed lice infestation class values in 78% of the cases. Using the modeled lice density, a binary forecast system is proposed to predict areas of elevated lice infestation pressure. For the 2015 test case, the prediction system is correct (elevated/non-elevated) in 32 of 36 cases (89%). |
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