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:	AD Sandvik, PA Bjørn, B Ådlandsvik, L Asplin, J Skarðhamar, IA Johnsen, M Myksvoll, MD Skogen
Formato:	article
Lenguaje:	EN
Publicado:	Inter-Research 2016
Materias:	Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/5fbae79156c944638a4dd45d67aabc7b
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spelling	oai:doaj.org-article:5fbae79156c944638a4dd45d67aabc7b2021-11-11T11:06:02ZToward a model-based prediction system for salmon lice infestation pressure1869-215X1869-753410.3354/aei00193https://doaj.org/article/5fbae79156c944638a4dd45d67aabc7b2016-09-01T00:00:00Zhttps://www.int-res.com/abstracts/aei/v8/p527-542/https://doaj.org/toc/1869-215Xhttps://doaj.org/toc/1869-7534High 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%).AD SandvikPA BjørnB ÅdlandsvikL AsplinJ SkarðhamarIA JohnsenM MyksvollMD SkogenInter-ResearcharticleAquaculture. Fisheries. AnglingSH1-691EcologyQH540-549.5ENAquaculture Environment Interactions, Vol 8, Pp 527-542 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5
spellingShingle	Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5 AD Sandvik PA Bjørn B Ådlandsvik L Asplin J Skarðhamar IA Johnsen M Myksvoll MD Skogen Toward a model-based prediction system for salmon lice infestation pressure
description	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%).
format	article
author	AD Sandvik PA Bjørn B Ådlandsvik L Asplin J Skarðhamar IA Johnsen M Myksvoll MD Skogen
author_facet	AD Sandvik PA Bjørn B Ådlandsvik L Asplin J Skarðhamar IA Johnsen M Myksvoll MD Skogen
author_sort	AD Sandvik
title	Toward a model-based prediction system for salmon lice infestation pressure
title_short	Toward a model-based prediction system for salmon lice infestation pressure
title_full	Toward a model-based prediction system for salmon lice infestation pressure
title_fullStr	Toward a model-based prediction system for salmon lice infestation pressure
title_full_unstemmed	Toward a model-based prediction system for salmon lice infestation pressure
title_sort	toward a model-based prediction system for salmon lice infestation pressure
publisher	Inter-Research
publishDate	2016
url	https://doaj.org/article/5fbae79156c944638a4dd45d67aabc7b
work_keys_str_mv	AT adsandvik towardamodelbasedpredictionsystemforsalmonliceinfestationpressure AT pabjørn towardamodelbasedpredictionsystemforsalmonliceinfestationpressure AT badlandsvik towardamodelbasedpredictionsystemforsalmonliceinfestationpressure AT lasplin towardamodelbasedpredictionsystemforsalmonliceinfestationpressure AT jskarðhamar towardamodelbasedpredictionsystemforsalmonliceinfestationpressure AT iajohnsen towardamodelbasedpredictionsystemforsalmonliceinfestationpressure AT mmyksvoll towardamodelbasedpredictionsystemforsalmonliceinfestationpressure AT mdskogen towardamodelbasedpredictionsystemforsalmonliceinfestationpressure
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Toward a model-based prediction system for salmon lice infestation pressure

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