Latent Gaussian models to decide on spatial closures for bycatch management in the Barents Sea shrimp fishery
In the Barents Sea and adjacent water, fishing grounds are closed for shrimp fishing by the Norwegian Directorate of Fisheries Monitoring and Surveillance Service (MSS) if the expected number of juvenile fish caught are predicted to exceed a certain limit per kilogram shrimp (Pandalus borealis). Tod...
Published in: | Canadian Journal of Fisheries and Aquatic Sciences |
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Online Access: | http://dx.doi.org/10.1139/cjfas-2015-0322 http://www.nrcresearchpress.com/doi/full-xml/10.1139/cjfas-2015-0322 http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfas-2015-0322 |
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crcansciencepubl:10.1139/cjfas-2015-0322 2023-12-17T10:27:00+01:00 Latent Gaussian models to decide on spatial closures for bycatch management in the Barents Sea shrimp fishery Breivik, Olav Nikolai Storvik, Geir Nedreaas, Kjell 2016 http://dx.doi.org/10.1139/cjfas-2015-0322 http://www.nrcresearchpress.com/doi/full-xml/10.1139/cjfas-2015-0322 http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfas-2015-0322 en eng Canadian Science Publishing http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining Canadian Journal of Fisheries and Aquatic Sciences volume 73, issue 8, page 1271-1280 ISSN 0706-652X 1205-7533 Aquatic Science Ecology, Evolution, Behavior and Systematics journal-article 2016 crcansciencepubl https://doi.org/10.1139/cjfas-2015-0322 2023-11-19T13:38:31Z In the Barents Sea and adjacent water, fishing grounds are closed for shrimp fishing by the Norwegian Directorate of Fisheries Monitoring and Surveillance Service (MSS) if the expected number of juvenile fish caught are predicted to exceed a certain limit per kilogram shrimp (Pandalus borealis). Today, a simple ratio estimator, which does not fully utilize all data available, is in use. In this paper, we construct a Bayesian hierarchical spatiotemporal model for improved prediction of the bycatch ratio in the Barents Sea shrimp fishery. More predictable bycatch will be an advantage for the MSS because of more correct decisions and better resource allocation and also for the fishermen because of more predictable fishing grounds. The model assumes that the occurrence of shrimp and juvenile Atlantic cod (Gadus morhua) can be modeled by linked regression models containing several covariates (including 0-group abundance estimates) and random effects modeled as Gaussian fields. Integrated nested Laplace approximations is applied for fast calculation. The method is applied to prediction of the bycatch ratio for Atlantic cod. Article in Journal/Newspaper atlantic cod Barents Sea Gadus morhua Pandalus borealis Canadian Science Publishing (via Crossref) Barents Sea Laplace ENVELOPE(141.467,141.467,-66.782,-66.782) Canadian Journal of Fisheries and Aquatic Sciences 73 8 1271 1280 |
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Open Polar |
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Canadian Science Publishing (via Crossref) |
op_collection_id |
crcansciencepubl |
language |
English |
topic |
Aquatic Science Ecology, Evolution, Behavior and Systematics |
spellingShingle |
Aquatic Science Ecology, Evolution, Behavior and Systematics Breivik, Olav Nikolai Storvik, Geir Nedreaas, Kjell Latent Gaussian models to decide on spatial closures for bycatch management in the Barents Sea shrimp fishery |
topic_facet |
Aquatic Science Ecology, Evolution, Behavior and Systematics |
description |
In the Barents Sea and adjacent water, fishing grounds are closed for shrimp fishing by the Norwegian Directorate of Fisheries Monitoring and Surveillance Service (MSS) if the expected number of juvenile fish caught are predicted to exceed a certain limit per kilogram shrimp (Pandalus borealis). Today, a simple ratio estimator, which does not fully utilize all data available, is in use. In this paper, we construct a Bayesian hierarchical spatiotemporal model for improved prediction of the bycatch ratio in the Barents Sea shrimp fishery. More predictable bycatch will be an advantage for the MSS because of more correct decisions and better resource allocation and also for the fishermen because of more predictable fishing grounds. The model assumes that the occurrence of shrimp and juvenile Atlantic cod (Gadus morhua) can be modeled by linked regression models containing several covariates (including 0-group abundance estimates) and random effects modeled as Gaussian fields. Integrated nested Laplace approximations is applied for fast calculation. The method is applied to prediction of the bycatch ratio for Atlantic cod. |
format |
Article in Journal/Newspaper |
author |
Breivik, Olav Nikolai Storvik, Geir Nedreaas, Kjell |
author_facet |
Breivik, Olav Nikolai Storvik, Geir Nedreaas, Kjell |
author_sort |
Breivik, Olav Nikolai |
title |
Latent Gaussian models to decide on spatial closures for bycatch management in the Barents Sea shrimp fishery |
title_short |
Latent Gaussian models to decide on spatial closures for bycatch management in the Barents Sea shrimp fishery |
title_full |
Latent Gaussian models to decide on spatial closures for bycatch management in the Barents Sea shrimp fishery |
title_fullStr |
Latent Gaussian models to decide on spatial closures for bycatch management in the Barents Sea shrimp fishery |
title_full_unstemmed |
Latent Gaussian models to decide on spatial closures for bycatch management in the Barents Sea shrimp fishery |
title_sort |
latent gaussian models to decide on spatial closures for bycatch management in the barents sea shrimp fishery |
publisher |
Canadian Science Publishing |
publishDate |
2016 |
url |
http://dx.doi.org/10.1139/cjfas-2015-0322 http://www.nrcresearchpress.com/doi/full-xml/10.1139/cjfas-2015-0322 http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfas-2015-0322 |
long_lat |
ENVELOPE(141.467,141.467,-66.782,-66.782) |
geographic |
Barents Sea Laplace |
geographic_facet |
Barents Sea Laplace |
genre |
atlantic cod Barents Sea Gadus morhua Pandalus borealis |
genre_facet |
atlantic cod Barents Sea Gadus morhua Pandalus borealis |
op_source |
Canadian Journal of Fisheries and Aquatic Sciences volume 73, issue 8, page 1271-1280 ISSN 0706-652X 1205-7533 |
op_rights |
http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining |
op_doi |
https://doi.org/10.1139/cjfas-2015-0322 |
container_title |
Canadian Journal of Fisheries and Aquatic Sciences |
container_volume |
73 |
container_issue |
8 |
container_start_page |
1271 |
op_container_end_page |
1280 |
_version_ |
1785578760665300992 |