Latent Gaussian models to predict historical bycatch in commercial fishery
Knowledge about how many fish that have been killed due to bycatch is an important aspect of ensuring a sustainable ecosystem and fishery. We introduce a Bayesian spatio-temporal prediction method for historical bycatch that incorporates two sources of available data sets, fishery data and survey da...
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ftoslouniv:oai:www.duo.uio.no:10852/59360 2023-05-15T15:38:52+02:00 Latent Gaussian models to predict historical bycatch in commercial fishery Breivik, Olav Nikolai Storvik, Geir Olve Nedreaas, Kjell Harald 2016-10-03T13:01:19Z http://hdl.handle.net/10852/59360 http://urn.nb.no/URN:NBN:no-62044 https://doi.org/10.1016/j.fishres.2016.09.033 EN eng Elsevier Science http://urn.nb.no/URN:NBN:no-62044 Breivik, Olav Nikolai Storvik, Geir Olve Nedreaas, Kjell Harald . Latent Gaussian models to predict historical bycatch in commercial fishery. Fisheries Research. 2017, 185, 62-72 http://hdl.handle.net/10852/59360 1388983 info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Fisheries Research&rft.volume=185&rft.spage=62&rft.date=2017 Fisheries Research 185 62 72 http://dx.doi.org/10.1016/j.fishres.2016.09.033 URN:NBN:no-62044 Fulltext https://www.duo.uio.no/bitstream/handle/10852/59360/2/ArticleTotalBycatchCod4th.pdf Attribution-NonCommercial-NoDerivs 3.0 Unported https://creativecommons.org/licenses/by-nc-nd/3.0/ CC-BY-NC-ND 0165-7836 Journal article Tidsskriftartikkel Peer reviewed AcceptedVersion 2016 ftoslouniv https://doi.org/10.1016/j.fishres.2016.09.033 2020-06-21T08:51:18Z Knowledge about how many fish that have been killed due to bycatch is an important aspect of ensuring a sustainable ecosystem and fishery. We introduce a Bayesian spatio-temporal prediction method for historical bycatch that incorporates two sources of available data sets, fishery data and survey data. The model used assumes that occurrence of bycatch can be described as a log-linear combination of covariates and random effects modeled as Gaussian fields. Integrated Nested Laplace Approximations (INLA) is used for fast calculations. The method introduced is general, and is applied on bycatch of juvenile cod (Gadus morhua) in the Barents Sea shrimp (Pandalus borealis) fishery. In this fishery we compare our prediction method with the well known ratio and effort methods, and make a strong case that the Bayesian spatio-temporal method produces more reliable historical bycatch predictions compared to existing methods. Article in Journal/Newspaper Barents Sea Gadus morhua Pandalus borealis Universitet i Oslo: Digitale utgivelser ved UiO (DUO) Barents Sea Laplace ENVELOPE(141.467,141.467,-66.782,-66.782) Fisheries Research 185 62 72 |
institution |
Open Polar |
collection |
Universitet i Oslo: Digitale utgivelser ved UiO (DUO) |
op_collection_id |
ftoslouniv |
language |
English |
description |
Knowledge about how many fish that have been killed due to bycatch is an important aspect of ensuring a sustainable ecosystem and fishery. We introduce a Bayesian spatio-temporal prediction method for historical bycatch that incorporates two sources of available data sets, fishery data and survey data. The model used assumes that occurrence of bycatch can be described as a log-linear combination of covariates and random effects modeled as Gaussian fields. Integrated Nested Laplace Approximations (INLA) is used for fast calculations. The method introduced is general, and is applied on bycatch of juvenile cod (Gadus morhua) in the Barents Sea shrimp (Pandalus borealis) fishery. In this fishery we compare our prediction method with the well known ratio and effort methods, and make a strong case that the Bayesian spatio-temporal method produces more reliable historical bycatch predictions compared to existing methods. |
format |
Article in Journal/Newspaper |
author |
Breivik, Olav Nikolai Storvik, Geir Olve Nedreaas, Kjell Harald |
spellingShingle |
Breivik, Olav Nikolai Storvik, Geir Olve Nedreaas, Kjell Harald Latent Gaussian models to predict historical bycatch in commercial fishery |
author_facet |
Breivik, Olav Nikolai Storvik, Geir Olve Nedreaas, Kjell Harald |
author_sort |
Breivik, Olav Nikolai |
title |
Latent Gaussian models to predict historical bycatch in commercial fishery |
title_short |
Latent Gaussian models to predict historical bycatch in commercial fishery |
title_full |
Latent Gaussian models to predict historical bycatch in commercial fishery |
title_fullStr |
Latent Gaussian models to predict historical bycatch in commercial fishery |
title_full_unstemmed |
Latent Gaussian models to predict historical bycatch in commercial fishery |
title_sort |
latent gaussian models to predict historical bycatch in commercial fishery |
publisher |
Elsevier Science |
publishDate |
2016 |
url |
http://hdl.handle.net/10852/59360 http://urn.nb.no/URN:NBN:no-62044 https://doi.org/10.1016/j.fishres.2016.09.033 |
long_lat |
ENVELOPE(141.467,141.467,-66.782,-66.782) |
geographic |
Barents Sea Laplace |
geographic_facet |
Barents Sea Laplace |
genre |
Barents Sea Gadus morhua Pandalus borealis |
genre_facet |
Barents Sea Gadus morhua Pandalus borealis |
op_source |
0165-7836 |
op_relation |
http://urn.nb.no/URN:NBN:no-62044 Breivik, Olav Nikolai Storvik, Geir Olve Nedreaas, Kjell Harald . Latent Gaussian models to predict historical bycatch in commercial fishery. Fisheries Research. 2017, 185, 62-72 http://hdl.handle.net/10852/59360 1388983 info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Fisheries Research&rft.volume=185&rft.spage=62&rft.date=2017 Fisheries Research 185 62 72 http://dx.doi.org/10.1016/j.fishres.2016.09.033 URN:NBN:no-62044 Fulltext https://www.duo.uio.no/bitstream/handle/10852/59360/2/ArticleTotalBycatchCod4th.pdf |
op_rights |
Attribution-NonCommercial-NoDerivs 3.0 Unported https://creativecommons.org/licenses/by-nc-nd/3.0/ |
op_rightsnorm |
CC-BY-NC-ND |
op_doi |
https://doi.org/10.1016/j.fishres.2016.09.033 |
container_title |
Fisheries Research |
container_volume |
185 |
container_start_page |
62 |
op_container_end_page |
72 |
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1766370263190994944 |