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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Online Access: | http://hdl.handle.net/11250/2471902 https://doi.org/10.1016/j.fishres.2016.09.033 |
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ftimr:oai:imr.brage.unit.no:11250/2471902 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 2017 application/pdf http://hdl.handle.net/11250/2471902 https://doi.org/10.1016/j.fishres.2016.09.033 eng eng http://publications.nr.no/1508504026/HistoricalBycatch-OlavNikolaiBreivik.pdf Fisheries Research. 2017, 185 62-72. urn:issn:0165-7836 http://hdl.handle.net/11250/2471902 https://doi.org/10.1016/j.fishres.2016.09.033 cristin:1388983 62-72 185 Fisheries Research Peer reviewed Journal article 2017 ftimr https://doi.org/10.1016/j.fishres.2016.09.033 2021-09-23T20:14:38Z 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. acceptedVersion Article in Journal/Newspaper Barents Sea Gadus morhua Pandalus borealis Institute for Marine Research: Brage IMR Barents Sea Laplace ENVELOPE(141.467,141.467,-66.782,-66.782) Fisheries Research 185 62 72 |
institution |
Open Polar |
collection |
Institute for Marine Research: Brage IMR |
op_collection_id |
ftimr |
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. acceptedVersion |
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 |
publishDate |
2017 |
url |
http://hdl.handle.net/11250/2471902 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 |
62-72 185 Fisheries Research |
op_relation |
http://publications.nr.no/1508504026/HistoricalBycatch-OlavNikolaiBreivik.pdf Fisheries Research. 2017, 185 62-72. urn:issn:0165-7836 http://hdl.handle.net/11250/2471902 https://doi.org/10.1016/j.fishres.2016.09.033 cristin:1388983 |
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 |
_version_ |
1766370257928192000 |