Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance
<qd> Simmonds, E. J., Portilla, E., Skagen, D., Beare, D., and Reid, D. G. 2010. Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance. – ICES Journal of Marine Science, 67: 1138–115...
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fthighwire:oai:open-archive.highwire.org:icesjms:67/6/1138 2023-05-15T17:41:32+02:00 Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance Simmonds, E. John Portilla, Enrique Skagen, Dankert Beare, Doug Reid, Dave G. 2010-09-01 00:00:00.0 text/html http://icesjms.oxfordjournals.org/cgi/content/short/67/6/1138 https://doi.org/10.1093/icesjms/fsq013 en eng Oxford University Press http://icesjms.oxfordjournals.org/cgi/content/short/67/6/1138 http://dx.doi.org/10.1093/icesjms/fsq013 Copyright (C) 2010, International Council for the Exploration of the Sea/Conseil International pour l'Exploration de la Mer Articles TEXT 2010 fthighwire https://doi.org/10.1093/icesjms/fsq013 2010-08-22T20:05:02Z <qd> Simmonds, E. J., Portilla, E., Skagen, D., Beare, D., and Reid, D. G. 2010. Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance. – ICES Journal of Marine Science, 67: 1138–1153. </qd>Bayesian Markov chain Monte Carlo methods are ideally suited to analyses of situations where there are a variety of data sources, particularly where the uncertainties differ markedly among the data and the estimated parameters can be correlated. The example of Northeast Atlantic (NEA) mackerel is used to evaluate the agreement between available data from egg surveys, tagging, and catch-at-age using multiple models within the Bayesian framework WINBUGS. The errors in each source of information are dealt with independently, and there is extensive exploration of potential sources of uncertainty in both the data and the model. Model options include variation by age and over time of both selectivity in the fishery and natural mortality, varying the precision and calculation method for spawning-stock biomass derived from an egg survey, and the extent of missing catches varying over time. The models are compared through deviance information criterion and Bayesian posterior predictive p -values. To reconcile mortality estimated from the different datasets the landings and discards reported would have to have been between 1.7 and 3.6 times higher than the recorded catches. Text Northeast Atlantic HighWire Press (Stanford University) Simmonds ENVELOPE(159.567,159.567,-70.333,-70.333) Skagen ENVELOPE(12.310,12.310,66.018,66.018) ICES Journal of Marine Science 67 6 1138 1153 |
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HighWire Press (Stanford University) |
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English |
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Articles |
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Articles Simmonds, E. John Portilla, Enrique Skagen, Dankert Beare, Doug Reid, Dave G. Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance |
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Articles |
description |
<qd> Simmonds, E. J., Portilla, E., Skagen, D., Beare, D., and Reid, D. G. 2010. Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance. – ICES Journal of Marine Science, 67: 1138–1153. </qd>Bayesian Markov chain Monte Carlo methods are ideally suited to analyses of situations where there are a variety of data sources, particularly where the uncertainties differ markedly among the data and the estimated parameters can be correlated. The example of Northeast Atlantic (NEA) mackerel is used to evaluate the agreement between available data from egg surveys, tagging, and catch-at-age using multiple models within the Bayesian framework WINBUGS. The errors in each source of information are dealt with independently, and there is extensive exploration of potential sources of uncertainty in both the data and the model. Model options include variation by age and over time of both selectivity in the fishery and natural mortality, varying the precision and calculation method for spawning-stock biomass derived from an egg survey, and the extent of missing catches varying over time. The models are compared through deviance information criterion and Bayesian posterior predictive p -values. To reconcile mortality estimated from the different datasets the landings and discards reported would have to have been between 1.7 and 3.6 times higher than the recorded catches. |
format |
Text |
author |
Simmonds, E. John Portilla, Enrique Skagen, Dankert Beare, Doug Reid, Dave G. |
author_facet |
Simmonds, E. John Portilla, Enrique Skagen, Dankert Beare, Doug Reid, Dave G. |
author_sort |
Simmonds, E. John |
title |
Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance |
title_short |
Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance |
title_full |
Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance |
title_fullStr |
Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance |
title_full_unstemmed |
Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance |
title_sort |
investigating agreement between different data sources using bayesian state-space models: an application to estimating ne atlantic mackerel catch and stock abundance |
publisher |
Oxford University Press |
publishDate |
2010 |
url |
http://icesjms.oxfordjournals.org/cgi/content/short/67/6/1138 https://doi.org/10.1093/icesjms/fsq013 |
long_lat |
ENVELOPE(159.567,159.567,-70.333,-70.333) ENVELOPE(12.310,12.310,66.018,66.018) |
geographic |
Simmonds Skagen |
geographic_facet |
Simmonds Skagen |
genre |
Northeast Atlantic |
genre_facet |
Northeast Atlantic |
op_relation |
http://icesjms.oxfordjournals.org/cgi/content/short/67/6/1138 http://dx.doi.org/10.1093/icesjms/fsq013 |
op_rights |
Copyright (C) 2010, International Council for the Exploration of the Sea/Conseil International pour l'Exploration de la Mer |
op_doi |
https://doi.org/10.1093/icesjms/fsq013 |
container_title |
ICES Journal of Marine Science |
container_volume |
67 |
container_issue |
6 |
container_start_page |
1138 |
op_container_end_page |
1153 |
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1766143150027440128 |