Parameter estimation using randomized phases in an integrated assessment model for Antarctic krill
An integrated model assessing the status and productivity of Antarctic krill (Euphausia superba, hereafter krill) was configured to estimate different subsets of 118 potentially estimable parameters in alternative configurations. We fixed the parameters that were not estimated in any given configura...
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ftpubmed:oai:pubmedcentral.nih.gov:6097675 2023-05-15T13:59:08+02:00 Parameter estimation using randomized phases in an integrated assessment model for Antarctic krill Kinzey, Douglas Watters, George M. Reiss, Christian S. 2018-08-17 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097675/ http://www.ncbi.nlm.nih.gov/pubmed/30118523 https://doi.org/10.1371/journal.pone.0202545 en eng Public Library of Science http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097675/ http://www.ncbi.nlm.nih.gov/pubmed/30118523 http://dx.doi.org/10.1371/journal.pone.0202545 https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. CC0 PDM Research Article Text 2018 ftpubmed https://doi.org/10.1371/journal.pone.0202545 2018-09-02T00:40:22Z An integrated model assessing the status and productivity of Antarctic krill (Euphausia superba, hereafter krill) was configured to estimate different subsets of 118 potentially estimable parameters in alternative configurations. We fixed the parameters that were not estimated in any given configuration at pre-specified values. The model was fitted to over forty years of fisheries and survey data for krill in Subarea 48.1, a statistical reporting area around the Antarctic Peninsula used by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). The number of estimated parameters was gradually increased across model configurations. Configurations that estimated more parameters fitted the data better, but the order in which the parameters were estimated became more important in finding the best fit. Twenty-two configurations estimating from 48 to 107 parameters were able to obtain an invertible Hessian matrix that was subsequently used to estimate parameter uncertainty. Parameter uncertainties calculated using asymptotic approximation around the maximum likelihood estimates were often larger than uncertainties based on Markov chain Monte Carlo sampling for the same parameters. Diagnostics applied to MCMC samples in the best model of each configuration that obtained an invertible Hessian indicated that the most highly parameterized configurations did not reach stationary distributions. A 96-parameter configuration was the best fitting model of those that passed the MCMC diagnostics. The ΔAIC and ΔBIC scores indicated essentially no support relative to the best model for the alternative models that also passed MCMC diagnostics. Simulated data using the configurations as operating models showed that while all configurations passed "self-tests" for spawning biomass and recruitment, there was a small negative bias due to model penalties in the fishing mortality estimates for years with the highest fishing mortalities. "Cross-tests" of configurations that estimated different parameters often ... Text Antarc* Antarctic Antarctic Krill Antarctic Peninsula Euphausia superba PubMed Central (PMC) Antarctic Antarctic Peninsula The Antarctic PLOS ONE 13 8 e0202545 |
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Research Article Kinzey, Douglas Watters, George M. Reiss, Christian S. Parameter estimation using randomized phases in an integrated assessment model for Antarctic krill |
topic_facet |
Research Article |
description |
An integrated model assessing the status and productivity of Antarctic krill (Euphausia superba, hereafter krill) was configured to estimate different subsets of 118 potentially estimable parameters in alternative configurations. We fixed the parameters that were not estimated in any given configuration at pre-specified values. The model was fitted to over forty years of fisheries and survey data for krill in Subarea 48.1, a statistical reporting area around the Antarctic Peninsula used by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). The number of estimated parameters was gradually increased across model configurations. Configurations that estimated more parameters fitted the data better, but the order in which the parameters were estimated became more important in finding the best fit. Twenty-two configurations estimating from 48 to 107 parameters were able to obtain an invertible Hessian matrix that was subsequently used to estimate parameter uncertainty. Parameter uncertainties calculated using asymptotic approximation around the maximum likelihood estimates were often larger than uncertainties based on Markov chain Monte Carlo sampling for the same parameters. Diagnostics applied to MCMC samples in the best model of each configuration that obtained an invertible Hessian indicated that the most highly parameterized configurations did not reach stationary distributions. A 96-parameter configuration was the best fitting model of those that passed the MCMC diagnostics. The ΔAIC and ΔBIC scores indicated essentially no support relative to the best model for the alternative models that also passed MCMC diagnostics. Simulated data using the configurations as operating models showed that while all configurations passed "self-tests" for spawning biomass and recruitment, there was a small negative bias due to model penalties in the fishing mortality estimates for years with the highest fishing mortalities. "Cross-tests" of configurations that estimated different parameters often ... |
format |
Text |
author |
Kinzey, Douglas Watters, George M. Reiss, Christian S. |
author_facet |
Kinzey, Douglas Watters, George M. Reiss, Christian S. |
author_sort |
Kinzey, Douglas |
title |
Parameter estimation using randomized phases in an integrated assessment model for Antarctic krill |
title_short |
Parameter estimation using randomized phases in an integrated assessment model for Antarctic krill |
title_full |
Parameter estimation using randomized phases in an integrated assessment model for Antarctic krill |
title_fullStr |
Parameter estimation using randomized phases in an integrated assessment model for Antarctic krill |
title_full_unstemmed |
Parameter estimation using randomized phases in an integrated assessment model for Antarctic krill |
title_sort |
parameter estimation using randomized phases in an integrated assessment model for antarctic krill |
publisher |
Public Library of Science |
publishDate |
2018 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097675/ http://www.ncbi.nlm.nih.gov/pubmed/30118523 https://doi.org/10.1371/journal.pone.0202545 |
geographic |
Antarctic Antarctic Peninsula The Antarctic |
geographic_facet |
Antarctic Antarctic Peninsula The Antarctic |
genre |
Antarc* Antarctic Antarctic Krill Antarctic Peninsula Euphausia superba |
genre_facet |
Antarc* Antarctic Antarctic Krill Antarctic Peninsula Euphausia superba |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097675/ http://www.ncbi.nlm.nih.gov/pubmed/30118523 http://dx.doi.org/10.1371/journal.pone.0202545 |
op_rights |
https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
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https://doi.org/10.1371/journal.pone.0202545 |
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PLOS ONE |
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13 |
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