Connecting single-stock assessment models through correlated survival

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Abstract

Fisheries management is mainly conducted via single-stock assessment models assuming that fish stocks do not interact, except through assumed natural mortalities. Currently, the main alternative is complex ecosystem models which require extensive data, are difficult to calibrate, and have long run times. We propose a simple alternative. In three case studies each with two stocks, we improve the single-stock models, as measured by Akaike information criterion, by adding correlation in the cohort survival. To limit the number of parameters, the correlations are parameterized through the corresponding partial correlations. We consider six models where the partial correlation matrix between stocks follows a band structure ranging from independent assessments to complex correlation structures. Further, a simulation study illustrates the
importance of handling correlated data sufficiently by investigating the coverage of confidence intervals for estimated fishing mortality. The results presented will allow managers to evaluate stock statuses based on a more accurate evaluation of model output uncertainty. The methods are directly implementable for stocks with an analytical assessment and do not require any new data sources
Original languageEnglish
JournalICES Journal of Marine Science
Volume75
Issue number1
Pages (from-to)235-244
ISSN1054-3139
DOIs
Publication statusPublished - 2017

Cite this

@article{cd494bdcf11a41b1b2351710475143e5,
title = "Connecting single-stock assessment models through correlated survival",
abstract = "Fisheries management is mainly conducted via single-stock assessment models assuming that fish stocks do not interact, except through assumed natural mortalities. Currently, the main alternative is complex ecosystem models which require extensive data, are difficult to calibrate, and have long run times. We propose a simple alternative. In three case studies each with two stocks, we improve the single-stock models, as measured by Akaike information criterion, by adding correlation in the cohort survival. To limit the number of parameters, the correlations are parameterized through the corresponding partial correlations. We consider six models where the partial correlation matrix between stocks follows a band structure ranging from independent assessments to complex correlation structures. Further, a simulation study illustrates theimportance of handling correlated data sufficiently by investigating the coverage of confidence intervals for estimated fishing mortality. The results presented will allow managers to evaluate stock statuses based on a more accurate evaluation of model output uncertainty. The methods are directly implementable for stocks with an analytical assessment and do not require any new data sources",
author = "Albertsen, {Christoffer Moesgaard} and Anders Nielsen and Thygesen, {Uffe H{\o}gsbro}",
year = "2017",
doi = "10.1093/icesjms/fsx114",
language = "English",
volume = "75",
pages = "235--244",
journal = "I C E S Journal of Marine Science",
issn = "1054-3139",
publisher = "Oxford University Press",
number = "1",

}

Connecting single-stock assessment models through correlated survival. / Albertsen, Christoffer Moesgaard; Nielsen, Anders; Thygesen, Uffe Høgsbro.

In: ICES Journal of Marine Science, Vol. 75, No. 1, 2017, p. 235-244.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Connecting single-stock assessment models through correlated survival

AU - Albertsen, Christoffer Moesgaard

AU - Nielsen, Anders

AU - Thygesen, Uffe Høgsbro

PY - 2017

Y1 - 2017

N2 - Fisheries management is mainly conducted via single-stock assessment models assuming that fish stocks do not interact, except through assumed natural mortalities. Currently, the main alternative is complex ecosystem models which require extensive data, are difficult to calibrate, and have long run times. We propose a simple alternative. In three case studies each with two stocks, we improve the single-stock models, as measured by Akaike information criterion, by adding correlation in the cohort survival. To limit the number of parameters, the correlations are parameterized through the corresponding partial correlations. We consider six models where the partial correlation matrix between stocks follows a band structure ranging from independent assessments to complex correlation structures. Further, a simulation study illustrates theimportance of handling correlated data sufficiently by investigating the coverage of confidence intervals for estimated fishing mortality. The results presented will allow managers to evaluate stock statuses based on a more accurate evaluation of model output uncertainty. The methods are directly implementable for stocks with an analytical assessment and do not require any new data sources

AB - Fisheries management is mainly conducted via single-stock assessment models assuming that fish stocks do not interact, except through assumed natural mortalities. Currently, the main alternative is complex ecosystem models which require extensive data, are difficult to calibrate, and have long run times. We propose a simple alternative. In three case studies each with two stocks, we improve the single-stock models, as measured by Akaike information criterion, by adding correlation in the cohort survival. To limit the number of parameters, the correlations are parameterized through the corresponding partial correlations. We consider six models where the partial correlation matrix between stocks follows a band structure ranging from independent assessments to complex correlation structures. Further, a simulation study illustrates theimportance of handling correlated data sufficiently by investigating the coverage of confidence intervals for estimated fishing mortality. The results presented will allow managers to evaluate stock statuses based on a more accurate evaluation of model output uncertainty. The methods are directly implementable for stocks with an analytical assessment and do not require any new data sources

U2 - 10.1093/icesjms/fsx114

DO - 10.1093/icesjms/fsx114

M3 - Journal article

VL - 75

SP - 235

EP - 244

JO - I C E S Journal of Marine Science

JF - I C E S Journal of Marine Science

SN - 1054-3139

IS - 1

ER -