Choosing the observational likelihood in state-space stock assessment models

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Abstract

Data used in stock assessment models result from combinations of biological, ecological, fishery, and sampling processes. Since different types of errors propagate through these processes it can be difficult to identify a particular family of distributions for modelling errors on observations a priori. By implementing several observational likelihoods, modelling both numbers- and proportions-at-age, in an age based state-space stock assessment model, we compare the model fit for each choice of likelihood along with the implications for spawning stock biomass and average fishing mortality. We propose using AIC intervals based on fitting the full observational model for comparing different observational likelihoods. Using data from four stocks, we show that the model fit is improved by modelling the correlation of observations within years. However, the best choice of observational likelihood differs for different stocks, and the choice is important for the short-term conclusions drawn from the assessment model; in particular, the choice can influence total allowable catch advise based on reference points.
Original languageEnglish
JournalCanadian Journal of Fisheries and Aquatic Sciences
Volume74
Issue number5
Pages (from-to)779-789
ISSN0706-652X
DOIs
Publication statusPublished - 2017

Keywords

  • stat.AP
  • q-bio.QM

Cite this

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title = "Choosing the observational likelihood in state-space stock assessment models",
abstract = "Data used in stock assessment models result from combinations of biological, ecological, fishery, and sampling processes. Since different types of errors propagate through these processes it can be difficult to identify a particular family of distributions for modelling errors on observations a priori. By implementing several observational likelihoods, modelling both numbers- and proportions-at-age, in an age based state-space stock assessment model, we compare the model fit for each choice of likelihood along with the implications for spawning stock biomass and average fishing mortality. We propose using AIC intervals based on fitting the full observational model for comparing different observational likelihoods. Using data from four stocks, we show that the model fit is improved by modelling the correlation of observations within years. However, the best choice of observational likelihood differs for different stocks, and the choice is important for the short-term conclusions drawn from the assessment model; in particular, the choice can influence total allowable catch advise based on reference points.",
keywords = "stat.AP, q-bio.QM",
author = "Albertsen, {Christoffer Moesgaard} and Anders Nielsen and Thygesen, {Uffe H{\o}gsbro}",
year = "2017",
doi = "10.1139/cjfas-2015-0532",
language = "English",
volume = "74",
pages = "779--789",
journal = "Canadian Journal of Fisheries and Aquatic Sciences",
issn = "0706-652X",
publisher = "N R C Research Press",
number = "5",

}

TY - JOUR

T1 - Choosing the observational likelihood in state-space stock assessment models

AU - Albertsen, Christoffer Moesgaard

AU - Nielsen, Anders

AU - Thygesen, Uffe Høgsbro

PY - 2017

Y1 - 2017

N2 - Data used in stock assessment models result from combinations of biological, ecological, fishery, and sampling processes. Since different types of errors propagate through these processes it can be difficult to identify a particular family of distributions for modelling errors on observations a priori. By implementing several observational likelihoods, modelling both numbers- and proportions-at-age, in an age based state-space stock assessment model, we compare the model fit for each choice of likelihood along with the implications for spawning stock biomass and average fishing mortality. We propose using AIC intervals based on fitting the full observational model for comparing different observational likelihoods. Using data from four stocks, we show that the model fit is improved by modelling the correlation of observations within years. However, the best choice of observational likelihood differs for different stocks, and the choice is important for the short-term conclusions drawn from the assessment model; in particular, the choice can influence total allowable catch advise based on reference points.

AB - Data used in stock assessment models result from combinations of biological, ecological, fishery, and sampling processes. Since different types of errors propagate through these processes it can be difficult to identify a particular family of distributions for modelling errors on observations a priori. By implementing several observational likelihoods, modelling both numbers- and proportions-at-age, in an age based state-space stock assessment model, we compare the model fit for each choice of likelihood along with the implications for spawning stock biomass and average fishing mortality. We propose using AIC intervals based on fitting the full observational model for comparing different observational likelihoods. Using data from four stocks, we show that the model fit is improved by modelling the correlation of observations within years. However, the best choice of observational likelihood differs for different stocks, and the choice is important for the short-term conclusions drawn from the assessment model; in particular, the choice can influence total allowable catch advise based on reference points.

KW - stat.AP

KW - q-bio.QM

U2 - 10.1139/cjfas-2015-0532

DO - 10.1139/cjfas-2015-0532

M3 - Journal article

VL - 74

SP - 779

EP - 789

JO - Canadian Journal of Fisheries and Aquatic Sciences

JF - Canadian Journal of Fisheries and Aquatic Sciences

SN - 0706-652X

IS - 5

ER -