Estimation of time-varying selectivity in stock assessments using state-space models

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Time-varying selectivity is one of the main challenges in single species age-based assessment models. In classical deterministic VPA-type models the fishing mortality rates are unfiltered representations of the observed catches. As a consequence the selectivity becomes time-varying, but this representation is too fluctuating, because it includes the observation noise. In parametric statistical catch at age models a common assumption is that the selectivity is constant in all years, although time-varying selectivity can be introduced by splitting the data period in blocks with different selectivities, or by using smoothing splines and penalized time-deviances. However, these methods require subjective choices w.r.t. the degree of time-varying allowed. A simple state-space assessment model is presented as an alternative, which among other benefits offers an objective way of estimating time-varying selectivity pattern. The fishing mortality rates are considered (possibly correlated) stochastic processes, and the corresponding process variances are estimated within the model. The model is applied to North Sea cod and it is verified from simulations that time-varying selectivity can be estimated
Original languageEnglish
JournalFisheries Research
Volume158
Pages (from-to)96-101
ISSN0165-7836
DOIs
Publication statusPublished - 2014

Cite this

@article{becb4117bfdd4c4f89d780f3249c2af0,
title = "Estimation of time-varying selectivity in stock assessments using state-space models",
abstract = "Time-varying selectivity is one of the main challenges in single species age-based assessment models. In classical deterministic VPA-type models the fishing mortality rates are unfiltered representations of the observed catches. As a consequence the selectivity becomes time-varying, but this representation is too fluctuating, because it includes the observation noise. In parametric statistical catch at age models a common assumption is that the selectivity is constant in all years, although time-varying selectivity can be introduced by splitting the data period in blocks with different selectivities, or by using smoothing splines and penalized time-deviances. However, these methods require subjective choices w.r.t. the degree of time-varying allowed. A simple state-space assessment model is presented as an alternative, which among other benefits offers an objective way of estimating time-varying selectivity pattern. The fishing mortality rates are considered (possibly correlated) stochastic processes, and the corresponding process variances are estimated within the model. The model is applied to North Sea cod and it is verified from simulations that time-varying selectivity can be estimated",
author = "Anders Nielsen and Berg, {Casper Willestofte}",
year = "2014",
doi = "10.1016/j.fishres.2014.01.014",
language = "English",
volume = "158",
pages = "96--101",
journal = "Fisheries Research",
issn = "0165-7836",
publisher = "Elsevier",

}

Estimation of time-varying selectivity in stock assessments using state-space models. / Nielsen, Anders; Berg, Casper Willestofte.

In: Fisheries Research, Vol. 158, 2014, p. 96-101.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Estimation of time-varying selectivity in stock assessments using state-space models

AU - Nielsen, Anders

AU - Berg, Casper Willestofte

PY - 2014

Y1 - 2014

N2 - Time-varying selectivity is one of the main challenges in single species age-based assessment models. In classical deterministic VPA-type models the fishing mortality rates are unfiltered representations of the observed catches. As a consequence the selectivity becomes time-varying, but this representation is too fluctuating, because it includes the observation noise. In parametric statistical catch at age models a common assumption is that the selectivity is constant in all years, although time-varying selectivity can be introduced by splitting the data period in blocks with different selectivities, or by using smoothing splines and penalized time-deviances. However, these methods require subjective choices w.r.t. the degree of time-varying allowed. A simple state-space assessment model is presented as an alternative, which among other benefits offers an objective way of estimating time-varying selectivity pattern. The fishing mortality rates are considered (possibly correlated) stochastic processes, and the corresponding process variances are estimated within the model. The model is applied to North Sea cod and it is verified from simulations that time-varying selectivity can be estimated

AB - Time-varying selectivity is one of the main challenges in single species age-based assessment models. In classical deterministic VPA-type models the fishing mortality rates are unfiltered representations of the observed catches. As a consequence the selectivity becomes time-varying, but this representation is too fluctuating, because it includes the observation noise. In parametric statistical catch at age models a common assumption is that the selectivity is constant in all years, although time-varying selectivity can be introduced by splitting the data period in blocks with different selectivities, or by using smoothing splines and penalized time-deviances. However, these methods require subjective choices w.r.t. the degree of time-varying allowed. A simple state-space assessment model is presented as an alternative, which among other benefits offers an objective way of estimating time-varying selectivity pattern. The fishing mortality rates are considered (possibly correlated) stochastic processes, and the corresponding process variances are estimated within the model. The model is applied to North Sea cod and it is verified from simulations that time-varying selectivity can be estimated

U2 - 10.1016/j.fishres.2014.01.014

DO - 10.1016/j.fishres.2014.01.014

M3 - Journal article

VL - 158

SP - 96

EP - 101

JO - Fisheries Research

JF - Fisheries Research

SN - 0165-7836

ER -