Hierarchical model-based estimation of population growth curves for redfish (Sebastes mentella and Sebastes fasciatus) off the Eastern coast of Canada

Noel G. Cadigan*, Steven E. Campana

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Northwest Atlantic (NWA) redfish (Sebastes mentella and Sebastes fasciatus) stocks are currently assessed using survey indicators and ageaggregated production models rather than age-based models because routine age readings are not available due to the difficulty in obtaining reliable measurements for these fish. However, recruitment is highly variable for redfish species so age-Aggregated production models are not a good approach to provide short-Term harvest advice. Recently a relatively large dataset of validated age readings was published that provide a good basis to model growth and its variability [i.e. population growth curve (PGC)]. In this article we propose a hierarchical random effects growth model that includes between-individual variation to estimate PGCs for 10 NWA redfish stocks and for males and females separately. These growth curves are required to develop age-based stock assessment models. External estimates of measurement error in length and age are included in our model to separate these sources of variation from the PGC variability. The hierarchical approach leads to more realistic growth curves than if each stock and sex are modelled separately. Model results indicated that S. mentella usually grow to larger sizes than S. fasciatus and that females of both these species grow to larger sizes than males. There was little evidence of a change in growth rates over time.

Original languageEnglish
Pages (from-to)687-697
Number of pages11
JournalICES Journal of Marine Science
Volume74
Issue number3
DOIs
Publication statusPublished - 1 Mar 2017

Bibliographical note

Publisher Copyright:
© International Council for the Exploration of the Sea 2016. All rights reserved.

Other keywords

  • ageing error
  • between-individual variation
  • covariate measurement error
  • mixed effects model
  • Von Bertalanffy growth curve

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