Comparison and evaluation of approaches aimed at correcting or reducing selectivity bias in growth parameter estimates for fishes

Paul N. Frater*, Gunnar Stefansson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Several techniques have been proposed and tested to alleviate the common problem of selectivity bias in fish size-at-age data used for fitting growth models. Many of these techniques have not been directly compared to each other, though. Here, we test six methods for correcting selectivity bias in fish growth models using two selectivity shapes and across three life history types. We also include an example of fitted growth curve parameters from each method using data on Atlantic cod (Gadus morhua L.) from standardized surveys in the waters surrounding Iceland. Lastly, we present simulations that incorporate three types of misspecification on each method, selectivity type, and life-history strategy to discern the impacts of incorrect assumptions stemming from each method. Age structured populations are simulated, and the basic von Bertalanffy growth curve is used as the mean to assign normally-distributed length-at-age data which are sampled under two selectivity shapes. Growth parameters are then estimated using these data for each of six bias-correction methods. In general, likelihood-based bias-correction methods resulted in growth parameter estimates that are closer to true values used as well as more similar to parameter estimates derived from data sampled with no selectivity. The method introduced by Troynikov (1999) performed best for both simulation and misspecification scenarios. This result is true of both dome-shaped and asymptotic selectivity as well as across life-history types.

Original languageEnglish
Article number105464
JournalFisheries Research
Publication statusPublished - May 2020

Bibliographical note

Publisher Copyright:
© 2019 Elsevier B.V.

Other keywords

  • Age-length data
  • Bias-correction
  • Growth
  • Likelihood
  • Selectivity bias
  • von Bertalanffy


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