Journal Article

Fitting growth models to length frequency data

Geoff M. Laslett, J. Paige Eveson and Tom Polacheck

in ICES Journal of Marine Science

Published on behalf of ICES/CIEM

Volume 61, issue 2, pages 218-230
Published in print January 2004 | ISSN: 1054-3139
Published online January 2004 | e-ISSN: 1095-9289 | DOI:
Fitting growth models to length frequency data

More Like This

Show all results sharing these subjects:

  • Environmental Science
  • Marine and Estuarine Biology


Show Summary Details


A novel two-stage procedure for fitting growth curves to length frequency data collected from commercial fisheries is described. The method is suitable for species in which cohorts are spawned over a limited time period, and samples of length frequency data are collected regularly (e.g. in weekly, fortnightly, or monthly time intervals) over an extended time period. In the first stage of analysis, Gaussian mixtures are fitted separately to the data for each time interval, and summary statistics (component means and standard errors) are extracted. In the second stage, parametric growth models, such as the von Bertalanffy seasonal growth curve, are fitted to the summary data. The error structure in this second stage of analysis incorporates random between-year effects, random within-year age-group effects, random within-year time-interval effects, random within-year age-group and time-interval interactions, and sampling errors. This complex error structure incorporating unbalanced crossed and nested random effects acknowledges that commercial fishing is not an exercise in random sampling, and allows for the inevitable additional sources of random variation in such an enterprise. The method is applied to South Australian southern bluefin tuna length frequency data collected from 1964 to 1989, and leads to the conclusion that juvenile tuna grew faster in the 1980s than in the 1960s, with the 1970s being a decade of highly variable growth.

Keywords: maximum likelihood; mixture decompositions; variance components

Journal Article.  6869 words.  Illustrated.

Subjects: Environmental Science ; Marine and Estuarine Biology

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.