Identifying growth morphs from mixtures of size-at-age data

Kyle W. Shertzer, John Fieberg, Jennifer C. Potts, Michael L. Burton

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Somatic growth is critical to the biology of individuals and to population dynamics. Variability in size at age can often be attributed to the existence of distinct groups, or growth morphs, that differ in their growth trajectories. We develop a framework for identifying multiple growth morphs from mixture data, with utility for describing somatic growth at the population level as well as for classifying individuals into their most likely groups. For illustration, growth trajectories are modeled using the von Bertalanffy function, but the framework is general enough to accommodate any suitable growth function. After describing the framework, we demonstrate proof of concept using a simulation study, and then apply the proposed method to size-at-age data for Cubera snapper Lutjanus cyanopterus. In addition, we compare several Bayesian model selection criteria for inferring the unknown, underlying number of morphs.

Original languageEnglish (US)
Pages (from-to)83-89
Number of pages7
JournalFisheries Research
Volume185
DOIs
StatePublished - Jan 1 2017

Bibliographical note

Publisher Copyright:
© 2016

Keywords

  • Bayesian
  • Cubera snapper
  • Mixture models
  • Somatic growth
  • Unsupervised classification
  • von Bertalanffy

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