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

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• 作者：Kyle W. Shertzer^a ; ^{Kyle.Shertzer@noaa.gov" class="auth_mail" title="E-mail the corresponding author} ; John Fieberg^b ; Jennifer C. Potts^a ; Michael L. Burton^a
• 关键词：Bayesian ; Cubera snapper ; Mixture models ; Somatic growth ; Unsupervised classification ; von Bertalanffy
• 刊名：Fisheries Research
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：185
• 期：Complete
• 页码：83-89
• 全文大小：1520 K

文摘

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.