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Bayesian modeling of individual growth variability using back-calculation: Application to pink cusk-eel (Genypterus blacodes) off Chile

Author

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  • Contreras-Reyes, Javier E.
  • López Quintero, Freddy O.
  • Wiff, Rodrigo

Abstract

The von Bertalanffy growth function (VBGF) with random effects has been widely used to estimate growth parameters incorporating individual variability of length-at-age. Trajectories of individual growth can be inferred using either mark-recapture or back-calculation of length-at-age from growth marks in hard body parts such as otoliths. Modern statistical methods evaluate individual variation usually from mark-recapture data, and the parameters describing this function are estimated using empirical Bayes methods assuming Gaussian error. In this paper, we combine recent studies in non-Gaussian distributions and a Bayesian approach to model growth variability using back-calculated data in harvested fish populations. We presumed that errors in the VBGF can be assumed as a Student-t distribution, given the abundance of individuals with extreme length values. The proposed method was applied and compared to the standard methods using back-calculated length-at-age data for pink cusk-eel (Genypterus blacodes) off Chile. Considering several information criteria, and comparing males and females, we have found that males grow significantly faster than females, and that length-at-age for both sexes exhibits extreme length observations. Comparisons indicated that a Student-t model with mixed effects describes best back-calculated data regarding pink cusk-eel. This framework merged the strengths of different approaches to estimate growth parameters in harvested fish populations, considering modeling of individual variability of length-at-age, Bayesian inference, and distribution of errors from the Student-t model.

Suggested Citation

  • Contreras-Reyes, Javier E. & López Quintero, Freddy O. & Wiff, Rodrigo, 2018. "Bayesian modeling of individual growth variability using back-calculation: Application to pink cusk-eel (Genypterus blacodes) off Chile," Ecological Modelling, Elsevier, vol. 385(C), pages 145-153.
    • Handle: RePEc:eee:ecomod:v:385:y:2018:i:c:p:145-153
    DOI: 10.1016/j.ecolmodel.2018.07.002
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    References listed on IDEAS

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    1. J. D. Metcalfe & G. P. Arnold, 1997. "Tracking fish with electronic tags," Nature, Nature, vol. 387(6634), pages 665-666, June.
    2. Chambers, Mark S. & Sidhu, Leesa A. & O’Neill, Ben & Sibanda, Nokuthaba, 2017. "Flexible von Bertalanffy growth models incorporating Bayesian splines," Ecological Modelling, Elsevier, vol. 355(C), pages 1-11.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Matteo Convertino & L James Valverde Jr, 2013. "Portfolio Decision Analysis Framework for Value-Focused Ecosystem Management," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-14, June.
    5. Kotz,Samuel & Nadarajah,Saralees, 2004. "Multivariate T-Distributions and Their Applications," Cambridge Books, Cambridge University Press, number 9780521826549, January.
    6. Simone Vincenzi & Marc Mangel & Alain J Crivelli & Stephan Munch & Hans J Skaug, 2014. "Determining Individual Variation in Growth and Its Implication for Life-History and Population Processes Using the Empirical Bayes Method," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-16, September.
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