Using an approximate length-conditional approach to estimate von Bertalanffy growth parameters of North Pacific albacore (Thunnus alalunga) Academic Article uri icon

abstract

  • 2015 Elsevier B.V. Growth models in stock assessments can strongly influence the estimated biomass that affect the conclusion of stock status and exploitation level. Recent studies on North Pacific albacore (Thunnus alalunga) growth obtained age-length data from hard parts and fit the age-length data to a von Bertalanffy growth model, assuming each observation of length is a random sample for a given age. However, these previous studies may have resulted in biased growth parameter estimates because these samples were not chosen at random and hence violated the assumptions of the method. In this study, we instead use an "approximate length-conditional" approach, which assumes that each fish is a random sample from that length bin based on an equilibrium population age structure, to fit age-length data from three previous studies. Results of the length-conditional approach resulted in a sex-combined growth curve that is similar to the previous estimates over the young and mid ages (age 2-6) but with different asymptotic lengths. Estimated growth parameters were not highly sensitive to assumed mortality rates but changing the data-weighting scheme can result in differences in estimated growth parameters. Although the length-conditional approach likely result in less biased estimated length-at-ages, especially for the youngest and oldest ages, the estimated growth curves from this study may not be representative of the stock due to potential regional differences in growth, and age and sex-specific movements. In order to successfully unravel the complexities of albacore growth observed in this and previous studies, given the complex life history, ocean-basin scale movements and multiple international fisheries, a well-coordinated and designed international sampling effort will be required.

published proceedings

  • Fisheries Research

altmetric score

  • 0.5

author list (cited authors)

  • Xu, Y. i., Teo, S., Piner, K. R., Chen, K., & Wells, R.

citation count

  • 8

complete list of authors

  • Xu, Yi||Teo, Steven LH||Piner, Kevin R||Chen, Kuo-Shu||Wells, RJ David

publication date

  • August 2016