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RESEARCH ARTICLE (Open Access)

A re-examination of the growth of the gummy shark (Mustelus antarcticus) from Queensland, Australia

Matthew J. Campbell https://orcid.org/0000-0002-4950-4264 A * and Cassandra L. Rigby B
+ Author Affiliations
- Author Affiliations

A Queensland Department of Agriculture and Fisheries, Agri-Science Queensland, Ecosciences Precinct, GPO Box 267, Brisbane, Qld 4001, Australia.

B Centre for Sustainable Tropical Fisheries and Aquaculture and College of Science and Engineering, James Cook University, 1 James Cook Drive, Townsville, Qld 4811, Australia.

* Correspondence to: matthew.campbell@daf.qld.gov.au

Handling Editor: Bradley Wetherbee

Marine and Freshwater Research 73(11) 1399-1403 https://doi.org/10.1071/MF22066
Submitted: 16 March 2022  Accepted: 2 June 2022   Published: 20 July 2022

© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution 4.0 International License (CC BY)

Abstract

The gummy shark (Mustelus antarcticus) is endemic to Australia and is the target of commercial fisheries in southern Australia. However, the Queensland population is subjected to low levels of fishing mortality. The present study re-analysed a limited length-at-age dataset collected from central Queensland to estimate growth parameters in a Bayesian framework, with informative priors for size-at-birth and maximum size. Growth parameters were estimated using a multi-model approach. This study showed that M. antarcticus caught in Queensland exhibits slow growth compared with conspecifics in southern Australia, and females grow slower and larger than do males. The combined length-at-age data for males and females was best described by the von Bertalanffy growth function and the growth parameters were L = 1852 mm (total length, TL), L0 = 261 mm TL and k = 0.044 year−1 for males and females combined. The ‘BayesGrowth’ R package offers a simple method to minimise bias in the estimation of growth parameters from a limited length-at-age dataset in a Bayesian framework.

Keywords: BayesGrowth, elasmobranch, growth, gummy shark, length-at-age, life history, Mustelus antarcticus.


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