Using length data in the Schnute Model to describe growth in a metapenaeid from waters off Australia
Steven S. Montgomery A C , Chris T. Walsh A , Malcolm Haddon B , Caitlin L. Kesby A and Daniel D. Johnson A
+ Author Affiliations
- Author Affiliations
A NSW Department of Primary Industries, Cronulla Fisheries Research Centre of Excellence, PO Box 21, Cronulla, NSW 2230, Australia.
B CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas 7001, Australia.
C Corresponding author. Email: steven.montgomery@dpi.nsw.gov.au
Marine and Freshwater Research 61(12) 1435-1445 https://doi.org/10.1071/MF10060
Submitted: 3 March 2010 Accepted: 14 August 2010 Published: 13 December 2010
Abstract
This paper presents a novel approach for fitting length data to the Schnute growth model. Cohorts were fitted manually to a time series of length distributions from two stocks (Clarence and Hunter Rivers) of Metapenaeus macleayi and considered analogous to individuals from tag–recapture data, in order to estimate growth parameters. Data for Clarence males best fitted the three-parameter Schnute Model (L∞ = 21.3 mm CL, κ = 0.025 day–1 and γ = –1.35), whereas those for Hunter males were best fitted to a two-parameter version of the model (L∞ = 33.5 mm CL, κ = 0.009 day–1 and γ = 0 fixed). The equivalent to the von Bertalanffy growth function was the best fit to female data from both stocks (L∞ = 36.6 and 40.2 mm CL, κ = 0.004 and 0.005 day–1 and γ = 1 fixed for Clarence and Hunter respectively). Females grew larger than males and took longer to achieve their maximum size. No significant differences in female growth were found between stocks; however, males from the Hunter grew to a longer mean maximum length but at a slower rate than those from the Clarence. This study shows how the Schnute Model can be fitted to length based data and thus include the flexibility of comparing fits between asymptotic and non-asymptotic growth functions.
Additional keywords: growth models, length–frequency analyses, Metapenaeus macleayi, school prawn.
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