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Article << Previous     |     Next >>   Contents Vol 60(2)

Performance of methods for estimating size–transition matrices using tag–recapture data

André E. Punt A B F, Rik C. Buckworth C, Catherine M. Dichmont D, Yimin Ye D E

A CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia.
B School of Aquatic and Fishery Sciences, Box 355020, University of Washington, Seattle, WA 98195-5020, USA.
C Fisheries, Department of Regional Development, Fisheries and Resources, GPO Box 3000, Darwin, NT 0810, Australia.
D CSIRO Marine and Atmospheric Research, PO Box 120, Cleveland, Qld 4163, Australia.
E Current address: Fishery Management & Conservation Service, FAO of the United Nations, Viale delle Termi diCaracalla, 00153, Rome, Italy.
F Corresponding author. Email: andre.punt@csiro.au
 
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Abstract

Management advice for hard-to-age species such as prawns, crabs and rock lobsters are usually based on size-structured population dynamics models. These models require a size–transition matrix that specifies the probabilities of growing from one size-class to the others. Many methods exist to estimate size–transition matrices using tag–recapture data. However, they have not been compared in a systematic way. Eight of these methods are compared using Monte Carlo simulations parameterised using the data for the tiger prawn (Penaeus semisulcatus). Four of the methods are then applied to tag–recapture data for three prawn species in Australia’s Northern Prawn Fishery to highlight the considerable sensitivity of model outputs to the method for estimating the size–transition matrix. The simulations show that not all methods perform equally well and that some methods are extremely poor. The ‘best’ methods, as identified in the simulations, are those that allow for individual variability in the parameters of the growth curve as well as the age-at-release. A method that assumes that l rather than k varies among individuals tends to be more robust to violations of model assumptions.

Keywords: Australia, prawns, size-structured models, tagging data.


   
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