Register      Login
Marine and Freshwater Research Marine and Freshwater Research Society
Advances in the aquatic sciences
Shopping Cart: ( empty )
You are here: Home > Journals > MF > MF05261 > Fulltext
RESEARCH ARTICLE
Contents Vol 57(5)

Bayesian hierarchical modelling of maturity-at-length for rock lobsters, Jasus edwardsii, off Victoria, Australia

André E. Punt A B D , David K. Hobday C and Rhonda Flint C
+ Author Affiliations
- Author Affiliations

A School of Aquatic and Fishery Sciences, Box 355020, University of Washington, Seattle, WA 98195-5020, USA.

B CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, TAS 7001, Australia.

C Marine and Freshwater Systems, Department of Primary Industries, PO Box 114, Queenscliff, VIC 3225, Australia.

D Corresponding author. Email: aepunt@u.washington.edu

Marine and Freshwater Research 57(5) 503-511 https://doi.org/10.1071/MF05261
Submitted: 24 December 2005  Accepted: 7 April 2006   Published: 10 July 2006

Abstract

Maturity-at-length is a key input to stock assessments when the management objectives are expressed in terms of the size of the spawning output relative to some reference level. Data for rock lobsters, Jasus edwardsii (Hutton, 1875), off Victoria, Australia, are used to estimate logistic relationships between carapace length and the probability of being mature. The analyses are based primarily on mixed-effects models in which the parameters governing maturity-at-length depend on year and region, fitted using a Bayesian hierarchical approach. Maturity-at-length differs among years and regions, and the length-at-50%-maturity increases from west to east and then remains relatively constant. However, the estimates for all years and regions are not equally precise, so there is value in using a mixed-effects approach to allow the years for which the dataset is large to ‘provide support’ for the years for which the data are sparse. The results provide the input needed to conduct assessments of rock lobster populations off Victoria and to form the basis for conducting population projections.

Extra keywords: crustacean, fixed effects models, mixed effects models.


Acknowledgments

This work was jointly supported by Fisheries Research and Development Corporation project 2004/037 and the Department of Primary Industries, Victoria. John Brandon, Jason Cope, Gavin Fay (University of Washington), Sandy Morrison (PirVic), and two anonymous reviewers kindly provided comments on an earlier draft of this paper.


References

Anon  (2003). Rock Lobster Fishery Management Plan. Management Report Series 1. Fisheries Victoria, Melbourne.

Chen, Y. , Kanaiwa, M. , and Wilson, C. (2005). Developing and evaluating a size-structured stock assessment model for the American lobster, Homarus americanus, fishery. New Zealand Journal of Marine and Freshwater Research 39, 645–660.
Gelman A., Carlin J. B., Stern H. S., and Rubin D. B. (1995). ‘Bayesian Data Analysis.’ (Chapman & Hall: New York.)

Geweke J. (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In ‘Bayesian Statistics 4’. (Eds J. M. Bernardo, J. Berger, A. P. Dawid and A. F. M. Smith.) pp. 169–193. (Oxford University Press: Oxford.)

Haist V., Breen P. A., Kim S. W., and Starr P. J. (2005). Stock assessment of red rock lobsters (Jasus edwardsii) in CRA 3 in 2004. New Zealand Fisheries Assessment Report 38. New Zealand Fisheries, Wellington.

Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97–109.
| Crossref | GoogleScholarGoogle Scholar | Minte-Vera C. V. (2004). Meta-analysis of density-dependent growth. Ph.D. Thesis, University of Washington, Seattle, WA.

Pinheiro J. C., and Bates D. M. (2000). ‘Mixed-effects models in S and SPLUS.’ (Springer: New York.)

Punt, A. E. , and Kennedy, R. B. (1997). Population modelling of Tasmanian rock lobster, Jasus edwardsii, resources. Marine and Freshwater Research 48, 967–980.
| Crossref | GoogleScholarGoogle Scholar | Raftery A. E., and Lewis S. (1992). How many iterations in the Gibbs sampler? In ‘Bayesian Statistics 4’. (Eds J. M. Bernardo, J. Berger, A. P. Dawid and A. F. M. Smith.) pp. 763–773. (Oxford University Press: Oxford.)