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Advances in the aquatic sciences
RESEARCH ARTICLE

Modified hierarchical Bayesian biomass dynamics models for assessment of short-lived invertebrates: a comparison for tropical tiger prawns

Shijie Zhou A E , André E. Punt A B , Roy Deng A , Catherine M. Dichmont A , Yimin Ye A C and Janet Bishop D
+ Author Affiliations
- Author Affiliations

A CSIRO Marine and Atmospheric Research, PO Box 120, Cleveland, Qld 4163, Australia.

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

C Present address: Fishery Management and Conservation Service, Food & Agriculture Organisation of the United Nations, Viele delle Terme di Caracalla, 00153 Rome, Italy.

D Present address: 20 Tooth Street, Nobby, Qld 4360, Australia.

E Corresponding author. Email: shijie.zhou@csiro.au

Marine and Freshwater Research 60(12) 1298-1308 https://doi.org/10.1071/MF09022
Submitted: 4 February 2009  Accepted: 18 May 2009   Published: 17 December 2009

Abstract

Conventional biomass dynamics models express next year’s biomass as this year’s biomass plus surplus production less catch. These models are typically applied to species with several age-classes but it is unclear how well they perform for short-lived species with low survival and high recruitment variation. Two alternative versions of the standard biomass dynamics model (Standard) were constructed for short-lived species by ignoring the ‘old biomass’ term (Annual), and assuming that the biomass at the start of the next year depends on density-dependent processes that are a function of that biomass (Stock-recruit). These models were fitted to catch and effort data for the grooved tiger prawn Penaeus semisulcatus using a hierarchical Bayesian technique. The results from the biomass dynamics models were compared with those from more complicated weekly delay-difference models. The analyses show that: the Standard model is flexible for short-lived species; the Stock-recruit model provides the most parsimonious fit; simple biomass dynamics models can provide virtually identical results to data-demanding models; and spatial variability in key population dynamics parameters exists for P. semisulacatus. The method outlined in this paper provides a means to conduct quantitative population assessments for data-limited short-lived species.

Additional keywords: maximum likelihood, observation error, process error, squid, state-space, surplus production.


Acknowledgements

Drs You-Gan Wang (CSIRO Mathematics and Statistics), Wayne Rochester, Malcolm Haddon and two anonymous reviewers are thanked for their comments on an earlier version of this paper. This work was supported by the Australian Fisheries Research and Development Corporation.


References

Askey, P. J. , Post, J. R. , Parkinson, E. A. , Rivot, E. , Paul, A. J. , and Biro, P. A. (2007). Estimation of gillnet efficiency and selectivity across multiple sampling units: a hierarchical Bayesian analysis using mark-recapture data. Fisheries Research 83, 162–174.
Crossref | GoogleScholarGoogle Scholar | Best N., Cowles M. K., and Vines K. (1996). ‘CODA Convergence Diagnosis and Output Analysis Software for Gibbs Sampling Output.’ (MRC Biostatistics Unit: Cambridge, UK.)

Bishop, J. (2006). Standardizing fishery-dependent catch and effort in a complex fishery where technology changed. Reviews in Fish Biology and Fisheries 16, 21–38.
Crossref | GoogleScholarGoogle Scholar | Dichmont C. M., Deng A. R., Venables W. N., Punt A. E., Haddon M., et al. (2005). A new approach to assessment in the NPF: spatial models in a management strategy environment that includes uncertainty. Fisheries Research and Development Corporation Report 2001/002, Cleverland, Australia.

Van Dongen, S. (2006). Prior specification in Bayesian statistics: three cautionary tales. Journal of Theoretical Biology 242, 90–100.
Crossref | GoogleScholarGoogle Scholar | PubMed | Gillman M., and Hails R. (1997). ‘An Introduction to Ecological Modelling—Putting Practice into Theory.’ (Blackwell Science: Oxford, UK.)

Ghosh, S. K. , and Norris, J. L. (2005). Bayesian capture-recapture analysis and model selection allowing for heterogeneity and behavioural effects. Journal of Agricultural Biological & Environmental Statistics 10, 35–49.
Crossref | GoogleScholarGoogle Scholar | Haddon M. (2001). ‘Modelling and Quantitative Methods in Fisheries.’ (Chapman and Hall/CRC: Boca Raton, FL.)

Harley, S. J. , and Myers, R. A. (2001). Hierarchical Bayesian models of length-specific catchability of research trawl surveys. Canadian Journal of Fisheries and Aquatic Sciences 58, 1569–1584.
Crossref | GoogleScholarGoogle Scholar | Punt A. E., and Hilborn R. (1996). ‘Biomass Dynamic Models. User’s Manual.’ FAO Computerized Information Series (Fisheries). (Rome: FAO.)

Quinn T. J., and Deriso R. B. (1999). ‘Quantitative Fish Dynamics.’ (Oxford University Press: New York.)

Rivot, E. , and Prevost, E. (2002). Hierarchical Bayesian analysis of capture-mark-recapture data. Canadian Journal of Fisheries and Aquatic Sciences 59, 1768–1784.
Crossref | GoogleScholarGoogle Scholar | Sheskin D. (1997). ‘Handbook of Parametric and Nonparametric Statistical Procedures.’ (CRC Press: Boca Raton, FL.)

Smith, M. T. , and Addison, J. T. (2003). Method for stock assessment of crustacean fisheries. Fisheries Research 65, 231–256.
Crossref | GoogleScholarGoogle Scholar | Venables W. N., Kenyon R. A., Bishop J. F. B., Dichmont C. M., et al. (2006). Species distribution and catch allocation: data and methods for the NPF, 2002–2004. Final report. AFMA Project No. R01/1149. Australian Fisheries Management Authority, Canberra.

Webster, R. A. , Pollock, K. H. , Ghosh, S. K. , and Hankin, D. G. (2008). Bayesian spatial modelling of data from unit-count surveys of fish in streams. Transactions of the American Fisheries Society 137, 438–453.
Crossref | GoogleScholarGoogle Scholar | Ye Y., Kenyon R. A., Burridge C., Dichmont C. M., Pendrey R., et al. (2007). An integrated monitoring program for the Northern Prawn Fishery 2005/06. Project Australian Fisheries Management Authority R05/0599, Australia.