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Advances in the aquatic sciences
RESEARCH ARTICLE

Performance of a fisheries catch-at-age model (Stock Synthesis) in data-limited situations

Chantell R. Wetzel A B C and André E. Punt B
+ Author Affiliations
- Author Affiliations

A Northwest Fisheries Science Center, National Marine Fisheries Service, 2725 Montlake Boulevard East, Seattle, WA 98112, USA.

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

C Corresponding author. Email: chantell.wetzel@noaa.gov

Marine and Freshwater Research 62(8) 927-936 https://doi.org/10.1071/MF11006
Submitted: 13 January 2011  Accepted: 23 April 2011   Published: 22 August 2011

Abstract

Limited data are a common challenge posed to fisheries stock assessment. A simulation framework was applied to examine the impact of limited data and data type on the performance of a widely used catch-at-age stock-assessment method (Stock Synthesis). The estimation method provided negatively biased estimates of current spawning-stock biomass (SSB) relative to the unfished level (final depletion) when only recent survey indices were available. Estimation of quantities of management interest (unfished SSB, virgin recruitment, target fishing mortality and final depletion) improved substantially even when only minimal-length-composition data from the survey were available. However, the estimates of some quantities (final depletion and unfished SSB) remained biased (either positively or negatively) even in the scenarios with the most data (length compositions, age compositions and survey indices). The probability of overestimating yield at the target SSB relative to the true such yield was ~50%, a risk-neutral result, for all the scenarios that included length-composition data. Our results highlight the importance of length-composition data for the performance of an age-structured assessment model, and are encouraging for the assessment of data-limited stocks.

Additional keywords: fisheries modelling, groundfish, US west coast.


References

Butterworth, D. S., and Punt, A. E. (1999). Experience in the evaluation and implementation of management procedures. ICES Journal of Marine Science 56, 985–998.
Experience in the evaluation and implementation of management procedures.Crossref | GoogleScholarGoogle Scholar |

Chen, Y., Chen, L., and Stergiou, K. I. (2003). Impacts of data quantity on fisheries stock assessment. Aquatic Sciences 65, 92–98.
Impacts of data quantity on fisheries stock assessment.Crossref | GoogleScholarGoogle Scholar |

Cope, J. M., and Punt, A. E. (2009). Length-based reference points for data-limited situations: applications and restrictions. Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science 1, 169–186.
Length-based reference points for data-limited situations: applications and restrictions.Crossref | GoogleScholarGoogle Scholar |

Fournier, D., and Archibald, C. P. (1982). A general theory for analyzing catch at age data. Canadian Journal of Fisheries and Aquatic Sciences 39, 1195–1207.
A general theory for analyzing catch at age data.Crossref | GoogleScholarGoogle Scholar |

Hicks, A. C., Haltuch, M. A., and Wetzel, C. (2009). Status of greenstriped rockfish (Sebastes elongates) along the outer coast of California, Oregon, and Washington. Pacific Fishery Management Council. Portland, OR.

Magnusson, A., and Hilborn, R. (2007). What makes fisheries data informative? Fish and Fisheries 8, 337–358.
What makes fisheries data informative?Crossref | GoogleScholarGoogle Scholar |

Maunder, M. N., and Starr, P. J. (2001). Bayesian assessment of the SNA 1 snapper (Pagrus auratus) stock on the north-east coast of New Zealand. New Zealand Journal of Marine and Freshwater Research 35, 87–110.
Bayesian assessment of the SNA 1 snapper (Pagrus auratus) stock on the north-east coast of New Zealand.Crossref | GoogleScholarGoogle Scholar |

Methot, R. D. (2005). Technical description of the Stock Synthesis II assessment program. NOAA Technical Memorandum SEDAR 16-AW-04.

Methot, R. D. (2009). ‘User Manual for Stock Synthesis. Model Version 3.02C.’ (NOAA Fisheries: Seattle, WA.)

Methot, R. D., and Taylor, I. G. (2011). Adjusting for bias due to variability of estimated recruitments in fishery assessment models. Can. J. Fish. Aquat. Sci., in press.

Punt, A. E., Smith, A. D. M., and Cui, G. (2002). Evaluation of management tools for Australia’s south-east fishery, 2. How well can management quantities be estimated? Marine and Freshwater Research 53, 631–644.
Evaluation of management tools for Australia’s south-east fishery, 2. How well can management quantities be estimated?Crossref | GoogleScholarGoogle Scholar |

Restrepo, V. R., Thompson, G. G., Mace, P. M., Gabriel, W. L., Low, L. L., MacCall, A. D., Methot, R. D., Powers, J. E., Taylor, B. L., Wade, P. R., and Witzig, J. F. (1998). Technical guidance on the use of precautionary approaches to implementing National Standard 1 of the Magnuson–Stevens Fishery Conservation and Management Act. NOAA Technical Memorandum NMFS-F/SPO-31.

Shertzer, K. W., and Prager, M. H. (2007). Delay in fishery management: diminished yield, longer rebuilding, and increased probability of stock collapse. ICES Journal of Marine Science 64, 149–159.

Shono, H., Satoh, K., Okamoto, H., and Nishida, T. (2009). Updated stock assessment for bigeye tuna in the Indian Ocean up to 2008 using Stock Synthesis III (SS3). National Research Institute of Far Seas Fisheries, Shizuoka, Japan. IOTC-2009-WPTT-20.

Smith, A. D. M. (1993). Risks of over- and under-fishing new resources. Canadian Special Publication of Fisheries and Aquatic Sciences 120, 261–267.

Smith, A. D. M., Smith, D. C., Tuck, G. N., Klaer, N., Punt, A. E., Knuckey, I., Prince, J., Morison, A., Kloser, R., Haddon, M., Wayte, S., Day, J., Fay, G., Pribac, F., Fuller, M., Taylor, B., and Little, L. R. (2008). Experience in implementing harvest strategies in Australia’s south-eastern fisheries. Fisheries Research 94, 373–379.
Experience in implementing harvest strategies in Australia’s south-eastern fisheries.Crossref | GoogleScholarGoogle Scholar |

Stewart, I. J., and Hamel, O. S. (2010). Stock assessment of Pacific hake, Merluccius productus (aka whiting) in US and Canadian waters in 2010. Pacific Fishery Management Council, Portland, OR.

Tuck, G. N. (Ed.) (2007). Stock assessment for the southern and eastern scalefish and shark fishery: 2006–2007. Australian Fisheries Management Authority and CSIRO Marine Atmospheric Research, Hobart.

US Office of the Federal Register (2009). Magnusson–Stevens Act provisions: national standard 1 – optimal yield. Federal Register 74: 11 (16 January 2009). pp. 3178–3213. Available at http://ecfr.gpoaccess.gov/ [Accessed March 2009].

Welch, D. J., Garrett, R. N., and Gribble, N. A. (2005). Fisheries uncertainty: a tropical Australian data-poor fishery. In ‘Fisheries Assessment and Management in Data-limited Situations’. (Eds G. H. Kruse, V. F. Gallucci, D. E. Hay, R. I. Perry, R. M. Peterman, T. C. Shirley, P. D. Spencer, B. Wilson and D. Woodby.) pp. 533–552. (University of Alaska Fairbanks: Fairbanks, AK.)

Yin, Y., and Sampson, D. B. (2004). Bias and precision of estimates from an age-structured stock assessment program in relation to stock and data characteristics. North American Journal of Fisheries Management 24, 865–879.
Bias and precision of estimates from an age-structured stock assessment program in relation to stock and data characteristics.Crossref | GoogleScholarGoogle Scholar |