Application of a Bayesian method to data-poor stock assessment by using Indian Ocean albacore (<i>Thunnus alalunga</i>) stock assessment as an example

GUAN Wenjiang; TANG Lin; ZHU Jiangfeng; TIAN Siquan; XU Liuxiong

doi:10.1007/s13131-016-0814-0

Volume 35 Issue 2

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > Acta Oceanologica Sinica > 2016 > 35(2): 117-125

GUAN Wenjiang, TANG Lin, ZHU Jiangfeng, TIAN Siquan, XU Liuxiong. Application of a Bayesian method to data-poor stock assessment by using Indian Ocean albacore (Thunnus alalunga) stock assessment as an example[J]. Acta Oceanologica Sinica, 2016, 35(2): 117-125. doi: 10.1007/s13131-016-0814-0

Citation:

GUAN Wenjiang, TANG Lin, ZHU Jiangfeng, TIAN Siquan, XU Liuxiong. Application of a Bayesian method to data-poor stock assessment by using Indian Ocean albacore (Thunnus alalunga) stock assessment as an example[J]. Acta Oceanologica Sinica, 2016, 35(2): 117-125. doi: 10.1007/s13131-016-0814-0

Citation:

PDF( 1349 KB)

Application of a Bayesian method to data-poor stock assessment by using Indian Ocean albacore (Thunnus alalunga) stock assessment as an example

doi: 10.1007/s13131-016-0814-0

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources (Shanghai Ocean University), Ministry of Education, Shanghai 201306, China
2.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

Received Date: 2015-06-15
Rev Recd Date: 2015-12-03

Abstract

Abstract

It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in data-poor situations through borrowing strength from prior information deduced from species with good-quality data or other known information. Because there is considerable uncertainty remaining in the stock assessment of albacore tuna (Thunnus alalunga) in the Indian Ocean due to the limited and low-quality data, we investigate the advantages of a Bayesian method in data-poor stock assessment by using Indian Ocean albacore stock assessment as an example. Eight Bayesian biomass dynamics models with different prior assumptions and catch data series were developed to assess the stock. The results show (1) the rationality of choice of catch data series and assumption of parameters could be enhanced by analyzing the posterior distribution of the parameters; (2) the reliability of the stock assessment could be improved by using demographic methods to construct a prior for the intrinsic rate of increase (r). Because we can make use of more information to improve the rationality of parameter estimation and the reliability of the stock assessment compared with traditional statistical methods by incorporating any available knowledge into the informative priors and analyzing the posterior distribution based on Bayesian framework in data-poor situations, we suggest that the Bayesian method should be an alternative method to be applied in data-poor species stock assessment, such as Indian Ocean albacore.
- data-poor stock assessment,
- Bayesian method,
- catch data series,
- demographic method,
- Indian Ocean,
- Thunnus alalunga

FullText(HTML)

References(32)

References

Babcock E A. 2014. Application of a Bayesian surplus production model to preliminary data for south Atlantic albacore. Collect Vol Sci Pap ICCAT, 73(3): 1326-1334

Bentley N, Stokes K. 2009. Contrasting paradigms for fisheries man-agement decision making: How well do they serve data-poor fisheries. Marine and Coastal Fisheries: Dynamics, Manage-ment, and Ecosystem Science, 1(1): 391-401

Chen I C, Lee Peifen, Tzeng W N. 2005. Distribution of albacore (Thunnus alalunga) in the Indian Ocean and its relation to en-vironmental factors. Fisheries Oceanography, 14(1): 71-80

Chen Y. 2003. Quality of fisheries data and uncertainty in stock as-sessment. Sci Mar, 67(Suppl 1): 75-87

Costello C, Ovando D, Hilborn R, et al. 2012. Status and solutions for the world's unassessed fisheries. Science, 338(6106): 517-520

Guan Wenjiang, Zhu Jiangfeng, Xu Liuxiong. 2014. Analyzing popula-tion dynamics of Indian Ocean albacore (Thunnus alalunga) using Bayesian biomass dynamics model. IOTC-2014-WPTmT05-21. 5th Working Party on Temperate Tunas. Busan, Korea: Indian Ocean Tuna Commission

Haddon M. 2001. Modelling and Quantitative Methods in Fisheries. Boca Raton: Chapman & Hall/CRC Press

Hilborn R, Walters C J. 1992. Quantitative Fisheries Stock Assess-ment: Choice, Dynamics and Uncertainty. New York: Chap-man & Hall

Hillary R M. 2008. Surplus production analyses for Indian Ocean al-bacore. IOTC-2008-WPTe-06. 2nd Working Party on Temper-ate Tunas. Bangkok, Thailand: Indian Ocean Tuna Commission

Honey K T, Moxley J H, Fujita R M. 2010. From rags to fishes: data-poor methods for fishery managers. Managing Data-Poor Fish-eries: Case Studies, Models & Solutions, 1: 159-184

IOTC. 2014. Report of the Fifth Session of the IOTC Working Party on Temperate Tunas. IOTC-2014-WPTmT05-R[E]. 5th Working Party on Temperate Tunas. Busan, Korea: Indian Ocean Tuna Commission

Jiao Y, Cortés E, Andrews K, et al. 2011. Poor-data and data-poor spe-cies stock assessment using a Bayesian hierarchical approach. Ecol Appl, 21(7): 2691-2708

Lee Liangkang, Hsu Chihcheng, Chang Fengchen. 2014. Albacore (Thunnus alalunga) CPUE trend from Indian ocean core alba-core areas based on Taiwanese longline catch and effort statist-ics dating from 1980 to 2013. IOTC-2014-WPTmT05-19. 5th Working Party on Temperate Tunas. Busan, Korea: Indian Ocean Tuna Commission

Liermann M, Hilborn R. 1997. Depensation in fish stocks: a hierarch-ic Bayesian meta-analysis. Can J Fish Aquat Sci, 54(9): 1976-1984

Lunn D J, Thomas A, Best N, et al. 2000. WinBUGS-a Bayesian model-ling framework: concepts, structure, and extensibility. Statistics and Computing, 10(4): 325-337

Kéry M. 2010. Introduction to WinBUGS for Ecologists: A Bayesian Approach to Regression, ANOVA, Mixed Models and Related Analyses. San Diego: Academic Press

Kuparinen A, Mäntyniemi S, Hutchings J A, et al. 2012. Increasing biological realism of fisheries stock assessment: towards hier-archical Bayesian methods. Environ Rev, 20(2): 135-151

Magnusson A, Hilborn R. 2007. What makes fisheries data informat-ive? Fish and Fisheries, 8(4): 337-358

Maravelias C D, Hillary R, Haralabous J, et al. 2010. Stochastic bioeconomic modelling of alternative management measures for anchovy in the Mediterranean Sea. ICES J Mar Sci, 67(6): 1291-1300

Márquez-Farías J F, Rosales-Juárez F J. 2013. Intrinsic rebound po-tential of the endangered (Totoaba macdonaldi) population, endemic to the Gulf of California, México. Fisheries Research,147: 150-153

Matsumoto T, Nishida T, Kitakado T. 2014. Stock and risk assess-ments of albacore in the Indian Ocean based on ASPIC. IOTC-2014-WPTmT05-22. 5th Working Party on Temperate Tunas. Busan, Korea: Indian Ocean Tuna Commission

McAllister M K, Kirkwood G P. 1998. Bayesian stock assessment: a re-view and example application using the logistic model. ICES J Mar Sci, 55(6): 1031-1060

McAllister M K, Pikitch E K, Babcock E A. 2001. Using demographic methods to construct Bayesian priors for the intrinsic rate of in-crease in the Schaefer model and implications for stock re-building. Can J Fish Aquat Sci, 58(9): 1871-1890

McAllister M K, Pikitch E K, Punt A E, et al. 1994. A Bayesian ap-proach to stock assessment and harvest decisions using the sampling/importance resampling algorithm. Can J Fish Aquat Sci, 51(12): 2673-2687

Millar R B, Meyer R. 2000. Non-linear state space modelling of fisher-ies biomass dynamics by using Hastings-Metropolis within-Gibbs sampling. Applied Statistics, 49(3): 327-342

Musick J A, Harbin M M, Berkeley S A, et al. 2000. Marine, estuarine, and diadromous fish stocks at risk of extinction in North Amer-ica (exclusive of Pacific salmonids). Fisheries, 25(11): 6-30

Nishida T, Kitakado T, Matsumoto T. 2014. Consideration and pro-posal of biological parameters for the 2014 albacore stock as-sessment in the Indian Ocean. IOTC-2014-WPTmT05-16. 5th Working Party on Temperate Tunas. Busan, Korea: Indian Ocean Tuna Commission

Nishida T, Matsumoto T, Kitakado T. 2012. Stock and risk assess-ments on albacore (Thunnus alalunga) in the Indian Ocean based on AD Model Builder implemented Age-Structured Pro-duction Model (ASPM) IOTC-2012-WPTmT04-21 Rev_4. 4th Working Party on Temperate Tunas. Shanghai, China: Indian Ocean Tuna Commission

Punt A E, Smith D C, Koopman M T. 2005. Using information for ‘data-rich’ species to inform assessments of ‘data-poor’ species through Bayesian stock assessment methods. Final Report to Fisheries Research and Development Corporation Project No. 2002/094. Primary Industries Research Victoria, Queenscliff: Department of Primary Industries

Punt A E, Smith D C, Smith A D M. 2011. Among-stock comparisons for improving stock assessments of data-poor stocks: the “Robin Hood” approach. ICES J Mar Sci, 68(5): 972-981

R Core Team. 2012. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/[2015-6-25]

Simon M, Fromentin J M, Bonhommeau S, et al. 2012. Effects of stochasticity in early life history on steepness and population growth rate estimates: an illustration on Atlantic Bluefin tuna. PLoS One, 7(10): e48583, doi: 10.1371/journal.pone.0048583

Relative Articles

Relative Articles

Supplements(0)

Cited By

Cited by

Periodical cited type(6)

1.	Yongchuang Shi, Xiaomin Zhang, Yuru He, et al. Stock Assessment Using Length-Based Bayesian Evaluation Method for Three Small Pelagic Species in the Northwest Pacific Ocean. Frontiers in Marine Science, 2022, 9 doi:10.3389/fmars.2022.775180
2.	Yongchuang Shi, Chuanxiang Hua, Qingcheng Zhu, et al. Applying the Catch-MSY model to the stock assessment of the northwestern Pacific saury Cololabis Saira. Journal of Oceanology and Limnology, 2020, 38(6): 1945. doi:10.1007/s00343-019-9156-z
3.	Ming Sun, Yunzhou Li, Chongliang Zhang, et al. Management of Data‐Limited Fisheries: Identifying Informative Data to Achieve Sustainable Fisheries. North American Journal of Fisheries Management, 2020, 40(3): 733. doi:10.1002/nafm.10438
4.	Fabio Bozzeda, Sandra L. Marín, Laura Nahuelhual. An uncertainty-based decision support tool to evaluate the southern king crab (Lithodes santolla) fishery in a scarce information context. Progress in Oceanography, 2019, 174: 64. doi:10.1016/j.pocean.2018.10.013
5.	Wenjiang Guan, Jiawen Wu, Siquan Tian. Evaluation of the performance of alternative assessment configurations to account for the spatial heterogeneity in age-structure: a simulation study based on Indian Ocean albacore tuna. Acta Oceanologica Sinica, 2019, 38(10): 9. doi:10.1007/s13131-019-1485-4
6.	Tiphaine Chouvelon, Christophe Brach-Papa, Dominique Auger, et al. Chemical contaminants (trace metals, persistent organic pollutants) in albacore tuna from western Indian and south-eastern Atlantic Oceans: Trophic influence and potential as tracers of populations. Science of The Total Environment, 2017, 596-597: 481. doi:10.1016/j.scitotenv.2017.04.048