Stock assessment for Atlantic yellowfin tuna based on extended surplus production model considering life history

Zhipan Tian; Fei Wang; Siquan Tian; Qiuyun Ma

doi:10.1007/s13131-021-1924-x

Volume 41 Issue 8

Aug. 2022

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Zhipan Tian, Fei Wang, Siquan Tian, Qiuyun Ma. Stock assessment for Atlantic yellowfin tuna based on extended surplus production model considering life history[J]. Acta Oceanologica Sinica, 2022, 41(8): 41-51. doi: 10.1007/s13131-021-1924-x

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Zhipan Tian, Fei Wang, Siquan Tian, Qiuyun Ma. Stock assessment for Atlantic yellowfin tuna based on extended surplus production model considering life history[J]. Acta Oceanologica Sinica, 2022, 41(8): 41-51. doi: 10.1007/s13131-021-1924-x

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Stock assessment for Atlantic yellowfin tuna based on extended surplus production model considering life history

doi: 10.1007/s13131-021-1924-x

Zhipan Tian^1,,
Fei Wang^4,,
Siquan Tian^{1, 2, 3},
Qiuyun Ma^{1, 2, 3
,
,}

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2.
National Distant-water Fisheries Engineering Research Center, Shanghai 201306, China
3.
Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China
4.
Fisheries College, Zhejiang Ocean University, Zhoushan 316022, China

Funds: The Fund of National Key R&D Programs of China under contract No. 2019YFD0901404; the China Postdoctoral Science Foundation under contract No. 2019M651475.

More Information

Corresponding author: E-mail: qyma@shou.edu.cn
Received Date: 2021-03-04
Accepted Date: 2021-05-12

Available Online: 2022-08-01

Publish Date: 2022-08-15

Abstract

Abstract

The modern fishery stock assessment could be conducted by various models, such as Stock Synthesis model with high data requirement and complicated model structure, and the basic surplus production model, which fails to incorporate individual growth, maturity, and fishery selectivity, etc. In this study, the Just Another Bayesian Biomass Assessment (JABBA) Select which is relatively balanced between complex and simple models, was used to conduct stock assessment for yellowfin tuna (Thunnus albacares) in the Atlantic Ocean. Its population dynamics was evaluated, considering the influence of selectivity patterns and different catch per unit effort (CPUE) indices on the stock assessment results. The model with three joint longline standardized CPUE indices and logistic selectivity pattern performed well, without significant retrospective pattern. The results indicated that the stock is not overfished and not subject to overfishing in 2018. Sensitivity analyses indicated that stock assessment results are robust to natural mortality but sensitive to steepness of the stock-recruitment relationship and fishing selectivity. High steepness was revealed to be more appropriate for this stock, while the fishing selectivity has greater influence to the assessment results than life history parameters. Overall, JABBA-Select is suitable for the stock assessment of Atlantic yellowfin tuna with different selectivity patterns, and the assumptions of natural mortality and selectivity pattern should be improved to reduce uncertainties.
- population dynamics,
- selectivity,
- tropical tuna,
- fishery management

†These authors contributed equally to this work.

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References(56)

References

[1]	Butterworth D S, Punt A E. 1990. Some preliminary examinations of the potential information content of age-structure data from Antarctic minke whale research catches. Reports-International Whaling Commission, 40: 301–315
[2]	Butterworth D S, Rademeyer R A, Brandão A, et al. 2014. Does selectivity matter? A fisheries management perspective. Fisheries Research, 158: 194–204. doi: 10.1016/j.fishres.2014.02.004
[3]	Carruthers T R, Punt A E, Walters C J, et al. 2014. Evaluating methods for setting catch limits in data-limited fisheries. Fisheries Research, 153: 48–68. doi: 10.1016/j.fishres.2013.12.014
[4]	Chang Yi-Jay, Brodziak J, O’Malley J, et al. 2015. Model selection and multi-model inference for Bayesian surplus production models: a case study for Pacific blue and striped marlin. Fisheries Research, 166: 129–139. doi: 10.1016/j.fishres.2014.08.023
[5]	Costello C, Ovando D, Hilborn R, et al. 2012. Status and solutions for the world’s unassessed fisheries. Science, 338(6106): 517–520. doi: 10.1126/science.1223389
[6]	Diaha N C, Zudaire I, Chassot E, et al. 2015. Present and future of reproductive biology studies of yellowfin tuna (Thunnus albacares) in the eastern Atlantic Ocean. Collective Volume of Scientific Papers ICCAT, 71(1): 489–509
[7]	Foss-Grant A P, Zipkin E F, Thorson J T, et al. 2016. Hierarchical analysis of taxonomic variation in intraspecific competition across fish species. Ecology, 97(7): 1724–1734. doi: 10.1890/15-0733.1
[8]	Froese R, Demirel N, Coro G, et al. 2017. Estimating fisheries reference points from catch and resilience. Fish and Fisheries, 18(3): 506–526. doi: 10.1111/faf.12190
[9]	Galland G, Rogers A, Nickson A. 2016. Netting Billions: A Global Valuation of Tuna. Washington: The Pew Charitable Trust
[10]	Geweke J. 1991. Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. Minneapolis: Federal Reserve Bank of Minneapolis
[11]	Guan Wenjiang, Gao Feng, Lei Lin, et al. 2012. Retrospective problem in fishery stock assessment. Journal of Shanghai Ocean University, 21(5): 841–847
[12]	Guéry L. 2020. SCRS/2019/066. Accounting for fishing days without a fishing set in the CPUE standardization of yellowfin tuna in free schools for the EU purse seine fleet operating in the eastern Atlantic Ocean during the 1993–2018 PERIOD. Collective Volume of Scientific Papers ICCAT, 76(6): 294–320
[13]	Heidelberger P, Welch P D. 1983. Simulation run length control in the presence of an initial transient. Operations Research, 31(6): 1109–1144. doi: 10.1287/opre.31.6.1109
[14]	Hilborn R. 2001. Calculation of biomass trend, exploitation rate, and surplus production from survey and catch data. Canadian Journal of Fisheries and Aquatic Sciences, 58(3): 579–584
[15]	Hoyle S D, Lauretta M, Lee M K, et al. 2019. Collaborative study of yellowfin tuna CPUE from multiple Atlantic Ocean longline fleets in 2019. Collective Volume of Scientific Papers ICCAT, 76(6): 241–293
[16]	Hurtado-Ferro F, Szuwalski C S, Valero J L, et al. 2015. Looking in the rear-view mirror: bias and retrospective patterns in integrated, age-structured stock assessment models. ICES Journal of Marine Science, 72(1): 99–110. doi: 10.1093/icesjms/fsu198
[17]	ICCAT. 2016. Report of the 2016 ICCAT Yellowfin Tuna Data Preparatory Meeting. San Sebastian: International Committee and Conservation of Atlantic Tunas
[18]	ICCAT. 2019a. Report of the Standing Committee on Research and Statistics (SCRS). Madrid: International Committee and Conservation of Atlantic Tunas
[19]	ICCAT. 2019b. Report of the 2019 ICCAT Yellowfin Tuna Stock Assessment Meeting. Grand- Bassam: International Committee and Conservation of Atlantic Tunas
[20]	ICCAT. 2019c. Report of the 2019 ICCAT Yellowfin Tuna Data Preparatory Meeting. Madrid: International Committee and Conservation of Atlantic Tunas
[21]	Kolody D S, Eveson J P, Preece A L, et al. 2019. Recruitment in tuna RFMO stock assessment and management: a review of current approaches and challenges. Fisheries Research, 217: 217–234. doi: 10.1016/j.fishres.2018.11.031
[22]	Langley A. 2019. Stock assessment of albacore tuna in the Indian Ocean using Stock Synthesis for 2019. IOTC–2019–WPTmT07 (AS)–11. https://iotc.org/sites/default/files/documents/2019/07/IOTC-2019-WPTmT07AS-11.pdf[2019-07-09/2021-03-05]
[23]	Lee Q, Lee A, Liu Zunlei, et al. 2020. Life history changes and fisheries assessment performance: a case study for small yellow croaker. ICES Journal of Marine Science, 77(2): 645–654. doi: 10.1093/icesjms/fsz232
[24]	Lee Hui-Hua, Maunder M N, Piner K R, et al. 2012. Can steepness of the stock–recruitment relationship be estimated in fishery stock assessment models?. Fisheries Research, 125–126: 254–261
[25]	Lewy P, Nielsen A. 2003. Modelling stochastic fish stock dynamics using Markov Chain Monte Carlo. ICES Journal of Marine Science, 60(4): 743–752. doi: 10.1016/S1054-3139(03)00080-8
[26]	Matsumoto T, Satoh K. 2017. Stock assessment for Atlantic yellowfin tuna using a non-equilibrium production model. Collective Volume of Scientific Papers ICCAT, 73(2): 451–474
[27]	Maunder M N. 2002. The relationship between fishing methods, fisheries management and the estimation of maximum sustainable yield. Fish and Fisheries, 3(4): 251–260. doi: 10.1046/j.1467-2979.2002.00089.x
[28]	McAllister M K, Pikitch E K, Babcock E A. 2001. Using demographic methods to construct Bayesian priors for the intrinsic rate of increase in the Schaefer model and implications for stock rebuilding. Canadian Journal of Fisheries and Aquatic Sciences, 58(9): 1871–1890
[29]	Merino G, Urtizberea A, Murua H, et al. 2019. Stock assessment for Atlantic yellowfin using a biomass production model. IOTC-2019-WPTT-49. https://iotc.org/sites/default/files/documents/2019/10/IOTC-2019-WPTT21-49.pdf[2019-10-10/2021-05-10]
[30]	Millar R B, Meyer R. 2000. Non-linear state space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling. Journal of the Royal Statistical Society: Series C (Applied Statistics), 49(3): 327–342. doi: 10.1111/1467-9876.00195
[31]	Mohn R. 1999. The retrospective problem in sequential population analysis: an investigation using cod fishery and simulated data. ICES Journal of Marine Science, 56(4): 473–488. doi: 10.1006/jmsc.1999.0481
[32]	Narvaez M. 2020. SCRS/2019/123. Standardized catch rates for yellowfin tuna (Thunnus albacares) from the Venezuelan pelagic longline fishery in the Caribbean Sea and adjacent waters of the western central Atlantic for the period of 1991–2018. Collective Volume of Scientific Papers ICCAT, 76(6): 662–673
[33]	Omori K L, Hoenig J M, Luehring M A, et al. 2016. Effects of underestimating catch and effort on surplus production models. Fisheries Research, 183: 138–145. doi: 10.1016/j.fishres.2016.05.021
[34]	Pella J J, Tomlinson P K. 1969. A generalized stock production model. Inter-American Tropical Tuna Commission Bulletin, 13(3): 416–497
[35]	Punt A E, Hilborn R. 1997. Fisheries stock assessment and decision analysis: the Bayesian approach. Reviews in Fish Biology and Fisheries, 7(1): 35–63. doi: 10.1023/A:1018419207494
[36]	Punt A E, Hurtado-Ferro F, Whitten A R. 2014a. Model selection for selectivity in fisheries stock assessments. Fisheries Research, 158: 124–134. doi: 10.1016/j.fishres.2013.06.003
[37]	Punt A E, Smith A D M, Smith D C, et al. 2014b. Selecting relative abundance proxies for B_MSY and B_MEY. ICES Journal of Marine Science, 71(3): 469–483. doi: 10.1093/icesjms/fst162
[38]	Punt A E, Su Nan-Jay, Sun Chilu. 2015. Assessing billfish stocks: a review of current methods and some future directions. Fisheries Research, 166: 103–118. doi: 10.1016/j.fishres.2014.07.016
[39]	R Core Team. 2013. R: a language and environment for statistical computing. In: R Foundation for Statistical Computing. http://www.R-project.org/[2017-06-30/2021-04-28]
[40]	Rankin P S, Lemos R T. 2015. An alternative surplus production model. Ecological Modelling, 313: 109–126. doi: 10.1016/j.ecolmodel.2015.06.024
[41]	Restrepo V R, Legault C M. 1998. A stochastic implementation of an age-structured production model. Fishery Stock Assessment Models, AK-SG-98-01: 435–450
[42]	Sant’Ana R, Mourato B, Kimoto A, et al. 2020. Atlantic yellowfin tuna stock assessment: an implementation of Bayesian state-space surplus production model using JABBA. Collective Volume of Scientific Papers ICCAT, 76(6): 699–724
[43]	Satoh K, Yokoi H, Nishida T, et al. 2017. SCRS/2016/111. Stock assessment for Atlantic yellowfin tuna using age structured production model. Collective Volume of Scientific Papers ICCAT, 73(2): 577–631
[44]	Stewart I J, Martell S J D. 2014. A historical review of selectivity approaches and retrospective patterns in the Pacific halibut stock assessment. Fisheries Research, 158: 40–49. doi: 10.1016/j.fishres.2013.09.012
[45]	Szuwalski C S, Ianelli J N, Punt A E. 2018. Reducing retrospective patterns in stock assessment and impacts on management performance. ICES Journal of Marine Science, 75(2): 596–609. doi: 10.1093/icesjms/fsx159
[46]	Thorson J T, Cope J M, Branch T A, et al. 2012. Spawning biomass reference points for exploited marine fishes, incorporating taxonomic and body size information. Canadian Journal of Fisheries and Aquatic Sciences, 69(9): 1556–1568. doi: 10.1139/f2012-077
[47]	Thorson J T, Rudd M B, Winker H. 2019. The case for estimating recruitment variation in data-moderate and data-poor age-structured models. Fisheries Research, 217: 87–97. doi: 10.1016/j.fishres.2018.07.007
[48]	Thorson J T, Taylor I G. 2014. A comparison of parametric, semi-parametric, and non-parametric approaches to selectivity in age-structured assessment models. Fisheries research, 158: 74–83. doi: 10.1016/j.fishres.2013.10.002
[49]	Tropical Tunas Species Group. 2012. SCRS/2011/205. Alternative virtual population analyses of yellowfin tuna (Thunnus albacares), 1970–2010. ICCAT, 68(3): 1044–1059
[50]	Walter J. 2019. SCRS/2019/121. Stock synthesis model for Atlantic yellowfin tuna. ICCAT, 76(6): 558–639
[51]	Walter J, Sharma R. 2017. Atlantic Ocean yellowfin tuna stock assessment 1950–2014 using stock synthesis. Collective Volume of Scientific Papers ICCAT, 73(2): 510–576
[52]	Wang Shengping, Maunder M N, Aires-da-Silva A. 2014. Selectivity's distortion of the production function and its influence on management advice from surplus production models. Fisheries Research, 158: 181–193. doi: 10.1016/j.fishres.2014.01.017
[53]	Winker H, Carvalho F, Kapur M. 2018. JABBA: just another Bayesian biomass assessment. Fisheries Research, 204: 275–288. doi: 10.1016/j.fishres.2018.03.010
[54]	Winker H, Carvalho F, Thorson J T, et al. 2020. JABBA-Select: incorporating life history and fisheries’ selectivity into surplus production models. Fisheries Research, 222: 105355. doi: 10.1016/j.fishres.2019.105355
[55]	Winker H, Kerwath S E, Attwood C G. 2013. Comparison of two approaches to standardize catch-per-unit-effort for targeting behaviour in a multispecies hand-line fishery. Fisheries Research, 139: 118–131. doi: 10.1016/j.fishres.2012.10.014
[56]	Xu Luoliang, Li Bai, Chen Xinjun, et al. 2019. A comparative study of observation-error estimators and state-space production models in fisheries assessment and management. Fisheries Research, 219: 105322