Abstract
The majority of fishery stocks in the world are data limited, which limits formal stock assessments. Identifying the impacts of input data on stock assessment is critical for improving stock assessment and developing precautionary management strategies. We compare catch advice obtained from applications of various data-limited methods (DLMs) with forecasted catch advice from existing data-rich stock assessment models for the Indian Ocean bigeye tuna (Thunnus obesus). Our goal was to evaluate the consistency of catch advice derived from data-rich methods and data-limited approaches when only a subset of data is available. The Stock Synthesis (SS) results were treated as benchmarks for comparison because they reflect the most comprehensive and best possible scientific information of the stock. This study indicated that although the DLMs examined appeared robust for the Indian Ocean bigeye tuna, the implied catch advice differed between data-limited approaches and the current assessment, due to different data inputs and model assumptions. Most DLMs tended to provide more optimistic catch advice compared with the SS, which was mostly influenced by historical catches, current abundance and depletion estimates, and natural mortality, but was less sensitive to life-history parameters (particularly those related to growth). This study highlights the utility of DLMs and their implications on catch advice for the management of tuna stocks.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arnold L M, Heppell S S. 2015. Testing the robustness of data-poor assessment methods to uncertainty in catch and biology: a retrospective approach. ICES Journal of Marine Science, 72(1): 243–250, doi: https://doi.org/10.1093/icesjms/fsu077
Beddington J R, Kirkwood G P. 2005. The estimation of potential yield and stock status using life-history parameters. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1453): 163–170, doi: https://doi.org/10.1098/rstb.2004.1582
Berkson J, Thorson J T. 2015. The determination of data-poor catch limits in the United States: is there a better way?. ICES Journal of Marine Science, 72(1): 237–242, doi: https://doi.org/10.1093/icesjms/fsu085
Beverton R J H, Holt S J. 1993. On the Dynamics of Exploited Fish Populations. Suffolk, UK: Springer Dordrecht, 35–38
Bull B, Francis R I C C, Dunn A, et al. 2012. CASAL (C++ algorithmic stock assessment laboratory) user manual v2.30-2012/03/21. NIWA Technical Report 135. New Zealand, Wellington: The National Institute of Water and Atmospheric Research. http://docs.niwa.co.nz/library/public/NIWAtr135.pdf[2012-3-21/2020-6-1]
Carruthers T R, Hordyk A R. 2018. The Data-Limited Methods Toolkit (DLMtool): an R package for informing management of data-limited populations. Methods in Ecology and Evolution, 9(12): 2388–2395, doi: https://doi.org/10.1111/2041-210X.13081
Carruthers T R, Hordyk A R. 2020. Data-limited methods toolkit (DLMtool 5.4. 2). Vancouver, Canada: UBC. https://dlmtool.git-hub.io/DLMtool/userguide/introduction.html[2020-2-24/2020-6-1]
Carruthers T R, Kell L T, Butterworth D D S, et al. 2016. Performance review of simple management procedures. ICES Journal of Marine Science, 73(2): 464–482, doi: https://doi.org/10.1093/icesjms/fsv212
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: https://doi.org/10.1016/j.fishres.2013.12.014
Carruthers T R, Walters C J, McAllister M K. 2012. Evaluating methods that classify fisheries stock status using only fisheries catch data. Fisheries Research, 119–120: 66–79, doi: https://doi.org/10.1016/j.fishres.2011.12.011
Cope J M. 2013. Implementing a statistical catch-at-age model (Stock Synthesis) as a tool for deriving overfishing limits in data-limited situations. Fisheries Research, 142: 3–14, doi: https://doi.org/10.1016/j.fishres.2012.03.006
Cummings N J, Karnauskas M, Michaels W L, et al. 2014. Report of a GCFI workshop. Evaluation of current status and application of data-limited stock assessment methods in the larger Caribbean Region. Corpus Christi: Gulf and Caribbean Fisheries Institute. https://www.regions.noaa.gov/secar/wp-content/uploads/2013/06/66th-GCFI-2013-Data-Limited-Assessment-Work-shop-Report-English-v26March2014.pdf[2013-11-4/2020-06-07]
Dick E J, MacCall A D. 2010. Estimates of sustainable yield for 50 data-poor stocks in the Pacific Coast Groundfish Fishery Management Plan. NOAA-TM-NMFS-SWFSC-460. https://www.pcouncil.org/documents/2010/06/estimates-of-sustainable-yield-for-50-data-poor-stocks-in-the-pacific-coast-groundfish-fishery-management-plan-noaa-technical-memorandum-noaa-tm-nmfs-swfsc-460-june-2010.pdf/[2010-06-03/2019-12-23]
Dick E J, MacCall A D. 2011. Depletion-Based Stock Reduction Analysis: a catch-based method for determining sustainable yields for data-poor fish stocks. Fisheries Research, 110(2): 331–341, doi: https://doi.org/10.1016/j.fishres.2011.05.007
Doonan I, Large K, Dunn A, et al. 2016. Casal2: New Zealand’s integrated population modelling tool. Fisheries Research, 183: 498–505, doi: https://doi.org/10.1016/j.fishres.2016.04.024
Fournier D, Archibald C P. 1982. A general theory for analyzing catch at age data. Canadian Journal of Fisheries and Aquatic Sciences, 39(8): 1195–1207, doi: https://doi.org/10.1139/f82-157
Fournier D A, Hampton J, Sibert J R. 1998. MULTIFAN-CL: a length-based, age-structured model for fisheries stock assessment, with application to South Pacific albacore, Thunnus alalunga. Canadian Journal of Fisheries and Aquatic Sciences, 55(9): 2105–2116, doi: https://doi.org/10.1139/f98-100
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: https://doi.org/10.1111/faf.12190
Froese R, Winker H, Coro G, et al. 2018. A new approach for estimating stock status from length frequency data. ICES Journal of Marine Science, 75(6): 2004–2015, doi: https://doi.org/10.1093/icesjms/fsy078
Fu Dan. 2019. Preliminary Indian Ocean bigeye tuna stock assessment 1950–2018 (Stock Synthesis). IOTC-2019-WPTT21-61. Virtual: IOTC. https://www.iotc.org/meetings/22nd-working-party-tropical-tuna-wptt22-stock-assessment-meeting[2019-10-10/2020-6-1]
Gedamke T, Hoenig J M. 2006. Estimating mortality from mean length data in nonequilibrium situations, with application to the assessment of goosefish. Transactions of the American Fisheries Society, 135(2): 476–487, doi: https://doi.org/10.1577/T05-153.1
Geromont H F, Butterworth D S. 2015a. Generic management procedures for data-poor fisheries: forecasting with few data. ICES Journal of Marine Science, 72(1): 251–261, doi: https://doi.org/10.1093/ices-jms/fst232
Geromont H F, Butterworth D S. 2015b. Complex assessments or simple management procedures for efficient fisheries management: a comparative study. ICES Journal of Marine Science, 72(1): 262–274, doi: https://doi.org/10.1093/icesjms/fsu017
Griffiths S P, Fay G. 2015. Integrating recreational fisheries data into stock assessment: implications for model performance and subsequent harvest strategies. Fisheries Management and Ecology, 22(3): 197–212, doi: https://doi.org/10.1111/fme.12117
Gulland J A. 1971. Science and fishery management. ICES Journal of Marine Science, 33(3): 471–477, doi: https://doi.org/10.1093/icesjms/33.3.471
Harford W J, Carruthers T R. 2017. Interim and long-term performance of static and adaptive management procedures. Fisheries Research, 190: 84–94, doi: https://doi.org/10.1016/j.fishres.2017.02.003
Hilborn R, Walters C J. 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty. Boston: Springer, doi: https://doi.org/10.1007/978-1-4615-3598-0
Hordyk A. 2019. LBSPR: an R package for simulation and estimation using life-history ratios and length composition data. Vancouver, Canada: Blue Matter Science. https://cran.r-project.org/web/packages/LBSPR/vignettes/LBSPR.html[2019-6-18/2020-6-1]
Hordyk A, Ono K, Valencia S, et al. 2015. A novel length-based empirical estimation method of spawning potential ratio (SPR), and tests of its performance, for small-scale, data-poor fisheries. ICES Journal of Marine Science, 72(1): 217–231, doi: https://doi.org/10.1093/icesjms/fsu004
ICES. 2012. ICES Implementation of Advice for Data-limited Stocks in 2012 in its 2012 Advice. ICES CM 2012/ACOM 68. Copenhagen, Denmark: ICES
IOTC Secretariat. 2020. Draft Resource Stock Status Summary Bigeye Tuna (BET: Thunnus Obesus). Rome: FAO
ISSF. 2018. ISSF Technical Report 2018-15: 2018 ISSF Stock Assessment Workshop: Review of Current t-RFMO Practice in Stock Status Determinations. Washington D C, USA: International Seafood Sustainability Foundation
Jardim E, Azevedo M, Brites N M. 2015. Harvest control rules for data limited stocks using length-based reference points and survey biomass indices. Fisheries Research, 171: 12–19, doi: https://doi.org/10.1016/j.fishres.2014.11.013
Li B. 2011. Report of the sixteenth meeting of the scientific committee. CCSBT-EC/1108/BGD 01. Indonesia: CCSBT. https://www.ccsbt.org/en/system/files/resource/en/4e6855b3742de/BGD01-SC_ChairReport_of_SC16.pdf[2011-7-19/2020-6-1]
MacCall A D. 2009. Depletion-corrected average catch: a simple formula for estimating sustainable yields in data-poor situations. ICES Journal of Marine Science, 66(10): 2267–2271, doi: https://doi.org/10.1093/icesjms/fsp209
Martell S, Froese R. 2013. A simple method for estimating MSY from catch and resilience. Fish and Fisheries, 14(4): 504–514, doi: https://doi.org/10.1111/j.1467-2979.2012.00485.x
Maunder M. 2014. Management strategy evaluation (MSE) implementation in Stock Synthesis: application to Pacific bluefin tuna. SAC-05-10b Management Strategy Evaluation. La Jolla, USA: IATTC. https://www.iattc.org/Meetings/Meetings2014/SAC-05/5thMeetingScientificAdvisoryCommitteeENG.htm [2014-5-12/2020-6-1]
Maunder M N, Crone P R, Punt A E, et al. 2017. Data conflict and weighting, likelihood functions and process error. Fisheries Research, 192: 1–4, doi: https://doi.org/10.1016/j.fishres.2017.03.006
Maunder M N, Piner K R. 2017. Dealing with data conflicts in statistical inference of population assessment models that integrate information from multiple diverse data sets. Fisheries Research, 192: 16–27, doi: https://doi.org/10.1016/j.fishres.2016.04.022
Maunder M N, Punt A E. 2013. A review of integrated analysis in fisheries stock assessment. Fisheries Research, 142: 61–74, doi: https://doi.org/10.1016/j.fishres.2012.07.025
Methot R D Jr. 2009. Stock assessment: operational models in support of fisheries management. In: Beamish R J, Rothschild B J, eds. The Future of Fisheries Science in North America. Dordrecht: Springer, 137–165
Methot R D Jr., Wetzel C R. 2013. Stock synthesis: a biological and statistical framework for fish stock assessment and fishery management. Fisheries Research, 142: 86–99, doi: https://doi.org/10.1016/j.fishres.2012.10.012
Methot R D Jr., Wetzel C R, Taylor I G, et al. 2020. Stock synthesis user manual version 3.30. 15. NOAA Processed Report NMFS-NWF-SC-PR-2020–05. Washington, D C: U. S. Department of Commerce, doi: https://doi.org/10.25923/5wpn-qt71[2020-5/2020-6-1
Newman D, Berkson J, Suatoni L. 2015. Current methods for setting catch limits for data-limited fish stocks in the United States. Fisheries Research, 164: 86–93, doi: https://doi.org/10.1016/j.fishres.2014.10.018
Newman D, Carruthers T, MacCall A, et al. 2014. Improving the science and management of data-limited fisheries: an evaluation of current methods and recommended approaches. Natural Resources Defense Council Report R: 14-09-B. New York: NRDC. https://www.nrdc.org/sites/default/files/improving-data-limited-fisheries-report.pdf[2014-10/2020-6-1]
Punt A E, Butterworth D S, de Moor C L, et al. 2016. Management strategy evaluation: best practices. Fish and Fisheries, 17(2): 303–334, doi: https://doi.org/10.1111/faf.12104
Punt A E, Dunn A, Elvarsson B Þ, et al. 2020. Essential features of the next-generation integrated fisheries stock assessment package: a perspective. Fisheries Research, 229: 105617, doi: https://doi.org/10.1016/j.fishres.2020.105617
Rudd M B. 2018. LIME: length-based integrated mixed effects (LIME) assessment method. R Package Version 2.1.0. Seattle: University of Washington. https://github.com/merrillrudd/LIME[2017-6-25/2020-10-28]
Rudd M B, Thorson J T. 2018. Accounting for variable recruitment and fishing mortality in length-based stock assessments for data-limited fisheries. Canadian Journal of Fisheries and Aquatic Sciences, 75(7): 1019–1035, doi: https://doi.org/10.1139/cjfas-2017-0143
Sagarese S R, Harford W J, Walter J F, et al. 2019. Lessons learned from data-limited evaluations of data-rich reef fish species in the Gulf of Mexico: implications for providing fisheries management advice for data-poor stocks. Canadian Journal of Fisheries and Aquatic Sciences, 76(9): 1624–1639, doi: https://doi.org/10.1139/cjfas-2017-0482
Van Beveren E, Duplisea D, Castonguay M, et al. 2017. How catch underreporting can bias stock assessment of and advice for northwest Atlantic mackerel and a possible resolution using censored catch. Fisheries Research, 194: 146–154, doi: https://doi.org/10.1016/j.fishres.2017.05.015
Walters C, Martell S J D. 2002. Stock assessment needs for sustainable fisheries management. Bulletin of Marine Science, 70(2): 629–638
Zhou Shijie, Fu Dan, DeBruyn P, et al. 2019. Improving data limited methods for assessing Indian Ocean neritic tuna species. IOTC-2019-WPNT09–15. Victoria, Seychelles: CSIRO. https://iotc.org/meetings/9th-working-party-neritic-tunas-wpnt09[2019-7-1/2020-9-16]
Zhu Jiangfeng, Kitakado T. 2019. Uncertainties in the 2019 stock assessment for Indian Ocean albacore tuna and suggestions of further researches in 2020 for improving the assessment and providing management advice. IOTC-2019-SC22–13. Karachi, Pakistan: IOTC Scientific Committee. https://iotc.org/documents/SC/22/13[2019-11-21/2020-9-16]
Acknowledgements
The senior author’s work was conducted at Yong Chen’s lab in the School of Marine Sciences, University of Maine. We thank the developers of DLMtool for their splendid work and technical support, and the data supported by the IOTC secretary. Jessica Chen helped edit the paper, which was supported by the Fisheries Learning Network.
Funding
The National Natural Science Foundation of China under contract No. 41676120.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, Y., Zhu, J., Dai, X. et al. Using data-limited approaches to assess data-rich Indian Ocean bigeye tuna: Data quantity evaluation and critical information for management implications. Acta Oceanol. Sin. 41, 11–23 (2022). https://doi.org/10.1007/s13131-021-1933-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13131-021-1933-9