Enhancing the utility of known-biomass production models: a case study of the Bay of Biscay and Iberian Coast ecoregion
DOI:
https://doi.org/10.3989/scimar.05400.083Keywords:
KBPMs, stock assessment models, environmental effects, biological reference points, multispecies, surplus production evolutionAbstract
Our general purpose is to support the use of known-biomass production models (KBPMs), illustrating their usefulness by addressing the evolution of surplus production (SP) over time and the factors affecting it (e.g. environment). We also demonstrate the utility of KBPMs for multispecies management objectives or for estimating maximum sustainable yield reference points without a stock recruitment function, among other worthwhile applications. To do so, we present different uses of KBPMs, illustrating their application on demersal species in the International Council for the Exploration of the Sea (ICES) area, specifically for megrim, white anglerfish and European hake stocks. The proposed analytical approach involved fitting single-species and multispecies KBPMs, conducting retrospective analyses and assessing the effects of environmental variability on SP. The findings show that, in general, stock SP increased after a decline in biomass and SP, except for white anglerfish in the southern area. Megrim stocks are the least productive, while hake and northern anglerfish are the most productive. Retrospective analysis revealed SP shifts in northern hake stock for reasons other than biomass variability. Hence, the North Atlantic Oscillation and the Atlantic Multidecadal Oscillation (AMO), two key climate variability modes in the North Atlantic, were tested for their links to SP, revealing a positive connection between SP and AMO, although further research is necessary. Beyond the specific results of our particular KBPM application, our main conclusion is that KBPMs can serve as a tool complementary to more complex assessment models for resolving unaddressed issues and crosschecking available assessment results.
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Abad E., Pennino M.G., Valeiras J., et al. 2020. Integrating spatial management measures into fisheries: The Lepidorhombus spp. case study. Mar. Policy 116: 103739. https://doi.org/10.1016/j.marpol.2019.103739
Beverton R.J.H., Holt S.J. 1957. On the dynamics of exploited fish populations. Fish. Invest. 19: 1-533.
Bundy A., Bohaboy E.C., Hjermann D.O., et al. 2012. Common patterns, common drivers: comparative analysis of aggregate surplus production across ecosystems. Mar. Ecol. Prog. Ser. 459: 203-218. https://doi.org/10.3354/meps09787
Chrysafi A., Kuparinen A. 2015. Assessing abundance of populations with limited data: Lessons learned from data-poor fisheries stock assessment. Environ. Rev. 24: 25-38. https://doi.org/10.1139/er-2015-0044
Colin M., Scott L., Arni M., Pinto C. 2022. icesSAG: Stock Assessment Graphs Database Web Services. R package version 1.4.0. https://CRAN.R-project.org/package=icesSAG
Cousido-Rocha M., Cerviño S., Alonso-Fernández A., et al. 2022a. Applying length-based assessment methods to fishery resources in the Bay of Biscay and Iberian Coast ecoregion: Stock status and parameter sensitivity. Fish. Res. 248: 106197. https://doi.org/10.1016/j.fishres.2021.106197
Cousido-Rocha M., Pennino M.G., Izquierdo F., et al. 2022b. Surplus Production Models: a practical review of recent approaches. Rev. Fish Biol. Fish. 32: 1085-1102. https://doi.org/10.1007/s11160-022-09731-w
Enfield D., Mestas-Nunez A., Trimble P. 2001. The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental U.S. Geophys. Res. Lett. 28: 2077-2080. https://doi.org/10.1029/2000GL012745
Hidalgo M., Massutí E., Guijarro B., et al. 2009. Population effects and changes in life history traits in relation to phase transitions induced by long-term fishery harvesting: European hake (Merluccius merluccius) off the Balearic Islands. Can. J. Fish. Aquat. Sci. 66: 1355-1370. https://doi.org/10.1139/F09-081
Hilborn R. 2001. Calculation of biomass trend, exploitation rate, and surplus production from survey and catch data. Can. J. Fish. Aquat. Sci. 58: 579-584. https://doi.org/10.1139/f01-018
Hilborn R., Walters C.J. 1992. Quantitative fisheries stock assessment: Choice dynamics, and uncertainty. London: Chapman & Hall. https://doi.org/10.1007/978-1-4615-3598-0 PMid:9908045
Hurrell J.W. 1995. Decadal trends in the North Atlantic Oscillation: Regional Temperatures and Precipitation. Science 269: 676-679. https://doi.org/10.1126/science.269.5224.676 PMid:17758812
Hurrell J.W., Deser C. 2009. North Atlantic climate variability: the role of the North Atlantic Oscillation. J. Mar. Syst. 78: 28-41. https://doi.org/10.1016/j.jmarsys.2008.11.026
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 Mar. Sci. 72: 99-110. https://doi.org/10.1093/icesjms/fsu198
Fogarty M.J. 2014. The art of ecosystem-based fishery management. Can. J. Fish. Aquat. Sci. 71: 479-490. https://doi.org/10.1139/cjfas-2013-0203
ICES 2015. Report of the fifth Workshop on the development of quantitative assessment methodologies based on life-history traits, exploitation characteristics and other relevant parameters for data-limited stocks (WKLIFE V). ICES Expert Group reports (until 2018). Report.
ICES 2021. Bay of Biscay and the Iberian Coast ecoregion - Ecosystem Overview. ICES Advice: Ecosystem Overviews. Report.
ICES 2023. Benchmark workshop on anglerfish and hake (WKANGHAKE). ICES Scientific Reports. Report.
Jacobson L.D., De Oliveira J.A.A., Barange M., et al. 2001. Surplus production, variability, and climate change in the great sardine and anchovy fisheries. Can. J. Fish. Aquat. Sci. 58: 1891-1903. https://doi.org/10.1139/f01-110
Jacobson L., Cadrin S., Weinberg J. 2002. Tools for Estimating Surplus Production and FMSY in Any Stock Assessment Model. N. Am. J. Fish. Manag. 22: 326-338. https://doi.org/10.1577/1548-8675(2002)022<0326:TFESPA>2.0.CO;2
Kerr R.A. 2000. A North Atlantic climate pacemaker for the centuries. Science 288 (5473): 1984-1986. https://doi.org/10.1126/science.288.5473.1984 PMid:17835110
MacCall A. 2002. Use of Known-Biomass Production Models to Determine Productivity of West Coast Groundfish Stocks. N. Am. J. Fish. Manag. 22: 272-279. https://doi.org/10.1577/1548-8675(2002)022<0272:UOKBPM>2.0.CO;2
Mueter F., Megrey B. 2006. Using multi-species surplus production models to estimate ecosystem-level maximum sustainable yields. Fish. Res. 81: 189-201. https://doi.org/10.1016/j.fishres.2006.07.010
Pedersen M.W., Berg C.W. 2017. A stochastic surplus production model in continuous time. Fish and Fish. 18: 226-243. https://doi.org/10.1111/faf.12174
Pella J. J., Tomlinson P. K. 1969. A generalized stock-production model. Bull I-ATTC 13: 421-58.
Pennino M.G., Cousido-Rocha M., Maia C., et al. 2022. This is what we know: Assessing the stock status of the data-poor common sole on the Iberian coast. Estuar. Coast. Shelf Sci. 107747. https://doi.org/10.1016/j.ecss.2022.107747 https://doi.org/10.1016/j.ecss.2022.107747
Prager M.H. 1992. ASPIC: A Surplus-Production Model Incorporating Covariates. Coll. Vol. Sci. Pap. Int. Comm. Conserv. Atl. Tunas (ICCAT) 28: 218-229.
R Core Team 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Schaefer M. B. 1957. A study of the dynamics of the fishery for yellowfin tuna in the Eastern Tropical Pacific Ocean. Bull I-ATTC 2: 247-285.
Sparholt H., Bogstad B., Christensen V., et al. 2021. Estimating Fmsy from an ensemble of data sources to account for density dependence in Northeast Atlantic fish stocks. ICES Mar. Sci. 78: 55-69. https://doi.org/10.1093/icesjms/fsaa175
Walters C.J., Hilborn R., Christensen V. 2008. Surplus production dynamics in declining and recovering fish populations. Can. J. Fish. Aquat. Sci. 65: 2536-2551. https://doi.org/10.1139/F08-170
Winker H., Carvalho F., Kapur M. 2018. JABBA: Just Another Bayesian Biomass Assessment. Fish. Res., 204: 275-288. https://doi.org/10.1016/j.fishres.2018.03.010
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Ministerio de Ciencia, Innovación y Universidades
Grant numbers RTI2018-099868-B-I00;PID-2022-140274OB-100
Agencia Estatal de Investigación
Grant numbers RTI2018-099868-B-I00;PID-2022-140274OB-100
European Regional Development Fund
Grant numbers RTI2018-099868-B-I00;PID-2022-140274OB-100
Axencia Galega de Innovación
Grant numbers IN607A 2022/04
Ministerio de Agricultura, Pesca y Alimentación
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Consejo Superior de Investigaciones Científicas
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Generalitat Valenciana
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