Oceanography The Official Magazine of
The Oceanography Society
Volume 27 Issue 04

View Issue TOC
Volume 27, No. 4
Pages 42 - 47

OpenAccess

Resolving Hjort's Dilemma: How Is Recruitment Related to Spawning Stock Biomass in Marine Fish?

By Philippe M. Cury , Jean-Marc Fromentin, Sarah Figuet, and Sylvain Bonhommeau 
Jump to
Article Abstract Citation Supplementary Materials References Copyright & Usage
Article Abstract

The relationship between spawning fish abundance and the number of offspring, the so-called stock-recruitment relationship, is crucial for fisheries management and conservation measures. Using the most comprehensive data set ever assembled, we quantify this relationship for 211 fish stocks worldwide, revealing a global pattern with a pervasive asymptotic shape that shows increasing recruitment reaching an upper limit for values around half to two-thirds of parental biomass. This corroborates previous theoretical and modeling results. However, parental biomass is a predictor for only 5% to 15% of the variance in recruitment, demonstrating the weak predictive power of the stock-recruitment relationship in marine fish populations. Thus, there is a need to move rapidly toward models that integrate environmental conditions and species interactions in fisheries stock assessment and management, as suggested by Johan Hjort 100 years ago.

Citation

Cury, P.M., J.-M. Fromentin, S. Figuet, and S. Bonhommeau. 2014. Resolving Hjort’s Dilemma: How Is recruitment related to spawning stock biomass in marine fish? Oceanography 27(4):42–47, https://doi.org/10.5670/oceanog.2014.85.

Supplementary Materials

» Supplementary Table 1 (204 KB pdf)
Time series used in the global analysis of stock-recruitment (SR) relationships. Each time series is one fish species at one location.The scientific name of the species, the geographic location of the population, the length of the time series, the type of data, and the source of the data are provided for (A) demersal, (B) small pelagic, and (C) large pelagic fish species.

References
    Akaike, H.A. 1974. New look at the statistical model identification. IEEE Transactions on Automatic Control 19:716–723, https://doi.org/10.1109/TAC.1974.1100705.
  1. Anderson, C.N.K., C.-h. Hsieh, S.A. Sandin, R. Hewitt, A. Hollowed, J. Beddington, R.M. May, and G. Sugihara. 2008. Why fishing magnifies fluctuations in fish abundance. Nature 452:835–839, https://doi.org/10.1038/nature06851.
  2. Begon, M., C.R. Townsend, and J.L. Harper. 2006. Ecology: From Individuals to Ecosystems. Blackwell Publishing, Oxford, UK, 752 pp.
  3. Beverton, R.J.H., and S.J. Holt. 1957. On the Dynamics of Exploited Fish Populations. Chapman and Hall, London, UK, 515 pp.
  4. Cury, P., and D. Pauly. 2000. Patterns and propensities in reproduction and growth of marine fishes. Ecological Research 15:101–106, https://doi.org/10.1046/j.1440-1703.2000.00321.x.
  5. Cury, P.M., I.L. Boyd, S. Bonhommeau, T. Anker-Nilssen, R.J.M. Crawford, R.W. Furness, J.A. Mills, E.J. Murphy, H. Österblom, M. Paleczny, and others. 2011. Global seabird response to forage fish depletion: One-third for the birds. Science 334:1,703–1,706, https://doi.org/10.1126/science.1212928.
  6. Cushing, D.H. 1990. Plankton production and year class strength in fish populations: An update of the match/mismatch hypothesis. Advances in Marine Biology 26:249–293, https://doi.org/10.1016/S0065-2881(08)60202-3.
  7. Deyle, E.R., M. Fogarty, C.H. Hsieh, L. Kaufman, A.D. MacCall, S.B. Munch, C.T. Perretti, H. Ye, and G. Sugihara. 2013. Predicting climate effects on Pacific sardine. Proceedings of the National Academy of Sciences of the United States of America 110:6,430–6,435, https://doi.org/10.1073/pnas.1215506110.
  8. Durant, J.M., D.Ø. Hjermann, T. Anker-Nilssen, G. Beaugrand, A. Mysterud, N. Pettorelli, and N.C. Stenseth. 2005. Timing and abundance as key mechanisms affecting trophic interactions in variable environments. Ecology Letters 8:952–958, https://doi.org/10.1111/j.1461-0248.2005.00798.x.
  9. Fréon, P., C. Mullon, and X. Pichon. 1993. CLIMPROD: Experimental Interactive Software for Choosing and Fitting Surplus Production Models including Environmental Variables. FAO Computerized Information Series (Fisheries) 5, 76 pp.
  10. Glaser, S.M., M.J. Fogarty, H. Liu, I. Altman, C.-h. Hsieh, L. Kaufman, A.D. MacCall, A.A. Rosenberg, H. Ye, and G. Sugihara. 2013. Complex dynamics may limit prediction in marine fisheries. Fish and Fisheries 15:616–633, https://doi.org/10.1111/faf.12037.
  11. Hastie, T.J., and R.J. Tibshirani. 1990. Generalized Additive Models. Chapman and Hall/CRC, 352 pp.
  12. Hilborn, R., and K. Stokes. 2010. Defining overfished stocks: Have we lost the plot? Fisheries 35:113–120, https://doi.org/10.1577/1548-8446-35.3.113.
  13. Hixon, M.A., and M.H. Carr. 1997. Synergistic predation, density-dependence, and population regulation in marine fish. Science 277:946–949, https://doi.org/10.1126/science.277.5328.946.
  14. Hjort, J. 1914. Fluctuations in the Great Fisheries of Northern Europe Viewed in the Light of Biological Research. Conseil Permanent International Pour l’Exploration de la Mer: Rapports et Procès-Verbaux des Réunions, vol. 20, 228 pp.
  15. Hjort, J. 1926. Fluctuations in the year classes of important food fishes. Journal du Conseil International pour l’Exploration de la Mer 1:5–38.
  16. Houde, E.D. 2008. Emerging from Hjort’s shadow. Journal of the Northwest Atlantic Fishery 41:53–70, https://doi.org/10.2960/J.v41.m634.
  17. Houde, E.D. 2009. Recruitment variability. Chapter 3 in Fish Reproductive Biology: Implications for Assessment and Management. T. Jakobsen, M.J. Fogarty, B.A. Megrey, and E. Moksness, eds, Wiley-Blackwell, Oxford, UK. https://doi.org/10.1002/9781444312133.ch3.
  18. Hsieh, C.-h., C.S. Reiss, J.R. Hunter, J.R. Beddington, R.M. May, and G. Sugihara. 2006. Fishing elevates variability in the abundance of exploited species. Nature 443:859–862, https://doi.org/10.1038/nature05232.
  19. Keith, D.M., and J.A. Hutchings. 2012. Population dynamics of marine fishes at low abundance. Canadian Journal of Fisheries and Aquatic Science 69:1,150–1,163, https://doi.org/10.1139/f2012-055.
  20. Myers, R.A., and N.J. Barrowman. 1996. Is fish recruitment related to spawner abundance? Fisheries Bulletin 94:707–724, http://fishbull.noaa.gov/944/myers.pdf.
  21. Neubauer, P., O.P. Jensen, J.A. Hutchings, and J.K. Baum. 2013. Resilience and recovery of overexploited marine populations. Science 340:347–349, https://doi.org/10.1126/science.1230441.
  22. Pikitch, E.K., K.J. Rountos, T.E. Essington, C. Santora, D. Pauly, R. Watson, U.R. Sumalia, P.D. Boersma, I.L. Boyd, D.O. Connover, and others. 2014. The global contribution of forage fish to marine fisheries and ecosystems. Fish and Fisheries 15:43–64, https://doi.org/10.1111/faf.12004.
  23. Ricard, D., C. Minto, O.P. Jensen, and J.K. Baum. 2012. Evaluating the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database. Fish and Fisheries 13:380–398, https://doi.org/10.1111/j.1467-2979.2011.00435.x.
  24. Ricker, W.E. 1954. Stock and recruitment. Journal of the Fisheries Research Board of Canada 11:559–623, https://doi.org/10.1139/f54-039.
  25. Rouyer, T., J.M. Fromentin, N.C. Stenseth, and B. Cazelles. 2008. Analysing multiple time series and extending significance testing in wavelet analysis. Marine Ecology Progress Series 359:11–23, https://doi.org/10.3354/meps07330.
  26. Sugihara, G., R. May, H. Ye, C.-h. Hsieh, E. Deyle, M. Fogarty, and S. Munch. 2012. Detecting causality in complex ecosystems. Science 338:496–500, https://doi.org/10.1126/science.1227079.
  27. Szuwalski, C.S., K.A. Vert-Pre, A.E. Punt, T.A. Branch, and R. Hilborn. 2014. Examining common assumptions about recruitment: A meta-analysis of recruitment dynamics for worldwide marine fisheries. Fish and Fisheries, https://doi.org/10.1111/faf.12083.
  28. Travis, J., F.C. Coleman, P.J. Auster, P.M. Cury, J.A. Estes, J. Orensanz, C.H. Peterson, M.E. Power, R.S. Steneck, and J.T. Wootton. 2014. Integrating the invisible fabric of nature into fisheries management. Proceedings of the National Academy of Sciences of the United States of America 111:581–584, https://doi.org/10.1073/pnas.1305853111.
  29. Vert-Pre, K.A., R.O. Amoroso, O.P. Jensen, and R. Hilborn. 2006. Frequency and intensity of productivity regime shifts in marine fish stocks. Proceedings of the National Academy of Sciences of the United States of America 110:1,779–1,784, https://doi.org/10.1073/pnas.1214879110.
  30. Walters, C.J., and S.J.D. Martell. 2004. Fisheries Ecology and Management. Princeton University Press, Princeton, NJ, 448 pp.
  31. Wood, S.N. 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society B 73:3–36, https://doi.org/10.1111/j.1467-9868.2010.00749.x.
Copyright & Usage

This is an open access article made available under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format as long as users cite the materials appropriately, provide a link to the Creative Commons license, and indicate the changes that were made to the original content. Images, animations, videos, or other third-party material used in articles are included in the Creative Commons license unless indicated otherwise in a credit line to the material. If the material is not included in the article’s Creative Commons license, users will need to obtain permission directly from the license holder to reproduce the material.