Spawning biomass reference points for exploited marine fishes, incorporating taxonomic and body size information

Canadian Journal of Fisheries and Aquatic Sciences

Volumn 69, Issue 9, 2012, Pages 1556-1568

  Thorson, James T ^a   Cope, Jason M ^b   Branch, Trevor A ^a   Jensen, Olaf P ^c  

^a UNIVERSITY OF WASHINGTON   (United States)

^b NORTHWEST FISHERIES SCIENCE CENTER   (United States)

^c RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMASS; BODY SIZE; DATA SET; DATABASE; ERROR ANALYSIS; EXPLOITATION; FISH; FISHERY MANAGEMENT; HARVESTING; LIFE HISTORY; SPAWNING; STOCK ASSESSMENT; SUSTAINABILITY; TAXONOMY;

EID: 84865383990     PISSN: 0706652X     EISSN: 12057533     Source Type: Journal    
DOI: 10.1139/F2012-077     Document Type: Article

References (88)

1. Adams, P.B. 1980. Life history patterns in marine fishes and their consequences for fisheries management. Fish Bull. 78: 1-12.
2. Akaike, H. 1974. New look at statistical model identification. IEEE Trans. Automat. Contr. 19(6): 716-723. doi:10.1109/TAC.1974. 1100705.
3. Beddington, J.R., and Kirkwood, G.P. 2005. The estimation of potential yield and stock status using life-history parameters. Philos. Trans. R. Soc. B Biol. Sci. 360(1453): 163-170. doi:10. 1098/rstb.2004.1582. PMID:15713595.
4. Bolker, B.M., Brooks, M.E., Clark, C.J., Geange, S.W., Poulsen, J.R., Stevens, M.H., and White, J.S. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24(3): 127-135. doi:10.1016/j.tree.2008.10.008. PMID: 19185386.
5. Bravington, M.V., Stokes, T.K., and O'Brien, C.M. 2000. Sustainable recruitment: the bottom line. Mar. Freshw. Res. 51(5): 465-475. doi:10.1071/MF99090.
6. Brooks, E.N., and Powers, J.E. 2007. Generalized compensation in stock-recruit functions: properties and implications for management. ICES J. Mar. Sci. 64(3): 413-424. doi:10.1093/icesjms/ fsl046.
7. Brooks, E.N., Powers, J.E., and Cortés, E. 2010. Analytical reference points for age-structured models: application to data-poor fisheries. ICES J. Mar. Sci. 67(1): 165-175. doi:10.1093/icesjms/ fsp225.
8. Charnov, E.L. 1993. Life history invariants: some explorations of symmetry in evolutionary ecology. Oxford University Press, Oxford, UK.
9. Cheung, W.W., Pitcher, T.J., and Pauly, D. 2005. A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing. Biol. Conserv. 124(1): 97-111. doi:10.1016/j. biocon.2005.01.017.
10. Clark, W.G. 1993. The effect of recruitment variability on the choice of a target level of spawning biomass per recruit. In Proceedings of the International Symposium on Management Strategies for Exploited Fish Populations. Edited by G. Kruse, D.M. Engers, R.J. Marasco, C. Pautzke, and T.J.I. Quinn. University of Alaska, Alaska Sea Grant Report 93-02, Fairbanks, Alaska. pp. 233-246.
11. Clark, J.S. 2003. Uncertainty and variability in demography and population growth: a hierarchical approach. Ecology, 84(6): 1370-1381. doi:10.1890/0012-9658(2003)084[1370:UAVIDA]2.0.CO;2.
12. Cortés, E. 2000. Life history patterns and correlations in sharks. Rev. Fish. Sci. 8(4): 299-344. doi:10.1080/10641260008951115.
13. de Valpine, P. 2009. Shared challenges and common ground for Bayesian and classical analysis of hierarchical statistical models. Ecol. Appl. 19(3): 584-588. doi:10.1890/08-0562.1. PMID: 19425421.
14. Dick, E.J., and MacCall, A.D. 2011. Depletion-based stock reduction Analysis: a catch-based method for determining sustainable yields for data-poor fish stocks. Fish. Res. 110(2): 331-341. doi:10.1016/ j.fishres.2011.05.007.
15. Dorn, M.W. 2002. Advice on West Coast rockfish harvest rates from Bayesian meta-analysis of stock-recruit relationships. N. Am. J. Fish. Manage. 22(1): 280-300. doi:10.1577/1548-8675(2002) 022<0280:AOWCRH>2.0.CO;2.
16. Dunham, A.E., and Miles, D.B. 1985. Patterns of covariation in life history traits of squamate reptiles: the effects of size and phylogeny reconsidered. Am. Nat. 126(2): 231-257. doi:10. 1086/284411.
17. Enberg, K., Jørgensen, C., and Mangel, M. 2010. Fishing-induced evolution and changing reproductive ecology of fish: the evolution of steepness. Can. J. Fish. Aquat. Sci. 67(10): 1708-1719. doi:10. 1139/F10-090.
18. FAO. 2010. FishStat [online]. Available from http://www.fao.org/fishery/statistics/software/fishstat/en#2.
19. Fletcher, R.I. 1978. On the restructuring of the Pella-Tomlinson system. Fish Bull. 76: 515-521.
20. Fournier, D.A., Skaug, H.J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M.N., Nielsen, A., and Sibert, J. 2012. AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optimization Methods and Software, 27(2): 233-249. doi:10.1080/10556788. 2011.597854.
21. Fowler, C.W. 1981. Density dependence as related to life history strategy. Ecology, 62(3): 602-610. doi:10.2307/1937727.
22. Fowler, C.W. 1988. Population dynamics as related to rate of increase per generation. Evol. Ecol. 2(3): 197-204. doi:10.1007/BF02214283.
23. Fox, W.W., Jr. 1970. An exponential surplus-yield model for optimizing exploited fish populations. Trans. Am. Fish. Soc. 99(1): 80-88. doi:10.1577/1548-8659(1970)99<80:AESMFO>2. 0.CO;2.
24. Garcia-Saez, C. 1997. Assessing populations of swordfish from the north Atlantic: density dependence, maximum sustainable yield levels and size-classified demographic models. Col. Vol. Sci. Pap. ICCAT, 46: 345-353.
25. Gelman, A., and Hill, J. 2007. Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, Cambridge, UK.
26. Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. 2003. Bayesian data analysis. 2nd ed. Chapman & Hall, Boca Raton, Fla.
27. He, X., Mangel, M., and MacCall, A. 2006. A prior for steepness in stock-recruitment relationships, based on an evolutionary persistence principle. Fish. Bull. 104: 428-433.
28. Hilborn, R. 2001. Calculation of biomass trend, exploitation rate, and surplus production from survey and catch data. Can. J. Fish. Aquat. Sci. 58(3): 579-584. doi:10.1139/f01-018.
29. Hilborn, R., and Liermann, M. 1998. Standing on the shoulders of giants: learning from experience in fisheries. Rev. Fish Biol. Fish. 8(3): 273-283. doi:10.1023/A:1008877912528.
30. Hilborn, R., and Stokes, K. 2010. Defining overfished stocks: have we lost the plot? Fisheries (Bethesda, Md.), 35(3): 113-120. doi:10.1577/1548-8446-35.3.113.
31. Hilborn, R., Stewart, I.J., Branch, T.A., and Jensen, O.P. 2012. Defining trade-offs among conservation, profitability, and food security in the California Current bottom-trawl fishery. Conserv. Biol. 26(2): 257-268. doi:10.1111/j.1523-1739.2011.01800.x. PMID:22443131.
32. Hutchings, J.A. 2000. Collapse and recovery of marine fishes. Nature, 406(6798): 882-885. doi:10.1038/35022565. PMID:10972288.
33. Jennings, S., Reynolds, J.D., and Mills, S.C. 1998. Life history correlates of responses to fisheries exploitation. Proc. R. Soc. Lond. B Biol. Sci. 265(1393): 333-339. doi:10.1098/rspb.1998.0300.
34. Kinzey, D., and Punt, A.E. 2008. Multispecies and single-species models of fish population dynamics: comparing parameter estimates. Nat. Resour. Model. 22(1): 67-104. doi:10.1111/j. 1939-7445.2008.00030.x.
35. Larkin, P.A. 1977. An epitaph for the concept of maximum sustained yield. Trans. Am. Fish. Soc. 106(1): 1-11. doi:10.1577/1548-8659 (1977)106<1:AEFTCO>2.0.CO;2.
36. Leader, J.J. 2004. Numerical analysis and scientific computation. Addison Wesley, Boston, Mass.
37. Liermann, M., and Hilborn, R. 1997. Depensation in fish stocks: a hierarchic Bayesian meta-analysis. Can. J. Fish. Aquat. Sci. 54(9): 1976-1984. doi:10.1139/cjfas-54-9-1976.
38. Longhurst, A. 2010. Mismanagement of marine fisheries. Cambridge University Press, Cambridge, UK.
39. Lorenzen, K., and Enberg, K. 2002. Density-dependent growth as a key mechanism in the regulation of fish populations: evidence from among-population comparisons. Proc. R. Soc. Lond. B Biol. Sci. 269(1486): 49-54. doi:10.1098/rspb.2001.1853. PMID: 11788036.
40. Ludwig, D., and Walters, C.J. 1981. Measurement errors and uncertainty in parameter estimates for stock and recruitment. Can. J. Fish. Aquat. Sci. 38(6): 711-720. doi:10.1139/f81-094.
41. MacCall, A.D. 2009. Depletion-corrected average catch: a simple formula for estimating sustainable yields in data-poor situations. ICES J. Mar. Sci. 66(10): 2267-2271. doi:10.1093/icesjms/fsp209.
42. Mace, P.M. 1994. Relationships between common biological reference points used as thresholds and targets of fisheries management strategies. Can. J. Fish. Aquat. Sci. 51(1): 110-122. doi:10.1139/f94-013.
43. Mangel, M., Brodziak, J., and DiNardo, G. 2010. Reproductive ecology and scientific inference of steepness: a fundamental metric of population dynamics and strategic fisheries management. Fish Fish. 11(1): 89-104. doi:10.1111/j.1467-2979.2009.00345.x.
44. Maunder, M.N. 2003a. Letter to the Editor: Is it time to discard the Schaefer model from the stock assessment scientist's toolbox? Fish. Res. 61(1-3): 145-149. doi:10.1016/S0165-7836(02)00273-4.
45. Maunder, M.N. 2003b. Paradigm shifts in fisheries stock assessment: from integrated analysis to Bayesian analysis and back again. Nat. Resour. Model. 16(4): 465-475. doi:10.1111/j.1939-7445.2003. tb00123.x.
46. Maunder, M.N. 2012. Evaluating the stock-recruitment relationship and management reference points: Application to summer flounder (Paralichthys dentatus) in the U.S. mid-Atlantic. Fish. Res. 125-126: 20-26. doi:10.1016/j.fishres.2012.02.006.
47. McAllister, M.K., Pikitch, E.K., and 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. Can. J. Fish. Aquat. Sci. 58(9): 1871-1890. doi:10. 1139/f01-114.
48. McGilliard, C.R., Hilborn, R., MacCall, A., Punt, A.E., and Field, J. C. 2011. Can information from marine protected areas be used to inform control-rule-based management of small-scale, data-poor stocks? ICES J. Mar. Sci. 68(1): 201-211. doi:10.1093/icesjms/ fsq151.
49. Methot, R.D. 2009. Stock assessment: operational models in support of fisheries management. In The future of fisheries science in North America. Edited by R.J. Beamish and B.J. Rothschild. Springer, Dordrecht, the Netherlands. pp. 137-165.
50. Minte-Vera, C.V., Branch, T.A., Stewart, I.J., and Dorn, M.W. 2005. Practical application of meta-analysis results: avoiding the double use of data. Can. J. Fish. Aquat. Sci. 62(4): 925-929. doi:10.1139/ f04-245.
51. Myers, R.A. 2001. Stock and recruitment: generalizations about maximum reproductive rate, density dependence, and variability using meta-analytic approaches. ICES J. Mar. Sci., 58(5): 937-951. doi:10.1006/jmsc.2001.1109.
52. Myers, R.A., and Barrowman, N.J. 1996. Is fish recruitment related to spawner abundance? Fish Bull. 94: 707-724.
53. Myers, R., and Mertz, G. 1998. Reducing uncertainty in the biological basis of fisheries management by meta-analysis of data from many populations: a synthesis. Fish. Res. 37: 51-60. doi:10.1016/S0165-7836(98)00126-X.
54. O'Brien, C.M. 1999. A note on the distribution of Gloss. ICES J. Mar. Sci. 56(2): 180-183. doi:10.1006/jmsc.1999.0447.
55. Ono, K., Punt, A.E., and Rivot, E. 2012. Model performance analysis for Bayesian biomass dynamics models using bias, precision and reliability metrics. Fish. Res. 125-126: 173-183. doi:10.1016/j. fishres.2012.02.022.
56. Patrick, W.S., Spencer, P., Ormseth, O., Cope, J., Field, J., Kobayashi, D., Gedamke, T., Cortés, E., Bigelow, K., Overholtz, W., Link, J., and Lawson, P. 2009. Use of productivity and susceptibility indices to determine the vulnerability of a stock: with example applications to six US fisheries. Vulnerability Evaluation Working Group Report, NOAA, National Marine Fisheries Service, Office of Sustainable Fisheries, Silver Spring, Md.
57. Patrick, W.S., Spencer, P., Link, J., Cope, J., Field, J., Kobayashi, D., Lawson, P., Gedamke, T., Cortés, E., Ormseth, O., Bigelow, K., and Overholtz, W. 2010. Using productivity and susceptibility indices to assess the vulnerability of United States fish stocks to overfishing. Fish Bull. NOAA/NMFS 108: 305-322.
58. Pella, J.J., and Tomlinson, P.K. 1969. A generalized stock production model. Inter-Am. Trop. Tuna Comm. Bull. 13: 416-497.
59. Pinheiro, J., and Bates, D. 2009. Mixed-effects models in S and SPLUS. Springer, New York.
60. Pinsky, M.L., Jensen, O.P., Ricard, D., and Palumbi, S.R. 2011. Unexpected patterns of fisheries collapse in the world's oceans. Proc. Natl. Acad. Sci. U.S.A. 108(20): 8317-8322. doi:10.1073/ pnas.1015313108. PMID:21536889.
61. Polacheck, T., Hilborn, R., and Punt, A.E. 1993. Fitting surplus production models: comparing methods and measuring uncertainty. Can. J. Fish. Aquat. Sci. 50(12): 2597-2607. doi:10.1139/ f93-284.
62. Porch, C.E., Eklund, A., and Scott, G.P. 2006. A catch-free stoack assessment model with application to goliath grouper (Epinephelus itajara) off southern Florida. Fish Bull. 104: 89.
63. Prager, M.H. 1992. ASPIC - a surplus-production model incorporating covariates. Coll. Vol. Sci. Pap. Int. Comm. Conserv. Atl. Tunas [ICCAT], 28: 218-229.
64. Prager, M.H. 2002. Comparison of logistic and generalized surplusproduction models applied to swordfish, Xiphias gladius, in the north Atlantic Ocean. Fish. Res. 58(1): 41-57. doi:10.1016/ S0165-7836(01)00358-7.
65. Prager, M.H. 2003. Reply to the Letter to the Editor by Maunder. Fish. Res. 61(1-3): 151-154. doi:10.1016/S0165-7836(02)00274-6.
66. Punt, A.E. 2003. Extending production models to include process error in the population dynamics. Can. J. Fish. Aquat. Sci. 60(10): 1217-1228. doi:10.1139/f03-105.
67. Punt, A.E., and Hilborn, R. 1997. Fisheries stock assessment and decision analysis: the Bayesian approach. Rev. Fish Biol. Fish. 7(1): 35-63. doi:10.1023/A:1018419207494.
68. Quinn, T.J., and Deriso, R.B. 1999. Quantitative fish dynamics. Oxford University Press, Oxford, UK.
69. R Development Core Team. 2009. R: a language and environment for statistical computing [online]. Available from http://www.Rproject.org.
70. Ricard, D., Minto, C., Jensen, O.P., and Baum, J.K. Examining the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database. Fish Fish. [In press.] doi:10.1111/j.1467-2979.2011.00435.x.
71. Rose, K.A., Cowan, J.H., Winemiller, K.O., Myers, R.A., and Hilborn, R. 2001. Compensatory density dependence in fish populations: importance, controversy, understanding and prognosis. Fish Fish. 2(4): 293-327. doi:10.1046/j.1467-2960.2001.00056.x.
72. Sainsbury, K.J., Punt, A.E., and Smith, A.D. 2000. Design of operational management strategies for achieving fishery ecosystem objectives. ICES J. Mar. Sci. 57(3): 731-741. doi:10.1006/jmsc. 2000.0737.
73. Schaefer, M.B. 1954. Some aspects of the dynamics of populations important to the management of the commercial marine fisheries. Bull. I-ATCC, 1: 27-56.
74. Sethi, S.A., Branch, T.A., and Watson, R. 2010. Global fishery development patterns are driven by profit but not trophic level. Proc. Natl. Acad. Sci. U.S.A. 107(27): 12163-12167. doi:10.1073/ pnas.1003236107. PMID:20566867.
75. Sibly, R.M., Barker, D., Denham, M.C., Hone, J., and Pagel, M. 2005. On the regulation of populations of mammals, birds, fish, and insects. Science, 309(5734): 607-610. doi:10.1126/science. 1110760. PMID:16040705.
76. Skaug, H., and Fournier, D. 2006. Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models. Comput. Stat. Data Anal. 51(2): 699-709. doi:10.1016/j.csda.2006.03.005.
77. Smith, T., and Punt, A.E. 2001. The gospel of maximum sustainable yield in fisheries management: birth, crucifixion and reincarnation. In Conservation of exploited species. Edited by J.D. Reynolds, G.M. Mace, K.H. Redford, and J.G. Robinson. Cambridge University Press, Cambridge, UK. pp. 41-66.
78. Stearns, S.C. 1983. The influence of size and phylogeny on patterns of covariation among life-history traits in the mammals. Oikos, 41(2): 173-187. doi:10.2307/3544261.
79. Thompson, G.G. 1992. Management advice from a simple dynamic pool model. Fish Bull. 90: 552-560.
80. Thorson, J.T., and Berkson, J. 2010a. Evaluating single-and multispecies procedures to estimate time-varying catchability functional parameters. Fish. Res. 101(1-2): 38-49. doi:10.1016/j.fishres. 2009.09.005.
81. Thorson, J.T., and Berkson, J. 2010b. Multispecies estimation of Bayesian priors for catchability trends and density dependence in the US Gulf of Mexico. Can. J. Fish. Aquat. Sci. 67(6): 936-954. doi:10.1139/F10-040.
82. Thorson, J.T., Branch, T.A., Jensen, O., and Quinn, T. 2012. Using model-based inference to evaluate global fisheries status from landings, location and life history data. Can. J. Fish. Aquat. Sci. 69(4): 645-655. doi:10.1139/f2012-016.
83. Venturelli, P.A., Murphy, C.A., Shuter, B.J., Johnston, T.A., van Coeverden de Groot, P.J., Boag, P.T., Casselman, J.M., Montgomerie, R., Wiegand, M.D., and Leggett, W.C. 2010. Maternal influences on population dynamics: evidence from an exploited freshwater fish. Ecology, 91(7): 2003-2012. doi:10.1890/09-1218. 1. PMID:20715623.
84. Walters, C., and Kitchell, J.F. 2001. Cultivation/depensation effects on juvenile survival and recruitment: implications for the theory of fishing. Can. J. Fish. Aquat. Sci. 58(1): 39-50. doi:10.1139/f00- 160.
85. Walters, C.J., Hilborn, R., and Christensen, V. 2008. Surplus production dynamics in declining and recovering fish populations. Can. J. Fish. Aquat. Sci. 65(11): 2536-2551. doi:10.1139/F08- 170.
86. Williams, E.H., and Shertzer, K.W. 2003. Implications of life-history invariants for biological reference points used in fishery management. Can. J. Fish. Aquat. Sci. 60(6): 710-720. doi:10.1139/f03- 059.
87. Winemiller, K.O., and Rose, K.A. 1992. Patterns of life-history diversification in North American fishes: implications for population regulation. Can. J. Fish. Aquat. Sci. 49(10): 2196-2218. doi:10.1139/f92-242.
88. Zhou, S., Yin, S., Thorson, J.T., Smith, A.D.M., and Fuller, M. 2012. Linking fishing mortality reference points to life history traits: an empirical study. Can. J. Fish. Aquat. Sci. 69(8): 1292-1301. doi:10.1139/f2012-060.