1932

Abstract

Fisheries science is concerned with the management and understanding of the raising and harvesting of fish. Fish stocks are assessed using biological and fisheries data with the goal of estimating either their total population or biomass. Stock assessment models also make it possible to predict how stocks will respond to varying levels of fishing pressure in the future. Such tools are essential with overfishing now reducing stocks and employment worldwide, with in turn many serious social, economic, and environmental implications. Increasingly, a state-space framework is being used in place of deterministic and standard parametric stock assessment models. These efforts have not only had considerable impact on fisheries management but have also advanced the supporting statistical theory and inference tools as well as the required software. An application of such techniques to the North Sea cod stock highlights what should be considered best practices for science-based fisheries management.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-031017-100427
2018-03-07
2024-03-29
Loading full text...

Full text loading...

/deliver/fulltext/statistics/5/1/annurev-statistics-031017-100427.html?itemId=/content/journals/10.1146/annurev-statistics-031017-100427&mimeType=html&fmt=ahah

Literature Cited

  1. Aanes S, Engen S, Sæther BE, Aanes R. 2007. Estimation of the parameters of fish stock dynamics from catch-at-age data and indices of abundance: can natural and fishing mortality be separated?. Can. J. Fish. Aquat. Sci. 64:1130–42 [Google Scholar]
  2. Aarts G, Poos JJ. 2009. Comprehensive discard reconstruction and abundance estimation using flexible selectivity functions. ICES J. Mar. Sci. 66:763–71 [Google Scholar]
  3. Andrieu C, Doucet A, Holenstein R. 2010. Particle Markov chain Monte Carlo methods. J. R. Stat. Soc. B 72:269–342 [Google Scholar]
  4. Baranov FI. 1918. On the question of the biological basis of fisheries. Nauchnye Issled. Ikhriologicheskii Inst. Izv. 1:81–128 [Google Scholar]
  5. Barndorff-Nielsen OE, Cox DR. 1989. Asymptotic Techniques for Use in Statistics London: Chapman & Hall
  6. Bell BM. 2012. CppAD: a package for differentiation of C++ algorithms Comput. Infrastruct. Operat. Res. Proj. https://www.coin-or.org/CppAD/Doc/cppad.htm
  7. Beverton RJH. 1954. Notes on the use of theoretical models in the study of the dynamics of exploited fish populations. Miscellaneous Contributions 2 Beaufort, NC: US Fishery Lab. [Google Scholar]
  8. Beverton RJH, Holt SJ. 1957. On the dynamics of exploited fish populations. Fisheries Investigation Series 2 19 London, UK: Minist. Agric. Fish. Food [Google Scholar]
  9. Bickel PJ, Ritov Y, Ryden T. 1998. Asymptotic normality of the maximum-likelihood estimator for general hidden Markov models. Ann. Stat. 26:1614–35 [Google Scholar]
  10. Bolker BM, Gardner B, Maunder M, Berg CW, Brooks M. et al. 2013. Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS. Methods Ecol. Evol. 4:501–12 [Google Scholar]
  11. Brinch CN, Eikeset AM, Stenseth NC. 2011. Maximum likelihood estimation in nonlinear structured fisheries models using survey and catch-at-age data. Can. J. Fish. Aquat. Sci. 68:1717–31 [Google Scholar]
  12. Buckland ST, Newman KB, Fernández C, Thomas L, Harwood J. 2007. Embedding population dynamics models in inference. Stat. Sci. 22:44–58 [Google Scholar]
  13. Butterworth DS, Ianelli JN, Hilborn R. 2003. A statistical model for stock assessment of southern bluefin tuna with temporal changes in selectivity. Afr. J. Mar. Sci. 25:331–61 [Google Scholar]
  14. Cadigan NG. 2015. A state-space stock assessment model for northern cod, including under-reported catches and variable natural mortality rates. Can. J. Fish. Aquat. Sci. 73:296–308 [Google Scholar]
  15. Chen Y, Davis TA, Hager WW, Rajamanickam S. 2008. Algorithm 887: CHOLMOD, supernodal sparse Cholesky factorization and update/downdate. ACM Trans. Math. Softw. 35:1–22 [Google Scholar]
  16. Costello C, Ovando D, Clavelle T, Strauss CK, Hilborn R. et al. 2016. Global fishery prospects under contrasting management regimes. PNAS 113:5125–29 [Google Scholar]
  17. Derzhavin AN. 1922. The stellate sturgeon (Acipenser stellatus pallas), a biological sketch. Byull. Bakin. Ikhtiologicheskoi Stanstsii 1:1–393 [Google Scholar]
  18. Doubleday WG. 1976. A least squares approach to analyzing catch at age data. Int. Comm. Northwest Atl. Res. Bull. 12:69–81 [Google Scholar]
  19. Douc R, Moulines E, Olsson J, Van Handel R. 2011. Consistency of the maximum likelihood estimator for general hidden Markov models. Ann. Stat. 39:474–513 [Google Scholar]
  20. Doucet A, de Freitas N, Gordon NJ. 2001. An introduction to sequential Monte Carlo methods. Sequential Monte Carlo Methods in Practice A Doucet, N de Freitas, NJ Gordon 3–14 New York: Springer-Verlag [Google Scholar]
  21. FAO (Food Agric. Organ. U.N.). 2014. The state of world fisheries and aquaculture: opportunities and challenges Rep FAO, Rome:
  22. Fearnhead P, Künsch HR. 2017. Particle filters. Annu. Rev. Stat. Appl. 5:421–49 [Google Scholar]
  23. Fournier DA, Archibald CP. 1982. A general theory for analyzing catch at age data. Can. J. Fish. Aquat. Sci. 39:1195–207 [Google Scholar]
  24. Fournier DA, Skaug HJ, Ancheta J, Ianelli J, Magnusson A. et al. 2012. AD model builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim. Methods Softw. 27:233–49 [Google Scholar]
  25. Fry FEJ. 1949. Statistics of a lake trout fishery. Biometrics 5:27–67 [Google Scholar]
  26. Fryer RJ. 2002. TSA: Is it the way?. Report of the Working Group on Methods of Fish Stock Assessment ICES CM 2002/D:01 Copenhagen: ICES [Google Scholar]
  27. Gilks WR, Richardson S, Spiegelhalter DJ. 1995. Markov Chain Monte Carlo in Practice London: Chapman & Hall/CRC
  28. Gong G, Samaniego FJ. 1981. Pseudo maximum likelihood estimation: theory and applications. Ann. Stat. 9:861–69 [Google Scholar]
  29. Griewank A, Walther A. 2008. Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation Philadelphia: SIAM. , 2nd. ed.
  30. Gudmundsson G. 1994. Time series analysis of catch-at-age observations. J. R. Stat. Soc. C 43:117–26 [Google Scholar]
  31. Gudmundsson G, Gunnlaugsson T. 2012. Selection and estimation of sequential catch-at-age models. Can. J. Fish. Aquat. Sci. 69:1760–72 [Google Scholar]
  32. Guennebaud G, Jacob B, Avery P, Bachrach A, Barthelemy Sea. 2010. Eigen: a C++ template library for linear algebra Matrix Libr. http://eigen.tuxfamily.org/index.php?title=Main_Page
  33. Gulland JA. 1961. Fishing and the stocks of fish at Iceland. Fish. Investig. Ser. 2 23:1–52 [Google Scholar]
  34. Gulland JA. 1965. Estimation of mortality rates. 1965 Annex to Arctic Fisheries Working Group Report ICES Doc. 3 Copenhagen: ICES [Google Scholar]
  35. Harvey AC. 1989. Forecasting, Structural Time Series Models, and the Kalman Filter Cambridge, UK: Cambridge Univ. Press
  36. Holmes L, Strauss CK, de Vos K, Bonzon K. 2014. Towards investment in sustainable fisheries: A framework for financing the transition Disc. Doc., Environ. Def. Fund and Prince of Wales's Int Sustain. Unit, EDF New York:
  37. Hosack GR, Peters GW, Ludsin SA. 2014. Interspecific relationships and environmentally driven catchabilities estimated from fisheries data. Can. J. Fish. Aquat. Sci. 71:447–63 [Google Scholar]
  38. Huber P, Ronchetti EM, Victoria-Feser M. 2004. Estimation of generalized linear latent variable models. J. R. Stat. Soc. B 66:893–908 [Google Scholar]
  39. Hürzeler M, Künsch HR. 2001. Approximating and maximising the likelihood for a general state-space model. Sequential Monte Carlo Methods in Practice A Doucet, N de Freitas, NJ Gordon 159–75 New York, NY: Springer-Verlag [Google Scholar]
  40. ICES (Int. Counc. Explor. Sea). 2015. Report of the Working Group on the Assessment of Demersal Stocks in the North Sea and Skagerrak (WGNSSK) Copenhagen: ICES
  41. Jardim E, Millar CP, Mosqueira I, Scott F, Osio GC. et al. 2015. What if stock assessment is as simple as a linear model? The a4a initiative. ICES J. Mar. Sci. 72:232–36 [Google Scholar]
  42. Jöreskog KG. 1969. A general approach to confirmatory maximum likelihood factor analysis. Psychometrika 34:183–202 [Google Scholar]
  43. Kalman RE. 1960. A new approach to linear filtering and prediction problems. J. Basic Eng. 82:35–45 [Google Scholar]
  44. Kalman RE, Bucy RS. 1961. New results in linear filtering and prediction theory. J. Basic Eng. 83:95–108 [Google Scholar]
  45. Kantas N, Doucet A, Singh SS, Maciejowski J, Chopin N. 2015. On particle methods for parameter estimation in state-space models. Stat. Sci. 30:328–351 [Google Scholar]
  46. Knape J, de Valpine P. 2012. Fitting complex population models by combining particle filters with Markov chain Monte Carlo. Ecology 93:256–63 [Google Scholar]
  47. Kristensen K, Nielsen A, Berg CW, Skaug H, Bell BM. 2016. TMB: automatic differentiation and Laplace approximation. J. Stat. Softw. 70:1–21 [Google Scholar]
  48. Larkin PA. 1977. An epitaph for the concept of maximum sustained yield. Trans. Am. Fish. Soc. 106:1–11 [Google Scholar]
  49. Lee Y, Nelder JA. 1996. Hierarchical generalized linear models. J. R. Stat. Soc. B 58:619–78 [Google Scholar]
  50. Lele SR, Nadeem K, Schmuland B. 2012. Estimability and likelihood inference for generalized linear mixed models using data cloning. J. Am. Stat. Assoc. 105:1617–25 [Google Scholar]
  51. Lewy P. 1988. Integrated stochastic virtual population analysis: estimates and their precision of fishing mortalities and stock sizes for the North Sea whiting stock. ICES J. Mar. Sci. 44:217–28 [Google Scholar]
  52. Link JS, Bundy A, Overholtz WJ, Shackell N, Manderson J. et al. 2011. Ecosystem-based fisheries management in the northwest Atlantic. Fish Fish 12:152–70 [Google Scholar]
  53. Liu J, West M. 2001. Combined parameter and state estimation in simulation-based filtering. Sequential Monte Carlo Methods in Practice A Doucet, N de Freitas, NJ Gordon 197–223 New York, NY: Springer-Verlag [Google Scholar]
  54. Lunn DJ, Thomas A, Best N, Spiegelhalter DJ. 2000. WinBUGS—a Bayesian modelling framework: concepts, structure, and extensibility. Stat. Comput. 10:325–37 [Google Scholar]
  55. MacDonald IL, Zucchini W. 1997. Hidden Markov and Other Models for Discrete-Valued Time Series London: Chapman & Hall/CRC [Google Scholar]
  56. McAllister MK, Pikitch EK, Punt AE, Hilborn R. 1994. A Bayesian approach to stock assessment and harvest decisions using the sampling/importance resampling algorithm. Can. J. Fish. Aquat. Sci. 51:2673–87 [Google Scholar]
  57. McCulloch CE, Searle SR. 2000. Generalized, Linear and Mixed Models New York: Wiley
  58. Methot RD, Wetzel CR. 2013. Stock synthesis: a biological and statistical framework for fish stock assessment and fishery management. Fish. Res. 142:86–99 [Google Scholar]
  59. Meyer R, Millar RB. 1999. BUGS in Bayesian stock assessments. Can. J. Fish. Aquat. Sci. 56:1078–87 [Google Scholar]
  60. Miller TJ, Legault CM. 2015. Technical details for ASAP version 4 Ref. Doc. 15–17 Northeast Fish. Sci. Cent NOAA, Woods Hole, MA:
  61. Minto C, Mills Flemming J, Britten GL, Worm B. 2013. Productivity dynamics of Atlantic cod. Can. J. Fish. Aquat. Sci. 71:203–16 [Google Scholar]
  62. Murphy GI. 1965. A solution of the catch equation. J. Fish. Board Can. 22:191–202 [Google Scholar]
  63. Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4:133–42 [Google Scholar]
  64. Neal RM. 2011. MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo S Brooks, A Gelman, G Jones, XL Meng 113–62 London: Chapman & Hall/CRC [Google Scholar]
  65. Nielsen A, Berg CW. 2014. Estimation of time-varying selectivity in stock assessments using state-space models. Fish. Res. 158:96–101 [Google Scholar]
  66. Paloheimo JE. 1980. Estimation of mortality rates in fish populations. Trans. Am. Fish. Soc. 109:378–86 [Google Scholar]
  67. Pedersen MW, Berg CW, Thygesen UH, Nielsen A, Madsen H. 2011. Estimation methods for nonlinear state-space models in ecology. Ecol. Model. 222:1394–400 [Google Scholar]
  68. Plummer M. 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proc. 3rd Int. Worksh. Distrib. Stat. Comput., Mar. 20–23, Vienna
  69. Pope JG. 1972. An investigation of the accuracy of virtual population analysis using cohort analysis. Int. Comm. Northwest Atlant. Res. Bull. 9:65–74 [Google Scholar]
  70. Pope JG, Shepherd JG. 1982. A simple method for the consistent interpretation of catch-at-age data. ICES J. Mar. Sci. 40:176–84 [Google Scholar]
  71. R Core Team. 2017. R: a language and environment for statistical computing R Found. Stat. Comput Vienna:
  72. Robert CP, Casella G. 2005. Monte Carlo Statistical Methods New York: Springer-Verlag. , 2nd ed..
  73. Royce WF. 1988. Centennial lecture IV: the historical development of fishery science and management. Mar. Fish. Rev. 50:30–40 [Google Scholar]
  74. Rue H, Martino S, Chopin N. 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. B 71:319–92 [Google Scholar]
  75. Schnute JT. 1977. Improved estimates from the Schaefer production model: theoretical considerations. J. Fish. Board Can. 34:583–603 [Google Scholar]
  76. Skaug HJ, Fournier DA. 2006. Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models. Comput. Stat. Data Anal. 51:699–709 [Google Scholar]
  77. Stan Development Team. 2016. Stan Modeling Language Users Guide and Reference Manual Version 2. 14.0. http://mc-stan.org
  78. Sullivan PJ. 1992. A Kalman filter approach to catch-at-length analysis. Biometrics 48:237–57 [Google Scholar]
  79. Tanner MA, Wong WH. 1987. The calculation of posterior distributions by data augmentation. J. Am. Stat. Assoc. 82:528–40 [Google Scholar]
  80. Thorson JT, Kristensen K. 2016. Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples. Fish. Res. 175:66–74 [Google Scholar]
  81. Thygesen UH, Albertsen CM, Berg CW, Kristensen K, Nielsen A. 2017. Validation of state space models fitted as mixed effects models. Environ. Ecol. Stat. 24:317–39 [Google Scholar]
  82. Tierney L, Kass RE, Kadane JB. 1989. Fully exponential Laplace approximations to expectations and variances of nonpositive functions. J. Am. Stat. Assoc. 84:710–16 [Google Scholar]
  83. Virtala M, Kuikka S, Arjas E. 1998. Stochastic virtual population analysis. ICES J. Mar. Sci. 55:892–904 [Google Scholar]
  84. Williams EH, Shertzer KW. 2015. Technical documentation of the Beaufort Assessment Model (BAM) NOAA Tech. Memo. NMFS-SEFSC-671, US Dep. Commer NOAA, Beaufort, NC:
  85. Xu X, Cantoni E, Mills Flemming J, Field C. 2015. Robust state space models for estimating fish stock maturities. Can. J. Stat. 43:133–50 [Google Scholar]
/content/journals/10.1146/annurev-statistics-031017-100427
Loading
/content/journals/10.1146/annurev-statistics-031017-100427
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error