Multi-model inference for incorporating trophic and climate uncertainty into stock assessments

Deep-Sea Research Part II: Topical Studies in Oceanography

Volumn 134, Issue , 2016, Pages 379-389

  Ianelli, James ^a   Holsman, Kirstin K ^b   Punt, André E ^c   Aydin, Kerim ^a  

^a NOAA Alaska Fisheries Science Center   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

^c UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Alaska Bering Sea; Arrowtooth flounder; Biological reference points; Climate change; Fisheries; Groundfish; Model averaging; Model ensemble; Multi species model; Pacific cod; Predator prey; Stock assessment; USA; Walleye Pollock

Indexed keywords

BAYESIAN NETWORKS; CLIMATE CHANGE; ECOLOGY; ECOSYSTEMS; FISHERIES;

ARROWTOOTH FLOUNDER; BERING SEA; GROUNDFISH; MODEL AVERAGING; MODEL ENSEMBLES; MULTI SPECIES MODEL; PACIFIC COD; PREDATOR- PREYS; REFERENCE POINTS; STOCK ASSESSMENT; USA; WALLEYE POLLOCK;

CLIMATE MODELS;

AGE STRUCTURE; CLIMATE CHANGE; ECOLOGICAL MODELING; ECOSYSTEM MANAGEMENT; FISHERY MANAGEMENT; FISHERY PRODUCTION; FLATFISH; LENGTH-WEIGHT RELATIONSHIP; MARICULTURE; PREDATOR-PREY INTERACTION; STOCK ASSESSMENT; TROPHIC INTERACTION; UNCERTAINTY ANALYSIS;

ALASKA; BERING SEA; PACIFIC OCEAN; UNITED STATES;

GADUS MACROCEPHALUS; REINHARDTIUS STOMIAS; THERAGRA CHALCOGRAMMA;

EID: 84945411724     PISSN: 09670645     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsr2.2015.04.002     Document Type: Article

References (78)

1. A'mar, Z.T., Punt, A.E., Dorn., M.W., The evaluation of two management strategies for the Gulf of Alaska walleye pollock fishery under climate change. ICES J. Mar. Sci. 66 (2009), 1614–1632.
2. Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle, In: Proceedings of the 2nd International Symposium on Information Theory. Academiai Kiado, Budapest, pp. 267–281.
3. Aitkin, M., Posterior Bayes factors. J. R. Soc. Ser. B 53 (1991), 111–142.
4. Aydin, K., Gaichas, S., Ortiz, I., Kinzey, D., Friday, N., A Comparison of the Bering Sea, Gulf of Alaska, and Aleutian Islands Large Marine Ecosystems Through Food Web Modeling. NOAA Tech Memo NMFS AFSC-178, 2007, U.S. Department of Commerce, Seattle, 298.
5. Brandon, J.R., Wade, P.R., Assessment of the Bering-Chukchi-Beaufort Seas stock of bowhead whales using Bayesian model averaging. J. Cetacean Res. Manag. 8 (2006), 225–239.
6. Brodziak, J., Legault, C.M., Model averaging to estimate rebuilding targets for overfished stocks. Can. J. Fish. Aquat. Sci. 62 (2005), 544–562.
7. Brodziak, J., Piner, K., Model averaging and probable status of North Pacific striped marlin, Tetrapturus audaz. Can. J. Fish. Aquat. Sci. 67 (2010), 793–805.
8. Buckland, S.T., Burnham, K.P., Augustin, N.H., Model selection: an integral part of inference. Biometrics 53 (1997), 603–618.
9. Burnham, Kenneth P., Anderson, David R., Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach (2nd Edition), 2002, Springer, Secaucus, NJ, USA ProQuest ebrary. Web. 26 July 2015.
10. Burnham, K.P., Anderson, D.R., Model Selection and Multimodel Inference: a Practical Information-Theoretical Approach. 1998, Springer Verlag, New York, 353.
11. Butterworth, D.S., Punt, A.E., Smith, A.D.M., 1996. On plausible hypotheses and their weighting, with implications for selection between variants of the Revised Management Procedure. Reports of the International Whaling Commission. 46, pp. 637–640.
12. Butterworth, D.S., Why a management procedure approach? Some positives and negatives. ICES J. Mar. Sci. 64 (2007), 613–617.
13. Christensen, V., Walters, C.J., Ecopath with Ecosim: methods, capabilities and limitations. Ecol. Model. 172 (2004), 109–130.
14. Chimielechi, R.M., Raftery, A.E., Probabilistic visibility forecasting using Bayesian model averaging. Mon. Weather Rev. 139 (2011), 1626–1636.
15. Collie, J.S., Gislason, H., Biological reference points for fish stocks in a multispecies context. Can. J. Fish. Aquat. Sci. 58:11 (2001), 2167–2176.
16. Davies, N., Hoyle, S., Hampton, J., 2012. Stock assessment of striped marlin (Kajikia audax) in the southwest Pacific Ocean. In: Proceedings of the Western and Central Pacific Fisheries Commission. WCPFC document WCPFC-SC8-2012/SA-WP-05, 90 p.
17. Dick, E.J., Ralston, S., Cowcod Rebuilding Analysis. 2009, Pacific Fishery Management Council, Portland, OR, 32.
18. Durban, J.W., Elston, D.A., Ellifrit, D.K., Dickson, E., Hammond, P.S., Thompson, P.M., Multisite mark-recapture for cetaceans: population estimates with Bayesian model averaging. Mar. Mamm. Sci. 21 (2005), 80–92.
19. Flato, G.M., Boer, G.J., Lee, W.G., McFarlane, N.A., Ramsden, D., Reader, M.C., Weaver, A.J., The Canadian centre for climate modelling and analysis global coupled model and its climate. Clim. Dyn. 16 (2000), 451–467.
20. Flato, G.M., Boer, G.J., Warming asymmetry in climate change simulations. Geophys. Res. Lett. 28 (2001), 195–198.
21. Field, J.C., Francis, R.C., Aydin, K., Top-down modelling and bottom-up dynamics: linking a fisheries-based ecosystem model with climate hypotheses in the Northern California Current. Prog. Oceanogr. 68 (2006), 238–270.
22. Francis, R.I.C.C., Data weighting in statistical fisheries stock assessment models. Can. J. Fish. Aquat. Sci. 68 (2011), 1124–1138.
23. Fournier, D.A., Skaug, H.J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M.N., Nielsen, A., Sibert, J., AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim. Methods Softw. 27 (2012), 233–249.
24. Fulton, E.A., Smith, A.D.M., Johnson, C.R., Effects of spatial resolution on the performance and interpretation of marine ecosystem models. Ecol. Model. 176 (2004), 27–42.
25. Fulton, E.A., Link, J.S., Kaplan, I.C., Savina-Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D.M., Smith, D.C., Lessons in modelling and management of marine ecosystems: the Atlantis experience. Fish Fish. 12 (2011), 171–188.
26. Fulton, E.A., Smith, A.D.M., Smith, D.C., van Putten, I.E., Human behavior: the key source of uncertainty in fisheries management. Fish Fish. 12 (2011), 2–17.
27. Gardmark, A., Lindegren, M., Neuenfeldt, S., Blenckner, T., Heikinheimo, O., Muller-Karulis, B., Niiranen, S., Tomczak, M.T., Aro, E., Wikstrom, A., Mollmann, C., Biological ensemble modeling to evaluate potential futures of living marine resources. Ecol. Appl. 23:4 (2013), 742–754.
28. Hamel, O.S., Rebuilding analysis for Pacific Ocean Perch in 2011. 2011, Pacific Fishery Management Council, Portland, OR, 15.
29. Hamel, O.S., Ono, K., Stock Assessment of Pacific Ocean Perch in waters off of the U.S. West Coast in 2011. 2011, Pacific Fishery Management Council, Portland, OR, 165.
30. Hill, S.L., Watters, G.M., Punt, A.E., McAllister, M.K., LeQuere, C., Turner, J., Model uncertainty in the ecosystem approach to fisheries. Fish Fish. 8 (2007), 315–336.
31. Hoeting, J.A., Madigan, D., Raftery, A.E., Volinsky, C.T., Bayesian model averaging: a tutorial. Stat. Sci. 14 (1999), 382–417.
32. Holsman, K.K., Ianelli, J., Aydin, K., Punt, A.E., Moffitt, E.A., A comparison of fisheries biological reference points estimated from temperature-specific multi-species and single-species climate-enhanced stock assessment models. Deep Sea Res. II 134 (2016), 360–378.
33. Horner, P.J., Kaplan, I.C., Marshall, K.N., Levin, P.S., Harvey, C.J., Hermann, A.J., Fulton, E.A., Design and parameterization of a spatially explicit ecosystem model of the Central California Current. NOAA Technical Memorandum NMFS-NWFSC-104, 2010, NOAA, USA, 140.
34. Hulson, P-J,F., Quinn, T.J. II, Hanselman, D.H., Ianelli, J.N., Spatial modeling of Bering Sea walleye pollock with integrated age-structured assessment models in a changing environment. Can. J. Fish. Aquat. Sci. 70 (2013), 1402–1416.
35. Ianelli, J.N., Honkalehto, T., Barbeaux, S., Kotwicki, S., Aydin, K., Williamson, N., In Stock Assessment and Fishery Evaluation Report for the Groundfish Resources for the Bering Sea/Aleutian Islands Regions. 2012, North Pacific Fishery Management Council, Anchorage, AK, 51–155.
36. Ianelli, J.N., Hollowed, A.B., Haynie, A.C., Mueter, F.J., Bond, N.A., Evaluating management strategies for eastern Bering Sea walleye pollock (Theragra chalcogramma) in a changing environment. ICES J. Mar. Sci. 68 (2011), 1297–1304.
37. Jurado-Molina, J., Livingston, P., Climate-forcing effects on trophically linked groundfish populations: implications for fisheries management. Can. J. Fish. Aquat. Sci. 59 (2002), 1941–1951.
38. Kaplan, I.C., Brown, C.J., Fulton, E.A., Gray, I.A., Field, J.C., Smith, A.D.M., Impacts of depleting forage species in the California current. Environ. Conserv. 40:4 (2013), 380–393.
39. Kass, R.E., Raftery, A.E., Bayes factors. J. Am. Stat. Assoc. 90 (1995), 773–795.
40. K-1 model developers, K-1 Technical Report 1 Hasumi, H., Emori, S., (eds.) K-1 Coupled Model (MIROC) Description, 2004, Center for Climate System Research, University of Tokyo, Tokyo, Japan, 34.
41. Kinzey, D., Multispecies Stock Assessment With Predator-Prey Interactions. 2010, University of Washington, Seattle WA, 123 (Ph.D. thesis).
42. Kinzey, D., Punt, A.E., Multispecies and single-species models of fish population dynamics: comparing parameter estimates. Nat. Resour. Model. 22 (2009), 167–204.
43. Kim, S.J., Flato, G.M., Boer, G.J., McFarlane, N.A., A coupled climate model simulation of the Last Glacial Maximum, Part 1: transient multi-decadal response. Clim. Dyn. 19 (2002), 515–537.
44. Kim, S.J., Flato, G.M., Boer, G.J., A coupled climate model simulation of the last glacial maximum Part 2: approach to equilibrium. Clim. Dyn. 20 (2003), 635–661.
45. Kolody, D. 2011. An age-, sex- and spatially-structured stock assessment of the Indian Ocean swordfish fishery 1950–2009, including special emphasis on the south-west region. IOTC Document IOTC-2011-WPB-17. 75 p.
46. Kolody, D., Campbell, R., Davies, N., 2008. A MULTIFAN-CL stock assessment of southwest Pacific swordfish 1952–2007. WCPFC document WCPFC-SC4-2008/SA-WP-6 (Rev1). 90 p.
47. Legutke, S., Voss, R., ECHO-G, the Hamburg Atmosphere-Ocean Coupled Circulation Model. DKRZ Technical Report 18, 1999, DKRZ, Hamburg.
48. Magnusson, A., Punt, A.E., Hilborn, R., Measuring uncertainty in fisheries stock assessment: the delta method, bootstrap, and MCMC. Fish Fish. 14 (2013), 325–342.
49. Marasco, R.J., Goodman, D., Grimes, C.B., Lawson, P.W., Punt, A.E., Quinn, T.J. II, Ecosystem-based fisheries management: some practical suggestions. Can. J. Fish. Aquat. Sci. 64 (2007), 928–939.
50. Millar, C., Jardim, E., Model averaging to streamline the stock assessment process. ICES J. Mar. Sci. 72:1 (2015), 93–98.
51. Mueter, F.J., Megrey, B.A., Using multispecies surplus production models to estimate ecosystem-level maximum sustainable yields. Fish. Res. 81 (2006), 189–201.
52. Mueter, F.J., Bond, N.A., Ianelli, J.N., Hollowed, A.B., Expected declines in recruitment of walleye pollock (Theragra chalcogramma) in the eastern Bering Sea under future climate change. ICES J. Mar. Sci. 68 (2011), 1284–1296.
53. Myers, R.A., Rosenberg, A.A., Mace, P.M., Barrowman Restrepo, V.R., N., In search of thresholds for recruitment overfishing. ICES J. Mar. Sci. 51:2 (1994), 191–205.
54. North Pacific Fishery Management Council, Stock Assessment and Fishery Evaluation Report for the Groundfish Resources of the Bering Sea/Aleutian Islands Regions. 2012, North Pacific Fishery Management Council, Anchorage, AK.
55. Overland, J.E., Wang, M., Future climate of the North Pacific Ocean. Eos Trans. Am. Geophys. Union, 88, 2007, 182.
56. Pacific Fishery Management Council, Pacific Coast Groundfish Fishery Management Plan for the California. 2011, Oregon and Washington Grounfish Fishery, Portland, OR.
57. Parslow, J., Cressie, N., Campbell, E.P., Jones, E., Murray, L., Bayesian learning and predictability in a stochastic nonlinear dynamical model. Ecol. Appl. 23 (2013), 679–698.
58. Patterson, K.R., Evaluating uncertainty in harvest control law catches using Bayesian Markov chain Monte Carlo virtual population analysis with adaptive rejection sampling and including structural uncertainty. Can. J. Fish. Aquat. Sci. 56 (1999), 208–221.
59. Patterson, K., Cook, R., Darby, C., Gavaris, S., Kell, L., Lewy, P., Mesnil, B., Punt, A.E., Restrepo, V, Skagen, D.W., Stefansson, G., Estimating uncertainty in fish stock assessment and forecasting. Fish Fish. 2 (2001), 125–157.
60. Plagányi, É.E., Models for an Ecosystem Approach to Fisheries. FAO Fisheries Technical paper 477, 2007, FAO, Rome, 108.
61. Plagányi, É.E., Punt, A.E., Hillary, R., Morello, E.B., Thébaud, O., Hutton, T., Pillans, R.D., Thorson, J.T., Fulton, E.A., Smith, A.D.M., Smith, F., Bayliss, P., Haywood, M., Lyne, V., Rothlisberg, P.C., Models of intermediate complexity for ecosystem assessment to support tactical management decisions in fisheries and conservation. Fish Fish. 15 (2014), 1–22.
62. Prager, M.H., Shertzer, K.W., Deriving acceptable biological catch from the overfishing limit: implications for assessment models. N. Am. J. Fish. Manag. 30 (2010), 289–294.
63. Prager, M.H., Porch, C.E., Shertzer, K.W., Caddy, J.F., Targets and limits for management of fisheries: a simple probability-based approach. N. Am. J. Fish. Manag. 23 (2003), 349–361.
64. Punt, A.E., Donovan, G., Developing management procedures that are robust to uncertainty: lessons from the International Whaling Commission. ICES J. Mar. Sci. 64 (2007), 603–612.
65. Punt, A.E., Siddeek, M.S.M., Garber-Yonts, B., Dalton, M., Rugolo, L., Stram, D., Turnock, B.J., Zheng, J., Evaluating the impact of buffers to account for scientific uncertainty when setting TACs: application to red king crab in Bristol Bay, Alaska. ICES J. Mar. Sci. 69 (2012), 624–634.
66. Raftery, A.E., Gneiting, T., Balabdaoui, F., Polakowski, M., Using Bayesian model averaging to calibrate forecast ensembles. Mon. Weather Rev. 133 (2005), 1155–1174.
67. Ralston, S., Punt, A.E., Hamel, O.S., DeVore, J., Conser, R.J., An approach to quantifying scientific uncertainty in stock assessment. Fish. Bull. 109 (2011), 217–231.
68. Reifen, C., Toumi, R., Climate projections: past performance no guarantee of future skill?. Geophys. Res. Lett., 36, 2009, L13704.
69. Ricker, W.E., Stock and recruitment. J. Fish. Res. Board Can. 11 (1954), 559–623.
70. Shertzer, C.E., Prager, M.H., Williams, E.H., A probability-based approach to setting annual catch levels. Fish. Bull. 106 (2008), 225–232.
71. Spiegelhalter, D.J., Best, N.G., Carlin, B.P., van der Linde, A., Bayesian measures of model complexity and fit. J. R. Soc. B 64 (2002), 583–639.
72. Spies, I., Wilderbuer, T.K., Nichol, D.G., Aydin, A., Arrowtooth flounder. Stock Assessment and Fishery Evaluation Report for the Groundfish Resources for the Bering Sea/Aleutian Islands Regions, 2012, North Pacific Fishery Management Council, Anchorage, AK, 895–965.
73. Schwartz, G., Estimating the dimension of a model. Ann. Stat. 6 (1978), 461–464.
74. Taylor, I.G., Stephens, A., Stock Assessment of Shortspine Thornyhead in 2013. 2013, Pacific Fishery Management Council, Portland, OR.
75. Thompson, G.G., Lauth, RR., Assessment of the Pacific cod stock in the Eastern Bering Sea and Aleutian Islands area. Stock Assessment and Fishery Evaluation Report for the Groundfish Resources for the Bering Sea/Aleutian Islands Regions, 2012, North Pacific Fishery Management Council, Anchorage, AK, 245–254.
76. Wang, M., Overland, J.E., Bond, N.A., Climate projections for selected large marine ecosystems. J. Mar. Syst. 79 (2010), 258–266.
77. Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., Kawamiya, M., MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 4 (2011), 845–872.
78. Wilberg, M.J., Bence, J.R., Performance of deviance information criterion model selection in statistical catch-at-age analysis. Fish. Res. 93 (2008), 212–221.