Stock-environment-recruitment models for North Atlantic albacore (Thunnus alalunga)

Fisheries Oceanography

Volumn 15, Issue 5, 2006, Pages 402-412

  Arregui, Igor ^a   Arrizabalaga, Haritz ^a   Kirby, David S ^b   Martín González, Juan Manuel ^c  

^a MARINE RESEARCH DIVISION   (Spain)

^b SECRETARIAT OF THE PACIFIC COMMUNITY   (New Caledonia)

^c ULPGC   (Spain)

Author keywords

Albacore; Environment; Neural network; Prediction; Stock recruitment; Thunnus alalunga

Indexed keywords

DENSITY DEPENDENCE; ENVIRONMENTAL CONDITIONS; FISH; NORTH ATLANTIC OSCILLATION; SPAWNING; STOCK ASSESSMENT;

ATLANTIC OCEAN; ATLANTIC OCEAN (NORTH);

THUNNUS ALALUNGA;

EID: 33746833258     PISSN: 10546006     EISSN: 13652419     Source Type: Journal    
DOI: 10.1111/j.1365-2419.2005.00399.x     Document Type: Article

References (55)

1. Aoki, I. and Komatsu, T. (1997) Analysis and prediction of the fluctuation of sardine abundance using a neural network. Oceanol. Acta 20, 81-88.
2. Anonymous (1989) Thons et Environment. Colloq. Sémin. Paris: ORSTOM, 84pp.
3. Anonymous (2001) Report of the ICCAT SCRS albacore stock assessment session (Madrid, Spain - October 9 to 15, 2000). Collect. Vol. Sci. Pap. ICCAT 52:1283-1390.
4. Bakun, A. and Broad, K. (2003) Environmental 'loopholes' and fish population dynamics: comparative pattern recognition with focus on El Niño effects in the Pacific. Fish. Oceanogr. 12, 458-473.
5. Bard, F.X. (1981) Le thon germon (Thunnus alalunga Bonnaterre 1788) de l'ocean Atlantique. De la dynamique des populations à la strategie demographique. PhD Thesis. Universite de Paris, Paris, 333pp.
6. Bard, F.X. (2001) Extension of geographical and vertical habitat of albacore (Thunnus alalunga) in the North Atlantic. Possible consequences on the true rate of exploitation of this stock. Collect. Vol. Sci. Pap. ICCAT 52:1447-1456.
7. Beamish, R.J. (1995) Climate change and northern fish populations. Can. Spec. Publ. Fish. Aquat. Sci. 121:739.
8. Beamish, R.J., Neville, C.E. and Cass, A.J. (1997) Production of Fraser River sockeye salmon (Oncorhynchus nerka) in relation to decadal-scale changes in the climate and the ocean. Can. J. Fish. Aquat. Sci. 54:543-554.
9. Berg, M. and Getz, W. (1988) Stability of discrete age structured and aggregated delay difference population models. Bull. Math. Biol. 26:551-581.
10. Beverton, R.J.H. and Holt, S.J. (1957) On the dynamics of exploited fish populations. Fishery Invest., Lond., Ser. 2 19:553.
11. Brander, K.M. (2005) Cod recruitment is strongly affected by climate when stock biomass is low. ICES J. Mar. Sci. 62:339-343.
12. Brander, K.M. and Mohn, R. (2004) Effect of the North Atlantic Oscillation on recruitment of Atlantic cod (Gadus morhua). Can. J. Fish. Aquat. Sci. 61:1558-1564.
13. Chen, D.G. and Irvine, J.R. (2001) A semiparametric model to examine stock-recruitment relationships incorporating environmental data. Can. J. Fish. Aquat. Sci. 58:1178-1186.
14. Chen, D.G. and Ware, D.M. (1999) A neural network model for forecasting fish stock recruitment. Can. J. Fish. Aquat. Sci. 56:2385-2396.
15. Conan, G.Y. (1994) Can simple linear correlation satisfactorily detect environmental or interespecific effects on fisheries landings in a chaotic ocean universe? ICES C. M. P. 1994/P:8.
16. Cushing, D.H. (1973) The dependence of recruitment on parent stock. J. Fish. Res. Board Can. 30:1965-1976.
17. Cushing, D.H. (1982) Climate and Fisheries. London and New York: Academic Press, 387pp.
18. Deriso, R.B. (1980) Harvesting strategies and parameter estimation for an age structured model. Can. J. Fish. Aquat. Sci. 37:268-282.
19. Efron, B. and Tibshirani, R.J. (1993) An Introduction to the Bootstrap. London: Chapman & Hall, 436 pp.
20. Fournier, D.A., Hampton, J. and Sibert, J.R. (1998) MULTI-FAN-CL: a length-based, age-structured model for fisheries stock assessment, with application to South Pacific albacore, Thunnus alalunga. Can. J. Fish. Aquat. Sci. 55:2105-2116.
21. Fromentin, J.M. and Restrepo, V. (2001) Recruitment variability and environment: issues related to stock assessments of Atlantic tunas. Collect. Vol. Sci. Pap. ICCAT 52:1780-1792.
22. Gilbert, D.J. (1997) Towards a new recruitment paradigm for fish stocks. Can. J. Fish. Aquat. Sci. 54:969-977.
23. Goutte, C. (1997) Note on free lunches and cross-validation. Neur. Comput. 9:1245-1249.
24. Hertz, J., Krogh, A. and Palmer, R.G. (1991) Introduction to the Theory of Neural Computation. Santa Fe Institute Studies in the Sciences of Complexity. Redwood City: Addisson Wesley, 352pp.
25. Hilborn, R. and Walters, C.J. (1992) Quantitative Fisheries Stock Assessment. Choice, Dynamics and Uncertainty. London: Chapman and Hall, 570pp.
26. Hunter, J.R., Macewicz, B.J. and Sibert, J.R. (1986) The spawning frequency of skipjack tuna, Katsuwonus pelamis, from the South Pacific. Fish. Bull. 84:895-903.
27. Hurrell, J.W. (1995) Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation. Science 269:676-669.
28. Hurrell, J.W. (1996) Influence of variations in extratropical wintertime teleconnections on northern hemisphere temperature. Geophys. Res. Lett. 23:655-668.
29. Hurrell, J.W. and van Loon, H. (1997) Decadal variations associated with the North Atlantic Oscillation. Clim. Chang. 36:301-326.
30. Huse, G. and Ottersen, G. (2003) Forecasting recruitment and stock biomass of Northeast Artic cod using neural networks. Sci. Mar. 67(Suppl. 1), 325-335.
31. Itano, D.G. (2000) The reproductive biology of yellowfin tuna (Thunnus albacares) in Hawaiian Waters and the western tropical Pacific Ocean: project summary. SOEST Publication 00-01, JIMAR Contribution 00-328:69.
32. Köster, W.F., Hinrichsen, H.H., St John, M.A. et al. (2001) Developing Baltic cod recruitment models II. Incorporating environmental variability and species interaction. Can. J. Fish. Aquat. Sci. 58:1535-1557.
33. Köster, W.F., Hinrichsen, H.H., Shnack, D. et al. (2003) Recruitment of Baltic cod and sprat stocks: identification of critical life stages and incorporation of environmental variability into stock-recruitment relationships. Sci. Mar. 67(Suppl. 1), 129-154.
34. Lamb, P.J. and Peppeler, R.A. (1987) North Atlantic Oscillation. Concept and application. Bull. Am. Meteorol. Soc. 68:1218-1225.
35. Lehodey, P., Bertignac, M., Hampton, J., Lewis, A. and Picaut, J. (1997) El Niño Southern Oscillation and tuna in the western Pacific. Nature 398:715.
36. Maravelias, C.D., Reid, D.G., Simmonds, E.J. and Haralabous, J. (1996) Spatial analysis and mapping of acoustic survey data in the presence of high local variability: geostatistical application to North sea herring (Clupea arengus). Can. J. Fish. Aquat. Sci. 53:1497-1505.
37. Maunder, M.N. and Watters, G.M. (2001) Status of yellowfin tuna in the eastern Pacific Ocean. Inter-Amer. Trop- Tuna Comm., Stock Assess. Rep. 1:5-86.
38. Maunder, M.N. and Watters, G.M. (2003) A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example. Fish. Bull. 101:89-99.
39. Myers, R.A. (1998) When do environment-recruitment correlations work? Rev. Fish. Biol. Fish. 8:285-305.
40. Myers, R.A. (2001) Stock and recruitment: generalizations about maximum reproductive rate, density dependence, and variability using meta-analytic approaches. ICES J. Mar. Sci. 58:937-951.
41. Myers, R.A. and Barrowman, N.J. (1996) Is fish recruitment related to spawner abundance?. Fish. Bull. 94:707-724.
42. Myers, R.A., Barrowman, N.J., Hutchings, J.A. and Rosenberg, A.A. (1995) Population dynamics of exploited fish stocks at low population levels. Science 269:1106-1108.
43. Nishikawa, Y., Honma, M., Ueyanagi, S. and Kikawa, S. (1985) Average distribution of larvae of oceanic species of scombroid fishes, 1956-1981. Far Seas Fish. Res. Lab., S. Ser. 12:99.
44. O'Brien, C.M., Fox, C.J., Planque, B. and Casey, J. (2000) Climate variability and North Sea Cod. Nature 404:142.
45. Ortiz de Zárate, V., Lavin, A. and Moreno-Ventas, X. (1997) Existe alguna relación entre las variables ambientales y las capturas de superficie de atún blanco (T. alalunga) en el Atlántico norte?. Collect. Vol. Sci. Pap. ICCAT 48:250-259.
46. Press, W., Teukolsky, S.A., Vetterling, W. and Flannery, B.P. (1992) Numerical Recipes in Fortran: The Art of Scientific Computing. Cambridge: Cambridge Univiversity Press, 933pp.
47. Ricker, W.E. (1975) Computation and interpretation of biological statistics of fish populations. Bull. Fish. Res. Board Can. 191:382.
48. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning representations by back-propagating errors. Nature 323:533-536.
49. Santiago, J. (1998) The North Atlantic Oscillation and recruitment of temperate tunas. Collect. Vol. Sci. Pap. ICCAT 48:240-249.
50. Santiago, J. (2004) Dinámica de la población de atún blanco (Thunnus alalunga Bonaterre 1788) del Atlántico Norte. PhD Thesis. Euskal Herriko Unibertsitatea, Bilbao, 354pp.
51. Schnute, J. (1985) A general theory for analysis of catch and effort data. Can. J. Aquat. Sci. 42:414-429.
52. Schweight, J.F. and Noakes, D.J. (1990) Forecasting pacific herring (Cuplea arengus pallasi) recruitment from spawner abundance and environmental information. In: Proceedings of the International Herring Symposium, October 1990, Anchorage, AK: Alaska Sea Grant College Program, pp. 373-387.
53. Solow, A.R. (2000) Fisheries recruitment and North Atlantic Oscillation. Fish. Res. 54:295-297.
54. Watters, G.M. and Maunder, M.N. (2001) Status of bigeye tuna in the eastern Pacific Ocean. Inter-Amer. Trop-Tuna Comm., Stock Assess. Report 1:109-210.
55. Williams, E.H. and Terrance, J.Q. (2000) Pacific herring, Clupea pallasi, recruitment in the Bering Sea and northeast Pacific Ocean, II: relationships to environmental variables and implications for forecasting. Fish. Oceanogr. 9:300-315.