Forecasting annual harvests of Atlantic and Gulf Menhaden

North American Journal of Fisheries Management

Volumn 26, Issue 3, 2006, Pages 753-764

  Hanson, Peter J ^a   Vaughan, Douglas S ^a   Narayan, Sridhar ^b  

^a NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

^b UNIVERSITY OF NORTH CAROLINA WILMINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AKAIKE INFORMATION CRITERION; ARTIFICIAL NEURAL NETWORK; FISH; FISHING EFFORT; FORECASTING METHOD; HARVESTING; MULTIPLE REGRESSION; TIME SERIES ANALYSIS;

ATLANTIC OCEAN; GULF OF MEXICO;

BREVOORTIA PATRONUS; BREVOORTIA TYRANNUS;

EID: 33845411914     PISSN: 02755947     EISSN: 15488675     Source Type: Journal    
DOI: 10.1577/M04-096.1     Document Type: Article

References (46)

1. Anonymous. 2002. 35th Northeast Regional Stock Assessment Workshop. Northeast Fisheries Science Center, Reference Document 02-13, Woods Hole, Massachusetts.
2. Anonymous. 2004. Stock assessment of the deepwater snapper-grouper complex in the South Atlantic. South Atlantic Fisheries Management Council, Southeast Data and Assessment Review 4, Stock Assessment Report 1, Charleston, South Carolina.
3. ASMFC (Atlantic States Marine Fisheries Commission). 2004. Atlantic menhaden stock assessment report for peer review. ASMFC, Stock Assessment Report 04-01, Washington, D.C.
4. Box, G. E. P., and G. M. Jenkins. 1976. Time series analysis: forecasting and control, 2nd edition. Holden-Day, San Francisco.
5. Brocklebank, J. C., and D. A. Dickey. 1986. SAS system for forecasting time series. SAS Institute, Inc., Cary, North Carolina.
6. Brockwell, P. J., and R. A. Davis. 2002. Introduction to time series and forecasting, 2nd edition. Springer-Verlag, New York.
7. Brosse, S., S. Lek, and F. Dauba. 1999. Predicting fish distribution in a mesotrophic lake by hydroacoustic survey and artificial neural networks. Limnology and Oceanography 44:1293-1303.
8. Burnham, K. P., and D. R. Anderson. 1998. Model selection and inference: a practical information-theoretic approach. Springer-Verlag, New York.
9. Cadrin, S. X., and D. S. Vaughan. 1997. Retrospective analysis of virtual population estimates for Atlantic menhaden stock assessment. Fishery Bulletin 95:445-455.
10. Chen, D. G., and D. M. Ware. 1999. A neural network model for forecasting fish stock recruitment. Canadian Journal of Fisheries and Aquatic Sciences 56:2385-2396.
11. Cisneros-Mata, M. A., T. Brey, A. Jarre-Teichmann, W. García-Franco, and G. Montemayor-López. 1996. Artificial neural networks to forecast biomass of Pacific sardine and its environment. Ciencias Marinas 22:427-442.
12. Dorn, M., S. Barbeaux, S. Gaichas, M. Guttormsen, B. Megrey, K. Spalinger, and M. Wilkins. 2004. Assessment of walleye pollock in the Gulf of Alaska. North Pacific Fishery Management Council, Stock Assessment and Fishery Evaluation Report, Anchorage, Alaska. Available: www.afsc.noaa.gov/refm/stocks/ assessments. htm. (February 2005).
13. Govoni, J. J. 1997. The association of the population recruitment of Gulf menhaden, Brevoortia patronus, with Mississippi River discharge. Journal of Marine Systems 12:101-108.
14. Gudmundsson, G. 2004. Time series analysis of abundance indices of young fish. ICES Journal of Marine Science 61:176-183.
15. Guégan, J.-F., S. Lek, and T. Oberdorff. 1998. Energy availability and habitat heterogeneity predict global riverine fish diversity. Nature (London) 391:382-384.
16. Guillory, V., J. Geaghan, and J. Roussel. 1983. Influence of environmental factors on Gulf menhaden recruitment. Louisiana Department of Wildlife and Fisheries, Technical Bulletin 37, New Orleans.
17. Guillory, V. 1993. Predictive models for Louisiana Gulf menhaden harvests: an update. Louisiana Department of Wildlife and Fisheries, Technical Bulletin 43, Bourg.
18. Haykin, S. 1999. Neural networks: a comprehensive foundation, 2nd edition. Prentice-Hall, Upper Saddle River, New Jersey.
19. Huse, G., and J. Giske. 1998. Ecology in Mare Pentium: an individual-based spatio-temporal model for fish with adapted behavior. Fisheries Research 37:163-178.
20. Jensen, A. L. 1976. Time series analysis and forecasting of Atlantic menhaden catch. Chesapeake Science 17:305-307.
21. Jensen, A. L. 1985. Time series analysis and the forecasting of menhaden catch and CPUE. North American Journal of Fisheries Management 5:78-85.
22. Komatsu, T., I. Aoki, I. Mitani, and T. Ishii. 1994. Prediction of the catch of Japanese sardine larvae in Sagami Bay using a neural network. Fisheries Science 60:385-391.
23. Mendelssohn, R. 1980. Using Box-Jenkins models to forecast fishery dynamics: identification, estimation, and checking. Fishery Bulletin 78:887-896.
24. NOAA (National Oceanic and Atmospheric Administration). 1996. Magnuson-Stevens Fishery Conservation and Management Act, as amended through October 11, 1996. NOAA Technical Memorandum NMFS-F/SPO-23.
25. Pankratz, A. 1983. Forecasting with univariate Box-Jenkins models. Wiley, New York.
26. Peterman, R. M., B. J. Pyper, and B. W. MacGregor. 2003. Use of the Kalman filter to reconstruct historical trends in productivity of Bristol Bay sockeye salmon (Oncorhynchus nerka). Canadian Journal Fisheries and Aquatic Sciences 60:809-824.
27. Quinn, G. P., and M. J. Keough. 2002. Experimental design and data analysis for biologists. Cambridge University Press, Cambridge, UK.
28. Restrepo, V. R., G. G. Thompson, P. M. Mace, W. L. Gabriel, L. L. Low, A. D. MacCall, R. D. Methot, J. E. Powers, B. L. Taylor, P. R. Wade, and J. F. Witzig. 1998. Technical guidance on the use of precautionary approaches to implementing national standard 1 of the Magnuson-Stevens Fishery Conservation and Management Act. NOAA Technical Memorandum NMFS-F/SPO-31.
29. Ripley, B. D. 1997. Pattern recognition and neural networks. Cambridge University Press, Cambridge, UK.
30. Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1986. Learning internal representations by error propagation. Pages 318-362 in D. E. Rumelhart and J. L. McClelland editors. Parallel distributed processing: explorations in the microstructure of cognition. MIT Press, Cambridge, Massachusetts.
31. SAFMC (South Atlantic Fishery Management Council). 1991. Final amendment 5 (wreckfish): regulatory impact review, initial regulatory flexibility determination, and environmental assessment for the fishery management plan for the snapper-grouper fishery of the South Atlantic region. South Atlantic Fisheries Management Council, Report to Snapper-Grouper Assessment Group, Charleston, South Carolina.
32. Saila, S. B., M. Wigbout, and R. J. Lermit. 1980. Comparison of some time series models for the analysis of fisheries data. Journal du Conseil International pour l'Exploration de la Mer 39:44-52.
33. SAS. 1984. SAS/ETS user's guide, version 5. SAS Institute, Cary, North Carolina.
34. SAS. 1986. SAS system for forecasting time series, 1986 edition. SAS Institute, Cary, North Carolina.
35. SAS. 1989. SAS/STAT user's guide, version 6, 4th edition. SAS Institute, Cary, North Carolina.
36. Schaaf, W. E., J. E. Sykes, and R. B. Chapoton. 1975. Forecasts of Atlantic and Gulf menhaden catches based on the historical relation of catch and fishing effort. Marine Fisheries Review 37:5-9.
37. Schnute, J. T., and A. R. Kronlund. 2002. Estimating salmon stock-recruitment relationships from catch and escapement data. Canadian Journal of Fisheries and Aquatic Sciences 59:433-449.
38. Sinclair, A., D. Gascon, R. O'Boyle, D. Rivard, and S. Gavaris. 1990. Consistency of some northwest Atlantic groundfish stock assessments. Northwest Atlantic Fisheries Organization, Scientific Council Research, Document 90/96, Dartmouth, Nova Scotia.
39. Sinclair, M., V. C. Anthony, T. D. Iles, and R. N. O'Boyle. 1985. Stock assessment problems in Atlantic herring (Clupea harengus) in the northwest Atlantic. Canadian Journal of Fisheries and Aquatic Sciences 42:888-898.
40. SNNS. 1998. Stuttgart neural network simulator, user's manual, version 4.2. Wilhelm Schickard Institute for Computer Science, Tübingen, Germany.
41. Stocker, M., and R. Hilborn. 1981. Short-term forecasting in marine fish stocks. Canadian Journal of Fisheries and Aquatic Sciences 38:1247-1254.
42. Stone, J. H. 1976. Environmental factors relating to Louisiana menhaden harvest. Center for Wetland Resources, Louisiana State University, Sea Grant Publication LSU-T-76-004, Baton Rouge.
43. Vaughan, D. S., S Manooch III, and J. C. Potts. 2001. Assessment of the wreckfish fishery on the Blake Plateau. Pages 105-120 in G. R. Sedberry editor. Island in the stream: oceanography and fisheries of the Charleston Bump. American Fisheries Society, Symposium 25, Bethesda, Maryland.
44. Vaughan, D. S., J. W. Smith, and M. H. Prager. 2000. Population characteristics of Gulf menhaden, Brevoortia patronus. NOAA TechNical Report NMFS 149.
45. Wei, W. W. S. 1994. Time series analysis. Addison-Wesley, Redwood City, California.
46. Werbos, P. J. 1974. Beyond regression: new tools for prediction and analysis in the behavioral sciences. Doctoral dissertation. Harvard University, Cambridge, Massachusetts.