Computers in fisheries population dynamics

Computers in Fisheries Research: Second Edition

Volumn 9781402086366, Issue , 2009, Pages 337-372

  Maunder, Mark N ^a   Schnute, Jon T ^b   Ianelli, James N ^c  

^a INTER AMERICAN TROPICAL TUNA COMMISSION   (United States)

^b PACIFIC BIOLOGICAL STATION   (Canada)

^c ALASKA FISHERIES SCIENCE CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; FISHERIES; POPULATION DYNAMICS; SENSITIVITY ANALYSIS;

ALMOST TRIVIAL; BIOLOGICAL PROCESS; CALCULATING MACHINES; ELECTRONIC COMPUTERS; FISH POPULATION DYNAMICS;

UNCERTAINTY ANALYSIS;

EID: 79960788042     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-1-4020-8636-6_11     Document Type: Chapter

References (119)

1. Anon (2004) Alaska Groundfish Fisheries Final Programmatic Supplemental Environmental Impact Statement. http://www.fakr.noaa.gov/sustainablefisheries/seis/intro.htm. 7000 pp
2. Bayarri MJ, Berger J (2004) The interplay between Bayesian and frequentist analysis. Statistical Science 19:58-80
3. Begley J, Howell D (2004) An overview of Gadget, the Globally applicable Area-Disaggregated General Ecosystem Toolbox ICES CM 2004/FF: 13. See http://www.hafro.is/gadget
4. Bentley N, Breen PA, Starr PJ, Kendrick T (2001) Assessment of the CRA3 and NSS stock of red rock lobster (Jasus edwardsii) for 2000. New Zealand Fisheries Assessment Report 2001/69
5. Beverton RJH, Holt SJ (1957) On the dynamics of exploited fish populations. U. K. Ministry of Agriculture, Fish & Fisheries Investigations (Series 2) 19
6. Bull B, Francis RICC, Dunn A, McKenzie A, Gilbert DJ, Smith MH (2005) CASAL (C++ algorithmic stock assessment laboratory): CASAL user manual v2.07-2005/08/21. NIWA Technical Report 127. See http://www.niwascience.co.nz/ncfa/tools/casal
7. Butterworth DS, Cochrane KL, De Oliveira JAA (1997) Management procedures: a better way to management fisheries? The South African experience. In: Pikitch EL, Huppert DD, Sissenwine MP (eds) Global Trends: Fisheries Management. American Fisheries Society Symposium 20, Bethesda, pp 83-90
8. Butterworth DS, Punt AE (1999) Experiences in the evaluation and implementation of management procedures. ICES Journal of Marine Science 56:985-998
9. Butterworth DS, Ianelli JN, Hilborn R (2003) A statistical model for stock assessment of southern bluefin tuna with temporal changes in selectivity. African Journal of Marine Science 25:331-361
10. Campbell SL, Chancellier JP, Nikoukhah R (2006) Modeling and simulation in Scilab/Scicos. Springer, New York (Software available at http://www.scicos.org/book.html)
11. Clifford P (1993) Discussion on the meeting on the Gibbs sampler and other Markov chain Monte Carlo methods. Journal of the Royal Statistical Society (Series B) 55:53-102
12. Dalgaard P (2002) Introductory statistics with R. Springer-Verlag, New York (Software in the R package "ISwR")
13. De La Mare WK (1986) Simulation studies on management procedures. Reports of the International Whaling Commission 36:429-450
14. De Oliveira JAA, Butterworth DS, Johnston SJ (1998) Progress and problems in the applicationofmanagement procedurestoSouth Africa's major fisheries. In: FunkF, Quinn II TJ, Heifetz J, Ianelli JN, Powers JE, Schweigert JJ, Sullivan PJ, Zhang CI (eds) Fishery Stock Assessment Models. Alaska Sea Grant College Program Report No. AK-SG-98-01, University of Alaska Fairbanks, pp 513-530
15. Deriso RB, Maunder MN, Skalski JR (2007) Variance estimation in integrated assessment models and its importance for hypothesis testing. Canadian Journal of Fisheries and Aquatic Science 64:187-197
16. Dorn MW (2002) Advice on West Coast rockfish harvest rates from Bayesian meta-analysis of stock-recruit relationships. North American Journal of Fisheries Management 22:280-300
17. Efron BE, Tibshirani RJ (1993) An Introduction to the Bootstrap. Chapman & Hall, New York
18. Field JC, Ralston S (2005) Spatial variability in rockfish (Sebastes spp.) recruitment events in the California Current System. Canadian Journal of Fisheries and Aquatic Science 62:2199-2210
19. Fournier D, Archibald CP (1982) A general theory for analyzing catch at age data. Canadian Journal of Fisheries and Aquatic Science 39:1195-1207
20. Fournier DA, Sibert JR, Majkowski J, Hampton J (1990) MULTIFAN a likelihood-based method for estimating growth parameters and age-composition from multiple length frequency data sets illustrated using data for southern bluefin tuna (Thunnus maccoyii). Canadian Journal of Fisheries and Aquatic Science 47:301-317
21. Fournier DA, Hampton J, Sibert JR (1998) MULTIFAN-CL: a length-based, age-structured model for fisheries stock assessment, with application to South Pacific albacore, Thunnus alalunga. Canadian Journal of Fisheries and Aquatic Science 55:2105-2116
22. Francis RICC (1992) Use of risk analysis to assess fishery management strategies: a case study using orange roughy (Hoplostethus atlanticus) on the Chatham Rise, New Zealand. New Zealand Journal of Marine and Freshwater Research 49:922-930
23. Francis RICC, Hurst RJ, Renwick JA (2003) Quantifying annual variation in catchability for commercial and research fishing. Fishery Bulletin 101:293-304
24. Frayn M (1965) The Tin Men. Collins, London
25. Gelman A, Carlin BP, Stern HS, Rubin DB (1995) Bayesian Data Analysis. Chapman and Hall, London
26. Gomez C (ed.) (1999) Engineering and scientific computing with Scilab. Birkhauser Boston (Book includes software on CD ROM.)
27. Griewank A (2000) Evaluating derivatives: principles and techniques of algorithmic differentiation. Frontiers in Applied Mathematics 19. Society for Industrial and Applied Mathematics, Philadelphia, PA.
28. Haddon M (2001) Modelling and quantitative methods in fisheries. Chapman & Hall/CRC Press, Boca Raton (Software available at http://www.crcpress.com/e-products/downloads/download.asp?cat-no=C1771)
29. Hampton J, Fournier DA (2001) A spatially disaggregated, length-based, age-structured population model of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean. Marine and Freswater Research 52:937-963
30. Heifetz J, Fujioka JT (1991) Movement dynamics of tagged sablefish in the northeastern Pacific. Fisheries Research 11:355-374
31. Hilborn R (1979) Comparison of fisheries control systems that utilize catch and effort data. Canadian Journal of Fisheries and Aquatic Science 36:1477-1489
32. Hilborn R (1996) Computers in fisheries population dynamics. In: Megrey BE, Moksness E (eds) Computers in Fisheries Research. Chapman and Hall, London, pp 176-189
33. Hilborn R, Liermann M (1998) Standing on the shoulders of giants: learning from experience in fisheries. Reviews in Fish Biology and Fisheries 8:273-283
34. Hilborn R, Maunder MN, Parma A., Ernst B, Payne J, Starr PJ (2000) Documentation for a general age-structured Bayesian stock assessment model: code named Coleraine. Fisheries Research Institute, University of Washington, FRI/UW 00/01. Software available at http://fish.washington.edu/research/coleraine
35. Hoggarth DD, Abeyasekera S, Arthur RI, Beddington JR, Burn RW, Halls AS, Kirkwood GP, McAllister M, Medley P, Mees CC, Parkes GB, Pilling GM, Wakeford RC, Welcomme RL (2006) Stock assessment for fishery management: a framework guide to the stock assessment tools of the Fisheries Management Science Programme. FAO Fisheries Technical Paper 487 (ftp://ftp.fao.org/docrep/fao/009/a0486e/a0486e00.pdf). Software available at http://www.fmsp.org.uk/Software.htm
36. Ianelli JN (2002) Simulation analyses testing the robustness of productivity determinations from West Coast Pacific ocean perch stock assessment data. North American Journal of Fisheries Management 22:301-310
37. Ianelli JN, Zimmerman M (1998) Status of the Pacific Ocean Perch Resource in 1998. Pacific Fishery Management Council, Portland
38. Ianelli JN, Fritz L, Walters G, Williamson N, Honkahleto T (1999) Bering Sea-Aleutian Islands Walleye Pollock Assessment for 2000. In: Stock Assessment and Fishery Evaluation Report for the Groundfish Resources of the Eastern Bering Sea and Aleutian Island Region, 2000. North Pacific Fishery Management Council, Anchorage
39. Ianelli JN, Barbeaux S, Honkalehto T, Lauth B, Williamson N (2005) Assessment of Alaska pollock stock in the eastern Bering Sea. In: Stock Assessment and Fishery Evaluation Report for the Groundfish Resources of the Bering Sea/Aleutian Islands Regions. North Pacific Fishery Management Council, Anchorage, pp 31-124
40. Jiao Y, Chen Y (2004) An application of generalized linear models in production model and sequential population analysis. Fisheries Research 70:367-376
41. Jurado-Molina J, Livingston PA, Ianelli JN (2005) Incorporating predation interactions in a statistical catch-at-age model for a predator-prey system in the eastern Bering Sea Canadian Journal of Fisheries and Aquatic Sciences 62:1865-1873
42. Kell LT, Mosqueira I, Grosjean P, Fromentin J-M, Garcia D, Hillary R, Jardim E, et al. (2007). FLR: an open-source framework for the evaluation and development of management strategies. ICES Journal of Marine Science, 64:640-646
43. Lehodey P, Bertignac M, Hampton J, Lewis A, Picaut J (1997) El Nino Southern Oscillation and tuna in the western Pacific. Nature 389:715-718
44. Lewy P, Nielsen A (2003) Modeling stochastic fish stock dynamics using Markov Chain Monte Carlo. ICES Journal of Marine Science 60:743-752
45. Ludwig D, Walters CJ (1985) Are age-structured models appropriate for catch-effort data? Canadian Journal of Fisheries and Aquatic Science 42:1066-1072
46. Luenberger DG (1979) Introduction to dynamic systems: theory, models, and applications. John Wiley and Sons, New York
47. Mahévas S, Pelletier D (2004) ISIS-Fish, a generic and spatially explicit simulation tool for evaluating the impact of management measures on fisheries dynamics. Ecological Modelling 171:65-84
48. Maindonald JM, Braun J (2003) Data analysis and graphics using R: an example-based approach. Cambridge University Press, Cambridge (Software in the R package "UsingR".)
49. Maunder MN (1998) Integration of Tagging and Population Dynamics Models in Fisheries Stock Assessment. PhD dissertation, University of Washington
50. Maunder MN (2001a) A general framework for integrating the standardization of catch-perunit-of-effort into stock assessment models. Canadian Journal of Fisheries and Aquatic Science 58:795-803
51. Maunder MN (2001b) Integrated Tagging and Catch-at-Age ANalysis (ITCAAN). In: Kruse GH, Bez N, Booth A, Dorn MW, Hills S, Lipcius RN, Pelletier D, Roy C, Smith SJ, Witherell D (eds) Spatial Processes and Management of Fish Populations, Alaska Sea Grant College Program Report No. AK-SG-01-02, University of Alaska Fairbanks, pp 123-146
52. Maunder MN (2003) Paradigm shifts in fisheries stock assessment: from integrated analysis to Bayesian analysis and back again. Natural Resource Modeling 16:465-475
53. Maunder MN (2004) Population viability analysis, based on combining integrated, Bayesian, and hierarchical analyses. Acta Oecologica 26:85-94
54. Maunder MN (ed.) (2005) Inter-American Tropical Tuna Commission Workshop On Stock Assessment Methods, La Jolla, California, 7-11 November 2005. Available at http://iattc.org/PDFFiles2/Assessment-methods-WS-Nov05-ReportENG.pdf
55. Maunder MN (2007) A reference list of papers that use AD Model Builder and its precursor AUTODIF. Available at http://admb-project.org/community/bibliography
56. Maunder MN, Starr PJ (2002) Industry participation in stock assessment: The New Zealand SNA1 snapper (pagrus auratus) fishery. Marine Policy 26:481-492
57. Maunder MN, Deriso RB (2003) Estimation of recruitment in catch-at-age models. Canadian Journal of Fisheries and Aquatic Sciences 60:1204-1216
58. Maunder MN, Langley AD (2004) Integrating the standardization of catch-per-unit-of-effort into stock assessment models: testing a population dynamics model and using multiple data types. Fisheries Research 70:389-395
59. Maunder MN, Punt AE (2004) Standardizing catch and effort data: a review of recent approaches. Fisheries Research 70:141-159
60. Maunder MN, Watters GM (2003a) A-SCALA: an age-structured statistical catch-at-length analysis for assessing tuna stocks in the eastern Pacific Ocean. Inter-American Tropical Tuna Commission Bulletin 22:433-582 (in English and Spanish)
61. Maunder MN, Watters GM (2003b) A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example. Fishery Bulletin 101:89-99
62. Maunder MN, Harley SJ, Hampton J (2006) Including parameter uncertainty in forward projections of computationally intensive statistical population dynamic models. ICES Journal of Marine Science 63:969-979
63. Maunder MN, Hoyle SD (2007) Status of bigeye tuna in the eastern Pacific Ocean in 2005 and outlook for 2006. Inter-American Tropical Tuna Commission Stock Assessment Report 7:117-248
64. McAllister MK, Ianelli JN (1997) Bayesian stock assessment using catch-age data and the sampling/importance resampling algorithm. Canadian Journal of Fisheries and Aquatic Sciences 54:284-300
65. Methot RD (1990) Synthesis model: an adaptable framework for analysis of diverse stock assessment data. International North Pacific Fishery Commission Bulletin 50:259-277
66. Millar RB (2002) Reference priors for Bayesian fisheries models. Canadian Journal of Fisheries and Aquatic Science 59:1492-1502
67. Miller TJ, Skalski JR (2006) Integrating design- and model-based inference to estimate length and age composition in North Pacific longline catches. Canadian Journal of Fisheries and Aquatic Sciences 63:1092-1114
68. Minte-Vera CV, Branch TA, Stewart IJ, Dorn MW (2004) Practical application of metaanalysis results: avoiding the double use of data. Canadian Journal of Fisheries and Aquatic Sciences 62:925-929
69. Murrell P (2006) R Graphics. Chapman & Hall/CRC Press, Boca Raton (Software in the R package "RGraphics")
70. Myers RA, Bowen KG, Barrowman NJ (1999) Maximum reproductive rate of fish at low population sizes. Canadian Journal of Fisheries and Aquatic Sciences 56:2404-2419
71. Nielsen A (2000) Fish Stock Assessment using Markov Chain Monte Carlo. MSc Thesis, University of Copenhagen (http://www.dina.kvl.dk/~anielsen/master/)
72. O'Hagan A, Forster J (1994;2004-2nd edition) Kendall's advanced theory of statistics, Volume 2B: Bayesian inference. Arnold, London
73. Parma AM (2001) Bayesian approaches to the analysis of uncertainty in the stock assessment of Pacific halibut. American Fisheries Society Symposium 24:111-132
74. Parma AM (2002) In search of robust harvest rules for Pacific halibut in the face of uncertain assessments and decadal changes in productivity. Bulletin of Marine Science 70:423-453
75. Patterson K, Cook R, Darby C, Gavaris S, Kell L, Lewy P, Mesnil B, Punt A, Restrepo V, Skagen DW, Stefansson G (2001) Estimating uncertainty in fish stock assessment and forecasting. Fish and Fisheries 2:125-157
76. Pella JJ, Tomlinson PK (1969) A generalized stock production model. Inter-American Tropical Tuna Commission Bulletin 13:421-458
77. Pennington M, Volstad JH (1994) Assessing the effect of intra-haul correlation and variabledensity on estimates of population characteristics for marine surveys. Biometrics 50:725-732
78. Pennington M, Burmeister LM, Hjellvik V (2002) Assessing the precision of frequency distributions estimated from trawl-survey samples. Fisheries Bulletin 100:74-80
79. Prager MH (1995) User's manual for ASPIC: a stock-production model incorporating covariates, program version 3.6x. NMFS, Southeast Fisheries Science Center Miami Lab Document MIA-92/93-55, Miami
80. Prager MH, Willaims EH (2003) From the golden age to the new industrial age: fishery modeling in the early 21st century. Natural Resource Modeling 16:477-489
81. Punt AE (1988) Model Selection for the Dynamics of Southern African Hake Resources. MSc thesis, University of Cape Town
82. Punt AE (1993) The comparitive performance of production-model and ad hoc tuned VPA based feedback-control management procedures for the stock of Cape hake off the west coast of South Africa. In: Smith SJ, Hunt JJ, Rivard D (eds) Risk Evaluation and Biological Reference Points for Fisheries Management. Canadian Special Publication in Fisheries and Aquatic Science 120, pp 283-299
83. Punt AE, Hilborn R (1996) Biomass Dynamic Models - User's Manual. FAO Computerized Information Series (Fisheries) No. 10, Rome
84. Punt AE, Hilborn R (1997) Fisheries stock assessment and decision analysis: the Bayesian approach. Reviews in Fish Biology and Fisheries 7:35-63
85. Punt AE, Hilborn R (2001) BAYES-SA - Bayesian Stock Assessment Methods in Fisheries - User's Manual. FAO Computerized Information Series (Fisheries) No. 12, Rome (http://www.fao.org/docrep/005/Y1958E/Y1958E00.HTM)
86. Quinn TJ II (2003) Ruminations on the development and future of population dynamics models in fisheries. Natural Resource Modeling 16:341-392
87. Restrepo NR, Patterson KR, Darby CD, Gavaris S, Kell LT, Lewy P, Mesnil B, Punt AE, Cook RM, O'Brien CM, Skagen DW, Stefansson G (2000) Do different methods provide accurate probability statements in the short term? ICES CM 2000/V: 08
88. Richards LJ (1991) Use of contradictory data sources in stock assessments. Fisheries Research 11:225-238
89. Ricker WE (1958) Handbook of computations for biological statistics of fish populations. Bulletin of the Fisheries Research Board of Canada No. 119
90. Ricker WE (1975) Computation and interpretation of biological statistics of fish populations. Bulletin of the Fisheries Research Board of Canada No. 191
91. Robert CP, Casella G (2004-2nd edition) Monte Carlo statistical methods. Springer, New York, NY
92. Schnute J (1987) Data uncertainty, model ambiguity, and model identification: "Mirror, mirror on the wall, what model's fairest of them all?". Natural Resource Modeling 2:159-212
93. Schnute JT (2006) Curiosity, recruitment, and chaos: a tribute to Bill Ricker's inquiring mind. Environmental Biology of Fishes 75:95-110
94. Schnute JT, Haigh R (2006) Reference points and management strategies: lessons from quantum mechanics. ICES Journal of Marine Science 63:4-11
95. Schnute JT, Haigh R (2007) Compositional analysis of catch curve data withanapplication to Sebastes maliger. ICES Journal of Marine Science 64:218-233
96. Schnute JT, Hilborn R (1993) Analysis of contradictory data sources in fisheries stock assessment. Canadian Journal of Fisheries and Aquatic Sciences 50:1916-1923
97. Schnute JT, Richards LJ (1995) The influence of error on population estimates from catch-age models. Canadian Journal of Fisheries and Aquatic Sciences 52:2063-2077
98. Schnute JT, Richards LJ, Olsen N (1998) Statistics, software, and fish stock assessment. In: Funk F, Quinn II TJ, Heifetz J, Ianelli JN, Powers JE, Schweigert JJ, Sullivan PJ, Zhang CI (eds) Fishery Stock Assessment Models. Alaska Sea Grant College Program Report No. AK-SG-98-01, University of Alaska Fairbanks, pp 171-184
99. Schnute JT, Boers NM, Haigh R (2004) PBS Mapping 2: User's Guide. Canadian Technical Report of Fisheries and Aquatic Sciences 2549 (Softwarein the R package "PBSmapping", which includes this report as "PBSmapping-UG.pdf")
100. Schnute JT, Couture-Beil A, Haigh R (2006) PBS Modelling 1: User's Guide. Canadian Technical Report of Fisheries and Aquatic Sciences 2674 (Software in the R package "PBSmodelling", which includes this report as "PBSmodelling-UG.pdf")
101. Schnute JT, Maunder MN, Ianelli JN (2007) Designing tools to evaluate fishery management strategies: can the scientific community deliver? ICES Journal of Marine Science, 64:1077-1084
102. Schweder T, Hjort NL (2002) Confidence and likelihood. Scandinavian Journal of Statistics 29:309-332
103. Shepherd JG (1999) Extended survivors analysis: an improved method for the analysis of catch-at-age data and abundance indices. ICES Journal of Marine Science 56:584-591
104. Skaug H (2002) Automatic differentiation to facilitate maximum likelihood estimation in nonlinear random effects models Journal of Computational and Graphical Statistics 11:458-470
105. Skaug H, Fournier D (2006) Automatic approximation of the marginal likelihood in nonlinear hierarchical models. Computational Statistics and Data Analysis 51:699-709
106. Skud BE (1975) Revised estimates of halibut abundance and the Thompson-Burkenroad debate. International Pacific Halibut Commission Scientific Report 56:36 pp (Available at http://www.iphc.washington.edu/HALCOM/pubs/scirep/SciReport0056.pdf)
107. Smith ADM, Punt AE (1998) Stock assessment of gemfish (Rexea solandri) in eastern Australia using maximum likelihood and Bayesian methods. In: Funk F, Quinn II TJ, Heifetz J, Ianelli JN, Powers JE, Schweigert JJ, Sullivan PJ, Zhang CI (eds) Fishery Stock Assessment Models. Alaska Sea Grant College Program Report No. AK-SG-98-01, University of Alaska Fairbanks, pp 245-286
108. Smith ADM, Sainsbury KJ, Stevens RA (1999) Implementing effective fisheries-management systems: management strategy evaluation and the Australian partnership approach. ICES Journal of Marine Science 56:967-979
109. Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society (Series B) 64:583-639
110. Spiegelhalter D, Thomas A, Best N, Lunn D (2004) WinBUGS User Manual, version 2.0 (http://mathstat.helsinki.fi/openbugs/)
111. Stefansson G (1998) Comparing different information sources in a multispecies context. In: Funk F, Quinn II TJ, Heifetz J, Ianelli JN, Powers JE, Schweigert JJ, Sullivan PJ, Zhang CI (eds) Fishery Stock Assessment Models. Alaska Sea Grant College Program Report No. AK-SG-98-01, University of Alaska Fairbanks, pp 741-758
112. Thomas N (2004) BRugs User Manual (the R interface to BUGS), version 1.0 (http://mathstat.helsinki.fi/openbugs/, Software in the R package "BRugs")
113. Thompson GG (1992) A Bayesian approach to management advice when stock-recruitment parameters are uncertain. Fishery Bulletin 90:561-573
114. Thompson GG (1999) Optimizing harvest control rules in the presence of natural variability and parameter uncertainty. NOAA Tech. Memo. NMFS-F/SPO-40
115. Venables WN, Ripley BD (1994, 1999-3rd edition) Modern applied statistics with S-PLUS. Springer Verlag New York (Software in the R package "MASS")
116. Venables WN, Ripley BD (2000) S Programming. Springer Verlag, New York (Software available at http://www.stats.ox.ac.uk/pub/MASS3/Sprog/)
117. Walther A, Kowarz A, Griewank A (2005) ADOL-C: a package for the automatic differentiation of algorithms written in C/C++, version 1.10.0. Report and software available from: http://www.math.tu-dresden. de/%04adol-c
118. Wilberg MJ (2005) Improving statistical catch-at-age stock assessments. PhD Dissertation, Michigan State University
119. Wood S (2006) Generalized Additive Models: An Introduction with R. Texts in Statistical Science 67. Chapman & Hall/CRC Press, Boca Raton (Software and data available in the R packages "mgcv" and "gamair")