Standardizing fishery-dependent catch and effort data in complex fisheries with technology change

Reviews in Fish Biology and Fisheries

Volumn 16, Issue 1, 2006, Pages 21-38

  Bishop, Janet ^a  

^a CSIRO   (Australia)

Author keywords

Catchability; Confounding; CPUE; Extrapolation; Natural experiment

Indexed keywords

ABUNDANCE INDEX; CATCHABILITY; FISHERY; STANDARDIZATION; STOCK ASSESSMENT; TECHNOLOGICAL CHANGE;

EID: 33748099725     PISSN: 09603166     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11160-006-0004-9     Document Type: Article

References (83)

1. Allen PM, Punsly R (1984) Catch rates as indices of abundance in yellowfin tuna, Thunnus albacares, in the Eastern Pacific Ocean. Int Am Trop Tuna Commiss Bull 18:301-379
2. Alverson DL (1959) Recent developments in fishing methods and effects of management on efficiency. In: Crutchfield JA (ed) Conference proceedings: Biological and economic aspects of fisheries management, February 17-19 1959. University of Washington, Seattle, pp 160
3. Arreguin-Sanchez F (1996) Catchability: A key parameter for fish stock assessment. Rev Fish Biol Fish 6:221-242
4. Box GEP, Hunter JS, Hunter WG (2005) Statistics for experimenters: design, discovery and innovation. Wiley Interscience, Hoboken NJ, pp 633
5. Breiman L (1992) The little bootstrap and other methods for dimensionality selection in regression-X-fixed prediction error. JASA 87:738-754
6. Brewer D, Heales D, Jones P (2003) Chapter 10. Change in Northern Prawn Fishery Catches due to TEDs and BRDs. In: CSIRO Marine Research (ed) Assessment and improvement of BRDs and TEDs in the Northern Prawn Fishery: A co-operative approach by fishers, scientists, fisheries technologists, economists and conservationists. CSIRO/FRDC 2000/173, Cleveland, Australia
7. Brunenmeister SL (1984) Standardization of fishing effort and production models for brown, white and pink shrimp stocks fished in US waters of the Gulf of Mexico. In: Gulland JA, Rothschild BJ (eds) Penaeid shrimps: their biology and management. Fishing News Books, Farnham, Surrey, UK, pp 308
8. Campbell RA (2004) CPUE standardization and the construction of indices of stock abundance in a spatially varying fishery using general linear models. Fish Res 70:209-227
9. Chatfield C (1995) Model uncertainty, data mining and statistical inference. J R Stat Soc A 158:419-466
10. Chen C, Chock DP, Winkler S (1999) A simulation study of confounding in generalized linear models for air pollution epidemiology. Environ Health Persp 107:217-222
11. Chen Y, Chen LQ, Stergiou KI (2003) Impacts of data quantity on fisheries stock assessment. Aquat Sci 65:92-98
12. Cochran WG (1977) Sampling techniques, 3rd edn. Wiley, New York, pp 428
13. Cochran WG (1983) Planning and analysis of observational studies. Wiley, New York, pp 149
14. Cochran WG, Rubin DB (1973) Controlling bias in observational studies: A review. Sankhya A 35:417-446
15. Cramer EM (1985) Multicollinearity. In: Kotz S, Johnson NL (eds) Encyclopedia of statistical sciences, Vol 5. Wiley, New York, pp 639-643
16. De Boer EJ (1975) On the use of brake horsepower as a parameter for fishing power. In: Pope JA (ed) Measurement of fishing effort. Cons. Int. Explor. Mer. - Rapports et process-verbaux des reunions 168, Special meeting 25-26 Sept. 1970, Charlottenlund Slot, Denmark, pp 30-34
17. De Boer EJ, de Veen JF (1975) On the fishing power of Dutch beam trawlers. In: Pope JA (ed) Measurement of fishing effort. Cons. Int. Explor. Mer. - Rapports et process-verbaux des reunions 168, Special meeting 25-26 Sept. 1970. Charlottenlund Slot, Denmark, pp 11-12
18. Diamond J (1986) Overview: Laboratory experiments, field experiments and natural experiments. In: Diamond J, Case TJ (eds) Community ecology. Harper and Row, New York
19. Dickson W (1993) Estimation of the capture efficiency of trawl gear. 2: testing a theoretical model. Fish Res 16:255-272
20. Draper N, Smith H (1981) Applied regression analysis, 2nd edn. John Wiley and Sons, New York, pp 709
21. Duncan OD, Cuzzort RP, Duncan B (1961) Statistical geography: Problems in analyzing areal data. Greenwood Press, Westport, CT, pp 195
22. Ellison A (1996) An introduction to Bayesian inference for ecological research and environmental decision-making. Ecol Appl 6:1036-1046
23. Fayyad UM (1997) Editorial. Data Min Knowl Disc 1:5-10
24. Fisher RA (1926) The arrangement of field experiments. J Min Agr GB 33:503-513
25. Freeman DH, Holford TR (1980) Summary rates. Biometrics 36:195-205
26. Garcia S, Le Reste L (1981) Life cycles, dynamics, exploitation and management of coastal Penaeid shrimp stocks. FAO Fish. Tech. Pap. 203, United Nations, Rome, Italy, pp 215
27. Garstang W (1900) The impoverishment of the sea. J Mar Biol Assoc 6:1-69
28. Gavaris S (1980) Use of a multiplicative model to estimate catch rate and effort from commercial data. Can J Fish Aquat Sci 3:2272-2275
29. Geisser S (1971) The inferential use of predictive distributions. In: Godambe VP, Sprott DA (eds) Foundations of statistical inference. Holt, Rinehart and Winston, Toronto, pp 519
30. Geisser S (1982) Aspects of the predictive and estimative approaches in the determination of probabilities. Biometrics supplement: Current topics in biostatistics and epidemiology. March 1982, pp 75-85
31. Geisser S (1985) Predictive analysis. In: Kotz S, Johnson NL (eds) Encyclopedia of statistical sciences, vol 7. Wiley, New York, pp 158-170
32. Gillis DM, Peterman RM (1998) Implications of interference among fishing vessels and the ideal free distribution to the interpretation of CPUE. Can J Fish Aquat Sci 55:37-46
33. Glymour C, Madigan D, Pregibon D, Smyth P (1997) Statistical themes and lessons for data mining. Data Min Knowl Disc 1:11-28
34. Grimm V (1999) Ten years of individual-based modeling in ecology: What have we learned and what could we learn in the future? Ecol Model 115:129-148
35. Gulland JA (1956) On the fishing effort in English demersal fisheries. Fishery Investigations Series II, vol XX, Number 5. G.B. Ministry Agriculture, Fisheries and Food, London, pp 45
36. Gulland JA (1983) Fish stock assessment: A manual of basic methods. John Wiley and Sons, Chichester, pp 223
37. Gustafson P (2002) On the simultaneous effects of model misspecification and errors in variables. Can J Stat 30:463-474
38. Gustafson P (2003) Measurement error and misclassification in statistics and epidemiology: Impacts and Bayesian adjustments. Chapman and Hall/CRC Press, Boca Raton FL, pp 195
39. Hairston NG Sr (1989) Ecological experiments: Purpose, design and execution. Cambridge University Press, Cambridge, pp 359
40. Hansen KE, Ferro RST, Hopper A et al. (1998) Investigation of the relative fishing effort exerted by towed demersal gears on North Sea consumption species. In: Barthel KG et al. (eds) Proceedings of the third conference of European marine science and technology, vol 5. Lisbon, Portugal, pp 148-149
41. Holden M (1994) The common fisheries policy: Origin, evaluation and future. Fishing News Books, Oxford, pp 274
42. Johannes R (1998) The case for data-less marine resource management: examples from tropical nearshore fin-fisheries. Trends Ecol Evol 13:243-2460
43. Joseph J, Calkins TP (1969) Population dynamics of skipjack tuna (Katsuwonus pelamis) of the Eastern Pacific Ocean. Int Am Trop Tuna Comm Bull 13:1-273
44. Kimura DK (1981) Standardized measures of relative abundance based on modelling log c.p.u.e., and their application to Pacific ocean perch Sebastes alutus. J Cons Int Explor Mer 39:211-218
45. Klima EF (1989) Approaches to research and management of US Fisheries for the Penaeid shrimp in the Gulf of Mexico. In: Caddy JF (ed) Marine invertebrate fisheries: Their assessment and management. John Wiley and Sons, New York, pp 752
46. Larkin PA (1996) The costs of fisheries management, information and fisheries research. In: Proceedings of the second world fisheries congress. Brisbane, Australia, pp 713-718
47. Leamer EE (1978) Specification searches: Ad hoc inference with non-experimental data. John Wiley and Sons, New York, pp 372
48. Levin R (1966) The strategy of model building in population biology. Am Sci 54:42-431
49. Ljung L (1987) System identification: Theory for the user. Prentice Hall, Eaglewood Cliffs, NJ, pp 519
50. MacKinnon MJ, Glick N (1999) Data mining and knowledge discovery in databases - an overview. Aust NZ J Stat 41:255-275
51. Mahevas S, Sandon Y, Biseau A (2003) Quantification of annual variations in fishing power due to vessel characteristics: An application to the bottom trawlers of South-Brittany targeting anglerfish Lophius budegassa and Lophius piscatorius. ICES J Mar Sci 61:71-83
52. Mallows C (1998) The zeroth problem. Am Statist Assoc 52:1-9
53. Mangel M (1982) Search effort and catch rates in fisheries. Eur J Operat Res 11:361-366
54. Marchal P, Ulrich C, Pastoors M (2002) Area-based management and fishing efficiency. Aquat Living Resour 15:73-85
55. Matricciani E (2001) The "missing" damage-temperature relationship in the Challenger incident. IEEE Trans Eng Manage 48:267-271
56. Maunder MM, Punt AE (2004) Standardizing catch and effort data: A review of recent approaches. Fish Res 70:141-159
57. McCullagh P, Nelder JA (1989) Generalized linear models. Chapman and Hall, New York, pp 511
58. McKinlay SM (1975) The design and analysis of the observational study - a review. JASA 70:503-523
59. Morgenstern H (1982) Uses of ecologic analyses in epidemiologic research. Am J Public Health 72:1336-1344
60. Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33:161-174
61. Pauly D (1995) Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol Evol 10:430
62. Pearse PH (1980) Regulation of fishing effort. FAO Fisheries Technical Paper No. 197 (FIPL/T197). Food and Agriculture Organization of the United Nations, Rome, pp 82
63. Pope JA (ed) (1975) Measurement of fishing effort. In: Cons. Int. Explor. Mer. - Rapports et process-verbaux des reunions, vol 168. Special meeting, 25-26 September 1970, Charlottenlund Slot, Denmark, pp 102
64. Prevost E, Parent E, Crozier W et al. (2003) Setting biological reference points for Atlantic salmon stocks: Transfer of information from data-rich to sparse-data situations by Bayesian hierarchical modeling. ICES J Mar Sci 60:1177-1193
65. Quinn TJ, Hoag SH, Southward GM (1982) Comparison of two methods of combining catch-per-unit-effort data from geographic regions. Can J Fish Aquat Sci 39:837-846
66. Rahikainen M, Kuikka S (2002) Fleet dynamics of herring trawlers-change in gear size and implications for interpretation of catch per unit effort. Can J Fish Aquat Sci 59:531-541
67. Reade-Christopher S (1995) On the effects of predictor misclassification in multiple linear regression analysis. Commun Statist Theory Meth 24:13-37
68. Ricker WE (1975) Computation and interpretation of biological statistics of fish populations. In: Bull. Fish. Res. Board Canada, vol 191. Dept. Fisheries and Oceans, Ottawa, Canada, pp 472
69. Robins CM, Wang Y-G, Die D (1998) The impact of Global Positioning Systems and plotters on fishing power in the Northern Prawn Fishery, Australia. Can J Fish Aquat Sci 55:1645-1651
70. Saltelli A, Tarantola S, Campolongo F (2000) Sensitivity analysis as an ingredient of modeling. Stat Sci 15:377-395
71. Shultz KS, Whitney DJ (2004) Measurement theory in action. Sage Publications, California
72. Simpson CH (1951) The interpretation of interaction in contingency tables. J R Stat Soc Ser B 1:238-241
73. Sterling DJ (2005) Modeling the physics of prawn trawling for fisheries management. School of Applied Physics, Curtin University of Technology, Perth, Australia
74. Swain DP, Sinclair AF (1994) Fish distribution and catchability: What is the appropriate measure of distribution? Can J Fish Aquat Sci 51:1046-1054
75. Tweedie RL, Mengersen KL (1994) Garbage in, garbage out: Can statisticians quantify the effects of poor data? Chance 7:20-27
76. Venables WN, Ripley BD (2002) Modern applied statistics using S-PLUS, 4th edn. Springer-Verlag, New York, pp 495
77. Vignaux M (1996a) Analysis of spatial structure in fish distribution using commercial catch and effort data from the New Zealand hoki fishery. Can J Fish Aquat Sci 53:963-973
78. Vignaux M (1996b) Analysis of vessel movements and strategies using commercial catch and effort data from the New Zealand hoki fishery. Can J Fish Aquat Sci 53:2126-2136
79. Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE (2005) Regression methods in biostatistics: Linear, logistic, survival, and repeated measures models. Springer, New York, pp 340
80. Walters CJ (2003) Folly and fantasy in the analysis of spatial catch rate data. Can J Fish Aquat Sci 60:1433-1436
81. Westrheim SJ, Foucher RP (1985) Relative fishing power for Canadian trawlers landing Pacific Cod (Gadus macrocephalus) and important shelf cohabitants from major offshore areas of western Canada, 1960-1981. Can J Fish Aquat Sci 42:1614-1626
82. Wold H (1956) Causal inference from observational data: A review of ends and means. J R Stat Soc A 119:28-60
83. Zeller D, Froese R, Pauly D (2005) On losing and recovering fisheries and marine science data. Mar Policy 29:69-73