Abstract
Standardization of commercial catch and effort data is important in fisheries where standardized abundance indices based on fishery-dependent data are a fundamental input to stock assessments. The goal of the standardization is then to minimize bias due to the confounding of apparent abundance patterns with fishing power. There is a high risk of confounding between fishing power and abundance in fisheries where the fleet has altered their fishing technology over the years. Also, the spatial aspects and the fishing history can be so heterogeneous that any standardization really involves an extrapolation, for example to a hypothetical standard vessel. When the standardization involves an extrapolation, then the appropriate modeling strategy is to build a so-called estimation model, rather than a predictive model. Strategies to build such an estimation model from fishery-dependent data include: pay careful attention to subject matter, and collect information about potential confounding effects to include in the model (putting a high value on the acquisition of data on covariates); model variable catchability at a highly disaggregated scale; aim for realistic coefficients when fitting the model and pay relatively less attention to achieving precision or maximizing explained variance; adopt modern statistical methods to combine data from different sources; and if data are deficient, then apply precautionary allowances. These strategies offer some protection against bias due to confounding, in the absence of formal criteria for identifying the best model.
Similar content being viewed by others
References
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
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
Arreguin-Sanchez F (1996) Catchability: a key parameter for fish stock assessment. Rev Fish Biol Fish 6:221–242
Box GEP, Hunter JS, Hunter WG (2005) Statistics for experimenters: design, discovery and innovation. Wiley Interscience, Hoboken NJ, pp 633
Breiman L (1992) The little bootstrap and other methods for dimensionality selection in regression—X-fixed prediction error. JASA 87:738–754
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
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
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
Chatfield C (1995) Model uncertainty, data mining and statistical inference. J R Stat Soc A 158:419–466
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
Chen Y, Chen LQ, Stergiou KI (2003) Impacts of data quantity on fisheries stock assessment. Aquat Sci 65:92–98
Cochran WG (1977) Sampling techniques, 3rd edn. Wiley, New York, pp 428
Cochran WG (1983) Planning and analysis of observational studies. Wiley, New York, pp 149
Cochran WG, Rubin DB (1973) Controlling bias in observational studies: a review. Sankhya A 35:417–446
Cramer EM (1985) Multicollinearity. In: Kotz S, Johnson NL (eds) Encyclopedia of statistical sciences, Vol 5. Wiley, New York, pp 639–643
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
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
Diamond J (1986) Overview: laboratory experiments, field experiments and natural experiments. In: Diamond J, Case TJ (eds) Community ecology. Harper and Row, New York
Dickson W (1993) Estimation of the capture efficiency of trawl gear. 2: testing a theoretical model. Fish Res 16:255–272
Draper N, Smith H (1981) Applied regression analysis, 2nd edn. John Wiley and Sons, New York, pp 709
Duncan OD, Cuzzort RP, Duncan B (1961) Statistical geography: problems in analyzing areal data. Greenwood Press, Westport, CT, pp 195
Ellison A (1996) An introduction to Bayesian inference for ecological research and environmental decision-making. Ecol Appl 6:1036–1046
Fayyad UM (1997) Editorial. Data Min Knowl Disc 1:5–10
Fisher RA (1926) The arrangement of field experiments. J Min Agr GB 33:503–513
Freeman DH, Holford TR (1980) Summary rates. Biometrics 36:195–205
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
Garstang W (1900) The impoverishment of the sea. J Mar Biol Assoc 6:1–69
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
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
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
Geisser S (1985) Predictive analysis. In: Kotz S, Johnson NL (eds) Encyclopedia of statistical sciences, vol 7. Wiley, New York, pp 158–170
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
Glymour C, Madigan D, Pregibon D, Smyth P (1997) Statistical themes and lessons for data mining. Data Min Knowl Disc 1:11–28
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
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
Gulland JA (1983) Fish stock assessment: a manual of basic methods. John Wiley and Sons, Chichester, pp 223
Gustafson P (2002) On the simultaneous effects of model misspecification and errors in variables. Can J Stat 30:463–474
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
Hairston NG Sr (1989) Ecological experiments: purpose, design and execution. Cambridge University Press, Cambridge, pp 359
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
Holden M (1994) The common fisheries policy: origin, evaluation and future. Fishing News Books, Oxford, pp 274
Johannes R (1998) The case for data-less marine resource management: examples from tropical nearshore fin-fisheries. Trends Ecol Evol 13:243–2460
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
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
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
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
Leamer EE (1978) Specification searches: ad hoc inference with non-experimental data. John Wiley and Sons, New York, pp 372
Levin R (1966) The strategy of model building in population biology. Am Sci 54:42–431
Ljung L (1987) System identification: theory for the user. Prentice Hall, Eaglewood Cliffs, NJ, pp 519
MacKinnon MJ, Glick N (1999) Data mining and knowledge discovery in databases—an overview. Aust NZ J Stat 41:255–275
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
Mallows C (1998) The zeroth problem. Am Statist Assoc 52:1–9
Mangel M (1982) Search effort and catch rates in fisheries. Eur J Operat Res 11:361–366
Marchal P, Ulrich C, Pastoors M (2002) Area-based management and fishing efficiency. Aquat Living Resour 15:73–85
Matricciani E (2001) The “missing” damage-temperature relationship in the Challenger incident. IEEE Trans Eng Manage 48:267–271
Maunder MM, Punt AE (2004) Standardizing catch and effort data: a review of recent approaches. Fish Res 70:141–159
McCullagh P, Nelder JA (1989) Generalized linear models. Chapman and Hall, New York, pp 511
McKinlay SM (1975) The design and analysis of the observational study—a review. JASA 70:503–523
Morgenstern H (1982) Uses of ecologic analyses in epidemiologic research. Am J Public Health 72:1336–1344
Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33:161–174
Pauly D (1995) Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol Evol 10:430
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
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
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
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
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
Reade-Christopher S (1995) On the effects of predictor misclassification in multiple linear regression analysis. Commun Statist Theory Meth 24:13–37
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
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
Saltelli A, Tarantola S, Campolongo F (2000) Sensitivity analysis as an ingredient of modeling. Stat Sci 15:377–395
Shultz KS, Whitney DJ (2004) Measurement theory in action. Sage Publications, California
Simpson CH (1951) The interpretation of interaction in contingency tables. J R Stat Soc Ser B 1:238–241
Sterling DJ (2005) Modeling the physics of prawn trawling for fisheries management. School of Applied Physics, Curtin University of Technology, Perth, Australia
Swain DP, Sinclair AF (1994) Fish distribution and catchability: what is the appropriate measure of distribution? Can J Fish Aquat Sci 51:1046–1054
Tweedie RL, Mengersen KL (1994) Garbage in, garbage out: can statisticians quantify the effects of poor data? Chance 7:20–27
Venables WN, Ripley BD (2002) Modern applied statistics using S-PLUS, 4th edn. Springer-Verlag, New York, pp 495
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
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
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
Walters CJ (2003) Folly and fantasy in the analysis of spatial catch rate data. Can J Fish Aquat Sci 60:1433–1436
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
Wold H (1956) Causal inference from observational data: a review of ends and means. J R Stat Soc A 119:28–60
Zeller D, Froese R, Pauly D (2005) On losing and recovering fisheries and marine science data. Mar Policy 29:69–73
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bishop, J. Standardizing fishery-dependent catch and effort data in complex fisheries with technology change. Rev Fish Biol Fisheries 16, 21–38 (2006). https://doi.org/10.1007/s11160-006-0004-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11160-006-0004-9