Scientia Marina, Vol 75, No 4 (2011)

Generalized linear Bayesian models for standardizing CPUE: an application to a squid-jigging fishery in the northwest Pacific Ocean


https://doi.org/10.3989/scimar.2011.75n4679

Jie Cao
College of Marine Sciences, Shanghai Ocean University, China

Xinjun Chen
College of Marine Sciences, Shanghai Ocean University - The Key Laboratory of Shanghai Education Commission for Oceanic Fisheries Resources Exploitation - The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, China

Yong Chen
School of Marine Sciences, University of Maine - The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, United States

Bilin Liu
College of Marine Sciences, Shanghai Ocean University - The Key Laboratory of Shanghai Education Commission for Oceanic Fisheries Resources Exploitation - The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, China

Jin Ma
College of Marine Sciences, Shanghai Ocean University, China

Siliang Li
College of Marine Sciences, Shanghai Ocean University, China

Abstract


Generalized linear Bayesian (GLBM) non-hierarchical and hierarchical models were developed for standardization of catch per unit effort (CPUE). The GLBM containing the covariates of month, latitude, sea surface temperature (SST), sea surface salinity (SSS) and sea level height (SLH) had the best fit for the Chinese squid-jigging fishery of Ommastrephes bartramii in the northwest Pacific Ocean based on deviance information criteria. This best-fitting model tends to be more ecologically sound than other CPUE standardization models, such as generalized linear models and generalized additive models. GLBM was also used to deal with the problems of estimating stock abundance index (i.e. standardized CPUE) resulting from increased spatial heterogeneity of spatial dynamics of fishing efforts in the squid fishery by predicting the standardized CPUE for unfished areas. The standardized CPUE based on data including predicted CPUE of unfished areas was lower than the derived CPUE based on data with observed CPUE alone, in particular during the fishing peak of August to October. This study indicates that it is more appropriate to use the standardized CPUE derived from data including both predicted CPUE of unfished areas and observed CPUE of fished area as a stock abundance index. We suggest that the proposed method be used in CPUE standardization to account for impacts of large spatial heterogeneity of fishing efforts in fisheries.

Keywords


generalized linear Bayesian models; CPUE standardization; Ommastrephes bartramii; Chinese squid-jigging fishery; northwest Pacific Ocean

Full Text:


PDF

References


Bellido, J.M., G.J. Pierce and J. Wang. – 2001. Modelling intra-annual variation in abundance of squid Loligo forbesi in Scottish waters using generalized additive models. Fish. Res., 52(1): 23-29. http://dx.doi.org/10.1016/S0165-7836(01)00228-4

Bigelow, K.A., C.H. Boggs and X. He. – 1999. Environmental effects on swordfish and blue shark catch rates in the US North Pacific longline fishery. Fish. Oceanogr., 8(3): 178-198. http://dx.doi.org/10.1046/j.1365-2419.1999.00105.x

Brooks, S. and A. Gelman. – 1997. General methods for monitoring convergence of iterative simulations. JCGS, 7: 434-455.

Campbell, R.A. – 2004. CPUE standardisation and the construction of indices of stock abundance in a spatially varying fishery using general linear models. Fish. Res., 70: 209-227. http://dx.doi.org/10.1016/j.fishres.2004.08.026

Campbell, R.A. and G. Tuck. – 1996. Spatial and temporal analyses of SBT fine-scale catch and effort data. Working Paper SBFWS/96/18 Presented at the Second CCSBT Scientific Meeting, Hobart, Australia, August 26-September 6 (1996). pp. 37.

Cao, J., X.J. Chen and Y. Chen. – 2009. Influence of surface oceanographic variability on abundance of the western winter-spring cohort of neon flying squid Ommastrephes bartramii in the NW Pacific Ocean. Mar. Ecol. Prog. Ser., 381: 119-127. http://dx.doi.org/10.3354/meps07969

Chen, X.J. – 1997. An analysis on marine environment factors of fishing ground of Ommastrephes bartramii in northwest Pacific. J. Shanghai Fish. Univ., 6: 285-287 (in Chinese).

Chen, X.J. – 1999. Study on the formation of fishing grounds of the large squid, Ommastrephes bartramii in the waters 160°E -170°E North Pacific Ocean. J. Shanghai Fish. Univ., 8: 197-201 (in Chinese).

Chen, X.J., Y. Chen, S.Q. Tian, B.L. Liu and W.G. Qian. – 2008. An assessment of the west winter-spring cohort of neon flying squid (Ommastrephes bartramii) in the Northwest Pacific Ocean. Fish. Res., 92: 221-230. http://dx.doi.org/10.1016/j.fishres.2008.01.011

Chen, X.J. and S.Q. Tian. – 2005. Study on the catch distribution and relationship between fishing grounds and surface temperature for Ommastrephes bartramii in the northwestern Pacific Ocean. Periodical of Ocean University of China, 35: 101-107 (in Chinese).

Chen, X.J., L.X. Xu and S.Q. Tian. – 2003. Spatial and temporal analysis of Ommastrephes bartramii resources and its fishing ground in North Pacific Ocean. J. Fish. China., 27(4): 334-342. (in Chinese)

Chen, X.J., X.H. Zhao and Y. Chen. – 2007. El Niño/La Niña influence on the Western Winter-Spring Cohort of neon flying squid (Ommastrephes bartarmii) in the northwestern Pacific Ocean. ICES J. Mar. Sci., 64: 1152-1160.

Cowles, M.K. and B.P. Carlin. – 1996. Markov Chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association, 91: 883-904. http://dx.doi.org/10.2307/2291683

Daniel, G. and D. Michel. – 2004. Analysis of non-linear relationships between catch per unit effort and abundance in a tuna purse-seine fishery simulated with artificial neural networks. ICES J. Mar. Sci., 61(5): 812-820. http://dx.doi.org/10.1016/j.icesjms.2004.05.002

Everitt, B.S. – 2002. The Cambridge Dictionary of Statistics. 2nd edition. Cambridge University Press, Cambridge, UK.

Gavaris, S. – 1980. Use of a multiplicative model to estimate catch rate and effort from commercial data. Can. J. Fish. Aquat. Sci., 37(12): 2272-2275. http://dx.doi.org/10.1139/f80-273

Gelman, A. and D.B. Rubin. – 1992. Inference from iterative simulation using multiple sequences (with discussion). Statistical Science, 7: 457-511. http://dx.doi.org/10.1214/ss/1177011136

Geweke, J. – 1992. Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In: J.M. Bernardo, J. Berger, A.P. Dawid and A.F.M. Smith (eds.), Bayesian Statistics 4, pp.169-193. Oxford University Press, Oxford. Hastie, T.J. and R.J. Tibshirani. – 1990. Generalized Additive Models. Chapman and Hall, Washington, DC. 352 pp.

Hilborn, R. and C.J. Walters. – 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty. Chapman and Hall, New York. 570 pp.

Ichii, T. – 2003. Akaika (Red flying squid). In: The Current Status of International Fishery Stocks, pp. 304-308. Japan Fisheries Agency/Fisheries Research Agency, (in Japanese)

Ichii, T. and K. Mahapatra. – 2004. Stock assessment of the autumn cohort of neon flying squid (Ommastrephes bartramii) in the North Pacific based on the past driftnet fishery data. In: Report of the 2004 Meeting on Squid Resources, pp. 21-34. Japan Sea National Fisheries Research Institute, Niigata. (in Japanese).

Ichii, T., K. Mahapatra, M. Sakai and Y. Okada. – 2009. Life history of the neon flying squid: effect of the oceanographic regime in the North Pacific Ocean. Mar. Ecol. Prog. Ser., 378: 1-11. http://dx.doi.org/10.3354/meps07873

Maunder, M.N. and A.E. Punt. – 2004. Standardizing catch and effort data: a review of recent approaches. Fish. Res., 70: 141-159. http://dx.doi.org/10.1016/j.fishres.2004.08.002

McAllister, M.K. and G.P. Kirkwood. – 1998. Bayesian stock assessment: a review and example application using logistic model. ICES J. Mar. Sci., 55: 1031-1060. http://dx.doi.org/10.1006/jmsc.1998.0425

Mori, J. – 1997. Geographical differences between the parasites’ infection levels of the neon flying squid (Ommastrephes bartramii) from the North Pacific Ocean (Abstract). In: Report of the 1995 Meeting on Squid Resources, pp. 85-86. Contributions to the Fisheries Researches in the Japan Sea Block, N. 36. Japan Sea National Fisheries Research Institute, Niigata. (in Japanese)

Murata, M. and Y. Nakamura. – 1998. Seasonal migration and diel vertical migration of the neon flying squid, Ommastrephes bartramii, in the North Pacific. In: T. Okutani (ed.), Contributed Papers to the International Symposium on Large Pelagic Squids, pp.13-30. Japan Marine Fishery Resources Research Center. Tokyo.

Nagasawa, K., J. Mori and H. Okamura. – 1998a. Parasites as biological tags of stocks of neon flying squid (Ommastrephes bartramii) in the North Pacific Ocean. In: T. Okutani (ed.), Contributed Papers to International Symposium on Large Pelagic Squids, pp. 49-64. Japan Marine Fishery Resources Research Center, Tokyo.

Nagasawa, K., J. Mori and H. Okamura. – 1998b. Parasites as biological tags of stocks of neon flying squid (Ommastrephes bartramii) in the North Pacific Ocean. In: Report of the 1996 Meeting on Squid Resources, pp. 120. National Research Institute of Far Seas Fisheries, Shimizu.

Nelder, J.A. and R.W.M. Wedderburn. – 1972. Generalized linear models, J. R. Statist. Soc. A., 137: 370–384.

Ntzoufras, L. – 2009. Bayesian Modeling Using Winbugs. Wiley press. Hoboken, NJ.

Pinheiro, J.C. and D.M. Bates. – 2000. Mixed-Effects Models in S and S-plus. Springer, New York.

Quinn, T.J. and R.B. Deriso. – 1999. Quantitative fish dynamics. Oxford Univ. Press, New York.

Rodhouse, P.G. – 2001. Managing and forecasting squid fisheries in variable environments. Fish. Res., 54: 3-8. http://dx.doi.org/10.1016/S0165-7836(01)00370-8

Roper, C.F.E., M.J. Sweeney and C.E. Nauen. – 1984. FAO species catalogue. Vol 3: cephalopods of the world. An annotated and illustrated catalogue of species of interest to fisheries. FAO Fish. Synop. 125, Rome.

Spiegelhalter, D., N. Best, B. Carlin and A. Van der Linde. – 2002. Bayesian measures of model complexity and fit (with discussion). J. Roy. Statist. Soc. B., 64: 583-639. http://dx.doi.org/10.1111/1467-9868.00353

Spiegelhalter, D., A.Thomas, N. Best and D. Lunn. – 2003. WinBUGS Version 1.4 user manual. MRC Biostatistics Unit, Cambridge.

Tian, S.Q., X.J. Chen, Y. Chen, L.X. Xu and X.J. Dai. – 2009. Standardizing CPUE of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean. Chin. J. Oceanol. Limnol., 27(4): 729-739. http://dx.doi.org/10.1007/s00343-009-9199-7

Wang, Y.G. and X.J. Chen. – 2005. Oceanic Ommastrephidae Squids and their Fisheries in the World. Ocean Press of China, Beijing. 124-155 pp. (in Chinese).

Walters, C.J. – 2003. Folly and fantasy in the analysis of spatial catch rate data. Can. J. Fish. Aquat. Sci., 60: 1433-1436. http://dx.doi.org/10.1139/f03-152

Yatsu, A., H. Tanaka and J. Mori. – 1998. Population structure of the neon flying squid, Ommastrephes bartramii, in the North Pacific. In: T. Okutani (ed.), Contributed Papers to International Symposium on Large Pelagic Squid, pp. 31-48. Japan Marine Fishery Resources Research Center, Tokyo.

Yatsu, A. and T. Watanabe. – 1996. Interannual variability in neon flying squid abundance and oceanographic conditions in the central North Pacific, 1982–1992. Bull. Natl. Res. Inst. Far Seas Fish., 33(3): 123-138.

Zhang, Z., J. Lessard and A. Campbell. – 2009. Use of Bayesian hierarchical models to estimate northern abalone, Haliotis kamtschatkana, growth parameters from tag- recapture data. Fish. Res., 95: 289-295. http://dx.doi.org/10.1016/j.fishres.2008.09.035




Copyright (c) 2011 Consejo Superior de Investigaciones Científicas (CSIC)

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Contact us scimar@icm.csic.es

Technical support soporte.tecnico.revistas@csic.es