Analysis and standardization of landings per unit effort of red shrimp Aristeus antennatus from the trawl fleet of Barcelona (NW Mediterranean)
Keywords:LPUE, standardized LPUE, Aristeus antennatus, generalized additive models, NW Mediterranean, deep-water fisheries
Monthly landings and effort data from the Barcelona trawl fleet (NW Mediterranean) were selected to analyse and standardize the landings per unit effort (LPUE) of the red shrimp (Aristeus antennatus) using generalized additive models. The dataset covers a span of 15 years (1994-2008) and consists of a broad spectrum of predictors: fleet-dependent (e.g. number of trips performed by vessels and their technical characteristics, such as the gross registered tonnage), temporal (inter- and intra-annual variability), environmental (North Atlantic Oscillation [NAO] index) and economic (red shrimp and fuel prices) variables. All predictors individually have an impact on LPUE, though some of them lose their predictive power when considered jointly. That is the case of the NAO index. Our results show that six variables from the whole set can be incorporated into a global model with a total explained deviance (ED) of 43%. We found that the most important variables were effort-related predictors (trips, tonnage, and groups) with a total ED of 20.58%, followed by temporal variables, with an ED of 13.12%, and finally the red shrimp price as an economic predictor with an ED of 9.30%. Taken individually, the main contributing variable was the inter-annual variability (ED=12.40%). This high ED value suggests that many factors correlated with inter-annual variability, such as environmental factors (the NAO in specific years) and fuel price, could in turn affect LPUE variability. The standardized LPUE index with the effort variability removed was found to be similar to the fishery-independent abundance index derived from the MEDITS programme.
Akaike H. 1973. Information theory as an extension of the maximum likelihood principle. In: Petrov B.N., Csáki F. (eds), Proc. 2nd Int. Symp. Information Theory, Akadémiai Kiadó, Budapest, pp. 267-281.
Bas C., Maynou F., Sardà F., Lleonart J. 2003. Variacions demogràfiques a les poblacions d'espècies demersals explotades: els darrers quaranta anys a Blanes i Barcelona. Inst. Est. Catalans. Arxiu de la Sec. Ciències, Barcelona. PMCid:PMC1808826
Bertrand J.A., de Sola L.G., Papaconstantinou C., Relini G., Souplet A. 2002. The general specifications of the MEDITS surveys. Sci. Mar. 66: 9-17.
Brauner N., Shacham M. 1998. Role of range and precision of the independent variable in regression of data. Am. Inst. Chem. Eng. J. 44: 603-611. http://dx.doi.org/10.1002/aic.690440311
Brodziak J., O'Brien L. 2005. Do environmental factors affect recruits per spawner anomalies of New England groundfish? ICES J. Mar. Sci. 62: 1394-1407. http://dx.doi.org/10.1016/j.icesjms.2005.04.019
Carbonell A., Carbonell M.S., Demestre M., Grau A., Montserrat S. 1999. The red shrimp Aristeus antennatus (Risso, 1816) fishery and biology in the Balearic Islands, western Mediterranean. Fish. Res. 44: 1-13. http://dx.doi.org/10.1016/S0165-7836(99)00079-X
Cardinale M., Osio G.C., Charef A. (eds). 2012. Report of the Scientific, Technical and Economic Committee for Fisheries on Assessment of Mediterranean Sea stocks – part 1. JRC Scientific and Policy Reports. European Commission.
Cartes J.E., Sardà F. 1992. Abundance and diversity of decapod crustaceans in the deep-Catalan sea (western Mediterranean). J. Nat. Hist. 26: 1305-1323. http://dx.doi.org/10.1080/00222939200770741
Craven P., Wahba G. 1979. Smoothing noisy data with spline functions: estimating the correct degree of smoothing by the method of generalized cross-validation. Numer. Mat. 31: 377-403. http://dx.doi.org/10.1007/BF01404567
Denis V., Lejeune J., Robin J.P. 2002. Spatio-temporal analysis of commercial trawler data using General Additive models: patterns of Loliginid squid abundance in the north-east Atlantic. ICES J. Mar. Sci. 59: 633-648. http://dx.doi.org/10.1006/jmsc.2001.1178
Dennard S.T., MacNeil M.A., Treble M.A., Campana S., Fisk A.T. 2010. Hierarchical analysis of a remote, Arctic, artisanal longline fishery. ICES J. Mar. Sci. 67: 41-51. http://dx.doi.org/10.1093/icesjms/fsp220
FAO-FISHSTAT. 2011. FAO Fisheries Department, Fishery information, Data and Statistics Unit. FishstatJ, a tool for fishery statistical analysis, release 2.0.0. FAO, Rome.
Hastie T., Tibshirani R. 1990. Generalized additive models. Chapman Hall, London. PMCid:PMC332745
Hilborn R., Walters C.J. 1992. Quantitative Fisheries Stock Assessment. Chapman & Hall, New York. http://dx.doi.org/10.1007/978-1-4615-3598-0 PMid:9908045
Lassen H., Medley P. 2000. Virtual population analysis. A practical manual for stock assessment. FAO Fisheries Technical Paper. No. 400. FAO, Rome. 129 p.
Lleonart J., Maynou F. 2003. Fish stock assessments in the Mediterranean: state of the art. Sci. Mar. 67: 37-49.
Marriott R.J., Wise B., St John J. 2011. Historical changes in fishing efficiency in the west coast demersal scalefish fishery, Western Australia: implications for assessment and management. ICES J. Mar. Sci. 68: 76-86. http://dx.doi.org/10.1093/icesjms/fsq157
Marx B.D., Eilers P.H.C. 1998. Direct generalized additive modelling with penalized likelihood. Comput. Statist. Data Anal. 28: 193-209. http://dx.doi.org/10.1016/S0167-9473(98)00033-4
Maunder M. N., Punt A. E. 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
Maunder M. N., Sibert J. R., Fonteneau A., Hampton J., Kleiber P., Harley S.J. 2006. Interpreting catch per unit effort data to assess the status of individual stocks and communities. ICES J. Mar. Sci. 63: 1373-1385. http://dx.doi.org/10.1016/j.icesjms.2006.05.008
Maynou F. 2008. Environmental causes of the fluctuations of red shrimp (Aristeus antennatus) landings in the Catalan Sea. J. Mar. Sys. 71: 294-302. http://dx.doi.org/10.1016/j.jmarsys.2006.09.008
Maynou F., Demestre M., Sánchez P. 2003. Analysis of catch per unit effort by multivariate analysis and generalized linear models for deepwater crustacean fisheries off Barcelona (NW Mediterranean). Fish. Res. 64: 257-269. http://dx.doi.org/10.1016/j.fishres.2003.09.018
Neal R.A., Maris R.C. 1985. Fishing biology of shrimps and shrimplike animals. In: Provenzano A.J. (ed.) The Biology of Crustacea Vol 10: Economic aspects: fisheries and culture. Academic Press Inc.
Orsi Relini L., Mannini A., Relini G. 2013. Updating knowledge on growth, population dynamics, and ecology of the blue and red shrimp, Aristeus antennatus (Risso, 1816), on the basis of the study of its instars. Mar. Ecol. 34: 90-102. http://dx.doi.org/10.1111/j.1439-0485.2012.00528.x
Sardà F., Maynou F. 1998. Assessing perceptions: do Catalan fishermen catch more shrimp on Fridays? Fish. Res. 36: 149-157. http://dx.doi.org/10.1016/S0165-7836(98)00102-7
Sardà F., Maynou F., Talló L. 1997. Seasonal and spatial mobility patterns of rose shrimps Aristeus antennatus in the Western Mediterranean: results of a long-term study. Mar. Ecol. Prog. Ser. 159: 133-141. http://dx.doi.org/10.3354/meps159133
Scott D.W. 1992. Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, New York. http://dx.doi.org/10.1002/9780470316849
Stefánsson G. 1996. Analysis of groundfish survey abundance data: combining the. GLM and delta approaches. ICES J. Mar. Sci. 53: 577-588. http://dx.doi.org/10.1006/jmsc.1996.0079
Su N.J., Yeh S.Z., Sun C.L., Punt A.E., Chen Y., Wang S.P. 2008. Standardizing catch and effort data of the Taiwanese distant-water longline fishery in the western and central Pacific Ocean for bigeye tuna, Thunnus obesus. Fish. Res. 90: 235-246. http://dx.doi.org/10.1016/j.fishres.2007.10.024
Wasserman L. 2005. All of Nonparametric Statistics. Springer, New York.
Wood S. N. 2006. Generalized Additive Models: An Introduction with R. CRC/Chapman Hall, Boca Raton, Florida.
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