SCRS/2008/039 Collect. Vol. Sci. Pap. ICCAT, 64(6): (2009)

SCRS/2008/039 Collect. Vol. Sci. Pap. ICCAT, 64(6): 1833-1843 (2009) UPDATE OF STANDARDIZED CATCH RATES FOR SAILFISH (ISTIOPHORUS ALBICANS) FROM THE VENEZUELAN PELAGIC LONGLINE FISHERY OFF THE CARIBBEAN SEA AND ADJACENT AREAS: PERIOD 1991-2006 Freddy Arocha 1* Mauricio Ortiz 2 and José Silva 1 SUMMARY Indices of abundance of sailfish (Istiophorus albicans) from the Venezuelan Pelagic Longline fishery are presented for the period 1991-2006. The index of number of fish per number of hooks (thousand) was estimated from numbers and estimated weight of sailfish caught and reported in the observer data forms recorded by scientific observers aboard longline (Venezuelan Pelagic Longline Observer Program) vessels since 1991. The standardization analysis procedure included the following variables; year, vessel, area, season, bait, and fishing depth. The standardized index was estimated using Generalized Linear Mixed Models under a delta lognormal model approach. RÉSUMÉ Ce document présente les indices d abondance du voilier (Istiophorus albicans) de la pêcherie palangrière pélagique du Venezuela pour la période 1991-2006. L indice de nombre de poissons par nombre d hameçons (mille) a été estimé à partir des nombres et du poids estimé des voiliers capturés et déclarés sur les formulaires de données des observateurs, enregistrés par les observateurs scientifiques embarqués à bord de palangriers (Programme d observateurs des palangriers pélagiques du Venezuela) depuis 1991. La procédure de l analyse de standardisation incluait les variables suivantes : année, navire, zone, saison, appât et profondeur de pêche. L indice standardisé a été estimé à l aide de Modèles linéaires généralisés mixtes dans le cadre d une approche de modèle delta-lognormale. RESUMEN Se presentan los índices de abundancia del pez vela (Istiophorus albicans) de la pesquería palangrera pelágica venezolana para el periodo 1991-2006. Se estimó el índice en número de ejemplares por número de anzuelos (mil) a partir del número y peso estimados para los peces vela capturados y comunicados en los formularios de datos de observadores recopilados por observadores científicos a bordo de los palangreros (Programa de observadores de palangre pelágico de Venezuela) desde 1991. El procedimiento de análisis de estandarización incluía las siguientes variables: año, buque, zona, temporada, cebo y profundidad de la pesca. El índice estandarizado se estimó mediante Modelos lineales generalizados mixtos con un enfoque de modelo delta lognormal. KEYWORDS Sailfish, catch rates, Caribbean Sea, Venezuelan longline fishery 1 Instituto Oceanográfico de Venezuela, Universidad de Oriente, Apartado de Correos No. 204, Cumaná 6101, Venezuela; farocha@sucre.udo.edu.ve/farochap@gmail.com 2 U.S. Department of Commerce, National Marine Fisheries Service, Southeast Fisheries Science Center, 75 Virginia Beach Drive, Miami, Florida 33149 USA. 1833

1. Introduction Information about changes in the abundance of sailfish, Istiophorus albicans, is necessary to tune stock assessment models that are required for the management of sailfish in the Atlantic. The utility of indices of abundance based on catch and effort data can be improved by standardizing them to remove the impact of factors such as changes over time in the efficiency of the fleet. Since 1991, ICCAT s Enhanced Billfish Research Program (EBRP) started placing scientific observers on board Venezuelan pelagic longliners targeting tuna and swordfish. Due to the difficulties in obtaining pelagic longline log book data by species, the data collected from the EBRP from the Venezuelan fleet was chosen to develop standardized catch per unit of effort (CPUE) indices of abundance for the billfish caught by the Venezuelan fleet (ICCAT, 2001; Ortiz and Arocha, 2004). The Venezuelan longline fleet operates over an important geographical area in the western central Atlantic and its main target species is yellowfin tuna, thus imposing important fishery mortality on by-catch species (e.g. all billfish species) fished in the western tropical Atlantic. The present report, review and updates the catch and effort information from 1991 until 2006 and standardize catch rates of sailfish using a Generalized Linear Mixed Model with random factor interactions particularly for the year effect. 2. Materials and methods The data used in this paper came from the database of the Venezuelan Pelagic Longline Observer Program (VPLOP) for the period 1991-2006. Arocha and Marcano (2001) describe the main features of the fleet and Marcano et al. (2005, 2007) review the available catch and effort data from the Venezuelan Pelagic Longline fishery covered by the observer program. Yellowfin tuna and swordfish are the main target species for the Venezuelan Pelagic Longline fleet, where sailfish constitutes about 2% of the total catch in numbers for the overall period (Arocha and Marcano, 2001). Since 1991, trained scientific observers have recorded detailed information on gear characteristics, fishing operations as well morphometric and biological information from a sub-sample of the Venezuelan pelagic longline vessels (Arocha and Marcano, 2001). The VPLOP surveys on average 13.2% of the Venezuela longline fleet trips (1991-2006). The data collected comprises a total of 5,350 record-sets from 1991 through 2006. Of these sets, sailfish was reported caught in 1,210 sets (22.62%). Detailed information collected in the VPLOP, as well fishing grounds for the Venezuelan fleet are the same as described in Ortiz and Arocha (2004). Factors included in the analyses of catch rates included: bait type, depth of the hooks, area of fishing, and season, defined to account for seasonal fishery distribution through the year (i.e., Jan-Mar, Apr-Jun, Jul-Sep and Oct-Dec). In prior analysis vessels were classified into 3 categories, mainly associated with their size (Ortiz and Arocha, 2004), in the present case, vessels were also included in the analysis but as individual units rather than groups. By using a repeated measurement GLM structure is possible to estimate individual vessel variability (Bishop, 2006). There were 48 different vessels that reported catches of sailfish. However, not all were fishing during the 1991-2006 period. The GLM model used an autoregressive 1 variance-covariance matrix; such correlation of catch rates of each vessel is higher for adjacent years (Little et al., 1996). Fishing effort is reported in terms of the total number of hooks per trip and number of set per trip, as the number of hooks per set, vary; catch rates were calculated as number of fish and estimated weight (using sex specific length-weight conversion factors; Prager et al., 1995) of sailfish caught per 1000 hooks. For the Venezuelan longline observer data, relative indices of abundance for sailfish were estimated by Generalized Linear Modeling approach assuming a delta lognormal model distribution. The delta model estimates separately the proportion of positive to total number of sets assuming a binomial error distribution, and the mean catch rate of positive for a given fish (sailfish) by assuming a lognormal error distribution. The standardized index is the product of these model-estimated components. The logit function was used as link between the linear factor component and the binomial error. For sets that caught at least one sailfish (positive observations), estimated CPUE rates were assumed to follow a lognormal error distribution (lncpue) of a linear function of fixed factors and random effect interactions, particularly when the year effect was within the interaction. A step-wise regression procedure was used to determine the set of systematic factors and interactions that significantly explained the observed variability. The difference of between two consecutive models follows a χ2 (Chi-square) distribution; this statistic was used to test for the significance of an additional factor in the model. The number of additional parameters associated with the added factor minus one corresponds to the number of degrees of freedom in the χ 2 test (McCullagh and Nelder, 1989). Deviance analysis tables are 1834

presented for the data series, including the for the proportion of positive observations (i.e., positive sets/total sets), and the for the positive catch rates. Final selection of explanatory factors was conditional to: a) the relative percent of explained by adding the factor in evaluation (normally factors that explained more than 5% were selected), and b) The χ 2 significance. Once a set of fixed factors was specified, possible interactions were evaluated, in particular interactions between the year effect and other factors. Selection of the final mixed model was based on the Akaike s Information Criterion (AIC), the Bayesian Information Criterion (BIC), and a χ 2 test of the difference between the [ 2 loglikelihood] statistic of a successive model formulations (Littell et al., 1996). Relative indices for the delta model formulation were calculated as the product of the year effect least square means (LSmeans) from the binomial and the lognormal model components. The LSmeans estimates use a weighted factor of the proportional observed margins in the input data to account for the non-balance characteristics of the data. LSMeans of lognormal positive trips were bias corrected using Lo et al., (1992) algorithms. Analyses were done using the Glimmix and Mixed procedures from the SAS statistical computer software (SAS Institute Inc. 1997). 3. Results and discussion Sailfish spatial distribution of nominal CPUE from the VPLOP data set is presented in Figure 1. Important catch rates were obtained in the Caribbean Sea area, followed by high catch rates in the Guyana-Amazon area. Very few catch were observed southwest of the Sargasso Sea. In general, the highest sailfish catch rates were closer to land masses compared to other marlin species, due to the more coastal nature of sailfish. Figures 2 and 3 are diagnostic plots for the positive observations and positive model. The frequency distribution of log-transformed nominal CPUE in weight is closer to normality than that of nominal CPUE in numbers of fish, although the residual histograms of both nominal CPUE follow the normal distribution (Figure 2). The distribution of residuals by years for the positive observation component of the delta lognormal model fits show no apparent trends or bias (Figure 3, top). The cumulative normalized residuals or qq-plot indicate possible deviations from the assumed error distribution of the model (pattern do not follow a straight line) in catch rates based on numbers of fish, but in catch rates based on weight the small deviations appear at the upper end of the distribution (Figure 3, middle). Distribution residuals by vessel show deviations from the null value but with no apparent trends (Figure 3, bottom). The analysis for sailfish from the Venezuela Longline Observer data analyses are presented in table 1 for analysis based on the weight of fish as catch rate, and in table 2 for those based on the numbers of fish as catch rates. For the proportion of positive/total sets; year, area, season, and baittype; and the interactions year area, year baittype, year season, year depth, season depth and area season were the major factors that explained whether or not a set caught at least one fish. For the mean catch rate given that it is a positive set, the factors: year, baittype, depth, and area; and the interactions year area, year season, year baittype, area season, and season baittype were more significant. Once a set of fixed factors were selected, we evaluated first level random interaction between the year and other effects. The results from the random test analyses for sailfish and the three-model selection criterion indicate, that for the proportion of positive/total sets, the final model included the year, area, bait, and depth as main fixed factors and the random interactions of area season, and year area (and year season for CPUE in weight) (Table 3 and 4). For the conditional mean catch rate (i.e., positive observations), the final mixed model included the year, area, season, and bait as fixed factor and the random interaction of year season and year area. Standardized CPUE series for sailfish are shown in Table 5 and 6 and Figure 4. Coefficients of variation range from 44.6 to 63.4% for model fit based on catch rates of weight of fish; while model fit based on catch rates of numbers of fish coefficients of variation range from 49.0 to 91.8%. The standardized CPUE series (in weight) show that the relative abundance of sailfish caught by the observed Venezuelan longline fleet reflects downward trend from the beginning of the series to 2001, with the exception of a strong spike in 1999, after 2002 the standardized catch rates showed signs of a moderate recovery. The spike in 1999, was caused by changes in the gear configuration of a particular vessel during several trips conducted during the year. The standardized CPUE series (in numbers) follows a similar trend as in the standardized CPUE series (in weight), but with a moderate steeper trend and broader confidence intervals. 1835

Although the standardized CPUE series based on the delta lognormal model approach estimated large confidence intervals for sailfish, the resulting standardized series from the VPLOP account for about 13 % of the annual trips of the Venezuelan pelagic longline fleet during the period analyzed. Considering that the information that exists in logbooks do not reflect the catch of sailfish and that there is a high degree of under-reporting, the standardized CPUE index based on the VPLOP data can be used as a proxy to reflect the overall trend in relative abundance of sailfish caught by the Venezuelan longline fleet in the southeastern Caribbean Sea and east of the Guiana s. References Arocha, F. and Marcano, L., 2001. Monitoring large pelagic fishes in the Caribbean Sea and the western central Atlantic by an integrated monitoring program from Venezuela. pp. 557-576. In: Proceedings of the 52 nd GCFI meeting. Key West, Fl. November 1999. Arocha, F., Marcano, L., Marcano, J., Gutierrez, X. and Sayegh, J., 2001. Captura incidental observada de peces de pico en la pesquería industrial de palangre venezolana en el mar Caribe y en el Atlántico centrooccidental: 1991-1999. Collect. Vol. Sci. Pap. ICCAT, 53: 131-140. Bishop, J., 2006. Standardizing fishery-dependent catch and effort data in complex fisheries with technology change. Rev. Fish. Biol. Fisheries 16(1):21-38. ICCAT, 2001. Report of the fourth ICCAT Billfish Workshop. Collect. Vol. Sci. Pap. ICCAT,53: 1-110. Littell, R.C., Milliken, G.A., Stroup, W.W. and Wolfinger, R.D., 1996. SAS System for Mixed Models, Cary NC:SAS Institute Inc., 1996. 663 pp. Lo, N.C., Jacobson, L.D. and Squire, J.L., 1992. Indices of relative abundance from fish spotter data based on delta-lognormal models. Can. J. Fish. Aquat. Sci. 49: 2515-2526. Marcano, L., Arocha, F., Alió, J. Marcano, J., Larez, A., Gutierrez, X. and Vizcaino, G., 2007. Actividades desarrolladas en el Programa expandido de ICCAT para Peces pico en Venezuela: período 2006-2007. ICCAT SCRS/2007/121. Marcano, L., Arocha, F., Alió, J., Marcano, J. and Larez, A., 2005. Actividades desarrolladas en el Programa expandido de ICCAT para Peces pico en Venezuela: período 2003-2004. Collect. Vol. Sci. Pap. ICCAT,, 58: 1603-1615. McCullagh, P. and Nelder, J.A., 1989. Generalized Linear Models 2nd edition. Chapman & Hall. Ortiz, M. and Arocha, F., 2004. Alternative error distribution models for standardization of catch rates of nontarget species from pelagic longline fishery: billfish species in the Venezuelan tuna longline fishery. Fish. Res., 70:275-297. Prager, M.H., Lee, D.W. and Prince, E.D., 1995. Empirical length and weight conversion equations for blue marlin, white marlin, and sailfish from the North Atlantic. Bull. Mar. Sci., 56:201-210. SAS Institute Inc. 1997, SAS/STAT Software: Changes and Enhancements through Release 6.12. Cary, NC:Sas Institute Inc., 1997. 1167 pp. 1836

Table 1. Deviance analysis table for explanatory variables in the delta lognormal model for sailfish catch rates (in weights) from the Venezuelan Pelagic Longline Observer Program (VPLOP). Percent of total refers to the explained by the full model; p value refers to the probability Chi-square test between two nested models. The mean catch rate for positive observations assumed a lognormal error distribution. Model factors positive catch rates values d.f. Residual Change in % of total p 1 1 1235,12591 Year 15 1167,40088 67,73 11,6% < 0.001 Year Area 2 1070,94001 96,46 16,5% < 0.001 Year Area Season 3 1061,53212 9,41 1,6% 0,024 Year Area Season Baittype 3 846,747388 214,78 36,7% < 0.001 Year Area Season Baittype Depth 1 777,517649 69,23 11,8% < 0.001 Year Area Season Baittype Depth Season*Depth 3 773,334725 4,18 0,7% 0,242 Year Area Season Baittype Depth Area*Depth 1 772,108933 5,41 0,9% 0,020 Year Area Season Baittype Depth Baittype*Depth 3 770,393829 7,12 1,2% 0,068 Year Area Season Baittype Depth Area*Baittype 2 769,611505 7,91 1,4% 0,019 Year Area Season Baittype Depth Year*Depth 12 752,169396 25,35 4,3% 0,013 Year Area Season Baittype Depth Season*Baittype 9 741,771008 35,75 6,1% < 0.001 Year Area Season Baittype Depth Area*Season 4 733,082778 44,43 7,6% < 0.001 Year Area Season Baittype Depth Year*Baittype 23 721,453783 56,06 9,6% < 0.001 Year Area Season Baittype Depth Year*Season 44 699,370293 78,15 13,3% 0,001 Year Area Season Baittype Depth Year*Area 18 649,487407 128,03 21,9% < 0.001 Model factors proportion positives d.f. Residual Change in % of total p 1 1 1449,338 Year 15 1332,661 116,68 14% < 0.001 Year Area 2 996,446 336,21 41% < 0.001 Year Area Season 3 890,758 105,69 13% < 0.001 Year Area Season Baittype 3 854,334 36,42 4% < 0.001 Year Area Season Baittype Depth 1 854,311 0,02 0% 0,879 Year Area Season Baittype Depth Area*Depth 2 840,545 13,77 2% 0,001 Year Area Season Baittype Depth Area*Baittype 6 833,481 20,83 3% 0,002 Year Area Season Baittype Depth Season*Baittype 9 822,665 31,65 4% < 0.001 Year Area Season Baittype Depth Season*Depth 3 816,968 37,34 5% < 0.001 Year Area Season Baittype Depth Year*Depth 12 807,798 46,51 6% < 0.001 Year Area Season Baittype Depth Area*Season 5 790,717 63,59 8% < 0.001 Year Area Season Baittype Depth Year*Area 24 762,089 92,22 11% < 0.001 Year Area Season Baittype Depth Year*Season 45 646,984 207,33 25% < 0.001 Year Area Season Baittype Depth Year*Baittype 30 636,165 218,15 27% < 0.001 1837

Table 2. Deviance analysis table for explanatory variables in the delta lognormal model for sailfish catch rates (in numbers) from the Venezuelan Pelagic Longline Observer Program (VPLOP). Percent of total refers to the explained by the full model; p value refers to the probability Chi-square test between two nested models. The mean catch rate for positive observations assumed a lognormal error distribution. Model factors positive catch rates values d.f. Residual Change in % of total p 1 1 1094,32211 Year 15 1023,66387 70,66 14,1% < 0.001 Year Area 2 940,501415 83,16 16,6% < 0.001 Year Area Season 3 930,529951 9,97 2,0% 0,019 Year Area Season Baittype 3 757,205823 173,32 34,7% < 0.001 Year Area Season Baittype Depth 1 695,237287 61,97 12,4% < 0.001 Year Area Season Baittype Depth Area*Depth 1 689,475104 5,76 1,2% 0,016 Year Area Season Baittype Depth Season*Depth 3 689,441921 5,80 1,2% 0,122 Year Area Season Baittype Depth Area*Baittype 3 683,938305 11,30 2,3% 0,010 Year Area Season Baittype Depth Baittype*Depth 3 683,224242 12,01 2,4% 0,007 Year Area Season Baittype Depth Year*Depth 12 666,816864 28,42 5,7% 0,005 Year Area Season Baittype Depth Season*Baittype 9 661,465797 33,77 6,8% < 0.001 Year Area Season Baittype Depth Area*Season 4 658,665331 36,57 7,3% < 0.001 Year Area Season Baittype Depth Year*Baittype 23 629,648557 65,59 13,1% < 0.001 Year Area Season Baittype Depth Year*Season 44 625,427405 69,81 14,0% 0,008 Year Area Season Baittype Depth Year*Area 20 594,169663 101,07 20,2% < 0.001 Model factors proportion positives d.f. Residual Change in % of total p 1 1 1447,775 Year 15 1335,769 112,01 14% < 0.001 Year Area 2 1008,200 327,57 41% < 0.001 Year Area Season 3 898,892 109,31 14% < 0.001 Year Area Season Baittype 3 861,889 37,00 5% < 0.001 Year Area Season Baittype Depth 1 861,815 0,07 0% 0,785 Year Area Season Baittype Depth Area*Depth 2 846,700 15,11 2% < 0.001 Year Area Season Baittype Depth Area*Baittype 6 843,852 17,96 2% 0,006 Year Area Season Baittype Depth Season*Baittype 9 830,085 31,73 4% < 0.001 Year Area Season Baittype Depth Year*Depth 12 819,703 42,11 5% < 0.001 Year Area Season Baittype Depth Season*Depth 3 819,684 42,13 5% < 0.001 Year Area Season Baittype Depth Area*Season 5 798,477 63,34 8% < 0.001 Year Area Season Baittype Depth Year*Area 24 772,466 89,35 11% < 0.001 Year Area Season Baittype Depth Year*Season 45 646,344 215,47 27% < 0.001 Year Area Season Baittype Depth Year*Baittype 30 646,138 215,68 27% < 0.001 1838

Table 3. Analyses of delta lognormal mixed model formulations for sailfish catch rates (in numbers) from the Venezuelan Pelagic Longline Observer Program (VPLOP). Likelihood ratio tests the deference of 2 REM log likelihood between two nested models. The star and bold lettering model indicates the selected model for each component of the delta mixed model. GLMixed Model -2 REM Log likelihood Akaike's Information Criterion Bayesian Information Criterion Likelihood Ratio Test Dispersion Proportion Positives Year Area Season Bait 985,7 987,7 991,3 3,0491 Year Area Season Bait Year:Area 980,5 984,5 988 5,2 0,0226 2,8395 * Year Area Season Bait Year:Area Year:Season 976 982 987,3 4,5 0,0339 2,2557 Year Area Season Bait Year:Area Year:Season Year:Depth 976,2 984,2 991,2-0,2 N/A 2,2443 Positives catch rates Year Area Season Bait Depth Area*Season 2793,4 2795,4 2800,4 0,554 * Year Area Season Bait Depth Area*Season Year*Area 2692,6 2969,6 2696,6 100,8 0,0000 0,4931 Year Area Season Bait Depth Area*Season Year*Area Year*Season 2865,7 2691,7 2696,6-173,1 N/A 0,4822 Table 4. Analyses of delta lognormal mixed model formulations for sailfish catch rates (in weights) from the Venezuelan Pelagic Longline Observer Program (VPLOP). Likelihood ratio tests the deference of 2 REM log likelihood between two nested models. The star and bold lettering model indicates the selected model for each component of the delta mixed model. GLMixed Model -2 REM Log likelihood Akaike's Information Criterion Bayesian Information Criterion Likelihood Ratio Test Dispersion Proportion Positives Year Area Season Bait 982,3 984,3 987,8 3,0546 Year Area Season Bait Year:Area 978 982 985,5 4,3 0,0381 2,8534 * Year Area Season Bait Year:Area Year:Season 972,6 978,6 983,8 5,4 0,0201 2,2862 Year Area Season Bait Year:Area Year:Season Year:Depth 972,7 980,7 987,6-0,1 N/A 2,265 Positives catch rates Year Area Season Bait Depth Area*Season 2954 2956 2961,1 Year Area Season Bait Depth Area*Season Year*Area 2800,9 2804,9 2808 153,1 0,0000 * Year Area Season Bait Depth Area*Season Year*Area Year*Season 2789,9 2795,9 2800,7 11 0,0009 1839

Table 5. Nominal and standardized (Delta lognormal mixed model) CPUE series (number of fish /1000 hooks) for sailfish catch rates from the Venezuelan Pelagic Longline Observer Program (VPLOP). Year N Obs Nominal CPUE Standard CPUE Lower CI Upper CI CV std error 1991 110 1,79 0,79 0,27 2,35 58,5% 0,46 1992 272 0,75 0,83 0,30 2,25 53,4% 0,44 1993 472 1,27 0,68 0,24 1,94 56,4% 0,38 1994 336 2,20 0,92 0,35 2,46 52,1% 0,48 1995 484 1,43 0,59 0,23 1,50 49,0% 0,29 1996 399 0,45 0,28 0,09 0,90 64,1% 0,18 1997 368 0,72 0,50 0,17 1,42 56,3% 0,28 1998 469 0,91 0,28 0,09 0,87 61,9% 0,17 1999 351 1,78 1,06 0,41 2,71 49,7% 0,53 2000 319 0,67 0,31 0,08 1,14 72,8% 0,23 2001 281 0,31 0,16 0,03 0,78 91,8% 0,15 2002 221 0,36 0,35 0,10 1,28 72,2% 0,25 2003 324 0,47 0,34 0,10 1,14 67,3% 0,23 2004 336 0,73 0,29 0,08 1,03 70,9% 0,20 2005 270 0,96 0,31 0,09 1,05 67,3% 0,21 2006 313 1,21 0,43 0,14 1,34 61,2% 0,27 Table 6. Nominal and standardized (Delta lognormal mixed model) CPUE series (weight of fish /1000 hooks) for sailfish catch rates from the Venezuelan Pelagic Longline Observer Program (VPLOP). Nominal Standard Year N Obs Lower CI Upper CI CV std error CPUE CPUE 1991 110 1,75 14,41 5,34 38,85 52,8% 7,61 1992 272 0,70 10,83 4,14 28,29 50,9% 5,51 1993 472 1,26 8,08 3,00 21,76 52,7% 4,26 1994 336 2,09 11,02 4,22 28,79 50,9% 5,61 1995 484 1,46 11,04 4,71 25,88 44,6% 4,92 1996 399 0,46 5,27 1,96 14,14 52,6% 2,77 1997 368 0,76 8,19 3,27 20,52 48,4% 3,97 1998 469 0,90 4,64 1,77 12,17 51,1% 2,37 1999 351 1,74 22,83 9,18 56,81 48,1% 10,97 2000 319 0,66 5,78 1,87 17,90 61,4% 3,55 2001 281 0,32 3,79 1,19 12,13 63,4% 2,40 2002 221 0,38 5,90 2,05 16,98 56,8% 3,35 2003 324 0,49 4,68 1,67 13,11 55,1% 2,58 2004 336 0,80 6,58 2,35 18,41 55,0% 3,62 2005 270 0,96 4,85 1,72 13,72 55,7% 2,70 2006 313 1,25 6,00 2,19 16,42 53,7% 3,22 1840

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25 No. of sailfish/1000 hks 20 15 10 5 0 80 75 70 65 60 55 50 45 40 Figure 1. Spatial distribution of nominal CPUE of sailfish (numbers/1000 hooks) caught by the Venezuelan pelagic longline fleet during 1991-2006 and recorded by the VPLOP. Figure 2. Frequency distribution of log-transformed nominal CPUE (fish/1000 hooks and weight of fish/1000 hooks) and residuals histograms of the positive model (left panel), from trips that caught sailfish in the Venezuela Pelagic Longline Fishery from 1991 through 2006. 1842

Figure 3. Diagnostic plots for the delta lognormal positive observations (in numbers and in weight) model component of the positive trips for the sailfish fit of the lognormal error distribution; cumulative normalized residuals or qq-plot (middle); and boxplot residuals by Vessel (bottom). 1843

Scaled CPUE (dressed weight kg/1000 hks) Scaled CPUE (number of SAI/1000 hks) 8 7 6 5 4 3 2 1 0 8 7 6 5 4 3 2 1 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Figure 4. Estimated nominal (circles) and standardized (rhombs and line) CPUE in weight (top) and numbers of fish (bottom) for sailfish from the Venezuelan Pelagic Longline Observer Program data set. Dotted lines correspond to 95% confidence intervals of the standardized CPUE. 1844