SCRS/2005/031 Col. Vol. Sci. Pap. ICCAT, 59(1): (2006)

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1 SCRS/2005/031 Col. Vol. Sci. Pap. ICCAT, 59(1): (2006) UPDATED WHITE MARLIN (TETRAPTURUS ALBIDUS) AND BLUE MARLIN (MAKAIRA NIGRICANS) CATCH RATES FROM THE U.S. PELAGIC LONGLINE FISHERY IN THE NORTHWEST ATLANTIC AND GULF OF MEXICO Guillermo A. Diaz and Mauricio Ortiz 1 SUMMARY Indices of abundance of white marlin and blue marlin for the U.S. pelagic longline fishery in the Atlantic are presented for the period The index of weight (kg) per thousand hooks was estimated from the number of marlins caught reported by commercial fisherman through a logbook reporting system, and from mean annual weight estimated from observations obtained by scientific observers aboard longline vessels since 1992 (Pelagic Observer Program). The standardization analysis procedure included the following variables: year, area of fishing, gear characteristics (e.g., main line length, number of hooks, light sticks, etc.), and fishing characteristics (e.g., bait type, target species, etc.). The standardized indices were estimated using Generalized Linear Mixed Models under a Delta lognormal model approach. RÉSUMÉ Ce document présente les indices d abondance du makaire blanc et du makaire bleu pour la pêcherie palangrière pélagique des Etats-Unis dans l Atlantique, pour la période L indice de poids (kg) par mille hameçons a été estimé à partir du nombre de makaires capturés et déclarés par les pêcheurs sportifs au moyen d un système de déclaration des livres de bord, et à partir du poids annuel moyen, estimé d après les observations obtenues par les observateurs scientifiques embarqués à bord des palangriers depuis 1992 (Programme d observateurs pélagiques). La procédure d analyse de standardisation incluait les variables suivantes : année, zone de pêche, caractéristiques des engins (c est-à-dire longueur de la ligne principale, nombre d hameçons, baguettes lumineuses, etc.) et des caractéristiques de pêche (c est-à-dire type d appât, espèce cible, etc.). Les indices standardisés ont été estimés à l aide de Modèles linéaires généralisés mixtes dans le cadre d une approche de modèle delta lognormal. RESUMEN Se presentan índices de abundancia de aguja azul y aguja blanca para la pesquería de palangre pelágico de Estados Unidos, para el periodo Los índices de peso por mil anzuelos se estimaron a partir del número de marlines capturados y comunicados por los pescadores comerciales a través del sistema de comunicación mediante cuadernos de pesca y a partir del peso medio anual estimado mediante las observaciones realizadas por los observadores científicos a bordo de los palangreros desde 1992 (Programa de observadores pelágicos). El procedimiento de análisis de estandarización incluía las siguientes variables: año, zona de pesca, características del arte (por ejemplo, longitud de la línea madre, número de anzuelos, bastones luminosos, etc.) y características de la pesca (por ejemplo, tipo de cebo, especie objetivo, etc.). Se estimaron los índices estandarizados utilizando modelos lineales mixtos generalizados con un enfoque de modelo delta lognormal. KEYWORDS 1. Introduction Catch/effort; U.S. commercial longlining; White marlin; Blue marlin The US longline fleet operates over a wide geographical range of the western north Atlantic ocean and the Caribbean 1 U.S. Department of Commerce, National Oceanographic and Atmospheric Administration, National Marine Fisheries Service, Southeast Fisheries Science Center, Sustainable Fisheries Division, 75 Virginia Beach Drive, Miami, FL USA *this is a Sustainable Fisheries Division Contribution SFD no * 196

2 targeting swordfish and tunas. Although billfish are now not targeted nor landed by the US longline fleet, they are still incidentally caught (bycatch). Bycatch data collected from the US pelagic longline fleet has been previously used to develop standardized indices of abundance for white and blue marlin (Cramer 1998, Ortiz and Scott 2003). This information of relative abundance can be used to tune stock assessment models. The present report documents the analytical methods applied and updates the blue and white marlin indices of abundance through Materials and methods Hoey and Bertolino (1988) described the US longline fleet and numerous authors have reviewed the available catch and effort data (e.g., Hoey et al. 1989, Scott et al. 1993, Cramer and Bertolino 1998, Ortiz et al. 2000, Ortiz and Diaz 2004). Logbook records from the US pelagic longline fleet were collected on a voluntary basis from 1986 to 1991, and thereafter the submission of logbook records became mandatory for all vessels. The main targets of the US longline fleets are swordfish (Xiphias gladius), yellowfin tuna (Thunnus albacares), and other tunas. Although marlin are not retained by the US fleet, fishermen are still required to record their incidental catch. Each record in the Pelagic Longline Logbook (PLL) data set provides information of the catch in numbers of all species caught including bycatch, fishing effort (as total number of hooks), gear configuration (main line length, number of hooks per basket, etc), date, time, and geographical location of a longline set. Since 1992, an average of 5% of the US pelagic longline fleet is sampled by the Pelagic Observer Program (POP). The POP places trained observers aboard commercial longliners to record detailed information on gear characteristics, fishing operations and biological information of the catch and bycatch that would not otherwise be collected (Lee and Brown 1998). The ICCAT-SCRS Billfish Species Group has recommended that when possible, indices of abundance be estimated in both weight and number of fish. The PLL only records catch in numbers. Thus, to convert number of marlin caught to weight we used reported lower jaw-fork length (LJFL) by the POP of marlin caught by the sampled fraction of the US pelagic longline fleet. The total number of blue and white marlin measured by the POP program in the period was1,044 and 1,463, respectively lower jaw-fork lengths were converted to weight using the size-weight relationships (for combined sex) developed by Prager et al. (1995). Mean weight was estimated for each year-areaseason stratum with at least 20 observations (measurements), for those cells with less than 20 observations then a mean weight for the year-area or year stratum was used. For years previous to 1992 (before the implementation of the POP), the mean weight values for 1992 were applied. The longline fishing grounds of the US fleet extends from the Grand Banks in the north Atlantic to approximately 5-10 o S, off the South American coast, and include the Caribbean Sea and the Gulf of Mexico. For analysis purposes, nine geographical areas were defined as follows (Figure 1): Caribbean, Gulf of Mexico, Florida East coast, South Atlantic bight, Mid-Atlantic Bight, Northeast coastal. Northeast distant, Sargasso-North central Atlantic, and Southern offshore area. Since 1999, the U.S. has implemented time-area restrictions for the pelagic longline fleet (Figure 1). These restrictions included three permanent closure: (1) the Desoto Canyon (Gulf of Mexico) since November 1, 2000, (2) the U.S. EEZ within the Florida East Coast area since March 1, 2001, and (3) the entire Northeast distant area since 2001 (the Northeast distant area is only open to U.S. longline fishing boats participating in fishing gear configuration experiments), and two temporary closures imposed in 1999: (1) the Charleston Bump from February 1 to April 30 and (2) the Bluefin tuna protection area closed from June 1to June 30. Calendar quarters were used to account for seasonal fishery distribution through the year (Jan-Mar, Apr-Jun, Aug-Sep, Oct-Dec). Besides year, geographical area, and season, other factors describing gear configurations, target species, and type and size of vessels (operational procedure) were included in the factor selection process. Factors that were continuous variables (e.g. length of the mainline, density of light sticks), were transformed into discrete variables by defining ranges (e.g. mainline length < 30 and 30). Thus, all factors considered for the final models were categorical. Relative indices of abundances for both the PLL and POP data were estimated by a GLM approach assuming a deltalognormal model distribution. The delta model fits separately the proportion of positive sets (proportion of sets that caught at least one marlin) assuming a binomial error distribution and the catch rate estimated only from positive sets assuming a lognormal error distribution. The standardized index is the product of these model-estimated components. Ortiz and Scott (2003) used a step-wise procedure to determine the set of systematic factors and interactions that significantly explained the observed variability in the proportion of positive sets and the catch rates. The significance of a factor added to the model in the step-wise procedure was tested using a Chi-square test. They conditioned the final selection of explanatory factors to: 1) the relative percent of deviance explained by adding the factor in evaluation, 2) the Chi-square test of significance, and 3) the Type III test of significance within the final specified 197

3 model. After selecting the fixed factors, they evaluated all possible interactions, in particular interactions between effect and other factors. Because the purpose of this document is to update the standardized catch rates estimated by Ortiz and Scott (2003), we choose to use the same factors they determined as significant. Besides year, area, and season, the other factors included in the models were: Target: defined based on the proportion of the number of swordfish caught to the total number of fish (tunas and swordfish) per set as four discrete categories: 0-25%, 25-50%, 50-75% and %. OP (Operations Procedure): a categorical classification of US longline vessels based on their fishing configuration, type and size of the vessel, main area of operations, and main target species. Light: a proportion of hooks in a set with light sticks as three discrete categories. Mainline: categorical variable defined by the length of the main line. Bait kind: categorical variable that explained the different type/s of bait used in the set (e.g. mackerel, squid, sardine, scad, and different combinations of bait kind, etc). Table 1 provides a list of the fixed factors and interactions included in each model. All interaction terms that included the year factor were treated as random interactions. Analyses were done using the GLIMMIX and MIXED procedures from the SAS _ Statistical computer software (SAS Institute Inc. 1997). 3. Results and discussion A total of 226,874 PLL set records were used in the present analysis. Of these, 19,382 (8.5%) reported catching at least one white marlin and 19,826 (8.7%) at least one blue marlin. The POP data set consisted of 6,589 set records; of which 1,534 (23.3%) reported white marlin catch (es) and 1,133 (17.2%) reported blue marlin catch (es). White marlin standardized and nominal (in number of fish and weight) series estimated from the PLL data are presented in Tables 2 and 3, and Figures 2 and 3. Coefficients of variation for the estimated standardized ranged from 24% to 43%. The same information for blue marlin is shown in Tables 4 and 5 and Figure 4 and 5. For blue marlin, the coefficients of variation ranged from 22% to 49%. For each species, nominal and standardized series for the PLL data showed similar trends, and the nominal series were within the 95% confidence interval of the standardized series during the entire time period analyzed. A comparison of the estimated standardized series (in weight) from the PLL and POP data for each species is shown in Figure 6. For comparison purposes, the series from the PLL and POP data were scaled to their mean of the overlapping time period ( ). In general, 95% confidence intervals of the series (Tables 6 and 7) were wider for the POP estimates in both species. In the case of white marlin, both series showed a close correlation with only one POP estimate outside the 95% of the PLL series (year 1994) and a different trend only in the period Blue marlin series also showed a good correlation. Only POP estimates for years 1993 and 1994 laid outside the 95% confidence interval of the POP series. Different trends were observed for the period For both species, series showed a declining trend through the late 90's and seemed to have stabilized at low levels for the past 5 years. The potential impact of the closing areas on the marlin series was assessed by estimating standardized in weight for the entire period without including data from the time-area restrictions. These estimated series were scaled to their overall mean value and plotted against the scaled series estimated using data from all areas (Tables 3 and 5). Figure 7 shows the obtained plots and indicates that for both species, the time-area restrictions had no impact on the estimation of series. 198

4 References CRAMER, J Large Pelagic Logbook catch rate indices for Billfish. Col. Vol. Sci. Pap. ICCAT, 47: CRAMER, J. and A. Bertolino Standardized catch rates for swordfish (Xiphias gladius) from the U.S. longline fleet through Col. Vol. Sci. Pap. ICCAT, 49(1): HOEY, J.J. and A. Bertolino Review of the U.S. fishery for swordfish, 1978 to Col. Vol. Sci. Pap. ICCAT, 27: HOEY, J.J., R. Conser and E. Duffie Catch per unit effort information from the U.S. swordfish fishery. Col. Vol. Sci. Pap. ICCAT, 29: LEE, D.W. and C.J. Brown SEFSC Pelagic Observer Program Data Summary for NOAA Tech. Memo. NMFS-SEFSC-408, 21 pp. MCCULLAGH, P. and J.A. Nelder Generalized Linear Models 2 nd edition. Chapman & Hall. ORTIZ, M. J. Cramer, A. Bertolino and G. P. Scott Standardized catch rates by sex and age for swordfish (Xiphias gladius) from the U.S. Longline Fleet Col. Vol. Sci. Pap. ICCAT, 51: ORTIZ, M and G. P. Scott Standardized catch rates for white marlin (Tetrapturus albidus) and blue marlin (Makaira nigricans) from the pelagic longline fishery in the northwest Atlantic and the Gulf of Mexico. Col. Vol. Sci. Pap. ICCAT, 55(2): ORTIZ, M and G. A. Diaz Standardized catch rates for yellowfin tuna (Thunnus albacares) from the U.S. pelagic longline fleet. Col. Vol. Sci. Pap. ICCAT, 56(2): PRAGER, M.H., E. D. Prince and D. W. Lee Empirical length and weight conversion equation: for blue marlin, white marlin, and sailfish from the North Atlantic Ocean. Bull of Mar. Sci. 56(1): SAS Institute Inc. 1997, SAS/STAT Software: Changes and Enhancements through Release Cary, NC, USA:Sas Institute Inc., pp. SCOTT, G. P., V. R. Restrepo and A. R. Bertolino Standardized catch rates for swordfish (Xiphias gladius) from the US longline fleet through Col. Vol. Sci. Pap. ICCAT, 40(1):

5 Table 1. List of fixed factors and interactions included in the proportion positive and catch rate models for each marlin species and each data source. Refer to text for a detailed explanation of each factor. Pelagic Longline Logbooks Pelagic Observer Program White marlin Blue Marlin White marlin Blue Marlin Prop. Posit Catch rate Prop. Posit Catch rate Prop. Posit Catch rate Prop. Posit Catch rate Area Area Area Area Area Area Area Area Season Season Season OP Season OP Season Mainlength OP OP OP Lights OP Target OP OP Target Lights Target *Area Bait *Area *Area *Area *Area *Area *Area *OP Target *OP *Seas. *OP *OP *OP *OP Area*OP Light Area*Seas. Area*Seas. Area*Seas. *Lght *Area Area*OP Area*OP 200

6 Table 2. White marlin nominal and standardized catch rate (number of fish/1000 hooks), coefficient of variation and limits of the 95% confidence interval () from the Pelagic Longline Logbook program data. The index corresponds to the standardized scaled to its maximum value of the series. Nominal Standardized Coefficient Variation Index Upper 95% Lower 95% % % % % % % % % % % % % % % % % % % %

7 Table 3. White marlin nominal and standardized catch rate (kg/1000 hooks), coefficient of variation and limits of the 95% confidence interval () from the Pelagic Longline Logbook program data. The index corresponds to the standardized scaled to its maximum value of the series. Nominal Standardized Coefficient Variation Index Upper 95% Lower 95% % % % % % % % % % % % % % % % % % % %

8 Table 4. Blue marlin nominal and standardized catch rate (number of fish/1000 hooks), coefficient of variation and limits of the 95% confidence interval () from the Pelagic Longline Logbook program data. The index corresponds to the standardized scaled to its maximum value of the series. Nominal Standardized Coefficient Variation Index Upper 95% Lower 95% % % % % % % % % % % % % % % % % % % %

9 Table 5. Blue marlin nominal and standardized catch rate (kg/1000 hooks), coefficient of variation and limits of the 95% confidence interval () from the Pelagic Longline Logbook program data. The index corresponds to the standardized scaled to its maximum value of the series. Nominal Standardized Coefficient Variation Index Upper 95% Lower 95% % % % % % % % % % % % % % % % % % % %

10 Table 6. White marlin nominal and standardized catch rate (kg/1000 hooks), coefficient of variation and limits of the 95% confidence interval () from the Pelagic Observer Program data. The index corresponds to the standardized scaled to its maximum value of the series. Nominal Standardized Coefficient Variation Index Upper 95% Lower 95% % % % % % % % % % % % % % Table 7. Blue marlin nominal and standardized catch rate (kg/1000 hooks), coefficient of variation and limits of the 95% confidence interval () from the Pelagic Observer Program data. The index corresponds to the standardized scaled to its maximum value of the series. Nominal Standardized Coefficient Variation Index Upper 95% Lower 95% % % % % % % % % % % % % %

11 Figure 1. Geographical area classification used in the analysis of the US Pelagic Longline fleet data: Caribbean (CAR), Gulf of Mexico (GOM), Florida East Coast (FEC), South Atlantic Bight (SAB), Mid-Atlantic Bight (MAB), Northeast Coastal waters (NEC), Northeast Distant waters (NED), Sargargasso-North Central Atlantic (SAR-NCA), and Southern Offshore area (SOF). Blue areas correspond to time-area restrictions to the U.S. longline fishing fleet: (1) Desoto Canyon, (2) Florida East Coast, (3) Charleston Bump, (4) Bluefin tuna protection area, (5) Northeast Distant waters area. 206

12 (fish/1000 hooks) (kg/1000 hooks) Scaled White marlin numbers weight s Figure 2. Estimated nominal (squares) and standardized (diamonds and line) in number of fish and weight for white marlin from the U.S. Pelagic Longline Logbook data set. Dotted lines correspond to 95% confidence interval of the standardized. White marlin White marlin s 0.0 s Figure 3. White marlin standardized series in weight and number of fish. For comparison purposes, the series were scaled to their overall mean. 207

13 Scaled (fish/1000 hooks) (kg/1000 hooks) Blue marlin Blue marlin s s Figure 4. Estimated nominal (squares) and standardized (diamonds and line) in number of fish and weight for blue marlin from the U.S. Pelagic Longline Logbook data set. Dotted lines correspond to 95% confidence interval of the standardized. Blue marlin numbers Weight s Figure 5. Blue marlin standardized series in weight and number of fish. For comparison purposes, the series were scaled to their overall mean. 208

14 Scaled (kg/1000 hooks) Scaled (kg/1000 hooks) White marlin Blue marlin Figure 6. Standardized (kg/1000 hooks) series and 95% confidence intervals estimated from the Pelagic Longline Logbook data (blue lines and symbols) and the Pelagic Observer Program data (red line and symbols). Both series were scaled to their mean value of the overlapping years ( ). 209

15 White marlin 4,0 Scaled (kg/1000 hooks) 3,0 2,0 1,0 0, Blue marlin 2,0 Scaled (kg/1000 hooks) 1,8 1,5 1,3 1,0 0,8 0,5 0,3 0, s Figure 7. White and blue marlin series estimated from all available data (red line) and from data excluding records within the time-area restrictions (see text for explanation of time-area restrictions). 210