SCRS/2004/133

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Col. Vol. Sci. Pap. ICCAT, 58(1): 137-152 (2005)

STANDARDIZED CATCH RATES FOR BIGEYE TUNA (THUNNUS OBESUS) FROM THE PELAGIC LONGLINE FISHERY IN THE NORTHWEST ATLANTIC

AND THE GULF OF MEXICO

Mauricio Ortiz1

SUMMARY Two indices of abundance of bigeye tuna from the United States pelagic longline fishery in the Atlantic are presented for the period 1982-2003: An index of number of fish per thousand hooks estimated from numbers of bigeye tuna caught and reported in the Pelagic logbooks data. And, a biomass index kg (dress weight) per thousand hooks estimated from the weight-out data. The standardization analysis procedure included the following variables; year, area, season, gear characteristics (light sticks, main line length, hook density, etc) and fishing characteristics (bait type, operations procedure, and target species). The standardized index was estimated using Generalized Linear Mixed Models under a delta lognormal model approach.

RÉSUMÉ

Deux indices d’abondance du thon obèse émanant de la pêcherie palangrière pélagique des Etats-Unis opérant dans l’Atlantique sont présentés pour la période 1982-2003 : un indice du nombre de poissons pour mille hameçons estimé à partir du nombre de thons obèses capturés et consignés dans les carnets de bord pélagiques, et un indice de biomasse en kg (poids manipulé) pour mille hameçons estimé d’après les données de poids au débarquement. La procédure d’analyse de la standardisation a inclus les variables suivantes : année, zone, saison, caractéristiques des engins (baguettes lumineuses, profondeur de la ligne principale, densité de l’hameçon, etc.) et caractéristiques de la pêche (type d’appât, procédure d’opération et espèces-cibles). L’indice standardisé a été estimé à l’aide de modèles mixtes linéaires généralisés selon une approche du modèle delta-lognormal.

RESUMEN Se presentan dos índices de abundancia para el patudo a partir de los datos de la pesquería palangrera pelágica estadounidense en el océano Atlántico para el período 1982-2003: un índice de número de peces por mil anzuelos, estimado a partir del número de especimenes de patudo capturados y comunicados en los datos de los cuadernos de pesca de la pesquería pelágica, y un índice de biomasa en kg (peso canal) por mil anzuelos, estimado a partir de los datos de peso en el desembarque. El procedimiento de análisis de estandarización incluía las siguientes variables: año, zona, temporada, características del arte (bastones de luz, longitud de la línea madre, densidad de anzuelos, etc.) y las características de la pesca (tipo de cebo, procedimiento operativo y especies objetivo). El índice estandarizado se estimó mediante modelos lineales mixtos generalizados con un enfoque de modelo delta lognormal.

KEYWORDS

Catch/effort, Abundance, Longline, Fish catch statistics, Logbooks, Multivariate analyses

1 U.S. Department of Commerce, National Marine Fisheries Service, Southeast Fisheries Science Center, 75 Virginia Beach Drive, Miami, Florida 33149 U.S.A. Email: Mauricio.Ortiz@noaa.gov

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1. Introduction Indices of abundance from commercial fisheries have been used for tuning stock assessment models of the bigeye tuna Atlantic stock (ICCAT 2003). Data collected from the US longline fleet has been used to develop standardized catch per unit of effort (CPUE) indices of abundance for several tuna species including bigeye (Ortiz and Brown 2002, Cramer and Ortiz 1998, Ortiz et al. 2000). This report documents the analytical methods applied to the available data from the US longline fleet through 2003, and presents correspondent standardized CPUE indices of abundance for the Atlantic bigeye tuna stock unit. Catch in numbers and effort data were obtained from the Pelagic Longline Logbooks (PLL) data that reports catch and effort information for each longline set. While biomass catches (carcass dress-weight) information was gathered from the Weight-out pelagic sheets, which records carcass weight per vessel-trip at the fish houses, of landed catch. 2. Materials and methods Hoey and Bertolino (1988) described the main features of the fleet and several authors (Hoey et al. 1989, Scott et al. 1993, Ortiz et al. 2000) have reviewed the available catch and effort data from the US Pelagic Longline fishery. The present report updates the catch and effort information through 2003, and includes analyses of variability associated with random factor interactions particularly for interactions that include the Year effect, following the suggestion of the statistics and methods working group of the SCRS in 1999. Logbook records from the US Pelagic Longline fleet have been collected since 1986. From 1986 to 1991, submission of logbooks was voluntary, and thereafter, submission of logbook reports became mandatory. Swordfish, yellowfin, and other tunas including bigeye are the main target species for the US Pelagic Longline fleet. Longline fishers are also required to submit trip level summary of individual carcasses weight for the main market species, at the fish houses that process the landed catch. This constitutes the Weight-out database, which started in 1982. The Pelagic Longline Logbook data comprises a total of 218,316 record-sets from 1986 through 2003. Each record contains information of catch by set, including: date and time, geographical location, catch in numbers of targeted and bycatch species, and fishing effort (as number of hooks per set). Of these sets, bigeye tuna was reported as being caught in 66,459 sets (30.5%). Logbooks only record numbers of fish. As per the recommendation of the SCRS Species Group, indices of abundance should be reported both in weight and numbers of fish, when possible. The weight-out data comprises a total 35,800 from 1982 through 2003. Each record represents information of catch by vessel-trip, including date, geographical area of the catch (Figure 1), catch in numbers and weight for swordfish, tunas, and other market species, and fishing effort (total number of hooks per set, and number of sets per trip). The pelagic longline fishing grounds for the US fleet extends from the Grand Banks in the North Atlantic to latitudes of 5-10° south, off the South America coast, including the Caribbean Sea and the Gulf of Mexico. Eight geographical areas of longline fishing have been commonly used for classification (Figure 1). These include: the Caribbean, Gulf of Mexico, Florida East coast, South Atlantic Bight, Mid-Atlantic Bight, New England coastal, Northeast distant waters, the Sargasso Sea, and the Offshore area. Calendar quarters were used to account for seasonal fishery distribution through the year (Jan-Mar, Apr-Jun, Jul-Sep, and Oct-Dec). Other factors reviewed in the analyses of catch rates included; the use and number of light-sticks per hook deployed, and a variable named operations procedure (OP), which is a represents a categorical classification of US longline vessels based on their fishing configuration, type and size of the vessel, and main target species and area of operation (Hoey and Bertolino 1988). Fishing effort is reported in terms of the total number of hooks per trip and number of sets per trip. As number of hooks per set varies, catch rates were calculated as number of bigeye tuna caught per 1000 hooks. The U.S. Atlantic longline fleet targets mainly swordfish and yellowfin tuna, but other tuna species are also targets including bigeye tuna and albacore (to a lesser extent, some of the trips-sets target other pelagic species including sharks, dolphin and small tunas). A target variable was defined based on the proportion of the number of swordfish caught to the total number of fish caught per set, with four discrete target categories corresponding to the ranges 0-25%, 25-50%, 50-75%, and 75-100%. Due to management regulations related to swordfish and other species, time-area restrictions were implemented in 2000 that affected significant areas of the Pelagic Longline fishing grounds (Federal Register 2000, Figure 1).

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These restrictions included two permanent closures to pelagic longline fishing, one in the Gulf of Mexico known as the Desoto Canyon, effective since November 1st 2000, and the second permanent closure was the Florida East Coast effective since March 1st 2001. In addition, three time-area restrictions were also imposed for the pelagic longline gear in the US Atlantic coast: the Charleston Bump, an area off the North Carolina coast closed from February 1st to April 30th starting in 2001 year, The Bluefin tuna protection area off the South New England coast closed from June 1st to June 30th starting in 1999, and the Grand Banks area that was closed from July 17 2001 to January 9 2002 as a result of an emergency rule implementation. Because of the time-area restrictions mentioned above, it is important to evaluate their possible effect on catch rates of bigeye tuna and if necessary account for this source of variability in the standardization process to obtain relative indices of biomass. An approximation is to evaluate historic catch trends within and outside of the management areas prior to the implementation of time-closures, particularly for the two permanent closures. For the Pelagic Logbook data, it is possible to assign most of the longline sets to specific lat-lon positions and evaluate historic catch trends in and out of the time-area restrictions. An explanatory variable called MngArea was created that defined whether a particular set was within or outside of any of the restricted areas mention before. Figure 2 presents a summary of the average annual nominal catch rates of bigeye tuna (plus symbols) and the corresponding mean number of hooks deployed per year for one degree square areas. For the period 1990-2000, the map indicates that most of the fishing effort concentrated in areas near the Atlantic coast, the Grand Banks and the northern Gulf of Mexico areas. Likewise, bigeye catch rates were higher in the Grand Banks, off the Florida east coast and offshore Atlantic areas north of the Brazil coast. By comparison, in the latest years (2001-2003), annual fishing effort has declined and restricted to fewer areas, where traditional higher fishing effort were allocated previously. Catch rates had also declined in the recent years, particularly in those areas where time-area restrictions were implemented, such the Grand Banks. Figure 3 presents the annual trend of nominal CPUE for each of the management area, and the mean of non-closure areas. The Grand Banks and the Bluefin tuna protected area had overall higher catch rates than the rest of management areas or the non-closure mean. The permanent closures; Florida EC and DeSoto areas had overall much lower catch rates for bigeye compared to the non-closure overall mean. The plot of fishing effort, measured as the number of hooks deployed annually shows that the overall fishing effort has declined in comparison with the mid 1990’s. There is evident the drop of effort for the permanent closures of the DeSoto and Florida EC areas, and a reduction of effort in the Bluefin tuna and Charleston areas (Figure 3). With the Weight-out data that represents total catch by trip, the information collected includes only a general geographic zone of the area fished that are larger than the time-area closures, therefore it is not possible to properly allocate the bigeye tuna catch to a non-closure or closure location. An approximate solution was to assign an average latitude-longitude position to the catch-trip record in the weight-out data. This was possible for records from 1996 on, as it is possible to link logbooks with the Weight-out records using the trip ID number for each vessel. A mean position (latitude and longitude) was estimated for each trip based on location of the longline sets for the each trip reported in the logbooks. And for the analysis of catch rates, those records that on average were within time-area management areas were excluded from 1996 on. Relative indices of abundance for bigeye were estimated by a GLMM approach assuming a delta-lognormal model distribution due to the large percentage of zero observations in both the logbook and the weight-out data (Figure 4). The delta model fits separately the proportion of positive sets assuming a binomial error distribution and the mean catch rate of sets where at least one bigeye tuna was caught assuming a lognormal error distribution. The standardized index is the product of these model-estimated components. The log-transformed frequency distribution of observations with positive catch of bigeye tuna for the Logbook and the weight-out data are shown in Figure 4. The estimated proportion of successful sets per stratum is assumed to be the result of r positive sets of a total n number of sets, and each one is an independent Bernoulli-type realization. The estimated proportion is a linear function of fixed effects and interactions. The logit function was used as a link between the linear factor component and the binomial error. For sets that caught at least one bigeye tuna (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. Because, the difference of deviance between two consecutive (nested) 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 χ2 test (McCullagh and Nelder, 1989). Deviance analysis tables are presented for both data series, each table includes the deviance for the proportion of positive

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observations (i.e. positive trips/total trips), and the deviance for the positive catch rates. Final selection of explanatory factors was conditional to: a) the relative percent of deviance explained by adding the factor in evaluation (normally factors that explained more than 5% were selected), b) the χ2 test of significance, and c) the Type-III test significance within the final specified model. Once a set of fixed factors was specified, possible interactions were evaluated, and 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), Schwarz’s Bayesian Criterion (SBC), and a chi-square ratio test of the –2 loglikelihood statistic between nested 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 un-balanced characteristics of the data. LSmeans of lognormal positive trips were bias corrected using Lo et al., (1992) algorithms. Analyses were done using the GLIMMIX procedure from the SAS® statistical computer software (SAS Institute Inc. 1997). 3. Results and discussion Table 1 and 2 show the deviance analysis for bigeye, respectively from the Logbook and the weight-out data analyses, respectively. Deviance analyses of the logbooks show that for bigeye tuna the proportion of positive sets was mainly explained by the variables of area, season and the interactions of year*area, year*OP, and area*season. The mean catch rate for sets with bigeye tuna catch was primarily explained by the main variables of year, area, season, OP, and the interactions year*area, year*OP, year*season, and area*season (Table 1). In the case of the weight-out data for bigeye tuna, the fixed effects of area and target and the interactions year*OP and year*area were the main explanatory variables of the probability of capture bigeye tuna. Mean catch rate of positive sets was primarily explained by the fixed effects of year, area, quarter, and target, and the interactions year*area, year*OP and year*quarter (Table 2). Interactions between the variable year and other variables were assumed to be random, evaluation of the mixed model formulations are shown in Table 4, for the logbook and the weight-out data, respectively. Table 4 indicates the final model selected for each of the delta model standardization components. Figure 5 presents diagnostic plots for the delta lognormal component fit to the logbook data. To help identify the trends in these plots a loess smoother was plotted as a solid line. The plot of deviance residuals against the predicted or fitted values indicates that the assumed variance distribution remains more and less constant as the mean value increases; the range of the residuals however is much lower and asymmetric for the smallest catch rates as also seen in the qq-plot. The plot of the dependent variable, log CPUE against the fitted values follow a linear trend, indicating that the model used an appropriate link function. Finally the qq-plot or quartile normal plot indicates that for most of the data, the model follows the assumed lognormal distribution, with exception of the smallest catch rates. Similar diagnostic plots are also presented for the weight-out delta lognormal component in Figure 6. Standardized CPUE indices for bigeye are presented in Table 5 and 6 and Figure 7. Coefficients of variation for the bigeye tuna analysis range from 18 to 21% for the logbook data; and 21 and 74% for the weight-out data. Both indices show a declining trend; from the logbook data higher catch rates were observed in 1986-88, followed by a stable period between 1990 and 1998 with some increase in 1999, followed by a decline in the latest years. The weight-out index also indicated higher catch rates in the early 1980’s followed by declines to lowest values in 1996 an increase in 1998/99, and a low values again in the latest years. Figure 8 compares the two indices in a common scale; trends are similar between 1987 and 1995. In 1996 -2000 the weight-out data or biomass index oscillates more than the logbook data or number of fish index. However the estimated confidence intervals between both indices overlap substantially. Finally, Figure 9 presents histograms of bigeye size frequency catch by sex and year collected by the Observer Pelagic program on this fishery. Figure 10 shows the histograms of dressed weight (pounds) of the catch by year. In terms of size no differences were observed between male and female landings, in terms of weight, smaller fish appear to be more common in the last year.

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Literature cited CRAMER, J. and M. Ortiz. 1998. Standardized catch rates for bigeye tuna (Thunnus obesus) and yellowfin tuna

(T. albacares) from the U.S. longline fleet through 1997. Col. Vol. Sci. Pap. ICCAT, 49(3):33-356.

FEDERAL REGISTER. 2000. Atlantic highly migratory species; Pelagic longline management Final rule. 50 CFR part 635. Vol. 65(56) August 1 2000.

HOEY, J.J. and A. Bertolino. 1988. Review of the U.S. fishery for swordfish, 1978 to 1986. Col. Vol. Sci. Pap. ICCAT, 27:256-266.

HOEY, J.J., R. Conser and E. Duffie. 1989. Catch per unit effort information from the U.S. swordfish fishery. Col. Vol. Sci. Pap. ICCAT, 29:195-249.

ICCAT 2003. Report of the 2002 ICCAT Bigeye tuna stock assessment. Col. Vol. Sci. Pap. ICCAT, 55:1728-1842.

LITTELL, R.C., G.A. Milliken, W.W. Stroup, and R.D Wolfinger. 1996. SAS® System for Mixed Models, Cary NC, USA:SAS Institute Inc., 1996. 663 pp.

LO, N.C., L.D. Jacobson, and J.L. Squire. 1992. Indices of relative abundance from fish spotter data based on delta-lognormal models. Can. J. Fish. Aquat. Sci. 49: 2515-2526.

MCCULLAGH, P. and J.A. Nelder. 1989. Generalized Linear Models 2nd edition. Chapman & Hall.

ORTIZ, M. and C. Brown. 2002. Standardized catch rates for bigeye tuna (Thunnus obesus) from the pelagic longline fishery in the northwest Atlantic and the Gulf of Mexico. Col. Vol. Sci. Pap. ICCAT, 55:1880-1891.

ORTIZ, M. J. Cramer, A. Bertolino and G. P. Scott. 2000. Standardized catch rates by sex and age for swordfish (Xiphias gladius) from the U.S. Longline Fleet 1981-1998. Col. Vol. Sci. Pap. ICCAT, 51:1559-1620.

SAS Institute Inc. 1997, SAS/STAT® Software: Changes and Enhancements through Release 6.12. Cary, NC, USA:Sas Institute Inc. 1997. 1167 pp.

SCOTT, G. P., V. R. Restrepo and A. R. Bertolino. 1993. Standardized catch rates for swordfish (Xiphias gladius) from the US longline fleet through 1991. Col. Vol. Sci. Pap. ICCAT, 40(1):458-467.

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Bigeye tuna biomass CPUE Index from Weight-out data

Model factors positive catch rates values d.f.Residual deviance

Change in deviance

% of total deviance p

1 1 9031.37Year 21 8600.06 431.31 9.7% < 0.001Year Area 6 7466.92 1133.13 25.6% < 0.001Year Area Qtr 3 7061.04 405.88 9.2% < 0.001Year Area Qtr Op 6 6918.69 142.35 3.2% < 0.001Year Area Qtr Op Targ 3 5785.71 1132.98 25.5% < 0.001Year Area Qtr Op Targ Year*Area 95 5296.56 489.15 11.0% < 0.001Year Area Qtr Op Targ Year*Area Year*Qtr 52 5204.42 92.14 2.1% < 0.001Year Area Qtr Op Targ Year*Area Year*Qtr Area*Qtr 17 5027.73 176.69 4.0% < 0.001Year Area Qtr Op Targ Year*Area Year*Qtr Area*Qtr Year*Op 82 4917.49 110.24 2.5% 0.020Year Area Qtr Op Targ Year*Area Year*Qtr Area*Qtr Year*Op Area*Op 28 4853.01 64.47 1.5% < 0.001Year Area Qtr Op Targ Year*Area Year*Qtr Area*Qtr Year*Op Area*Op Qtr*Op 18 4821.50 31.52 0.7% 0.025Year Area Qtr Op Targ Year*Area Year*Qtr Area*Qtr Year*Op Area*Op Qtr*Op Year*Targ 49 4736.51 84.99 1.9% 0.001Year Area Qtr Op Targ Year*Area Year*Qtr Area*Qtr Year*Op Area*Op Qtr*Op Year*Targ Area*Targ 18 4642.13 94.38 2.1% < 0.001Year Area Qtr Op Targ Year*Area Year*Qtr Area*Qtr Year*Op Area*Op Qtr*Op Year*Targ Area*Targ Qtr*Targ 9 4620.19 21.93 0.5% 0.009Year Area Qtr Op Targ Year*Area Year*Qtr Area*Qtr Year*Op Area*Op Qtr*Op Year*Targ Area*Targ Qtr*Targ Op*Targ 17 4596.89 23.30 0.5% 0.140

Model factors proportion positives d.f.Residual deviance

Change in deviance

% of total deviance p

1 1 9345.976Year 21 9239.306 106.67 2% < 0.001Year Area 6 5042.256 4197.05 68% < 0.001Year Area Qtr 3 4888.770 153.49 2% < 0.001Year Area Qtr Op 6 4659.586 229.18 4% < 0.001Year Area Qtr Op Targ 3 3608.230 1051.36 17% < 0.001Year Area Qtr Op Targ Area*Op 29 3526.327 81.90 1% < 0.001Year Area Qtr Op Targ Area*Qtr 17 3453.486 154.74 2% < 0.001Year Area Qtr Op Targ Area*Targ 18 3437.483 170.75 3% < 0.001Year Area Qtr Op Targ Year*Targ 63 3411.712 196.52 3% < 0.001Year Area Qtr Op Targ Year*Qtr 63 3384.547 223.68 4% < 0.001Year Area Qtr Op Targ Year*Op 111 3207.572 400.66 6% < 0.001Year Area Qtr Op Targ Year*Area 108 3143.168 465.06 7% < 0.001

Table 1. Deviance table analysis of bigeye tuna catch rates from the Logbook data US Pelagic longline fishery from 1987 to 2003. Percent of total deviance refers to the deviance explained by the full model; p value refers to the Chi-square probability between consecutive models.

Table 2. Deviance table analysis of bigeye tuna catch rates from the weight out longline fleet 1981-2003. Percent of total deviance refers to the deviance explained by the full model; p value refers to the Chi-square probability between consecutive models.

Bigeye Tuna Logbook Catch (Numbers of fish)

Model factors positive catch rates values d.f.Residual deviance

Change in deviance

% of total deviance p

1 1 59715.0Year 16 57953.8 1761.11 14.4% < 0.001Year Area 8 51160.4 6793.42 55.5% < 0.001Year Area Season 3 50206.3 954.13 7.8% < 0.001Year Area Season Op 7 49418.5 787.84 6.4% < 0.001Year Area Season Op Lghtc 3 49180.4 238.11 1.9% < 0.001Year Area Season Op Lghtc Mngarea2 1 49167.6 12.76 0.1% < 0.001Year Area Season Op Lghtc Mngarea2 Year*Mngarea2 16 48895.3 272.31 2.2% < 0.001Year Area Season Op Lghtc Mngarea2 Year*Lghtc 48 48890.4 277.15 2.3% < 0.001Year Area Season Op Lghtc Mngarea2 Area*Op 42 48839.3 328.26 2.7% < 0.001Year Area Season Op Lghtc Mngarea2 Year*Season 48 48689.2 478.42 3.9% < 0.001Year Area Season Op Lghtc Mngarea2 Year*Op 103 48617.3 550.30 4.5% < 0.001Year Area Season Op Lghtc Mngarea2 Area*Season 24 48412.0 755.60 6.2% < 0.001Year Area Season Op Lghtc Mngarea2 Year*Area 128 47485.0 1682.56 13.8% < 0.001

Model factors proportion positives d.f.Residual deviance

Change in deviance

% of total deviance p

1 1 94519.657Year 16 92846.824 1672.83 3% < 0.001Year Area 8 42495.545 50351.28 79% < 0.001Year Area Season 3 39300.888 3194.66 5% < 0.001Year Area Season Op 8 38229.309 1071.58 2% < 0.001Year Area Season Op Lghtc 3 37825.526 403.78 1% < 0.001Year Area Season Op Lghtc Mngarea2 1 37234.741 590.79 1% < 0.001Year Area Season Op Lghtc Mngarea2 Year*Mngarea2 16 36812.153 422.59 1% < 0.001Year Area Season Op Lghtc Mngarea2 Area*Op 54 36105.616 1129.13 2% < 0.001Year Area Season Op Lghtc Mngarea2 Year*Lghtc 48 35890.758 1343.98 2% < 0.001Year Area Season Op Lghtc Mngarea2 Year*Season 48 35584.573 1650.17 3% < 0.001Year Area Season Op Lghtc Mngarea2 Year*Op 112 34779.132 2455.61 4% < 0.001Year Area Season Op Lghtc Mngarea2 Area*Season 24 33341.462 3893.28 6% < 0.001Year Area Season Op Lghtc Mngarea2 Year*Area 128 31103.942 6130.80 10% < 0.001

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Bigeye tuna GLM Mixed Models Deviance Scaled Dev

-2 REM Log

likelihood

Akaike's Information

Criterion

Schwartz's Bayesian Criterion

Proportion Positives Year Area Target OP 4085.01 1.83 10352.4 10354.4 10360.2Year Area Target OP Year*OP 3787.29 1.72 10346.1 10350.1 10356 6.3 0.0121

* Year Area Target OP Year*OP Year*Area 3642.89 1.67 10314.6 10320.6 10329.4 31.5 0.0000

Positives Catch RatesYear Area Qtr OP Target 36236.38 1.05 17324.5 17326.5 17333.2Year Area Qtr OP Target Year*area 5812.64 0.99 17100.1 17104.1 17109.8 224.4 0.0000Year Area Qtr OP Target Year*area Year*OP 5751.32 0.98 17090.7 17096.7 17105.2 9.4 0.0022

* Year Area Qtr OP Target Year*area Year*OP Year*Qtr 5680.48 0.98 17069 17077 17088.3 21.7 0.0000

Likelihood Ratio Test

Table 3. Analysis of GLM mixed model formulations for bigeye catch rates from the logbook data US Pelagic longline fleet. Likelihood ratio tests the difference of –2 REM loglikelihood between two nested models.

Table 4. Analysis of GLM mixed model formulations for bigeye catch rates from the weight out US Pelagic longline fleet. Likelihood ratio tests the difference of –2 REM loglikelihood between two nested models.

Bigeye tuna GLMixed Model Scale Deviance

-2 REM Log

likelihood

Akaike's Information

Criterion

Schwartz's Bayesian Criterion

Proportion Positives Year Area OP Season 6.09 32308.8 32310.8 32317.7Year Area OP Season Year*Area 5.28 31834.1 31838.1 31844.1 474.7 0.0000Year Area OP Season Year*Area Year*OP 5.2 31822.4 31828.4 31837.5 11.7 0.0006

* Year Area OP Season Year*Area Year*OP Area*Season 4.75 31550.6 31558.6 31570.7 271.8 0.0000

Positives Catch RatesYear Area Season OP Area*season 0.7425 166963.2 166965.2 166974.3Year Area Season OP Area*Season Year*area 0.7186 165191.2 165195.2 165201.2 1772 0.0000Year Area Season OP Area*Season Year*area Year*OP 0.7156 165039.8 165045.8 165054.9 151.4 0.0000

* Year Area Season OP Area*Season Year*area Year*OP Year*season 0.7102 164651.5 164659.5 164671.6 388.3 0.0000

Likelihood Ratio Test

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Table 5. Nominal and standardized catch rates of bigeye tuna from the logbook US Pelagic longline fleet. Catch rates express as numbers of fish per thousand hooks.

Table 6. Nominal and standardized catch rates of bigeye tuna from the weight out data. Biomass catch rates express as dressed weight (lbs) per thousand hooks.

Year Nominal CPUE

Standard CPUE Coeff Var Std Error Numb

obs Index Upp CI 95%

Low CI 95%

1982 299.61 714.00 0.523 373.4 90 3.41 9.11 1.271983 351.63 417.56 0.358 149.4 128 1.99 3.99 1.001984 353.56 304.28 0.305 92.7 162 1.45 2.64 0.801985 256.01 269.30 0.296 79.8 168 1.29 2.30 0.721986 315.23 373.37 0.249 92.9 320 1.78 2.91 1.091987 235.56 302.52 0.229 69.4 729 1.44 2.27 0.921988 156.35 287.77 0.214 61.7 930 1.37 2.10 0.901989 153.36 248.21 0.216 53.6 731 1.18 1.82 0.771990 151.22 189.30 0.213 40.4 796 0.90 1.38 0.591991 157.75 211.10 0.220 46.4 1221 1.01 1.56 0.651992 113.50 127.11 0.219 27.8 1769 0.61 0.94 0.391993 134.45 128.63 0.216 27.8 2015 0.61 0.94 0.401994 124.02 116.05 0.216 25.1 2134 0.55 0.85 0.361995 113.89 106.09 0.218 23.2 2253 0.51 0.78 0.331996 49.42 54.90 0.388 21.3 52 0.26 0.55 0.121997 58.29 77.38 0.379 29.3 65 0.37 0.77 0.181998 31.20 174.44 0.605 105.6 34 0.83 2.54 0.271999 140.54 169.22 0.395 66.9 36 0.81 1.73 0.382000 46.07 87.45 0.737 64.4 18 0.42 1.56 0.112001 211.57 105.11 0.367 38.6 22 0.50 1.02 0.252002 128.33 80.57 0.394 31.8 19 0.38 0.82 0.182003 52.14 63.84 0.549 35.0 12 0.30 0.85 0.11

Year Nominal CPUE

Standard CPUE Coeff Var Std Error Numb

obs Index Upp CI 95%

Low CI 95%

1987 3.74 3.83 0.183 0.702 11154 1.75 2.52 1.221988 2.87 2.72 0.192 0.522 12066 1.24 1.82 0.851989 3.22 2.93 0.183 0.536 14231 1.34 1.93 0.931990 2.76 2.00 0.198 0.396 14018 0.92 1.35 0.621991 2.72 2.13 0.195 0.414 13739 0.97 1.43 0.661992 2.04 1.71 0.201 0.344 14010 0.78 1.16 0.521993 2.47 1.91 0.197 0.377 13574 0.87 1.29 0.591994 2.37 1.92 0.196 0.376 14280 0.88 1.29 0.591995 2.14 1.77 0.195 0.345 15338 0.81 1.19 0.551996 1.74 1.99 0.191 0.381 15546 0.91 1.33 0.621997 2.18 1.86 0.194 0.360 14875 0.85 1.25 0.581998 2.45 1.95 0.192 0.375 11675 0.89 1.31 0.611999 2.74 2.47 0.191 0.472 11888 1.13 1.65 0.772000 1.81 1.99 0.198 0.394 11290 0.91 1.35 0.622001 2.38 2.31 0.190 0.439 10160 1.06 1.54 0.732002 1.80 2.27 0.185 0.421 9298 1.04 1.50 0.722003 1.24 1.41 0.211 0.297 8766 0.64 0.98 0.42

145

Figure 1. Geographic area classification for the US Pelagic longline fishery: CAR Caribbean, GOM Gulf of Mexico, FEC Florida east coast, SAB south Atlantic bight, MAB mid Atlantic bight, NEC north east coastal, NED north east distant waters, SNA Sargasso area, and OFS offshore waters. Shaded areas represent the current time-area closures affecting the pelagic longline fisheries. Permanent closures: the DeSoto area in the Gulf of Mexico, and the Florida east coast area. Time-area closures: the Charleston Bump in the SAB area closed Feb-Apr, the Bluefin tuna protected area in the MAB and NEC areas closed Jun, and the Grand Banks in the NED area closed from Oct 10/00 to Apr 9/01.

100°

-90°

-80°

-70°

-60°

-50°

-40°

-30°

-20°

-10° 0°

-10°

0°

10°

20°

30°

40°

50°

60°

CAR

GOMFEC

SAB

MAB

NEC

NED

SNASNA

OFS

OFS

146

Figure 2. Geographic distribution of fishing effort (mean annual number of hooks deployed, shade areas), and mean catch rates (numbers of fish/1000 hooks, plus symbols) of bigeye tuna by 1° square degree areas from the pelagic logbook data for the periods of 1990-2000 (top) and 2001-2003 (bottom).

Annual hooksdeployed

500 - 2000

2000 - 10000

10000 - 25000

25000 - 50000

50000 - 100000

100000 - 200000

200000 - 250000

Mean anual CPUEBigeye tuna

0.045.0

90.0

Annual hooksdeployed

500 - 2000

2000 - 10000

10000 - 25000

25000 - 50000

50000 - 100000

100000 - 200000

200000 - 450000

Mean annual CPUEBigeye tuna PLL

0.120.040.0

147

Bigeye tuna mean annual nominal CPUE by Management area

-2

0

2

4

6

8

10

12

14

1985 1990 1995 2000 2005

Num

bers

of f

ish

per t

hous

and

hook

s

Bluefin Charleston DeSoto

FloridaEC GrandBanks No Closure

Number of hooks deployed annually by Management area

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1985 1990 1995 2000 2005

Tota

l num

ber o

f hoo

ks d

eplo

yed

(mill

ions

)

0

1

2

3

4

5

6

7BluefinCharlestonDeSotoFloridaECGrandBanks No Closure

Figure 3. Mean annual nominal catch rates of bigeye tuna by (top) and annual number of hooks deployed (bottom) by management area from the pelagic logbook data 1986-2003. See Figure 1 for definition and location of management areas.

148

0 2 4 6 8

020

0040

0060

0080

00

log CPUE+1%meanCPUE log CPUE positive trips only

2 4 6 8

0.0

0.10

0.20

0.30

0 2 4 6

050

000

1000

0015

0000

log CPUE+10%meanCPUE log CPUE positive sets only

0 2 4 6

0.0

0.1

0.2

0.3

0.4

0.5

Biomass per trip data bigeye tunaWeight-out Database

Number of bigeye caught per setPelagic logbook data

Figure 4. Frequency distribution of nominal catch rates for bigeye tuna from the weight-out data (top) and the logbook data (bottom). Left panels show all observations including zero catch, right panel shows the log transformed catch rates of positive observations only.

149

Figure 5. Diagnostic plots for the delta lognormal model fit to the logbook US Pelagic longline data. Top left deviance residuals against fitted values, top right square root of absolute deviance residuals against predicted values, bottom left dependent variable against the fitted values, and bottom right qq-plot of deviance residuals. Solid line represents the loess smoother of the plotted data.

Figure 6. Diagnostic plots for the delta lognormal model fit to the weight-out data. Top left deviance residuals against fitted values, top right square root of absolute deviance residuals against predicted values, bottom left dependent variable against the fitted values, and bottom right qq-plot of deviance residuals. Solid line represents the loess smoother of the plotted data.

150

Bigeye Tuna Standardized Logbook CPUE Pelagic Longline US Fishery 95% CI

0.0

0.5

1.0

1.5

2.0

2.5

1985 1990 1995 2000 2005

Sca

led

CP

UE

(fis

h/10

00 h

ooks

)

Bigeye Tuna Standardized Biomass CPUE Pelagic Longline US Fishery

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1980 1985 1990 1995 2000 2005

Sca

led

CPU

E (d

ress

ed w

gt lb

s/10

00 h

ooks

)

Figure 7. Nominal and standardized catch rates for bigeye from the US Pelagic longline fishery. Top, logbook data reported as number of fish per thousand hooks. Bottom, weight out data reported as dress weight (lbs) per thousand hooks.

151

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1980 1985 1990 1995 2000 2005

Sca

led

stan

dard

ized

CP

UE

to o

verl

appi

ng y

ears

Index biomassIndex Number

Figure 8. Comparison of the standardized indices for bigeye tuna from the weight-out data (biomass index) and the logbook data (number of fish index). Thin lines represent the estimates 95% confidence intervals. Indices are plotted in a common scale of their mean for the overlapping years (1987-2003).

25 75 125 175 225

25 75 125 175 225

Fork length (cm)

0.0

0.1

0.2

0.3

0.0

0.1

0.2

0.3

0.0

0.1

0.2

0.3

0.0

0.1

0.2

0.3

0.0

0.1

0.2

0.3

1992 1993 1994

1995 1996 1997

1998 1999 2000

2001 2002 2003

2004

Male Male Male

Male Male Male

Male Male Male

Male Male Male

Male

25 75 125 175 225

25 75 125 175 225

Fork length (cm)

0.0

0.1

0.2

0.3

0.0

0.1

0.2

0.3

0.0

0.1

0.2

0.3

0.0

0.1

0.2

0.3

0.0

0.1

0.2

0.3

1992 1993 1994

1995 1996 1997

1998 1999 2000

2001 2002 2003

2004

Female Female Female

Female Female Female

Female Female Female

Female Female Female

Female

Figure 9. Size frequency distribution of bigeye tuna from the pelagic observer program by sex and year. Size bin 5 cm, number of fish measured (straight fork length): females 5030 and males 5352.

152

5 45 85 125165205245285

5 45 85 125165205245285

Dressed weight pounds

0.0

0.1

0.2

0.0

0.1

0.2

0.0

0.1

0.2

0.0

0.1

0.2

0.0

0.1

0.2

1992 1993 1994

1995 1996 1997

1998 1999 2000

2001 2002 2003

2004

Figure 10. Weight frequency distribution of bigeye tuna from the pelagic observer program by year. Number of fish weighed 9956.