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

SCRS/2007/069 Collect. Vol. Sci. Pap. ICCAT, 62(2): 445-468 (2008) STANDARDIZED CATCH RATES FOR BIGEYE TUNA (THUNNUS OBESUS) FROM THE PELAGIC LONGLINE FISHERY IN THE NORTHWEST ATLANTIC AND THE GULF OF MEXICO John Walter 1, Mauricio Ortiz and Craig Brown SUMMARY Two indices of abundance of bigeye tuna from the United States pelagic longline fishery in the Atlantic are presented; an index of number of fish per thousand hooks estimated from numbers of bigeye tuna caught and reported from Pelagic longline logbook data from 1986-2006 and a biomass index (dressed weight) per thousand hooks estimated from the dealer weight-out data for the period 1982-2006.The standardization analysis procedure included the following variables; year, area, quarter of the year, gear characteristics (light sticks, etc) and fishing characteristics (operations procedure, and target species). Standardized indices of abundance by year and quarter of the year were also constructed from the dealer weight-out data for 1984-2006. Standardized indices were estimated using Generalized Linear Mixed Models under a delta lognormal model. Both indices indicate an overall decline since the mid-1980s with slight increases since 2004. RÉSUMÉ Ce document présente deux indices d abondance du thon obèse de la pêcherie palangrière pélagique des Etats-Unis dans l Atlantique : un indice du nombre de poissons par mille hameçons, estimé d après le nombre de thons obèses capturés et déclarés dans les livres de bord des palangriers pélagiques de 1986 à 2006 et un indice de biomasse (poids manipulé) par mille hameçons, estimé d après les données des pesées des négociants pour la période 1982-2006. La procédure d analyse de standardisation incluait les variables suivantes : année, zone, trimestre, caractéristiques des engins (baguettes lumineuses, etc.) et caractéristiques de pêche (procédure des opérations et espèces cibles). Les indices standardisés de l abondance par année et trimestre ont également été élaborés d après les données des pesées des négociants pour 1984-2006. Les indices standardisés ont été estimés à l aide de Modèles Linéaires Généralisés Mixtes (GLMM) dans le cadre d un modèle delta lognormal. Les deux indices indiquent un déclin global depuis le milieu des années 1980, avec de légères augmentations depuis 2004. RESUMEN Se presentan dos índices de abundancia de patudo de la pesquería de palangre pelágico de Estados Unidos en el Atlántico; un índice en número de ejemplares por mil anzuelos, estimado a partir del número de patudos capturados y comunicados en los datos de los cuadernos de pesca de la pesquería de palangre pelágico de 1986 a 2006, y un índice de biomasa (peso eviscerado) por mil anzuelos estimado a partir de los datos de venta al peso de los comerciantes para el periodo 1982-2006. El procedimiento de análisis de estandarización incluía las siguientes variables: año, zona, trimestre del año, características del arte (bastones luminosos, etc.) y características de la pesca (procedimiento de las operaciones y especies objetivo). También se obtuvieron los índices estandarizados de abundancia por año y trimestre del año utilizando los datos de venta al peso de los comerciantes para el periodo 1984-2006. Los índices estandarizados se estimaron utilizando modelos lineales generalizados mixtos con un enfoque de modelo delta-lognormal. Ambos índices indican un descenso global desde mediados de los ochenta con ligeros incrementos desde 2004. KEYWORDS Bigeye tuna, abundance indices, catch/effort, catch rate standardization, Generalized Linear Model, pelagic longline fisheries 1 NOAA Fisheries, Southeast Fisheries Center, Sustainable Fisheries Division, 75 Virginia Beach Drive, Miami, FL, 33149-1099, USA. E- mail: john.f.walter@noaa.gov 445

1. Introduction Relative abundance indices are critical indicators of stock status as well as essential inputs into stock assessments. Fishery catch per unit effort (CPUE) indices operate under the basic assumption that catch per unit effort is proportional to stock size. Numerous factors including changes in vessel characteristics, fishing locations and methods, stock size and distribution and targeting of alternative species erode this basic relationship and complicate the interpretation of raw CPUE. Provided that data on covariates that influence the relationship between CPUE and abundance exist, these can be incorporated into standardized measures that presumably more accurately reflect population relative abundance (Maunder and Punt 2004). The report presents updated standardized CPUE estimates in numbers of bigeye tuna obtained from pelagic longline logbook data (PLL) for years 1986 to 2006 and in weight from dealer weigh-out data (DLS) for the years 1982-2006. We use a delta lognormal approach implemented as a generalized linear mixed model in SAS using methodology similar to Ortiz and Diaz (2003) and Ortiz (2004). We also present nominal catch rates by geographic area and as a function of various explanatory variables and length-frequency histograms and plots of mean length by year and area. 2. Materials and methods Data for this analysis comes from the United States Atlantic and Gulf of Mexico Pelagic longline fishery described in detail by Hoey and Bertolino (1988). Swordfish, yellowfin and bigeye tuna are the predominant target species. The pelagic longline fishing grounds of the US fleet extends from the Grand Banks in the North Atlantic to 5-10 south of the Equator, mainly in the Western Atlantic including the Caribbean Sea and the Gulf of Mexico (Fig. 1).The fishery has operated under several time-area restrictions since 2000 due to management regulations related to swordfish and other species (Federal Register 2000, Fig. 1). These restrictions included two permanent closures to pelagic longline fishing, one in the Gulf of Mexico known as the Desoto Canyon, effective since 1 st November 2000, and the second permanent closure on the Florida East Coast effective since 1 st March 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 South Carolina coast closed from 1 st February to 30 th April starting in year 2001, The Bluefin tuna protection area off the South New England coast closed from 1 st June to 30 th June starting in 1999, and the Grand Banks area that was closed from 17 July 2001 to 9 January 2002 as a result of an emergency rule implementation (Cramer 2002). Catch and effort data is available from three sources: pelagic logbooks reported daily by vessel captains (Scott et al 1993, Cramer and Bertolino 1998, Ortiz et al 2000), pelagic observer program data collected beginning in 1992 by onboard fishery observers on approximately 4-5% of the Pelagic Longline trips (Lee and Brown 1999) and dealer weigh-out data sheets submitted at the completion of each trip. Dealer weight-out data consists of summarized catch per total effort for an entire trip so that spatial and temporal information on fishing locations and catch rates represent averages for an entire trip. In contrast, logbook and observer daily reports generally have spatially and temporally explicit information on individual longline sets. U.S. Pelagic logbook data are available from 1986. However, from 1986 to 1991, submission of logbooks was voluntary, and became mandatory in 1992. Weigh-out data collection began in 1981 and observer data collection began in 1992. In this paper we use both vessel logbook reports and dealer weigh-out data. This data and further descriptions of the data collection and processing are available from the National Marine Fisheries Service Southeast Fisheries Science Center (http://www.sefsc.noaa.gov/commercialprograms.jsp). The Pelagic Longline Logbook data comprises a total of 292,507 recorded sets from 1986 through 2006. Each record contains information of catch by set, including: date-time, geographical location, catch in numbers of targeted and bycatch species, number of hooks, light stick and various other gear parameters for each set as well as environmental conditions such as temperature. Various data restrictions were necessary to eliminate incomplete or erroneous records or records that were non-standard such as very short sets of fewer than 100 hooks, weight of fish incorrectly recorded as number or set with zero fish of any species captured. Restricting the logbook data only to sets from 1986 onward, those with greater than 100 hooks per set, and with complete catch, effort, location and date information, resulted in a total of 266,296 sets, of which 74,933 (28.1%) reported positive catches of bigeye tuna. The pelagic dealer weigh-out data comprises a total of 40,025 records. Similarly we applied a set of data restrictions that eliminated incomplete or clearly erroneous records resulting in a total of 37,040 reports of which 14,764 (39.8%) reported positive catches of bigeye tuna. 446

2.1 Dependent variables Two dependent variables were used for the analysis, catch per unit effort (1000 hooks) in number (CPUEN) and in weight (CPUEW). CPUEN was obtained from the logbook data as the number of bigeye tuna caught per 1000 hooks for each longline set. This number included kept as well as live and dead discarded bigeye tuna, however discarded bigeye tuna represented less than 5% of the total number of caught tuna. CPUEW was obtained from the DLS weigh-out database as the total dressed carcass weight of bigeye tuna landed for an entire trip divided by the total number of hooks set for the trip. For comparison nominal catch rates of bigeye tuna caught per 1000 hooks was also obtained from the DLS database. Note that the DLS recorded tuna did not include discarded fish. 2.2 Factors Five fixed factors and a random effect of year and interactions between the factors were evaluated in the analysis. Eight geographical areas (area) of longline fishing have been traditionally used for classification; 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 (Figure 1). Calendar quarters (qtr) were used to account for seasonal fishery distribution through the year (Jan-Mar, Apr-Jun, Jul-Sep, and Oct- Dec). Other factors included in the analyses of catch rates included; the use and number of light-sticks (lightc) expressed as the ratio of light-sticks per hook, and a variable named operations procedures (OP), which is a categorical classification of US longline vessels based on their fishing configuration, type and size of vessel, main target species, and area of operation(s). Fishing effort is reported as number of hooks per set, and nominal catch rates were calculated as number of bigeye tuna caught per 1000 hooks for each observation. The U.S. Atlantic longline fleet targets mainly swordfish and yellowfin tuna, but other tuna species are also targeted including bigeye tuna and albacore (to a lesser extent, some of the trips-sets target other pelagic species including sharks, dolphin and small tunas). We define targeting (targ) as a categorical variable with four levels based on the proportion of the number of swordfish caught to the total number of fish per set, with four discrete target categories corresponding to the ranges 0-25%, 25-50%, 50-75%, and 75-100%. As fishing practices targeting swordfish generally diverge from those targeting bigeye this provides an empirical means to determine whether vessels are targeting particular species. Note that there is a record for target species within the PLL database, however it is deemed to be uninformative (L. Beerkircher, SEFSC pers comm.) Factors related to bigeye-specific fishing strategy such as longer gangions, more hooks between floats and longer floatlines were examined. Due to changes in the units with which gangion length were recorded this variable proved impossible to use at this time and no clear relationship between floatline length or number of hooks between floats were observed. Indices of abundance of bigeye were estimated by a generalized linear modeling approach assuming a delta lognormal model (Lo et al. 2002). The standardization procedure splits the dependent data into two parts, one of which models the proportion of positive sets which is assumed to have a binomial error distribution. The second part models the mean catch rate (CPUEN or CPUEW) of successful sets and assumes a lognormal error distribution. The standardized index is the product of the two. Parameterization of the model used the GLM structure where the proportion of successful sets is a linear function of the fixed factors the random year effect and random year-interaction effects when the year term was within the interaction. The logit function was selected as link between the linear factor component and the binomial error. Analyses were done using Glimmix and Mixed procedures from the SAS statistical computer software (SAS Institute Inc., 1997, Littell et al. 1996). A step-wise regression procedure was used to determine the set of factors and interactions that significantly explained the observed variability. We used criterion which assumes that the difference in deviance between two consecutive models follows a Chi-square distribution. The deviance is essentially a measure of the residuals explained by the model. Using this statistic, with degrees of freedom equal to the number of additional parameters estimated minus one, a Chi-square test was constructed which indicates if the additional factor is or is not statistically significant (McCullagh and Nelder, 1989). Deviance analysis tables were constructed for both components of the delta model: Proportion of successful trips/sets, and mean catch rate in both numbers and weights of positive trips/sets. Each deviance table includes the deviance explained by the additional factor or interaction, the overall percent explained by each factor, and the Chi-square probability test. Final selection of explanatory factors was conditional to: a) the relative percent of deviance explained by the added factor, normally factors that explained more than 5-10% of deviance were included and b) the Chi-square significant test and c) if an interaction was deemed significant, the main effects of each component of the 447

interaction were included. Once a set of fixed factors was specified, all possible 1 st level interactions were evaluated, in particular random interactions between the year effect and other factors. Standard indices of abundance were calculated as the product of the year effect least square means (LSmeans) from the binomial and the lognormal components of the delta lognormal model. LSmeans estimates included a weight proportional to the observed margins of the input data, to account for the unbalanced distribution of the data. Lognormal estimates also included a bias back-transformation correction factor as describe by Lo et al. (1992). Variances were obtained by the delta method for the approximate variance of the product of two random variables (Zhou 2002). Quarterly indices of abundance were obtained as the least square mean estimates obtained by defining a new variable as year*qtr however the quarterly index was only estimated for years 1984-2006 as years 1982-83 had several quarters with zero catch. Note that indices in both weight and number are not age or size-specific. 2.3 Time-area restrictions Because of the time-area restrictions mentioned above, it was important to evaluate their possible effect on catch rates of bigeye tuna. We did so in a manner similar to Ortiz (2004) by constructing a separate standardized catch indices for only the areas that are outside of any of the closed areas and comparing these indices to the all-area indices. For the Pelagic Logbook data, it was possible to assign most of the longline sets to specific latitudelongitude positions and evaluate historic catch trends in and out of the time-area restrictions. In contrast, the DLS data represents total catch for a trip with only a general geographic zone that is larger than the time-area closures. Therefore it was not possible to precisely allocate the bigeye tuna catch to closure or non-closure locations. An approximate solution was to assign an average latitude-longitude by linking the weight-out records to the logbook set by set information using the an unique trip identifier number. However, prior to 1996 the trip identifier did not exist so it was not possible to assign these trips to closed or open areas. 3. Results and discussion 3.1 Differences from previous standardization This analysis differs from Ortiz (2004) in several substantive ways. First sample sizes are larger in this study because Ortiz (2004) removed all observations if they lacked an operations code and if they were of OP code 1. Since 2002 and increasing percentage of the total logbook records do not have an OP code, potentially resulting in the removal of 20-30% of the total logbook records. For this reason I kept all unknown OP code records except OP code 1. In addition several logbook records appeared to have anomalous numbers of bigeye tuna and inspection of the datasheets indicated that weight of fish was recorded as number. In this paper the presented standardized catch rates are for all areas, both inside and outside of the area closures. In general, however, the catch rates trends differ little between indices including or excluding the closed areas (Figure 13). Particularly the DLS index is essentially the same because DLS records prior to 1996 cannot be assigned a latitude and longitude and likely due to the loss of spatial resolution when the location of all trips within a set was averaged. Lastly the models selected differ from Ortiz (2004) in that no interaction terms were deemed significant. For the time period 1986-2006, an average of 11,369 longline sets were recorded in the pelagic logbook database. These comprise the most complete records of US pelagic longline CPUE available for the Atlantic and Gulf of Mexico. During this time period, there has been a reduction in the total numbers of vessels in the fishery, with a concomitant increase in the number of hooks set per vessel. This has kept overall fishing effort relatively constant. However, effort has been displaced from a series of time area closure areas (Figure 1 and 2). Figure 2 presents the geographic distribution of fishing effort and nominal catch rates for bigeye tuna from the pelagic logbooks for eight selected years from 1987 through 2005. The plotted values are the sum of annual log(hooks) deployed and the mean catches in number per 1000 hooks calculated on grids of approximately 40x40 nautical miles. The plots show the substantial reduction of fishing effort, particularly after the implementation of time/area closures. 3.2 Nominal catch rates Nominal catch rates by area and year indicate higher catch rates in the Mid-Atlantic Bight, Northeast Coast and Northeast distant waters (Figures 3-6). Catch rates are very low in the Gulf of Mexico throughout the entire time period. In recent years, however, catch rates on the Florida East coast have increased primarily to vessels displaced from closed areas in the Florida Straits to the waters north of the Bahamas where bigeye tuna appear to be more abundant (Figure 2). Comparison of the DLS and the logbook standardized catch rates in number of 448

bigeye tuna per 1000 hooks shows fairly close agreement as would be expected since the logbook data comes from individual set reports and the DLS data are trip summaries (Figure 7). Deviations may be due to released fish which were included in the logbook CPUEN. 3.3 Model selection The fixed factors in the analysis were chosen through deviance table analysis (Tables 1 and 2) and no fixed factor interactions were deemed significant. Random effects interactions were chosen on the basis of likelihood ratio tests and reduction in AIC values (Tables 3 and 4). The final models selected for the binomial and lognormal components for the CPUEN were as follows (note that factor OP was included as a fixed factor in the proportion positive model because it was included in the interactions): Proportion Positive= Year Area qtr targ OP Year*area Year* OP Year*qtr log(cpuen) = Year Area qtr targ OP Year*area Year* OP Year*qtr The final model selected for the binomial and lognormal components for the CPUEW in biomass were: Proportion Positive = Year Area targ OP Year*area Year*op Year*targ log(cpuew) = Year Area targ qtr OP Year*area Year*OP Year*targ Year*qtr The final model selected for the binomial and lognormal components of the quarterly CPUEW index in biomass were: Proportion Positive = YearQtr Area targ OP YearQtr*area YearQtr*targ log(cpuew) = YearQtr Area targ OP YearQtr*area YearQtr*targ 3.4 Standardized catch rates Histograms of nominal CPUEN from the logbooks and CPUEW indicate no severe departures from the assumed lognormality (Figure 8). Diagnostic plots for the delta lognormal model fit to the Pelagic Logbook data of residuals for positive CPUE, chi-square residuals of proportion positive by year, Q-Q plot of cumulative normalized for positive CPUE and a histogram of residuals for positive CPUE indicate no severe violations (Figure 9). The same plots for CPUE from the DLS weigh-out data also indicate fairly well-behaved data with no clear patterns or trends in the residuals (Figure 10). Standardized catch rates of bigeye tuna numbers and biomass per 1000 hooks (Figures 11, Tables 3 and 4) indicate a decline since 1987, a stable trend from 1992-2002 and slight increases since 2003. Ortiz (2004) noted a similar decreasing trend for the period 1987-2003 with both logbook and weigh-out indices. Nominal catch rates show less of a trend and diverge from standardized indices in the early part of the time series being higher in the logbook number index (Figure 11A) and lower than the DLS weight index (Figure 11B). Standardized catch rates in weight from the DLS data by quarter or season of the year were also created which indicated seasonal variation in catch rates (Figure 12, Table 5) with catch rates highest in the fall. Both standardized catch in number and weight show very similar patterns (Figure 13) reflective of the absence of a trend in mean size over the time period for the entire fishery (Figures 14-16). Mean sizes, do, however differ between regions with the larger bigeye tuna in the Mid-Atlantic and South Atlantic Bight (Figure 14). 3.5 Closed Area analysis Standardized indices were constructed for all areas and for just areas the areas that have been open to fishing throughout the entire time period (Figure 17A, B). Removing all observations that occurred in a area that was closed as some time reduced the number of observations in the Logbook database by almost ½ for the time period 1987-1997 but the indices show similar trends. With the reduction in overall effort and in particular in the closed areas, the total number of observations has decreased since 1998 but, again the trends between the open areas and all the areas appear similar. The DLS indices for all areas and for just the open areas do not differ. This 449

is due to the inability to assign DLS data to locations prior to 1996 so that the data is the same and to the lack of spatial precision in assigning locations after 1996. 4. Literature cited BROWN, C. 2008. Standardized catch rates of yellowfin tuna, Thunnus albacares, based upon the United States pelagic longline observer program data, 1992-2006. Collect. Vol. Sci. Pap. ICCAT, 62, in press (SCRS/ 2007/056). CRAMER, J. 2002. Large Pelagic Logbook Newsletter 2000. NOAA Tech. Mem. NMFS SEFSC 471, 26 pages. CRAMER, J. and A. Bertolino. 1998. Standardized catch rates for swordfish (Xiphias gladius) from the U.S. longline fleet through 1997. Collect Vol. Sci. Pap. ICCAT, 49(1): 449-456. CRAMER, J. and M. Ortiz. 1999. Standardized catch rates for bigeye (Thunnus obesus) and yellowfin (T. albacares) from the U.S. longline fleet through 1997. Collect. Vol. Sci. Pap. ICCAT, 49(2): 333-356. FEDERAL REGISTER. 2000. Atlantic Highly Migratory Species; Pelagic Longline Management; Final rule. 50 CFR part 635. Vol. 65, No. 148 August 1, 2000. HOEY, J.J. and A. Bertolino. 1988. Review of the U.S. fishery for swordfish, 1978 to 1986. Collect. Vol. Sci. Pap. ICCAT, 27: 256-266. LEE, D.W. and C.J. Brown. 1999. Overview of the SEFSC Pelagic Observer Program in the northwest Atlantic from 1992-1996. Collect. Vol. Sci. Pap. ICCAT, 49(4): 398-409. LITTELL, R.C., G.A. Milliken, W.W. Stroup and R.D. Wolfinger. 1996. SAS System for Mixed Models, Cary NC: 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. MAUNDER, M. and A. Punt. 2004. Standardization of catch and effort data: a review of recent approaches. Fisheries Research 70:141-159. MCCULLAGH, P. and J.A. Nelder. 1989. Generalized Linear Models 2 nd edition, Chapman & Hall. ORTIZ, M. 2004. Standardized catch rates for bigeye tuna (Thunnus obesus) from the Pelagic longline fishery in the Northwest Atlantic and the Gulf of Mexico. ICCAT SCRS/04/133. ORTIZ, M. and G. A. Diaz. 2004. Standardized catch rates for yellowfin tuna (Thunnus albacares) from the U.S. pelagic longline fleet. Collect. Vol. Sci. Pap. ICCAT, 56(2): 660-675. 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. Collect. Vol. Sci. Pap. ICCAT, 51(5): 1559-1620. PINHEIRO, J.C. and D.M. Bates. 2000. Mixed-effect models in S and S-Plus. Statistics and Computing. Springer-Verlag New York, Inc. SAS INSTITUTE INC. 1997. SAS/STAT Software: Changes and Enhancements through release 6.12. Cary NC: 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. Collect. Vol. Sci. Pap. ICCAT, 40(1): 458-467. ZHOU, S. 2002. Estimating Parameters of Derived Random Variables: Comparison of the Delta and Parametric Bootstrap Methods. Transctions of the American Fisheries Society. 131: 667-675. 450

Table 1. Deviance analysis table of BET CPUE in number of tuna/1000 hooks from the pelagic logbooks. Percent of total deviance refers to the deviance explained by the full model; p value refers to the Chi-square probability test between two 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 0 69340.2 year 20 67194.8 2145.37 11.7% year area 8 58344.6 8850.14 48.1% year area qtr 3 57194.3 1150.31 6.2% year area qtr targ 3 54531.5 2662.83 14.5% year area qtr targ op 9 53363.1 1168.36 6.3% year area qtr targ op lghtc 3 52850.9 512.22 2.8% year area qtr targ op lghtc targ*lghtc 9 52781.8 69.12 0.4% year area qtr targ op lghtc qtr*lghtc 9 52623.4 158.39 0.9% year area qtr targ op lghtc qtr*op 27 52581.9 41.56 0.2% year area qtr targ op lghtc year*targ 60 52550.7 31.14 0.2% year area qtr targ op lghtc year*lghtc 9 52505.2 45.47 0.2% year area qtr targ op lghtc qtr*targ 60 52495.6 9.60 0.1% year area qtr targ op lghtc op*lghtc 27 52434.4 61.25 0.3% year area qtr targ op lghtc area*op 27 52356.5 77.87 0.4% year area qtr targ op lghtc targ*op 57 52327.3 29.23 0.2% year area qtr targ op lghtc year*qtr 58 52257.6 69.71 0.4% year area qtr targ op lghtc area*qtr 24 52231.0 26.55 0.1% year area qtr targ op lghtc year*op 151 52015.7 215.37 1.2% year area qtr targ op lghtc area*lghtc 24 51911.0 104.63 0.6% year area qtr targ op lghtc area*targ 24 51559.6 351.38 1.9% year area qtr targ op lghtc year*area 159 50926.8 632.86 3.4% Model factors proportion positives d.f. Residual deviance Change in deviance % of total deviance 0 137330.131 year 20 135454.104 1876.03 2% year area 8 71050.992 64403.11 70% year area qtr 3 67159.379 3891.61 4% year area qtr targ 3 54303.111 12856.27 14% year area qtr targ op 9 51978.602 2324.51 3% year area qtr targ op lghtc 3 50686.828 1291.77 1% year area qtr targ op lghtc targ*lghtc 9 50577.044 109.78 0% year area qtr targ op lghtc qtr*targ 9 50040.013 537.03 1% year area qtr targ op lghtc op*lghtc 27 49716.544 323.47 0% year area qtr targ op lghtc qtr*lghtc 9 49590.296 126.25 0% year area qtr targ op lghtc year*targ 60 49395.605 194.69 0% year area qtr targ op lghtc area*op 62 49167.071 228.53 0% year area qtr targ op lghtc year*lghtc 60 49118.119 48.95 0% year area qtr targ op lghtc qtr*op 27 49025.101 93.02 0% year area qtr targ op lghtc year*qtr 59 48469.926 555.17 1% year area qtr targ op lghtc targ*op 27 48312.352 157.57 0% year area qtr targ op lghtc area*lghtc 24 48094.742 217.61 0% year area qtr targ op lghtc year*op 157 47157.511 937.23 1% year area qtr targ op lghtc area*qtr 24 46959.534 197.98 0% year area qtr targ op lghtc area*targ 24 45547.839 1411.70 2% year area qtr targ op lghtc year*area 159 42396.875 3150.96 3% 451

Table 2. Deviance analysis table of BET CPUE in kg of tuna/1000 hooks from the dealer weight out data. Percent of total deviance refers to the deviance explained by the full model; p value refers to the Chi-square probability test between two consecutive models. Model factors positive catch rates values d.f. Residual deviance Change in deviance % of total deviance p 1 0 24136.64 Year 24 22861.28 1275.35 12.4% < 0.001 Year Area 7 18402.18 4459.10 43.2% < 0.001 Year Area Targ 3 16155.13 2247.05 21.8% < 0.001 Year Area Targ Qtr 3 15457.91 697.23 6.8% < 0.001 Year Area Targ Qtr Op 7 14924.28 533.63 5.2% < 0.001 Year Area Targ Qtr Op Area*Op 34 14767.33 156.95 1.5% < 0.001 Year Area Targ Qtr Op Targ*Qtr 9 14751.15 16.18 0.2% 0.063 Year Area Targ Qtr Op Qtr*Op 20 14719.94 31.21 0.3% 0.053 Year Area Targ Qtr Op Year*Targ 72 14690.04 29.90 0.3% 1.000 Year Area Targ Qtr Op Targ*Op 20 14661.15 28.89 0.3% 0.090 Year Area Targ Qtr Op Area*Qtr 21 14659.08 2.07 0.0% 1.000 Year Area Targ Qtr Op Year*Qtr 70 14563.22 95.86 0.9% 0.022 Year Area Targ Qtr Op Year*Op 140 14321.53 241.69 2.3% < 0.001 Year Area Targ Qtr Op Area*Targ 21 14249.66 71.87 0.7% < 0.001 Year Area Targ Qtr Op Year*Area 144 13819.21 430.44 4.2% < 0.001 Model factors proportion positives d.f. Residual deviance Change in deviance % of total deviance p 1 0 21198.214 Year 23 20785.378 412.84 3% < 0.001 Year Area 6 12120.961 8664.42 62.0% < 0.001 Year Area Targ 3 9207.745 2913.22 20.8% < 0.001 Year Area Targ Qtr 3 8685.060 522.69 3.7% < 0.001 Year Area Targ Qtr Op 6 8407.089 277.97 2.0% < 0.001 Year Area Targ Qtr Op Targ*Op 18 8260.900 146.19 1.0% < 0.001 Year Area Targ Qtr Op Area*Op 29 8234.770 26.13 0.2% 0.619 Year Area Targ Qtr Op Targ*Qtr 9 8133.526 101.24 0.7% < 0.001 Year Area Targ Qtr Op Qtr*Op 18 8062.019 71.51 0.5% < 0.001 Year Area Targ Qtr Op Year*Targ 69 8002.598 59.42 0.4% 0.788 Year Area Targ Qtr Op Year*Qtr 69 7994.912 7.69 0.1% 1.000 Year Area Targ Qtr Op Area*Targ 18 7948.536 46.38 0.3% < 0.001 Year Area Targ Qtr Op Area*Qtr 18 7936.258 12.28 0.1% 0.833 Year Area Targ Qtr Op Year*Op 130 7564.328 371.93 2.7% < 0.001 Year Area Targ Qtr Op Year*Area 134 7222.092 342.24 2.4% < 0.001 452

Table 3. Random factor interaction table for BET CPUE in # of tuna/1000 hooks from the logbook data. Percent of total deviance refers to the deviance explained by the full model; p value refers to the likelihood ratio test between two consecutive models. Bigeye tuna GLM Mixed Models Deviance -2 REM Log likelihood Akaike's Information Criterion Schwartz's Bayesian Criterion Likelihood Ratio Test Proportion positives Year Area Qtr Target OP 53782.012 80517.9 80519.9 80527.7 Year Area Qtr Target OP Year*area 45341.741 79419.5 79423.5 79429.9 1098.4 0.0000000 Year Area Qtr Target OP Year*area Year*Target 44586.8808 79480.6 79486.6 79496.3-61.1 #NUM! Year Area Qtr Target OP Year*area Year*Target Year*op 41373.1841 79283.1 79291.1 79304.1 197.5 0.0000000 * Year Area Qtr Target OP Year*area Year*op Year*targ Year*qtr 40099.2724 79272.9 79282.9 79299.1 10.2 0.0014044 Positive catch rates Year Area Qtr Target OP 53129.1338 186722 186724 186733 Year Area Qtr Target OP Year*area 51195.4094 184588 184592 184599 2134 0.0000000000 Year Area Qtr Target OP Year*area Year*Target 50924.3512 184349 184355 184365 239 0.0000000000 Year Area Qtr Target OP Year*area Year*Target Year*op 50522.6726 184081 184089 184102 268 0.0000000000 * Year Area Qtr Target OP Year*area Year*op Year*Target Year*qtr 50128.9348 183672 183682 183698 409 0.0000000000 453

Table 4. Random factor interaction table for BET CPUE in kg of tuna/1000 hooks from the dealer weight out data. Percent of total deviance refers to the deviance explained by the full model; p value refers to the likelihood ratio test between two consecutive models. Bigeye tuna GLM Mixed Models Deviance -2 REM Log likelihood Akaike's Information Criterion Schwartz's Bayesian Criterion Likelihood Ratio Test Proportion positives Year Area Qtr Target OP 8849.0607 21948.9 21950.9 21957.3 Year Area Qtr Target OP Year*area 7663.0668 21731.5 21735.5 21741.9 217.4 0.0000000 Year Area Qtr Target OP Year*area Year*op 7460.8084 21609.7 21615.7 21625.3 121.8 0.0000000 * Year Area Qtr Target OP Year*area Year*op Year*targ 7343.9668 21548.3 21556.3 21569.1 61.4 0.0000000 Year Area Qtr Target OP Year*area Year*op Year*targ Year*qtr 6999.5101 21608.5 21618.5 21634.5-60.2 #NUM! Positive catch rates Year Area Qtr Target OP 14924.2811 39156.3 39158.3 39165.8 Year Area Qtr Target OP Year*area 13871.9758 38587.8 38591.8 38598.2 568.5 0.0000000000 Year Area Qtr Target OP Year*area Year*op 13717.4501 38560.6 38566.6 38576.1 27.2 0.0000001835 Year Area Qtr Target OP Year*area Year*op Year*targ 13615.5177 38535.6 38543.6 38556.3 25 0.0000005733 * Year Area Qtr Target OP Year*area Year*op Year*targ Year*qtr 13349.4332 38427 38437 38452.9 108.6 0.0000000000 454

Table 5. Nominal and standardized catch rates of bigeye tuna in #/1000 hooks (CPUEN) from the pelagic logbook data. Note that the nominal CPUE includes zero catches. Year Nominal CPUE Stand. CPUE Coeff Var Std Error Numb obs Index Upp CI 95% Low CI 95% Obs prop pos 1986 4.72 2.39 0.357 0.853 2026 1.40 2.80 0.70 0.32 1987 3.00 3.86 0.231 0.891 14844 2.26 3.57 1.44 0.29 1988 2.41 2.96 0.245 0.725 16033 1.74 2.82 1.07 0.29 1989 2.87 3.25 0.234 0.760 17751 1.90 3.02 1.20 0.33 1990 2.54 2.01 0.262 0.528 16210 1.18 1.98 0.71 0.30 1991 2.55 1.58 0.276 0.436 15061 0.93 1.59 0.54 0.32 1992 1.96 1.10 0.301 0.330 15046 0.64 1.16 0.36 0.27 1993 2.37 1.21 0.294 0.357 14675 0.71 1.27 0.40 0.31 1994 2.16 1.06 0.302 0.320 15787 0.62 1.12 0.34 0.30 1995 2.01 1.02 0.301 0.308 16881 0.60 1.08 0.33 0.31 1996 1.69 1.29 0.287 0.370 16236 0.76 1.33 0.43 0.25 1997 2.17 1.38 0.280 0.386 15343 0.81 1.40 0.47 0.28 1998 2.40 1.56 0.272 0.424 11975 0.91 1.56 0.54 0.30 1999 2.74 2.04 0.267 0.546 12319 1.20 2.03 0.71 0.29 2000 1.77 1.66 0.273 0.453 11715 0.97 1.66 0.57 0.26 2001 2.33 2.01 0.260 0.522 10489 1.18 1.97 0.71 0.28 2002 1.76 2.01 0.252 0.507 9832 1.18 1.94 0.72 0.28 2003 1.09 0.93 0.316 0.295 9702 0.55 1.01 0.29 0.19 2004 1.23 0.62 0.353 0.217 9867 0.36 0.72 0.18 0.16 2005 1.72 0.89 0.318 0.281 8018 0.52 0.97 0.28 0.23 2006 2.26 0.97 0.316 0.307 6460 0.57 1.06 0.31 0.28 Table 6. Nominal and standardized catch rates of bigeye tuna in kg/1000 hooks (CPUEW) from the dealer weighout system (DLS). Year Nominal CPUE Standar d CPUE Coeff Var Std Error Numb obs Index Upp CI 95% Low CI 95% Obs prop pos 1982 312.96 746.47 0.460 343.2 98 3.40 8.17 1.42 0.26 1983 384.64 442.54 0.338 149.8 156 2.02 3.90 1.04 0.47 1984 350.43 364.28 0.304 110.6 167 1.66 3.01 0.92 0.44 1985 232.70 311.52 0.302 94.2 186 1.42 2.57 0.79 0.37 1986 294.83 410.68 0.256 105.0 349 1.87 3.10 1.13 0.45 1987 222.46 333.92 0.239 79.9 838 1.52 2.44 0.95 0.45 1988 141.14 321.80 0.221 71.1 1139 1.47 2.27 0.95 0.47 1989 137.64 276.03 0.221 61.0 886 1.26 1.95 0.81 0.47 1990 138.93 215.40 0.218 47.0 916 0.98 1.51 0.64 0.43 1991 143.67 233.78 0.227 53.1 1372 1.07 1.67 0.68 0.40 1992 110.05 144.08 0.226 32.6 1894 0.66 1.03 0.42 0.39 1993 137.69 146.64 0.223 32.7 2194 0.67 1.04 0.43 0.42 1994 121.84 130.13 0.224 29.1 2316 0.59 0.92 0.38 0.40 1995 115.41 129.08 0.227 29.3 2479 0.59 0.92 0.38 0.42 1996 70.66 119.07 0.217 25.8 2677 0.54 0.83 0.35 0.33 1997 84.52 106.17 0.224 23.7 2874 0.48 0.75 0.31 0.34 1998 84.82 131.79 0.215 28.4 2435 0.60 0.92 0.39 0.33 1999 153.46 169.04 0.216 36.5 2346 0.77 1.18 0.50 0.39 2000 85.86 132.39 0.212 28.1 2354 0.60 0.92 0.40 0.38 2001 136.93 119.99 0.216 25.9 1856 0.55 0.84 0.36 0.44 2002 101.31 134.39 0.208 28.0 1676 0.61 0.92 0.41 0.47 2003 64.12 84.42 0.219 18.5 1628 0.38 0.59 0.25 0.39 2004 89.06 60.58 0.236 14.3 1696 0.28 0.44 0.17 0.33 2005 122.64 92.47 0.223 20.7 1373 0.42 0.66 0.27 0.45 2006 201.21 126.32 0.230 29.1 1052 0.58 0.91 0.37 0.49 455

Table 7. Nominal and standardized catch rates of bigeye tuna in kg/1000 hooks (CPUEW) from the dealer weighout system (DLS) by year and quarter. Year quarter Nom- inal CPUE Stand. CPUE Coeff Var Std Error Numb obs Index Upp CI 95% Low CI 95% Obs prop pos 1984 Jan-Mar 6.787 35.43 0.83 29.53 29 0.19 0.80 0.04 0.103 1984 Apr-June 210.338 580.08 0.32 183.27 56 3.08 5.70 1.66 0.429 1984 Jul-Sept 319.755 418.86 0.28 118.71 48 2.22 3.88 1.27 0.604 1984 Oct-Dec 709.396 675.18 0.30 203.86 45 3.58 6.47 1.98 0.533 1985 Jan-Mar 80.081 216.21 0.46 100.27 32 1.15 2.77 0.47 0.313 1985 Apr-June 125.989 228.78 0.40 91.27 43 1.21 2.62 0.56 0.326 1985 Jul-Sept 283.348 299.43 0.32 95.52 74 1.59 2.96 0.85 0.419 1985 Oct-Dec 336.859 454.59 0.31 141.56 46 2.41 4.43 1.31 0.457 1986 Jan-Mar 176.870 389.28 0.52 201.35 29 2.07 5.47 0.78 0.379 1986 Apr-June 63.357 223.77 0.32 71.45 88 1.19 2.21 0.64 0.284 1986 Jul-Sept 496.951 522.51 0.24 125.97 88 2.77 4.46 1.72 0.591 1986 Oct-Dec 331.998 476.82 0.22 106.01 146 2.53 3.93 1.63 0.486 1987 Jan-Mar 138.709 312.89 0.23 70.54 215 1.66 2.59 1.06 0.414 1987 Apr-June 124.396 254.44 0.22 56.00 225 1.35 2.09 0.87 0.387 1987 Jul-Sept 357.163 304.56 0.19 58.74 222 1.62 2.37 1.10 0.527 1987 Oct-Dec 272.831 384.11 0.22 84.34 182 2.04 3.15 1.32 0.473 1988 Jan-Mar 149.502 300.69 0.20 60.23 270 1.60 2.37 1.07 0.504 1988 Apr-June 93.150 220.74 0.22 47.57 309 1.17 1.79 0.76 0.369 1988 Jul-Sept 101.417 201.04 0.19 37.52 317 1.07 1.54 0.74 0.476 1988 Oct-Dec 238.392 419.98 0.17 73.35 257 2.23 3.15 1.58 0.549 1989 Jan-Mar 178.300 421.22 0.18 75.46 273 2.24 3.19 1.57 0.520 1989 Apr-June 75.321 195.32 0.19 36.38 267 1.04 1.50 0.72 0.412 1989 Jul-Sept 111.787 171.02 0.22 37.43 179 0.91 1.40 0.59 0.486 1989 Oct-Dec 196.066 280.25 0.20 57.31 178 1.49 2.23 0.99 0.494 1990 Jan-Mar 109.359 250.30 0.18 45.87 238 1.33 1.91 0.92 0.458 1990 Apr-June 34.225 97.10 0.24 23.71 210 0.52 0.83 0.32 0.276 1990 Jul-Sept 132.058 203.07 0.20 41.10 183 1.08 1.61 0.72 0.492 1990 Oct-Dec 246.739 356.57 0.18 65.80 292 1.89 2.73 1.31 0.490 1991 Jan-Mar 91.018 211.51 0.21 44.28 382 1.12 1.70 0.74 0.293 1991 Apr-June 31.482 80.05 0.23 18.10 363 0.42 0.66 0.27 0.262 1991 Jul-Sept 212.396 278.41 0.19 53.14 326 1.48 2.16 1.01 0.494 1991 Oct-Dec 271.356 289.40 0.18 51.49 301 1.54 2.19 1.08 0.605 1992 Jan-Mar 84.670 186.17 0.18 33.17 417 0.99 1.41 0.69 0.393 1992 Apr-June 32.322 44.98 0.25 11.45 481 0.24 0.39 0.14 0.222 1992 Jul-Sept 144.242 119.40 0.20 23.91 560 0.63 0.94 0.43 0.452 1992 Oct-Dec 176.145 206.62 0.18 37.37 436 1.10 1.57 0.77 0.491 1993 Jan-Mar 62.662 166.89 0.18 30.32 399 0.89 1.27 0.62 0.404 1993 Apr-June 27.072 50.45 0.25 12.70 532 0.27 0.44 0.16 0.214 1993 Jul-Sept 210.503 140.53 0.19 26.81 749 0.75 1.09 0.51 0.491 1993 Oct-Dec 204.322 249.40 0.17 41.18 514 1.32 1.84 0.95 0.541 1994 Jan-Mar 71.367 118.12 0.20 23.90 413 0.63 0.94 0.42 0.341 1994 Apr-June 46.812 63.55 0.23 14.51 626 0.34 0.53 0.21 0.243 1994 Jul-Sept 169.163 101.48 0.20 20.79 706 0.54 0.81 0.36 0.462 1994 Oct-Dec 182.099 200.88 0.17 34.80 571 1.07 1.50 0.76 0.552 1995 Jan-Mar 42.608 64.35 0.24 15.27 433 0.34 0.55 0.21 0.263 1995 Apr-June 46.594 61.99 0.24 14.98 651 0.33 0.53 0.20 0.257 1995 Jul-Sept 147.929 130.09 0.18 23.62 827 0.69 0.99 0.48 0.520 1995 Oct-Dec 202.413 228.96 0.18 40.81 568 1.21 1.73 0.85 0.581 1996 Jan-Mar 42.238 94.83 0.23 21.43 542 0.50 0.79 0.32 0.269 1996 Apr-June 43.861 75.25 0.21 15.62 742 0.40 0.60 0.26 0.259 1996 Jul-Sept 94.877 98.35 0.19 18.83 865 0.52 0.76 0.36 0.410 1996 Oct-Dec 97.810 134.62 0.19 24.91 528 0.71 1.03 0.49 0.373 456

Table 7. Continued. Year quarter Nom- inal CPUE Stand. CPUE Coeff Var Std Error Numb obs Index Upp CI 95% Low CI 95% Obs prop pos 1997 Jan-Mar 46.727 91.30 0.21 18.88 677 0.48 54.00 0.34 0.292 1997 Apr-June 54.276 71.26 0.24 16.94 784 0.38 54.00 0.47 0.241 1997 Jul-Sept 121.542 94.11 0.19 18.30 814 0.50 54.00 0.38 0.421 1997 Oct-Dec 116.498 130.15 0.19 24.68 599 0.69 54.00 0.28 0.412 1998 Jan-Mar 50.482 107.30 0.21 22.29 500 0.57 54.00 0.42 0.280 1998 Apr-June 38.011 84.01 0.23 19.27 745 0.45 54.00 0.83 0.188 1998 Jul-Sept 95.008 114.44 0.18 21.10 654 0.61 54.00 0.57 0.425 1998 Oct-Dec 169.468 220.98 0.17 38.45 536 1.17 54.00 0.55 0.461 1999 Jan-Mar 60.548 152.82 0.18 27.70 543 0.81 54.00 0.53 0.335 1999 Apr-June 112.797 152.40 0.19 29.08 628 0.81 54.00 0.76 0.325 1999 Jul-Sept 246.464 144.89 0.19 27.17 645 0.77 54.00 0.52 0.457 1999 Oct-Dec 183.632 205.69 0.19 38.20 530 1.09 54.00 0.34 0.430 2000 Jan-Mar 63.546 142.96 0.19 27.88 502 0.76 54.00 0.38 0.303 2000 Apr-June 63.853 94.79 0.19 18.43 614 0.50 54.00 0.61 0.287 2000 Jul-Sept 94.737 101.55 0.18 18.06 702 0.54 54.00 0.43 0.423 2000 Oct-Dec 120.340 162.37 0.18 28.66 536 0.86 54.00 0.34 0.506 2001 Jan-Mar 77.970 119.79 0.19 23.32 386 0.64 54.00 0.33 0.360 2001 Apr-June 83.642 96.08 0.21 19.78 463 0.51 54.00 0.73 0.339 2001 Jul-Sept 137.777 90.99 0.19 17.07 595 0.48 54.00 0.48 0.452 2001 Oct-Dec 250.823 194.42 0.18 34.55 412 1.03 54.00 0.30 0.600 2002 Jan-Mar 90.035 131.69 0.19 25.11 340 0.70 54.00 0.39 0.424 2002 Apr-June 47.747 83.23 0.20 16.34 443 0.44 54.00 0.88 0.339 2002 Jul-Sept 110.386 104.55 0.18 18.60 532 0.55 54.00 0.38 0.476 2002 Oct-Dec 164.288 231.03 0.17 38.24 361 1.23 54.00 0.19 0.659 2003 Jan-Mar 54.308 102.75 0.18 18.94 335 0.55 54.00 0.18 0.484 2003 Apr-June 23.127 56.48 0.23 12.80 443 0.30 54.00 0.76 0.223 2003 Jul-Sept 39.381 51.14 0.21 10.76 453 0.27 54.00 0.30 0.327 2003 Oct-Dec 146.375 199.66 0.17 33.63 397 1.06 54.00 0.08 0.552 2004 Jan-Mar 54.517 85.35 0.22 18.51 346 0.45 54.00 0.12 0.303 2004 Apr-June 8.342 27.29 0.28 7.72 509 0.14 54.00 0.75 0.171 2004 Jul-Sept 60.414 36.04 0.26 9.26 434 0.19 54.00 0.42 0.288 2004 Oct-Dec 249.923 204.22 0.19 38.32 407 1.08 54.00 0.24 0.607 2005 Jan-Mar 45.443 112.53 0.18 19.81 355 0.60 54.00 0.21 0.490 2005 Apr-June 45.300 72.54 0.23 16.96 390 0.38 54.00 0.92 0.269 2005 Jul-Sept 154.707 61.62 0.21 13.20 379 0.33 54.00 0.62 0.491 2005 Oct-Dec 305.051 247.83 0.18 45.14 249 1.32 54.00 0.30 0.639 2006 Jan-Mar 102.079 175.02 0.21 35.92 212 0.93 54.00 0.47 0.458 2006 Apr-June 75.708 87.68 0.23 19.94 295 0.47 54.00 0.96 0.363 2006 Jul-Sept 270.414 138.66 0.23 31.57 355 0.74 54.00 0.00 0.501 2006 Oct-Dec 377.349 264.75 0.19 50.19 190 1.40 54.00 0.00 0.726 457

NED NEC 5 MAB 4 GOM 1 SAB 3 2 FEC SAR SAR CAR OFS OFS Figure 1. Geographical areas 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 Atlantic, NED North east distant waters, SNA Sargasso Sea, and OFS Offshore waters. Shaded areas represent the current timearea closures affecting the pelagic longline fisheries. Permanent closures: (1) the DeSoto Canyon in the Gulf of Mexico and (2) The Florida east coast areas. Non-permanent closures: (3) the Charleston Bump area closed Feb- Apr, (4) the Bluefin tuna protection area closed in June, and (5) the Grand Banks closed since Oct-2000. 458

Figure 2. Spatial distribution of catch and effort in the US Pelagic longline fishery for selected years 1987-2006. Scale is log(hooks set) per grid cell. Cell size is approximately 40 x 40 nautical miles. 459

Figure 3. Nominal bigeye tuna catch per 1000 hooks for each area from logbook data. Upper and lower limits are 95% confidence intervals. Numbers are the total number of daily logbook sets reporting positive bigeye catches. Figure 4. Nominal percentage of positive sets for bigeye tuna upper and lower limits are approximate 95% confidence intervals, based on normality. Numbers are the total number of daily logbooks reporting positive bigeye catches. 460

Figure 5. Nominal bigeye tuna catch per 1000 hooks for each area from dealer weight-out data. Upper and lower limits are 95% confidence intervals. Numbers are the total number of trips reporting positive bigeye catches. Figure 6. Nominal percentage of positive sets for bigeye tuna from dealer weight-out data. Upper and lower limits are approximate 95% confidence intervals, based on normality. Numbers are the total number of trips reporting positive bigeye catches. 461

comparison of nominal logbook and DLS number per 1000 hooks for positive records scaled mean 0.0 0.5 1.0 1.5 2.0 Logbook DLS number 1990 1995 2000 2005 year Figure 7. Comparison of nominal logbook and DLS number per 1000 hooks for positive records. A. B. C. D. Figure 8. Histograms of nominal catch rates for bigeye tuna from the U.S. Pelagic longline fishery. A. Log(catch per 1000 hooks) for positive logbook sets. B) Frequency distribution of proportion of positive trips. C) Log(weight per 1000 hooks) for positive dealer weigh out data, D) Frequency distribution of proportion of positive trips from dealer weigh out data. 462

Figure 9. Diagnostic plots for the delta lognormal model fit to the Pelagic Logbook data for CPUEN (A) residuals for positive CPUE and (B) chi-square residuals of proportion positive by year. (C) Q-Q plot of cumulative normalized for positive CPUE. (D) Histogram of residuals for positive CPUE. Figure 10. Diagnostic plots for the delta lognormal model fit to the dealer weight-out data for CPUEN (A) residuals for positive CPUE and (B) chi-square residuals of proportion positive by year. (C) Q-Q plot of cumulative normalized for positive CPUE. (D) Histogram of residuals for positive CPUE. 463

A. Bigeye Tuna Standardized Logbook CPUE US (95% C)I Scaled CPUE (fish/1000 hooks) 3.5 3.0 2.5 2.0 1.5 1.0 0.5 CPUE obs 0.0 1985 1990 1995 2000 2005 B. Scaled CPUE (dressed wgt lbs/1000 hooks) 6.0 5.0 4.0 3.0 2.0 1.0 Bigeye Tuna Standardized CPUE in weight US Longline Dealer weigh-out standardized observed 0.0 1981 1986 1991 1996 2001 2006 Figure 11. Nominal and standardized catch rates for bigeye from the U.S. Pelagic longline fishery. A. Top, logbook data reported as number of fish per thousand hooks. B. Dealer weight out data reported as dress weight (lbs) per thousand hooks. 464

standardized CPUEW from DLS 5 4.5 4 3.5 3 2.5 2 1.5 1 observed winter spring summer fall annual 0.5 0 year*season Figure 12. Quarterly index obtained from dealer weight out data reported as dressed weight (lbs) per thousand hooks. Black line is the annual standardized index. 3.5 Scaled standardized CPUE to overlapping years 3.0 Index biomass Index Number 2.5 2.0 1.5 1.0 0.5 0.0 1985 1990 1995 2000 2005 Figure 13. Comparison of standardized catch rates for bigeye tuna from the U.S. Pelagic longline fishery logbook #/1000 hooks and dealer weighout kg/1000 hooks. Each index is scaled to the mean value for the continuous period 1986-2006. 465

Figure 14. Histograms of skipjack tuna straight line fork length (cm) by area. Red lines and numbers indicate mean fork length. Figure 15. Histograms of skipjack tuna straight line fork length (cm) by year. Red lines and numbers indicate mean fork length. 466

A. FL, mm 50 100 150 200 329 1936 648 3927 2244 1469 811 76 479 B. Caribbean Gulf of Mexico Florida East South Atl. Bight Mid-Atl. Bight NE Coast NE Distant Sargasso Sea South offshore FL, mm 50 100 150 200 849 1595 1083 1099 346 779 576 1082 366 337 863 524 552 822 999 77 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Figure 16. Box and whisker plots (median, 1 st, 3 rd quartile, minimum and maximum) of bigeye tuna straight line fork length by (A) area and (B) year. Box widths are proportional to sample size and dots represent lengths that are outside the 1.5*interquartile range. 467

A. B. scaled CPUEN from logbooks scaled CPUEN from logbooks 2.5 2 1.5 1 0.5 4 3.5 3 2.5 2 1.5 1 0.5 0 0 1982 1983 1986 1987 1988 all areas closed areas removed number all areas number closures removed 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 all areas closed areas removed number closures removed number all areas 2001 2002 2003 2004 2005 2006 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Figure 17. Comparison of standardized indices constructed using observations from all areas and observations that occur in closed areas removed. A. logbook CPUEN in #/1000 hooks. B. Dealer weighout CPUEW in kg/1000 hooks. Note that prior to 1996 it was not possible to assign the DLS data to spatial locations. 20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 3500 3000 2500 2000 1500 1000 500 0 total observations total observations 468