SCRS/2006/083 Col. Vol. Sci. Pap. ICCAT, 60(4): (2007)

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1 SCRS/2006/083 Col. Vol. Sci. Pap. ICCAT, 60(4): (2007) STANDARDIZED CATCH RATES OF LARGE BLUEFIN TUNA (THUNNUS THYNNUS) FROM THE U.S. PELAGIC LONGLINE FISHERY IN THE GULF OF MEXICO AND OFF THE FLORIDA EAST COAST DURING Shannon L. Cass-Calay 1 SUMMARY Two indices of abundance of bluefin tuna from the United States pelagic longline fishery in the Atlantic are presented for the period The first index is an extension of the previous U.S. pelagic longline index (Cramer 2002) that uses the same linear models. The second index was constructed using a repeated measures procedure to account for the variance in catch rates between vessels. Both indices were standardized using Generalized Linear Mixed Models, and a delta-lognormal approach. The indices are different in that the extension of the 2002 index suggests that catch rates increased from , then declined during and remained low thereafter. The newly constructed index suggest a fairly consistent decline from , with low catch rates continuing through RÉSUMÉ Ce document présente deux indices d abondance du thon rouge de la pêcherie palangrière pélagique des Etats-Unis dans l Atlantique pour la période Le premier indice est une extension de l indice palangrier pélagique des Etats-Unis développé auparavant (Cramer 2002) utilisant les mêmes modèles linéaires. Le second indice a été élaboré à l aide d une procédure de mesures répétées afin de tenir compte de la variance dans les taux de capture entre les navires. Ces deux indices ont été standardisés en utilisant des Modèles linéaires généralisés mixtes ainsi qu une approche delta-lognormal. Les indices sont différents en ce que l extension de l indice de 2002 suggère que les taux de capture ont augmenté de 1988 à 1990, puis ont diminué en et sont restés faibles par la suite. Le nouvel indice élaboré suggère un déclin assez constant de 1987 à 1993, avec de faibles taux de capture jusqu en RESUMEN Se presentan dos índices de abundancia de atún rojo para la pesquería de palangre pelágico de Estados Unidos en el Atlántico para el periodo El primer índice es una extensión del índice previo de palangre pelágico de Estados Unidos (Cramer, 2002) que utiliza los mismos modelos lineales. El segundo índice se construyó utilizando un procedimiento de medidas repetidas para tener en cuenta la varianza en las tasas de captura entre barcos. Ambos índices fueron estandarizados utilizando modelos lineales generalizados mixtos y un enfoque delta-lognormal. Los índices se diferencian en que la extensión del índice de 2002 sugiere que las tasas de captura aumentaron durante , luego descendieron durante y desde entonces han permanecido bajas. El índice nuevo sugiere un descenso bastante constante desde , con tasas de captura bajas que continúan hasta KEYWORDS Catch/effort, abundance, longlining, logbooks, multivariate analyses 1 U.S. Department of Commerce, NOAA Fisheries, Southeast Fisheries Science Center, Miami Laboratory, 75 Virginia Beach Drive, Miami, Florida U.S.A. Shannon.Calay@noaa.gov. Sustainable Fisheries Division Contribution No. SFD

2 1. Introduction Large bluefin tuna are caught by longline vessels within the Gulf of Mexico and off the Florida east coast. Although bluefin are considered incidental bycatch to this fishery, which primarily targets swordfish, bigeye and yellowfin tunas, previous studies have used logbook data from this fishery to construct catch rate indices for the western stock of Atlantic bluefin (Cramer and Ortiz, 2000; Cramer, 2002). These indices were utilized during previous assessments of Atlantic bluefin tuna. This report updates the catch and effort information through 2005, and describes two delta-lognormal catch rate indices. 2. Materials and methods The main features of the U.S. pelagic longline fleet are described by Hoey and Bertolino (1988). The operational area of the U.S. pelagic longline fleet is large, extending from the Grand Banks in the NW Atlantic to the South American coast, including the Gulf of Mexico and the Caribbean Sea (Figure 1). Logbook records have been collected since Although submission of logbooks was initially voluntary, mandatory submission began in The dataset contains 266,690 records from 1986 through Of these, 11,058 (4.1%) reported catching bluefin tuna (either landed, discarded or released). Each logbook record contains trip information by fishing day or set, including: vessel ID, date and time, fishing location, catch in numbers and fishing effort (hooks per set). The majority of records describe a unique longline set, other types of records (fishing day) were excluded from the analysis. Because very few sets were reported in 1986, records from 1986 were also excluded. 2.1 Extension of 2002 index This index was fit using the same methods and GLM formulations described by Cramer (2002), and is intended to be an extension of that index. Since the index was intended to reflect the abundance of mature western Atlantic bluefin, the analysis dataset was restricted to sets that occurred in two areas, the Gulf of Mexico (GOM) and the Florida east coast (FEC) (Figure 1). These areas are considered to be the primary spawning grounds of the western bluefin stock. Although bluefin are also caught in other areas, particularly off the Grand Banks, other areas are thought to contain bluefin from both the western and eastern stocks. To minimize the inclusion of effort targeting species unlikely to co-occur with bluefin tuna, the analysis dataset was restricted to sets occurring from January 1 to May 31 of each year. During this time period, vessels were substantially more likely to report bluefin tuna. In addition, only those vessels identified by Cramer (2002) were included in the analysis dataset (n = 11). The selected vessels did not report catching bluefin tuna consistently throughout the time series (Figure 2). As of January 1, 2001, some areas of the GOM and FEC regions are permanently closed to the pelagic longline fishery, including the Desoto region and the portion of the FEC that lies within the U.S. EEZ (Figure 1). Eightyfour sets were reported within the Desoto area during the closure. Of these, only one set reported catching bluefin tuna. All 84 sets were excluded from the analysis. A delta-lognormal approach (Lo et al. 1992) was used to develop the standardized catch rate index. This method combines separate generalized linear modeling (GLM) analyses of the proportion positive sets (sets that caught bluefin tuna) and the catch rates of successful sets to construct a single standardized index of abundance. Parameterization of each model was accomplished using a GLM procedure (GENMOD; Version 8.02 of the SAS System for Windows SAS Institute Inc. Cary, NC, USA). For the lognormal models, the response variable, log(cpue), was calculated: log(cpue) = 1000*[log(Number of Bluefin Tuna / Hooks)] The GLM formulations described by Cramer (2002) were used to fit the delta-lognormal model. The factors are summarized in Table 1. The final models were: PROPORTION POSITIVE SETS = YEAR + MONTH + VESSEL_ID + ZONE + YEAR*VESSEL_ID + YEAR*ZONE LOG(CPUE) = YEAR + VESSEL_ID + YEAR*VESSEL_ID 1071

3 2.2 Updated index of abundance To construct the analysis dataset, identical exclusions to those described in Section 2.1 were made, with one exception. The vessels included by Cramer (2002) were not considered, instead all vessels that caught, released or discarded at least one bluefin tuna during two or more years were included in the analysis (N = 223). A forward stepwise regression procedure was used to determine the set of fixed factors and interaction terms that explained a significant portion of the observed variability. The factors examined are summarized in Table 1 (VESSEL_ID was not examined as a fixed factor). For both the binomial and lognormal portions of the deltalognormal model, deviance tables were constructed to determine the proportion of total variance explained by the addition of each factor or interaction term. In addition, a χ 2 analysis was preformed to test the significance of the reduction in deviance between each consecutive set of nested models (McCullagh and Nelder 1989). Factors and interaction terms were selected for final analysis if: 1) the relative percent of deviance explained by adding the factor exceeded one percent, 2) the χ 2 test was significant and 3) the Type-III test was significant for the specified model. Once a set of fixed factors was identified, the influence of the YEAR*FACTOR interactions were examined. As per the recommendation of the statistics and methods working group of the SCRS (1999), YEAR*FACTOR interaction terms were included in the model as random effects. Selection of the final mixed model was based on the Akaike s Information Criterion (AIC), Schwarz s Bayesian Criterion (BIC), and a chi-square test of the difference between the 2 loglikelihood statistics between successive model formulations (Littell et al. 1996). The final delta-lognormal model was fit using the SAS macro GLIMMIX and the SAS procedure PROC MIXED (SAS Institute Inc. 1997) following the procedures described by Lo et al. (1992). Most vessels did not consistently report catching bluefin tuna during the time series. Instead, vessels tended to catch bluefin during a shorter interval of consecutive years, or sporadically (Figure 3). Due to the large number of vessels (N = 223) and the unbalanced nature of the Year*Vessel interaction (Figure 3). The variance in catch rates, by vessel, was modeled using repeated measures within the SAS PROC MIXED procedure (Littell et al., 1998). The term repeated measures refers to multiple measurements taken over time on the same experimental unit (i.e. vessel). Specifying the repeated measure VESSEL and the subject VESSEL(YEAR) allows PROC MIXED to model the covariance structure of the data. This is particularly important because catch rates of sets fished close in time can be more highly correlated that those fished far apart in time (Littell et al., 1998). 3. Results and discussion During January-May of each year, the 11 vessels selected by Cramer (2002) caught a small portion of the total catch (10-25%), and also comprised a small fraction (10-25%) of the total vessels that reported catching bluefin tuna within the GOM and FEC (Figure 4). When the dataset was expanded to include all vessels that landed at least one bluefin tuna in two or more years, the 223 selected vessels were responsible for at least 80% of the total catch and comprised at least 65% of the total vessels that reported catching bluefin (Figure 4). 3.1 Extension of 2002 index The annual proportion of positive sets (PPS: sets that caught bluefin) was moderate, ranging from 5% to 21% (Figure 5; Table 2). Before 1990, PPS varied without apparent trend. PPS fell to 3-10% from 1992 to 1998, and then increased to nearly 20% by In 2005 the proportion of positive sets was 15%. Nominal CPUE follows a similar pattern, but a decrease in CPUE after 2002 is notable (Figure 6; Table 2). The highest values occur during 1990 and 1991, followed by a significant decline in catch rates in Catch rates increase again in 2001 and 2002, but decline two lower levels from 2002 to Diagnostic plots were constructed to examine the fit of the components of the delta-lognormal model. The frequency distribution of nominal catch rates is shown in Figure 7. It is clear that the assumption of a normal fit the distribution of log(cpue) is violated to some degree. The distribution of residuals from the binomial model on proportion positive sets, by year, and the cumulative normalized residuals from the lognormal model on the catch rates of positive sets are shown in Figure 8. Some lack of fit is noted. The residuals of the binomial, by year, are typically less than zero, indicating the strong influence of a few large positive residuals (Figure 8). The QQ-Plot of the residuals of the lognormal model also indicates some departure from the assumption of a normal distribution (Figure 8, red line). Figure 9 illustrates the residuals of the lognormal model on catch rates, by 1072

4 vessel. The even distribution of the residuals above and below zero indicates that the model is able to effectively account for variability in catch rates between vessels. The delta-lognormal catch rate index, with 95% confidence intervals, is shown in Figure 10 and summarized in Table 2. The standardized abundance index is roughly similar to the nominal CPUE series (Figure 10). This index (extension of Cramer 2002) suggests the catch rates were highest in 1987, 1990 and 1991 then fell to low levels after A slow increase in CPUE is apparent from 1994 to Since then, CPUE has fluctuated just below the series mean. 3.2 Updated index Deviance tables were constructed to identify factors and interaction terms that explained a significant portion of the observed variability, and met all inclusion criteria. The deviance table constructed for the binomial model (Table 3) indicates that the significant fixed factors include: YEAR, ZONE, MONTH and the interaction term ZONE*MONTH. The deviance table constructed for the lognormal model (Table 4) indicates that the significant fixed factors include: YEAR, ZONE and MONTH. Once a set of fixed factors was selected, first level random interactions between year and other effects were examined. The results of this procedure are shown in Table 5. The final model was selected based on the loglikelihood, Akaike s Information Criterion (AIC), Schwarz s Bayesian Criterion (BIC) and the Likelihood Ratio Test of the difference between successive model formulations. All criteria used showed agreement for the best model selection: PROPORTION POSITIVE SETS = YEAR + ZONE + MONTH + ZONE*MONTH + YEAR*ZONE + YEAR*MONTH LOG(CPUE) = YEAR + ZONE + MONTH + YEAR*ZONE + YEAR*MONTH + the effect of the repeated measure VESSEL_ID with the covariance structure VESSEL_ID(YEAR) The SAS PROC MIXED code used to fit the lognormal model was as follows. PROC MIXED data=bft.posit info IC; CLASS year zone month vessel_id; MODEL logcpue = year zone month / SOLUTION CL outpm=bft.posit; RANDOM year*zone year*month / SOLUTION CL ; REPEATED vessel_id / type=cs subject=vessel_id(year) r rcorr; LSMEANS year /CL COV OBSMARGINS=BFT.POSIT ; ODS listing exclude covparms lsmeans; ODS output covparms=itcpu1 lsmeans=estrawp; RUN; The annual proportion of positive sets (PPS: sets that caught bluefin) was low, ranging from 3% to 14% (Figure 11; Table 6). PPS generally declined from 14% in 1987 to 3% in Thereafter, PPS generally increased to 13% in In 2005, the PPS was 8%. Nominal CPUE follows a similar pattern (Figure 12; Table 6). The highest levels were observed during , and then a substantial decline occurred during From , nominal CPUE increases, then remains at intermediate values throughout the remainder of the time series. Diagnostic plots were constructed to examine the fit of the components of the delta-lognormal model. The frequency distribution of nominal catch rates is shown in Figure 13. Like the previous index, there is evidence that the assumption of a normal fit the distribution of log(cpue) is violated to some degree. The distribution of residuals from the binomial model on proportion positive sets, by year, and the cumulative normalized residuals from the lognormal model on the catch rates of positive sets are shown in Figure 14. Again, some lack of fit is noted. The residuals of the binomial, by year, are typically less than zero, indicating the strong influence of a few large positive residuals (Figure 14) and the QQ-Plot indicates a departure from the assumption of a normal distribution (Figure 14, red line). Figure 15 illustrates the residuals of the lognormal model on catch rates, by vessel (N = 223). In general, the residuals are evenly distributed above and below zero, indicating that the model is able to effectively account for variability in catch rates between vessels. The delta-lognormal catch rate index, with 95% confidence intervals, is shown in Figure 16 and summarized in Table 6. The standardized abundance index is roughly similar to the nominal CPUE series (Figure 16). The 1073

5 updated index suggests the catch rates were highest in 1987, and that CPUE generally declined until Since that time, catch rates have remained below the series mean. The two indices are compared in Figure 17. They are roughly similar. The most important difference is the behavior of the index during the early years of the time-series ( ). The extension of the Cramer (2002) index fluctuates without apparent trend while the updated index shows a general decline. It is important to note that restricting the dataset to 11 vessels (Cramer, 2002) excludes 90% of the total catch in the early years, and about 80% thereafter, while the updated approach includes 80% of the catch throughout the time-series. It is also important to note that of the 11 vessels identified by Cramer (2002), only 3 to 5 continue to fish during Thus, it may be that the updated approach is preferable since it accounts for vessel effects without restricting the dataset to a small proportion of the available records. 4. Acknowledgments I would like to acknowledge the assistance of Jean Cramer, Guillermo Diaz, Mauricio Ortiz and Craig Brown and Steve Turner of NOAA Fisheries (SEFSC) who helped to extract relevant data, and provided advice regarding analytical techniques. Clay Porch reviewed drafts of the manuscript, and provided useful comments. 5. References CRAMER, J Standardized catch rates for large bluefin tuna, Thunnus thynnus, from the U.S. pelagic longline fishery in the Gulf of Mexico and off the Florida east coast. Col. Vol. Sci. Pap. ICCAT, 55(3): CRAMER, J. and M. Ortiz Standardized catch rates for large bluefin tuna, Thunnus thynnus, from the U.S. pelagic longline fishery in the Gulf of Mexico and off the Florida east coast. Col. Vol. Sci. Pap. ICCAT, 52: HOEY, J.J. and A. Bertolino Review of the U.S. fishery for swordfish, 1978 to Col. Vol. Sci. Pap. ICCAT, 27: LITTELL, R.C., G.A. Milliken, W.W. Stroup, and R.D Wolfinger SAS System for Mixed Models, Cary NC, USA:SAS Institute Inc., pp. LITTELL, R.C., P.R. Henry and C.B. Ammerman Statistical analysis of repeated measures data using SAS procedures. J. Anim. Sci. 76: LO, N.C., L.D. Jacobson, and J.L. Squire Indices of relative abundance from fish spotter data based on delta-lognormal models. Can. J. Fish. Aquat. Sci. 49: MCCULLAGH, P. and J.A. Nelder Generalized Linear Models edition. Chapman & Hall. SAS Institute Inc. 1997, SAS/STAT Software: Changes and Enhancements through Release Cary, NC, USA: SAS Institute Inc., pp. 1074

6 Table 1. Description of the analysis factors. FACTOR LEVELS VALUES YEAR MONTH 5 Jan, Feb, Mar, Apr, May ZONE 4 Zone 1 = Lat 18 N-25N and Lon 88 W to 97 W Zone 2 = Lat 26 N-29N and Lon 88 W to 98 W Zone 3 = Lat 22 N-30N and Lon 79 W to 87 W Zone 4 = Lat 24 N-29N and Lon 73 W to 78 W VESSEL ID 11 Table 2. Relative nominal CPUE, number of sets, number of positive sets, proportion positive sets (PPS) and abundance index statistics (extension of Cramer 2002). YEAR SETS POSITIVE SETS PPS Relative Nominal CPUE Relative Index Lower 95% CI Upper 95% CI CV 1075

7 Table 3. The deviance table for the binomial model on the proportion of positive sets. Factors were assumed to be significant if the explained >1% of the total deviance, and were significant according to a Chi-Square test. Residual Deviance Reduction in Deviance % of Total Deviance Chi Square Binomial Model Factors - Proportion Positive DF DF Log Like P Null Year <0.001 Year + Zone <0.001 Year + Zone + Month <0.001 Year + Zone + Month + Zone*Month <0.001 Year + Zone + Month + Zone*Month + Year*Zone <0.001 Year + Zone + Month + Zone*Month + Year*Month <0.001 Table 4. The deviance table for the lognormal model on catch rates of positive sets. Factors were assumed to be significant if the explained >1% of the total deviance, and were significant according to a Chi-Square test. Residual Deviance Reduction in Deviance % of Total Deviance Chi Square Lognormal Model Factors - CPUE DF DF Log Like P Null Year <0.001 Year + Zone <0.001 Year + Zone + Month <0.001 Year + Zone + Month + Zone*Month Year + Zone + Month + Zone*Month + Year*Zone <0.001 Year + Zone + Month + Zone*Month + Year*Month <0.001 Table 5. Analysis of the mixed model formulations. The likelihood ratio was used to test the difference of 2 REM logliklehood between two nested models. The final model is indicated with gray shading. Proportion Positive -2 REM Log likelihood Akaike's Information Criterion Schwartz's Bayesian Criterion Likelihood Ratio Test P Scaled Deviance Dispersion Year + Zone + Month Year + Zone + Month + Zone*Month < Year + Zone + Month + Zone*Month + Year*Zone < Year + Zone + Month + Zone*Month + Year*Month < Year + Zone + Month + Zone*Month + Year*Zone + Year*Month < Catch Rates on Positive Trips -2 REM Log likelihood Akaike's Information Criterion Schwartz's Bayesian Criterion Likelihood Ratio Test P Year + Zone Year + Zone + Month Year + Zone + Month + Year*Zone < Year + Zone + Month + Year*Month < Year + Zone + Month + Year*Zone + Year*Month < Year + Zone + Month + Year*Zone + Year*Month + ID(Year) < Year + Zone + Month + Year*Zone + Year*Month + ID(Zone) < Year + Zone + Month + Year*Zone + Year*Month + ID(Year*Zone) Year + Zone + Month + Year*Zone + Year*Month + ID(Year*Zone*Month) No Convergence 1076

8 Table 6. Relative nominal CPUE, number of sets, number of positive sets, proportion positive sets (PPS) and abundance index statistics (updated index). YEAR SETS POSITIVE SETS PPS Relative Nominal CPUE Relative Index Lower 95% CI Upper 95% CI CV 1077

9 Figure 1. Geographic area classifications 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. Note that permanent closures include the DeSoto area in the Gulf of Mexico, and the Florida east coast area. VESSEL ID YEAR Figure 2. Years that a vessel reported catching (landed, released and discarded) at least one bluefin tuna. (Vessels are coded with a numeric identifier). 1078

10 1079 YEAR VESSEL ID YEAR YEAR Figure 3. Years that a vessel reported catching (landed, released and discarded) at least one bluefin tuna. (Vessels are coded with a numeric identifier).

11 120% %Catch or %Vessels 100% 80% 60% 40% 20% 0% Year %Catch (Updated) %Vessels (Updated) % Catch (Cramer) % Vessels (Cramer) Figure 4. The annual fraction of the total catch landed by the 11 vessels selected by Cramer 2002 (solid red line) and the 223 vessels selected during this analysis (solid blue line); and the annual fraction the selected vessels comprise of the total vessels that reported catching bluefin for the 11 vessels selected by Cramer 2002 (dashed red line) and the 223 vessels selected during the analysis (dashed blue line). Figure 5. The annual trend in the proportion of positive sets (extension of Cramer, 2002). Figure 6. The annual trend in nominal CPUE (extension of Cramer, 2002). 1080

12 Figure 7. Frequency distribution of nominal catch rates (fish per 1000 hooks) on positive sets (extension of Cramer, 2002). Figure 8. Diagnostic plots for the delta-lognormal model (extension of Cramer, 2002). Left) the distribution of residuals from the binomial model on the proportion of positive set, by year. Right) the cumulative normalized residuals from the lognormal model on the catch rates of positive sets Figure 9. The distribution of residuals from the binomial model on the proportion of positive set, by vessel ID. Vessels are coded with a numeric identifier (extension of Cramer, 2002). 1081

13 Figure 10. Extension of the standardized index of Cramer (2002) with 95% confidence intervals and nominal CPUE. 1082

14 Figure 11. The annual trend in the proportion of positive sets (updated index). Figure 12. The annual trend in nominal CPUE (updated index). Figure 13. Frequency distribution of nominal catch rates (fish per 1000 hooks) on positive trips. 1083

15 Figure 14. Diagnostic plots for the delta-lognormal model (updated index). Left) the distribution of residuals from the binomial model on the proportion of positive set, by year. Right) the cumulative normalized residuals from the lognormal model on the catch rates of positive sets. 4 3 Residual Vessel Code 3 Residual Vessel Code 3 Residual Vessel Code Figure 15. Residuals of the lognormal model on catch rates of bluefin tuna during positive sets, by vessel. Vessels are coded with a numeric identifier (updated index). 1084

16 Figure 16. Standardized index (updated index) with lower and upper 95% confidence intervals and nominal CPUE. 1085

17 Figure 17. Comparison of the two indices of abundance; the extension of Cramer (2002) and the associated confidence intervals (blue), the updated index and the associated confidence intervals (red) and the area where the confidence intervals (purple). 1086