JAPANESE LONGLINE CPUE FOR YELLOWFIN TUNA (THUNNUS ALBACARES) IN THE ATLANTIC OCEAN STANDARDIZED USING GLM UP TO 2014

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1 SCRS/2016/035 Collect. Vol. Sci. Pap. ICCAT, 73(1): (2017) JAPANESE LONGLINE CPUE FOR YELLOWFIN TUNA (THUNNUS ALBACARES) IN THE ATLANTIC OCEAN STANDARDIZED USING GLM UP TO 2014 Keisuke Satoh 1, Takayuki Matsumoto 1 SUMMARY Japanese longline CPUE in number for yellowfin tuna caught in the Atlantic Ocean was standardized in quarter and annual base using GLM (General Linear Model) for the period from 1965 to 2014 in order to provide indicator of the stock. Annual CPUE in weight was also estimated from 1970 to During the period analyzed, standardized CPUEs for number was divided into four periods according to its level of CPUE, before 1979 (high), (middle), (low) and after 2006 (low to middle). RÉSUMÉ La CPUE palangrière japonaise en nombre d'albacores capturés dans l'océan Atlantique a été standardisée par trimestre et année à l'aide d'un GLM (modèle linéaire généralisé) pour la période afin de fournir un indicateur du stock. La CPUE annuelle en poids a également été estimée de 1970 à Au cours de la période analysée, la CPUE standardisée en nombre a été divisée en quatre périodes en fonction du niveau de la CPUE, avant 1979 (élevé), (moyen), (faible) et après 2006 (faible à moyen). RESUMEN Se estandarizó la CPUE del palangre japonés en número de rabiles capturados en el océano Atlántico por trimestre y por año utilizando un GLM (Modelo lineal generalizado) para el periodo para obtener un indicador del stock. También se estimó la CPUE anual en peso desde 1970 hasta Durante el periodo analizado, la CPUE estandarizada en número se dividió en cuatro periodos en función de su nivel, antes de 1979 (alta), (media), (baja) y después de 2006 (baja a media). KEYWORDS Atlantic, Yellowfin, Longline, Catch/effort 1 National Research Institute of Far Seas Fisheries Orido Shimizu, Shizuoka-City, Shizuoka , Japan. matumot@affrc.go.jp 270

2 1. Introduction Longline is the only tuna-fishing gear deployed by Japan at present in the Atlantic Ocean, and yellowfin tuna is one of the target species (Anonymous, 2013). Fishing effort for Japanese longline fishery covers almost entire Atlantic (Appendix Figure 1), and yellowfin tuna is mainly caught in the tropical area (Appendix Figure 2). Japanese longline CPUE for yellowfin in the Atlantic Ocean was standardized by GLM with lognormal error assumption up to 2014 in order to provide stock indicator of this species. For being easy to compare, the updated CPUE is calculated basically by the same method in previous studies (Okamoto and Satoh, 2008, Satoh et al., 2012 and Matsumoto and Satoh, 2015). 2. Materials and methods 2.1 Data The Japanese longline catch (in number and in weight) and effort statistics from 1965 (from 1970 for weight data) to 2014 were used. Data for 2014 are preliminary. The catch and effort data set was aggregated by month, 5-degree square and the number of hooks between floats (NHBF). The data in which the number of hooks was less than 10,000 in a stratum were not used for analyses. The NHBF from 1965 to 1974 is not available from logbook, therefore the NHBF was regarded to be 5 for these years. Area definition for the quarterly indices is whole Atlantic, east Atlantic and west Atlantic (Figure 1), and for the annual indices is consisted of three sub-areas (Figure 2). As environmental factor, the monthly SST (Sea Surface Temperature) data for 1-degree latitude and 1-degree longitude was obtained from NEAR-GOOS Regional Real Time Data Base of Japan Meteorological Agency (JMA; Monthly Mean Global Sea Surface Temperatures (COBE-SST), The SST data was averaged into 5-degree latitude and 5-latitude longitude by month from 1965 to Model used for standardization The initial annual and quarterly models are the same as those for the previous Japanese longline CPUE standardization analyses (Okamoto and Satoh, 2008, Satoh et al., 2012 and Matsumoto and Satoh, 2015). The effects of several factors on yellowfin catch were assessed using GLM procedure (log normal error structure model, SAS ver. 9.4, SAS Inst., Inc.). We intended to select the final model after variable selection with backward stepwise F test with a criterion of P-value = In the cases in which the factor is not significant as main factor but is significant as interaction with other factor, the main factor was kept in the model. For alternative model, In order to explain two dimensional distributions of CPUE, the nonlinear relationship between SST and CPUE is applied into the model as explanation variables as SST 2 and SST 3. The interaction term between latitude (Lat) and longitude (Lon) was included in the model as the cubic expression (i.e., Lat + Lon + (Lat + Lon) 2 + (Lat + Lon) 3 ). The number of hooks between float (NHBF) were categorized into 5 classes (CNHBF 1: less than 6, CNHBF 2: 6-8, CNHBF 3: 9-12, CNHBF 4: 13-16, CNHBF 5: larger than 16). The details of the initial models are as follows; For annual CPUE in number base and weight base Log (CPUE +constant) = mean + year + month + area + CNHBF + SST + SST 2 + SST 3 + ML + BL + interaction + error where Log: natural logarithm, CPUE: catch in number (weight) of bigeye per 1000 hooks, constant: 10% of overall mean of CPUE mean: overall mean, year: effect of year, month: effect of fishing season (month), area: effect of sub-area (Figure 2), CNHBF: effect of gear type (category of the number of hooks between floats), SST: effect of SST (as a continuous variable), SST 2 : effect of SST 2 (=SST x SST, as a continuous variable), 271

3 SST 3 : effect of SST 3 (=SST x SST x SST, as a continuous variable), ML: effect of material of main line, BL: effect of material of branch line, Interaction: year*area + year*month + month*area + area*cnhbf + month*sst + month*sst + area*sst + CNHBF*SST + ML*CNHBF + BL*CNHBF error: error term. Effect of year was obtained by the method used in Ogura and Shono (1999) that uses lsmeans of year-area interaction as the following equation. CPUE i = Σ W j * (exp (lsmean (year i * area j ))-constant), where CPUE i = CPUE in year i, W j = area rate of area j; (ΣW j = 1), lsmean (year i * area j ) = least square mean of year * area interaction in year i and area j, constant = 10% of overall mean of CPUE. As for the quarter based CPUE, least square mean of year*quarter*area was used instead. The sub-area is shown in Figure 2, which was used in last assessment in For quarterly CPUE in number base Log (CPUE + constant) = mean + year + quarter + Lat + Lon + Lat 2 + Lat-Lon + Lon 2 + Lat 3 + Lat 2 -Lon + Lat-Lon 2 + Lon 3 + CNHBF + SST + SST 2 + SST 3 + ML + BL + error Explanatory variables which is not included in the model for annual CPUE were included in the list as follows; Where quarter: effect of fishing season (quarter) Lat 2, Lat-Lon, Lon 2, Lat 3, Lat 2 -Lon, Lat-Lon 2, Lon 3 : the products of latitude (Lat) and longitude (Lon) expressed as third order polynomial functions. These values are included as continuous variables. Interaction: year*quarter + quarter*sst + CNHBF*SST + ML + CNHBF + BL + CNHBF + quarter*lat + quarter*lat 2 + quarter*lat 3 + quarter*lon + quarter*lon 2 + quarter*lon 3 + CNHBF*Lat + CNHBF*Lat 2 + CNHBF*Lat 3 + CNHBF*Lon + CNHBF*Lon 2 + CNHBF*Lon 3 + SST*Lat + SST*Lat 2 + SST*Lat 3 + SST*Lon + SST*Lon 2 + SST*Lon 3 Error fixation The wrong assignment of the area definition was fixed. In previous analyses (Okamoto and Satoh, 2008, Satoh et al., 2012 and Matsumoto and Satoh, 2015), the catch and effort in the range of 10-20W and 10-20S was wrongly assigned to the area 2 instead of the area 3 (Figure 2), and the missing catch and effort had occurred in the range for 10-20W and 20-40S, which accounted from 0.0 to 1.7% of the total effort (hooks), and from 0.0 to 0.2% of the total yellowfin catch in number. The misspecification of the effort criteria for excluding data for GLM analysis was fixed. For quarterly index the criteria of 5,000 hook per a stratum had been used in the previous three studies instead of 10,000 hooks which was described in the text. For the explanation quarterly CPUE index, in previous studies analyses there was certain error for the explanation of the model in regards to a series of latitudinal and longitudinal related variables (Lat-Lon, Lat 2 -Lon and Lat-Lon 2 ), which were the products of two or three variables and were included as main effect, however in the previous documents there were explained as interaction variables instead. 3. Results and discussion Annual CPUEs Annual CPUEs in number and weight base standardized by GLM were shown in Figure 3 in relative scale with nominal CPUE. In the relative scaled CPUE, average CPUE during the period analyzed (1965 (1970)-2014) was regarded as 1.0. ANOVA results were shown in Table 1 and histograms and QQ-plots of standardized residual were shown in Figure 4. Distributions of the standard residual did not show remarkable difference from the normal distribution for both analyses. During the period analyzed, standardized CPUEs for number was divided into four periods according to its level of CPUE (before 1979 (high), (middle), (low) and after 2006 (low to middle)), whose average of standardized annual CPUEs of each group were significant different (Number; F (3, 46) = 47.38, P< 0.001, post hoc HSD test, P < 0.05 for all combinations of the periods. Weight; F (3, 41) = 40.59, P< 0.001, post hoc HSD test, P < 0.05 for all combinations of the periods., Appendix Figure 3). The weight base index also showed similar trend. The trends of these standardized CPUEs of previous studies and the present one were almost identical even though the fixation of errors (Figure 5). 272

4 Quarterly CPUEs Trends of quarterly standardized and nominal CPUEs in number for all Atlantic and those divided into two areas were shown in Figure 6. Results of ANOVA and distributions of the standardized residual in each analysis were shown in Table 2 and Figure 7, respectively. In both areas and whole Atlantic, seasonal oscillation in standardized CPUE was observed. The historical changes of CPUEs of last assessment and this study were quite similar (Figure 8). References Anonymous Annual Report of Japan. ICCAT ANN-022/ p. Okamoto H Japanese longline CPUE for bigeye tuna standardized for two area definitions in the Atlantic Ocean from 1961 up to SCRS/2007/067; Collect. Vol. Sci. Pap. ICCAT, 62(2): Okamoto, H. and K. Satoh Japanese longline CPUE for yellowfin tuna (Thunnus albacares) in the Atlantic Ocean standardized using GLM up to SCRS/2008/108; Collect. Vol. Sci. Pap. ICCAT, 64(3): Matsumoto, T., and K. Satoh Japanese longline CPUE for yellowfin tuna (Thunnus albacares) in the Atlantic Ocean standardized using GLM up to SCRS/2014/081; Collect. Vol. Sci. Pap. ICCAT, 71(1): Satoh, K., H. Okamoto, and H. Ijima. (2012): Japanese longline CPUE for yellowfin tuna (Thunnus albacares) in the Atlantic Ocean using GLM up to SCRS/2011/128; Collect. Vol. Sci. Pap. ICCAT, 68(2): Shono, H. and M. Ogura. (1999): The standardized skipjack CPUE including the effect of searching devices, of the Japanese distant water pole and line fishery in the Western Central Pacific Ocean. SCRS/1999/59; Collect. Vol. Sci. Pap. ICCAT, 51(1):

5 Table 1. ANOVA table for annual CPUE in weight (left) and number (right) standardized by log-normal error structured model. The area is defined in figure 2 in this report. CV, the coefficient of variation, which describes the amount of variation in the population, is 100 times the standard deviation estimate of the dependent variable (CPUE), Root MSE (Mean Square for Error), divided by the Mean. Weight Number Year Year Source DF Type III SS Mean Square F Value Pr > F R-Square= Source DF Type III SS Mean Square F Value Pr > F R-Square= Model < Model < Error CV = Error CV = Corrected Total Corrected Total year <.0001 year <.0001 month <.0001 month <.0001 area <.0001 area <.0001 CNHBF <.0001 CNHBF <.0001 sst <.0001 sst <.0001 sst <.0001 sst <.0001 sst <.0001 sst <.0001 main main bran bran <.0001 year*area <.0001 year*area <.0001 year*month <.0001 year*month <.0001 area*month <.0001 area*month <.0001 area*cnhbf <.0001 area*cnhbf <.0001 sst*month <.0001 sst*month <.0001 sst*area <.0001 sst*area <.0001 sst*cnhbf <.0001 sst*cnhbf <.0001 main*cnhbf <.0001 main*cnhbf <.0001 bran*cnhbf bran*cnhbf 274

6 Table 2. ANOVA table for quarterly CPUE standardized by log-normal error structured model for each of two areas (west and east), which are defined in Figure 1 in this report. CV, the coefficient of variation, which describes the amount of variation in the population, is 100 times the standard deviation estimate of the dependent variable (CPUE), Root MSE (Mean Square for Error), divided by the Mean. WEST EAST ALL Source DF Type III SS Mean Square F Value Pr > F R-Square= Source DF Type III SS Mean Mean F Value Pr > F R-Square= Source DF Type III SS Square Square F Value Pr > F R-Square= Model < Model < Model < Error CV= Error CV= Error CV= Corrected Corrected Corrected Total Total Total year <.0001 year <.0001 year <.0001 quarter <.0001 quarter <.0001 quarter <.0001 lat <.0001 lat <.0001 lat <.0001 lon <.0001 lon <.0001 lon <.0001 lt lt <.0001 lt <.0001 ltln <.0001 ltln <.0001 ltln ln <.0001 ln <.0001 ln <.0001 lt3 lt <.0001 lt lt2ln lt2ln <.0001 lt2ln <.0001 ltln <.0001 ltln ltln <.0001 ln <.0001 ln <.0001 ln <.0001 CNHBF <.0001 CNHBF <.0001 CNHBF <.0001 sst <.0001 sst <.0001 sst sst <.0001 sst <.0001 sst sst3 sst <.0001 sst main main <.0001 main <.0001 bran bran bran <.0001 year*quarter <.0001 year*quarter <.0001 year*quarter <.0001 sst*quarter <.0001 sst*quarter <.0001 sst*quarter <.0001 sst*cnhbf <.0001 sst*cnhbf <.0001 sst*cnhbf <.0001 main*cnhbf main*cnhbf <.0001 main*cnhbf <.0001 bran*cnhbf bran*cnhbf bran*cnhbf lat*quarter <.0001 lat*quarter <.0001 lat*quarter <.0001 lt2*quarter <.0001 lt2*quarter <.0001 lt2*quarter <.0001 lt3*quarter <.0001 lt3*quarter <.0001 lt3*quarter lon*quarter <.0001 lon*quarter <.0001 lon*quarter <.0001 ln2*quarter <.0001 ln2*quarter <.0001 ln2*quarter <.0001 ln3*quarter <.0001 ln3*quarter <.0001 ln3*quarter <.0001 lat*cnhbf <.0001 lat*cnhbf <.0001 lat*cnhbf <.0001 lt2*cnhbf <.0001 lt2*cnhbf <.0001 lt2*cnhbf <.0001 lt3*cnhbf <.0001 lt3*cnhbf <.0001 lt3*cnhbf <.0001 lon*cnhbf <.0001 lon*cnhbf <.0001 lon*cnhbf ln2*cnhbf <.0001 ln2*cnhbf <.0001 ln2*cnhbf <.0001 ln3*cnhbf <.0001 ln3*cnhbf <.0001 ln3*cnhbf <.0001 lat*sst <.0001 lat*sst <.0001 lat*sst <.0001 lt2*sst lt2*sst <.0001 lt2*sst <.0001 lt3*sst lt3*sst <.0001 lt3*sst sst*lt lon*sst <.0001 lon*sst <.0001 ln2*sst <.0001 ln2*sst <.0001 ln2*sst ln3*sst <.0001 ln3*sst <.0001 ln3*sst <

7 Figure 1. Area definition (west and east are divided by pink dashed line) which is used in yellowfin stock assessment using Multifan-CL (from Figure 2. Sub-area definitions used for Japanese longline CPUE standardization, which is the same as that in last yellowfin assessment (Okamoto and Satoh, 2008; Satoh et al., 2012; Matsumoto and Satoh, 2015). However, in these previous studies the catch and effort in the range of 10-20W and 10-20S was wrongly assigned to the area 2 instead of the area 3, and the missing catch and effort had occurred in the range for 10-20W and 20-40S in the CPUE standardization program. 276

8 Relative CPUE in number number standardized Year Relative CPUE in weight weight standardized Year Figure 3. Standardized (solid line) and nominal (open circle) annual CPUE in number (top) and weight (bottom) expressed in relative scale in which the average from 1965 (1970) to 2014 is 1.0. The red lines present the average of the standardized CPUE for each period (before 1979, , and after 2006).. 277

9 Figure 4. Overall histogram and QQ-plot of standardized residuals from the GLM analyses for annual CPUEs in number (upper) and weight (bottom) applying the final model in this study. 278

10 3 number_2014 number_2016 Relative CPUE Year 3 weight_2014 weight_2016 Relative CPUE Year Figure 5. Comparison of standardized CPUE between last one of SCRS tropical tuna meeting in 2014 (dashed line) and this study (solid line) in number (upper) and weight (bottom) base. 279

11 relative CPUE relative CPUE relative CPUE 6 4 standardized (west) nominal (west) 2 0 year-quarter 6 4 standardized (east) nominal (east) 2 0 year-quarter 6 4 standardized (all) nominal (all) 2 0 year-quarter Figure 6. Standardized (solid line) and nominal (open circle) quarterly CPUEs in number for west, east and all Atlantic Ocean in relative scale in which the average from 1965 to 2014 is

12 Figure 7. Overall histogram and QQ-plot of standard residuals from the GLM analyses for quarterly CPUE in west (top), east (middle) and all (bottom) Atlantic Ocean of the final model in this study. 281

13 Relative CPUE Relative CPUE Relative CPUE 6 west_2014 west_ east_2014 east_ all_2014 all_ Figure 8. Comparison of standardized CPUE between last one of SCRS tropical tuna meeting in 2014 (dashed line) and this study (solid line) in west (upper), east (middle) and all (bottom) Atlantic Ocean. 282

14 Appendix Figure 1. Geographical distribution of fishing effort for Japanese longline fishery in recent years. 283

15 Appendix Figure 2 Geographical distribution of yellowfin catch by Japanese longline fishery in recent years. 284

16 Appendix Figure 3. Differences of distribution of average standardized annual CPUE (natural log transformed real scale) in number (upper) and in weight (bottom) of yellowfin tuna caught by Japanese longline in the Atlantic Ocean among periods (before 1979, , and after

17 Appendix Table 1. Nominal and standardize CPUE in number (number of fish per 1000 hooks) in real scale and relative scale in which the average from 1965 to 2014 is 1.0. The point in real scale is calculated by exp (lsmean)-mean/10, the upper is calculated as exp (lsmean+1.96*standard error)-mean/10, the lower is calculated by exp (lsmean-1.96 standard error)-mean/10. The lsmean is least square means of year effect. These values are conditioned by the area effect as mentioned in the text. CPUE in number nominal standardized Year relative real scale relative real scale scale upper point lower scale

18 Appendix Table 1 (continue) CPUE in number nominal standardized Year relative real scale relative real scale scale upper point lower scale

19 Appendix Table 2. Nominal and standardize CPUE in weight (kg of fish per 1000 hooks) in real scale and relative scale in which the average from 1970 to 2014 is 1.0. The point in real scale is calculated by exp (lsmean)-mean/10, the upper is calculated as exp (lsmean+1.96*standard error)-mean/10, the lower is calculated by exp (lsmean-1.96 standard error)-mean/10. The lsmean is least square means of year effect. These values are conditioned by the area effect as mentioned in the text. Year real scale nominal CPUE in weight standardized relative real scale scale upper point lower relative scale

20 Appendix Table 2 (continue) CPUE in weight nominal standardized Year real relative real scale relative scale scale upper point lower scale