SCRS/2003/061 Col. Vol. Sci. Pap. ICCAT, 56(2): (2004)

SCRS/2003/061 Col. Vol. Sci. Pap. ICCAT, 56(2): 676-685 (2004) UPDATING STANDARDIZED CATCH RATES FOR YELLOWFIN TUNA (Thunnus albacares) IN THE GULF OF MEXICO LONGLINE FISHERY FOR 1992-2002 BASED UPON OBSERVER PROGRAMS FROM MEXICO AND THE UNITED STATES Craig A. Brown 1, Rubén Urbina Pastor 2, and Rafael Solana Sansores SUMMARY Abundance indices for yellowfin tuna (Thunnus albacares) in the Gulf of Mexico for the period 1992-2002 were estimated using data obtained through pelagic longline observer programs conducted by Mexico and the United States. Individual longline set catch per unit effort data, collected by scientific observers, were analyzed to assess effects of environmental factors such as sea surface temperature and depth, time-area factors, and fishery factors such as bait and fleet. Standardized catch rates were estimated through generalized linear models by applying a Poisson error distribution assumption. A stepwise approach was used to quantify the relative importance of the main factors explaining the variance in catch rates. Sea surface temperature, year, area fished, time of set start, and quarter were the factors included in the final model. RÉSUMÉ Les indices d abondance pour l albacore (Thunnus albacares) en provenance du Golfe du Mexique pour la période 1992-2002 ont été estimés en utilisant des données obtenues au moyen des programmes d observateurs de palangre pélagique menés par le Mexique et les Etats-Unis. Les données de capture par unité d effort de chaque opération individuelle de palangre, recueillies par des observateurs scientifiques, ont été analysées pour évaluer les effets de facteurs environnementaux, comme la température de surface de la mer, la profondeur, les facteurs spatiotemporels, et de facteurs halieutiques, comme l appât et la flottille. Les taux de capture standardisés ont été estimés au moyen de modèles linéaires généralisés en appliquant un postulat de distribution d erreur de type Poisson. Une approche pas-à-pas a été utilisée pour quantifier l importance relative des principaux facteurs qui expliquent la variation dans les taux de capture. Le modèle définitif a inclus les facteurs : température de surface de la mer, année, zone de pêche, heure du début de l opération et trimestre. RESUMEN Se estimaron los índices de abundancia para el rabil (Thunnus albacares) en el Golfo de México para el periodo 1992-2002, utilizando datos obtenidos mediante los programas de observadores de palangre pelágico llevados a cabo por México y Estados Unidos. Los datos de captura por unidad de esfuerzo de cada lance individual de palangre, recopilados por observadores científicos, fueron analizados para evaluar los efectos de factores medioambientales como la temperatura de la superficie del mar, profundidad, factores espaciotemporales, y de factores de la pesquería como el cebo y la flota. Las tasas de captura estandarizadas se estimaron mediante modelos lineales generalizados aplicando un supuesto de distribución de error Poisson. Se utilizó un enfoque paso a paso para cuantificar la importancia relativa de los principales factores que explican la varianza en las tasas de captura KEYWORDS Catch/effort, Abundance, Longline, Fish catch statistics, Observer data collection, Multivariate analyses, Stock assessments, Catch rate standardization, Generalized linear model, Pelagic fisheries. 1 U.S. Department of Commerce National Marine Fisheries Service Southeast Fisheries Science Center, 75 Virginia Beach Drive, Miami, Florida 33149 U.S.A. email: Craig.Brown@noaa.gov 2 Depto. de Estadística e Informática de la Dirección General de Investigación para el Desarrollo Sustentable Instituto Nacional de la Pesca/SAGARPA Av. Pitágoras 1320 Col. Sta. Cruz Atoyac 3er. Piso México D.F., 03310 México email: rurbina@inp.semarnat.gob.mx 676

1. Introduction 1.1 The fisheries Longline fisheries for yellowfin tuna in the Gulf of Mexico are currently conducted by fleets from two nations: the U.S.A. and Mexico. The U. S. fishery can be divided into two phases. First, an increase in catches from the beginning of operations (1984) up to a maximum of 7,500 t (150,581 fish) in 1988, when the U.S. longline fishery consisted of 350-400 vessels (Russell 1992). It is believed that this increase was due in part to the transition towards using live bait (Browder et al. 1990). Second, catches and number of vessels both decreased, with a slight increase in 1992. Three phases can be distinguished in the catch series of the Mexican fishery: first, an increase to 772 t (18,825 fish), caught by 16 vessels, followed by a decrease till the ceasing of operations in 1988. Annual average catch (1982-1987) was 437 t. During this first period Japanese-style longlines and dead bait were used in the fishery. The fleet was heterogeneous in terms of vessel dimensions and fishing power. The second period (1989-1991) was characterized by low yield, with an annual average catch of 71 t. The fleet has been homogeneous since then, using American-style monofilament longline gear, often with live bait. The most recent period is characterized by an increasing trend in catches with an annual average (1992-2001) of 996 t. The pelagic longline used by the Mexican fleet is a selective gear, with yellowfin tuna making up over 70% of the catches. Incidental bycatch consists of a variety of pelagic predatory fishes in variable proportions. The nominal catch rate of yellowfin tuna, expressed as the average number of fish caught per 1000 hooks (nominal CPUE), varies by season, with higher values occurring between May and August, and in November (González-Ania et al. 2001). The geographical distribution of nominal CPUE also varies owing to mesoscale movements of the resource, which are probably due in turn to trophic and reproductive causes. During spring and summer, intermediate and high values of nominal CPUE are found in the central, southern, and western portions of the Mexican EEZ, where fleet activity concentrates. In fall and winter, the fishing zone extends more to the north and east. During that time, the highest values of nominal CPUE are found off the state of Tamaulipas, to the north of the Yucatan peninsula, and near the center of the Mexican EEZ, but the values are quite lower than those from the spring-summer. 1.2 Catch rate standardization Catch and effort data are being increasingly used to construct indices of relative abundance for commercial and recreational fisheries (Hoey et al. 1996; Brown 2002; Goñi et al. 1999). However, nominal catch rates obtained from fishery statistics or observer programs require standardization to correct for the effect of factors not related to regional fish abundance but assumed to affect fish availability and vulnerability (Bigelow et al. 1999). The use of generalized linear models (GLMs) is becoming standard practice in catch rate standardization because this approach allows identification of the factors that influence catch rates and calculation of standardized abundance indices through the year effect (Goñi et al. 1999). A variety of error distributions of catch rate data have been assumed in GLM analyses (Lo et al. 1992; Bigelow et al. 1999; Goñi et al. 1999; Punt et al. 1999). Brown (2002) used a two-step GLM analysis based on a delta-lognormal model proposed by Lo et al. (1992) to model the proportion of trips that caught bluefin tuna (Thunnus thynnus) and the catch rate for the trips with catch in the Virginia-Massachusetts rod and reel fishery. González-Ania et al. (2001) modeled standardized indices of relative abundance of yellowfin tuna using observer data from the Gulf of Mexico longline fleets of the U.S. and Mexico, assuming that the errors in the dependent variable follow a Poisson distribution. This present study repeats this analysis, using the same assumption and incorporating more recent data to develop the model. 2. Material and methods Under Mexico s fisheries regulations, vessels fishing longline gear have observers on board during all fishing trips. The objective of the United States observer program is to achieve a representative, 5% sampling level of the fishing effort in the Gulf of Mexico and other fishing areas during each calendar quarter of the year, although that sampling level is frequently not reached due to problems such as difficulties in scheduling observers to serve aboard qualified vessels as conflicting priorities; actual sampling levels tend to range between 3% and 5%. Observers of both programs record detailed, set-specific data needed to describe the catch and effort of the longline fishery. A combined data set was created which included the variables common to both observer programs. For this analysis, data were available from the Mexican observer program for the period 1993-2001 and from the United States observer 677

program, for the period 1992-2002. It should be noted that the Mexican database is in the midst of changes designed to improve the structure and quality of the relational database; as a result, not all variables in the data were available for analysis. An exploratory analysis also revealed that the proportion of observed sets from the Mexican data with catches of zero yellowfin tuna was an anomalous 33% in 1999, whereas this proportion varied between 0% and 11% for the other years for both fleets (Tab. 1). No explanation was made for this dramatic difference in 1999; an assumption was made that 1999 contained many records with missing catch data, and the model was developed using only those sets (in all years) wherein at least one yellowfin tuna was caught. After additional exploratory analysis, factors which were considered as possible influences on catch rates included environmental factors such as mean sea surface temperature (MEANTEMP) and depth (SEADEPTH), time-area factors such as YEAR, QUARTER, fishing area (ZONE) and two measures of the time of day during which a set was initiated (SETSTART, 2AM-11AM or 11AM-2AM as well as DAYNIGHT, day or night starts), and FLEET (Mexico or United States). Mean sea surface temperature (MEANTEMP) was calculated for each set as the average of temperature data measured in situ at the beginning and end of gear setting for the U. S. fleet, and at the beginning and end of both gear setting and retrieval in the case of the Mexican fleet. Five fishing areas (ZONE) were defined based upon the latitude and longitude of the sets (Fig. 1). The additional fishery factors related to bait category (BAITCAT, fish or cephalopod) and bait status (BAITLD, live or dead), which had been investigated by González-Ania et al. (2001) but which did not remain in their final model, were not presently available for the Mexican data. Standardized indices were developed using generalized linear models. Catch rates were modeled as a function of the various factors. A Poisson regression was fitted to the number of yellowfin tuna per set (log link) and the natural log of the mean operating time for the set (in hours) was used as the offset term. The mean operating time of each set is intended to reflect the average time that each hook was in the water. It was calculated by dividing the total time to set out and to retrieve the gear by two, then adding the soak time during which the gear is left undisturbed. A forward stepwise approach was used to quantify the relative importance of the main factors explaining the variance in catch rates. First, a null model was run with no factors entered into the model. Results from the null model reflect the distribution of the nominal data. Each potential factor was then tested one at a time. The results were then ranked from greatest to least reduction in deviance per degree of freedom when compared to the null model. The factor which resulted in the greatest reduction in deviance per degree of freedom was then incorporated into the model, provided two conditions were met: 1) the effect of the factor was determined to be significant at least at the 5% level based on a Chi-Square test, and 2) the deviance per degree of freedom was reduced by at least 1% from the less complex model. This process was repeated, adding factors one at a time at each step, until no factor met the criteria for incorporation into the final model. After the model was complete based upon these main effects, two-way interactions between all factors (except year interactions) were also tested. All models in the stepwise approach were fitted with the SAS GENMOD procedure, whereas the final model was run with the SAS MIXED procedure (SAS Inst. Inc.). The relative indices of abundance by year were determined based upon the standardized year effects. 3. Results and discussion The stepwise construction of the model is shown in Table 2. The final model included the factors YEAR, MEANTEMP, SETSTART, QUARTER, ZONE, and the two-way interaction MEANTEMP*QUARTER, ranked by decreasing importance. The results of the relative abundance analyses for yellowfin tuna in the Gulf of Mexico (1992-2002) are shown in Table 3. Table 4 and Figure 2 show the final model and relative index trend. In order to investigate the possible effect of excluding observations with yellowfin catches of zero from the analysis, the final model was applied to a data set which included those observations. A comparison of the results is shown in Figure 3. These results are quite similar, with the exception of 1999. This suggests that most of the information is contained in the overall CPUE distribution; that is, the proportion of zeros appears to be dictated by the overall distribution, rather than being a primary indicator of abundance trends. These results lend credence to the assumption that the proportion of observations without catches of yellowfin in 1999 is in error. Spatial-temporal heterogeneity in the marine environment is believed to greatly affect the biology, dynamics, and availability of tuna stocks, as well as their vulnerability to fishing gear, thus introducing a source of variability in nominal catch rates. Sea surface temperature is one of the most important physical factors because it modifies the geographical and vertical aggregation patterns of tuna, through its effect on feeding, reproductive, and migratory behavior and body thermoregulation (Fonteneau 1998). Acoustic telemetry studies of the microscale movement 678

patterns of yellowfin tuna conducted since 1982 have demonstrated that this species occurs in the warm-water mixed surface layer and the upper part of the thermocline in tropical and subtropical seas, moving occasionally into colder waters below the thermocline, probably to feed or thermoregulate (Block et al. 1997, Bard 1998). The consistent occurrence of yellowfin tuna in this layer of homogeneous temperature allows us to assume that sea surface temperatures taken simultaneously to fishing operations, either measured in situ from fishing vessels as in the present study or from satellites, are representative of the thermal habitat available to this species. The importance of sea surface temperature as an explanatory variable in the present analysis suggests the potential utility of exploring other possible relationships between catch rate and mesoscale oceanic features, such as including thermal gradients in the model. Detection of a strong relationship between nominal CPUE and temperature was due at least in part to the space-time microscale approach used. In that respect, our results differ from those by Power and May (1991), who did not find any perceptible relationship between satellite observations of sea surface temperature and yellowfin tuna nominal CPUE in the longline fishery of the northwestern Gulf of Mexico. The relationship may have been masked by data limitations and uncertainty in the geographical locations of the sets in that study. It is possible, however, that the relationship found between nominal CPUE and temperature may not only be due to specific temperature preferences by yellowfin tuna, especially because over 99% of the sets analyzed occurred in waters with surface temperatures above 21º C, considered to be the thermal minimum for the distribution of this species (Fonteneau 1998). Variability in nominal catch rates can also be related to other physical, chemical, and biological processes or factors in the ocean (e.g. water transparency, circulation patterns, frontal zones, salinity, plankton, nekton), which together with temperature define the identity, structure, and interaction of water masses and can affect the availability of potential prey and the capture efficiency of tuna (Laurs et al. 1984, Bigelow et al. 1999). The significant effect of time of set start (SETSTART) on catch rate may be related to predatory behavior. Yellowfin tuna tracked by acoustic telemetry have displayed a behavioral pattern in which they rapidly ascend to the surface at dawn; a similar behavior has been observed in the bluefin tuna (Block et al. 1997). This behavioral pattern may likely increase the vulnerability of yellowfin tuna to fishing gear. Although the factors BAITCAT and BAITLD did not remain in the final model calculated by González-Ania et al. (2001), it is unfortunate that these factors could not be tested during the present study. Live bait has typically been used by U.S. fishermen in order to target yellowfin tuna in the Gulf of Mexico; the perception among fishermen is that this practice increases catch rates. However, the U.S. imposed a ban on the use of live bait in this fishery beginning in 2001, as a means of mitigating the bycatch of billfish. It is hoped that the factors related to bait will be available in future versions of the Mexican observer data, as these factors may prove important in the standardization of catch rates. The present study represents an important cooperative effort to merge fishery and environmental information from the complete distribution range of the yellowfin tuna in the Gulf of Mexico, estimating the best available relative abundance indices, and modeling recent trends in CPUE. In future analyses, results might be improved by adding other predictor variables to the model, extending the time series, and taking into account the size-age structure and sex of the catches. Variable transformation and use of generalized additive models (GAMs) may also increase the explanatory power of the model, due to the likely nonlinearity of many of the functional relationships between catch rate and the predictor variables. The authors stress the importance of maintaining this cooperative effort in the future, due to the importance of these results in the assessment of yellowfin tuna stocks as well as the potential for providing much additional information about the fishery and fish populations within the Gulf of Mexico. 4. References BARD, F.X., P. Bach et E. Josse. 1998. Habitat, écophysiologie des thons: Quoi de neuf depuis 15 ans?. Int. Comm. Conserv. Atl. Tunas, Col. Vol. Sci. Pap. 50(1): 319-341. BIGELOW, K.A., C.H. Boggs, and X. He. 1999. Environmental effects on swordfish and blue shark catch rates in the US North Pacific longline fishery. Fisheries Oceanography 8: 178-198. 679

BLOCK, B.A., J.E. Keen, B. Castillo, H. Dewar, E.V. Freund, D.J. Marcinek, R.W. Brill and C. Farwell. 1997. Environmental preferences of yellowfin tuna (Thunnus albacares) at the northern extent of its range. Marine Biology 130: 119-132. BROWDER, J.A., E.B. Brown and M.L. Parrack. 1990. The U.S. longline fishery for yellowfin tuna in perspective. ICCAT Working Document SCRS/89/76 (YYP/89/15). BROWN, C.A. 2002. Updated standardized catch rates of bluefin tuna, Thunnus thynnus, from the rod and reel/handline fishery off the northeast United States during 1980-2000. Int. Comm. Conserv. Atl. Tunas, Col. Vol. Sci. Pap. 54(2): 477-497. FONTENEAU, A. 1998. Introduction aux problèmes des relations thons-environnement dans l Atlantique. Int. Comm. Conserv. Atl. Tunas, Col. Vol. Sci. Pap. 50(1): 275-317. GONZALEZ-ANIA, L.V., P.A. Ulloa-Ramírez, D.W. Lee, C.J. Brown and C.A. Brown. 1998. Description of Gulf of Mexico longline fisheries based upon observer programs from Mexico and the United States. Int. Comm. Conserv. Atl. Tunas, Col. Vol. Sci. Pap. 48(3): 308-316. GONZÁLEZ-ANIA, L.V., C.A. Brown, E. Cortés. 2001. Standardized catch rates for yellowfin tuna (Thunnus albacares) in the 1992-1999 Gulf of Mexico longline fishery based upon observer programs from Mexico and the United States. Int. Comm. Conserv. Atl. Tunas, Col. Vol. Sci. Pap. 52: 222-237. GOÑI, R., F. Alvarez and S. Adlerstein. 1999. Application of generalized linear modeling to catch rate analysis of Western Mediterranean fisheries: the Castellón trawl fleet as a case study. Fisheries Research 42: 291-302. HOEY, J.J., J. Mejuto, J.M. Porter, H.H. Stone and Y. Uozomi. 1996. An updated biomass index of abundance for North Atlantic swordfish. ICCAT, SCRS/96/144, 9 pp. LAURS, R.M., P.C. Fiedler and D.R. Montgomery. 1984. Albacore tuna catch distributions relative to environmental features observed from satellites. Deep-Sea Research 31(9): 1085-1099. LO, N.C.H., L.D. Jacobson and J.L. Squire. 1992. Indices of relative abundance from fish spotter data based on deltalognormal models. Canadian Journal of Fisheries and Aquatic Sciences 49: 2515-2526. POWER, J.H. and L.N. May Jr. 1991. Satellite observed sea-surface temperatures and yellowfin tuna catch and effort in the Gulf of Mexico. Fishery Bulletin, U.S. 89(3): 429-439. PUNT, A.E., T.I. Walker, B.L. Taylor and F. Pribac. 1999. Standardization of catch and effort data in a spatiallystructured shark fishery. Fisheries Research 956: 1-17. RUSSELL, S.J. 1992. Shark bycatch in the northern Gulf of Mexico tuna longline fishery, 1988-91, with observations on the nearshore directed shark fishery. NOAA Technical Report NMFS 115: 19-29. 680

Table 1. Number of Observations by Year and Fleet, including proportion with zero catches. Fleet 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 no. pos. obs. 36 651 1266 748 264 474 328 1016 749 no. zeros 1 17 29 40 3 5 159 17 96 MEX % zeros 3% 3% 2% 5% 1% 1% 33% 2% 11% no. pos. obs. 37 175 101 146 58 105 49 129 145 98 110 no. zeros 0 5 4 6 6 2 4 1 2 11 4 USA % zeros 0% 3% 4% 4% 9% 2% 8% 1% 1% 10% 4% Table 2. Results of the stepwise procedure to develop the catch rate model. There are no explanatory factors in the base model. BASE 6684 77166.1 11.5449 176457.2 YEAR 6674 68518.8 10.2665 11.07 180780.9 8647.30 0.00000 MEANTEMP 6683 72998.1 10.9230 5.39 178541.2 4167.99 0.00000 SETSTART 6683 73207.5 10.9543 5.12 178436.5 3958.59 0.00000 QUARTER 6681 73239.7 10.9624 5.05 178420.4 3926.40 0.00000 ZONE 6680 73294.6 10.9722 4.96 178393.0 3871.52 0.00000 FLEET 6683 74003.8 11.0734 4.08 178038.4 3162.29 0.00000 SEADEPTH 6683 76802.1 11.4922 0.46 176639.3 364.04 0.00000 DAYNIGHT 6683 76841.7 11.4981 0.41 176619.4 324.40 0.00000 The explanatory factors in the base model are: YEAR BASE 6674 68518.8 10.2665 180780.9 MEANTEMP 6673 65307.3 9.7868 4.67 182386.7 3211.52 0.00000 QUARTER 6671 65363.8 9.7982 4.56 182358.4 3155.05 0.00000 SETSTART 6673 65461.0 9.8098 4.45 182309.8 3057.77 0.00000 ZONE 6670 65590.8 9.8337 4.22 182244.9 2927.96 0.00000 FLEET 6673 65867.4 9.8707 3.86 182106.6 2651.40 0.00000 DAYNIGHT 6673 68393.9 10.2493 0.17 180843.4 124.94 0.00000 SEADEPTH 6673 68420.2 10.2533 0.13 180830.2 98.59 0.00000 The explanatory factors in the base model are: YEAR MEANTEMP BASE 6673 65307.3 9.7868 182386.7 SETSTART 6672 62692.6 9.3964 3.99 183694.0 2614.66 0.00000 ZONE 6669 63051.8 9.4545 3.40 183514.4 2255.46 0.00000 FLEET 6672 63343.7 9.4940 2.99 183368.5 1963.58 0.00000 QUARTER 6670 63610.6 9.5368 2.55 183235.0 1696.68 0.00000 DAYNIGHT 6672 65120.4 9.7602 0.27 182480.1 186.93 0.00000 SEADEPTH 6672 65299.6 9.7871-0.00 182390.5 7.64 0.00570 The explanatory factors in the base model are: YEAR MEANTEMP SETSTART BASE 6672 62692.6 9.3964 183694.0 QUARTER 6669 60959.3 9.1407 2.72 184560.7 1733.36 0.00000 ZONE 6668 61231.0 9.1828 2.27 184424.8 1461.67 0.00000 FLEET 6671 61597.9 9.2337 1.73 184241.3 1094.69 0.00000 DAYNIGHT 6671 62671.3 9.3946 0.02 183704.7 21.37 0.00000 SEADEPTH 6671 62686.6 9.3969-0.01 183697.0 6.01 0.01425 The explanatory factors in the base model are: YEAR MEANTEMP SETSTART QUARTER BASE 6669 60959.3 9.1407 184560.7 ZONE 6665 59894.3 8.9864 1.69 185093.2 1064.98 0.00000 FLEET 6668 59990.2 8.9967 1.58 185045.2 969.11 0.00000 DAYNIGHT 6668 60920.4 9.1362 0.05 184580.1 38.85 0.00000 SEADEPTH 6668 60946.9 9.1402 0.01 184566.9 12.41 0.00043 The explanatory factors in the base model are: YEAR MEANTEMP SETSTART QUARTER ZONE BASE 6665 59894.3 8.9864 185093.2 FLEET 6664 59679.2 8.9555 0.34 185200.7 215.06 0.00000 SEADEPTH 6664 59742.7 8.9650 0.24 185168.9 151.57 0.00000 DAYNIGHT 6664 59852.7 8.9815 0.05 185114.0 41.58 0.00000 681

Table 2 cont. Results of the stepwise procedure to develop the catch rate model. The explanatory factors in the base model are: YEAR MEANTEMP SETSTART QUARTER ZONE BASE 6665 59894.3 8.9864 185093.2 MEANTEMP*QUARTER 6662 57937.5 8.6967 3.22 186071.6 1956.80 0.00000 MEANTEMP*ZONE 6661 59377.6 8.9142 0.80 185351.5 516.70 0.00000 QUARTER*ZONE 6653 59408.3 8.9296 0.63 185336.1 485.95 0.00000 QUARTER*FLEET 6661 59509.8 8.9341 0.58 185285.4 384.52 0.00000 MEANTEMP*FLEET 6664 59575.2 8.9399 0.52 185252.7 319.05 0.00000 QUARTER*SEADEPTH 6661 59550.7 8.9402 0.51 185264.9 343.54 0.00000 ZONE*SEADEPTH 6660 59554.4 8.9421 0.49 185263.1 339.84 0.00000 DAYNIGHT*FLEET 6660 59554.4 8.9421 0.49 185263.1 339.84 0.00000 DAYNIGHT*SEADEPTH 6660 59554.4 8.9421 0.49 185263.1 339.84 0.00000 FLEET*SEADEPTH 6660 59554.4 8.9421 0.49 185263.1 339.84 0.00000 SETSTART*FLEET 6663 59660.4 8.9540 0.36 185210.1 233.89 0.00000 ZONE*FLEET 6661 59649.4 8.9550 0.35 185215.6 244.90 0.00000 ZONE*DAYNIGHT 6660 59699.9 8.9639 0.25 185190.4 194.43 0.00000 SETSTART*SEADEPTH 6663 59742.7 8.9663 0.22 185169.0 151.58 0.00000 MEANTEMP*SEADEPTH 6664 59769.3 8.9690 0.19 185155.7 124.98 0.00000 QUARTER*DAYNIGHT 6661 59760.4 8.9717 0.16 185160.1 133.84 0.00000 SETSTART*DAYNIGHT 6663 59819.1 8.9778 0.10 185130.7 75.17 0.00000 MEANTEMP*DAYNIGHT 6664 59857.2 8.9822 0.05 185111.7 37.08 0.00000 MEANTEMP*SETSTART 6664 59878.9 8.9854 0.01 185100.8 15.37 0.00009 SETSTART*ZONE 6661 59862.2 8.9870-0.01 185109.2 32.07 0.00000 SETSTART*QUARTER 6662 59879.4 8.9882-0.02 185100.6 14.87 0.00193 The explanatory factors in the base model are: YEAR MEANTEMP SETSTART QUARTER ZONE MEANTEMP*QUARTER BASE 6662 57937.5 8.6967 186071.6 ZONE*SEADEPTH 6657 57525.1 8.6413 0.64 186277.8 412.40 0.00000 DAYNIGHT*FLEET 6657 57525.1 8.6413 0.64 186277.8 412.40 0.00000 DAYNIGHT*SEADEPTH 6657 57525.1 8.6413 0.64 186277.8 412.40 0.00000 FLEET*SEADEPTH 6657 57525.1 8.6413 0.64 186277.8 412.40 0.00000 QUARTER*FLEET 6658 57556.0 8.6446 0.60 186262.3 381.50 0.00000 MEANTEMP*FLEET 6661 57592.9 8.6463 0.58 186243.8 344.57 0.00000 QUARTER*SEADEPTH 6658 57572.0 8.6470 0.57 186254.3 365.47 0.00000 QUARTER*ZONE 6650 57527.7 8.6508 0.53 186276.5 409.80 0.00000 ZONE*FLEET 6658 57615.1 8.6535 0.50 186232.7 322.33 0.00000 SETSTART*FLEET 6660 57635.5 8.6540 0.49 186222.6 301.99 0.00000 SETSTART*SEADEPTH 6660 57709.0 8.6650 0.36 186185.8 228.48 0.00000 MEANTEMP*SEADEPTH 6661 57727.5 8.6665 0.35 186176.6 210.01 0.00000 MEANTEMP*ZONE 6658 57754.8 8.6745 0.26 186162.9 182.64 0.00000 ZONE*DAYNIGHT 6657 57747.7 8.6747 0.25 186166.4 189.74 0.00000 QUARTER*DAYNIGHT 6658 57807.7 8.6824 0.16 186136.4 129.75 0.00000 SETSTART*DAYNIGHT 6660 57860.2 8.6877 0.10 186110.2 77.28 0.00000 MEANTEMP*DAYNIGHT 6661 57895.5 8.6917 0.06 186092.6 41.98 0.00000 SETSTART*ZONE 6658 57894.4 8.6955 0.01 186093.1 43.10 0.00000 MEANTEMP*SETSTART 6661 57932.8 8.6973-0.01 186073.9 4.71 0.02996 SETSTART*QUARTER 6659 57929.4 8.6994-0.03 186075.6 8.07 0.04450 FINAL MODEL: YEAR+MEANTEMP+SETSTART+QUARTER+ZONE+MEANTEMP*ZONE %REDUCTION: percent difference in deviance/df between the newly included factor and the previous factor entered into the model; LOGLIKE: log likelihood; CHISQ: Pearson Chi-square statistic; PROBCHISQ: significance level of the Chi-square statistic. 682

Table 3. Results of final model fit. Class Levels Values year 11 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 setstart 2 11AM-2AM 2AM-11AM QUARTER 4 1 2 3 4 zone 5 1 2 3 4 5 Criteria For Assessing Goodness Of Fit Criterion DF Value Value/DF Deviance 6662 57937.4821 8.6967 Scaled Deviance 6662 57937.4821 8.6967 Pearson Chi-Square 6662 71552.0976 10.7403 Scaled Pearson X2 6662 71552.0976 10.7403 Log Likelihood 186071.5627 Analysis Of Parameter Estimates Standard Wald 95% Confidence Chi- Parameter DF Estimate Error Limits Square Pr > ChiSq Intercept 1-4.2347 0.1237-4.4772-3.9923 1171.89 <.0001 year 1992 1 0.7630 0.0594 0.6466 0.8794 164.93 <.0001 year 1993 1 0.5074 0.0421 0.4249 0.5900 145.26 <.0001 year 1994 1 0.8480 0.0391 0.7713 0.9247 469.42 <.0001 year 1995 1 0.4109 0.0390 0.3344 0.4873 110.90 <.0001 year 1996 1 0.1607 0.0397 0.0829 0.2385 16.38 <.0001 year 1997 1 0.4392 0.0401 0.3605 0.5179 119.65 <.0001 year 1998 1 0.2931 0.0403 0.2141 0.3721 52.91 <.0001 year 1999 1 0.5344 0.0399 0.4562 0.6127 179.04 <.0001 year 2000 1 0.1113 0.0394 0.0340 0.1886 7.97 0.0048 year 2001 1-0.0052 0.0403-0.0842 0.0738 0.02 0.8976 year 2002 0 0.0000 0.0000 0.0000 0.0000.. meantemp 1-0.0184 0.0043-0.0269-0.0098 17.88 <.0001 setstart 11AM-2AM 1-0.3547 0.0082-0.3708-0.3386 1862.33 <.0001 setstart 2AM-11AM 0 0.0000 0.0000 0.0000 0.0000.. QUARTER 1 1 0.5644 0.1962 0.1799 0.9489 8.28 0.0040 QUARTER 2 1-4.6980 0.1433-4.9790-4.4171 1074.28 <.0001 QUARTER 3 1-0.3471 0.1865-0.7127 0.0185 3.46 0.0628 QUARTER 4 0 0.0000 0.0000 0.0000 0.0000.. zone 1 1 0.4727 0.0189 0.4356 0.5098 623.70 <.0001 zone 2 1 0.3670 0.0194 0.3289 0.4050 357.01 <.0001 zone 3 1 0.1658 0.0203 0.1261 0.2055 67.01 <.0001 zone 4 1 0.2871 0.0356 0.2173 0.3569 65.03 <.0001 zone 5 0 0.0000 0.0000 0.0000 0.0000.. meantemp*quarter 1 1-0.0345 0.0079-0.0500-0.0190 19.01 <.0001 meantemp*quarter 2 1 0.1812 0.0053 0.1709 0.1916 1182.09 <.0001 meantemp*quarter 3 1 0.0189 0.0066 0.0060 0.0318 8.29 0.0040 meantemp*quarter 4 0 0.0000 0.0000 0.0000 0.0000.. Scale 0 1.0000 0.0000 1.0000 1.0000 NOTE: The scale parameter was held fixed. LR Statistics For Type 3 Analysis Chi- Source DF Square Pr > ChiSq year 10 6629.65 <.0001 meantemp 1 88.98 <.0001 setstart 1 1980.11 <.0001 QUARTER 3 1769.80 <.0001 zone 4 1159.63 <.0001 meantemp*quarter 3 1956.80 <.0001 Table 4 Relative Abundance Indices for yellowfin tuna. YEAR INDEX LCI* UCI* CV 1992 1.427 1.107 1.839 0.154 1993 1.105 0.983 1.242 0.071 1994 1.553 1.455 1.658 0.04 1995 1.003 0.942 1.068 0.038 1996 0.781 0.722 0.846 0.048 1997 1.032 0.946 1.126 0.053 1998 0.892 0.819 0.971 0.052 1999 1.135 1.047 1.231 0.049 2000 0.744 0.692 0.799 0.044 2001 0.662 0.609 0.719 0.051 2002 0.665 0.540 0.820 0.127 *Approximate 95% lower and upper confidence intervals. 683

Figure 1. Fishing areas defined for the GLM analyses and distribution of pelagic longline sets sampled by observer programs. 684

RELATIVE INDEX 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Standardized CPUE Nominal CPUE 19921993199419951996199719981999200020012002 YEAR Figure 2. Nominal CPUE trend and relative abundance indices (standardized CPUE) for yellowfin tuna with approximate 95% confidence intervals. (Yellowfin caught per set, offset: natural log of mean hours each hook is in the water, error distribution: Poisson). Model = YEAR+MEANTEMP+SETSTART+QUARTER+ZONE+MEANTEMP*QUARTER 2 1.8 1.6 1.4 including YFT catches of 0 excluding YFT catches of 0 Relative Index 1.2 1 0.8 0.6 0.4 0.2 0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 YEAR Figure 3. Comparison of results when the final model (YEAR+MEANTEMP+SETSTART+QUARTER+ ZONE+MEANTEMP*QUARTER) is applied to data which include or exclude observations where the catch of yellowfin tuna = zero. 685