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SCRS/2014/091 Collect. Vol. Sci. Pap. ICCAT, 71(1): 374-389 (2015)

STANDARDIZED CATCH INDICES OF SKIJACK TUNA, KATSUWONUS PELAMIS, FROM THE UNITED STATES

PELAGIC LONGLINE OBSERVER PROGRAM

Matthew V. Lauretta1 and John F. Walter1

SUMMARY Catch and effort data from the United States pelagic longline observer program in the Atlantic Ocean and Gulf of Mexico were analyzed to estimate indices of relative abundance for Skipjack for the period 1992 to 2013. A delta-lognormal, generalized linear model was used to evaluate multiple factors, including year, season, fishing area, target species, and number of light sticks. Significant fixed factors included year, fishing area, and target species in the binomial (logit link) regressions of the presence of skipjack, with year*fishing_area interaction modeled as a random effect. Significant factors in the Gaussian (identity link) regressions of the loge-transformed positive catch rates included year, fishing area, and target species with year*fishing_area interaction modeled as a random effect. Standardized abundance indices are presented along with estimates of 95% confidence intervals of the predicted means.

RÉSUMÉ

Les données de prise et d'effort des observateurs palangriers pélagiques des États-Unis opérant dans l’océan Atlantique et le golfe du Mexique ont été analysées pour estimer les indices de l'abondance relative du listao de la période courant de 1992 à 2013. Un modèle linéaire généralisé delta-lognormal a été utilisé pour évaluer de multiples facteurs, dont l'année, la saison, la zone de pêche, les espèces cibles et le nombre de baguettes lumineuses. Les facteurs fixes significatifs incluaient l'année, la zone de pêche et les espèces cibles dans les régressions binomiales (lien logarithmique) de la présence des listaos, l'interaction année*zone de pêche étant modélisée comme effet aléatoire. Des facteurs significatifs dans les régressions gaussiennes (lien d'identité) des taux de capture positive traités par transformation logarithmique incluaient l'année, la zone de pêche et les espèces cibles, l'interaction année*zone de pêche étant modélisée comme effet aléatoire. Des indices d’abondance standardisés sont présentés avec des estimations d'intervalles de confiance de 95% des moyennes prédites.

RESUMEN

Se analizan los datos de captura y esfuerzo del programa de observadores de palangre pelágico de Estados Unidos que opera en el Atlántico y golfo de México para estimar los índices de abundancia relativa para el listado para el periodo desde 1992 a 2013. Se utilizó un modelo lineal generalizado delta lognormal para evaluar múltiples factores que incluían año, temporada, zona de pesca, especie objetivo y número de bastones de luz. Los factores significativos fijos incluían año, zona de pesca y especie objetivo en las regresiones binomiales (vínculo logarítmico) de la presencia de listado con una interacción año*zona de pesca modelada como efecto aleatorio. Los factores significativos en las regresiones gaussianas (vínculo de identidad) de las tasas de captura positivas transformadas logarítmicamente incluían año, zona de pesca y especie objetivo con la interacción año*zona de pesca modelada como un efecto aleatorio. Se presentan los índices de abundancia estandarizados junto con estimaciones de los intervalos de confianza del 95% de las medias predichas.

KEYWORDS

Catch/effort, Tuna fisheries, Commercial fishing, Longlining, Skipjack

1 U.S. National Marine Fisheries Service, Southeast Fisheries Center, Sustainable Fisheries Division, 75 Virginia Beach Drive, Miami, FL, 33149-1099, USA. E-mail: [email protected]

374

1. Introduction The United States pelagic longline fishery has operated in the Atlantic Ocean and Gulf of Mexico since the 1960s, primarily targeting swordfish and tunas, and occasionally sharks. Regulatory requirements to document longline fishing activities in vessel logbooks have been in effect since 1987, which provide a record of fishing effort and catch for the fleet. An observer program was initiated in 1992, with a target coverage of 5% of the deployed longline sets which was later expanded to 8% target coverage. Skipjack (Katsuwonus pelamis) are not targeted by the U.S. longline fleet, but are caught and retained for commercial sale in addition to target species. The observer program documents both retained and discarded Skipjack, and therefore the data represent a measure of relative abundance. Data from the pelagic longline observer program were analyzed to estimate standardized indices of relative abundance for skipjack. This report documents the analytical methods and provides standardized indices for the period 1992 to 2013. 2. Material and methods The U.S. Pelagic Longline Observer Program (program information can be found at http://www.sefsc.noaa.gov/fisheries/observers/pelagic.htm) database was examined to identify appropriate sample strata and evaluate fishing areas and seasons in which skipjack were frequently encountered. Table 1 lists the number of positive observations by strata, which was used to determine appropriate sample strata to include in the analysis. The following section describes the determined strata and data exclusions. 2.1 Data exclusions The following filters were applied to the U.S. pelagic longline logbook database for the analysis of skipjack standardized catch rates:

− Years 1992 to 2013 were assessed. − Data from regions with closed area regulation in effect at any time were excluded for the entire time

series. − Fishing areas included the Gulf of Mexico, Florida East Coast, South-Atlantic Bight, Mid-Atlantic

Bight, and Northeast Coastal Waters; skipjack were infrequently sampled or observed in other fishing areas, or area closures were in effect during the time series.

− Records with the # hooks less than 100 or the # hooks less than the # of fish caught were excluded − Bottom longline sets were excluded. − Longline sets that targeted sharks were excluded; Skipjack were not observed in these samples.

2.2 Data classifications

The following classifications were made to define

factors: Class variables:

− Year: 1992 to 2013 − Season: Jan-Mar, Apr-Jun, Jul-Sep, Oct-Dec − Fishing Area: Gulf of Mexico (22 to 30 latitude, -82 to -98 longitude), Florida East Coast (22 to 30

latitude,-71 to -82 longitude), South Atlantic Bight (30 to 35 latitude, -71 to -82 longitude), Mid-Atlantic Bight (35 to 43 latitude, -71 to -78 longitude), Northeast Coastal Atlantic (35 to 45 latitude, -65 to -71 longitude and 35 to 50 latitude, -60 to -65 longitude)

− Target species: the recorded species targeted by the longliner − Light sticks: the ratio of the number of light sticks to hooks on the longline set binned into categories

Continuous variables:

− Temperature

375

http://www.sefsc.noaa.gov/fisheries/observers/pelagic.htm

2.3 Generalized linear models The Lo method (Lo et al. 1992) was applied to develop abundance indices, with separate analyses conducted on the proportions of longline sets that captured at least one skipjack (positive set), and the catch rates of the positive sets. An individual longline set was considered a sample unit with fishing effort measured as number of hooks. The catch rate of skipjack was modeled as the number of fish per 1,000 hooks. The procedure involved the standardization of yearly changes in proportion positives and catch rates of positive sets, accounting for covariates which have a significant effect on either response variable. Factors considered included year, season (Jan-Mar, Apr-Jun, Jul-Sep, Oct-Dec), area (Gulf of Mexico, South Atlantic bight, Mid-Atlantic bight, northeast coastal Atlantic), target species, and the number of light sticks. A binomial generalized linear model (GLM) with logit link was applied to the presence/absence of skipjack on a longline set with the estimated probability of success a function of fixed factors, including year, season, fishing area, target species, or light stick category. Similarly, the loge-transformed catch rate on positive sets was modeled as a function of similar fixed factors with an assumed Gaussian distribution, using an identity linear link. Forward model selection was used to determine the set of significant fixed factors to include in the GLM. First, a Null model was run, in which no factors were entered in the model (intercept only model). These results reflect the distribution of the nominal data. Each potential factor was then tested iteratively. The results were 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 the 5% probability based upon a Chi-Square test, and 2) the deviance per degree of freedom was reduced by at least 3% 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. Next interactions were modeled and tested iteratively and those found to be significant were modeled as random effects. The final model included fixed single factors and random interaction terms. The product of the standardized proportion positives and the standardized positive catch rates was used to calculate overall standardized catch rates. A relative index of abundance was obtained by dividing the standardized catch rates in each series by the mean value across the time series. Upper and lower limits were also scaled to the index mean. 3. Results and discussion 3.1 Geographic overage The distributions of observed longline sets used in the analysis are shown in Figure 1. Data are in number of sets per 5 degree longitude by 5 degree latitude spatial cell. In general, the geographic coverage of the data included the Gulf of Mexico and U.S. eastern coast. Notable trends in the geographical distribution of sets include the increase in observed sets over time, particularly in the Gulf of Mexico. Figure 2 shows the spatial distribution of Skipjack catches. Skipjack are caught in the U.S. pelagic longline fishery along the U.S. eastern coast and Gulf of Mexico and an increase in the catch of skipjack along the northeast coast was observed (Figure 2). 3.2 Time series continuity A continuous time series was modeled from 1992 to 2013, although it is important to note the regulatory change in fishing gear to the use of circle hooks exclusively in 2004, while J hooks were commonly used prior to the regulation. Further regulation to use weak circle hooks in the Gulf of Mexico was implemented in 2012. The effects of these regulations on the catchability of skipjack to longlines is not known, and the potential bias in standardized indices was not assessed.

376

3.3 Observed trends and standardized indices In general, both the observed proportion positive and mean CPUE demonstrated an increasing trend across the time series (Figure 3). Observed mean positive catch rates varied considerably across most strata (Figure 4). Target species, year, season, and fishing area were identified as significant factors in regression models of Skipjack presence/absence and target species, year, and season were significant factors in the positive catch rate regression (Tables 2 and 3). Year*season interactions were also significant in both models, and year*target interaction was significant in the positive catch model (Tables 2 and 3). The final generalized linear model were:

𝑝𝑝1 − 𝑝𝑝

= 𝑒𝑒𝛽𝛽0+𝛽𝛽𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌+𝛽𝛽𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌+𝛽𝛽𝑇𝑇𝑌𝑌𝑌𝑌𝑇𝑇𝑌𝑌𝑇𝑇_𝑆𝑆𝑆𝑆𝑌𝑌𝑆𝑆𝑆𝑆𝑌𝑌𝑆𝑆+𝜀𝜀𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌∗𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌 + 𝜀𝜀

p = probability of success β0 = intercept βYEAR = Year specific coefficient βAREA = Fishing area specific coefficient βTARGET = Target species specific coefficient εYEAR*AREA = Year-area random interaction effect ε = error term and

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 = 𝑒𝑒𝛽𝛽0+𝛽𝛽𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌+𝛽𝛽𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌+𝛽𝛽𝑇𝑇𝑌𝑌𝑌𝑌𝑇𝑇𝑌𝑌𝑇𝑇_𝑆𝑆𝑆𝑆𝑌𝑌𝑆𝑆𝑆𝑆𝑌𝑌𝑆𝑆+𝜀𝜀𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌∗𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌 + 𝜀𝜀 where CPUE = catch rate on positive sets β0 = intercept βYEAR = Year specific coefficient βAREA = Fishing area specific coefficient βTARGET = Target species specific coefficient εYEAR*AREA = Year-area random interaction effect ε = error term

Descriptive statistics and annual standardized indices for the time series model are presented in Table 4. In general, the normal probability model demonstrated a lack of fit to the annual positive catch rates (Figure 5). Comparison among the nominal catch rate time series and the standardization model estimates are presented in Figure 6. Trends in model predicted means were similar to the observed mean CPUE, with the exception of 2006, when the observed mean was greater than the GLM predicted mean and 2011 which demonstrated an opposite trend. 3.4 Size structure of skipjack catches The majority of skipjack caught on pelagic longlines ranged between 30 and 100 cm, with a mode at approximately 70 to 80 cm for most years (Figure 7); however, a bimodal distribution was apparent in some years. The mean size of skipjack ranged from approximately 66 cm to 72 cm between 2001 and 2013 with a general cyclical trend but no indication of a long-term declining or increasing trend in mean over the entire time series (Figure 8). Reference Lo, N.C., Jackson, L.D., and Squire, J.L. 1992. Indices of relative abundance form fish spotter data based on delta-lognormal models. Can. J. Fish.Aquat. Sci. 49: 2515-2526.

377

Table 1. Number of positive observations of skipjack per factor strata. Shaded columns shows strata that were filtered out of the index standardization due to low frequency of occurrence of Skipjack or inconsistent sampling across years.

Season Fishing Area Target Lightstick Category Year Jan-Mar Apr-Jun Jul-Sep Oct-Dec CAR FEC GOM MAB NCA NEC SAB SAR TUN TUS UNK BET DOL MIX SHX SWO TUN YFT 0-25 25-50 50-100 100+ 1992 3 2 4 0 0 8 1 0 0 2 0 7 7 0 2 0 1993 2 13 4 8 2 0 19 2 0 3 0 1 4 0 2 21 19 6 0 2 1994 6 7 2 13 1 0 20 2 2 2 1 3 0 6 19 19 2 5 2 1995 6 10 7 18 0 0 34 0 4 3 0 0 0 1 0 4 2 34 30 4 2 5 1996 6 3 8 8 0 0 18 0 2 1 1 2 1 0 1 8 0 16 9 8 3 5 1997 5 10 3 9 0 0 19 0 1 4 3 0 0 0 2 0 4 2 2 17 17 8 0 2 1998 0 6 8 8 0 1 14 0 0 6 1 0 0 6 0 2 3 11 14 6 0 2 1999 8 9 12 20 0 0 45 2 0 0 2 0 0 0 0 0 0 1 2 6 40 41 6 2 0 2000 2 15 9 8 0 29 1 0 0 4 0 2 3 3 8 18 24 6 2 2 2001 2 14 11 5 0 1 27 1 0 0 3 2 4 3 7 16 20 7 5 0 2002 2 0 3 4 0 0 8 1 0 0 0 0 5 0 0 4 4 3 2 0 2003 5 9 15 15 0 1 30 10 0 1 1 1 0 0 25 6 2 11 14 19 10 1 2004 5 8 23 21 0 1 51 2 1 2 0 0 0 13 0 4 6 34 40 8 7 2 2005 22 16 5 19 0 0 59 2 0 0 0 1 22 6 1 33 32 11 15 4 2006 6 23 13 31 1 69 2 0 0 0 1 0 30 0 1 3 39 40 11 21 1 2007 9 77 27 28 0 3 129 2 0 2 0 5 0 37 5 2 97 91 23 24 3 2008 4 91 17 37 1 140 6 1 1 0 0 1 83 0 1 64 87 38 23 1 2009 21 95 52 46 1 185 18 5 3 1 1 6 110 3 4 91 114 73 23 4 2010 24 49 7 4 0 3 69 4 2 3 2 1 0 0 28 0 5 4 47 68 6 3 7 2011 2 62 36 42 3 105 18 8 6 1 1 5 41 0 8 13 75 102 23 6 11 2012 18 68 7 23 4 96 4 2 2 3 5 1 50 10 3 52 54 37 13 12 2013 18 105 22 31 0 4 152 11 0 3 3 0 2 1 1 54 9 38 74 115 45 6 10

378

Table 2. Forward model selection of the binomial regression (logit link) of presence/absence of Skipjack on observed U.S. pelagic longline sets.

n Deviance Deviance/df % Reduction LogLikelihood

NULL (INTERCEPT ONLY) 12653 9284.2 0.73 -4642 TARGET 12648 8150.3 0.64 12.2 -4075 LIGHTS 12650 8223.1 0.65 11.4 -4112 FISHING_AREA 12649 8452.9 0.67 8.9 -4226 TEMP_C 12199 8708.8 0.71 2.4 -4354 YEAR 12632 9101.2 0.72 1.8 -4551 SEASON 12650 9156.0 0.72 1.4 -4578 Iteration 2 NULL (TARGET) 12648 8150.3 0.64 -4075 FISHING_AREA 12644 7863.1 0.62 3.5 -3932 YEAR 12627 7950.8 0.63 2.3 -3975 TEMP_C 12194 7709.0 0.63 1.4 -3855 LIGHTS 12645 7962.3 0.63 2.3 -3981 SEASON 12645 8065.3 0.64 1.0 -4033 Iteration 3 NULL(TARGET FISHING_AREA) 12644 7863.1 0.62 -3932 YEAR*SEASON 12105 7059.8 0.58 5.7 -3530 YEAR*FISHING_AREA 12086 7188.7 0.59 3.8 -3594 YEAR*TEMP_C 12169 7329.1 0.60 2.6 -3665 YEAR*TEMP_C 12169 7329.1 0.60 2.6 -3665 SEASON*TEMP_C 12187 7387.7 0.61 1.9 -3694 FISHING_AREA*SEASON 12177 7383.6 0.61 1.9 -3692 TARGET*FISHING_AREA 12173 7471.1 0.61 0.7 -3736 FISHING_AREA*TEMP_C 12186 7493.9 0.62 0.5 -3747 Iteration 4 NULL(TARGET FISHING_AREA YEAR*SEASON) 12105 7059.8 0.58 -3530 YEAR*TEMP_C 12083 6960.2 0.58 1.2 -

379

3480 YEAR*FISHING_AREA 12021 6928.5 0.58 1.2 -3464 TARGET*FISHING_AREA 12087 6984.7 0.58 0.9 -3492 TARGET*TEMP_C 12099 7025.3 0.58 0.4 -3513 FISHING_AREA*SEASON 12094 7026.2 0.58 0.4 -3513 SEASON*TEMP_C 12101 7030.9 0.58 0.4 -3515 FISHING_AREA*TEMP_C 12100 7036.9 0.58 0.3 -3518

380

Table 3. Forward model selection of the Gaussian regression (identity link) of loge-transformed positive catch rates of Skipjack on observed U.S. pelagic longline sets.

n Deviance Deviance/df % Reduction LogLikelihood

NULL (INTERCEPT_ONLY) 1455 987 0.68 -1782.7 TARGET 1450 844 0.58 14.2 -1669.2 LIGHTS 1452 848 0.58 13.9 -1672.1 YEAR 1434 897 0.63 7.8 -1713.3 FISHING_AREA 1451 951 0.66 3.4 -1755.9 SEASON 1452 964 0.66 2.1 -1766.1 TEMP_C 1454 972 0.67 1.5 -1771.5 Iteration 2 NULL(TARGET) 1450 844 0.58 -1669.2 YEAR 1429 761 0.53 8.6 -1593.2 LIGHTS 1447 817 0.56 3.0 -1645.3 SEASON 1447 827 0.57 1.9 -1654.0 FISHING_AREA 1446 838 0.58 0.5 -1663.4 TEMP_C 1449 841 0.58 0.3 -1666.3 Iteration 3 NULL(TARGET YEAR) 1429 761 0.53 -1593.2 LIGHTS 1426 734 0.51 3.3 -1567.4 SEASON 1426 742 0.52 2.2 -1575.5 FISHING_AREA 1425 754 0.53 0.6 -1587.1 TEMP_C 1428 760 0.53 0.1 -1592.2 Iteration 4 NULL(TARGET YEAR) 1429 761 0.53 -1593.2 YEAR*SEASON 1367 647 0.47 11.1 -1474.9 YEAR*TARGET 1370 669 0.49 8.3 -1499.8 SEASON*TEMP_C 1425 735 0.52 3.1 -1568.2 FISHING_AREA*SEASON 1411 733 0.52 2.46 -1565.9 YEAR*FISHING_AREA 1370 720 0.53 1.26 -1553.2 FISHING_AREA*TEMP_C 1424 754 0.53 0.49 -1587.1 Iteration 5 NULL(TARGET YEAR YEAR*SEASON) 1367 647 0.47 -1474.9 YEAR*TARGET 1309 572 0.44 7.53 -1386.4 YEAR*FISHING_AREA 1308 607 0.46 1.86 -

381

1429.1 SEASON*TEMP_C 1363 636 0.47 1.32 -1463.1 FISHING_AREA*TEMP_C 1362 636 0.47 1.23 -1463.3 FISHING_AREA*SEASON 1352 634 0.47 0.87 -1460.5 Iteration 6 NULL (TARGET YEAR YEAR*SEASON YEAR*TARGE 1309 572 0.44 -1386.4 SEASON*TEMP_C 1305 566 0.43 0.82 -1378.1 FISHING_AREA*SEASON 1294 563 0.44 0.51 -1374.2 FISHING_AREA*TEMP_C 1304 568 0.44 0.44 -1380.4 YEAR*FISHING_AREA 1261 554 0.44 -0.36 -1361.8

Table 4. Descriptive statistics and standardized catch rate indices of observed skipjack on U.S. pelagic longlines.

Year

Observed Trips

Positive trips

Effort (hooks)

Catch

Observed CPUE

Standardized CPUE

LL

UL

CV

1992 181 9 101766 22 0.20 1.00 0.00 2.43 0.73 1993 527 24 364541 39 0.08 0.19 0.00 0.50 0.81 1994 404 25 274264 62 0.20 0.66 0.00 1.50 0.65 1995 421 37 303680 52 0.16 0.19 0.00 0.44 0.69 1996 207 20 130557 49 0.47 0.81 0.00 1.74 0.59 1997 294 26 207118 37 0.17 0.25 0.00 0.59 0.69 1998 224 22 138854 92 0.60 1.47 0.00 3.06 0.55 1999 275 49 189124 93 0.47 0.61 0.00 1.26 0.54 2000 351 34 235057 55 0.28 0.52 0.00 1.07 0.54 2001 362 32 253562 54 0.31 0.80 0.00 1.61 0.52 2002 313 9 234753 9 0.04 0.11 0.00 0.41 1.33 2003 481 43 367252 54 0.14 0.51 0.00 1.19 0.68 2004 490 56 346866 161 0.58 1.78 0.17 3.38 0.46 2005 488 61 363390 191 0.68 1.34 0.10 2.57 0.47 2006 492 72 357638 435 1.56 1.87 0.14 3.60 0.47 2007 857 136 641931 294 0.57 1.30 0.13 2.47 0.46 2008 1159 149 864101 226 0.31 0.81 0.05 1.56 0.48 2009 1297 212 984570 402 0.51 1.62 0.27 2.97 0.43 2010 770 81 569163 144 0.31 0.94 0.00 1.89 0.51 2011 793 140 502015 441 1.04 2.90 0.50 5.30 0.42 2012 892 108 608112 193 0.38 1.18 0.10 2.26 0.47 2013 1376 173 912512 282 0.36 1.15 0.20 2.11 0.42

382

Figure 1. Geographic distribution of observed U.S. pelagic longline fishing effort used in the analysis, by number of sets.

Figure 2. Geographic distribution of observed U.S. pelagic longline skipjack catches.

383

Pro

porti

on o

f trip

s w

ith S

kipj

ack

Obs

erve

d M

ean

CP

UE

(Ski

pjac

k/10

00

4 2 Proportion Positive Observed Mean CPUE

0.3 1.5

0.2 1

0.1 0.5

0.0 0

1995 2000 2005 2010

Year

Figure 3. Proportion of U.S. observer monitored pelagic longline sets that captured skipjack and observed mean catch rates.

384

Figure 4. Observed mean of loge-transformed positive catch rates across factors examined.

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

0

1

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3

4

Year

Jan-Mar Apr-Jun Jul-Sep Oct-Dec

0

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FEC GOM MAB NEC SAB

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Area

BET DOL MIX SWO TUN YFT

0

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2

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4

Target Species

0 1 2 3

0

1

2

3

4

Light Sticks

log

e(C

PU

E)

385

Figure 5. Distribution of loge-transformed positive catch rates of Skipjack by year, and normal probability

densities.

1992

OBS$lncpue[OBS$YEAR == i]

Density

0.5 1.0 1.5 2.0

0.0

0.5

1.0

1.5

1993


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1994


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1995


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1996


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1997


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1998


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1999


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2006


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2007


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2008


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2009


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2010


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2011


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2012


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2013


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0 1 2 3

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loge(CPUE)

De

nsity

386

Figure 6. Comparison of observed mean catch rates and standardized mean estimates from the delta-lognormal

generalized linear mixed model. The dashed line shows the recommended break point resulting from fleet wide

change in gear.

1995 2000 2005 2010

0

1

2

3

4

5

6

Year

Ind

ex

Nominal

Standardized

Confidence Interval

387

Figure 7. Size frequency distributions of Skipjack tuna measured by on-board observers in the U.S. pelagic

longline fishery.

2001

SKJ$FL[SKJ$YEAR == i]

Fre

quency

0 50 100 150

050

100

150

200 2002


Fre

quency

0 50 100 1500.0

1.0

2.0

3.0

2003


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quency

0 50 100 150

02

46

810

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2004


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quency

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010

30

50

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quency

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020

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2006


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quency

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Fork Length (cm)

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Figure 8. Mean size of Skipjack tuna measured by on-board observers in the U.S. pelagic longline fishery.

2001 2003 2005 2007 2009 2011 2013

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STANDARDIZED CATCH INDICES OF SKIJACK TUNA, … › Documents › CVSP › CV071_2015 › n... ·...

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Transcript of STANDARDIZED CATCH INDICES OF SKIJACK TUNA, … › Documents › CVSP › CV071_2015 › n... ·...