SCRS/2011/139 Collect. Vol. Sci. Pap. ICCAT, 68(3): (2012)

SCRS/2011/139 Collect. Vol. Sci. Pap. ICCAT, 68(3): 953-966 (2012) A STANDARDIZED CATCH RATE INDEX FOR YELLOWFIN TUNA (THUNNUS ALBACARES) FROM THE U.S. RECREATIONAL FISHERY IN THE WESTERN NORTH ATLANTIC OCEAN, 1986-2010 Shannon L. Cass-Calay 1 SUMMARY Catch and effort data from the U. S. Marine Recreational Fisheries Statistical Survey (MFRSS) off the Atlantic coast and Gulf of Mexico (excluding Texas) were used to construct an index of abundance for yellowfin tuna. Standardized catch rates were estimated using a Generalized Linear Mixed modeling approach assuming a delta-lognormal error distribution. The explanatory variables considered for standardization included: year, geographic area, season, and fishing mode (a factor that classifies recreational fishing as charter or private/rental boat). The indices suggest that the catch rates of yellowfin have declined to a historic low in the most recent years (2008-2010). It is not clear whether this is an effect of reduced availability of yellowfin to the U.S. Recreational Fishery or a true reflection of a decrease in stock abundance. A reduction in availability could occur if yellowfin tuna were distributed further offshore than the recreational boats can reach. RÉSUMÉ Les données de prise et d'effort de l'enquête statistique des pêcheries récréatives marines des États-Unis (MFRSS) opérant au large du littoral atlantique et du golfe du Mexique (Texas exclus) ont été utilisées pour obtenir un indice d'abondance de l'albacore. Les taux de capture standardisés ont été estimés en utilisant une approche de modèle linéaire généralisé mixte postulant une distribution d erreur delta lognormale. Les variables explicatives considérées pour la standardisation incluaient : année, zone géographique, saison et mode de pêche (facteur qui classe la pêche récréative selon que l'embarcation est affrétée ou privée/en location). Les indices suggèrent que les taux de capture de l'albacore ont chuté à un faible historique au cours de ces toutes dernières années (2008-2010). On ne sait pas au juste si cela est dû à la disponibilité réduite de l'albacore à la pêcherie récréative des États-Unis ou si cela reflète réellement une baisse de l'abondance du stock. Une réduction de la disponibilité pourrait survenir si l'albacore était distribué plus loin des côtes, hors de portée des bateaux récréatifs. RESUMEN Se utilizaron los datos de captura y esfuerzo de la prospección estadística de las pesquerías de recreo marítimas de Estados Unidos (MFRSS) en aguas de la costa atlántica y del Golfo de México (con la exclusión de Tejas) para obtener el índice de abundancia para el rabil. Se estimaron las tasas de captura estandarizadas mediante modelos lineales mixtos generalizados asumiendo una distribución de error delta-lognormal. Las variables explicativas consideradas para la estandarización incluían: año, zona geográfica, temporada y modo de pesca (un factor que clasifica la pesca de recreo en barco fletado o buque privado/alquilado). Los índices sugieren que las tasas de captura de rabil han descendido hasta un nivel bajo histórico en los años más recientes (2008-2010). No está claro si esto es un efecto de una reducción en la disponibilidad de rabil para la pesquería de recreo estadounidense o si se trata de un reflejo real de una disminución de la abundancia del stock. Podría producirse un descenso en la disponibilidad si los rabiles se distribuyesen en una zona más alejada de la costa a la que no pueden acceder los barcos de recreo. KEY WORDS Catch/effort, abundance, MRFSS, recreational statistics, multivariate analyses 1 U.S. Department of Commerce, NOAA Fisheries, Southeast Fisheries Science Center, Miami Laboratory, 75 Virginia Beach Drive, Miami, Florida 33149 U.S.A. Email: Shannon.Calay@noaa.gov 953

1. Introduction Data collected and estimated by the Marine Recreational Fisheries Statistical Survey (MRFSS) were used to develop standardized catch per unit effort (CPUE) indices for yellowfin tuna in the Gulf of Mexico and western North Atlantic. The MRFSS survey began in 1979, and its purpose was to establish a reliable database for estimating the impact of marine recreational fishing on marine resources. More detailed information on the methods and protocols of the survey can be found at http://www.st.nmfs.gov/st1/recreational/overview/ overview.html. 2. Materials and methods The Marine Recreational Fisheries Statistical Survey (MRFSS) program provides estimates of catch and effort for the U.S. recreational fishery. Data were collected by scientific samplers during dockside interviews. Each record includes the following information: catch (by species) in numbers, and whether the catch was retained, released alive or discarded dead, the number of participating anglers, the number of fishing hours, information on gear used, target species, mode (shore, headboat, charter, or private/rental), area (inshore, ocean < 3 miles, 3 < ocean < 10 miles, ocean > 10 miles), county/state, and date. One potential problem with indices derived from the MRFSS database is the selection of trips/interviews that are relevant to the analysis. The MRFSS program includes information from recreational trips by shore anglers, inshore fishing trips, as well as large charter vessels fishing offshore. The task is then to identify trips that had a significant probability of catching yellowfin tuna. During the interview, anglers are asked which species were targeted during the fishing trip (primary and secondary target). For the yellowfin index, trips were included in the analysis if the primary or secondary target was a member of the group of large pelagic fish (Table 1). However, because a substantial proportion of trips do not report any target, trips were also included if they caught at least one species that occurred on at least 1% of trips that targeted a large pelagic fish (Table 2). As described subsequently, additional criteria were applied to further limit trips. The MRFSS data includes estimates of catch and effort from 1981 through 2007 from the U.S. States of Louisiana through Maine. Because very few trips reported catching yellowfin tuna before 1986, the indices were constructed for the period 1986-2010. Nearly all yellowfin (>99%) were landed using hook and line. Therefore, the indices were constructed using only hook and line trips. Additionally, shore and shelf effort were excluded from the analyses because it is unlikely to land a yellowfin tuna from a dock or shoreline, or within the shallow continental shelf. Finally, because headboat sampling is not reported consistently in the dataset in time and space, that fishing mode was also excluded from the analysis. Effort was excluded in certain time/area combinations because fishing did not generally occur there, or was not directed at tropical tunas. In the northeastern and mid Atlantic U.S. (CT, RI, MA, NH, ME, DE, NJ, NY, VA, MD) fishing effort that occurred during the winter and spring (Dec-May) were excluded from the analysis. The following factors were considered as possible influences on the proportion of trips that observed yellowfin tuna (proportion positive), and the catch rates on trips that caught yellowfin tuna. Because of the small number of records for some states, regional areas were defined and used as a spatial factor. Months were aggregated into seasons to account for seasonal fishery distribution through the year. Factor Levels Values YEAR 25 1986-2010 SEASON 4 WIN = (Dec-Feb) SPR = (Mar-May) SUM = (Jun-Aug) AUT = (Sep-Nov) MODE 2 Charter (CB) and Private (PB) REGION 4 NE U.S. (CT, RI, MA, NH, ME, DE, NJ, NY) Mid Atlantic U.S. (VA, MD, NC) Southeast U.S. (FL East Coast, GA, SC) Gulf of Mexico (FL West Coast, AL, MS, LA) Catch per unit effort (CPUE) was defined as the total kept, discarded or released (AB1B2, in number of fish) per 1000 angler hours. 954

CPUE = (Number Landed + Discarded Dead + Released Alive) / 1000 Angler Hours A delta-lognormal approach (Lo et al. 1992) was used to develop the standardized catch rate indices. This method combines separate generalized linear modeling (GLM) analyses of the proportion positive trips (trips that caught yellowfin tuna) and the catch rates of successful trips 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 2000. SAS Institute Inc. Cary, NC, USA). 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. For both the binomial and lognormal portions of the delta-lognormal 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 χ2 analysis was performed 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 5%, 2) the χ2 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 log likelihood 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). 3. Results and discussion The GLM model construction, results and statistics are summarized in Tables 3-4 (binomial component) and Tables 5-6 (lognormal component). The final models selected were as follows. PPT = REGION+MODE+SEASON+YEAR+YEAR*REGION+YEAR*SEASON LOG(CPUE) = YEAR+SEASON+REGION+YEAR*SEASON+YEAR*REGION The analysis dataset included 103,719 trips that either targeted a large pelagic species, or caught at least one associated species. Of these, only 10,646 (10.3%) reported landing, discarding or releasing yellowing tuna. The annual proportion of positive trips (PPT: trips that caught yellowfin) was low, ranging from 3% to 21% (Figure 1, Table 7). PPT was generally less than 10% before 1992, and then increased to 10 to 21% until 2007. However, since that time, the proportion positive tips are the lowest on record, 3% to 5%. Nominal CPUE follows a very similar pattern (Figure 2, Table 7). The lowest levels were also observed during 2008 to 2010. Diagnostic plots were constructed to examine the fit of the components of the delta-lognormal model. The chisquare residuals, by factor, are shown in Figure 3. The strong effect of a few positive outliers is noted. The frequency distribution of the proportion of positive trips by strata (year, region, fishing mode and season) is shown in Figure 4. It is evident that most strata have low numbers of positive trips. To use the binomial model, it is generally recommended that at least 10-20% of trips observe the species of interest. Therefore, it is possible that the low proportion of positive trips violates the assumptions of this model component. The residuals of the lognormal model, by factor, are shown in Figure 5. In this case, the residuals are more evenly distributed above and below zero, supporting an appropriate fit of the lognormal component. The frequency distribution of nominal catch rates is shown in Figure 6. Ideally, the frequency distribution of log(cpue) should resemble the normal distribution overlaid in red (Figure 6). In this case, some departure from the expectation is noted, but the model fit appears adequate. The QQ-Plot (Figure 7) also indicates the degree of departure from the assumption of a normal distribution (red line). In this case, the QQ-Plot indicates an appropriate fit to the lognormal component of the delta-model. The standardized index and the nominal CPUE are shown in Figure 8 and Table 7. To facilitate comparison, both series were scaled by dividing the annual estimates by the series mean. The standardized index suggests that 955

catch rates of yellowfin tuna varied without obvious trend until 2000. Since that time a generally decline is noted. The most recent years (2008-2010) are the lowest on record. It is not assured that this standardized index represents a true abundance trend of western Atlantic yellowfin tuna. The recent reduction in catch rates could be the result of a change in the distribution of yellowfin tuna that has altered their availability to the U.S. Recreational Fishery. For example, the fish could be distributed farther offshore than the fleet generally operates. Therefore, the reliability of this index should be carefully considered before inclusion in a quantitative stock assessment context. 4. Acknowledgments I would like to acknowledge the assistance of John F. Walter and Craig A. Brown of NOAA Fisheries (SEFSC) who provided advice regarding analytical techniques. 5. References Littell, R.C., Milliken, G.A., Stroup, W.W. and Wolfinger, R.D. 1996, SAS System for Mixed Models, Cary NC, USA:SAS Institute Inc., 1996. 663 pp. Littell, R.C., Henry, P.R. and Ammerman, C.B. 1998, Statistical analysis of repeated measures data using SAS procedures. J. Anim. Sci. 76: 1216-1231. Lo, N.C., Jacobson, L.D., and Squire, J.L. 1992, Indices of relative abundance from fish spotter data based on delta-lognormal models. Can. J. Fish. Aquat. Sci. 49: 2515-2526. SAS Institute Inc. 1997, SAS/STAT Software: Changes and Enhancements through Release 6.12. Cary, NC, USA: SAS Institute Inc., 1997. 1167 pp. Table 1. Members of the large pelagic species group. Genus - species Tuna genus Yellowfin tuna Dolphin Bluefin tuna Wahoo Billfish family Blue marlin White marlin King mackerel Mackerel family Blackfin tuna Bigeye tuna Albacore Sailfish Shortfin mako Skipjack tuna Common name Thunnus spp. Thunnus albacares Coryphaena hippurus Thunnus thynnus Acanthocybium solandri Istiophoridae Makaira nigricans Tetrapturus albidus Scomberomorus cavalla Scombridae Thunnus atlanticus Thunnus obesus Thunnus alalunga Istiophorus platypterus Isurus oxyrinchus Katsuwonus pelamis 956

Table 2. Species caught on at least 1% of trips that targeted a large pelagic species Common name Scientific name Percent occurrence on trips that targeted a large pelagic species Yellowfin tuna Thunnus albacares 15.7% Dolphin Coryphaena hippurus 13.0% Bluefin tuna Thunnus thynnus 10.8% Little tunny Euthynnus alletteratus 5.9% King mackerel Scomberomorus cavalla 4.3% Sailfish Istiophorus platypterus 4.2% Wahoo Acanthocybium solandri 3.3% Bluefish Pomatomus saltatrix 3.2% Blackfin tuna Thunnus atlanticus 2.9% Blue shark Prionace glauca 2.2% Great barracuda Sphyraena barracuda 2.1% Skipjack tuna Katsuwonus pelamis 1.6% Atlantic bonito Sarda sarda 1.5% White marlin Tetrapturus albidus 1.3% Ballyhoo Hemiramphus brasiliensis 1.2% Greater amberjack Seriola dumerili 1.1% Table 3. The deviance table for the binomial model on the proportion of positive trips. Factors were assumed to be significant if they explained >5% of the total deviance (shaded cells), and were significant according to a Chi- Square test. GENMOD (FIXED-FACTOR) OUTPUT Binomial Model Factors - Proportion Positive DF DF Residual Deviance Reduction in Deviance % of Total Deviance Log Like Chi Square Null 1 103718 68631.1 0.0 0.0-34315.6 Region 3 103715 54625.1 14006.0 68.2-27312.5 14006.0 <0.001 Region + Mode 1 103714 52554.7 2070.3 10.1-26277.4 2070.3 <0.001 Region + Mode + Season 3 103711 50445.5 2109.2 10.3-25222.8 2109.2 <0.001 Region + Mode + Season + Year 24 103687 48965.7 1479.8 7.2-24482.8 1479.8 <0.001 Region + Mode + Season + Year + Mode*Region 3 103684 48537.9 427.8 2.1-24268.9 427.8 <0.001 Region + Mode + Season + Year + Mode*Region + Season*Region 7 103677 48115.2 422.7 2.1-24057.6 422.7 <0.001 Region + Mode + Season + Year + Mode*Region + Season*Region + Season*Mode 3 103674 48090.5 24.7 0.1-24045.3 24.7 <0.001 Final Model: PPT = Region + Mode + Season + Year P Table 4. Analysis of the mixed model formulations for the binomial component of the delta-model. The likelihood ratio was used to test the difference of 2 REM log likelihood 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 Region + Mode + Season + Year 3036.7 3038.7 3043.2 - - Region + Mode + Season + Year*Region 2967.5 2971.5 2976.7 69.2 <0.0001 Region + Mode + Season + Year*Region + Year*Season 2928.0 2934.0 2937.1 39.5 <0.0001 Region + Mode + Season + Year*Region + Year*Season + Year*Mode 2928.0 2934.0 2941.8 0.0 1.0000 P 957

Table 5. The deviance table for the lognormal model on catch rates of positive trips. Factors were assumed to be significant if they explained >5% of the total deviance (shaded cells), and were significant according to a Chi- Square test. (NOTE: The model containing the term SEASON*REGION did not converge in GLMMIX to produce lsmeans for YEAR. Therefore, this factor was removed). Lognormal Model Factors - CPUE DF DF Residual Deviance Reduction in Deviance % of Total Deviance Log Like Chi Square Null 1 10645 9653.7 0.0 0.0-14585.2 Year 24 10621 9193.6 460.0 43.6-14325.3 519.8 <0.001 Year + Season 3 10618 8843.9 349.7 33.2-14118.9 412.87 <0.001 Year + Season + Region 3 10615 8726.7 117.3 11.1-14047.8 142.12 <0.001 Year + Season + Region + Mode 1 10614 8709.7 16.9 1.6-14037.5 20.66 <0.001 Year + Season + Region + Mode + Season*Region 7 10607 8616.0 93.7 8.9-13979.9 115.18 <0.001 Year + Season + Region + Mode + Season*Region + Season*Mode 3 10604 8603.0 13.0 1.2-13971.8 16.06 0.001 Year + Season + Region + Mode + Season*Region + Season*Mode + Mode*Region 3 10601 8599.6 3.4 0.3-13969.8 4.18 0.243 Final Model: log(cpue) = Year + Season + Region + Season*Region Table 6. YFT: Analysis of the mixed model formulations for the lognormal component of the delta-model. The likelihood ratio was used to test the difference of 2 REM log likelihood between two nested models. The final model is indicated with gray shading. P Catch Rates on Positive Trips -2 REM Log likelihood Akaike's Information Criterion Schwartz's Bayesian Criterion Likelihood Ratio Test P Year + Season + Region 28231.9 28233.9 28241.2 - - Year + Season + Region + Year*Season 27990.4 27994.4 27999.5 241.5 <0.0001 Year + Season + Region + Year*Season + Year*Region 27775.1 27781.1 27788.7 456.8 <0.0001 Table 7. Nominal CPUE, number of trips, number of positive trip, proportion positive trips (PPT), standardized index of abundance and index statistics. Year Nom CPUE Trips Pos trips PPT Relative index CV LCI UCI 1986 28.132 2480 214 0.086 2.211 0.516 0.837 5.845 1987 37.866 2817 282 0.100 1.218 0.550 0.436 3.406 1988 19.098 2974 235 0.079 0.699 0.580 0.238 2.053 1989 19.591 3712 323 0.087 0.750 0.550 0.268 2.097 1990 12.295 2947 167 0.057 0.430 0.596 0.143 1.297 1991 21.324 3086 251 0.081 0.702 0.560 0.247 1.995 1992 11.819 3633 232 0.064 0.474 0.540 0.172 1.304 1993 38.454 3071 409 0.133 1.253 0.513 0.477 3.291 1994 72.368 3443 724 0.210 2.552 0.496 0.999 6.521 1995 55.060 2998 515 0.172 1.978 0.522 0.741 5.276 1996 47.242 5052 658 0.130 0.448 0.557 0.158 1.267 1997 21.759 5582 494 0.089 0.424 0.538 0.155 1.161 1998 47.167 5517 719 0.130 0.878 0.489 0.348 2.219 1999 43.696 5339 574 0.108 1.561 0.500 0.607 4.011 2000 49.783 5845 681 0.117 1.431 0.495 0.561 3.649 2001 40.727 5488 766 0.140 1.410 0.470 0.577 3.442 2002 27.485 6102 491 0.080 1.006 0.484 0.401 2.519 2003 41.828 5988 642 0.107 1.069 0.471 0.436 2.618 2004 32.258 4898 506 0.103 0.863 0.491 0.341 2.187 2005 25.910 4508 404 0.090 0.835 0.492 0.329 2.121 2006 33.694 4620 539 0.117 1.048 0.493 0.412 2.664 2007 21.817 3969 358 0.090 0.866 0.504 0.335 2.243 2008 10.606 3678 167 0.045 0.442 0.574 0.152 1.287 2009 7.426 2294 79 0.034 0.211 0.706 0.059 0.753 2010 10.689 3678 216 0.059 0.240 0.610 0.078 0.739 958

Figure 1. YFT: Proportion of positive trips, by year. Figure 2. YFT: Nominal CPUE (fish per 1000 angler hours), by year. 959

A) B) C) D) Figure 3. Chi-square residuals for the fit to the binomial model, by year (A), region (B), fishing mode (C) and season (D). Figure 4. Frequency distribution of proportion positive trips by the strata year, region and fishing mode. 960

A) B) C) Figure 5. Residuals of the fit to the lognormal model, by year (A), region (B) and season (C). Figure 6. Frequency distribution of nominal catch rates (fish per 1000 angler hours) on positive trips. 961

Figure 7. The cumulative normalized residuals (QQ-Plot) from the lognormal model on the catch rates of positive trips. Figure 8. Nominal CPUE (blue line) and the delta-lognormal index (red line) with 95% confidence intervals (dashed lines). Both series are scaled to a mean of 1.0 to facilitate comparison. 962

Appendix 1 Distribution of observations by year, region, season, fishing mode (charter boat, private boat) and season Table A1. Trips that observed YFT by YEAR. Cumulative year Frequency Frequency ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 1986 214 214 1987 282 496 1988 235 731 1989 323 1054 1990 167 1221 1991 251 1472 1992 232 1704 1993 409 2113 1994 724 2837 1995 515 3352 1996 658 4010 1997 494 4504 1998 719 5223 1999 574 5797 2000 681 6478 2001 766 7244 2002 491 7735 2003 642 8377 2004 506 8883 2005 404 9287 2006 539 9826 2007 358 10184 2008 167 10351 2009 79 10430 2010 216 10646 Table A2. Trips that observed YFT by SEASON. Cumulative SEASON Frequency Frequency ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ AUT 3257 3257 SPR 3180 6437 SUM 3678 10115 WIN 531 10646 Table A3. Trips that observed YFT by REGION. Cumulative Region Frequency Frequency ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ GOM 425 425 Mid_ATL 8665 9090 NE_US 1343 10433 SE_US 213 10646 Table A4. Trips that observed YFT by fishing MODE. Cumulative mode Frequency Frequency ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ CB 8965 8965 PB 1681 10646 963

Table A5. Trips that observed YFT by fishing YEAR and REGION. Frequency GOM Mid_ATL NE_US SE_US Total ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 1986 2 86 75 51 214 1987 2 227 42 11 282 1988 6 207 17 5 235 1989 2 211 107 3 323 1990 0 88 79 0 167 1991 7 204 38 2 251 1992 20 174 29 9 232 1993 15 264 122 8 409 1994 10 546 165 3 724 1995 4 405 106 0 515 1996 3 635 18 2 658 1997 1 449 34 10 494 1998 20 649 23 27 719 1999 17 518 25 14 574 2000 15 611 36 19 681 2001 37 634 81 14 766 2002 49 396 39 7 491 2003 40 522 67 13 642 2004 32 426 41 7 506 2005 34 301 64 5 404 2006 41 435 63 0 539 2007 31 286 38 3 358 2008 27 128 12 0 167 2009 6 66 7 0 79 2010 4 197 15 0 216 ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total 425 8665 1343 213 10646 Table A6. Trips that observed YFT by fishing YEAR and fishing MODE. Frequency CB PB. Total ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 1986 108 106 214 1987 229 53 282 1988 181 54 235 1989 200 123 323 1990 125 42 167 1991 202 49 251 1992 183 49 232 1993 330 79 409 1994 573 151 724 1995 430 85 515 1996 573 85 658 1997 438 56 494 1998 675 44 719 1999 525 49 574 2000 619 62 681 2001 645 121 766 2002 408 83 491 2003 543 99 642 2004 460 46 506 2005 343 61 404 2006 445 94 539 2007 317 41 358 2008 152 15 167 2009 73 6 79 2010 188 28 216 ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total 8965 1681 10646 964

Table A7. Trips that observed YFT by fishing YEAR and SEASON. Frequency AUT SPR SUM WIN Total ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 1986 68 47 99 0 214 1987 101 41 140 0 282 1988 74 28 133 0 235 1989 61 53 209 0 323 1990 54 33 80 0 167 1991 79 75 93 4 251 1992 70 61 98 3 232 1993 213 71 124 1 409 1994 283 151 284 6 724 1995 184 147 158 26 515 1996 187 292 154 25 658 1997 96 235 127 36 494 1998 209 246 145 119 719 1999 143 224 195 12 574 2000 161 242 269 9 681 2001 298 262 184 22 766 2002 106 164 122 99 491 2003 219 115 270 38 642 2004 158 151 179 18 506 2005 152 109 125 18 404 2006 156 139 219 25 539 2007 91 154 60 53 358 2008 48 37 75 7 167 2009 0 27 52 0 79 2010 46 76 84 10 216 ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total 3257 3180 3678 531 10646 Table A8. Trips that observed YFT by fishing SEASON and REGION. Frequency GOM Mid_ATL NE_US SE_US Total ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ AUT 78 2480 674 25 3257 SPR 75 2996 0 109 3180 SUM 138 2799 669 72 3678 WIN 134 390 0 7 531 ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total 425 8665 1343 213 10646 Table A9. Trips that observed YFT by fishing SEASON and REGION. Frequency CB PB Total ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ AUT 2749 508 3257 SPR 2803 377 3180 SUM 2927 751 3678 WIN 486 45 531 ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total 8965 1681 10646 Table A10. Trips that observed YFT by fishing REGION and fishing MODE. Frequency CB PB Total ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ GOM 332 93 425 Mid_ATL 7736 929 8665 NE_US 744 599 1343 SE_US 153 60 213 ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total 8965 1681 10646 965

Table A11. Trips that observed YFT by STATE. Frequency LA MS AL FL W FL E GA SC NC Total ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 1986 1 0 0 1 4 0 47 64 1987 0 0 0 2 0 0 11 201 1988 1 0 0 5 1 0 4 177 1989 2 0 0 0 0 0 3 187 1990 0 0 0 0 0 0 0 83 1991 5 0 1 1 0 0 2 192 1992 16 0 1 3 2 0 7 163 1993 13 0 1 1 1 0 7 254 1994 10 0 0 0 1 0 2 514 1995 4 0 0 0 0 0 0 389 1996 3 0 0 0 0 0 2 616 1997 0 0 0 1 5 0 5 439 1998 18 0 1 1 2 0 25 629 1999 16 0 0 1 5 0 9 495 2000 13 0 0 2 0 0 19 600 2001 30 0 5 2 5 0 9 615 2002 34 0 6 9 2 0 5 373 2003 36 0 3 1 1 2 10 477 2004 29 0 1 2 1 0 6 393 2005 28 0 1 5 4 0 1 258 2006 36 0 3 2 0 0 0 397 2007 27 0 3 1 1 0 2 255 2008 22 0 5 0 0 0 0 117 2009 2 1 2 1 0 0 0 51 2010 2 0 0 2 0 0 0 184 ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total 348 1 33 43 35 2 176 8123 Continued Frequency VA MD DE NJ NY CT RI MA ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 1986 16 6 0 5 64 0 6 0 1987 19 7 0 5 10 6 20 1 1988 9 21 0 1 4 2 9 1 1989 3 21 10 40 49 3 4 1 1990 0 5 25 9 26 0 18 1 1991 2 10 11 17 7 0 3 0 1992 7 4 4 10 12 0 3 0 1993 5 5 2 2 54 15 49 0 1994 27 5 12 4 116 2 30 1 1995 9 7 8 5 60 0 23 10 1996 7 12 7 1 6 0 4 0 1997 4 6 7 4 18 0 5 0 1998 10 10 11 3 6 0 3 0 1999 12 11 1 12 2 0 9 1 2000 6 5 10 9 13 0 4 0 2001 10 9 51 8 20 0 2 0 2002 5 18 19 13 1 0 6 0 2003 9 36 8 39 11 0 6 3 2004 26 7 7 32 1 0 1 0 2005 10 33 32 29 2 0 1 0 2006 4 34 28 34 1 0 0 0 2007 13 18 11 26 1 0 0 0 2008 7 4 4 7 0 0 0 1 2009 4 11 5 2 0 0 0 0 2010 0 13 2 13 0 0 0 0 ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total 224 318 275 330 484 28 206 20 Total 10646 966