A Catch Rate Index of Yellowfin Tuna (Thunnus Albacares) from the United States Recreational Fishery in the Western North Atlantic Ocean, 1986-2006

SCRS/2007/052 Collect. Vol. Sci. Pap. ICCAT, 62(1): 240-251 (2008)

A CATCH RATE INDEX OF YELLOWFIN TUNA (THUNNUS ALBACARES) FROM THE UNITED STATES RECREATIONAL FISHERY IN THE WESTERN NORTH ATLANTIC OCEAN, 1986-2006

Shannon L. Cass-Calay1

SUMMARY

Catch and effort data from the U.S Marine Recreational Fisheries Statistical Survey (MFRSS) of the Atlantic coast and Gulf of Mexico (excluding Texas) were used to construct indices 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: geographic area, season, and fishing mode (a factor that classifies recreational fishing as charter or private/rental boat). The index suggests that the catch rates of yellowfin vary annually, largely without trend. However, recent catch rates are higher than they were during the mid 1980s and early 1990s.

RÉSUMÉ

Les données de prise et d’effort provenant de l’Enquête statistique sur les pêcheries marines récréatives des Etats-Unis (Marine Recreational Fisheries Statistical Survey - MFRSS) de la côte atlantique et du Golfe du Mexique (à l’exclusion du Texas) ont été utilisées pour élaborer les indices d’abondance de l’albacore. Les taux de capture standardisés ont été estimés à l’aide d’un Modèle Linéaire Généralisé Mixte en postulant une distribution d’erreur delta-lognormale. Les variables explicatives prises en compte dans la standardisation incluaient la zone géographique, la saison et la modalité de pêche (facteur classant la pêche récréative comme navire affrété ou privé/en location). Les indices donnent à penser que les taux de capture d’albacore varient chaque année, sans présenter de tendance générale. Toutefois, les récents taux de capture sont plus élevés que ceux du milieu des années 1980 et du début des années 1990.

RESUMEN

Se utilizaron los datos de captura y esfuerzo de la Encuesta estadística de pesca de recreo marítimo de Estados Unidos (MFRSS) de la costa atlántica y del Golfo de México (excluyendo Texas) para obtener índices de abundancia de rabil. Se estimaron las tasas de captura estandarizadas mediante un enfoque de modelación mixto lineal generalizado, asumiendo una distribución de error delta-lognormal. Entre las variables explicativas consideradas para la estandarización se incluían: zona geográfica, temporada y modo de pesca (un factor que clasifica la pesca de recreo como buque fletado o privado/alquilado). El índice sugiere que las tasas de captura de rabil varían anualmente, sin tendencias. Sin embargo, las tasas de capturas recientes son más elevadas de lo que lo fueron durante mediados de los ochenta y comienzos de los noventa.

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: [email protected] 240

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 started 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), and in general the catch composition reflects the species found in the habitat associated with the targeted species. The fishing trips were classified into “guilds” based on the intended target. The “guilds” identified were: sharks, pelagic species, inshore species, reef species, and non-reef species. When no primary or secondary target was specified, the record was assigned an unclassified status. Trips were included in the analysis if the primary or secondary target was a member of the pelagic guild, or if at least one yellowfin tuna was caught on the trip. Pelagic guild members are listed in Table 1.

The MRFSS data includes estimates of catch and effort from 1981 through 2006 from the U.S. States of Louisiana, Mississippi, Alabama, Florida, Georgia, South Carolina, North Carolina, Virginia, Maryland, Delaware, New Jersey, New York, Connecticut, Rhode Island, Massachusetts, New Hampshire and Maine. Because very few trips reported catching yellowfin tuna before 1986, the index was constructed for the period 1986-2006. The index was constructed using only hook and line trips (>99% YFT were landed using hook and line). Additionally, shore and shelf effort were excluded from the analysis because it is unlikely to land a yellowfin tuna in these areas. 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 21 1986-2006 WIN = (Dec-Feb) SPR = (Mar-May) Season 4 SUM = (Jun-Aug) AUT = (Sep-Nov) Mode 2 Charter (CB) and Private (PB) NE U.S. (CT, RI, MA, NH, ME, DE, NJ, NY) Mid Atlantic U.S. (VA, MD, NC) Region 4 Southeast U.S. (FL East Coast, GA, SC) Gulf of Mexico (FL West Coast, AL, MS, LA)

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Fishing effort (angler hours) was estimated as the number of anglers times the number of hours fishing, and nominal catch rates were defined as the total catch kept and released (AB1B2, in number of fish) per angler hour.

CPUE = (Number Landed + Discarded Dead + Released Alive) / Angler Hours

A delta-lognormal approach (Lo et al. 1992) was used to develop the standardized catch rate index. This method combines separate generalized linear modeling (GLM) analyses of the proportion positive sets (sets that caught bluefin tuna) and the catch rates of successful sets to construct a single standardized index of abundance. Parameterization of each model was accomplished using a GLM procedure (GENMOD; Version 8.02 of the SAS System for Windows © 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. Each potential factor was added to the null model individually, and the resulting reduction (%RED) in deviance per degree of freedom (DEV/DF) was examined. The factor that caused the greatest reduction in deviance per degree of freedom was added to the model if the factor was significant based upon a Chi-Square test (PROB > CHISQ), and the reduction in deviance per degree of freedom was ≥1%. This model then became the base model, and the process was repeated, adding factors and two-way interaction terms individually until no factor or interaction met the criteria for incorporation into the final model. Higher order interaction terms were not examined.

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 results and statistics for the binomial component are summarized in Tables 2-3 (binomial component) and Tables 4-5 (lognormal component). The final models selected were as follows.

PPT = REGION+MODE+SEASON+YEAR+MODE*REGION+YEAR*SEASON+YEAR*REGION

LOG(CPUE) = SEASON+YEAR+REGION+YEAR*SEASON+YEAR*REGION

The analysis dataset included 86,236 trips that either targeted pelagic species, or caught at least one yellowfin tuna. Of these, only 9,841 (11.4%) reported landing, discarding or releasing yellowing tuna. The annual proportion of positive trips (PPT: trips that caught yellowfin) was low, ranging from 5% to 17% (Figure 1; Table 6). PPT was generally less than 9% before 1992, and then increased to 10 to 17% thereafter. Nominal CPUE follows a similar pattern (Figure 2; Table 6). The lowest levels were observed during 1986 to 1991. Nominal CPUE increased to a higher level after 1991.

Diagnostic plots were constructed to examine the fit of the components of the delta-lognormal model. The chi- square residuals, by factor, are shown in Figure 3. The residuals are more frequently above zero than below, indicating a strong effect of the positive outliers. 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, indicating a proper 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. 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.

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The standardized index and the nominal CPUE are shown in Figure 8 and Table 6. To facilitate comparison, both series were scaled by dividing the annual estimates by the series mean. Although annual catch rates are quite variable, the index suggests that yellowfin tuna catch rates were lowest during 1987-1992, and have increased since.

The quality of this index is uncertain due to the violation of the assumptions of the binomial component. This problem causes an inflation of the variance, which is apparent in Table 6 and Figure 8. Coefficients of variation are greater than 1 in every year, and values larger than 2 occur in several years. Due to this fact, the tropical tuna species group should carefully consider whether this index if appropriate for use in formal stock assessment proceedings.

4. Acknowledgments

I would like to acknowledge the assistance of Patty Phares, Mauricio Ortiz and Craig Brown of NOAA Fisheries (SEFSC) who helped to extract relevant data, and provided advice regarding analytical techniques. Clay Porch reviewed drafts of the manuscript, and provided useful comments.

5. References

LITTELL, R.C., G.A. Milliken, W.W. Stroup, and R.D Wolfinger. 1996. SAS® System for Mixed Models, Cary NC, USA:SAS Institute Inc., 1996. 663 pp. LITTELL, R.C., P.R. Henry and C.B. Ammerman. 1998. Statistical analysis of repeated measures data using SAS procedures. J. Anim. Sci. 76: 1216-1231. LO, N.C., L.D. Jacobson, and J.L. Squire. 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. p. 1167

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Table 1. Members of the “guild” of pelagic species.

Genus - species Common name Genus - species Common name Acanthocybium wahoo Scomber japonicus chub mackerel solandri Auxis rochei bullet mackerel Scomber scombrus atlantic mackerel Auxis thazard frigate mackerel Scomberomorus king mackerel cavalla Coryphaena equisetis pompano dolphin Scomberomorus spanish mackerel maculatus Coryphaena hippurus dolphin Scomberomorus cero regalis Coryphaena spp. dolphin genus Scomberomorus spp. mackerel genus Coryphaenidae dolphin family Scombridae mackerel family Echeneidae remora family Sphyrna lewini scalloped hammerhead Echeneis naucrates sharksucker Sphyrna mokarran great hammerhead Echeneis whitefin sharksucker Sphyrna tiburo bonnethead neucratoides Elegatis bipinnulatus rainbow runner Sphyrna tudes smalleye hammerhead Euthynnus little tunny Sphyrna zygaena smooth hammerhead alletteratus Euthynnus spp. Euthynnus genus Tetrapturus albidus white marlin Gempylus serpens snake mackerel Tetrapturus pfleugeri longbill spearfish Istiophoridae billfish family Thunnus alalunga albacore Istiophorus sailfish Thunnus albacares yellowfin tuna platypterus Katsuwonus pelamis skipjack tuna Thunnus atlanticus blackfin tuna Lepidocybium escolar Thunnus obesus bigeye tuna flavobrunneum Lobotes surinamensis tripletail Thunnus spp. tuna genus Makaira nigricans blue marlin Thunnus thynnus bluefin tuna Phtheirichthys slender suckerfish Thunnus thynnus young school bluefin lineatus Pomatomus saltatrix bluefish (juveniles) Thunnus thynnus school bluefin Pomatomus saltatrix bluefish Thunnus thynnus large school bluefin Rachycentron cobia Thunnus thynnus small medium bluefin canadum Remora australis whalesucker Thunnus thynnus large medium bluefin Remora brachyptera spearfish remora Thunnus thynnus giant bluefin Remora osteochir marlinsucker Trichiuridae snake mackerel family Remora remora remora Trichiuridae cutlassfish, unidentified Ruvettus pretiosus oilfish Trichiurus lepturus atlantic cutlassfish Sarda chilensis pacific bonito Xiphias gladius swordfish Sarda orientalis striped bonito Sarda sarda atlantic bonito Sarda spp. Sarda genus

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Table 2. Results of the binomial model on the proportion of positive trips.

Source DF Chi-Square Prob > Chi-Square Region 3 5007.5 <0.0001 Mode 1 1170.6 <0.0001 Season 3 1973.8 <0.0001 Year 20 1043.4 <0.0001 Mode*Region 3 527.3 <0.0001

Table 3. 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 Akaike’s Schwartz’s Likelihood Ratio P Scaled Dispersion Likelihood Information Bayesian Test Deviance Criterion Criterion Region + Mode + Season + Year + Mode*Region 2512.3 2514.3 2518.6 - - 333.96 10.18

Region + Mode + Season + Year + Mode*Region + Year*Season 2470.0 2474.0 2478.9 42.3 <0.0001 313.69 9.09

Region + Mode + Season + Year + Mode*Region + 2433.3 2439.3 2446.6 36.7 <0.0001 299.89 8.05 Year*Season + Year*Region

Table 4. Results of the lognormal model on the proportion of positive trips.

Source DF Chi-Square Prob > Chi-Square Season 3 397.7 <0.0001 Year 20 66.6 <0.0001 Region 3 79.0 <0.0001 Year*Season 55 399.0 <0.0001 Year*Region 56 346.6 <0.0001

Table 5. 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 logliklehood between two nested models. The final model is indicated with gray shading.

Catch Rates on Positive Trips -2 REM Log Akaike’s Schwartz’s Likelihood Ratio P Scaled Dispersion Likelihood Information Bayesian Test Deviance Criterion Criterion Season + Year + Region 26123.7 26125.7 26132.9 - - 9814.0 0.82 Season + Year + Region + Year*Season 25918.2 25922.2 25926.9 205.5 <0.0001 9770.8 0.80

Season + Year + Region + Year*Season + Year*Region 25720.4 25726.4 25733.6 197.8 <0.0001 9732.7 0.78

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Table 6. Nominal CPUE, number of trips, number of positive trip, proportion positive trips (PPT), standardized index of abundance and index statistics.

Relative YEAR Nom CPUE Trips Pos Trips PPT CV LCI UCI Index 1986 0.028 2456 214 0.087 1.880 1.091 0.320 11.049 1987 0.034 3148 282 0.090 0.906 1.569 0.098 8.421 1988 0.016 3584 237 0.066 0.548 2.066 0.042 7.218 1989 0.017 4164 323 0.078 0.554 1.979 0.044 6.917 1990 0.011 3354 168 0.050 0.344 2.700 0.019 6.299 1991 0.018 3666 252 0.069 0.552 1.981 0.044 6.903 1992 0.010 4100 233 0.057 0.379 2.309 0.025 5.733 1993 0.033 3617 409 0.113 0.867 1.495 0.099 7.570 1994 0.059 4204 724 0.172 1.667 1.084 0.286 9.723 1995 0.045 3685 516 0.140 1.367 1.235 0.199 9.366 1996 0.047 5076 659 0.130 0.448 2.150 0.032 6.206 1997 0.024 5088 494 0.097 0.403 2.198 0.028 5.730 1998 0.054 4854 719 0.148 0.800 1.458 0.095 6.768 1999 0.055 4255 574 0.135 1.663 1.081 0.286 9.658 2000 0.060 4814 682 0.142 1.432 1.147 0.229 8.943 2001 0.046 4877 767 0.157 1.527 1.054 0.271 8.596 2002 0.032 5191 492 0.095 1.070 1.261 0.152 7.529 2003 0.052 4815 644 0.134 1.201 1.176 0.186 7.738 2004 0.040 3924 506 0.129 1.045 1.302 0.143 7.651 2005 0.032 3604 405 0.112 0.954 1.363 0.123 7.409 2006 0.042 3760 541 0.144 1.394 1.150 0.222 8.747

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Figure 1. Proportion of positive trips, by year.

Figure 2. Nominal CPUE, by year.

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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).

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Figure 4. Frequency distribution of proportion positive trips by the strata year, region and fishing mode.

A) B)

Figure 5. Residuals of the fit to the lognormal model, by year (A), region (B) and season (C).

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Figure 6. Frequency distribution of nominal catch rates (fish per angler hour) on positive trips.

Figure 7. QQ-Plot: The cumulative normalized residuals from the lognormal model on the catch rates of positive trips.

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Figure 8. Nominal CPUE (blue solid line open symbols) and the delta-lognormal index (red solid line filled symbols) with 95% confidence intervals (dashed lines). Both series are scaled to a mean of 1.0 to facilitate comparison.

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