STANDARDIZED CATCH RATES FOR ALBACORE TUNA (THUNNUS ALALUNGA) FROM THE U.S. PELAGIC LONGLINE FLEET

Transcription

1 SCRS/29/1 Collect. Vol. Sci. Pap. ICCAT, 65(4): (21) STANDARDIZED CATCH RATES FOR ALBACORE TUNA (THUNNUS ALALUNGA) FROM THE U.S. PELAGIC LONGLINE FLEET Mauricio Ortiz 1 SUMMARY An index of relative abundance of albacore tuna was generated by standardizing catch and effort data from the United States pelagic longline fishery in the North Atlantic. This fleet also has an observer program with an average annual coverage of 5% of the trips (PLOP) since The standardization procedure evaluated the following factors: year, area, season, gear characteristics (light sticks, main line length, hook density, etc.) and fishing operation characteristics (bait type, fleet type and target species). Standard indices were estimated using Generalized Linear Mixed Models under a delta-lognormal modeling approach. RÉSUMÉ Un indice d abondance relative du germon a été créé en standardisant les données de prise et d effort provenant des pêcheries palangrières pélagiques des États-Unis menées dans l Atlantique Nord. Cette flottille dispose également d un programme d observateurs présentant une couverture annuelle moyenne de 5 % des navires (PLOP) depuis La procédure de standardisation a évalué les facteurs suivants : l année, la zone, la saison, les caractéristiques de l engin de pêche (baguettes lumineuses, longueur de la ligne principale, densité des hameçons, etc.) et les caractéristiques des opérations de pêche (type d appât, type de flottille et espèces cibles). Les indices estandard ont été estimés à l aide des modèles linéaires généralisés mixtes selon une approche de modélisation delta-log normale. RESUMEN Se generó un índice de abundancia relativa del atún blanco estandarizando los datos de captura y esfuerzo de la pesquería de palangre pelágico de Estados Unidos en el Atlántico norte. Esta flota cuenta también con un programa de observadores con una cobertura media anual del 5% de las mareas (PLOP) desde El procedimiento de estandarización evaluaba los siguientes factores: año, área, temporada, características del arte (bastones de luz, longitud de la línea madre, densidad de anzuelos, etc.) y las características de las operaciones pesqueras (tipo de cebo, tipo de flota y especie objetivo). Se estimaron índices estándar utilizando modelos lineales mixtos generalizados con una aproximación delta-lognormal. KEYWORDS Albacore, abundance, time series analysis 1. Introduction Assessment models for the northern albacore stock require relative indices of abundance for tuning population trends. In the past data collected from the U.S. pelagic longline fleet has been used to develop standardized catch per unit of effort (CPUE) indices for albacore and other highly migratory species (Ortiz and Diaz 24, Ortiz and Cramer 21, Ortiz et al. 2, Cramer and Ortiz 1999). Catch and effort data is submitted by fishers through a logbook program started in Since 1992, this fleet has also an observer program (PLOP) with an annual average coverage of 5% of the fleet trips. The Observer program collects detailed information on the fishing operations, gear characteristics and setting operations. The present report documents the analytical procedures for standardization of catch rates derived from the pelagic longline fleet data through 28. Catch in numbers and effort information for each longline set were obtained from the pelagic longline logbook database. 1 U.S. Dpt of Commerce NOAA/NMFS Southeast Fisheries Science Center SFD. 75 Virginia Beach Drive, Miami FL USA. Mauricio.ortiz@noaa.gov. 1338

2 2. Materials and methods Hoey and Bertolino (1998) described the characteristics and general operations of the U.S. Pelagic Longline Fleet that operates in the Atlantic US coast, the Gulf of Mexico and the Caribbean Sea. This fleet started to submit information of catch, gear and fishery operations in 1986 through a logbook program that become mandatory for all the fleet in For the U.S. pelagic fleet the main target species are swordfish and yellowfin tuna, although it also catches other tunas including bigeye, bluefin and albacore, and pelagic sharks. The pelagic longline logbook (PLL) data comprises a total of 29,152 record-sets from 1986 to 28 with complete catch and effort information. Of these 38,464 (13.2%) records had reported catches of albacore. Each record contains information of catch by set, date, geographical location, number of fish caught and number of hooks deployed (effort unit). In 1992, an Observer Program (PLOP) was initiated for this fishery. Currently about 5-7% of the pelagic fleet trips are monitored by observers (Keene et al. 26). Observers collect much more detailed information of the fishery operations, gear characteristics, and environmental-related information (sea surface temperature, wind, etc.), and fate of the fish hooked (kept or release). Observers also measure length and weight of each individual fish. The U.S. pelagic longline fishery extends from the Grand Banks in the north Atlantic to roughly the Equator, in the western Atlantic including the Caribbean Sea and the Gulf of Mexico (Figure 1). Eight geographical areas of longline fishing have been traditionally used for spatial classification including: the Caribbean Sea, the Gulf of Mexico, Florida east coast, south Atlantic Bight, mid-atlantic Bight, New England coastal, northeast distant waters, the Sargasso Sea, and the central Atlantic offshore waters. Calendar quarters were used to account for seasonal fishery distribution through the year (Jan-Mar, Apr-Jun, Jul-Sep, and Oct-Dec). Other factors evaluated in the analysis of catch rates were the use and proportion of light-sticks in the gear (expressed as the quartiles of the ratio light-sticks per hook), a classification of the fleet based on vessel features (type of vessel, size, tonnage, main target species, and main area of operation) denominated operations procedure variable (OP) (Hoey and Bertolino 1988). Fishing effort is reported as number of hooks per set, and nominal catch rates were estimated as number of albacore caught per 1 hooks for each record. Figure 2 shows the frequency distribution of the nominal logtransformed CPUE for positive set/trips of albacore and the annual trends of percent positive sets for albacore from the logbook database. The main target species of the US pelagic fleet are swordfish and yellowfin tuna, other tunas and pelagic species are mostly secondary targets for this fishery. Because fishing operations are largely determine by the species targeted, a proxy for a target variable was defined based on the proportion of swordfish catch reported by observation. This variable was categorized using the.25,.5, 75, and 1. quartiles of swordfish catch proportion. Sets targeting bottom or non-pelagic sharks were excluded, as well set with 1 or less hooks deployed. Due to management regulations related to swordfish and other pelagic species, time-area restrictions were implemented in 2 that affected significant areas of the pelagic longline fleet fishing grounds (Federal Register 2, Figure 1). These restrictions included two permanent closures to pelagic longline gear: a) on in the Gulf of Mexico known as The DeSoto Canyon, effective since Nov 2, and b) off the east coast of Florida effective since Mar 21. Other restrictions were time-closures in the US Atlantic coast: c) the Charleston Bump, off the South Carolina and Georgia coast close to pelagic longlines during February and April, d) the bluefin tuna protection area off the south New England coast closed for the month of June, and e) the Grand Banks area that was closed from Jul to Jan 9 22 (Figure 1). The effects of these time-closures on catch rates of albacore were evaluated by including a variable that defined if an observation belong to time-close area or not. Standardized index for albacore was estimated by Generalized Linear Mixed model approach. Because of the large proportion of zero observations, it was assumed that the observed albacore catch rates were the result of a two dependent stage processes: a) a probability of catch at least one albacore, times b) the conditional expected mean catch rate given that there is positive probability of capturing albacore. This model is commonly refer as a delta-model, in this case it was assumed that the probability of capture follow a binomial distribution, while the mean catch rate follow a normal error distribution of the log-transformed CPUE observed. Parameterization of the model used the GLM structure, where the probability of a successful set/trip (positive catch of albacore) was the result of a linear combination of significant factors and interactions evaluated. The logit function was used as link between the linear predictor and the binomial error response variable. In the case of the mean catch rate, the expected log-scaled CPUE was the result of a linear combination of significant factors and interactions evaluated assuming a normal error distribution and the identity link function. A deviance table analysis was used to 1339

3 determine the set of systematic factors and interactions that statistically explained the observed variability. The null model was set as the model with only the year factor (as the objective is to generate annual relative indices of abundance) while the full model included all factors and second-order interactions that lead to a model solution. Deviance analysis tables were constructed for each of the delta-model components. Each deviance table includes a) the deviance explained by the addition of each factor/interaction, b) the overall percentage of deviance explained by each factor/interaction in reference to the full model, and c) the Chi-square test between two consecutive nested models (with degrees of freedom equal to the number of additional parameters estimated by the nested) (McCullagh and Nelder, 1989). Final selection of explanatory factors and interactions was conditional on a) the relative percent of deviance explained by the factor/interaction, normally factors that explained 5% or more of the deviance were selected, and b) the Chi-square significant test. Once a set of fixed factors and interactions was specified, all interactions that included the year factor were evaluated as random interactions in order to obtain year estimates (Cooke 1997). The statistical significance of random interactions were evaluated between nested models using three criteria; the likelihood ratio test (Pinhero and Bates 2), the Akaike information criteria (AIC), and the Schwarz Bayesian information criteria (BIC) (Littell et al. 1996). For the last two criteria smaller values of AIC or BIC indicated best model fit. Analyses were done using the Glimmix and Mixed Procedures from the SAS statistical computer software (SAS institute Inc. 1997, Littell et al. 1996). The overall index of relative abundance was estimated as the product of the year effect least square means (LSMeans) of the binomial predicted probability of capture times the expected catch rate of the lognormal positive catch rates component. LSMeans estimates included a weight proportional to the observed margins of the input data, to account for the unbalance distribution of data. Lognormal estimates also included a logarithmic bias back-transformation correction factor as described by Lo et al. (1992). 3. Results and discussion Figure 3 shows the annual trends of albacore catch and effort from the logbook data. The first years increases were due to the increasing number of vessels submitting information, full reporting become mandatory in Fishing effort measured as number of hooks deployed, peak in the mid 199 s reaching 8.7 million hooks in 1997, since then effort has decrease to about 5.5 million hooks per year in 28. Catches of albacore (after 1992) have fluctuated from 4.1 to 12.7 thousand fish. They peak in 1995 with 12.7 thousand fish caught, then decline rapidly in 1996, increase again in subsequent years reaching 6.6 thousand fish in 21. Since 22 catches declined and have been about 4.8 thousand fish per year. A scatter plot corroborates the positive correlation between catch of albacore and fishing effort (Figure 4). Figures 5 and 6 show the spatial distribution of fishing effort and albacore catch (numbers of fish) by 5 squared degrees since Most of the albacore catch of the US Pelagic longline fleet is concentrated in the North east Atlantic coast, although catches in the Caribbean were also significant in the mid 199s. As indicated before, the pelagic observer data collects more detailed information of the fishing operations and environmental related data. Preliminary analysis with this data indicated that factors such hook type, length of main line, number of hooks per length unit of main line (hook density), and light-sticks (use and proportion in relation to number of hooks) do have an effect on catch rates of albacore (Table 1). However, the proportion of deviance explained by these factors is minor compared with the area and season, vessel type (OP), and year factor. As the main factors information is available from the logbook data, analysis of standardization continued with the overall PLL database. Table 2 presents the deviance analysis from the PLL data. The deviance table shows the model factor(s), the degrees of freedom for the added factor/interaction, the residual deviance of each model (row), the change in deviance due to the added factor/interaction (in the case of models with interactions, the change of deviance is compared to the last model without interactions i.e. all fix factors), the percent of total deviance explained by the added factor/interaction (This percent is in reference to the model with the minimum residual deviance in the table which represents the 1% deviance explained or so called full model ), and the Chi-square test probability between two consecutive models with degrees of freedom equal to the number of additional parameters estimated by the added factor/interaction minus one. The Chi-square test for models with interactions is also compared to the last model without interactions all fix factors. The deviance table highlights those factors and interactions that explained 5% or more of the overall deviance explained by the full model. In the deviance tables, interactions are formulated as fix factor interactions. The probability of capture albacore is primarily explained by the area, season, vessel type (OP), and target species (Targ2). Management areas (Mngarea, closure/non-closure areas) have also impact on the probability of catching albacore, however when included in the final model it prevented for obtaining yearly estimates of 134

4 LSMeans due to the lack of contrast between years. The conditional expected catch rates are also mainly explained by the same factors (area, year, season, OP, target spp, and Mngarea). Several interactions including the factor year were also considered important explanatory components in the model. Given the objective to generate annual indices, these interactions were assumed to be random interactions in the final model, and their significance was evaluated using the three criteria of likelihood ratio, AIC and BIC (Table 4). Final models for each of the delta model components were: log, positive catch for albacore, and Proportion of log. Mean expected catch rate given a positive probability catch of albacore. Once a final model was defined, a set of diagnostics were evaluated to determine; a) deviations from the model assumptions, and b) observations with significant influence or departure from the main trends. Figures 7 and 8 show the general diagnostic plots for each of the components in the delta lognormal model. These plots include: a) a diagnostic for the link function selection of the linear predictor versus the dependent variable transformed to a constant information scale, b) a diagnostic for the variance function of the linear predictor versus the deviance residuals, and c) a diagnostic for the error distribution of the linear predictor versus the standardized deviance residuals. In the case of the binomial sub-component, the variance function and error distribution plots indicated an over-dispersion of the data, with observed variance greater than the one estimated by a binomial distribution. The link function plot also suggests a possible missing interaction or explanatory variable. For the subcomponent of positive observations (lognormal submodel), the plots agree with the assumption of the model formulation. There is evidence of positive deviance particularly in the low catch rates, indicating a higher than expected number of observations with very low catch-rates of albacore. The review of observations with high influence and leverage indicated that most of these records came from logbooks of vessels that have very sporadic catches of albacore, with less than 5 years of catch reports. With the PLL database it is possible to identify vessel and the number of years that a given vessel has reported catch of albacore since On average vessels with less than 5 years of albacore catch (between ) reports account for less than 16% of the annual catch. Figure 9 shows the average annual proportion and cumulative percent of total albacore catch versus the number of years with catches of albacore from the PLL database. It is expected that vessels that have greater number of years of catch represent a more consistent sampling unit for catch rate evaluation of albacore. Thus it was decided to restrict the standardization of CPUE to include only records from vessels with at least 8 or more years of historic albacore catch. This restriction substantially removes observations prior considered as outliers by their high residuals in the GLM model in general; it however did not change the estimated standardized CPUE trend (Figure 1) (Appendix 1). Figure 11 and Table 4 show the estimated relative index of abundance derived from the PLL data analyses. Overall, the index indicates a stable trend through the time series, with a peak in 21 and values above the mean in 199/91, 1994, 1997, 2/2, and 25. In 28 the index is below the overall average at about.8. As some of the model assessments for northern albacore stock require indices of abundance by season, the same final delta-lognormal model was use to estimate seasonal indices by including in the model the Year*Season fix interaction. In 1986 there were not observations for all seasons (trimesters), thus the season index was only estimated from 1987 forward. Table 5 and Figure 12 show the estimated seasonal relative index for north Atlantic albacore. References Keene, K.F. Beerkircher, L.R. and Lee, D.W. 26, SEFSC Pelagic Observer Program data summary for NOAA tech Memo NMFS-SEFSC p. Cramer, J. and Ortiz, M. 1999, Standardized catch rates for bigeye (Thunnus obesus) and yellowfin (T. albacares) from the U.S. longline fleet through Collect. Vol. Sci. Pap. ICCAT, 49(2): Federal Register, 2, Atlantic Highly Migratory Species; Pelagic Longline Management; Final rule. 5CFR part 635. Vol. 65, No. 148 August 1,

5 Hoey, J.J. and Bertolino, A. 1988, Review of the U.S. fishery for swordfish, 1978 to Colloect. Vol. Sci. Pap. ICCAT, 27: Lee, D.W. and Brown, C.J. 1999, Overview of the SEFSC Pelagic Observer Program in the northwest Atlantic from Collect. Vol. Sci. Pap. ICCAT, 49(4) : Littell, R.C., Milliken, G.A., Stroup, W.W. and Wolfinger, R.D. 1996, SAS System for Mixed Models, CaryNC: SAS Institute Inc., pp. 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: McCullagh, P. and Nelder, 1989, Generalized Linear Models 2nd edition, Chapman & Hall. Ortiz, M and Diaz, G.A. 24, Standardized catch rates for albacore (Thunnus alalunga) from the U.S. pelagic longline fleet. Collect. Vol. Sci. Pap. ICCAT 56(4): Ortiz, M. and Cramer, J. 21, Standardized catch rates for albacore (Thunnus alalunga) from the U.S. Pelagic Longline fleet Collect. Vol. Sci. Pap. ICCAT, 52(4): Ortiz, M., Cramer, J., Bertolino, A, and Scott, G.P. 2, Standardized catch rates by sex and age for swordfish (Xiphias gladius) from the U.S. longline fleet Collect. Vol. Sci. Pap. ICCAT, 51(5): Pinheiro, J.C. and Bates, D.M. 2, Mixed-effect models in S and S-Plus. Statistics and Computing. Springer- Verlag New York, Inc. SAS Institute Inc. 1997, SAS/STAT Software: Changes and Enhancements through release Cary NC: SAS Institute Inc., pp. Scott, G.P., Restrepo, V.R., and Bertolino, A.R. 1993, Standardized catch rates for swordfish (Xiphias gladius) from the US longline fleet through Collect. Vol. Sci. Pap. ICCAT, 4(1):

6 Table 1. Deviance table for catch rates of albacore from the Pelagic Observer Program database. ALBACORE NORTH ATLANTIC Model factors positive catch rates values degrees of Residual Change in % of total freedom deviance deviance deviance p Year %.112 Year Location % <.1 Year Location Season % <.1 Year Location Season Op %.11 Year Location Season Op Lghtc %.931 Year Location Season Op Lghtc Targetsp %.466 Year Location Season Op Lghtc Targetsp Mainlenc %.59 Year Location Season Op Lghtc Targetsp Mainlenc Hktype %.751 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc %.169 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Lghtc*Targetsp %.728 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Op*Lghtc %.785 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Location*Targetsp %.495 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Season*Lghtc %.39 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Location*Op %.635 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Op*Targetsp %.35 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Mainlenc %.491 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Hktype %.552 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Season*Targetsp %.15 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Hkdenc %.786 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Lghtc %.319 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Location*Season %.2 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Season*Op %.2 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Targetsp %.158 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Season %.63 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Op %.472 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Location %.499 Model factors proportion of positive / total obs degrees of Residual Change in % of total freedom deviance deviance deviance p Year % <.1 Year Location % <.1 Year Location Season % <.1 Year Location Season Op % <.1 Year Location Season Op Lghtc % <.1 Year Location Season Op Lghtc Targetsp % <.1 Year Location Season Op Lghtc Targetsp Mainlenc % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype %.35 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Lghtc*Targetsp %.8 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Location*Targetsp %.14 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Op*Targetsp %.43 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Op*Lghtc %.3 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Mainlenc %.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Location*Op % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Hktype % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Season*Lghtc % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Lghtc % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Season*Targetsp % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Hkdenc % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Season*Op % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Targetsp % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Season % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Location*Season % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Op % <.1 Year Location Season Op Lghtc Targetsp Mainlenc Hktype Hkdenc Year*Location % <

7 Table 2. Deviance table catch rates of albacore from the pelagic longline Logbook database. ALBACORE ATLANTIC PELAGIC LONGLINE Model factors positive catch rates values degrees of Residual Change in % of total freedom deviance deviance deviance p Year % <.1 Year Area % <.1 Year Area Season % <.1 Year Area Season Op % <.1 Year Area Season Op Targ % <.1 Year Area Season Op Targ2 Lghtc % <.1 Year Area Season Op Targ2 Lghtc Mngarea % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty %.296 Year Area Season Op Targ2 Lghtc Mngarea Baitty Op*Targ %.18 Year Area Season Op Targ2 Lghtc Mngarea Baitty Op*Lghtc % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Area*Targ % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Season*Lghtc % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Season*Targ % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Area*Op % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Year*Targ % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Season*Op % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Year*Lghtc % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Year*Season % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Area*Season % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Year*Op % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Year*Area % <.1 Model factors proportion of positive / total obs degrees of Residual Change in % of total freedom deviance deviance deviance p Year % <.1 Year Area % <.1 Year Area Season % <.1 Year Area Season Op % <.1 Year Area Season Op Targ % <.1 Year Area Season Op Targ2 Lghtc % <.1 Year Area Season Op Targ2 Lghtc Mngarea % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Season*Targ % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Year*Targ % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Op*Targ % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Area*Targ % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Year*Lghtc % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Area*Lghtc % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Season*Lghtc % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Area*Op % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Year*Season % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Year*Op % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Season*Op % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Year*Area % <.1 Year Area Season Op Targ2 Lghtc Mngarea Baitty Area*Season % <

8 Table 3. Table of random interaction(s) evaluation for selected model of the delta-lognormal standardization analysis. ALBACORE Pelagic Longline -2 REM Log likelihood Akaike's Information Criterion Schwartz's Bayesian Criterion Likelihood Ratio Test Proportion Positives Year Area Season OP Target MngArea * Year Area Season OP Target MngArea Year*Area Positive Catch Year Area Season OP Target MngArea Year Area Season OP Target MngArea Year*Area Year Area Season OP Target MngArea Year*Area Year*OP * Year Area Season OP Target MngArea Year*Area Year*OP Year*Lights Table 4. Estimated standardized relative index of abundance for north Atlantic albacore from the U.S. pelagic longline fishery. Year N obs Nominal Standardized Coeff Var Index 95% confidence intervals % % % % % % % % % % % % % % % % % % % % % % %

9 Table 5. Estimated standardized seasonal relative index of abundance for North Atlantic albacore from the U.S. pelagic longline fishery Year Season N obs Nominal Standardized Coeff Var Index 95% confidence intervals % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %

10 % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %

11 Figure 1. Geographical area distribution of the US Pelagic longline fishery: CAR Caribbean Sea, GOM Gulf of Mexico, FEC Florida east coast, SAB south Atlantic bight, MAB mid Atlantic bight, NEC north east coastal Atlantic, NED north east distant waters, SAR Sargasso Sea, and OFS mid Atlantic offshore waters. Shaded areas represent the time-area closures affecting the pelagic longline fishery. Permanent closures: (1) DeSoto Cayon in the US Gulf of Mexico, (2) Florida east coast, Non-permanent closures: (3) the Charleston bump area closed Feb-Apr, (4) the bluefin tuna protection area closed Jun, and (5 the Grand Banks closed Jul-21 to Jan

12 Millions of hooks deployed Mandatory report logbooks Number of fish thousands Effort Albacore N fish / 1 hooks CPUE Proportion sets percent sets with albacore catch Figure 3. Annual trends of fishing effort (hooks deployed) and number of albacore caught by the U.S. Pelagic longline fleet since Full reporting by the fleet started in thousand albacore Figure 2. Frequency distribution of the nominal logtransformed CPUE for positive set/trips of albacore from the Pelagic Longline fishery (top), and annual trends of nominal CPUE (blue) and percent of trip/sets with catch of albacore (bottom) Million of hooks deployed Figure 4. Scatter plot of fishing effort (hooks deployed) versus number of albacore caught per year. 1349

13 latitude longitude Figure 5. Trend of total annual fishing effort (1 hooks deployed) distribution by 5 lat.-long. areas from the U.S. pelagic longline fleet since latitude longitude Figure 6. Trend of total annual catch of albacore (numbers of fish) by the U.S. Pelagic longline fleet by 5 lat.- long. areas since

14 Figure 7. Diagnostic plots for the binomial sub-component of the delta model. Figure 8. Diagnostic plots for the lognormal sub-component of the delta model. 16% 14% 12% Avg Yr CumPercent 1% 8% % 8% 6% 6% 4% Number of fish Total Catch Catch Vess 8+ 4% 2% % Number of years with report of albacore catch 2% % Figure 9. Average and cumulative percent of total albacore catch accounted by vessels that have reported x number of years since 1986 in the Pelagic longline logbook database (left). Total catch (numbers of fish) and partial catch of albacore from vessels that have at least 8 or more years of albacore records in the PLL database (right)

15 2 Nominal Vess 5+ Vess 1+3 Vess Scaled CPUE (fish / 1 hooks) Figure 1. Comparison of standard relative indices of albacore estimated when observations were restricted to vessels with a) 1 or more years of historic catch (all records, Vess 1+), b) 5 or more years of historic catch (Vess 5+), and c) 1 or more years of historic catch (Vess 1+). 3.5 ALBACORE STANDARDIZED CPUE DELTA-LOGNORMAL MODEL Vessels 8+ Scaled CPUE (fish / 1 hooks Standardized Nominal Figure 11. Standardized relative index of abundance for north Atlantic albacore from the U.S. pelagic longline fishery Red-diamond markers show the nominal annual CPUE, the solid blue line the relative index with 95% confidence intervals including only observations from vessels with 8 or more years of historic catch of albacore. 1352

16 ALBACORE STANDARDIZED PLL CPUE DELTA-LOGNORMAL MODEL 6. Scaled CPUE (fish / 1 hooks Standardized Nominal Figure 12. Standardized seasonal relative index of abundance for north Atlantic albacore from the U.S. pelagic longline fishery

17 Appendix 1 Diagnostic plots for observations in GLM models The relative influence of a given observation in a GLM is measured by the difference between parameter estimates with and without the given point. Depending on the indicator watched, influence statistics can be grouped into: a) measures of changes in the GLM objective function such likelihood distance and restricted likelihood distance (RLD), b) influence on GLM parameter estimates such Cook s distances and MDFFITS statistic, c) influence on precision of estimates such covariance ratios (CovRatio) and CovTrace, d) influence on fitted and predicted individual observations such as PRESS residuals, PRESS statistic and DFFITS, and e) outliers such studentized residuals, standardized residuals and leverage estimates. For GLM models with uncorrelated data, it is not required to actually refit every possible model combination, influence statistics can be estimated from the full data fitted variance-covariance matrix (Ref). In the case of mixed GLM models, however observations not only affect fix parameters estimates but the variance-covariance matrix, on which fix estimates depend upon. Therefore in some cases influence diagnostics cannot be fully estimated. Influence diagnostics can be estimated for single observation removals or for grouping of observations usually associated with a classification characteristic to be evaluated. Leverage estimates are given by the diagonal of the projection matrix (or H matrix), and in the GLMs fitted by iterative reweighting least squares (IRLS) will depend upon the weighting factors. Large leverage values indicate unusual observations. While leverage may indicate potential effects in model fitting, influence diagnostic do actually measure the change in parameter estimates associated with a given observation (or set of observations). Cook s distances measures the relative difference in the parameter estimates of the model without a given observation, while DIFFTS measures the relative difference in the predicted values (ref). Similarly, large values of Cook s distances of DIFFTS indicate observations with high influence in the model fitting results. Restricted likelihood distance and likelihood distance represent the two times difference between the likelihood values of the model with and without the given observation, thus it reflects the influence of a given observation on the overall parameter estimates. 1354

18 A B C D E F Figure A1. Diagnostic plots for the binomial submodel of observations with significant influence or departure from the main trends. A) deviance residuals, b) Cook s distance versus leverage scatter plot, c) restricted likelihood distances, d) PRESS residuals, e) DFFITS residuals, and f) covariance ratio plot (see Appendix 1 text for detailed explanations). 1355

19 A B C D E F Figure A2. Diagnostic plots for the lognormal submodel of positive observations with significant influence or departure from the main trends. A) deviance residuals, b) Cook s distance versus leverage scatter plot, c) restricted likelihood distances, d) PRESS residuals, e) DFFITS residuals, and f) covariance ratio plot (see Appendix 1 text for detailed explanations). 1356