Makaira Nigricans) Stock in the Atlantic Ocean 2018: Pre-Decional Stock Assessement Model

SCRS/2018/097 Collect. Vol. Sci. Pap. ICCAT, 75(5): 1026-1050 (2018)

CURRENT STATUS OF THE BLUE MARLIN (MAKAIRA NIGRICANS) STOCK IN THE ATLANTIC OCEAN 2018: PRE-DECIONAL STOCK ASSESSEMENT MODEL

M. Schirripa1

SUMMARY

This document describes the pre-decisional base case model configured to estimate the status of the blue marlin (Makaira nigricans) stock for the June 2018 stock assessment meeting. The model configuration is based on the 2011 model used to provide management advice. Uncertainties specifically accounted for were growth, length at 50% maturity, stock-recruitment steepness, natural mortality and conflicting CPUE trends. Uncertainties not accounted for where, inter alia, seasonal and/or aerial differences in life history traits and illegal, unreported and unregulated (IUU) landings. Several assumptions were investigated via different model configurations, namely three steepness values (0.40, 0.50 and 0.60) and three natural mortality values (0.10, 0.122 and 0.139). Uncertainty distributions around all nine combinations the terminal year estimates of B/BMSY and F/FMSY were constructed using the means and standard deviations and assuming bivariate normal distributions. When considering all combinations simultaneously, 81 percent of the points were in the red zone of the KOBE matrix (both overfished and overfishing) 18 percent in the yellow, and 1 percent in the green (neither overfishing nor overfished).

RÉSUMÉ

Le présent document décrit le cas de base du modèle pré-décisionnel configuré pour estimer l'état du stock de makaire bleu (Makaira nigricans) pendant la session d'évaluation du stock réalisée en juin 2018. La configuration du modèle est basée sur le modèle de 2011 utilisé pour formuler un avis de gestion. Les incertitudes spécifiquement prises en compte étaient la croissance, la longueur à 50% de la maturité, la pente à l'origine de la relation stock-recrutement (steepness), la mortalité naturelle et les tendances contradictoires de la CPUE. Les incertitudes non prises en compte étaient, entre autres, les différences saisonnières et/ou géographiques dans les caractéristiques du cycle vital et les débarquements illégaux, non déclarés et non réglementés (IUU). Plusieurs postulats ont été étudiés par le biais de différentes configurations du modèle, à savoir trois valeurs de steepness (0,40, 0,50 et 0,60) et trois valeurs de mortalité naturelle (0,10, 0,122 et 0,139). On a élaboré des distributions de probabilités pour les neuf combinaisons des estimations de l'année terminale de B/BPME et F/FPME en utilisant les moyennes et les écarts-types et en postulant des distributions normales à deux variables. Si l’on tenait compte de toutes les combinaisons simultanément, 81% des points se trouvaient dans la zone rouge de la matrice de Kobe (surexploitée et victime de surpêche), 18% dans la zone jaune et 1% dans la zone verte (ni surexploitée ni victime de surpêche).

RESUMEN

Este documento describe el caso base del modelo predecisivo para estimar el estado del stock de aguja azul (Makaira nigricans) para la evaluación de stock de junio de 2018. La configuración del modelo se basa en el modelo de 2011 utilizado para proporcionar asesoramiento en materia de ordenación. Las incertidumbres tenidas en cuenta específicamente fueron crecimiento, talla al 50 % de madurez, inclinación stock-reclutamiento, mortalidad natural y tendencias contradictorias de la CPUE. Las incertidumbres no tenidas en cuenta fueron, entre otras, las diferencias estacionales y/o de área en las características del ciclo vital y los desembarques ilegales, no declarados y no reglamentados (IUU). Se investigaron diversos supuestos mediante diferentes configuraciones del modelo, principalmente tres valores de inclinación (0,40, 0,50 y 0,60) y tres valores de mortalidad natural (0,10, 0,122 y 0,139). Las distribuciones de la

1 NOAA Fisheries, Southeast Fisheries Center, Sustainable Fisheries Division, 75 Virginia Beach Drive, Miami, FL, 33149-1099, USA. [email protected] 1026 probabilidad para las nueve combinaciones de las estimaciones del año terminal de B/BRMS y F/FRMS se construyeron utilizando las medias y las desviaciones estándar y asumiendo distribuciones normales bivariables. Al considerar todas las combinaciones de manera simultánea, el 81 por ciento de los puntos se encontraba en la zona roja de la matriz de KOBE (tanto sobrepescado como experimentando sobrepesca), el 18 por ciento en la amarilla y el 1 por ciento en la verde (ni sobrepescado ni experimentando sobrepesca).

KEYWORDS

Blue Marlin, Stock assessment, Population modeling

1. Introduction

Blue marlins (Makaira nigricans) are a large apex pelagic predator species within the family of Istiophorida (the billfish). Their growth rate is among the fastest documented for a fish species and can reach weights in excess of one thousand pounds with females growing significantly larger than males (cite). They display extensive movements within and across the Atlantic, as demonstrated from various tagging studies (cite). Spawning takes place over a wide seasonal time range with a peak occurring in the summer months. Little information is available on the larval and juvenile stages as they rarely occur in biological samples.

The International Commission for the Conservation of Atlantic Tuna (ICCAT) is responsible for the management of the Atlantic blue marlin. Catches of blue marlin are predominately incidental from surface longline fisheries targeting tuna, however, there are also documented catches from near-shore gillnet fisheries. There is also a substantial targeted recreational fishery in North and South America as well as the Caribbean, with most of the fish being released. The ICCAT manages the Atlantic as a single unit stock.

2. Method

Continuity Model

Methods. With the goal of examining how only the updated data would change the perception of the stock status, the 2011 base model was re-run with the only updating the catch, CPUE, as well with and without the updated length data. Details of this model are given in (ref). Some new indices (Brazilian longline and Brazilian recreational) were only presented at the 2018 blue marlin Data Preparatory (DP) so these could not be included in the continuity model. Trends in spawning stock biomass, F/FMSY and B/BMSY were used to compare the results of the 2011 base model and the 2018 continuity model.

As a means to test the prediction capabilities of the continuity model forecasts were made with the 2011 model using the actual 2010-2016 landings. Two key parameters were fixed at the values estimated by the 2018 continuity model, virgin recruitment (R0) and steepness (h). This process asks the question: If the values of R0 and h from the 2018 continuity model are in fact correct, how do the forecasted values of B/BMSY from the 2011 model compare with those estimated from the 2018 continuity model?

Results. The introduction of the updated data resulted in the estimated blue marlin population to be smaller and more productive than previously estimated (Figure 1, top). Trends in spawning stock biomass (SSB) and the relative benchmarks, B/BMSY (Figure 1, center) and F/FMSY (Figure 1, bottom), were in general agreement between all three models. The 2011 assessment suggested a steadily declining SSB whereas the updated data suggests a slowing of the decline when the lengths were included and even a slight increasing trend when the lengths were not used. When both the CPUEs and lengths are used in the model the declining trend in B/BMSY slows. But when only the CPUE are included in the fit the beginning of a possible increasing trend in B/BMSY was evident. Both of the continuity models suggest a continued decline in the trend in mortality.

The 2018 continuity model estimated a lower SSB at MSY when using the length information and even lower when only the CPUE data was considered. Fishing mortality at MSY was higher in the 2018 model with lengths included and even higher when not. The estimate of steepness increased from 0.34 in the 2011 assessment to 0.47 with lengths and 0.49 without lengths. Consequently, the estimated increase in productivity is being driven more by the updated CPUEs and to a lesser extent the updated length compositions.

1027 The forecast of the 2011 model using the actual 2010-2016 mt landings resulted in a very similar 2016 value of B/Bmsy as did the 2018 continuity model including the lengths, although the path to this value differed (Figure 2, top). When the lengths were not considered not only where the 2016 values very similar, the path to this value was also very similar (Figure 2, bottom). The 2011 assessment suggested for the stock to increase landings should not exceed 2000 mt. In response, ICCAT recommendation 2011-07 that a TAC of 2000 mt for blue marline to be established for 2012. Landings for 2012-2016 were 2698, 2055, 2710, 2107 and 2016 mt for an average of 2325 mt. This level of landings. Projections from the 2011 predicted that landings at 2500 mt would result in a relatively flat population trajectory in the near term.

In summary, compared to the 2011 assessment, when only the CPUE and length data is updated the population is showing signs of increase and higher productivity. This increase in productivity is being driven in great part by the updated CPUEs and to a lesser extent the updated length compositions. It has yet to be determined if the newly introduced fishery fleet structure, CPUEs and length information will change this perception. Forecast of 2011 model to 2016 is in good agreement with 2018 continuity model with regard to status of the stock and 2011 management advice.

Base Model

Fleet Structure. The assessment model was configured with five fleets: (1) artisanal-gillnet, (2) longline, (3) purse seine, (4) rod & reel, and (5) Fish Aggregation Device (FAD) fleet. The artisanal fleet uses mostly gillnet gear. The FAD fishery uses mostly purse seine gear. The FAD fishery fleet is an additional fleet not considered in the 2011 assessment. Even though no CPUE and only a small number of lengths are available from this fishery, given the increased use of FADs in general, it was believe to be important to maintain the individual identity of this fishery for future examination.

The decisions regarding the reclassification of “gillnet” to “artisanal’ as well as the addition of a FAD fishery were made at the blue marlin DP meeting. The classification from “gillnet” to “artisanal” was made to better reflect the variety of gears used for this fishery: drift-gillnets, trolling-hooks, longline). The FAD fleet is primarily industrial purse seins fishing for tropical tuna. The addition of the FAD fleet was made to provide a finer resolution and to more accurately classify the landings of this type of fishing as it has become apparent that fishing on FADs has been gradually increasing.

Axes of Uncertainty in model parameters. While there is a great deal of uncertainty associated with the landings of blue marlin, the unreported discards and discard mortality, the two primary axes of uncertainty regarding model parameters that were identified during the DP meeting where natural mortality (M) and S/R steepness (h). Values of M to be evaluated were to be 0.10, 0.122 (from longevity), and 0.139 (from the previous assessment). Steepness would first be attempted to be estimated from within the assessment model based on the observational data. If estimates were not deemed reasonable for slow growing, long lived highly migratory species (see DP meeting report) then values of 0.50, 0.60 and 0.70 would be investigated.

Observational Data

The entire suite of observational data used in the base model is shown in Figure 3.

Landings. Blue marlin landings were provided by the ICCAT Secretariat. Landings by the fleet structure used here are shown in Figure 4.

Discards. Only one CPC (US) reports discards of blue marlin from longline gear. In order to utilize these discards one would have to make the assumption that the entire longline fleet discards at the same rate as the US. Or, that the US represents the vast majority of total discards. Given the unique and more stringent regulations regarding the landing of blue marlin in the US, it was not reasonable to use this as a representative fleet.

Percent discards were available from the US rod & reel fleet. Discards were quite low from 1970 to approximately 1987, after which there was a general trend to release more fish until approximately 200 when nearly all fish were release. As in the 2011 assessment, a release mortality of 5 percent was assumed. Discards data is collected from tournaments and are assumed not only to represent the entire US fishery, but the rod & reel fleet in this assessment. While this assumption may be questionable, as the rod & reel fleet is thought to be essentially a trophy fishery it is based on a trophy blue marlin is characterized the same regardless of where it is caught.

1028 Indices of abundance. Thirteen CPUE time series were available at the DP meeting. These included several longline, several rod & reel, and a gillnet indices: US longline (observer), US rod & reel, Venezuelan longline, Venezuelan rod & reel, Chinese-Taipei longline (broken into 3 stanzas), Brazilian longline, Brazilian rod $ reel, Ghana gillnet, and a historical Japanese longline index. The CV of each index of abundance was taken directly from the work presented at the DP meeting. However, in the case that the CV was less than 0.30, the value 0.30 was used. Length Compositions. Length compositional data were available for the artisanal-gillnet, longline, and rod & reel fleets. Expansion of the length compositions to the landings (, as is done in VPA analysis.

Mean length at age. The estimation of growth of blue marlin has been particularly difficult. Wilson et al. (1991) found that the sex-related size differences in blue marlin are related to differential growth between sexes and are not due to differential mortality, a justification for using the same natural mortality for each. Goodyear (2003) modeled growth for Atlantic blue marlin by combining data for the first 16 months of life from Prince et al. (1991) with sizes for older fish from Wilson (1991). Growth estimates for Pacific blue marlin were also available (Shimose 2011).

Attempts were made to utilize preliminary age and growth data presented during the DP meeting (SCRS_P_2018_001). However, the results of this study were preliminary as not all samples had been processed at the time of this assessment. The growth function (a SS modified von Bertalanffy function) parameters were estimated using informative priors based on the means of Goodyear (2003) and Shimose (2015) values, normally distributed and a CV of 10 percent.

Ratio of yellowfin tuna to bigeye tuna for the early Japanese fleet. As agreed upon at the DP meeting, this ratio was used to account for time varying catchability of the early Japanese CPUE time series. This ratio was calculated from the ICCAT landings from 1959-1998. The addition of this parameter greatly reduced the negative log likelihood of the model (i.e. .resulted in a much better fit to the CPUE). Even though, a healthy skepticism should be maintained regarding a possible spurious correlation. While the hypothesis that the ratio could be used as proxy for depth was derived before the inclusion into the model, there still exists the possibility that the early drop in the Japanese CPUE was due to, at least in part, a sharp decline in the blue marlin population.

Model Configuration

Natural mortality. The only actual estimated value for natural mortality (M) was reported by Lauretta (2014). A capture-recapture model for estimating M was used to arrive at the estimate. Estimated values of M were 0.10 with a CV = 0.82, with a range of +/-2SD (<0 – 0.26). Based on the age and growth and longevity data provided during the DP meeting, the natural mortality for the base model was set at 0.122. However, values of 0.10 and 0.139 (previous assessment) were also investigated.

Fecundity and Maturity. Values from the ICCAT handbook were used as they represented the most up to date information http://www.iccat.int/Documents/SCRS/Manual/CH2/2_1_6_BUM_ENG.pdf Length at 50% maturity for females was set at 256 cm LJFL.

Stock-recruitment relation. Despite the importance of the value of the steepness (h) of the stock-recruitment function, this parameter is very often difficult to estimate accurately with the available data. The discussions of the DP Group concluded that the values of steepness based on simulation studies (approximately 0.87) do not necessarily represent the best prior for Atlantic blue marlin. Based on these discussions the Group decided to use the three possible values for steepness of 0.4, 0.5 and 0.6. The lower bound was selected based on the value estimated in the last blue marlin assessment. The upper bound was based on the informed decision that white marlin are more productive than blue marlin. The ICCAT estimated value of steepness for white marlin is approximately 0.6. A 0.5 steepness value was adopted as the base case value. However, the model would also be allowed to provide an estimate as dictated by the observational data.

Recruitment standard deviations (sigma r). Sigma_r, or standard deviation in annual recruitment deviations, dictates to what extend the estimated recruitments deviate from the S/R curve. Standard practices are mixed with no definitive method being hailed as the best. Analysis of the blue marlin sigma_r in this assessment showed that the model fit best at a very (unreasonably) high value; 1.73. Several model configurations (sigma_r = 0.2, 0.4, 0.6 and 0.8) were made and the resulted examined for trends. Examination of the likelihoods for each sigma_r value revealed that the CPUE time series were driving the estimate of sigma_r to high values. This is because most of the variation in the observational data is seen in the CPUE trends. These trends have large fluctuations that can

1029 mathematically be best accounted for by large deviations in recruitment. Given the sharp increases and subsequent decreases in the CPUE fluctuations, and that fact that most CPUE are an index of larger fish, it is highly unlikely that the fluctuations are reflective of high variation in recruitment. Consequently, sigma_r was fixed at a commonly used level of 0.60 and the log-normal bias accounted for using the methods of Methot and Taylor (2011).

Selectivity. Selectivity was assumed to be asymptotic for the longline and the rod & reel fisheries. The artisanal/gillnet fishery fish was allow to have a dome-shaped selectivity. Due to a sparse sampling of lengths from the FAD fishery, its selectivity was mirrored to the artisanal/gillnet selectivity. The rod & reel fishery selectivity was configured to account for changes in minimum legal size, based on US regulations. Examination of several of the selectivity parameters where found to have very large standard deviations, suggesting that the estimated value was not well defined. When some consistency was found in estimating the values of the parameters the values were used as informative priors with CV’s of 20%. This allowed for the parameter to still be estimated but provided increased model stability. The resulting fit to the length composition data was deemed acceptable and indicated that the parameter priors were set to reasonable values.

Data weighting. All CPUE time series were given an equal weighting of 1.0 (except when particular times series were set to 0 for sensitivity analysis, see below). The effective sample size of the length compositional data were adjusted using the Francis (2011) variance adjustment method.

3. Model Fit to Observational Data

Parameter estimates. A total of 63 parameters were estimated. The parameter values and standard deviations are given in Table 1. Some selectivity parameters had essentially infinite standard deviations and required informative priors to maintain stability in the model in the subsequent variations in natural mortality and steepness. Priors where chosen by the ending model fit of the base model, which did converge. Inspection of the length compositions suggested that these value were appropriate for use as priors. The estimated values of these parameters did not move off the assigned prior value, indicating the lack of the signal in the data.

Fit to growth: The estimated growth function is shown in Figure 5 (top). While the mean of both the Goodyear (2003) and Shimose (2015) values were used as informative priors, the resulting estimated curve was more similar to Goodyear (2003) than to Shimose (2015). As a reference, the female L∞ (284.65 cm LJFL) was compared to the history of length compositional data collected from the longline fishery (Figure 5, bottom). There was substantial agreement between the estimated L∞ as it corresponded in an expected manner to the largest fish observed in the fishery.

Fit to indices and discards. The fit to the various CPUE time series are shown in Figures 6-8. There were obvious conflicts in the trends of the various CPUEs. This could have been due to some of the CPUE’s coming from geographically restricted areas, or direct targeting of blue marlin in some fisheries and as non-targeted bycatch in others. It may also be due to model misspecification, either the assessment model or the GLM standardization models. Several of the fleets are known to have changed gear configuration within the time frame of the CPUE and these are not always possible to standardize for within the GLM model.

Fit to length compositions. The fit of the model to the overall length compositions is show in Figure 9. Fits to the lengths comps were considered reasonable. The best fits were to the Artisanal-gillnet and longline lengths while the fit to the rod & reel was not as good. The rod & reel lengths were subject to a variety of management regulations (minimum legal size) which were not consistent between all CPC data that went into the compositions. Expected mean weights were derived from the length information and the length-weight relationship. The expected and observed mean weights by gear are shown in Figure 10.

4. Model Diagnostics

Fit to independent index of mortality. The estimate of F/Fmsy from the Base model was compared to the estimated trend in the mortality indicator NZ50 (Goodyear 2015) (Figure 11). The NZ50 estimate used the same length compositional data as used in the Base model. However, to ensure that the same length data was not used twice, once in each analysis, the Base model was fit without using the length compositional data. Trends in the overall mortality from 1970-2016 were in overall directional agreement. Both indices increase from 1970 to approximately 1999 and then showed a decreasing trend. Bother trends were in unison with the landings.

1030 Profile analysis. Profiling was applied to the steepness parameter. Steepness was fixed at values of 0.40 to 0.95 and the resulting profile of likelihood was examined for a global minimum in the negative log likelihood (-LL) (Figure 12). The total -LL showed a clear minimum at h = 0.70. However, it should be noted that the difference between the minimum and maximum negative log likelihood values was on 7 units. This is not a large number of units given the wide range of steepness values explored.

Overall the length compositional data also showed a minimum –LL at a steepness of around h=0.68. The survey data however showed a minimum at a steepness of 0.50. Given that the minimum –LL of the length compositions was very close to the minimum of the total it might be concluded that the length information was having a substantial influence on the estimation of steepness. Examined individually the Artisanal-gillnet lengths had a minimum –LL are the lowest steepness values (h=0.40) while the longline showed a minimum of h=0.90, demonstrating a direct tension between these two data sources. The rod & reel lengths had essentially no curvature within the range of steepness values examined. This lack of signal is likely due to the irregular and time varying shape of the length composition that were highly influenced by management regulations.

There was also obvious tension between the CPUE time series with some fitting better at low steepness values and other at high values. The strongest trend was exhibited by the Chinese-Taipei_early time series, which showed a minimum –LL at h=0.4. Chinese-Taipei_early also demonstrated a propensity for low steepness values, but with a difference of only about 2 –LL units. Venezuela sport CPUE also showed a minimum at lowest steepness values but a difference of approximately 1.7 units. Differences of less than 2 units are considered inconsequential. In conflict with the above trends were the US rod & reel and the Ghana gillnet indices, which had the small –LL at the highest steepness values. The two indices that demonstrated the most tension where the Chinese-Taipie_early (low steepness) and the US rod & reel (high steepness).

Influence of individual CPUE time series. The above analysis demonstrated that it should be determined to what extent the Chinese-Taipei and US rod & real CPUEs influenced the trends in estimated bench marks. This was investigated by removing each of the CPUE time series one at a time and examining the trends in B/BMSY and F/FMSY (Figure 13). The two indices with the most influence on the benchmark trends were the US rod & real index (positive influence) and the Chinese-Taipei_late index (negative influence). Removal of the US rod & reel index resulted in a terminal year B/BMSY of 0.98 and an F/FMSY of 0.98, while removal of the Chinese-Taipei_late index in a B/BMSY of 0.42 and an F/FMSY of 1.70. Removal of the Chinese-Taipei_late resulted in an upward trend in B/BMSY beginning in 2006 and continuing up to the terminal year of the assessment (2016). Removal of Chinese- Taipei_late also resulted in the steepest decline in F/FMSY in the most recent years. Removal of the remaining CPUE’s had less influence with the B/BBMSY ranging from 0.62 to 0.80 and the F/FMSY ranging from 1.1 to1.6.

Management Benchmarks

The estimated maximum sustainable yield from the Base model is 2,432 t (2,202 – 2,663 t) (Table 2). The estimated relative biomass in 2016 is 0.74 (0.53 – 0.96). The estimated relative fishing mortality in 2016 is 1.20 (0.82 – 1.60). This results in a stock status of overfished and well as overfishing. Benchmark values for the entire matrix of natural mortality and steepness are also shown in Table 2.

The trends in B/BMSY and F/FMSY from the final Base model are shown in Figure 14. Fishing mortality has shown a decisive decrease since 1998, possible evidence that management regulations have had the intended effect. The trend is B/BMSY continues on a downward trajectory. If these patterns are accurate, it suggests that even though fishing mortality has been reduced, the rate of population growth isn’t high enough for the stock to have demonstrated a rebuilding trend yet.

1031 References

Forestall, F.C., Goodyear, C.P., Schirripa, M., Babcock, E., Lauretta, M., and Sharma, R. 2017. Testing robustness of CPUE standardization using simulated data: findings of initial blind trials. Collect. Vol. Sci. Pap. ICCAT, 74(2): 391-403.

Francis, R.I.C.C. (2011). Data weighting in statistical fisheries stock assessment models. Can. J. Fish. Aquat. Sci. 68: 1124-1138. https://doi.org/10.1139/f2011-025

Goodyear, C. P. 2003. Blue Marlin mean length: simulated response to increasing fishing mortality. Marine and Freshwater Research 54:401–408.

Goodyear, C.P., 2015. Understanding maximum size in the catch: Atlantic blue marlin as an example. Transactions of the American Fisheries Society, 144(2), pp.274-282.

Lauretta, M.V. 2014. A simulated capture-recapture model for estimating mortality and stock mixing rates of migratory Alantic fishes. Collect. Vol. Sci. Pap. ICCAT, 70(6): 2868-2888.

Methot, R.D. and Taylor, I.G., 2011. Adjusting for bias due to variability of estimated recruitments in fishery assessment models. Can. J. Fish. Aquat. Sci., 68:1744-1760.

Prince, E. D., D. W. Lee, J. R. Zweifel, and E. B. Brothers. 1991. Estimating age and growth of young Atlantic Blue Marlin Makaira nigricans from otolith microstructure. U.S. National Marine Fisheries Service Fishery Bulletin 89:441–459.

Shimose, T., Yokawa, K. and Tachihara, K., 2015. Age determination and growth estimation from otolith micro- increments and fin spine sections of blue marlin (Makaira nigricans) in the western North Pacific. Marine and Freshwater Research, 66(12), pp.1116-1127.

Wilson, C.A., Dean, J.M., Prince, E.D. and Lee, D.W., 1991. An examination of sexual dimorphism in Atlantic and Pacific blue marlin using body weight, sagittae weight, and age estimates. Journal of Experimental Marine Biology and Ecology, 151(2), pp.209-225.

1032 Table 1. Parameter values used to configure Base model. PARAMETERS Num Label Value Active_Cnt Phase Min Max Init StatusParm_StDevPR_type Prior Pr_SD 1 NatM_p_1_Fem_GP_1 0.122 _ -3 0.04 0.4 0.122 NA _ No_prior 2 L_at_Amin_Fem_GP_1 180.299 1 2 160 210 185.850 OK 4.32118 Normal 185.85 5.58 3 L_at_Amax_Fem_GP_1 284.650 2 2 270 310 288.800 OK 2.68215 Normal 288.8 5 4 VonBert_K_Fem_GP_1 0.209 3 3 0.1 0.3 0.226 OK 0.019699 Normal 0.226 0.05 5 Age_K_Fem_GP_1_a_1 1.000 _ -1 -5 5 1 NA _ No_prior 6 Age_K_Fem_GP_1_a_2 1.000 _ -1 -5 5 1 NA _ No_prior 7 Age_K_Fem_GP_1_a_3 1.000 _ -1 -5 5 1 NA _ No_prior 8 CV_young_Fem_GP_1 0.120 _ -6 0.1 0.5 0.120 NA _ Normal 0.12 0.2 9 CV_old_Fem_GP_1 0.120 _ -6 0.1 0.5 0.120 NA _ Normal 0.12 0.2 10 NatM_p_1_Mal_GP_1 0.122 _ -3 0.3 0.5 0.122 NA _ No_prior 11 L_at_Amin_Mal_GP_1 172.100 _ -1 160 210 172.1 NA _ Normal 172.1 5.58 12 L_at_Amax_Mal_GP_1 209.247 4 2 200 220 209.95 OK 1.97837 Normal 209.95 4 13 VonBert_K_Mal_GP_1 3.04E-01 5 3 0.2 0.8 0.504 OK 0.056428 Normal 0.504 0.1 14 Age_K_Mal_GP_1_a_1 1.000 _ -1 -5 5 1.000 NA _ No_prior 15 Age_K_Mal_GP_1_a_2 1.000 _ -1 -5 5 1 NA _ No_prior 16 Age_K_Mal_GP_1_a_3 1.000 _ -1 -5 5 1 NA _ No_prior 17 CV_young_Mal_GP_1 0.12 _ -6 0.1 0.5 0.12 NA _ Normal 0.12 0.2 18 CV_old_Mal_GP_1 0.12 _ -6 0.1 0.5 0.12 NA _ Normal 0.12 0.2 19 Wtlen_1_Fem 1.9E-06 _ -2 0 1 1.9E-06 NA _ Normal 1.9E-06 0.8 20 Wtlen_2_Fem 3.284 _ -2 0 4 3.284 NA _ Normal 3.2842 0.8 21 Mat50%_Fem 256.430 _ -3 0 300 256.430 NA _ No_prior 22 Mat_slope_Fem -0.125 _ -3 -3 3 -0.125 NA _ No_prior 23 Eggs/kg_inter_Fem 1.000 _ -3 -3 3 1 NA _ No_prior 24 Eggs/kg_slope_wt_Fem 0.000 _ -3 -3 3 0 NA _ No_prior 25 Wtlen_1_Mal 0.000 _ -2 0 1 0.000 NA _ Normal 2.47E-06 0.8 26 Wtlen_2_Mal 3.224 _ -2 0 4 3 NA _ Normal 3.2243 0.8 27 RecrDist_GP_1 0.000 _ -4 0 0 0.000 NA _ No_prior 28 RecrDist_Area_1 0.000 _ -4 0 0 0.000 NA _ No_prior 29 RecrDist_Seas_1 0 _ -4 0 0 0 NA _ No_prior 30 CohortGrowDev 0 _ -4 0 0 0 NA _ No_prior 31 SR_LN(R0) 4.834 6 1 5 5 4.84 OK 0.056816 No_prior 32 SR_BH_steep 0.500 _ -2 0.3 0.99 0.5 NA _ No_prior 33 SR_sigmaR 0.600 _ -4 0 2 0.6 NA _ No_prior 34 SR_envlink 0.000 _ -3 -5 5 0.000 NA _ No_prior 35 SR_R1_offset 0.000 _ -4 -5 5 0.000 NA _ No_prior 36 SR_autocorr 0 _ -99 0 0 0 NA _ No_prior 72 InitF_1Art_Gillnet_1 0 _ -1 0 1 0 NA _ Normal 0.1 99 73 InitF_2LongLine_2 0 _ -1 0 1 0 NA _ Normal 0.1 99 74 InitF_3Purse_Seine_3 0 _ -1 0 1 0 NA _ Normal 0.1 99 75 InitF_4RR_4 0 _ -1 0 1 0 NA _ Normal 0.1 99 76 InitF_5FAD_5 0 _ -1 0 1 0 NA _ Normal 0.1 99 77 Q_envlink_17_Japan_00_17 1.31243 42 4 0 3 0 OK 0.205935 No_prior 90 LnQ_base_17_Japan_00_17 -7.01768 43 1 -7.2 -6.4 -7.02 OK 0.137972 No_prior 91 SizeSel_1P_1_Art_Gillnet_1 207.498 44 2 200 240 232 OK 0.018449 No_prior 92 SizeSel_1P_2_Art_Gillnet_1 -11.7235 45 3 -15 -8 -11.72 OK 2.19512 Normal -11.72 2.2 93 SizeSel_1P_3_Art_Gillnet_1 7.37245 46 4 1 10 8.2696 OK 0.178647 No_prior 94 SizeSel_1P_4_Art_Gillnet_1 -8.99992 47 3 -12 -6 -9 OK 1.79943 Normal -9 1.8 95 SizeSel_1P_5_Art_Gillnet_1 -15 _ -2 -16 5 -15 NA _ No_prior 96 SizeSel_1P_6_Art_Gillnet_1 1.37655 48 2 0.2 5 1 OK 0.537738 No_prior 97 SizeSel_2P_1_LongLine_2 91.1887 49 2 90 120 91.226 OK 36.3488 No_prior 98 SizeSel_2P_2_LongLine_2 0 _ -3 -10 7 0 NA _ No_prior 99 SizeSel_2P_3_LongLine_2 10.5362 50 3 3 12 10.5456 OK 30.7805 No_prior 100 SizeSel_2P_4_LongLine_2 5.52616 _ -5 0 17 5.52616 NA _ No_prior 101 SizeSel_2P_5_LongLine_2 -15 _ -2 -15 5 -15 NA _ No_prior 102 SizeSel_2P_6_LongLine_2 12 _ -6 -5 12 12 NA _ No_prior 103 SizeSel_3P_1_Purse_Seine_3 1 _ -1 1 1 1 NA _ Normal 1 99 104 SizeSel_3P_2_Purse_Seine_3 89 _ -6 89 89 89 NA _ Normal 89 99 105 SizeSel_4P_1_RR_4 255.387 51 2 160 270 255 OK 13.8374 Normal 220 44 106 SizeSel_4P_2_RR_4 -0.00079 52 3 -1 1 0.199 OK 22.1078 No_prior 107 SizeSel_4P_3_RR_4 9.55966 53 4 5 12 9.3806 OK 0.410276 No_prior 108 SizeSel_4P_4_RR_4 2 54 5 -2 6 2 OK 3.996 Normal 2 4 109 SizeSel_4P_5_RR_4 -15 _ -2 -15 5 -15 NA _ No_prior 110 SizeSel_4P_6_RR_4 15 _ -5 -5 15 15 NA _ No_prior 111 Retain_4P_1_RR_4 161 _ -2 15 370 161 NA _ No_prior 112 Retain_4P_2_RR_4 1 _ -4 -1 40 1 NA _ No_prior 113 Retain_4P_3_RR_4 1 _ -2 0 1 1 NA _ No_prior 114 Retain_4P_4_RR_4 0 _ -4 -1 2 0 NA _ No_prior 115 DiscMort_4P_1_RR_4 10 _ -2 -1 30 10 NA _ No_prior 116 DiscMort_4P_2_RR_4 1 _ -4 -1 2 1 NA _ No_prior 117 DiscMort_4P_3_RR_4 0.05 _ -2 -1 2 0.05 NA _ No_prior 118 DiscMort_4P_4_RR_4 0 _ -4 -1 2 0 NA _ No_prior 145 AgeSel_1P_1_Art_Gillnet_1 1 _ -1 0 50 1 NA _ No_prior 146 AgeSel_1P_2_Art_Gillnet_1 50 _ -1 0 50 50 NA _ No_prior 147 AgeSel_4P_1_RR_4 1 _ -1 0 50 1 NA _ No_prior 148 AgeSel_4P_2_RR_4 50 _ -1 0 50 50 NA _ No_prior 149 Retain_4P_1_RR_4_BLK1repl_1987 222 _ -6 220 250 222 NA _ Sym_Beta 222 99 150 Retain_4P_1_RR_4_BLK1repl_1994 225 _ -6 220 250 225 NA _ Sym_Beta 225 99 151 Retain_4P_1_RR_4_BLK1repl_1999 251 _ -6 200 260 251 NA _ Sym_Beta 251 99 152 Retain_4P_2_RR_4_BLK1repl_1987 24.2423 55 4 -1 30 26 OK 4.5218 No_prior 153 Retain_4P_2_RR_4_BLK1repl_1994 3.69457 56 4 -1 10 4 OK 0.93927 No_prior 154 Retain_4P_2_RR_4_BLK1repl_1999 6.49222 57 4 -1 10 6 OK 1.81026 No_prior 155 Retain_4P_3_RR_4_BLK2repl_1956 1 _ -6 0 1 1 NA _ No_prior 156 Retain_4P_3_RR_4_BLK2repl_1987 0.792066 58 6 0 1 0.519 OK 0.164535 No_prior 157 Retain_4P_3_RR_4_BLK2repl_1989 0.58941 59 6 0 1 0.57 OK 0.124311 No_prior 158 Retain_4P_3_RR_4_BLK2repl_1994 0.521274 60 6 0 1 0.52 OK 0.171609 No_prior 159 Retain_4P_3_RR_4_BLK2repl_1998 0.325462 61 6 0 1 0.324 OK 0.369182 No_prior 160 Retain_4P_3_RR_4_BLK2repl_1999 0.282462 62 6 0 1 0.466 OK 0.259322 No_prior 161 Retain_4P_3_RR_4_BLK2repl_2005 0.217238 63 6 0 1 0.328 OK 0.160851 No_prior

1033 Table 2. Management benchmarks in 2016 for the nine combinations of natural mortality and steepness. Base model is M = 0.122 and h = 0.50. This table represents results from previous versions of the Base model as vary slightly (+/- 0.005) from the most recent version.

B/Bmsy M h 0.1 0.122 0.139 0.4 0.720 0.738 0.757 0.5 0.717 0.736 0.760 0.6 0.723 0.759 0.786

F/Fmsy M h 0.1 0.122 0.139 0.4 1.590 1.413 1.299 0.5 1.344 1.212 1.122 0.6 1.198 1.070 0.997

1034

Figure 1. SSB (top), B/BMSY (middle), and F/FMSY from the 2011 assessment, the 2011 model with updated catch only, and the 2001 model with updated catch and lengths.

1035 Figure 2. Forecast of the 2011 model using actual 2010-2016 landings with lengths (top) and without lengths. The 2018 R0 and steepness from the 2018 were maintained.

1036

Figure 3. Data presence by year for each fleet, where circle area is relative within a data type, and proportional to precision for indices and compositions, and absolute catch for catches. Note that since the circles are scaled relative to maximum, sca

1037

Figure 4. Atlantic blue marlin landings by total (top) and by percentages (bottom).

1038

Figure 5. Female-at-age used to model growth (top); approximately L inf overlaid on observed length compositions from Japanese longline fishery with assumed asymptotic selectivity.

1039

Figure 6. Fit to the CPUEs and rod & reel discards.

1040

Figure 7. Fit the log CPUEs

1041

Figure 8. Fit to Japanese longline CPUE without yellowfin: bigeye ratio (tops) and with (middle) and resulting time varying catchability (bottom).

1042 Figure 9. Fit to length compositions from each gear type.

1043

Figure 10. Observed and expected mean weight by gear type. Note that FAD lengths were not used in the modeling fitting.

1044

Figure 9. Landings (bars), Base model fit of F/FMSY without lengths (red line) and NZ50 trend. Trends are 3 point moving average (Top). Regression fit between the two trends.

1045

Figure 10. Profile analysis on the Base model with an M=0.122. Note that the Chinese_Taipei_early line is associated with the right axis.

1046

Figure 11. Trends in B/BMSY and F/FMSY with each CPUE time series removed one at a time.

1047 Figure 12. Trends in B/BMSY and F/FMSY from the Base Case model (M=0.122, h=0.50)

1048

Figure 13. KOBE Plot with tracks and ending B/BMSY and F/FMSY for the nine combinations of M and steepness considered. Larger point is the Base model with M=0.122 and h=0.50.

1049

Figure 14. KOBE plot with uncertainty in ending year B/BMSY and F/FMSY for the nine combinations of M considered.

1050