ATLANTIC OCEAN YELLOWFIN TUNA STOCK ASSESSMENT 1950 …€¦ · SCRS/2016/110 Collect. Vol. Sci. Pap. ICCAT, 73(2): 510-576 (2017) 510 ATLANTIC OCEAN YELLOWFIN TUNA STOCK ASSESSMENT

SCRS/2016/110 Collect. Vol. Sci. Pap. ICCAT, 73(2): 510-576 (2017)

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ATLANTIC OCEAN YELLOWFIN TUNA STOCK ASSESSMENT

1950-2014 USING STOCK SYNTHESIS

John Walter1, Rishi Sharma1

SUMMARY

A stock assessment of the Atlantic Ocean Yellowfin tuna (Thunnus albacares, YFT) population

from 1950 to 2014 using Stock Synthesis is presented here. We present initial scoping runs

and four initial model runs that use various treatments of growth and four models that fix

growth at model-estimated values. The initial model runs 1-4 uses all indices and were unable

to estimate key parameters such as steepness and showed evidence of very poor model

convergence. To reconcile conflicting indices, two index ‘clusters’ were proposed. Model runs

5-8 use either cluster 1 or cluster 2 indices and have growth fixed at estimates obtained from

previous models. Growth was estimated either with a formulation mimicking a two-stanza

model or a von Bertalannfy model and, in each case, the model estimates of Linf were lower

than the current ICCAT model. The base advice models (run 5 and 7) use the newly estimated

growth with a two-stanza model and cluster 1 and cluster 2 indices, respectively. Overall

model performance was improved when growth was subsequently fixed at the estimated values

and when individual index clusters were used rather than forcing the model to try to fit

conflicting indices. Final model diagnostic plots and fits are included in the stock assessment

report and are not duplicated in this report.

RÉSUMÉ

Ce document présente une évaluation de la population d'albacore (Thunnus albacares) de

l'océan Atlantique entre 1950 et 2014 à l'aide de Stock synthèse. Nous présentons des

scénarios exploratoires initiaux et quatre scénarios du modèle initial qui utilisent divers

traitements de croissance et quatre modèles qui fixent la croissance à des valeurs estimées par

le modèle. Les scénarios 1-4 du modèle initial utilisent tous les indices et n'ont pas pu estimer

les paramètres clefs, tels que la pente à l'origine de la relation stock-recrutement (steepness) et

ils ont montré des preuves d'une convergence très médiocre du modèle. Pour réconcilier les

indices contradictoires, deux "clusters" d'indices ont été proposés. Les scénarios du modèle 5-

8 utilisent les indices du cluster 1 ou du cluster 2 et leur croissance est fixée aux estimations

obtenues de modèles précédents. La croissance a été estimée soit avec une formulation imitant

un modèle à deux stances ou un modèle von Bertalanffy et, dans chaque cas, les estimations du

modèle de Linf étaient inférieures à celles du modèle actuel de l'ICCAT. Les modèles de base

de l'avis (scénarios 5 et 7) utilisent la croissance nouvellement estimée avec un modèle à deux

stances et les indices de cluster 1 et cluster 2, respectivement. Les performances générales du

modèle ont été améliorées lorsque la croissance a été ultérieurement fixée aux valeurs estimées

et lorsque les clusters des indices individuels ont été utilisés plutôt que de forcer le modèle à

essayer d’ajuster les indices contradictoires. Les diagrammes de diagnostic et les ajustements

au modèle final sont inclus dans le rapport d'évaluation du stock et ne sont pas répétés dans le

présent rapport.

RESUMEN

Se presenta una evaluación del stock de rabil del océano Atlántico (Thunnus albacares, YFT)

desde 1950 a 2014 utilizando Stock Synthesis. Presentamos los ensayos exploratorios iniciales

y cuatro ensayos del modelo inicial que utilizan diversos tratamientos del crecimiento y cuatro

modelos que ajustan el crecimiento a valores estimados por el modelo. Los ensayos 1-4 del

modelo inicial utilizan todos los índices, no pudieron estimar parámetros clave como la

inclinación y presentaban pruebas de una convergencia del modelo muy pobre. Para

reconciliar los índices contradictorios, se propusieron dos "conglomerados" de índices. Los

1 NOAA Fisheries, Southeast Fisheries Center, Sustainable Fisheries Division, 75 Virginia Beach Drive, Miami, FL, 33149-1099, USA.

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ensayos 5-8 del modelo utilizan los índices tanto del conglomerado 1 como del conglomerado

2 y el crecimiento se ha fijado en las estimaciones obtenidas a partir de modelos previos. El

crecimiento se estimó bien con una formulación que imitaba un modelo de dos estanzas o un

modelo von Bertalanffy y, en cada caso, las estimaciones del modelo de Linf eran inferiores a

las del modelo actual de ICCAT. Los modelos de base del asesoramiento (ensayos 5 y 7)

utilizan el crecimiento recientemente estimado con un modelo de dos estanzas y los índices del

conglomerado 1 y el conglomerado 2, respectivamente. En general, el rendimiento del modelo

mejoraba cuando el crecimiento se fijaba en los valores estimados y cuando se utilizaban los

conglomerados de índices individuales en lugar de forzar el modelo para tratar de ajustar

índices conflictivos. En el informe de evaluación de stock se incluyen los diagnósticos y ajustes

finales del modelo y no se duplican en este informe.

KEYWORDS

Stock assessment, yellowfin tuna

Introduction

Model Executive Summary

The analysis generally follows the Multifan assessment structure developed in 2011 and 2008 respectively. In

this assessment spatial structure was not considered due to its limited utility in the recent BET stock assessment

and the limited reliability of tag recapture data. Core assumptions in all models included:

Spatial one area model, age-structured population, iterated on a quarterly time-step 1950-2014.

17 fisheries (catch in mass extracted without error):

8 Relative abundance indices:

Initial model runs had 8 indices that were subsequently split into two clusters:

Cluster 1: "Japan_N_1976_2014", "VEN_LL_N", "US_LL_N", "CH_TAI_LLN_1_70_92",

Cluster 2: "CH_TAI_LLN_1_70_92", "URU_W_1", "URU_W_2", "BR_LL_N",

"CHTAI_N_93_14_M4”.

Indices were input with a common, fixed CV of 0.2

Beverton-Holt stock-recruit dynamics, with steepness fixed or estimated and spawning biomass

proportional to the total biomass of mature fish. Models were compared with deterministic and

stochastic recruitment (annual deviates 1970-2010 with estimated variance with recruitment allocation

by quarter estimated.

One von Bertalanffy length-at-age relationship was used to contrast with the two-stanza model:

o Draganick and Pelczarski growth: L245 – Linf = 175cm, k = 0.44, a0=-0.7 (Draganick and

Pelczarski 1984), 175*(1-exp(-0.44*(t-0.7)))

o Two stanza growth curve with the k-devs option. Where at age we multiply the original k by a

value that would mimic the Gascual et. al. (1992) growth curve. This curve approximates the

Gascual multi-stanza growth curves. FL (cm) = 37.8 + 8.93 * t + (137.0 – 8.93 * t) * [1 –

exp(-0.808 * t)]^7.49 (Linf ~175 cm)

a. Growth was initially fixed at the above values then estimated using otolith data from Lang et

al (2016) and Shuford et al 2007 and mean size at assumed age based on modal progression

from Gascuel et al (1992).

Eleven ages (0-10+) were modeled.

Natural mortality was determined according to SCRS-2016-109 according to Lorenzen-scaled M-at age

functions for the Gascuel et al growth curve for ages 0-10:1.588 1.194 0.748 0.55 0.476 0.447 0.435

0.431 0.429 0.428 0.428.and the Draganick and Pelczarski growth curve: 1.758 0.889 0.672 0.576

0.525 0.495 0.476 0.463 0.455 0.450 0.446

Maturity was invariant over time with 50% mature at length 115 cm with a slope of -0.11 according to

estimates from SCRS_2016_062.

Non-parametric (cubic spline) length-based selectivity was estimated for Purse Seine fleets

independently (with sufficient flexibility to describe logistic, dome-shaped or polymodal functions).

Logistic curves were followed for the LL fleets

Double normal selectivity (can be either domed or flat-topped) was used for all other fleets including

Baitboat.

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Time blocks- Two time blocks were imposed after visual inspection of the length composition data

indicated clear separation of length composition for Fleet 4 (ESFR_FADS2_PS_9114) between years

2002 and 2003 and for Fleet 6 BB_area2Sdak between 2009 and 2010. Another time block was

considered for the Chinese Taipei (CH_TAI_LL_2_93_14_allareas) fleet between 2004 and 2005, but

was not implemented.

Objective function terms included:

o likelihoods for:

CPUEs on the main fleets.

Catch-at-length from all fleets (with assumed sample sizes generally much lower

than observed).

Priors on some estimated parameters.

In addition, sensitivity to different assumptions namely steepness, natural mortality and the growth curve and

the use of CPUE series was examined, including the Japanese series from 1965. Sensitivity to each CPUE series

was examined with steepness either being estimated in the model or being kept fixed. Two additional clusters of

CPUE’s were examined and models were fit to both hypotheses.

Fishery History

The Atlantic Tuna Yellowfin fishery history extends back to the early 1950’s. The fleets were primarily from

West Africa and some far seas fleet activity which increased dramatically in the 1960s. By the 1950s both sides

of the Atlantic were fishing for yellowfin in both the northern and southern hemispheres. The advent of the

purse seine fleets through the 1970s and its increase in the 1980s and 1990s resulted in much higher total

landings. Traditionally purse seine landings focused on free schools of larger YFT; with the advent of the

fishery on FADs, the fishery has substantially increased the catches of smaller yellowfin. The most recent stock

assessment was conducted in 2011 using VPA and surplus production models.

The Atlantic Ocean YFT catch history is shown in Figure 1.

Catches increased steadily from the 1980s to a peak in 1990 of ~194K t, and catches from then have declined

steadily to around 100000t. The primary fleets are Longline, purse seine and baitboat. Much of the earlier period

was dominated by the LL fleets operating in the Atlantic and with the advent of the Purse seine fleet in the

1970’s and 1980’s this changed dramatically (Figure 1 a-c). Figure 2 illustrates the spatial distribution of the

catches by the primary gear types (the locations are not very accurate for most of the coastal fleets). While the

fishery has remained fairly stable over the time period, effort of the longline fleet has gradually declined in

recent years and the purse seine and baitboat fleet increased in contrast. While the LL fleet has operated in all

parts of the ocean the PS fleet has been concentrated off the coast of Africa and central/South American

primarily (Figure 2 and Figure 3).

For the primary LL fleet we also examined the effort and catch distribution by decade, and it appears that the 3

primary fleets (Japan, Chinese Taipei and Korea) have varied fleet activity through much of the early 2000’s

and the effort and catches dramatically reduce for the Japanese and Korean fleets in recent years (Figures 4-6).

Data and Model Assumptions

For continuity of the arguments, related data and model assumptions are described together. The SS3 control

file (for final model, run 5, is appended (Appendix 1).

Spatial Structure

A single area covering the entire Atlantic Ocean was used in the assessments: The model examined was similar

in spatial structure to models conducted in the 2011 assessment. This model examined the entire Atlantic Ocean

area as one unit, with the different fisheries operating in this one area.

Temporal units

Data were disaggregated by quarter (quarter 1 = Jan-Mar, quarter 2 =Apr-June, etc.), and the model was iterated

on quarterly time-steps, to represent the rapid dynamics of this population, over the period 1950-2014 (plus 10

years of projections).

Age Structure

The YFT population was represented with an annual/four season configuration. SS3 can resolve many

population features on a seasonal basis (e.g. recruitment, fishery removals, Mage). The age structure in 1950 was

assumed to be in unfished equilibrium (ignoring the small artisanal catches that were taken historically).

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Sex Structure

The model was sex-aggregated (and reported spawning biomass is the summed mass of all mature fish).

Fishery definitions

See table 1 for fishery definitions. The purse seine fleet was split into four fleets; an early, mixed fleet

(1_PS_ESFR2_6585 (early)), a transition fleet (2_PS_ESFR2_8690) and separate FAD

(4_ESFR_FADS2_PS_9114) and free-school fleets 93_PS_ESFR2_9114) in later years.

Total catch

The total catches were calculated by the Secretariat. This is a complicated process that requires a number of

approximations and substitutions for fleets with poor data (including those discussed below under size

composition data). The catch time series for the fleets is shown in Figure 1. The model uses the standard

difference form of the Baranov catch equations to describe the population dynamics. Catch in mass was used in

the model for all fleets, and was assumed to be known essentially without error and extracted precisely to within

the numerical tolerance in the iterative solving of the (SS3 ‘hybrid’) catch equations. Initial catch (1948) and

fishing mortality was assumed to be zero and all catch was input with a very low standard error (0.01). Fishing

mortality was reported as exploitation rates in biomass.

CPUE as a relative abundance index

Catch per unit effort indices form the most basic of indicators of relative stock status and are critical input to

most stock assessment models. In the current assessment eight indices were recommended for inclusion in the

SS models as follows:

"Japan_N_1976_2014", "URU_W_1", "URU_W_2", "BR_LL_N", "VEN_LL_N", "US_LL_N",

"CH_TAI_LLN_1_70_92", "CHTAI_N_93_14_M4”.

The eight indices included in the initial model construction were input in either number or weight according to

the native units of the data collection. Indices were input a lognormal error structure with equal log scale

standard errors (0.2) for all indices and all years. However due to conflicts in the indices index ‘clusters’ were

proposed and run in subsequent SS models:

CLUSTER_1=c("CH_TAI_LLN_1_70_92", "US_LL_W" , "VEN_LL_N","Japan_N_76_14" ) ")

CLUSTER_2=c("CH_TAI_LLN_1_70_92","URU_W_1","URU_W_2","BR_LL_N","CHTAI_N_93_14_M4")

Due to time constraints the initial CLUSTER1_Sens index that started the Japanese longline in 1965 was not run

and all ‘cluster 1’ SS model runs use the short time series of Japan longline.

Size Composition Data

The catch-at-length data were compiled by the secretariat. This process involves a number of approximations

and substitutions because some fleets have very poor data, and some fleets do not report data at the appropriate

resolution.

Catch-at-length distributions aggregated over time (Figure 4) and by season and fleet (Figure 5) are shown.

There is no obvious pattern to indicate strong seasonal recruitment. The bimodal distribution in the purse seine

fishery suggests a heterogeneous mix of two life history stages (or possibly two different fleets being aggregated

into one fleet, or fleets fishing in different areas giving the appearance of one fleet with a bimodal structure).

Brief exploration did not reveal any obvious spatial/seasonal explanation for the two modes, but this is worth

further investigation. The recent decline in mean size in the Other fleet probably reflects the erratic sampling

from this fleet or the mixture of fleets that comprise this category. In the future it might be worth further

partitioning these fleets to reflect likely differences in selectivity to the extent possible (but this is expected to be

a low priority for the assessment overall).

Catch-at-length sample sizes are often very large, however, in these sorts of models, it is generally

recommended not to weight the size composition data too highly relative to other data inputs. The size

composition data influence these models in two main ways: i) ensuring that the correct age distribution is

removed from the population by the fishery, and ii) providing information about relative year class strength

through the stationary selectivity assumption.

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In this assessment, all length composition samples were down-weighted to a considerable degree, and a range of

options were explored to test if the model was sensitive to these assumptions. A uniform effective sample size

of 20 was applied across all fisheries (low weight, largely uninformative) initially. The effective samples sizes

were estimated and then the input sample sizes were iteratively adjusted so that they matched approximately the

input sample sizes.

The catch-at-size distributions are aggregated in 86 bins of length 2 cm (≤10 to >180 cm). The multinomial

likelihood was used in the model, with an additional 1% added to each length bin (predicted and observed) to

make the term more robust to outliers; with the tail compression option turned off.

Selectivity

A cubic spline function was estimated independently for the selectivity of the purse seine fleets. Selectivity

parameters were estimated for a series of length-class nodes, with cubic spline interpolation between nodes (the

default node spacing within SS3 corresponding to the first node is at the size corresponding to the 2.5%

percentile of the cumulative size distribution and the last at the 97.5% percentile). The length-based concept is

applied in the calculation of the predicted catch-at-length distribution. However, the length-based selectivity is

converted to an age-based selectivity for purposes of removing the appropriate portion of the population in the

catch (i.e. cumulative effects of length-based selectivity on the length-at-age distribution are not described in the

model). The function is flexible enough to represent dome-shaped, monotonically increasing (e.g. logistic), and

polymodal functions (and was motivated by the clear bimodal distribution of the PS fleet). Stationary selectivity

was used in the final analysis due to problems in convergence in time-varying selectivity as a number of

parameters were hitting the boundary conditions for the time varying component.

The LL fleets used logistic selectivity with an asymptote to full selectivity at an estimated length, and the BB

and other fisheries used the double normal selectivity functions which could be estimated as either domed or

asymptotic. For estimation of the bait boat length-based selectivities, several constraints were imposed. First,

parameter 5 of the double normal selectivity function that defines the initial selectivity at the first size bin was

fixed to be zero and not estimated, as fish in the smallest size bin (10 cm) were unlikely to be captured by any of

the bait boat fisheries. Second, for the baitboat fishery the ascending and descending limbs were allowed to have

a smooth increase and a smooth decay using the SS technical specification (-999) for parameters 5 and 6.

Initially two time blocks of for selectivity were imposed corresponding to apparent changes in the selectivity of

two fisheries, however it is not clear why these changes may have occurred. These two time blocks were

imposed after visual inspection of the length composition data indicated clear separation of length composition

for Fleet 4 (ESFR_FADS2_PS_9114) between years 2002 and 2003 and for Fleet 6 BB_area2Sdak between

2009 and 2010. An additional time block was imposed for the = for the Chinese Taipei

(CH_TAI_LL_2_93_14_allareas) fleet between 2002 and 2003 And a sensitivity run to evaluate removing the

Chinese-Taipei data altogether (input effective sample size scalar of 0.01) was conducted to explore the

potential influence of including or excluding this composition data. In this case the selectivity was mirrored to

that of the Japanese longline.

Size-at-Age

Two relationships for mean length-at-age were examined though only the two-stanza growth model was used as

the base-advice. The two curves followed the standard von Bertalanffy growth function or a parameterization

that mimics the Gascuel et al 1992 two-stanza growth curve. This was done using multiple age-specific K

estimates. For both growth models, Length at age a=0.38 (Lamin) fixed at 25cm. The two growth curve options

were:

Draganick and Pelczarski Von Bertalanffy growth model (Linf=192.4 cm, k=0.37, t0=-0.003

Two stanza growth curve with the k-devs option which multiply the original k by a value that would

mimic the Gascuel et. al. growth curve. This curve approximates the Gascual multi-stanza growth

curves. FL (cm) = 37.8 + 8.93 * t + (137.0 – 8.93 * t) * [1 – exp(-0.808 * t)]^7.49 (Linf ~175 cm)

The mass-length relationship is mass = 1.766E-5 Length3.03542.

Conditional age at length input

A conditional age-at-length likelihood approach was used: the expected age composition within each length bin

was fit to age data conditioned on length (conditional age-at-length) in the objective function, rather than fitting

the expected marginal age-composition to age data (which are typically calculated external to the model as a

function of the conditional age-at-length data and the length-composition data). Both a von Bertalanffy growth

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curve and a multi-stanza growth curve was estimated by the model using the conditional age-at-length data.

Three sets of aging data were used corresponding the Lang et al dataset from the Gulf of Mexico, United States,

the Shuford et al dataset from Gulf of Guinea and North Carolina, U.S and the mean length at age derived from

model progression (from Gascuel et al 1992).

For input into SS the Lang et al 2016 dataset was assigned to the US rod and reel fishery and assigned to the

year and season they were collected. The Shuford dataset was assigned to either the US rod and reel fishery or to

the Free school purse seine fishery (3_PS_ESFR2_9114). Otoliths from very small fish captured in stomachs

were not used as these fish could not be assigned to a gear and were below the minimum population size bin.

Mean length at assumed age from the Gascuel dataset was assigned to 1_PS_ESFR2_6585 and the

corresponding year and season. Input sample sizes were equal to the number of observations in each dataset.

Fish in the Lang dataset above age 10 were assigned to age the plus group age 10.

Eleven age classes (0-10) with 10 as a plus group were modeled. A plus group of 10 was used asvery few fish

were present in any aging dataset beyond this age.

Aging error. Lang et al provided a matrix of aging error from repeated ages readings:

This vector indicates relatively high precision. It was used as the aging effort vector for all data inputs.

0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5

0.140 0.140 0.173 0.135 0.187 0.612 0.587 0.549 0.554 0.574 0.245

Maturity

Maturity estimates from SCRS-2016-163 were adopted: invariant over time, with 50% maturity at 115 cm.

Stock Recruitment

A Beverton-Holt stock recruit relationship was assumed (the SS3 ‘flat-top’ version in which Rt does not increase

beyond R0 if SBt happens to exceed SB0). It was assumed that spawning biomass is equal to the mass of the

mature population. In recognition of the difficulty in estimating steepness (h), different fixed values were

examined. Values of 0.7, 0.8 and 0.9 were examined for yellowfin tuna which is a resilient fecund species that

spawns multiple times over a year. The value of 0.9 was used in the base case assessment.

Deviations from the stock-recruitment relationship were assumed to follow a lognormal distribution, with

constant recruitment until we have more informative data on age structure, i.e. annual deviates from 1970-2010

(σR, annual estimated). The lognormal bias correction (-0.5σ2) for the mean of the stock recruit relationship was

applied during the period 1975-2011 with a bias correction ramp applied prior to 1975 and after 2011 according

to the Methot and Taylor (2011) recommended bias correction ramping.

Natural Mortality

Natural mortality was discussed extensively in SCRS-2016-116. Various natural mortality curves were

examined namely, the following:

Gascuel 2-Stanza (ages 0-10+) : :1.588 1.194 0.748 0.55 0.476 0.447 0.435 0.431 0.429 0.428 0.428

Draganick & Pelczarski (ages 0-10+): 1.758 0.889 0.672 0.576 0.525 0.495 0.476 0.463 0.455

0.450 0.446

Model Specifications

Six initial model specifications were conducted.

Model Class 1. SS-Lite and Fast (similar to an ASPM). The objective is to have a fast, light version of SS that

estimates R0 and steepness. Can mimic production model, while maintaining some flexibility and is a bridge to

SS heavy. Can quickly evaluate the multiple index hypothesis developed for the production models

Run0. Indices only, surplus production-like model. This model run represents one of the initial scoping runs

performed to evaluate solely the signal in the indices and the landings. Essentially this model run is an analog to

a production model. The only estimated parameters are R0 and steepness and the fleet and year-specific fishing

mortality rates. Biological parameters were fixed at initial values. Selectivity was fixed at constant for all ages.

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Run 0.5. Age-structured surplus production-like model. Indices and selectivity fixed at estimates from age

structure model. The only estimated parameters are R0, steepness, and sigmaR, recruitment deviations and the

fleet and year-specific fishing mortality rates.

Model Class 2. SS-Heavy using length composition information, *Runs 5 and 7 were chosen as equally

plausible base models.

Run 1. Base model, all indices, growth fixed at Gascuel et al. growth model

Run 2. Fixed Draganick and Pelczarski growth, von Bertalannfy growth

Run 3. Estimated multi-stanza growth- this model uses conditional length at age data from three data sources,

described above and estimates a multi-stanza growth model parameters.

Run 4. Estimated Draganick and Pelczarski growth this model uses conditional length at age data from three

data sources, described above and estimates a von Bertalannfy growth model.

*Run 5. Cluster 1 indices, growth fixed at Estimated multi-stanza growth values from Run 3

Run 6. Cluster 1 indices, growth fixed at Estimated Von bertanlannfy values from Run 4

*Run 7. Cluster 2 indices, growth fixed at Estimated multi-stanza growth values from Run 3

Run 8. Cluster 2 indices, growth fixed at Estimated Von bertanlannfy values from Run 4

Parameters Estimated

Of the 116 parameters estimated in the Run 5 model (Appendix Table 2) 45 were recruitment deviations. Of

the remaining 71 parameters, 3 were seasonal allocations of recruitment and 62 were selectivity parameters.

Table Appendix 2 includes predicted parameter values and their associated asymptotic standard errors, initial

parameter values, and minimum and maximum bounds, priors, if any, and phase of estimation for run 5 which

was chosen as the base advice model equally with run 7. Parameter bounds were selected to be sufficiently

wide to avoid truncating the searching procedure during model fitting. The soft bounds option in SS was

utilized when fitting the assessment model. This option creates a weak symmetric beta penalty to keep

parameters off of bounds (Methot and Wetzel 2013). Parameters designated as fixed were held at their initial

values.

Model Diagnostics

Model convergence was assessed using several means. The first diagnostic was whether the Hessian, (i.e., the

matrix of second derivatives of the likelihood with respect to the parameters) inverts. The second measure is the

maximum gradient component which, ideally, should be low. The third diagnostic was a jitter analysis of

parameter starting values to evaluate whether the model has converged to a global solution, rather than a local

minimum. Starting values of all estimated parameters were randomly perturbed by 10% and 50 trials were run.

Other diagnostics performed included likelihood profiling of key parameters, evaluation of fits to residuals for

indices and length composition, retrospective analyses and sensitivity to different indices and compositional

data inputs. Likelihood profiles were completed for three key model parameters: steepness of the stock-recruit

relationship (h) and the log of unexploited equilibrium recruitment (R0) and sigmaR. Likelihood profiles

elucidate conflicting information among various data sources, determine asymmetry around the likelihood

surface surrounding point estimates and evaluate the precision of parameter estimation.

Another model diagnostic is parametric bootstrapping and MCMC analysis. Uncertainty in parameter estimates

and derived quantities can as well bias between the maximum likelihood estimates and estimates obtained by

bootstrapping were investigated using a parametric bootstrap approach. Bootstrapping is a standard technique

used to estimate confidence intervals for model parameters or other quantities of interest. There is a built-in

option to create bootstrapped data-sets using SS. This feature performs a parametric bootstrap using the error

assumptions and sample sizes from the input data to generate new observations about the fitted model

expectations.

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Retrospective analyses are also standard diagnostic practice and were conducted on models 1-8. Not all model

results are shown in this document to avoid overlap with the final assessment report.

Data weighting

Francis (2011) indicates that often in complex integrated models there is conflicting sources of information,

stemming from fitting to either the length composition data, or abundance index data and often the numerically

abundant length composition information dominates the likelihood. Length composition data was initially input

with a sample size of 20, however inspection of the effective N indicated that in most cases the effective N was

much higher than the input N indicating that that the effective sample should be reduced for most fleets using

the recommended Francis (2011) method to adjust the input sample size. After these sample sizes were

determined the model was estimated and the ratio of the input/effective N calculated and a new set of variance

scalars determined so that the input N/effective N was close to 1. The resulting input vectors are in the input

control file.

An additional data weighting criterion of weighting according to the ratio of the percent of the size sample to

percent catch by fleet. This accounts for variable sampling fractions of the total catch by as an indicator of the

variable reliability of the sampling. This metric was calculated by calculating the fraction of the total size

samples and dividing this by the fraction of the total catch that each fishery represents. Then these fractional

numbers were converted to quantiles to allow a ranking of data quality on a scale of 1-5 with 5 being the highest

quantile. Then the overall initial input size was obtained by multiplying the initial input size of 20 (of 10, if prior

to 1980) by 1/the rank to get the input sample size. The reduction in input sample size for years prior to 1980

was done to reflect the overall increasing reliability of recent size samples. The resulting input size samples then

ranged from 2-20. This data weighting approach was proposed at the assessment meeting but would have

required entirely refitting the model and reevaluating diagnostics it could not be completed due to time

constraints.

MSY Calculations

MSY, BMSY, FMSY and equilibrium yield estimates are calculated on the basis of the Fage distribution (selectivities)

estimated for 2014. Proxy benchmarks of SPR 30 and 40% are also provided as the absence of an estimate of

steepness means that no estimate of MSY was possible and necessitates invoking proxies for MSY.

Uncertainty Quantification

Uncertainty in parameter estimates and derived quantities was evaluated using multiple approaches. First,

uncertainty in parameter estimates was quantified by computing asymptotic standard errors for each parameter

(Table 3.1). Asymptotic standard errors are calculated by inverting the Hessian matrix after the model fitting

process. Asymptotic standard errors are based upon the maximum likelihood estimates of parameter variances

at the converged solution. As parametric bootstrapping and the hessian-based standard errors are often similar

we only report the hessian based estimates.

Projections

Projections were conducted for years 2015-2025 at various catch levels and are described in more detail in

subsequent stock assessment documents.

Results

Initial diagnostics and scoping runs

Run0. Indices only, surplus production-like model Likelihood profiling for this model indicates that it is

unlikely to show substantial signal in the indices alone for the key parameters R0 and steepness and that the

model is exceedingly prone to crashing. Furthermore the steepness parameter hit the upper bound (Table 3)

indicating that the model did not estimate the leading parameter and that without some restraints or priors then

the index data alone appears unlikely to provide enough signal to estimate the productivity of the stock. Of

particular concern is the conflict between the indices that appear to favor a lower R0, while the model crashes

with any R0 lower than 11.5 (Figure 11). This indicates that there is particularly strong tension in the model

between the landings history and the indices. The failure of many of the fixed estimates during the profiling is

518

indicative of some severe model instability. Nonetheless the model did converge on a solution and the hessian

inverted. Retrospective analysis indicates that two of the retrospective peels found a very different solution

(Figure 12) and likely did not converge.

Model 0.5 Age-structured production model. Likelihood profiles for this model are also indicative of some

strong structural conflict between the indices and the landings history with the model crashing – or running out

of fish- for any R0 lower than 12.5, while the indices show some favor towards lower values (Figure 12).

Adding in the recruit deviations and allowing the indices and the removals to occur as a function of the

estimated selectivity from a previous fully age-structured run did not improve the likelihood profiling. This

model also freely estimated steepness which hits a bound at 0.99 (Table 3). Retrospective patterns were not

particularly bad however the minus 4 and minus 5 runs crashed (Figure 19).

Model 1. Gascuel growth, all indices and length composition model

This model represents the initial construction of the model. Inferences from Runs 0 and 0.5 that steepness is

likely not estimable from the survey data, free estimation of steepness that hit the bound of 0.99 and the

profiling (Figure 13) indicate that steepness is likely not estimable. Most fixed value of steepness less than 0.8

crashed. Hence it was necessary to fix steepness at the upper value recommended as fixed values to use in the

data workshop report (0.9). Likelihood profiling for model 1 (Figure 13) indicates that the model crashes with

fixed R0 values below 12, however when freely estimated in the full model run, it is estimated to be 11.91

indicating a very thin margin (somewhere between 11 and 12) between crashing and the lower log likelihood

values. The length composition data is the most influential factor and clearly favors values lower than 13. Given

the innate correlation between steepness and R0 this clearly creates tension in the model. The surveys are not in

direct conflict though their overall contribution is substantially lower than the length composition. The primary

problem is the very sharp gradient between the length composition favoring ln(R0) below 12 and the model

crashing at 11.5.

Jitter analysis was performed on this model and indicated that the models sometimes exhibited another, different

solution but usually most runs were at or close to the maximum likelihood estimate from the initial run (Figure

17).

Three of the selectivity parameters hit their bounds in this model (Table 3). This is a fairly common occurrence

in different formulations of these models, particularly for the spline gradients which seem to often hit upper or

lower bounds. A possible solution might be to fix these spline parameters these values for a future model, but

given the evidence of major structural problems identified by the likelihood profiling, this is of secondary

concern. The maximum gradient on this model is also slightly higher than generally desired, indicative of some

poor model convergence. Of also notable importance, this model does not converge when using the SS-safe

executable, though it does converge with SS-opt. SS-safe does more array checking and the failure to converge

likely indicates that this model cannot, in its current form be used for advice. Nonetheless we present the results

as it represents the basic structure of any SS models and is the initial model from which improvements are in

progress.

Further diagnostics analysis of Run 1 were retrospectives (Figs 24 and 25) that showed no problems, MCMCs

that showed a very low acceptance rate (11% in 300,000 runs so far) and substantial runs that crash. There are

also several very highly correlated parameters (Table 5), though most of these are selectivity parameters.

Likelihood profiling for runs 3 and 4 (Figures 15 and 16) show some improved performance, at least less run

crashes and some evidence of signal in the index data for R0, though there is still a very narrow window of

estimability where the length comp favors lower R0 while maintaining the population requires it to be above a

certain level.

Preliminary Model results

Fits to indices for model 1 (Figure 25) showed substantial lack of fit to some indices, as evidence by the high

RMSE above 0.5 for TAI_LL2, URU_LL_1 and URU_LL_2 (Table 4). It is not particularly surprising that

these indices all are included in Cluster 2, to be evaluated later.

Selectivity estimates (Figure 35) indicate that the early PS fisheries all had selectivities that, while bimodal

were estimated to be asymptotic for the largest fish. This was particularly true for the PS_ESFR2 fishery which

was the later free school fishery for large fish. The 4_ESFR_FADS_PS_9114 fishery was split in 2002 and the

6_BB_area2_Sdak was split in 2010 and estimated in two different time blocks to reflect apparent changes in

519

either the fisheries or the incomplete separation of FAD and Free school catches in the Purse seine (Figure 36).

These can be seen in the presence of some very large fish early in the FAD fishery time series and at the end of

the BB, resulting in two different selectivity estimates (Fig. 36). Given the growing nature of the FAD fishery it

is critical that the selectivity of this fisher reflect current practice which does indicate a focus on smaller fish.

Selectivities of some of the bait boat fleets and the Oth_Oth fleet were not particularly well estimated as

evidenced by some parameter bounding in different runs.

Fits to the length composition over all years show relatively decent fits (Figure 27) though there are some

notable lack of fits to larger fish where the model expects substantial fish at larger sizes for most fleets and,

conversely, fewer fish at around 150 cm. This manifests itself in a systematic lack of fit to almost all longline

fisheries and a systematic pattern in the Pearson residuals (Figure 28). One potential explanation for this pattern

was that the growth model could be incorrect which is addressed in runs 2-4.

The stock recruitment relationship shows little evidence of estimability and is driven by the assumed steepness

of 0.9 (Figure 30). Given the very low estimated sigmaR (~0.2) the expected recruitment after the bias

adjustment exp(sigmaR^2/2) is very close to the expected recruitment without the bias correction. The

recruitment deviation show a distinct trend with large positive deviation in the middle of the time series (1988-

2000) and low deviations in the early time period. As the deviations are required to sum to 1 this may be one of

the reasons.

Time series of total biomass, SSB and SSB relative to SSB0 indicate severe depletion to around 13% of virgin

biomass, a decline that the model estimates occurred largely by 1980 after which the population stayed

relatively constant (Figure 31). Recruitment allocation by season was highest in Season 1 and lowest in Season

2. Fishing mortality (exploitation rate in biomass) estimates indicate that annually between 50 and 60% of the

population was removed each year since 1982. These patterns are almost exactly similar to Run0.5 and Run2.

In contrast runs 3 and 4 show substantially reduced depletion levels of 31-34% of SSB0 much lower time series

of fishing mortality rates and a different pattern of recruits (Figure 32) without as much of a trend over time

(Figure 32). Fits to indices are also relatively similar with the exception that runs 3 and 4 do not fit the early

high value in 1970 (and subsequent decline) for the 19_TAI_LL (Figure 33). The fit or lack of fit to these data

points- and really just a single 1970 value seems particularly influential on the results. Otherwise the fits to the

indices are relatively similar, and poor.

The comparison of run1 with runs 2-4 provide an evaluation of whether a different growth model or whether

growth estimated within the assessment model might fit the length composition data better than fixed growth.

These analyses indicate an improved fit to the models that estimate growth, either Von Bertalanffy or multi-

stanza, though the performance of the mult-stanza estimation is problematic as it tends to hit bounds on some of

the older age K parameters and the Linf. Nonetheless the two growth curves look quite similar and substantially

different than the input growth models (Figure 36).

Comparison of just the length likelihood allows for a comparison of which growth formulation fits the same

dataset better which cannot be done on the total log-likelihood because runs 1 and 2 and 3 and 4 differ in the

number of data inputs. Runs 3 should be considered preliminary as the estimated Linf and the K on the last ages

exhibit a high negative correlation, the Linf hits an upper bound and the multiple K estimates actually slightly

negative growth. Hence it is likely that this model is somewhat overparameterized and that fewer age-specific

Ks should be estimated. Nonetheless the estimated growth models (Figure 36) are very similar with the multi-

stanza model showing a slight dip at ages 2 and 3. Both indicate a practical Linf (length at age 10) of 150 or 160

cm. These growth estimates differ fairly substantially from the input Gascuel and Draganick and Pelzcarski

growth models, notably in estimating a much lower Linf. Furthermore the slow growth of ages 2-4 is not as

pronounced as for the Gascuel model (Figure 36, Table 8).

Run 3 (estimated multistanza growth) and Run 4 (estimated von Bertalanffy growth) show improved fits to the

length composition data (Figure 27 and 29 and Table 6). The runs that estimate growth obtain a much lower

length composition log-likelihood by 455 and 565 points, respectively, with the mult-stanza growth better (by

70 LL points) than the von bert. The Pearson residuals show somewhat of a better fit but are still plagued

(comparing Figure 28 (Run1) vs 29 (Run3) by some systematic lack of fit, though the lack of fit to the larger

fish is noticeably reduced (compare Figure 26 to 27). Alternative explanations for the lack of large fish could

be U-shaped natural mortality or dome-shaped selectivity for the long-line fisheries.

520

Model runs 2-4 still exhibit some poor diagnostic patterns. Run 4 has a strong retrospective bias (Figure 23) not

seen in Run 3 (Figure 22). There also is some parameter bounding, particularly for some of the spline

selectivity parameters.

Another useful diagnostic tool is the dynamic B0 metric (Wang et al 2009) which plots the trajectory of the

population with and without fishing (Figure 37). This metric is a useful indicator of the relative influence of

fishing vs potential environmental factors, but it could also indicate model mis-specification. For Run0 which

allowed no recruitment deviations, the population is estimated to decline to about 3% of virgin without any

signs of increase. Runs 0.5, 1 and 2 indicate a strong trend in recruitment that maintains the stock after an initial

period of substantial decline. This recruitment is much higher than virgin recruitment and results in a strong

trend in the recruitment deviations and the resulting SSB. Runs 3 and 4 with the growth estimated both show

recruitment fluctuating around virgin levels without as strong of a trend. This divergence of patterns, and the

fact that the increased recruitment occurred in the late 1980s and early 1990s when the fishery switched to more

FAD fishing is a peculiar pattern that warrants further exploration.

Final models results

After a series of scoping runs and sensitivity analysis four runs (5-8) displayed improved performance with

reduced retrospective patterns. Results of these models are shown in the assessment report and not copied here

for brevity. These runs used either index cluster 1 or 2 and had growth fixed at previously estimated values.

Estimating growth resulted in strong retrospective patterns as the model updated the growth models with each

new year of growth information. Hence it was determined that the best course of action was to fix growth at the

estimated values.

Advice models Runs 5 and 7. Model runs 5 and 7 with two-stanza growth fixed at previously estimated values

and cluster 1 and 2 indices displayed the best model performance. Diagnostics are shown in the assessment

report (Anon 2016). For brevity we do not include complete diagnostics for these model runs but do report

estimated quantities and likelihoods (Tables 9 and 10) and parameter estimates (Appendix 2).

Discussion

Overall models 1-4 represented ‘scoping models’ necessary to obtain final advice models. Runs 1-4 each had

some convergence issues and that were largely resolved by fixing growth at values previously estimated in Runs

3 and 4 and splitting the indices into two clusters, thereby reducing conflicts between the indices. The resulting

models (5-8) showed substantially improved model performance, reduced run times and improved fits.

Ultimately model 5 (cluster 1) and 7 (cluster 2) with two-stanza growth were chosen for the base advice models.

While runs 3 and 4 appear to have slightly better performance than run 1 and 2, none of the models proposed

here are ready for advice. While we report benchmark quantities and relative stock status they are likely

unreliable for all models at this point. These preliminary models show relatively poor fits to indices, decent fits

to the length composition overall years but some substantial lack of fit to individual years. Diagnostic

performance of this model was very poor as evidenced by the inability to profile over a range of key parameters

where the model crashed for any steepness less than 0.8 and any R0 less than 12. Furthermore run times for

these models are particularly long and substantially longer than run times for the index clusters. Further

refinement of the models involved addressing some of the parameters that hit bounds and exploring the two

different index clusters.

The separation of indices into two clusters and fixing growth at the estimated values greatly improved model

performance and improved upon many of the issues documented in this report for the preliminary model runs.

Acknowledgements

We are grateful for the contributions of various parties toward the compilation of data and preparatory analyses.

The authors are extremely grateful to Ian Taylor for insights into Stock Synthesis and graphical output routines

and to Rick Methot for developing the Stock Synthesis software.

521

References

Draganik, B. & Pelczarski, W. 1984. Growth and age of bigeye and yellowfin tuna in the Central Atlantic as per

data gathered by R/V Wieczno. Col.Vol.Sci.Pap. ICCAT, 20 (1): 96-103.

Gascuel, D., A. Fonteneau, A. Capisano. 1992. A two-stanza growth model for the yellowfin tuna (Thunnus

albacares) in the eastern Atlantic. Aquatic Living Resources, Vol. 5, No. 3, pp. 155-172.

Methot, R.D. and Taylor, R.G. (2011) Adjusting for bias due to variability of estimated recruitments in fishery

assessment models. Canadian Journal of Fisheries and Aquatic Sciences 68:1744-1760

Methot, R.D. and Wetzel C.R. (2013) Stock synthesis: A biological and statistical framework for fish stock

assessment and fishery management, Fisheries Research 142: 86-99.

Wang, S-P, M. N. Maunder, A Aires-da-Silva,W H. Bayliff.2009. Evaluating fishery impacts: Application to

bigeye tuna (Thunnus obesus) in the eastern Pacific Ocean. Fisheries Research 99 (2009) 106–111.

522

Table 1. Names and fishery definitions of the fleets used in the model.

1_PS_ESFR2_6585 (early)

2_PS_ESFR2_8690 (transition)

3_PS_ESFR2_9114 (Free school)

4_ESFR_FADS2_PS_9114

5_BB+PS_Ghana_6514

6. BB area 2, south of Dakar

7_BB_DAKAR_62_80

8_BB_DAKAR_81_14

9_Japan_LL_75_14_allAreas

10_BR_LL

11_VEN_LL

12_US_LL

13_CH_TAI_LL_1_70_92_allareas

14_CH_TAI_LL_2_93_14_allareas

15_OTHER_LL

16_US_RR

17_OTH_OTH

Table 2. Structural uncertainty examined in YFT Assessment in 2016.

Assumption Option

Spatial domain ao; Atlantic Ocean with one area

Beverton-Holt SR

Steepness (h)

h=0.90 (Base case)

Growth, and Maturity

VB;

Gascual (base case);

Estimated VB and Multistanza

Natural Mortality 2 vectors, Gascual (Base case),

Drag & Pelczarski

CPUE*

σ=SD lognormal errors

All indices (base case)Cluster 1

Cluster 2

Cluster 1, early Japan

Catch-at-Length

(SS=assumed sample)

CL20, reweighted;

523

Table 3. Table of key information for models 0-4, noting the specifications, log-likelihoods, run time, virgin

and ending SSB, parameters that hit bounds, derived quantities and relative status.

Label 0. PM, no LC, fixed Sel at 1

0.5 ASPM, no LC, fixed Sel 1.Gascuel_all_ Fix Growth0.9h

2.D & P_all_Fix Growth0.9h

3.Gascuel_all_EstGrowth0.9h

4.D& P_all_ EstGrowth0.9h

Growth Model Gasc, fix Gasc, fix Gasc, fix D&P fix Gasc, est D&P est grad 0.0000221 0.0000291 0.0632600 0.0000922 0.0000625 0.0001653 time 6 mins 19mins 136 mins 65 mins 35 mins 27 mins wts input input Francis wts Francis wts Francis wts Francis wts Stp est est 0.9 0.9 0.9 0.9

BOUNDED PARMS h h

SizeSpline_GradHi_4_ESFR_FADS_PS_9114_4_BLK1add_2003

SizeSel_12P_1_12_US_LL SizeSpline_GradHi_4_ESFR_FAD

S_PS_9114_4_BLK1add_2003

SizeSel_12P_1_12_

US_LL SizeSel_14P_1_14_CHTAI_LL_2_93_14 SizeSel_17P_1_17_

OTH_OTH SizeSel_17P_1_17 OTH_OTH

SizSplin_GradLo_3_PS_ESFR2_9114_3 SizSel_11P_1_11_V

EN_LL SizSel_14P_1_14_CHTAI_LL_2_93_14

SizSel_17P_1_17_OTH

Likelihoods

TOTAL 166.12 51.82 4492.31 4584.80 5188.79 5068.60 Equil catch 0.00 0.00 0.00 0.00 0.00 0.00 Survey 166.12 88.74 207.89 196.04 204.04 207.02 Recruitment 0.00 -37.51 -37.79 -41.86 -41.61 -38.19 Forecast Rec 0.00 0.59 0.31 0.76 2.35 5.05 Parm_priors 0.00 0.00 4.26 2.77 0.98 3.02 Parm softbnds 0.00 0.00 0.03 0.05 0.02 0.06 Parm_devs 0.00 0.00 0.00 0.00 0.00 0.00 Crash_Pen 0.00 0.00 0.00 0.00 0.00 0.00 Length comp NA NA 4317.62 4427.04 3862.08 3932.18 Age_comp NA NA NA NA 1160.93 959.45

524

Table 4. Index variance tuning check indicating fits to the indices.

Fleet Q N r.m.s.e.

Input+ VarAdj +

extra New_VarAdj

JP_LL 0.000354715 39 0.33847 0.2 0.13847

TAI_LL_1 0.000207778 23 0.312754 0.2 0.112754

TAI_LL_2 0.000521683 22 0.538267 0.2 0.338267

US_LL 0.000781185 28 0.184893 0.2 -0.0151074

VEN_LL 0.000696884 24 0.30905 0.2 0.10905

BRA_LL 0.00024145 35 0.425099 0.2 0.225099

URU_LL_1 4.99E-06 10 0.698456 0.2 0.498456

URU_LL_2 6.33E-06 19 0.883694 0.2 0.683694

Table 5. Correlated parameters above the 70% selected threshold for run 4.

label.i label.j corr correlation 1 RecrDist_Seas_3 RecrDist_Seas_2 0.885 2 RecrDist_Seas_4 RecrDist_Seas_2 0.932 3 RecrDist_Seas_4 RecrDist_Seas_3 0.830 4 SR_LN(R0) RecrDist_Seas_2 -0.879 5 SR_LN(R0) RecrDist_Seas_3 -0.821 6 SR_LN(R0) RecrDist_Seas_4 -0.966 7 SizeSpline_Val_5_2_PS_ESFR2_8690_2 SizeSpline_Val_4_2_PS_ESFR2_8690_2 0.845 8 SizeSpline_Val_1_3_PS_ESFR2_9114_3 SizeSpline_GradLo_3_PS_ESFR2_9114_3 -0.912 9 SizeSel_5P_3_5_BB_PS_Ghana_6514 SizeSel_5P_1_5_BB_PS_Ghana_6514 0.861

10 SizeSel_6P_3_6_BB_area2_Sdak SizeSel_6P_1_6_BB_area2_Sdak 0.933 11 SizeSel_6P_4_6_BB_area2_Sdak SizeSel_6P_2_6_BB_area2_Sdak -0.993 12 SizeSel_7P_3_7_BB_DAKAR_62_80 SizeSel_7P_1_7_BB_DAKAR_62_80 0.906 13 SizeSel_7P_4_7_BB_DAKAR_62_80 SizeSel_7P_2_7_BB_DAKAR_62_80 -0.847 14 SizeSel_8P_3_8_BB_DAKAR_81_14 SizeSel_8P_1_8_BB_DAKAR_81_14 0.970 15 SizeSel_8P_4_8_BB_DAKAR_81_14 SizeSel_8P_2_8_BB_DAKAR_81_14 -0.807 16 SizeSel_10P_2_10_BR_LL_5675 SizeSel_10P_1_10_BR_LL_5675 0.839 17 SizeSel_14P_2_14_CHTAI_LL_2_93_14 SizeSel_14P_1_14_CHTAI_LL_2_93_14 0.763 18 SizeSel_16P_3_16_US_RR SizeSel_16P_1_16_US_RR 0.910 19 SizeSel_17P_3_17_OTH_OTH SizeSel_17P_1_17_OTH_OTH 0.945 20 SizeSel_17P_4_17_OTH_OTH SizeSel_17P_2_17_OTH_OTH -1.000 21 SizeSplin_Val_3_4_ESFR_FADS_PS_9114_4_BLK1add_2003 SizeSplin_Val_2_4_ESFR_FADS_PS_9114_4_BLK1add_2003 0.996 22 SizeSplin_Val_4_4_ESFR_FADS_PS_9114_4_BLK1add_2003 SizeSplin_Val_2_4_ESFR_FADS_PS_9114_4_BLK1add_2003 0.894 23 SizeSplin_Val_4_4_ESFR_FADS_PS_9114_4_BLK1add_2003 SizeSplin_Val_3_4_ESFR_FADS_PS_9114_4_BLK1add_2003 0.881 24 SizeSplin_Val_5_4_ESFR_FADS_PS_9114_4_BLK1add_2003 SizeSplin_Val_2_4_ESFR_FADS_PS_9114_4_BLK1add_2003 0.866 25 SizeSplin_Val_5_4_ESFR_FADS_PS_9114_4_BLK1add_2003 SizeSplin_Val_3_4_ESFR_FADS_PS_9114_4_BLK1add_2003 0.877 26 SizeSplin_Val_5_4_ESFR_FADS_PS_9114_4_BLK1add_2003 SizeSplin_Val_4_4_ESFR_FADS_PS_9114_4_BLK1add_2003 0.796

525

Table 6. Derived quantities and benchmark values for runs 0-4.

Label 0. PM, no LC, fixed Sel at 1

0.5 ASPM, no LC,

fixed Sel 1.Gascuel_all_ FixGrowth0.9h

2.Drag& Pelcz_all

Fix Growth0

.9h 3.Gascuel_all

EstGrowth0.9h

4.Drag& Pelcz_all_

EstGrowth0.9h SSB_Unfished 1348180 788741 896443 818236 799328 883993 TotBio_Unfih 1634810 956426 1132140 1063640 1150870 1316760

SmryBio_Unfis 1633460 955636 1130390 1063030 1149180 1316020 Recr_Unfished 162128 94851 149161 72349 143504 104788

SSB_Btgt 539274 315496 358577 327294 319731 353597 SPR_Btgt 0.402 0.402 0.42 0.42 0.417 0.417 Fstd_Btgt 0.175 0.270 0.22 0.22 0.205 0.189

TotYield_Btgt 130330 124175 122570 109339 114394 115398 SSB_SPRtgt 537226 314298 343210 313268 306028 338443 Fstd_SPRtgt 0.18 0.271 0.23 0.23 0.215 0.198

TotYld_SPRtgt 130499 124368 124277 110819 116111 116802 SSB_MSY 338824 176716 239280 222164 200818 247344 SPR_MSY 0.25 0.23 0.29 0.29 0.27 0.30 Fstd_MSY 0.27 0.45 0.32 0.31 0.32 0.27

TotYield_MSY 139438 137033 130269 115767 123099 121047 RetYield_MSY 139438 137033 130269 115767 123099 121047

2014 catch

estimate 97032 97032 97032 97032 97032 97032 Mean catch last 5

years 100362 100362 100362 100362 100362 100362 Fcurrent 0.93 0.50 0.54 0.54 0.24 0.23293

SSB0 1348180 788741 896443 818236 7.99E+05 883993 SSB 2014 38150 100240 107803 107803 247725 297736

SSBmsy 338824 124368 124277 110819 116111 116802 SSBspr40 130330 124175 122570 109339 114394 115398

F(Current)/ F(MSY) 3.41 1.11 1.68 1.73 0.77 0.87

F(Current)/ F(SPR40) 5.29 1.83 2.30 2.37 1.13 1.18

SB(Current)/ SB(MSY) 0.11 0.57 0.45 0.49 1.23 1.20

SB(Current)/ SB(SPR40%) 0.07 0.32 0.31 0.34 0.81 0.88

SB(Current)/ SB(0) 0.03 0.13 0.12 0.13 0.31 0.34

526

Table 7. Length composition likelihoods by fleet for models 1-4. Right columns are the difference in log

likelihood from the lowest value, indicating the model with the best fit to each component.

Model 1 2 3 4 1 2 3 4

Fleets Overall 4317.6 4427.0 3862.1 3932.2 455.5 565.0 0.0 70.1

PS

1 176.2 174.5 137.9 129.8 46.5 44.7 8.1 0.0

2 84.3 74.5 70.7 67.8 16.5 6.7 2.9 0.0

3 564.5 567.5 518.0 515.8 48.6 51.6 2.1 0.0

4 136.2 263.7 201.1 216.9 0.0 127.5 64.9 80.7

5 157.1 234.3 187.0 212.7 0.0 77.2 29.9 55.6

BB

6 95.7 112.8 102.6 111.8 0.0 17.1 6.9 16.1

7 133.0 153.9 153.9 152.2 0.0 20.9 20.9 19.2

8 338.5 354.9 326.3 357.5 12.2 28.6 0.0 31.2

LL

9 427.0 405.2 339.9 341.0 87.1 65.3 0.0 1.1

10 175.5 166.0 169.3 172.9 9.6 0.0 3.3 6.9

11 35.4 32.4 25.9 25.2 10.2 7.3 0.7 0.0

12 3.6 3.6 3.5 3.4 0.2 0.2 0.1 0.0

13 499.1 421.2 370.8 364.7 134.4 56.5 6.0 0.0

14 166.1 147.6 157.0 153.9 18.4 0.0 9.4 6.3

15 866.9 863.3 618.9 622.7 248.1 244.4 0.0 3.9

RR 16 130.0 140.5 152.7 147.5 0.0 10.5 22.6 17.5

OTH 17 328.4 311.2 326.7 336.3 17.3 0.0 15.6 25.1

Table 8. Estimated growth parameters.

L_a_A2 K A_a_L0 age specific

K

Run1 175 NA 0.2257 0.17 0.17 0.20 0.55 0.69 0.82 0.90 0.96 1.00 1.00 1.00 Run2 192.4 0.3700 0.0038 Run3 188.9 NA 3.9477 0.28 0.28 0.42 0.27 0.29 -0.03 -0.01 0.06 -0.04 -0.04 -0.04 Run4 150.9 0.6045 0.0804

Table 9. Table of key information for models 5 (cluster 1) and 7 (cluster 2), noting the specifications, log-

likelihoods, run time, virgin and ending SSB, parameters that hit bounds, derived quantities and relative status.

Cluster 1 Cluster 2

Growth Model Multistanza est Multistanza est

grad 6.64E-05 3.98E-05

wts Adjusted for Fraction of

catch; Francis wts

Adjusted for Fraction of catch;

Francis wts

Stp fix 0.9 fix 0.9

Index wts = cv 0.3 = cv 0.3

Likelihoods

TOTAL 3711.51 4010.39

Equil catch 0 0

Survey -94.3694 175.204

Recruitment 3845.86 3863.79

Forecast Rec -41.6811 -32.4235

Parm_priors 0.602593 2.7285

Parm softbnds 1.06562 1.06553

Parm_devs 0.0289317 0.0246453

Crash_Pen 0 0

Length comp 0 0

Age_comp 0 0

527

Table 10. SS3 Models: Derived quantities and benchmark values for models 5 (cluster 1) and 7 (cluster 2).

Run5. Cluster 1 sd Run7. Cluster 2 sd

SSB_Unfished 784400 21912 789953 26782

TotBio_Unfih 1129120 31506 1137180 38530

SmryBio_Unfis 1127470 31464 1135530 38475

Recr_Unfished 141034 4127 141933 4988

SSB_Btgt 235320 6574 236986 8035

SPR_Btgt 0.319 0.0000 0.319 0.000

Fstd_Btgt 0.277 0.0039 0.275 0.004

TotYield_Btgt 122277 3060 122004 3296

SSB_SPRtgt 219632 6135 221187 7499

Fstd_SPRtgt 0.29 0.0041 0.292 0.004

TotYld_SPRtgt 122984 3088 122721 3326

SSB_MSY 197150 5389 197949 6452

SPR_MSY 0.272 0.0014 0.271 0.001

Fstd_MSY 0.320 0.0046 0.319 0.005

TotYield_MSY 123382 3114 123139 3354

RetYield_MSY 123382 3114 123139 3354

2014 catch estimate 97032 97032

Mean catch last 5

years 100362 100362

Fcurrent 0.21 0.02 0.36 0.05

SSB0 784400 21912 789953 26782

SSB2014 271286 26129 160085 21385

SSBMSY 197150 5389 197949 3296

SSBspr30 219632 6135 221187 7499

FCurrent/ FMSY 0.647 1.118

FCurrent/ FSPR30 0.704 0.84

SSBCurrent/ SSBMSY 1.38 0.81

SSBCurrent/ SSBSPR30% 1.24 0.72

SSBCurrent/ SSB0 0.35 0.20

528

Figure 1A. Aggregate Atlantic YFT catch in mass over time disaggregated by the fleets defined for the

assessment.

529

Figure 1B,C. YFT landings by major gear group (MT) as a percentage of the total landings.

0

20000

40000

60000

80000

100000

120000

140000

160000

19

50

19

53

19

56

19

59

19

62

19

65

19

68

19

71

19

74

19

77

19

80

19

83

19

86

19

89

19

92

19

95

19

98

20

01

20

04

20

07

20

10

20

13

M

T

PS

OTH

LL

PSFS

BB

PSFAD

0%

20%

40%

60%

80%

100%

120%

19

50

19

53

19

56

19

59

19

62

19

65

19

68

19

71

19

74

19

77

19

80

19

83

19

86

19

89

19

92

19

95

19

98

20

01

20

04

20

07

20

10

20

13

PS

OTH

LL

PSFS

BB

PSFAD

530

Figure 2. Distribution of the LL fleet catches by 5*5 degree area (scaled by 4500 tons by unique 5*5 degree

square and decade).

Figure 3. Distribution of the PS fleet by 5*5 degree area (scaled by 25,000 tons by area and decade).

531

Figure 4. Japanese LL catch and effort by area (catch scaled by 4500 tons by 5*5 degree and effort by 10M

hooks per 5*5 degree and decade).

532

Figure 5. Chinese Taipei catch and effort by area (catch scaled by 4500 tons by 5*5 degree and effort by 10M

hooks per 5*5 degree and decade).

533

Figure 6. Korea catch and effort by area (catch scaled by 4500 tons by 5*5 degree and effort by 10M hooks per

5*5 degree and decade).

534

Figure 7. Length composition of catch data over all years by fleet.

535

Figure 8. Different Growth curves examined (Multistanza Gascuel (1992) versus Draganick and Pelaski 2005).

Figure 9. Assumed YFT maturity-at-age (proportion).

0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 8 10 12

Len

gth

Age

All free like Gascual

SS-Approximation

VB (Dragnetz and Pelaski)

536

Figure 10. Natural Mortality rates that were examined in the base case, sensitivity and grid runs examined in

this assessment.

Figure 11. Likelihood profiles of key parameters for run 0.

537

Figure 12. Likelihood profiles of key parameters for run 0.5.

538

Figure 13.Likelihood profiles of key parameters for run 1.

539


540


541


Figure 17. Jitter analysis of model runs 1-4. Red line is lowest log likelihood from initial run.

542

Figure 18. Retrospective analysis of Run 0. A. SSB, B. Biomass relative to 25% of virgin SSB, C. Estimated

time series of recruits, D. 1-SPR as a measure of fishing mortality rate, E. Estimated virgin biomass, F.

Estimated virgin recruitment.

543

Figure 19. Retrospective analysis of Run 0.5. A. SSB, B. Biomass relative to 25% of virgin SSB, C. Estimated



544




545

Figure 21. Retrospective analysis of Run 2. A. SSB, B. Biomass relative to 25% of virgin SSB, C. 1-SPR as a

measure of fishing mortality rate, D. Estimated time series of recruits, , E. Estimated virgin biomass, F.


546

Figure 22. Retrospective analysis of Run 3. A. SSB, B. Biomass relative to 25% of virgin SSB, C. 1-SPR as a

measure of fishing mortality rate, D. Estimated time series of recruits, , E. Estimated virgin biomass, F.


547




548

Figure 24. Retrospective fits to CPUE time series for run 1.

549

Figure 25. Fits to indices for Run 1.

550

Figure 26. Fits to length composition aggregated over all years for Run 1.

551

Figure 27. Fits to length composition aggregated over all years for Run 3.

552

553

554

Figure 28. Pearson residuals to length composition overall years for Run 1.

555

556

557

Figure 29. Pearson residuals to length composition overall years for Run 3, estimate multistanza growth.

558

Figure 30. Spawner-recruit relationship and recruitment deviations.

559

Figure 31. a. Time series of total biomass, b. spawning biomass. c. spawning biomass relative to virgin, d.

recruits, e. recruits by birth season and f. exploitation rate.

560

Figure 32. A. SSB, B. Biomass relative to 25% of virgin SSB, C. Estimated time series of recruits, D. 1-SPR as

a measure of fishing mortality rate, E. Estimated virgin biomass, F. Estimated virgin recruitment, Run0 not

shown on density plots due to altering the scaling.

561

Figure 33. Comparison of fits to CPUE time series for models 0-4 (noted as models 1-6) in figure.

562

563

Figure 34. Estimated selectivities for models 1-4.

564

Figure 35. Length composition and estimated selectivities for fleet 4_ESFR_FADS_PS_9114 and

6_BB_area2_SDak.

565

Figure 36. Input and estimated growth curves.

566

Figure 37. Dynamic B0 with (black) and without (red) fishing indicates that, for the models that allow

recruitment deviations, these substantial trend occurs independent of the pattern of fishing. It could be

interpreted as an environmental-driven recruitment, however clearly there is a different pattern for runs 3 and 4.

567

Appendix 1. Control file for final SS model (Run 5, cluster 1 (cluster 2 control similar but with

different indices).

#V3.24j

#_data_and_control_files: DATA11.SS // CONTROL.SS

#_SS-V3.24j-safe;_11/14/2012;_Stock_Synthesis_by_Richard_Methot_(NOAA)_using_ADMB_10.1

1 #_N_Growth_Patterns

1 #_N_Morphs_Within_GrowthPattern

#_Cond 1 #_Morph_between/within_stdev_ratio (no read if N_morphs=1)

#_Cond 1 #vector_Morphdist_(-1_in_first_val_gives_normal_approx)

#

4 # number of recruitment assignments (overrides GP*area*seas parameter values)

0 # recruitment interaction requested

#GP seas area for each recruitment assignment

1 1 1

1 2 1

1 3 1

1 4 1

#

#_Cond 0 # N_movement_definitions goes here if N_areas > 1

#_Cond 1.0 # first age that moves (real age at begin of season, not integer) also cond on do_migration>0

#_Cond 1 1 1 2 4 10 # example move definition for seas=1, morph=1, source=1 dest=2, age1=4, age2=10

#

2 #_Nblock_Patterns

1 1 #_blocks_per_pattern

2003 2014 #4_ESFR_FADS_PS_9114

2010 2014 #fleet 6_BB_area2Sdak

#

0.5 #_fracfemale

3 #_natM_type:_0=1Parm; 1=N_breakpoints;_2=Lorenzen;_3=agespecific;_4=agespec_withseasinterpolate

#_Age_natmort_by gender x growthpattern

1.588 1.194 0.748 0.55 0.476 0.447 0.435 0.431 0.429 0.428 0.428

3 # GrowthModel: 1=vonBert with L1&L2; 2=Richards with L1&L2; 3=age_speciific_K; 4=not implemented

0.38 #_Growth_Age_for_L1

999 #_Growth_Age_for_L2 (999 to use as Linf)

7 # number of K multipliers to read

2 3 4 5 6 7 8 # ages for K multiplier

0 #_SD_add_to_LAA (set to 0.1 for SS2 V1.x compatibility)

0 #_CV_Growth_Pattern: 0 CV=f(LAA); 1 CV=F(A); 2 SD=F(LAA); 3 SD=F(A); 4 logSD=F(A)

1 #_maturity_option: 1=length logistic; 2=age logistic; 3=read age-maturity matrix by growth_pattern; 4=read age-fecundity; 5=read fec

and wt from wtatage.ss

#_placeholder for empirical age-maturity by growth pattern

0 #_First_Mature_Age

1 #_fecundity option:(1)eggs=Wt*(a+b*Wt);(2)eggs=a*L^b;(3)eggs=a*Wt^b; (4)eggs=a+b*L; (5)eggs=a+b*W

0 #_hermaphroditism option: 0=none; 1=age-specific fxn

1 #_parameter_offset_approach (1=none, 2= M, G, CV_G as offset from female-GP1, 3=like SS2 V1.x)

1 #_env/block/dev_adjust_method (1=standard; 2=logistic transform keeps in base parm bounds; 3=standard w/ no bound check)

#

#_growth_parms

#_LO HI INIT PRIOR PR_type SD PHASE env-var use_dev dev_minyr dev_maxyr dev_stddev Block Block_Fxn

1 45 25 25 0 10 -2 0 0 0 0 0.5 0 0 # L_at_Amin_Fem_GP_1

120 190 189.393 175 0 10 -4 0 0 0 0 0.5 0 0 # L_at_Amax_Fem_GP_1

0.05 0.8 0.278737 0.17 0 0.8 -4 0 0 0 0 0.5 0 0 # VonBert_K_Fem_GP_1

-5 5 1.47117 -0.4 -1 1 -1 0 0 0 0 0 0 0 # Age_K_Fem_GP_1_a_2

-15 5 0.663384 -0.4 -1 1 -1 0 0 0 0 0 0 0 # Age_K_Fem_GP_1_a_3

-15 5 1.09024 -0.4 -1 1 -1 0 0 0 0 0 0 0 # Age_K_Fem_GP_1_a_4

-15 5 -0.120885 -0.4 -1 1 -1 0 0 0 0 0 0 0 # Age_K_Fem_GP_1_a_5

-15 5 0.430951 -1 -1 1 -1 0 0 0 0 0 0 0 # Age_K_Fem_GP_1_a_6

-15 5 -4.42698 -1 -1 1 -1 0 0 0 0 0 0 0 # Age_K_Fem_GP_1_a_7

-15 5 -0.607075 -1 -1 1 -1 0 0 0 0 0 0 0 # Age_K_Fem_GP_1_a_8

0.05 0.25 0.1 0.1 0 0.1 -3 0 0 0 0 0.5 0 0 # CV_young_Fem_GP_1

0.05 0.25 0.1 0.1 0 0.1 -3 0 0 0 0 0.5 0 0 # CV_old_Fem_GP_1

-3 3 1.7665e-005 1.7665e-005 0 0.8 -3 0 0 0 0 0.5 0 0 # Wtlen_1_Fem

-3 4 3.03542 3.03542 0 0.8 -3 0 0 0 0 0.5 0 0 # Wtlen_2_Fem

-3 150 115.1 1 -1 0.8 -3 0 0 0 0 0 0 0 # Mat50%_Fem

-3 3 -0.15786 -0.25 0 0.8 -3 0 0 0 0 0 0 0 # Mat_slope_Fem

-3 3 1 1 0 0.8 -3 0 0 0 0 0.5 0 0 # Eggs/kg_inter_Fem

-3 3 0 0 0 0.8 -3 0 0 0 0 0.5 0 0 # Eggs/kg_slope_wt_Fem

-4 4 0 0 -1 99 -3 0 0 0 0 0.5 0 0 # RecrDist_GP_1

-4 4 0 0 -1 99 -3 0 0 0 0 0.5 0 0 # RecrDist_Area_1

-4 4 0 0 -1 99 -3 0 0 0 0 0.5 0 0 # RecrDist_Seas_1

-4 4 0.169908 0 -1 99 3 0 0 0 0 0.5 0 0 # RecrDist_Seas_2

-4 4 -0.719255 0 -1 99 3 0 0 0 0 0.5 0 0 # RecrDist_Seas_3

-4 4 -0.227821 0 -1 99 3 0 0 0 0 0.5 0 0 # RecrDist_Seas_4

1 1 1 1 -1 99 -3 0 0 0 0 0.5 0 0 # CohortGrowDev

568

#

#_Cond 0 #custom_MG-env_setup (0/1)

#_Cond -2 2 0 0 -1 99 -2 #_placeholder when no MG-environ parameters

#

#_Cond 0 #custom_MG-block_setup (0/1)

#_Cond -2 2 0 0 -1 99 -2 #_placeholder when no MG-block parameters

#_Cond No MG parm trends

#

#_seasonal_effects_on_biology_parms

0 0 0 0 0 0 0 0 0 0 #_femwtlen1,femwtlen2,mat1,mat2,fec1,fec2,Malewtlen1,malewtlen2,L1,K

#_Cond -2 2 0 0 -1 99 -2 #_placeholder when no seasonal MG parameters

#

#_Cond -4 #_MGparm_Dev_Phase

#

#_Spawner-Recruitment

6 #_SR_function: 2=Ricker; 3=std_B-H; 4=SCAA; 5=Hockey; 6=B-H_flattop; 7=survival_3Parm

#_LO HI INIT PRIOR PR_type SD PHASE

0 15 12.5 11 -1 10 1 # SR_R0 ##

0.201 0.99 0.9 0.9 -1 10 -2 # SR_steepness

0.1 2 0.6 0.2 -1 10 6 # SR_sigmaR

-5 5 0 0 0 1 -3 # SR_envlink

-5 5 0 0 0 1 -4 # SR_R1_offset

0 0.5 0 0 -1 99 -2 # SR_autocorr

0 #_SR_env_link

0 #_SR_env_target_0=none;1=devs;_2=R0;_3=steepness

1 #do_recdev: 0=none; 1=devvector; 2=simple deviations

1970 # first year of main recr_devs; early devs can preceed this era

2010 # last year of main recr_devs; forecast devs start in following year

3 #_recdev phase

1 # (0/1) to read 13 advanced options

0 #_recdev_early_start (0=none; neg value makes relative to recdev_start)

-4 #_recdev_early_phase

0 #_forecast_recruitment phase (incl. late recr) (0 value resets to maxphase+1)

1 #_lambda for Fcast_recr_like occurring before endyr+1

1922 #_last_early_yr_nobias_adj_in_MPD

1975 #_first_yr_fullbias_adj_in_MPD

2011.8 #_last_yr_fullbias_adj_in_MPD

2015 #_first_recent_yr_nobias_adj_in_MPD

0.955 #_max_bias_adj_in_MPD (-1 to override ramp and set biasadj=1.0 for all estimated recdevs)

0 #_period of cycles in recruitment (N parms read below)

-5 #min rec_dev

5 #max rec_dev

45 #_read_recdevs

#_end of advanced SR options

#

#_placeholder for full parameter lines for recruitment cycles

# Specified recr devs to read

#_Yr Input_value # Final_value

1970 -0.301529 # -0.303938

1971 -0.247946 # -0.249537

1972 -0.407569 # -0.409305

1973 -0.455231 # -0.456662

1974 -0.293296 # -0.293796

1975 -0.0911775 # -0.0906277

1976 -0.000898286 # -0.000155714

1977 -0.169368 # -0.169316

1978 -0.0228093 # -0.0224322

1979 -0.0326464 # -0.0324359

1980 0.154272 # 0.15467

1981 0.0491351 # 0.0493136

1982 -0.103416 # -0.103603

1983 0.281461 # 0.282006

1984 0.0997118 # 0.0998901

1985 0.410649 # 0.411189

1986 0.532033 # 0.53278

1987 -0.0819647 # -0.0823792

1988 0.100344 # 0.100551

1989 0.285215 # 0.285676

1990 0.00696976 # 0.00694938

1991 0.189425 # 0.189696

1992 0.432273 # 0.432853

1993 0.0432378 # 0.0433088

1994 0.0526936 # 0.0529471

1995 0.0293444 # 0.0295967

1996 0.0198663 # 0.0200593

569

1997 0.12029 # 0.120564

1998 0.228538 # 0.228961

1999 -0.0417999 # -0.0417945

2000 0.181633 # 0.181951

2001 0.361646 # 0.362168

2002 -0.106501 # -0.106471

2003 -0.143734 # -0.143552

2004 -0.0956336 # -0.095368

2005 -0.1087 # -0.108355

2006 -0.337385 # -0.337463

2007 -0.0417987 # -0.0413939

2008 -0.198967 # -0.198966

2009 -0.0855032 # -0.0855523

2010 -0.210865 # -0.212024

2011 -0.308539 # -0.306869

2012 -0.731854 # -0.730978

2013 -0.593674 # -0.596924

2014 -0.324737 # -0.334529

#

#Fishing Mortality info

0.3 # F ballpark for tuning early phases

2000 # F ballpark year (neg value to disable)

1 # F_Method: 1=Pope; 2=instan. F; 3=hybrid (hybrid is recommended)

0.9 # max F or harvest rate, depends on F_Method

# no additional F input needed for Fmethod 1

# if Fmethod=2; read overall start F value; overall phase; N detailed inputs to read

# if Fmethod=3; read N iterations for tuning for Fmethod 3

#

#_initial_F_parms

#_LO HI INIT PRIOR PR_type SD PHASE

0 1 0 0 0 99 -1 # InitF_11_PS_ESFR2_6585

0 1 0 0 0 99 -1 # InitF_22_PS_ESFR2_8690

0 1 0 0 0 99 -1 # InitF_33_PS_ESFR2_9114

0 1 0 0 0 99 -1 # InitF_44_ESFR_FADS_PS_9114

0 1 0 0 0 99 -1 # InitF_55_BB_PS_Ghana_6514

0 1 0 0 0 99 -1 # InitF_66_BB_area2_Sdak

0 1 0 0 0 99 -1 # InitF_77_BB_DAKAR_62_80

0 1 0 0 0 99 -1 # InitF_88_BB_DAKAR_81_14

0 1 0 0 0 99 -1 # InitF_99_Japan_LL_75_14

0 1 0 0 0 99 -1 # InitF_1010_BR_LL_5675

0 1 0 0 0 99 -1 # InitF_1111_VEN_LL

0 1 0 0 0 99 -1 # InitF_1212_US_LL

0 1 0 0 0 99 -1 # InitF_1313_CHTAI_LL_1_70_92

0 1 0 0 0 99 -1 # InitF_1414_CHTAI_LL_2_93_14

0 1 0 0 0 99 -1 # InitF_1515_OTHER_LL

0 1 0 0 0 99 -1 # InitF_1616_US_RR

0 1 0 0 0 99 -1 # InitF_1717_OTH_OTH

#

#_Q_setup

# Q_type options: <0=mirror, 0=float_nobiasadj, 1=float_biasadj, 2=parm_nobiasadj, 3=parm_w_random_dev, 4=parm_w_randwalk,

5=mean_unbiased_float_assign_to_parm

#_for_env-var:_enter_index_of_the_env-var_to_be_linked

#_Den-dep env-var extra_se Q_type

0 0 0 0 # 1 1_PS_ESFR2_6585

0 0 0 0 # 2 2_PS_ESFR2_8690

0 0 0 0 # 3 3_PS_ESFR2_9114

0 0 0 0 # 4 4_ESFR_FADS_PS_9114

0 0 0 0 # 5 5_BB_PS_Ghana_6514

0 0 0 0 # 6 6_BB_area2_Sdak

0 0 0 0 # 7 7_BB_DAKAR_62_80

0 0 0 0 # 8 8_BB_DAKAR_81_14

0 0 0 0 # 9 9_Japan_LL_75_14

0 0 0 0 # 10 10_BR_LL_5675

0 0 0 0 # 11 11_VEN_LL

0 0 0 0 # 12 12_US_LL

0 0 0 0 # 13 13_CHTAI_LL_1_70_92

0 0 0 0 # 14 14_CHTAI_LL_2_93_14

0 0 0 0 # 15 15_OTHER_LL

0 0 0 0 # 16 16_US_RR

0 0 0 0 # 17 17_OTH_OTH

0 0 0 0 # 18 JP_LL

0 0 0 0 # 19 TAI_LL_1

0 0 0 0 # 20 TAI_LL_2

0 0 0 0 # 21 US_LL

0 0 0 0 # 22 VEN_LL

570

0 0 0 0 # 23 BRA_LL

0 0 0 0 # 24 URU_LL_1

0 0 0 0 # 25 URU_LL_2

0 0 0 0 # 26 EC_PS_3

0 0 0 0 # 27 EUR_FAD_PS

0 0 0 0 # 28 TROP_PS

0 0 0 0 # 29 MEX_LL

0 0 0 0 # 30 US_RR

0 0 0 0 # 31 CA_BB

0 0 0 0 # 32 VEN_PS

0 0 0 0 # 33 BR_BB

0 0 0 0 # 34 Eudkr_BB

#

#_Cond 0 #_If q has random component, then 0=read one parm for each fleet with random q; 1=read a parm for each year of index

#_Q_parms(if_any);Qunits_are_ln(q)

#

#_size_selex_types

#discard_options:_0=none;_1=define_retention;_2=retention&mortality;_3=all_discarded_dead

#_Pattern Discard Male Special

27 0 0 5 # 1 1_PS_ESFR2_6585

27 0 0 5 # 2 2_PS_ESFR2_8690

27 0 0 5 # 3 3_PS_ESFR2_9114

27 0 0 5 # 4 4_ESFR_FADS_PS_9114

24 0 0 0 # 5 5_BB_PS_Ghana_6514

24 0 0 0 # 6 6_BB_area2_Sdak

24 0 0 0 # 7 7_BB_DAKAR_62_80

24 0 0 0 # 8 8_BB_DAKAR_81_14

1 0 0 0 # 9 9_Japan_LL_75_14

1 0 0 0 # 10 10_BR_LL_5675

1 0 0 0 # 11 11_VEN_LL

1 0 0 0 # 12 12_US_LL

1 0 0 0 # 13 13_CHTAI_LL_1_70_92

1 0 0 0 # 14 14_CHTAI_LL_2_93_14

1 0 0 0 # 15 15_OTHER_LL

24 0 0 0 # 16 16_US_RR

24 0 0 0 # 17 17_OTH_OTH

15 0 0 9 # 18 JP_LL

15 0 0 13 # 19 TAI_LL_1

15 0 0 14 # 20 TAI_LL_2

15 0 0 12 # 21 US_LL

15 0 0 11 # 22 VEN_LL

15 0 0 10 # 23 BRA_LL

15 0 0 15 # 24 URU_LL_1

15 0 0 15 # 25 URU_LL_2

15 0 0 2 # 26 EC_PS_3

15 0 0 3 # 27 EUR_FAD_PS

15 0 0 5 # 28 TROP_PS

15 0 0 12 # 29 MEX_LL

15 0 0 16 # 30 US_RR

15 0 0 8 # 31 CA_BB

15 0 0 17 # 32 VEN_PS

15 0 0 17 # 33 BR_BB

15 0 0 8 # 34 Eudkr_BB

#

#_age_selex_types

#_Pattern ___ Male Special

14 0 0 0 # 1 1_PS_ESFR2_6585

15 0 0 1 # 2 2_PS_ESFR2_8690

15 0 0 1 # 3 3_PS_ESFR2_9114

15 0 0 1 # 4 4_ESFR_FADS_PS_9114

15 0 0 1 # 5 5_BB_PS_Ghana_6514

15 0 0 1 # 6 6_BB_area2_Sdak

15 0 0 1 # 7 7_BB_DAKAR_62_80

15 0 0 1 # 8 8_BB_DAKAR_81_14

15 0 0 1 # 9 9_Japan_LL_75_14

15 0 0 1 # 10 10_BR_LL_5675

15 0 0 1 # 11 11_VEN_LL

15 0 0 1 # 12 12_US_LL

15 0 0 1 # 13 13_CHTAI_LL_1_70_92

15 0 0 1 # 14 14_CHTAI_LL_2_93_14

15 0 0 1 # 15 15_OTHER_LL

15 0 0 1 # 16 16_US_RR

15 0 0 1 # 17 17_OTH_OTH

15 0 0 1 # 18 JP_LL

15 0 0 1 # 19 TAI_LL_1

571

15 0 0 1 # 20 TAI_LL_2

15 0 0 1 # 21 US_LL

15 0 0 1 # 22 VEN_LL

15 0 0 1 # 23 BRA_LL

15 0 0 1 # 24 URU_LL_1

15 0 0 1 # 25 URU_LL_2

15 0 0 1 # 26 EC_PS_3

15 0 0 1 # 27 EUR_FAD_PS

15 0 0 1 # 28 TROP_PS

15 0 0 1 # 29 MEX_LL

15 0 0 1 # 30 US_RR

15 0 0 1 # 31 CA_BB

15 0 0 1 # 32 VEN_PS

15 0 0 1 # 33 BR_BB

15 0 0 1 # 34 Eudkr_BB

#_LO HI INIT PRIOR PR_type SD PHASE env-var use_dev dev_minyr dev_maxyr dev_stddev Block Block_Fxn

0 0 0 0 -1 0 -99 0 0 0 0 0 0 0 # SizeSpline_Code_1_PS_ESFR2_6585_1

-0.001 2 0.260179 -1 1 0.001 1 0 0 0 0 0.5 0 0 # SizeSpline_GradLo_1_PS_ESFR2_6585_1

-5 5 0.0452897 -1 1 0.001 3 0 0 0 0 0.5 0 0 # SizeSpline_GradHi_1_PS_ESFR2_6585_1

13 200 23.125 0 -1 0 -99 0 0 0 0 0.05 0 0 # SizeSpline_Knot_1_1_PS_ESFR2_6585_1





-9 7 -6.39992 -1 -1 0.001 3 0 0 0 0 0.05 0 0 # SizeSpline_Val_1_1_PS_ESFR2_6585_1

-9 7 0.0545398 -1 -1 0.001 2 0 0 0 0 0.05 0 0 # SizeSpline_Val_2_1_PS_ESFR2_6585_1

-9 7 0.278668 -1 -1 -1 -2 0 0 0 0 0.05 0 0 # SizeSpline_Val_3_1_PS_ESFR2_6585_1



0 0 0 0 -1 0 -99 0 0 0 0 0.5 0 0 # SizeSpline_Code_2_PS_ESFR2_8690_2


-1 0.02 0.01 0.02 1 0.001 -3 0 0 0 0 0.5 0 0 # SizeSpline_GradHi_2_PS_ESFR2_8690_2






-9 7 -8.71258 -8 -1 0.001 -2 0 0 0 0 0.05 0 0 # SizeSpline_Val_1_2_PS_ESFR2_8690_2

-9 7 -0.492945 -1 -1 0.001 2 0 0 0 0 0.05 0 0 # SizeSpline_Val_2_2_PS_ESFR2_8690_2


-9 7 2.60788 0 -1 0.001 2 0 0 0 0 0.05 0 0 # SizeSpline_Val_4_2_PS_ESFR2_8690_2


0 0 0 0 -1 0 -99 0 0 0 0 0.5 0 0 # SizeSpline_Code_3_PS_ESFR2_9114_3


-1 0.02 0.02 0.02 1 0.001 -3 0 0 0 0 0.5 0 0 # SizeSpline_GradHi_3_PS_ESFR2_9114_3






-9 7 -5.49541 0 -1 0.001 2 0 0 0 0 0.05 0 0 # SizeSpline_Val_1_3_PS_ESFR2_9114_3





0 0 0 0 -1 0 -99 0 0 0 0 0.5 0 0 # SizeSpline_Code_4_ESFR_FADS_PS_9114_4

-0.001 1 0.33898 -1 1 0.001 3 0 0 0 0 0.5 0 0 # SizeSpline_GradLo_4_ESFR_FADS_PS_9114_4

-1 0.02 -0.0311541 -1 1 0.001 3 0 0 0 0 0.5 1 1 # SizeSpline_GradHi_4_ESFR_FADS_PS_9114_4

13 200 23.125 0 -1 0 -99 0 0 0 0 0.05 0 0 # SizeSpline_Knot_1_4_ESFR_FADS_PS_9114_4





-9 7 -8.60135 0 -1 0.001 -2 0 0 0 0 0.05 1 1 # SizeSpline_Val_1_4_ESFR_FADS_PS_9114_4

-9 7 0.337476 0 -1 0.001 2 0 0 0 0 0.05 1 1 # SizeSpline_Val_2_4_ESFR_FADS_PS_9114_4

-9 7 0.278668 -1 -1 -1 -2 0 0 0 0 0.05 1 1 # SizeSpline_Val_3_4_ESFR_FADS_PS_9114_4

-9 7 -0.611907 0 -1 0.001 2 0 0 0 0 0.05 1 1 # SizeSpline_Val_4_4_ESFR_FADS_PS_9114_4

-9 7 0.342002 0 -1 0.001 2 0 0 0 0 0.05 1 1 # SizeSpline_Val_5_4_ESFR_FADS_PS_9114_4

20 60 44.3342 45.4487 -1 1000 3 0 0 0 0 0 0 0 # SizeSel_5P_1_5_BB_PS_Ghana_6514

-20 -3 -16.3475 -2 -1 1000 -4 0 0 0 0 0 0 0 # SizeSel_5P_2_5_BB_PS_Ghana_6514

-10 9 3.19621 3.38221 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_5P_3_5_BB_PS_Ghana_6514

-5 9 5.3482 5.40137 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_5P_4_5_BB_PS_Ghana_6514

-999 15 -999 -1 -1 5 -99 0 0 0 0 0 0 0 # SizeSel_5P_5_5_BB_PS_Ghana_6514

-999 15 -999 -1 -1 5 -99 0 0 0 0 0 0 0 # SizeSel_5P_6_5_BB_PS_Ghana_6514

572

20 100 48.3632 48.6783 -1 1000 3 0 0 0 0 0 2 1 # SizeSel_6P_1_6_BB_area2_Sdak

-10 3 -4.60086 0.620026 -1 1000 4 0 0 0 0 0 2 1 # SizeSel_6P_2_6_BB_area2_Sdak

-5 9 4.08711 4.01657 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_6P_3_6_BB_area2_Sdak

-5 9 8.01401 5.6106 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_6P_4_6_BB_area2_Sdak

-999 15 -999 -1 -1 5 -99 0 0 0 0 0 0 0 # SizeSel_6P_5_6_BB_area2_Sdak

-999 15 -999 -1 -1 5 -99 0 0 0 0 0 0 0 # SizeSel_6P_6_6_BB_area2_Sdak

40 100 62.2244 40 -1 1000 3 0 0 0 0 0 0 0 # SizeSel_7P_1_7_BB_DAKAR_62_80

-10 3 -7.95407 50 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_7P_2_7_BB_DAKAR_62_80

-5 9 4.74381 3 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_7P_3_7_BB_DAKAR_62_80

-5 9 7.63159 5 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_7P_4_7_BB_DAKAR_62_80

-999 15 -999 -1 -1 5 -99 0 0 0 0 0 0 0 # SizeSel_7P_5_7_BB_DAKAR_62_80


40 100 60.2161 63.8192 -1 1000 3 0 0 0 0 0 0 0 # SizeSel_8P_1_8_BB_DAKAR_81_14

-10 3 -0.694965 -1.16787 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_8P_2_8_BB_DAKAR_81_14

-5 9 5.05004 4.81298 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_8P_3_8_BB_DAKAR_81_14

-5 9 7.4374 6.75951 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_8P_4_8_BB_DAKAR_81_14



80 130 108.847 106.19 -1 1 3 0 0 0 0 0 0 0 # SizeSel_9P_1_9_Japan_LL_75_14

10 50 24.5454 18.1095 -1 1 3 0 0 0 0 0 0 0 # SizeSel_9P_2_9_Japan_LL_75_14

80 130 94.65 106.19 -1 1 3 0 0 0 0 0 0 0 # SizeSel_10P_1_10_BR_LL_5675

10 50 26.182 18.1095 -1 1 3 0 0 0 0 0 0 0 # SizeSel_10P_2_10_BR_LL_5675

80 130 125.898 115.072 -1 1 3 0 0 0 0 0 0 0 # SizeSel_11P_1_11_VEN_LL

10 50 28.5073 21.3215 -1 1 3 0 0 0 0 0 0 0 # SizeSel_11P_2_11_VEN_LL

80 130 119.265 117.782 -1 1 3 0 0 0 0 0 0 0 # SizeSel_12P_1_12_US_LL

10 50 31.3301 28.2096 -1 1 3 0 0 0 0 0 0 0 # SizeSel_12P_2_12_US_LL

80 130 103.303 105.124 -1 1 3 0 0 0 0 0 0 0 # SizeSel_13P_1_13_CHTAI_LL_1_70_92




70 130 109.281 100.366 -1 1 3 0 0 0 0 0 0 0 # SizeSel_15P_1_15_OTHER_LL

10 50 25.2448 23.2361 -1 1 3 0 0 0 0 0 0 0 # SizeSel_15P_2_15_OTHER_LL

70 130 80.4175 92.8438 -1 1000 3 0 0 0 0 0 0 0 # SizeSel_16P_1_16_US_RR

-20 -3 -2 -2 -1 1000 -5 0 0 0 0 0 0 0 # SizeSel_16P_2_16_US_RR

-5 9 4.90659 5.63576 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_16P_3_16_US_RR

-5 9 7.35351 7.7584 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_16P_4_16_US_RR

-999 15 -999 -1 -1 5 -99 0 0 0 0 0 0 0 # SizeSel_16P_5_16_US_RR

-999 15 -999 -1 -1 5 -99 0 0 0 0 0 0 0 # SizeSel_16P_6_16_US_RR

70 130 73.1157 97.9544 -1 1000 3 0 0 0 0 0 0 0 # SizeSel_17P_1_17_OTH_OTH

-5 3 0.980563 0.0461944 -1 1000 5 0 0 0 0 0 0 0 # SizeSel_17P_2_17_OTH_OTH

-5 9 5.32636 6.48914 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_17P_3_17_OTH_OTH

-5 10 -1.43949 5.5059 -1 1000 4 0 0 0 0 0 0 0 # SizeSel_17P_4_17_OTH_OTH

-999 15 -999 -1 -1 5 -99 0 0 0 0 0 0 0 # SizeSel_17P_5_17_OTH_OTH

-999 15 -999 -1 -1 5 -99 0 0 0 0 0 0 0 # SizeSel_17P_6_17_OTH_OTH

-5 9 9 9 -1 99 -3 0 0 0 0 0.5 0 0 # AgeSel_1P_1_1_PS_ESFR2_6585

-5 9 9 9 -1 99 -3 0 0 0 0 0.5 0 0 # AgeSel_1P_2_1_PS_ESFR2_6585

-5 9 9 9 -1 99 -3 0 0 0 0 0.5 0 0 # AgeSel_1P_3_1_PS_ESFR2_6585

-5 9 9 9 -1 99 -3 0 0 0 0 0.5 0 0 # AgeSel_1P_4_1_PS_ESFR2_6585

-5 9 9 9 -1 99 -3 0 0 0 0 0.5 0 0 # AgeSel_1P_5_1_PS_ESFR2_6585

-5 9 9 9 -1 99 -3 0 0 0 0 0.5 0 0 # AgeSel_1P_6_1_PS_ESFR2_6585

-5 9 9 9 -1 99 -3 0 0 0 0 0.5 0 0 # AgeSel_1P_7_1_PS_ESFR2_6585

-5 9 9 9 -1 99 -3 0 0 0 0 0.5 0 0 # AgeSel_1P_8_1_PS_ESFR2_6585

-5 9 9 9 -1 99 -3 0 0 0 0 0.5 0 0 # AgeSel_1P_9_1_PS_ESFR2_6585

-5 9 9 9 -1 99 -3 0 0 0 0 0.5 0 0 # AgeSel_1P_10_1_PS_ESFR2_6585

-5 9 9 9 -1 99 -3 0 0 0 0 0.5 0 0 # AgeSel_1P_11_1_PS_ESFR2_6585

#_Cond 0 #_custom_sel-env_setup (0/1)

#_Cond -2 2 0 0 -1 99 -2 #_placeholder when no enviro fxns

1 #_custom_sel-blk_setup (0/1)

-1 0.02 -0.661435 -1 1 0.001 -7 # SizeSpline_GradHi_4_ESFR_FADS_PS_9114_4_BLK1add_2003

-9 7 -1.03703 0 -1 0.001 7 # SizeSpline_Val_1_4_ESFR_FADS_PS_9114_4_BLK1add_2003

-9 7 -0.149582 0 -1 0.001 -7 # SizeSpline_Val_2_4_ESFR_FADS_PS_9114_4_BLK1add_2003

-9 7 -0.147089 0 -1 0 7 # SizeSpline_Val_3_4_ESFR_FADS_PS_9114_4_BLK1add_2003



20 100 60 48.6783 -1 1000 7 # SizeSel_6P_1_6_BB_area2_Sdak_BLK2add_2010

-10 3 2.09398 0.620026 -1 1000 7 # SizeSel_6P_2_6_BB_area2_Sdak_BLK2add_2010

#_Cond 0 #_custom_sel-env_setup (0/1)

#_Cond -2 2 0 0 -1 99 -2 #_placeholder when no enviro fxns

#_Cond 0 #_custom_sel-blk_setup (0/1)

#_Cond -2 2 0 0 -1 99 -2 #_placeholder when no block usage

#_Cond No selex parm trends

#_Cond -4 # placeholder for selparm_Dev_Phase

2 #_Cond 0 #_env/block/dev_adjust_method (1=standard; 2=logistic trans to keep in base parm bounds; 3=standard w/ no bound check)

# Tag loss and Tag reporting parameters go next

573

0 # TG_custom: 0=no read; 1=read if tags exist

#_Cond -6 6 1 1 2 0.01 -4 0 0 0 0 0 0 0 #_placeholder if no parameters

#

1 #_Variance_adjustments_to_input_values

#_fleet: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #_add_to_survey_CV

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #_add_to_discard_stddev

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #_add_to_bodywt_CV

0.2 0.2 1.02 0.34 0.12 0.06 0.46 0.23 0.21 0.2 0.07 0.08 0.743 0.25 0.76 0.14 0.29 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 #_mult_by_lencomp_N

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 #_mult_by_agecomp_N

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 #_mult_by_size-at-age_N

#

1 #_maxlambdaphase

1 #_sd_offset

#

8 # number of changes to make to default Lambdas (default value is 1.0)

# Like_comp codes: 1=surv; 2=disc; 3=mnwt; 4=length; 5=age; 6=SizeFreq; 7=sizeage; 8=catch;

# 9=init_equ_catch; 10=recrdev; 11=parm_prior; 12=parm_dev; 13=CrashPen; 14=Morphcomp; 15=Tag-comp; 16=Tag-negbin

#like_comp fleet/survey phase value sizefreq_method

1 18 1 1 1

1 19 1 1 1

1 20 1 1 1

1 21 1 1 1

1 22 1 1 1

1 23 1 1 1

1 24 1 1 1

1 25 1 1 1

#

0 # (0/1) read specs for more stddev reporting

# 0 1 -1 5 1 5 1 -1 5 # placeholder for selex type, len/age, year, N selex bins, Growth pattern, N growth ages, NatAge_area(-1 for all),

NatAge_yr, N Natages

# placeholder for vector of selex bins to be reported

# placeholder for vector of growth ages to be reported

# placeholder for vector of NatAges ages to be reported

999

574

Appendix 2. Parameter estimates for SS model, run 5. Num Label Value Active

Cnt Phase Min Max Init Status Parm

StDev PR_type

1 L_at_Amin_Fem_GP_1 25 _ -2 1 45 25.000 NA _ Normal 2 L_at_Amax_Fem_GP_1 189.39 _ -4 120 190 189.393 NA _ Normal 3 VonBert_K_Fem_GP_1 0.27873 _ -4 0.05 0.8 0.279 NA _ Normal 4 Age_K_Fem_GP_1_a_2 1.47117 _ -1 -5 5 1.471 NA _ No_prior 5 Age_K_Fem_GP_1_a_3 0.66338 _ -1 -15 5 0.663 NA _ No_prior 6 Age_K_Fem_GP_1_a_4 1.09024 _ -1 -15 5 1.090 NA _ No_prior 7 Age_K_Fem_GP_1_a_5 -0.12088 _ -1 -15 5 -0.121 NA _ No_prior 8 Age_K_Fem_GP_1_a_6 0.430951 _ -1 -15 5 0.431 NA _ No_prior 9 Age_K_Fem_GP_1_a_7 -4.4269 _ -1 -15 5 -4.427 NA _ No_prior

10 Age_K_Fem_GP_1_a_8 -0.60707 _ -1 -15 5 -0.607 NA _ No_prior 11 CV_young_Fem_GP_1 0.1 _ -3 0.05 0.25 0.100 NA _ Normal 12 CV_old_Fem_GP_1 0.1 _ -3 0.05 0.25 0.100 NA _ Normal 13 Wtlen_1_Fem 1.77E-05 _ -3 -3 3 0.000 NA _ Normal 14 Wtlen_2_Fem 3.03542 _ -3 -3 4 3.035 NA _ Normal 15 Mat50%_Fem 115.1 _ -3 -3 150 115.100 NA _ No_prior 16 Mat_slope_Fem -0.15786 _ -3 -3 3 -0.158 NA _ Normal 17 Eggs/kg_inter_Fem 1 _ -3 -3 3 1.000 NA _ Normal 18 Eggs/kg_slope_wt_Fem 0 _ -3 -3 3 0.000 NA _ Normal 19 RecrDist_GP_1 0 _ -3 -4 4 0.000 NA _ No_prior 20 RecrDist_Area_1 0 _ -3 -4 4 0.000 NA _ No_prior 21 RecrDist_Seas_1 0 _ -3 -4 4 0.000 NA _ No_prior 22 RecrDist_Seas_2 -0.54134 1 3 -4 4 0.170 OK 0.17 No_prior 23 RecrDist_Seas_3 -0.01980 2 3 -4 4 -0.719 OK 0.0840 No_prior 24 RecrDist_Seas_4 -1.24226 3 3 -4 4 -0.228 OK 0.1405 No_prior 25 CohortGrowDev 1 _ -3 1 1 1.000 NA _ No_prior 26 SR_LN(R0) 11.8568 4 1 0 15 12.500 OK 0.0293 No_prior 27 SR_BH_flat_steep 0.9 _ -2 0.201 0.99 0.900 NA _ No_prior 28 SR_sigmaR 0.22506 5 6 0.1 2 0.600 OK 0.0316 No_prior 29 SR_envlink 0 _ -3 -5 5 0.000 NA _ Normal 30 SR_R1_offset 0 _ -4 -5 5 0.000 NA _ Normal 31 SR_autocorr 0 _ -2 0 0.5 0.000 NA _ No_prior 32 Main_RecrDev_1970 0.327 6 _ _ _ _ act 0.1465 dev 33 Main_RecrDev_1971 0.164 7 _ _ _ _ act 0.1347 dev 34 Main_RecrDev_1972 -0.024 8 _ _ _ _ act 0.1154 dev 35 Main_RecrDev_1973 -0.083 9 _ _ _ _ act 0.1116 dev 36 Main_RecrDev_1974 -0.419 10 _ _ _ _ act 0.1386 dev 37 Main_RecrDev_1975 0.268 11 _ _ _ _ act 0.1044 dev 38 Main_RecrDev_1976 0.080 12 _ _ _ _ act 0.1154 dev 39 Main_RecrDev_1977 0.011 13 _ _ _ _ act 0.1163 dev 40 Main_RecrDev_1978 -0.130 14 _ _ _ _ act 0.1136 dev 41 Main_RecrDev_1979 0.089 15 _ _ _ _ act 0.0959 dev 42 Main_RecrDev_1980 -0.248 16 _ _ _ _ act 0.1122 dev 43 Main_RecrDev_1981 0.219 17 _ _ _ _ act 0.0853 dev 44 Main_RecrDev_1982 -0.362 18 _ _ _ _ act 0.1177 dev 45 Main_RecrDev_1983 0.352 19 _ _ _ _ act 0.0874 dev 46 Main_RecrDev_1984 -0.123 20 _ _ _ _ act 0.1194 dev 47 Main_RecrDev_1985 0.636 21 _ _ _ _ act 0.0830 dev 48 Main_RecrDev_1986 0.321 22 _ _ _ _ act 0.0973 dev 49 Main_RecrDev_1987 0.150 23 _ _ _ _ act 0.1001 dev 50 Main_RecrDev_1988 -0.218 24 _ _ _ _ act 0.1182 dev 51 Main_RecrDev_1989 0.239 25 _ _ _ _ act 0.0847 dev 52 Main_RecrDev_1990 -0.144 26 _ _ _ _ act 0.0918 dev 53 Main_RecrDev_1991 -0.290 27 _ _ _ _ act 0.0989 dev 54 Main_RecrDev_1992 0.162 28 _ _ _ _ act 0.0802 dev 55 Main_RecrDev_1993 0.025 29 _ _ _ _ act 0.0846 dev 56 Main_RecrDev_1994 -0.208 30 _ _ _ _ act 0.0906 dev 57 Main_RecrDev_1995 -0.099 31 _ _ _ _ act 0.0812 dev 58 Main_RecrDev_1996 -0.223 32 _ _ _ _ act 0.0850 dev 59 Main_RecrDev_1997 0.056 33 _ _ _ _ act 0.0745 dev 60 Main_RecrDev_1998 0.121 34 _ _ _ _ act 0.0730 dev 61 Main_RecrDev_1999 -0.199 35 _ _ _ _ act 0.0845 dev 62 Main_RecrDev_2000 -0.002 36 _ _ _ _ act 0.0790 dev 63 Main_RecrDev_2001 0.353 37 _ _ _ _ act 0.0728 dev 64 Main_RecrDev_2002 0.155 38 _ _ _ _ act 0.0814 dev 65 Main_RecrDev_2003 -0.161 39 _ _ _ _ act 0.0890 dev 66 Main_RecrDev_2004 0.048 40 _ _ _ _ act 0.0801 dev 67 Main_RecrDev_2005 -0.163 41 _ _ _ _ act 0.0884 dev 68 Main_RecrDev_2006 -0.256 42 _ _ _ _ act 0.0877 dev 69 Main_RecrDev_2007 -0.231 43 _ _ _ _ act 0.0875 dev 70 Main_RecrDev_2008 -0.125 44 _ _ _ _ act 0.0855 dev 71 Main_RecrDev_2009 -0.016 45 _ _ _ _ act 0.0821 dev 72 Main_RecrDev_2010 -0.052 46 _ _ _ _ act 0.0880 dev 73 Late_RecrDev_2011 -0.154 47 _ _ _ _ act 0.0996 dev 74 Late_RecrDev_2012 -0.108 48 _ _ _ _ act 0.1134 dev 75 Late_RecrDev_2013 0.042 49 _ _ _ _ act 0.1389 dev 76 Late_RecrDev_2014 0.155 50 _ _ _ _ act 0.1999 dev 77 ForeRecr_2015 0 51 _ _ _ _ act 0.2251 dev 78 ForeRecr_2016 0 52 _ _ _ _ act 0.2251 dev 79 ForeRecr_2017 0 53 _ _ _ _ act 0.2251 dev 80 ForeRecr_2018 0 54 _ _ _ _ act 0.2251 dev 81 ForeRecr_2019 0 55 _ _ _ _ act 0.2251 dev 82 ForeRecr_2020 0 56 _ _ _ _ act 0.2251 dev 83 ForeRecr_2021 0 57 _ _ _ _ act 0.2251 dev 84 ForeRecr_2022 0 58 _ _ _ _ act 0.2251 dev 85 ForeRecr_2023 0 59 _ _ _ _ act 0.2251 dev 86 ForeRecr_2024 0 60 _ _ _ _ act 0.2251 dev 87 Impl_err_2015 0 _ _ _ _ _ NA _ dev 88 Impl_err_2016 0 _ _ _ _ _ NA _ dev 89 Impl_err_2017 0 _ _ _ _ _ NA _ dev 90 Impl_err_2018 0 _ _ _ _ _ NA _ dev 91 Impl_err_2019 0 _ _ _ _ _ NA _ dev

575

92 Impl_err_2020 0 _ _ _ _ _ NA _ dev 93 Impl_err_2021 0 _ _ _ _ _ NA _ dev 94 Impl_err_2022 0 _ _ _ _ _ NA _ dev 95 Impl_err_2023 0 _ _ _ _ _ NA _ dev 96 Impl_err_2024 0 _ _ _ _ _ NA _ dev 97 InitF_11_PS_ESFR2_6585 0 _ -1 0 1 0.000 NA _ Normal 98 InitF_22_PS_ESFR2_8690 0 _ -1 0 1 0.000 NA _ Normal 99 InitF_33_PS_ESFR2_9114 0 _ -1 0 1 0.000 NA _ Normal

100 InitF_44_ESFR_FADS_PS_9114 0 _ -1 0 1 0.000 NA _ Normal 101 InitF_55_BB_PS_Ghana_6514 0 _ -1 0 1 0.000 NA _ Normal 102 InitF_66_BB_area2_Sdak 0 _ -1 0 1 0.000 NA _ Normal 103 InitF_77_BB_DAKAR_62_80 0 _ -1 0 1 0.000 NA _ Normal 104 InitF_88_BB_DAKAR_81_14 0 _ -1 0 1 0.000 NA _ Normal 105 InitF_99_Japan_LL_75_14 0 _ -1 0 1 0.000 NA _ Normal 106 InitF_1010_BR_LL_5675 0 _ -1 0 1 0.000 NA _ Normal 107 InitF_1111_VEN_LL 0 _ -1 0 1 0.000 NA _ Normal 108 InitF_1212_US_LL 0 _ -1 0 1 0.000 NA _ Normal 109 InitF_1313_CHTAI_LL_1_70_92 0 _ -1 0 1 0.000 NA _ Normal 110 InitF_1414_CHTAI_LL_2_93_14 0 _ -1 0 1 0.000 NA _ Normal 111 InitF_1515_OTHER_LL 0 _ -1 0 1 0.000 NA _ Normal 112 InitF_1616_US_RR 0 _ -1 0 1 0.000 NA _ Normal 113 InitF_1717_OTH_OTH 0 _ -1 0 1 0.000 NA _ Normal 114 SizeSpline_Code_1_PS_ESFR2_6585_1 0 _ -99 0 0 0.000 NA _ No_prior 115 SizeSpline_GradLo_1_PS_ESFR2_6585_1 0.26312 61 1 -.001 2 0.260 OK 0.120 Sym_Beta 116 SizeSpline_GradHi_1_PS_ESFR2_6585_1 0.04830 62 3 -5 5 0.045 OK 0.022 Sym_Beta 117 SizeSpline_Knot_1_1_PS_ESFR2_6585_1 23.125 _ -99 13 200 23.125 NA _ No_prior 118 SizeSpline_Knot_2_1_PS_ESFR2_6585_1 41.9035 _ -99 13 200 41.904 NA _ No_prior 119 SizeSpline_Knot_3_1_PS_ESFR2_6585_1 45.6322 _ -99 13 200 45.632 NA _ No_prior 120 SizeSpline_Knot_4_1_PS_ESFR2_6585_1 110.298 _ -99 13 200 110.298 NA _ No_prior 121 SizeSpline_Knot_5_1_PS_ESFR2_6585_1 145.923 _ -99 13 200 145.923 NA _ No_prior 122 SizeSpline_Val_1_1_PS_ESFR2_6585_1 -6.4857 63 3 -9 7 -6.400 OK 1.426 No_prior 123 SizeSpline_Val_2_1_PS_ESFR2_6585_1 0.08392 64 2 -9 7 0.055 OK 0.063 No_prior 124 SizeSpline_Val_3_1_PS_ESFR2_6585_1 0.27866 _ -2 -9 7 0.279 NA _ No_prior 125 SizeSpline_Val_4_1_PS_ESFR2_6585_1 0.83469 65 2 -9 7 1.166 OK 0.204 No_prior 126 SizeSpline_Val_5_1_PS_ESFR2_6585_1 2.29129 66 2 -9 7 2.416 OK 0.189 No_prior 127 SizeSpline_Code_2_PS_ESFR2_8690_2 0 _ -99 0 0 0.000 NA _ No_prior 128 SizeSpline_GradLo_2_PS_ESFR2_8690_2 0.57065 67 3 -.001 1 0.649 OK 1.345 Sym_Beta 129 SizeSpline_GradHi_2_PS_ESFR2_8690_2 0.01 _ -3 -1 0.02 0.010 NA _ Sym_Beta 130 SizeSpline_Knot_1_2_PS_ESFR2_8690_2 23.125 _ -99 13 200 23.125 NA _ No_prior 131 SizeSpline_Knot_2_2_PS_ESFR2_8690_2 41.9035 _ -99 13 200 41.904 NA _ No_prior 132 SizeSpline_Knot_3_2_PS_ESFR2_8690_2 45.6322 _ -99 13 200 45.632 NA _ No_prior 133 SizeSpline_Knot_4_2_PS_ESFR2_8690_2 110.298 _ -99 13 200 110.298 NA _ No_prior 134 SizeSpline_Knot_5_2_PS_ESFR2_8690_2 145.923 _ -99 13 200 145.923 NA _ No_prior 135 SizeSpline_Val_1_2_PS_ESFR2_8690_2 -8.71258 _ -2 -9 7 -8.713 NA _ No_prior 136 SizeSpline_Val_2_2_PS_ESFR2_8690_2 -0.4405 68 2 -9 7 -0.493 OK 0.233 No_prior 137 SizeSpline_Val_3_2_PS_ESFR2_8690_2 0.27866 _ -2 -9 7 0.279 NA _ No_prior 138 SizeSpline_Val_4_2_PS_ESFR2_8690_2 2.17748 69 2 -9 7 2.608 OK 0.518 No_prior 139 SizeSpline_Val_5_2_PS_ESFR2_8690_2 3.14675 70 2 -9 7 3.495 OK 0.606 No_prior 140 SizeSpline_Code_3_PS_ESFR2_9114_3 0 _ -99 0 0 0.000 NA _ No_prior 141 SizeSpline_GradLo_3_PS_ESFR2_9114_3 0.37985 71 3 -.001 1 0.401 OK 0.142 Sym_Beta 142 SizeSpline_GradHi_3_PS_ESFR2_9114_3 0.02 _ -3 -1 0.02 0.020 NA _ Sym_Beta 143 SizeSpline_Knot_1_3_PS_ESFR2_9114_3 23.125 _ -99 13 200 23.125 NA _ No_prior 144 SizeSpline_Knot_2_3_PS_ESFR2_9114_3 41.9035 _ -99 13 200 41.904 NA _ No_prior 145 SizeSpline_Knot_3_3_PS_ESFR2_9114_3 45.6322 _ -99 13 200 45.632 NA _ No_prior 146 SizeSpline_Knot_4_3_PS_ESFR2_9114_3 110.298 _ -99 13 200 110.298 NA _ No_prior 147 SizeSpline_Knot_5_3_PS_ESFR2_9114_3 145.923 _ -99 13 200 145.923 NA _ No_prior 148 SizeSpline_Val_1_3_PS_ESFR2_9114_3 -5.34246 72 2 -9 7 -5.495 OK 0.687 No_prior 149 SizeSpline_Val_2_3_PS_ESFR2_9114_3 0.10008 73 2 -9 7 0.083 OK 0.032 No_prior 150 SizeSpline_Val_3_3_PS_ESFR2_9114_3 0.27866 _ -2 -9 7 0.279 NA _ No_prior 151 SizeSpline_Val_4_3_PS_ESFR2_9114_3 0.11428 74 2 -9 7 0.468 OK 0.122 No_prior 152 SizeSpline_Val_5_3_PS_ESFR2_9114_3 3.91567 75 2 -9 7 4.004 OK 0.105 No_prior 153 SizeSpline_Code_4_ESFR_FADS_PS_9114_4 0 _ -99 0 0 0.000 NA _ No_prior 154 SizeSpline_GradLo_4_ESFR_FADS_PS_9114_4 0.32917 76 3 -.001 1 0.339 OK 0.047 Sym_Beta 155 SizeSpline_GradHi_4_ESFR_FADS_PS_9114_4 -0.02508 77 3 -1 0.02 -0.031 OK 0.021 Sym_Beta 156 SizeSpline_Knot_1_4_ESFR_FADS_PS_9114_4 23.125 _ -99 13 200 23.125 NA _ No_prior 157 SizeSpline_Knot_2_4_ESFR_FADS_PS_9114_4 41.9035 _ -99 13 200 41.904 NA _ No_prior 158 SizeSpline_Knot_3_4_ESFR_FADS_PS_9114_4 45.6322 _ -99 13 200 45.632 NA _ No_prior 159 SizeSpline_Knot_4_4_ESFR_FADS_PS_9114_4 110.298 _ -99 13 200 110.298 NA _ No_prior 160 SizeSpline_Knot_5_4_ESFR_FADS_PS_9114_4 145.923 _ -99 13 200 145.923 NA _ No_prior 161 SizeSpline_Val_1_4_ESFR_FADS_PS_9114_4 -8.60135 _ -2 -9 7 -8.601 NA _ No_prior 162 SizeSpline_Val_2_4_ESFR_FADS_PS_9114_4 0.33846 78 2 -9 7 0.337 OK 0.046 No_prior 163 SizeSpline_Val_3_4_ESFR_FADS_PS_9114_4 0.27866 _ -2 -9 7 0.279 NA _ No_prior 164 SizeSpline_Val_4_4_ESFR_FADS_PS_9114_4 -0.96546 79 2 -9 7 -0.612 OK 0.206 No_prior 165 SizeSpline_Val_5_4_ESFR_FADS_PS_9114_4 0.32274 80 2 -9 7 0.342 OK 0.195 No_prior 166 SizeSel_5P_1_5_BB_PS_Ghana_6514 44.3988 81 3 20 60 44.334 OK 0.583 No_prior 167 SizeSel_5P_2_5_BB_PS_Ghana_6514 -16.3475 _ -4 -20 -3 -16.348 NA _ No_prior 168 SizeSel_5P_3_5_BB_PS_Ghana_6514 3.16317 82 4 -10 9 3.196 OK 0.187 No_prior 169 SizeSel_5P_4_5_BB_PS_Ghana_6514 5.17213 83 4 -5 9 5.348 OK 0.143 No_prior 170 SizeSel_5P_5_5_BB_PS_Ghana_6514 -999 _ -99 -999 15 -999 NA _ No_prior 171 SizeSel_5P_6_5_BB_PS_Ghana_6514 -999 _ -99 -999 15 -999 NA _ No_prior 172 SizeSel_6P_1_6_BB_area2_Sdak 47.084 84 3 20 100 48.363 OK 1.992 No_prior 173 SizeSel_6P_2_6_BB_area2_Sdak -8.92364 85 4 -10 3 -4.601 OK 18.321 No_prior 174 SizeSel_6P_3_6_BB_area2_Sdak 3.88543 86 4 -5 9 4.087 OK 0.439 No_prior 175 SizeSel_6P_4_6_BB_area2_Sdak 7.95373 87 4 -5 9 8.014 OK 0.225 No_prior 176 SizeSel_6P_5_6_BB_area2_Sdak -999 _ -99 -999 15 -999 NA _ No_prior 177 SizeSel_6P_6_6_BB_area2_Sdak -999 _ -99 -999 15 -999 NA _ No_prior 178 SizeSel_7P_1_7_BB_DAKAR_62_80 60.3997 88 3 40 100 62.224 OK 1.482 No_prior 179 SizeSel_7P_2_7_BB_DAKAR_62_80 -9.3898 89 4 -10 3 -7.954 OK 15.338 No_prior 180 SizeSel_7P_3_7_BB_DAKAR_62_80 4.62445 90 4 -5 9 4.744 OK 0.235 No_prior 181 SizeSel_7P_4_7_BB_DAKAR_62_80 7.67762 91 4 -5 9 7.632 OK 0.142 No_prior 182 SizeSel_7P_5_7_BB_DAKAR_62_80 -999 _ -99 -999 15 -999. NA _ No_prior 183 SizeSel_7P_6_7_BB_DAKAR_62_80 -999 _ -99 -999 15 -999. NA _ No_prior 184 SizeSel_8P_1_8_BB_DAKAR_81_14 56.61 92 3 40 100 60.216 OK 1.823 No_prior 185 SizeSel_8P_2_8_BB_DAKAR_81_14 -0.568291 93 4 -10 3 -0.695 OK 0.303 No_prior 186 SizeSel_8P_3_8_BB_DAKAR_81_14 4.78509 94 4 -5 9 5.050 OK 0.237 No_prior

576

187 SizeSel_8P_4_8_BB_DAKAR_81_14 7.55199 95 4 -5 9 7.437 OK 0.741 No_prior 188 SizeSel_8P_5_8_BB_DAKAR_81_14 -999 _ -99 -999 15 -999.0 NA _ No_prior 189 SizeSel_8P_6_8_BB_DAKAR_81_14 -999 _ -99 -999 15 -999.0 NA _ No_prior 190 SizeSel_9P_1_9_Japan_LL_75_14 112.711 96 3 80 130 108.847 OK 1.928 No_prior 191 SizeSel_9P_2_9_Japan_LL_75_14 26.9819 97 3 10 50 24.545 OK 1.747 No_prior 192 SizeSel_10P_1_10_BR_LL_5675 99.1238 98 3 80 130 94.650 OK 4.459 No_prior 193 SizeSel_10P_2_10_BR_LL_5675 30.127 99 3 10 50 26.182 OK 3.887 No_prior 194 SizeSel_11P_1_11_VEN_LL 124.869 100 3 80 130 125.898 OK 6.839 No_prior 195 SizeSel_11P_2_11_VEN_LL 28.2667 101 3 10 50 28.507 OK 6.190 No_prior 196 SizeSel_12P_1_12_US_LL 101.297 102 3 80 130 119.265 OK 13.273 No_prior 197 SizeSel_12P_2_12_US_LL 26.542 103 3 10 50 31.330 OK 13.669 No_prior 198 SizeSel_13P_1_13_CHTAI_LL_1_70_92 105.226 104 3 80 130 103.303 OK 1.445 No_prior 199 SizeSel_13P_2_13_CHTAI_LL_1_70_92 24.5166 105 3 10 50 23.236 OK 1.318 No_prior 200 SizeSel_14P_1_14_CHTAI_LL_2_93_14 129.995 106 3 80 130 122.429 HI 0.149 No_prior 201 SizeSel_14P_2_14_CHTAI_LL_2_93_14 39.8248 107 3 10 50 37.369 OK 1.516 No_prior 202 SizeSel_15P_1_15_OTHER_LL 112.509 108 3 70 130 109.281 OK 1.309 No_prior 203 SizeSel_15P_2_15_OTHER_LL 27.2092 109 3 10 50 25.245 OK 1.059 No_prior 204 SizeSel_16P_1_16_US_RR 80.6645 110 3 70 130 80.418 OK 2.058 No_prior 205 SizeSel_16P_2_16_US_RR -2 _ -5 -20 -3 -2.000 NA _ No_prior 206 SizeSel_16P_3_16_US_RR 5.0245 111 4 -5 9 4.907 OK 0.252 No_prior 207 SizeSel_16P_4_16_US_RR 7.5279 112 4 -5 9 7.354 OK 0.242 No_prior 208 SizeSel_16P_5_16_US_RR -999 _ -99 -999 15 -999.0 NA _ No_prior 209 SizeSel_16P_6_16_US_RR -999 _ -99 -999 15 -999.0 NA _ No_prior 210 SizeSel_17P_1_17_OTH_OTH 70.9565 113 3 70 130 73.116 OK 2.043 No_prior 211 SizeSel_17P_2_17_OTH_OTH 0.824938 114 5 -5 3 0.981 OK 0.364 No_prior 212 SizeSel_17P_3_17_OTH_OTH 5.23335 115 4 -5 9 5.326 OK 0.210 No_prior 213 SizeSel_17P_4_17_OTH_OTH 4.62404 116 4 -5 10 -1.439 OK 4.706 No_prior 214 SizeSel_17P_5_17_OTH_OTH -999 _ -99 -999 15 -999.0 NA _ No_prior 215 SizeSel_17P_6_17_OTH_OTH -999 _ -99 -999 15 -999.0 NA _ No_prior 216 AgeSel_1P_1_1_PS_ESFR2_6585 9 _ -3 -5 9 9.000 NA _ No_prior 217 AgeSel_1P_2_1_PS_ESFR2_6585 9 _ -3 -5 9 9.000 NA _ No_prior 218 AgeSel_1P_3_1_PS_ESFR2_6585 9 _ -3 -5 9 9.000 NA _ No_prior 219 AgeSel_1P_4_1_PS_ESFR2_6585 9 _ -3 -5 9 9.000 NA _ No_prior 220 AgeSel_1P_5_1_PS_ESFR2_6585 9 _ -3 -5 9 9.000 NA _ No_prior 221 AgeSel_1P_6_1_PS_ESFR2_6585 9 _ -3 -5 9 9.000 NA _ No_prior 222 AgeSel_1P_7_1_PS_ESFR2_6585 9 _ -3 -5 9 9.000 NA _ No_prior 223 AgeSel_1P_8_1_PS_ESFR2_6585 9 _ -3 -5 9 9.000 NA _ No_prior 224 AgeSel_1P_9_1_PS_ESFR2_6585 9 _ -3 -5 9 9.000 NA _ No_prior 225 AgeSel_1P_10_1_PS_ESFR2_6585 9 _ -3 -5 9 9.000 NA _ No_prior 226 AgeSel_1P_11_1_PS_ESFR2_6585 9 _ -3 -5 9 9.000 NA _ No_prior 227 SizeSpline_GradHi_4_ESFR_FADS_PS_9114_4

BLK1add 2003 -0.66143 _ -7 -1 0.02 -0.661 NA

_ Sym_Beta

228 SizeSpline_Val_1_4_ESFR_FADS_PS_9114_4 BLK1add 2003

-0.98391 117 7 -9 7 -1.037 OK 6.398

No_prior


-0.14958 _ -7 -9 7 -0.150 NA _

No_prior


-0.14747 118 7 -9 7 -0.147 OK 0.008

No_prior


-0.40643 119 7 -9 7 -0.418 OK 0.052

No_prior


-0.54597 120 7 -9 7 -0.596 OK 0.073

No_prior

233 SizeSel_6P_1_6_BB_area2_Sdak_BLK2add 2010 60 121 7 20 100 60.000 OK 894.424 No_prior 234 SizeSel_6P_2_6_BB_area2_Sdak_BLK2add 2010 2.09795 122 7 -10 3 2.094 OK 10.643 No_prior

Documents

ATLANTIC OCEAN YELLOWFIN TUNA STOCK ASSESSMENT 1950 …€¦ · SCRS/2016/110 Collect. Vol. Sci. Pap. ICCAT, 73(2): 510-576 (2017) 510 ATLANTIC OCEAN YELLOWFIN TUNA STOCK ASSESSMENT