ALTERNATIVE VPA ASSESSMENTS OF ATLANTIC ......SCRS/2016/207 Collect. Vol. Sci. Pap. ICCAT, 73(2):...

SCRS/2016/207 Collect. Vol. Sci. Pap. ICCAT, 73(2): 662-716 (2017)

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ALTERNATIVE VPA ASSESSMENTS OF ATLANTIC YELLOWFIN TUNA

Shannon L. Cass-Calay1 and Michelle Sculley2

SUMMARY

During the 2016 yellowfin tuna stock assessment workshop, the group noted a diagnostic problem

pertaining to the VPA. In certain years, deterministic and median estimates were not located near

the center of their 80% confidence intervals, indicating a significant skew in estimates of one or

more parameters (e.g. F, SSB, SSBMSY and FMSY) during those years. This behavior was corrected,

and revised runs were included in the report of the assessment. Regrettably, after publication of

the report the group discovered an error. Examination revealed that an earlier, untested version

of VPA-2BOX was inadvertently used for the revised models, resulting in aberrant estimates of

spawning biomass, total biomass etcetera. This document describes “alternative” VPA models

and projections using the improvements described in the assessment report, and the appropriate

VPA executable. These models reiterate the historic patterns (e.g. SSB, Biomass, F) observed

during the assessment workshop, and retain the improved diagnostic behavior of bootstraps and

projections. After review by the 2016 ICCAT Tropical Tunas Species Group, the “alternative”

VPA models, were used (along with other models) to develop management advice for yellowfin

tuna.

RÉSUMÉ

Pendant l'évaluation du stock d'albacore de 2016, le groupe a noté un problème de diagnostic

relatif à la VPA. Au cours de certaines années, des estimations déterministes et médianes ne se

sont pas situées près du centre de leurs intervalles de confiance de 80%, ce qui indique une

asymétrie considérable dans les estimations d'un ou de plusieurs paramètres (p.ex. F, SSB, SSB

PME et FPME) au cours de ces années. Ce comportement a été corrigé et des scénarios révisés ont

été inclus dans le rapport de l'évaluation. Malheureusement, après la publication du rapport, le

groupe a découvert une erreur. L'examen a révélé qu'une version antérieure, non testée de VPA-

2BOX avait été utilisée par inadvertance pour les modèles révisés, ce qui avait entraîné des

estimations anormales de la biomasse de reproduction, de la biomasse totale, etc. Ce document

décrit des modèles et des projections de VPA "alternatives" en utilisant les améliorations décrites

dans le rapport d'évaluation, et la VPA appropriée exécutable. Ces modèles réitèrent les schémas

historiques (p.ex. SSB, biomasse, F) observés pendant la réunion d'évaluation et ils retiennent le

comportement diagnostique amélioré des bootstraps et des projections. Une fois que le groupe

d’espèces sur les thonidés tropicaux de l'ICCAT les a examinés en 2016, les modèles de VPA

"alternatifs" ont été utilisés (tout comme d'autres modèles) dans le but de formuler un avis de

gestion pour l'albacore.

RESUMEN

Durante la reunión de evaluación de stock de rabil de 2016, el Grupo observó un problema de

diagnóstico relacionado con el VPA. En ciertos años, las estimaciones deterministas y de la

mediana no estaban situadas cerca del centro de sus intervalos de confianza del 80%, lo que

indica una importante asimetría en las estimaciones de uno o más parámetros (por ejemplo, F,

SSB, SSBRMS y FRMS) durante esos años. Este comportamiento se corrigió y se incluyeron ensayos

revisados en el informe de la evaluación. Lamentablemente, después de la publicación del

informe, el Grupo descubrió un error. El examen reveló que se había utilizado inadvertidamente

una versión anterior no probada de VPA-2BOX para los modelos revisados, lo que dio lugar a

estimaciones anormales de la biomasa reproductora, la biomasa total, etc. Este documento

describe modelos y proyecciones VPA "alternativos" utilizando las mejoras descritas en el

informe de evaluación y el ejecutable VPA adecuado. Estos modelos reiteran los patrones

históricos (por ejemplo SSB, biomasa, F) observados durante la evaluación y mantienen el

comportamiento de diagnóstico mejorado de los bootstraps y las proyecciones. Tras la revisión

por parte del Grupo de especies de túnidos tropicales de ICCAT de 2016, los modelos VPA

"alternativos" se utilizaron (junto con otros modelos) para elaborar el asesoramiento de

ordenación para el rabil.

KEYWORDS

Yellowfin Tuna, stock Assessment, fishery management

1 NOAA Fisheries, Southeast Fisheries Science Center, 75 Virginia Beach Drive, Miami FL USA 33149. [email protected] 2 University of Miami Rosenstiel School of Marine and Atmospheric Science, 4600 Rickenbacker Causeway, Miami, FL 33149

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1. Introduction

During the 2016 yellowfin tuna stock assessment workshop, the working group noted a diagnostic problem when

the VPA projections and bootstraps were reviewed. Specifically, in certain years, the deterministic and median

estimates of certain quantities (e.g. F, SSB, F/FMSY, SSB/SSBMSY) were not located near the center of their 80%

confidence intervals, indicating a significant skew in estimates of one or more parameters during those years

(Figure 1). This behavior was subsequently corrected, and revised runs were included in the report of the

assessment meeting (Anon., 2016). Regrettably, after the report was published to the ICCAT website, the working

group discovered an error in the revised models. Examination revealed that an earlier, untested version of VPA-

2BOX was inadvertently used for the revised models, resulting in aberrant estimates of spawning biomass, total

biomass etcetera. This document describes “alternative” VPA models and projections using the improvements

described in the assessment report, and the appropriate VPA executable.

2. Methods

Alternative virtual population analyses (VPA) were conducted using the VPA-2BOX software featured in the

ICCAT Software Catalog (compiled Sept. 10, 2010). Regrettably, an earlier, untested version of VPA-2BOX

(compiled Aug. 18, 2004) was inadvertently used for the models that appear in the assessment report.

2.1 Data Inputs

The data inputs used for the “alternative” runs are identical to those used during the July 2016 assessment

workshop (Tab1e 1). They include:

1. Total Catch-at-Age, assumed known exactly

2. Catch per Unit Effort (or index of relative fishing pressure)

3. Fleet-Specific Catch-at-Age

4. Fleet-Specific Average Weight-at-Age

5. Average Weight-at-Age at peak of spawning season

6. Biological parameters:

a) Maturity, fecundity, month of peak spawning

b) Natural Mortality

c) Growth Parameters

2.2 VPA Parameterization and Specifications

The parameter specifications used for the “alternative” runs are identical to those used during the July 2016

assessment workshop (Table 2). The oldest age class represents 5+ age group (ages 5 and older) and the fishing

mortality rate (F) on that age is specified as the product of the fishing mortality rate on the next younger age (F4)

and an ‘F-ratio’ parameter that represents the ratio of F5 to F4. As in the 2011 assessment model, the F-ratio was

estimated in 1970, and thereafter allowed to vary from the 1970 estimate using a random walk (standard deviation

= 0.2).

The fishing mortality rates for ages 1-4 in the last year of the VPA were estimated as free parameters, but subject

to a constraint restricting the amount of change in the vulnerability pattern (on ages 1 to 5+) during the most recent

three years with a standard deviation of 0.4 (e.g. SCRS/2008/089). Recruitment estimates were subject to a

constraint restricting the amount of change during the most recent four years with a standard deviation of 0.4.

The indices of abundance were fitted assuming a lognormal error structure and equal weighting (i.e., the coefficient

of variation was represented by a single estimated parameter for all years and indices). The catchability (scaling)

coefficients for each index were assumed constant over the duration of that index and estimated by the

corresponding concentrated likelihood formula. All indices were weighted by a variance scalar which was

estimated for the first index, then applied to the others. This parameterization weighted all indices equally.

The control file was modified slightly after the assessment workshop. Specifically, the convergence specifications

were made more strict in order to reduce the number of outlying bootstraps (Table 3). Tested in isolation, this

change did not alter the VPA results significantly since the initial models converged to a nearly identical solution

with less strict criteria.

664

As done during the assessment workshop, two base models were run exploring the uncertainty in annual trends

of abundance, as described by the different general trends in Cluster 1 and Cluster 2 indices (Figure 2).

Essentially, Cluster 2 indices show an increase in abundance in the mid-1990s that is not present in Cluster 1

indices.

1) Base Model #1

a) 1970-2014

b) Ages 0-5+

c) Lorenzen M on Ages 0-5+ = 1.59, 1.19, 0.748, 0.550, 0.423.

d) Maturity on Ages 0-5+ = 0, 0, 0, 0.38, 0.99, 1

e) Used CLUSTER 1 Indices: CH_TAI_LLN_1_70_92, US_LL_N, VEN_LL_N, Japan_N_76_14

weighted equally.

2) Base Model #2: Same as #1 except:

a) Used CLUSTER 2 indices: CH_TAI_LLN_1_70_92, URU_W_1, URU_W_2, BR_LL_N,

CH_TAI_N_93_14_M4.

2.3 Projection Methods

VPA-2Box itself does not produce management benchmarks and references. These are produced by a companion

program, PRO-2Box, also included in the ICCAT software catalog. For the alternative model projections, a

number of changes were introduced to reduce the aberrant model behavior described in Section 1 and to improve

the assessment results.

1) The assessment workshop runs resampled historic recruitment (1970-2014) during the projection. This

method can be used to produce “average” recruitment during the projection interval, but this method

also continues to produce average recruits even as the stock biomass declines to zero. For the alternative

projections, a fixed Beverton and Holt spawner-recruit relationship was used. The relationship was

estimated externally from VPA estimates of spawning stock biomass and recruitment during 1970-2011

(Figure 3). An initial attempt to estimate the relationship within Pro-2Box was rejected because it

produced a large number of failed bootstraps.

2) The assessment workshop projections used the average weight-at-age computed from the landings to

calculate spawning stock biomass. This approach does not allow the plus group to increase in weight

during the projection. The alternative runs used average weight-at-age for ages 0-4, and an adjusted von

Bertalannfy curve developed to mimic the Gascuel et al. 1992 curve for ages 5+ (Figure 4). This

function was applied to ages 5+ to allow growth of the plus group during projections.

3) Implemented bias correction for spawner-recruit curves with lognormal error structure in Pro-2Box.

Specifically, the recruitments were adjusted for the point estimate projections and benchmarks (run 0)

and for the benchmark calculations for the bootstrap replicates using the correction factor exp(2/2).

This is a new improvement to Pro-2Box, and when tested in isolation, resulted in small changes in

estimated quantities.

4) Confidence intervals were derived from 500 bootstraps of the index residuals. Results presented at

assessment workshop used a maximum of 100 bootstraps, and may have inadvertently used fewer.

The general projection specifications were as follows:

1) Projections run from 2015-2050 (by agreement, only projections through 2025 are shown)

2) Predicted catches in 2015 and 2016 = 110,337 t.

3) To quantify uncertainty, 500 bootstraps of the index residuals were run.

4) Constant catch projections of 50,000 – 200,000 t, in 10,000 t increments applied 2017-2025

5) Projected recruitment assumed to follow a Beverton and Holt function estimated using annual estimates

of SSB and R from the VPA model (1970-2011). Recruitments from 2011-2013 replaced with estimates

from the S/R function.

6) Projected selectivity equal to the geometric mean selectivity 2011-2013.

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3. Results

3.1 Model Diagnostics

For both alternative base models, the VPA converged within the specified maximum iterations (500), and no

parameter estimates were bounded. Six parameters were estimated for each model, the F ratio (F5/F4) in 1970, the

F on Ages 1-4, and a single index variance scalar. All parameters were well estimated (CV ~ 0.2) except the F on

age 0, which was less well estimated (CV ~ 0.4).

Fits to the CPUE series for the VPA models are summarized in Figures 5 and 6. The fits to assessment workshop

and alternative models that used Cluster 1 (Figure 5) were visually identical, and were fairly good for the U.S.

longline, Japan longline, and the Chinese-Taipei longline indices; while the fit to the Venezuelan longline index

was poor. Fits to the assessment workshop and alternative models that used Cluster 2 (Figure 6) were also visually

identical, but were degraded (relative to Cluster 1) except for the fit to the Chinese-Taipei longline index, which

was the only index that was included in both clusters.

Retrospective analyses of fishing mortality at age, numbers-at-age, spawning stock biomass and recruitment were

conducted by sequentially removing one, two, three, four and five years of data inputs from the assessment

workshop and alternative VPA models (Figures 7-12). The retrospective analyses for Cluster 1 were very similar

for the assessment workshop and alternative VPA models, and showed no strong patterns in fishing mortality at

age (Figure 7), numbers-at-age (Figure 8), or spawning stock biomass and recruitment (Figure 9). The most

notable retrospective pattern was in the fishing mortality on age 5, which tended to increase as data was removed,

particularly in the 2000s. Moderate retrospective changes in fishing mortality at age (Figure 10), numbers-at-age

(Figure 11), and spawning stock biomass and recruitment (Figure 12) were noted for Cluster 2 models, but there

was no systematic directional pattern. The retrospective patterns were similar for the assessment workshop and

alternative VPA models. It should be noted that constraints were applied (SD = 0.4) during 2011-2014 to restrict

the magnitude of allowable change in vulnerability. This likely limited the amount of retrospective pattern to an

extent.

3.2 VPA Results 1970-2014

The fundamental results of the assessment workshop and alternative VPA models were nearly identical during

1970-2014. In both cases, the abundance at age for the Cluster 1 models showed that for ages 3-5 abundance

remained relatively constant after 1995 (Figure 13); while for Cluster 2 models, ages 3-5 abundance generally

declined after 1995 (Figure 14).

Fishing mortality at age, apical F, terminal F at age and the F-Ratio (F5+/F4) for the assessment workshop and

alternative VPA model runs are summarized in Figures 15 and 16. The results were nearly identical. For both

Cluster 1 and 2, fishing mortality at age was highest for age 4 followed by age 3; while the lowest fishing mortality

corresponded to age 0. While this overall pattern was the same for the two clusters, the annual trends in F-at-age

were quite different. For example, for the Cluster 1 models, F at ages 1-4 showed declining trends after 2000;

while cluster 2 models showed increasing trends in F on ages 1-4 during the same period. The estimated terminal

year F at age was very similar for all VPA models. Fishing mortality was highest on age 4 with a secondary peak

in F on age 1. Annual trends in the F-Ratio varied. The most notable difference was that for Cluster 1 models, the

F-ratio increased substantially during 1995-2005 (indicating higher F on ages 5+) while Cluster 2 models indicated

a small decrease in the F-ratio during the same period.

Spawning stock biomass, recruits (Age 0) and the spawner-recruit relationship for the assessment workshop and

alternative VPA model runs are summarized in Figures 17 and 18. The results were nearly identical. Both SSB

trajectories showed a decline at the beginning of the time series, but the Cluster 1 models showed an almost

continuous decline until year 2000 followed by a slow increase; while Cluster 2 models showed an increase from

the mid 1980’s to the mid 1990’s followed by a sharp decrease until the end of the time series. General recruitment

trends were also different for Cluster 1 and 2 models. The recruitment for the Cluster 1 models, although variable,

did not show an obvious trend, while for the Cluster 2 models the time series of recruitment indicated elevated

recruitment in the 1990s, and lower than average recruitment since 2005.

The trajectories of SSB and F relative to the level that produces MSY in equilibrium illustrate the problem noted

by the assessment working group. Specifically, for the assessment workshop models, the median and deterministic

results were located far from the center of the 80% confidence intervals in some years, indicating a significant

skew in estimates of one or more parameters (e.g. F, SSB, SSBMSY and FMSY) during those years, or an insufficient

666

number of bootstrap replicates (Figures 19 and 20). For the alternative model runs, that issue has been largely

resolved. The current status estimates produced by the assessment workshop and alternative models are similar.

For both clusters, the stock is currently at or near SSBMSY, and the stock is not presently subject to overfishing. It

is not surprising to note that the deterministic results are very similar for the assessment workshop and alternative

models. The primary differences between the two approaches were the estimates the bootstrap median and

confidence intervals, and the management references (e.g. SSBMSY and FMSY). These changes were due to the

increased number of bootstraps, and the changes in projection methodology described in Section 2.3.

For the alternative VPA models, the Cluster 1 results indicate that the stock is currently below the overfished

threshold (SSB2014/SSBMSY=0.86) but it is not currently undergoing overfishing (FCURRENT/FMSY=0.97), although

overfishing did occur previously. The estimated MSY was 121,763t. The Cluster 2 model also suggests that the

stock is currently below the overfished threshold (SSB2014/SSBMSY=0.79), and is not currently undergoing

overfishing (FCURRENT/FMSY=0.9). The estimated MSY was 136,326t. A complete summary of management

references for the assessment workshop models, and the alternative runs is summarized in Table 4-7.

Histograms of the bootstrap estimates of SSBMSY, FMSY and current stock status relative to MSY from the

alternative runs were constructed to examine the bias and normality of the distributions (Figure 21). In both

alternative VPA cases, the medians were very close to the point estimates (Tables 6 and 7), which was not the

case for the assessment workshop VPA runs (Tables 4 and 5). This improvement is likely due to the

implementation of the bias correction. However, there remains evidence of skew in the distribution of the bootstrap

estimates of SSB and SSB2014/SSBMSY for Cluster 2. This undesirable diagnostic behavior was not eliminated

entirely by the revisions described in this report.

It is generally agreed that the selectivity of fisheries that prosecute yellowfin tuna have changed over time due to

an increase in the proportion of catch landed by surface fleets, and the FAD fishing fleet in particular. Therefore,

trends in SSB/SSBMSY and F/FMSY were recomputed to account for annual changes in selectivity by allowing FMSY

to be estimated each year. Annual stock status estimates, adjusted for selectivity are shown in Figure 22.

Section 3.3 Projection Results

Projection results varied by model. For the assessment workshop Cluster 1 VPA, catches of 90,000 to 100,000 t

allowed increased SSB and had <50% probability of overfishing, and MSY = 102,000 t (Figure 23, Table 4).

The Cluster 2 VPA results were similar. Catches of 110,000 t built SSB and had <50% probability of overfishing,

and MSY = 102,000 t (Figure 24, Table 5). The alternative VPA runs produced somewhat higher estimates of

acceptable yield and MSY. For the alternative Cluster 1 VPA, catches of 120,000 t built SSB and had <50%

probability of, and MSY = 122,000 t (Figure 25, Table 6). For the alternative Cluster 2 VPA, catches of 130,000

t built SSB and had <50% probability of overfishing, and MSY = 136,000 t.

4. Conclusions

This “alternative” VPA models and projections described in this document reiterate the historic patterns (e.g. SSB,

Biomass, F) observed during the assessment workshop, and exhibit improved diagnostic behavior for model

results, bootstraps estimates of uncertainty and projections. After review by the 2016 ICCAT Tropical Tunas

Species Group, these “alternative” VPA models, were used (along with other models) to develop management

advice for yellowfin tuna.

References

Anon. 2016. Report of the 2016 ICCAT Yellowfin tuna data preparatory meeting. San Sebastián, Spain, March 7

to 11, 2016, 31 p.

Gascuel, D., Fonteneau, A., & Capisano, C. (1992). Modélisation d'une croissance en deux stances chez l'albacore

(Thunnus albacares) de l'Atlantique est. Aquatic Living Resources, 5(3), 155-172.

667

Table 1. VPA data inputs. Not modified after the assessment workshop ##############################################################################

### INPUT FILE FOR PROGRAM VPA-2BOX, Version 3.01 ###

##############################################################################

# INSTRUCTIONS: the control options are entered in the order specified by

# the existing comments. The existing comment blocks may be

# expanded to as many lines as desired, however new comment

# lines may not be placed anywhere in the file other than

# immediately before or immediately after existing comments.

# All comment lines must have one of the following four symbols

# in the first column of the line: #, !, *, or - .

# Otherwise the line is perceived as free-format data input.

##############################################################################

1970 2014 FIRST AND LAST YEAR

0 5 5 5 FIRST AGE, LAST AGE AND PLUSGROUP AGE

###############################################################################

# BEGIN INPUT FOR AREA 1

###############################################################################

9 NUMBER OF INDICES in this file (whether use or not)

2 SPAWNING SEASON

0 0 0 0.38 0.99 1 #Fecundity

# TITLE PDF OF CATCH

# | | SIGMA CATCH

# | | |

'Yellowfin Tuna: Gascuel Determ LorM Cluster 1' 0 1

#________________________X______________________________________________________

# NOW ENTER THE (TOTAL)CATCH-AT-AGE DATA. ROW=YEAR, COLUMN=AGE

#_______________________________________________________________________________

#YEAR 0 1 2 3 4 5

1970 437123 2404159 558769 735304 216314 64220

1971 301482 1986133 1214530 492653 260486 54163

1972 1172378 2108550 1256046 673669 219104 128086

1973 2064574 1641734 918188 776958 296080 89415

1974 2005082 3431764 1165551 826715 325237 92318

1975 930262 1562172 1226933 726704 590333 194832

1976 1641631 2945097 1116120 819055 527549 170061

1977 789701 2940598 1337467 921991 532739 143686

1978 968485 2414098 1432623 1114650 516429 107289

1979 1735512 2364188 901981 1278019 492952 74272

668

Table 1 (Continued). VPA data inputs. Not modified after the assessment workshop

1980 1825411 3339743 1033598 1148725 506190 73257

1981 5565066 3571713 1493852 939172 799983 117242

1982 3099446 6139796 1389825 1211338 695916 115629

1983 4185536 3750024 1902498 1071723 709188 114794

1984 2872855 6739863 1690128 697884 201484 27617

1985 3846412 4681510 2287446 921953 646418 53580

1986 4246340 4838982 1075272 1226597 578562 62269

1987 6263660 4434833 1461429 1000472 649807 53086

1988 4830710 5307459 1087436 1278760 405682 30064

1989 6182658 4097799 1116924 1039997 829700 103986

1990 7029088 4349131 944681 1381466 1012659 160618

1991 7391669 4937706 1225813 963653 845194 121870

1992 7216602 3778294 1447372 1122875 753281 111569

1993 9527020 5519745 1356708 1003129 725457 73715

1994 6443796 5504067 2175118 1027058 727520 83829

1995 6637223 4381711 1526478 995847 681611 70143

1996 4382349 4361628 1671715 1015062 655275 69626

1997 5390348 5151457 1327514 775551 694805 72426

1998 5372805 5724544 1347484 780162 724485 106591

1999 5864506 8950049 1740872 636021 542220 92783

2000 7244309 4769735 1752885 880081 458987 85998

2001 6103466 8138531 1818563 1102484 484305 69149

2002 6661650 6253658 985247 849258 580992 62324

2003 6451936 5867458 1081636 761955 447383 91590

2004 7399152 5252532 1098591 793849 395013 123602

2005 5603217 4969753 809509 569934 434524 77893

2006 3986040 5174455 1065904 614996 396039 104269

2007 6325397 3263028 794917 577746 384415 97050

2008 5868248 3941532 848477 554446 495188 111853

2009 4259929 4704600 782905 590485 605287 126719

2010 6583858 4326083 858278 523670 471151 140834

2011 5024267 5185441 988460 588699 394784 98435

2012 4841509 4621429 967860 671312 420631 79980

2013 3894888 4716500 977234 541392 391266 88985

2014 5930025 4539751 981111 633686 341763 61234

-1 end of catch data

#_______________________X___________________________________________________________

# NOW ENTER IN THE ABUNDANCE INDEX SPECIFICATIONS

#_______________________X___________________________________________________________

# GEAR or fishery type

# | PDF (0= do not use,1=lognormal, 2=normal)

# | | UNITS (1 = numbers, 2 = biomass)

# | | | SELECTIVITY (1 = fixed, 2 = fractional catches, 3 = Powers and Restrepo partial catches,4=Butterworth and Geromont eq 4)

669

Table 1 (Continued). VPA data inputs. Not modified after the assessment workshop.

# | | | | TIMING (-1 = AVERAGE DURING YEAR, POSITIVE INTEGER = NUMBER OF MONTHS ELAPSED)

# | | | | | FIRST AGE LAST AGE

# | | | | | | | TITLE (IN SINGLE QUOTES)

##################################################

# CLUSTER 1 INDICES; ADD #s to USE CLUSTER 2 #

##################################################

1 0 1 4 -1 0 5 Japan_N_65_14

2 1 1 4 -1 0 5 CH_TAI_LLN_1_70_92

3 1 2 4 -1 0 5 US_LL_W

4 1 1 4 -1 0 5 VEN_LL_N

5 0 2 4 -1 0 5 URU_W_1

6 0 2 4 -1 0 5 URU_W_2

7 0 1 4 -1 0 5 BR_LL_N

8 0 1 4 -1 0 5 CHTAI_N_93_14_M4

9 1 2 4 -1 0 5 Japan_W_76_14

##################################################

# CLUSTER 2 INDICES; REMOVE #s to USE CLUSTER 2#

##################################################

# 1 0 1 4 -1 0 5 Japan_N_65_14

# 2 1 1 4 -1 0 5 CH_TAI_LLN_1_70_92

# 3 0 2 4 -1 0 5 US_LL_W

# 4 0 1 4 -1 0 5 VEN_LL_N

# 5 1 2 4 -1 0 5 URU_W_1

# 6 1 2 4 -1 0 5 URU_W_2

# 7 1 1 4 -1 0 5 BR_LL_N

# 8 1 1 4 -1 0 5 CHTAI_N_93_14_M4

# 9 0 2 4 -1 0 5 Japan_W_76_14

-1 end index specifications

#_______________________X___________________________________________________________

# NOW ENTER IN THE INDICES OF ABUNDANCE

#________________________X___________________________________________________________

#INDEX YEAR CPUE CV NOTES

1 1970 1.5295 0.2 Japan_N_65_14

1 1971 1.4368 0.2 Japan_N_65_14

1 1972 1.2105 0.2 Japan_N_65_14

1 1973 1.0370 0.2 Japan_N_65_14

1 1974 1.5653 0.2 Japan_N_65_14

1 1975 0.8431 0.2 Japan_N_65_14

1 1976 1.1408 0.2 Japan_N_65_14

1 1977 0.6042 0.2 Japan_N_65_14

1 1978 1.0479 0.2 Japan_N_65_14

1 1979 1.3795 0.2 Japan_N_65_14

1 1980 0.8880 0.2 Japan_N_65_14

1 1981 0.8707 0.2 Japan_N_65_14

670


1 1982 0.7857 0.2 Japan_N_65_14

1 1983 0.8809 0.2 Japan_N_65_14

1 1984 1.1496 0.2 Japan_N_65_14

1 1985 0.5845 0.2 Japan_N_65_14

1 1986 1.1479 0.2 Japan_N_65_14

1 1987 1.1232 0.2 Japan_N_65_14

1 1988 1.0310 0.2 Japan_N_65_14

1 1989 0.7833 0.2 Japan_N_65_14

1 1990 1.4807 0.2 Japan_N_65_14

1 1991 1.0260 0.2 Japan_N_65_14

1 1992 0.8900 0.2 Japan_N_65_14

1 1993 0.4800 0.2 Japan_N_65_14

1 1994 0.7080 0.2 Japan_N_65_14

1 1995 0.4786 0.2 Japan_N_65_14

1 1996 0.4747 0.2 Japan_N_65_14

1 1997 0.4165 0.2 Japan_N_65_14

1 1998 0.4777 0.2 Japan_N_65_14

1 1999 0.5881 0.2 Japan_N_65_14

1 2000 0.5921 0.2 Japan_N_65_14

1 2001 0.5336 0.2 Japan_N_65_14

1 2002 0.4878 0.2 Japan_N_65_14

1 2003 0.5745 0.2 Japan_N_65_14

1 2004 0.7225 0.2 Japan_N_65_14

1 2005 0.5756 0.2 Japan_N_65_14

1 2006 0.6394 0.2 Japan_N_65_14

1 2007 0.6460 0.2 Japan_N_65_14

1 2008 0.5766 0.2 Japan_N_65_14

1 2009 0.5105 0.2 Japan_N_65_14

1 2010 0.4834 0.2 Japan_N_65_14

1 2011 0.8091 0.2 Japan_N_65_14

1 2012 0.9215 0.2 Japan_N_65_14

1 2013 1.2119 0.2 Japan_N_65_14

1 2014 0.8685 0.2 Japan_N_65_14

2 1970 2.0310 0.2 CH_TAI_LLN_1_70_92

2 1971 1.2610 0.2 CH_TAI_LLN_1_70_92

2 1972 1.2420 0.2 CH_TAI_LLN_1_70_92

671

2 1973 1.2970 0.2 CH_TAI_LLN_1_70_92

2 1974 0.7690 0.2 CH_TAI_LLN_1_70_92

2 1975 0.7750 0.2 CH_TAI_LLN_1_70_92

2 1976 0.9720 0.2 CH_TAI_LLN_1_70_92

2 1977 0.7970 0.2 CH_TAI_LLN_1_70_92

2 1978 0.8060 0.2 CH_TAI_LLN_1_70_92

2 1979 0.8730 0.2 CH_TAI_LLN_1_70_92

2 1980 0.9080 0.2 CH_TAI_LLN_1_70_92

2 1981 0.7960 0.2 CH_TAI_LLN_1_70_92

2 1982 0.7940 0.2 CH_TAI_LLN_1_70_92

2 1983 0.7420 0.2 CH_TAI_LLN_1_70_92

672


2 1984 0.7690 0.2 CH_TAI_LLN_1_70_92

2 1985 0.6800 0.2 CH_TAI_LLN_1_70_92

2 1986 0.9440 0.2 CH_TAI_LLN_1_70_92

2 1987 0.9320 0.2 CH_TAI_LLN_1_70_92

2 1988 0.8310 0.2 CH_TAI_LLN_1_70_92

2 1989 0.7390 0.2 CH_TAI_LLN_1_70_92

2 1990 0.9220 0.2 CH_TAI_LLN_1_70_92

2 1991 1.4050 0.2 CH_TAI_LLN_1_70_92

2 1992 1.2080 0.2 CH_TAI_LLN_1_70_92

3 1987 1.7408 0.2 US_LL_W

3 1988 1.7681 0.2 US_LL_W

3 1989 1.6832 0.2 US_LL_W

3 1990 1.3993 0.2 US_LL_W

3 1991 1.1286 0.2 US_LL_W

3 1992 1.3299 0.2 US_LL_W

3 1993 0.8202 0.2 US_LL_W

3 1994 0.7531 0.2 US_LL_W

3 1995 1.0730 0.2 US_LL_W

3 1996 0.9234 0.2 US_LL_W

3 1997 0.8928 0.2 US_LL_W

3 1998 0.6169 0.2 US_LL_W

3 1999 0.9196 0.2 US_LL_W

3 2000 0.8955 0.2 US_LL_W

3 2001 0.8777 0.2 US_LL_W

3 2002 0.7130 0.2 US_LL_W

3 2003 0.5819 0.2 US_LL_W

3 2004 1.1349 0.2 US_LL_W

3 2005 1.0010 0.2 US_LL_W

3 2006 1.0747 0.2 US_LL_W

3 2007 1.2887 0.2 US_LL_W

3 2008 0.6786 0.2 US_LL_W

3 2009 0.5811 0.2 US_LL_W

3 2010 0.8075 0.2 US_LL_W

3 2011 0.7408 0.2 US_LL_W

3 2012 0.9748 0.2 US_LL_W

3 2013 0.9701 0.2 US_LL_W

3 2014 0.8141 0.2 US_LL_W

4 1991 1.0445 0.2 VEN_LL_N

4 1992 0.9128 0.2 VEN_LL_N

4 1993 0.7108 0.2 VEN_LL_N

4 1994 0.7432 0.2 VEN_LL_N

4 1995 0.6855 0.2 VEN_LL_N

4 1996 0.9850 0.2 VEN_LL_N

4 1997 0.7252 0.2 VEN_LL_N

4 1998 0.8298 0.2 VEN_LL_N

4 1999 0.9868 0.2 VEN_LL_N

4 2000 1.0499 0.2 VEN_LL_N

673


4 2001 0.8966 0.2 VEN_LL_N

4 2002 0.9543 0.2 VEN_LL_N

4 2003 0.6224 0.2 VEN_LL_N

4 2004 0.8695 0.2 VEN_LL_N

4 2005 1.5731 0.2 VEN_LL_N

4 2006 1.1870 0.2 VEN_LL_N

4 2007 1.8942 0.2 VEN_LL_N

4 2008 1.2862 0.2 VEN_LL_N

4 2009 1.0553 0.2 VEN_LL_N

4 2010 1.0012 0.2 VEN_LL_N

4 2011 0.8948 0.2 VEN_LL_N

4 2012 0.9669 0.2 VEN_LL_N

4 2013 0.9922 0.2 VEN_LL_N

4 2014 1.1329 0.2 VEN_LL_N

5 1982 2.44 0.2 URU_W_1

5 1983 0.68 0.2 URU_W_1

5 1984 0.41 0.2 URU_W_1

5 1985 0.81 0.2 URU_W_1

5 1986 1.28 0.2 URU_W_1

5 1987 0.66 0.2 URU_W_1

5 1988 1.47 0.2 URU_W_1

5 1989 0.49 0.2 URU_W_1

5 1990 0.21 0.2 URU_W_1

5 1991 1.56 0.2 URU_W_1

6 1992 1.76 0.2 URU_W_2

6 1993 0.32 0.2 URU_W_2

6 1994 2.00 0.2 URU_W_2

6 1995 1.29 0.2 URU_W_2

6 1996 2.11 0.2 URU_W_2

6 1997 1.35 0.2 URU_W_2

6 1998 1.81 0.2 URU_W_2

6 1999 1.09 0.2 URU_W_2

6 2000 1.27 0.2 URU_W_2

6 2001 0.94 0.2 URU_W_2

6 2002 0.42 0.2 URU_W_2

6 2003 0.61 0.2 URU_W_2

6 2004 0.54 0.2 URU_W_2

674

6 2005 1.16 0.2 URU_W_2

6 2006 0.93 0.2 URU_W_2

6 2007 0.52 0.2 URU_W_2

6 2008 0.28 0.2 URU_W_2

6 2009 0.03 0.2 URU_W_2

6 2010 0.56 0.2 URU_W_2

7 1978 1.10 0.2 BR_LL_N

7 1979 1.40 0.2 BR_LL_N

7 1980 0.69 0.2 BR_LL_N

7 1981 1.04 0.2 BR_LL_N

675


7 1982 0.64 0.2 BR_LL_N

7 1983 0.54 0.2 BR_LL_N

7 1984 0.52 0.2 BR_LL_N

7 1985 0.50 0.2 BR_LL_N

7 1986 0.94 0.2 BR_LL_N

7 1987 1.83 0.2 BR_LL_N

7 1988 1.05 0.2 BR_LL_N

7 1989 0.95 0.2 BR_LL_N

7 1990 2.68 0.2 BR_LL_N

7 1991 0.67 0.2 BR_LL_N

7 1992 0.71 0.2 BR_LL_N

7 1993 2.25 0.2 BR_LL_N

7 1994 1.10 0.2 BR_LL_N

7 1995 0.84 0.2 BR_LL_N

7 1996 2.07 0.2 BR_LL_N

7 1997 1.50 0.2 BR_LL_N

7 1998 0.85 0.2 BR_LL_N

7 1999 1.29 0.2 BR_LL_N

7 2000 1.18 0.2 BR_LL_N

7 2001 0.73 0.2 BR_LL_N

7 2002 0.48 0.2 BR_LL_N

7 2003 0.66 0.2 BR_LL_N

7 2004 0.74 0.2 BR_LL_N

7 2005 0.54 0.2 BR_LL_N

7 2006 0.61 0.2 BR_LL_N

7 2007 0.71 0.2 BR_LL_N

7 2008 0.84 0.2 BR_LL_N

7 2009 0.69 0.2 BR_LL_N

7 2010 0.65 0.2 BR_LL_N

7 2011 0.65 0.2 BR_LL_N

7 2012 1.36 0.2 BR_LL_N

8 1993 1.2406 0.2 CHTAI_N_93_14_M4

8 1994 2.7416 0.2 CHTAI_N_93_14_M4

8 1995 3.4566 0.2 CHTAI_N_93_14_M4

8 1996 1.5716 0.2 CHTAI_N_93_14_M4

8 1997 1.0789 0.2 CHTAI_N_93_14_M4

8 1998 1.0603 0.2 CHTAI_N_93_14_M4

8 1999 0.7243 0.2 CHTAI_N_93_14_M4

8 2000 0.7386 0.2 CHTAI_N_93_14_M4

8 2001 0.3874 0.2 CHTAI_N_93_14_M4

8 2002 0.8002 0.2 CHTAI_N_93_14_M4

8 2003 1.6735 0.2 CHTAI_N_93_14_M4

8 2004 1.1869 0.2 CHTAI_N_93_14_M4

8 2005 1.4683 0.2 CHTAI_N_93_14_M4

8 2006 0.9341 0.2 CHTAI_N_93_14_M4

8 2007 0.5553 0.2 CHTAI_N_93_14_M4

8 2008 0.3880 0.2 CHTAI_N_93_14_M4

676


8 2009 0.5054 0.2 CHTAI_N_93_14_M4

8 2010 0.3847 0.2 CHTAI_N_93_14_M4

8 2011 0.4659 0.2 CHTAI_N_93_14_M4

8 2012 0.4181 0.2 CHTAI_N_93_14_M4

8 2013 0.4370 0.2 CHTAI_N_93_14_M4

8 2014 0.4290 0.2 CHTAI_N_93_14_M4

9 1976 1.6909 0.2 Japan_W_76_14

9 1977 1.0016 0.2 Japan_W_76_14

9 1978 1.7116 0.2 Japan_W_76_14

9 1979 1.6962 0.2 Japan_W_76_14

9 1980 1.7122 0.2 Japan_W_76_14

9 1981 1.7713 0.2 Japan_W_76_14

9 1982 1.4526 0.2 Japan_W_76_14

9 1983 1.4771 0.2 Japan_W_76_14

9 1984 2.0386 0.2 Japan_W_76_14

9 1985 0.9504 0.2 Japan_W_76_14

9 1986 1.6315 0.2 Japan_W_76_14

9 1987 1.4826 0.2 Japan_W_76_14

9 1988 1.3157 0.2 Japan_W_76_14

9 1989 1.0469 0.2 Japan_W_76_14

9 1990 1.6547 0.2 Japan_W_76_14

9 1991 1.2292 0.2 Japan_W_76_14

9 1992 1.1195 0.2 Japan_W_76_14

9 1993 0.5785 0.2 Japan_W_76_14

9 1994 0.8096 0.2 Japan_W_76_14

9 1995 0.5818 0.2 Japan_W_76_14

9 1996 0.6095 0.2 Japan_W_76_14

9 1997 0.4776 0.2 Japan_W_76_14

9 1998 0.5116 0.2 Japan_W_76_14

9 1999 0.6277 0.2 Japan_W_76_14

9 2000 0.5869 0.2 Japan_W_76_14

9 2001 0.4757 0.2 Japan_W_76_14

9 2002 0.4325 0.2 Japan_W_76_14

9 2003 0.6125 0.2 Japan_W_76_14

9 2004 0.7267 0.2 Japan_W_76_14

9 2005 0.5751 0.2 Japan_W_76_14

9 2006 0.7122 0.2 Japan_W_76_14

677

9 2007 0.6730 0.2 Japan_W_76_14

9 2008 0.5828 0.2 Japan_W_76_14

9 2009 0.5467 0.2 Japan_W_76_14

9 2010 0.4514 0.2 Japan_W_76_14

9 2011 0.6757 0.2 Japan_W_76_14

9 2012 0.7851 0.2 Japan_W_76_14

9 2013 1.1281 0.2 Japan_W_76_14

9 2014 0.8568 0.2 Japan_W_76_14

-1 end index data

678


#_______________________X___________________________________________________________

# NOW ENTER IN THE SELECTIVITIES OR PARTIAL CATCHES FOR THE INDICES OF ABUNDANCE

#INDEX YEAR AGE 0 AGE 1 AGE 2 AGE 3 AGE 4 AGE5 AGE6 AGE7 AGE8 AGE9 AGE10+ 'Yellowfin Tuna 2016 Run

1'

#______________________________________X_______________________________________

1 1970 0 11767 54839 105569 13943 1308 Japan_N_65_14

1 1971 78 5791 181544 113124 20530 1635 Japan_N_65_14

1 1972 19 2425 81199 97918 10283 842 Japan_N_65_14

1 1973 9 931 67236 44572 5340 857 Japan_N_65_14

1 1974 0 433 53916 40608 4185 212 Japan_N_65_14

1 1975 6 451 28948 51410 14004 1072 Japan_N_65_14

1 1976 3 705 49285 42579 5184 309 Japan_N_65_14

1 1977 0 858 23754 15407 2093 140 Japan_N_65_14

1 1978 24 1275 31586 20757 2430 112 Japan_N_65_14

1 1979 0 377 29585 24458 2895 143 Japan_N_65_14

1 1980 1 986 12542 26920 13427 3487 Japan_N_65_14

1 1981 8 3252 49357 41570 10139 1324 Japan_N_65_14

1 1982 207 3733 42622 55793 25385 6283 Japan_N_65_14

1 1983 1 223 16679 24850 5986 487 Japan_N_65_14

1 1984 47 1847 35187 47612 11353 1557 Japan_N_65_14

1 1985 32 3201 59604 47817 18919 1802 Japan_N_65_14

1 1986 0 466 23801 50586 8354 998 Japan_N_65_14

1 1987 0 534 33389 42135 8981 344 Japan_N_65_14

1 1988 15 1049 50537 75798 19944 477 Japan_N_65_14

1 1989 0 230 56586 78958 22168 1538 Japan_N_65_14

1 1990 16 89 23955 82495 24901 2885 Japan_N_65_14

1 1991 3 3452 50665 42121 19021 1921 Japan_N_65_14

1 1992 0 47 17595 59323 10956 1058 Japan_N_65_14

1 1993 0 65 6866 36048 16529 2017 Japan_N_65_14

1 1994 0 566 23467 50783 21572 3098 Japan_N_65_14

1 1995 0 10290 46832 36531 30222 5943 Japan_N_65_14

1 1996 0 36 7040 52628 25149 8762 Japan_N_65_14

1 1997 0 9 16127 35360 19249 2174 Japan_N_65_14

1 1998 0 1422 34108 59003 16662 2368 Japan_N_65_14

1 1999 0 3263 14522 36194 18600 1441 Japan_N_65_14

1 2000 0 61 8393 31826 27398 5186 Japan_N_65_14

1 2001 0 1070 11299 35368 11368 730 Japan_N_65_14

1 2002 0 2252 15276 24334 7205 531 Japan_N_65_14

679

1 2003 0 43 21203 30018 8196 1231 Japan_N_65_14

1 2004 0 0 11731 36122 45519 14682 Japan_N_65_14

1 2005 0 787 11172 31533 30961 5746 Japan_N_65_14

1 2006 36 5090 9109 39914 32318 3017 Japan_N_65_14

1 2007 0 75 102460 93115 33833 1634 Japan_N_65_14

1 2008 0 411 56092 44089 34475 6303 Japan_N_65_14

680

Table 1 (Continued). VPA data inputs. Not modified after the assessment workshop. 1 2009 0 1507 40848 34315 27961 3527 Japan_N_65_14

1 2010 1 2272 92453 45169 9893 2287 Japan_N_65_14

1 2011 0 379 33262 40468 16184 4597 Japan_N_65_14

1 2012 0 1346 22520 47516 25949 1602 Japan_N_65_14

1 2013 0 438 47100 40373 19837 3057 Japan_N_65_14

1 2014 0 188 16328 41192 17304 2433 Japan_N_65_14

2 1970 0 4477 36779 104348 16047 10205 CH_TAI_LLN_1_70_86

2 1971 1 384 19710 44955 30142 4619 CH_TAI_LLN_1_70_87

2 1972 0 58 3771 54340 24608 7265 CH_TAI_LLN_1_70_88

2 1973 0 25 18179 32373 9969 1790 CH_TAI_LLN_1_70_89

2 1974 0 17 20115 28245 6646 1193 CH_TAI_LLN_1_70_90

2 1975 0 22 16630 28786 8640 1551 CH_TAI_LLN_1_70_91

2 1976 0 0 6966 26957 8152 507 CH_TAI_LLN_1_70_92

2 1977 0 0 2804 6325 380 0 CH_TAI_LLN_1_70_92

2 1978 0 49 10169 5107 0 0 CH_TAI_LLN_1_70_92

2 1979 0 420 21258 7671 0 0 CH_TAI_LLN_1_70_92

2 1980 0 0 3309 12555 639 0 CH_TAI_LLN_1_70_92

2 1981 0 304 9656 11293 2631 226 CH_TAI_LLN_1_70_92

2 1982 0 254 4537 10367 1127 90 CH_TAI_LLN_1_70_92

2 1983 0 61 4644 8202 1057 131 CH_TAI_LLN_1_70_92

2 1984 32 693 7798 10426 803 174 CH_TAI_LLN_1_70_92

2 1985 5 578 9932 11706 2582 254 CH_TAI_LLN_1_70_92

2 1986 7 305 14129 28231 896 29 CH_TAI_LLN_1_70_92

2 1987 0 1390 8520 15371 1003 147 CH_TAI_LLN_1_70_92

2 1988 0 0 8060 30631 2035 0 CH_TAI_LLN_1_70_92

2 1989 675 436 13349 7435 1173 0 CH_TAI_LLN_1_70_92

2 1990 0 232 51483 94163 19473 1529 CH_TAI_LLN_1_70_92

2 1991 0 1687 81447 47429 15417 72 CH_TAI_LLN_1_70_92

2 1992 0 784 26742 71069 10452 1676 CH_TAI_LLN_1_70_92

3 1987 165 495 30348 64655 10061 0 US_LL_W

3 1988 292 875 53668 114336 17792 0 US_LL_W

3 1989 790 32403 225237 3161 0 0 US_LL_W

3 1990 0 2246 30322 52783 10107 1123 US_LL_W

3 1991 4401 145232 179340 1650 0 0 US_LL_W

3 1992 2201 66040 199221 17611 1651 1101 US_LL_W

3 1993 618 169258 91862 17433 3833 1236 US_LL_W

3 1994 0 0 10415 54003 6706 442 US_LL_W

3 1995 0 0 11695 60641 7530 497 US_LL_W

3 1996 0 90954 177366 4977 622 124 US_LL_W

3 1997 223 14946 172752 9012 1160 803 US_LL_W

3 1998 0 63242 123773 5532 1790 271 US_LL_W

3 1999 153 76033 185258 5030 351 62 US_LL_W

3 2000 0 454 32993 38827 5636 189 US_LL_W

3 2001 0 457 18412 30414 5008 201 US_LL_W

3 2002 0 759 22278 32623 7483 86 US_LL_W

3 2003 0 1455 20755 22967 8749 177 US_LL_W

3 2004 0 1178 28272 30476 6552 138 US_LL_W

681

Table 1 (Continued). VPA data inputs. Not modified after the assessment workshop

3 2005 1 1929 16821 21075 5930 219 US_LL_W

3 2006 0 5258 24166 23253 4866 193 US_LL_W

3 2007 0 569 28392 23683 8116 446 US_LL_W

3 2008 0 1883 17285 13500 4762 253 US_LL_W

3 2009 1 2255 18244 17913 5806 247 US_LL_W

3 2010 1 397 22755 8816 3708 248 US_LL_W

3 2011 0 3015 24228 14913 2040 155 US_LL_W

3 2012 0 731 32374 22769 6362 220 US_LL_W

3 2013 0 1773 15705 19341 4498 141 US_LL_W

3 2014 0 1911 20555 17086 2532 88 US_LL_W

4 1991 0 4479 1508 1718 1849 503 VEN_LL_N

4 1992 0 6079 2046 2332 2509 682 VEN_LL_N

4 1993 0 345 9117 6270 2031 307 VEN_LL_N

4 1994 0 337 21027 7443 982 138 VEN_LL_N

4 1995 16 48 223 8413 2714 718 VEN_LL_N

4 1996 9 27 125 4693 1514 401 VEN_LL_N

4 1997 9 27 124 4673 1508 399 VEN_LL_N

4 1998 0 5 210 8150 1279 716 VEN_LL_N

4 1999 0 0 101 3193 2334 1910 VEN_LL_N

4 2000 0 0 84 2666 1949 1595 VEN_LL_N

4 2001 0 0 90 2857 2089 1709 VEN_LL_N

4 2002 0 24 2283 4077 868 24 VEN_LL_N

4 2003 0 0 3907 2585 588 65 VEN_LL_N

4 2004 0 704 3520 2762 3447 939 VEN_LL_N

4 2005 0 1043 5217 4093 5110 1391 VEN_LL_N

4 2006 0 71 1420 3767 3172 1848 VEN_LL_N

4 2007 0 12 1097 2888 4355 1502 VEN_LL_N

4 2008 905 30532 33698 0 0 0 VEN_LL_N

4 2009 0 0 560 5245 6813 1904 VEN_LL_N

4 2010 0 182 730 6295 5656 5291 VEN_LL_N

4 2011 0 0 1489 7513 6881 2598 VEN_LL_N

4 2012 282 282 3238 7040 2886 1197 VEN_LL_N

4 2013 38 28620 18171 5878 115 115 VEN_LL_N

4 2014 41 30645 19456 6294 123 123 VEN_LL_N

5 1982 0 38 3545 1721 136 73 URU_W_1

5 1983 0.00 63 5914 2872 227 122 URU_W_1

5 1984 0 15 4946 5124 1121 24 URU_W_1

5 1985 0 259 11358 3056 219 18 URU_W_1

5 1986 0 200 4956 2586 49 2 URU_W_1

5 1987 0 795 5870 325 60 10 URU_W_1

5 1988 0 6 4565 1926 6 0 URU_W_1

5 1989 0 2 1651 696 2 0 URU_W_1

5 1990 0 1 464 196 1 0 URU_W_1

5 1991 0 2 1599 675 2 0 URU_W_1

6 1992 0 2 1909 805 2 0 URU_W_2

6 1993 0 1 516 218 1 0 URU_W_2

6 1994 0 2 1522 642 2 0 URU_W_2

682


6 1995 6 1 858 451 6 0 URU_W_2

6 1996 19 4 2768 1454 19 0 URU_W_2

6 1997 6 1 858 451 6 0 URU_W_2

6 1998 10 2 1424 748 10 0 URU_W_2

6 1999 0 215 2610 0 27 0 URU_W_2

6 2000 0 379 1696 24 12 0 URU_W_2

6 2001 0 0 2213 1043 0 0 URU_W_2

6 2002 45 783 2709 809 0 0 URU_W_2

6 2003 0 165 2226 1476 0 0 URU_W_2

6 2004 0 44 7625 917 0 0 URU_W_2

6 2005 0 203 8600 9910 9 0 URU_W_2

6 2006 0 14 578 5270 188 16 URU_W_2

6 2007 0 0 633 510 11 1 URU_W_2

6 2008 0 0 41 1420 15 0 URU_W_2

6 2009 11 1029 1192 545 453 18 URU_W_2

6 2010 0 132 4039 216 0 0 URU_W_2

7 1978 0 0 1375 18075 1375 51 BR_LL_N

7 1979 99 99 3557 15709 1680 0 BR_LL_N

7 1980 0 1008 5270 8771 297 258 BR_LL_N

7 1981 0 3849 46325 2231 0 0 BR_LL_N

7 1982 0 4733 9591 20182 572 0 BR_LL_N

7 1983 0 409 27331 3457 1046 177 BR_LL_N

7 1984 152 7857 10004 6106 0 0 BR_LL_N

7 1985 0 2502 17343 1824 469 28 BR_LL_N

7 1986 0 3472 19671 13468 200 64 BR_LL_N

7 1987 8 9112 18733 6114 634 0 BR_LL_N

7 1988 0 4475 22428 6390 1661 123 BR_LL_N

7 1989 0 39 11078 13042 2713 117 BR_LL_N

7 1990 1 160 15335 7649 744 46 BR_LL_N

7 1991 0 177 13917 5954 487 28 BR_LL_N

7 1992 0 14618 61189 3974 298 45 BR_LL_N

7 1993 0 5920 33744 10971 3544 503 BR_LL_N

7 1994 33 965 25272 8551 1348 178 BR_LL_N

7 1995 69 3745 27086 9981 2065 145 BR_LL_N

7 1996 1 941 8061 8292 2737 211 BR_LL_N

7 1997 0 434 4578 9207 4159 259 BR_LL_N

7 1998 0 461 3373 10261 6273 205 BR_LL_N

7 1999 5 645 13650 17723 12459 8549 BR_LL_N

7 2000 0 725 27437 44842 9992 242 BR_LL_N

7 2001 0 5793 63875 47749 11898 3601 BR_LL_N

7 2002 0 304 28484 50882 10834 304 BR_LL_N

7 2003 0 622 18904 15375 5129 804 BR_LL_N

7 2004 3 9 7453 29115 7782 735 BR_LL_N

7 2005 1039 6470 18423 26841 30001 9150 BR_LL_N

7 2006 297 1138 2210 5315 9647 4233 BR_LL_N

7 2007 703 11048 7556 7328 8920 3788 BR_LL_N

683


7 2008 2215 74767 82520 0 0 0 BR_LL_N

7 2009 0 0 1583 10242 7666 261 BR_LL_N

7 2010 76 1771 23857 6552 1631 1526 BR_LL_N

7 2011 23 592 4791 12903 9769 4445 BR_LL_N

7 2012 540 540 6755 14382 7852 4490 BR_LL_N

8 1993 74 11515 68662 33336 10159 1700 CHTAI_N_93_14_M4

8 1994 83 7207 176527 60995 7059 1285 CHTAI_N_93_14_M4

8 1995 1562 11950 83926 49445 5222 1019 CHTAI_N_93_14_M4

8 1996 77 17293 75329 70342 21833 4445 CHTAI_N_93_14_M4

8 1997 0 8806 37851 50928 15884 3784 CHTAI_N_93_14_M4

8 1998 0 9149 82025 61481 17678 604 CHTAI_N_93_14_M4

8 1999 33 6045 52437 43184 17718 3607 CHTAI_N_93_14_M4

8 2000 29 7105 68992 72904 18590 5389 CHTAI_N_93_14_M4

8 2001 45 10073 56068 53176 10279 2624 CHTAI_N_93_14_M4

8 2002 40 460 41108 69194 12306 660 CHTAI_N_93_14_M4

8 2003 0 355 21718 75010 31693 3410 CHTAI_N_93_14_M4

8 2004 7 101 29245 78371 19698 2762 CHTAI_N_93_14_M4

8 2005 1 93 16849 43762 12910 3550 CHTAI_N_93_14_M4

8 2006 11 219 2626 7909 6885 3367 CHTAI_N_93_14_M4

8 2007 0 14 1644 7952 10816 7617 CHTAI_N_93_14_M4

8 2008 2 1 669 6253 6075 3827 CHTAI_N_93_14_M4

8 2009 0 2 1855 7938 7578 4128 CHTAI_N_93_14_M4

8 2010 2 6 1519 6853 3892 1963 CHTAI_N_93_14_M4

8 2011 5 29 8807 14308 7448 3258 CHTAI_N_93_14_M4

8 2012 0 10 2916 9939 4934 1961 CHTAI_N_93_14_M4

8 2013 0 44 2994 9778 7446 2426 CHTAI_N_93_14_M4

8 2014 95 1344 3280 9042 4569 2191 CHTAI_N_93_14_M4

9 1976 3 705 49285 42579 5184 309 Japan_W_76_14

9 1977 0 858 23754 15407 2093 140 Japan_W_76_14

9 1978 24 1275 31586 20757 2430 112 Japan_W_76_14

9 1979 0 377 29585 24458 2895 143 Japan_W_76_14

9 1980 1 986 12542 26920 13427 3487 Japan_W_76_14

9 1981 8 3252 49357 41570 10139 1324 Japan_W_76_14

9 1982 207 3733 42622 55793 25385 6283 Japan_W_76_14

9 1983 1 223 16679 24850 5986 487 Japan_W_76_14

9 1984 47 1847 35187 47612 11353 1557 Japan_W_76_14

9 1985 32 3201 59604 47817 18919 1802 Japan_W_76_14

9 1986 0 466 23801 50586 8354 998 Japan_W_76_14

9 1987 0 534 33389 42135 8981 344 Japan_W_76_14

684

9 1988 15 1049 50537 75798 19944 477 Japan_W_76_14

9 1989 0 230 56586 78958 22168 1538 Japan_W_76_14

9 1990 16 89 23955 82495 24901 2885 Japan_W_76_14

9 1991 3 3452 50665 42121 19021 1921 Japan_W_76_14

9 1992 0 47 17595 59323 10956 1058 Japan_W_76_14

9 1993 0 65 6866 36048 16529 2017 Japan_W_76_14

9 1994 0 566 23467 50783 21572 3098 Japan_W_76_14

9 1995 0 10290 46832 36531 30222 5943 Japan_W_76_14

685


9 1996 0 36 7040 52628 25149 8762 Japan_W_76_14

9 1997 0 9 16127 35360 19249 2174 Japan_W_76_14

9 1998 0 1422 34108 59003 16662 2368 Japan_W_76_14

9 1999 0 3263 14522 36194 18600 1441 Japan_W_76_14

9 2000 0 61 8393 31826 27398 5186 Japan_W_76_14

9 2001 0 1070 11299 35368 11368 730 Japan_W_76_14

9 2002 0 2252 15276 24334 7205 531 Japan_W_76_14

9 2003 0 43 21203 30018 8196 1231 Japan_W_76_14

9 2004 0 0 11731 36122 45519 14682 Japan_W_76_14

9 2005 0 787 11172 31533 30961 5746 Japan_W_76_14

9 2006 36 5090 9109 39914 32318 3017 Japan_W_76_14

9 2007 0 75 102460 93115 33833 1634 Japan_W_76_14

9 2008 0 411 56092 44089 34475 6303 Japan_W_76_14

9 2009 0 1507 40848 34315 27961 3527 Japan_W_76_14

9 2010 1 2272 92453 45169 9893 2287 Japan_W_76_14

9 2011 0 379 33262 40468 16184 4597 Japan_W_76_14

9 2012 0 1346 22520 47516 25949 1602 Japan_W_76_14

9 2013 0 438 47100 40373 19837 3057 Japan_W_76_14

9 2014 0 188 16328 41192 17304 2433 Japan_W_76_14

-1 -1 end index selectivities

#______________________________________X_______________________________________

# NOW ENTER IN THE WEIGHTS AT AGE FOR THE INDICES OF ABUNDANCE (row=year, col=age)

#INDEX YEAR AGE 0 AGE 1 AGE 2 AGE 3 AGE 4 AGE 5+ 'Yellowfin Tuna 2003'

#______________________________________X________________________________________

3 1987 1.911396639 10.76335614 27.92805661 50.33145338 67.01770208

82.82300409 US_LL_W

3 1988 1.911396639 10.76335614 27.92805661 50.33145338 67.01770208

84.69461022 US_LL_W

3 1989 0.649097365 7.655477437 25.38305901 35.67762673 66.44674699

84.24698425 US_LL_W

3 1990 1.63595958 6.451618578 24.92625306 52.20968912 67.21336746

110.8343774 US_LL_W

3 1991 2.446253608 7.026616835 16.21642503 39.50208157 67.64167244

99.89959326 US_LL_W

3 1992 1.080678435 7.996978714 18.84954258 38.43891907 73.94659076

90.2711914 US_LL_W

3 1993 1.68449922 5.560180809 18.70977842 40.41983122 73.63079093

97.20739576 US_LL_W

3 1994 1.90275685 4.456586084 31.02397896 43.57586243 62.75585277

95.35995131 US_LL_W

3 1995 1.91054391 5.135867198 31.02397896 43.57586243 62.75585277

95.35995131 US_LL_W

3 1996 1.592527991 7.574394239 12.83560994 37.55066949 70.35177224

103.1052746 US_LL_W

3 1997 1.007695525 7.912795349 17.55053016 38.24337063 69.73063273

110.127478 US_LL_W

3 1998 1.596154508 7.976334285 12.12194573 39.34330148 67.94249738

91.70223449 US_LL_W

686

3 1999 1.458053296 8.23681567 13.93862123 37.83091909 61.51998908

84.01538127 US_LL_W

3 2000 1.632220336 10.74128022 22.47858975 44.32829722 62.1119421

84.83609604 US_LL_W

3 2001 1.616080916 9.449252828 24.34906945 43.95068889 63.56642534

89.42409124 US_LL_W

3 2002 1.690518111 9.650008677 24.38962167 45.91897725 61.29026776

85.10651368 US_LL_W

3 2003 1.492954454 10.34368572 22.20613165 47.04793714 62.13675022

84.61827179 US_LL_W

3 2004 1.619827712 10.05831486 24.56355031 42.74372683 63.13887112

85.4179975 US_LL_W

3 2005 2.169488904 9.436570903 20.78732349 45.75840874 62.46017294

82.49002989 US_LL_W

687


3 2006 1.571608252 12.10311594 21.76241612 45.4124963 65.13728161

84.94544591 US_LL_W

3 2007 1.613629613 11.84031655 23.82032062 46.62448729 63.53816153

83.19275269 US_LL_W

3 2008 1.665774336 9.51041092 22.86880748 46.23431397 63.61547768

85.55646935 US_LL_W

3 2009 1.082479036 9.418089125 23.77671176 44.87496148 63.27863157

84.96230925 US_LL_W

3 2010 2.169488904 8.931139116 24.43180362 42.32904972 63.41470117

83.11710994 US_LL_W

3 2011 1.441871646 9.572616094 22.90818396 46.64982867 66.01404472

85.24380165 US_LL_W

3 2012 1.504095956 10.12878999 23.35698649 45.83244432 61.50738575

83.97776701 US_LL_W

3 2013 1.373760239 10.32266628 22.59576071 43.63322175 62.14730344

86.03700775 US_LL_W

3 2014 1.290574319 10.83960481 24.04391054 43.09067492 64.32470637

87.13796274 US_LL_W

5 1982 2.223863552 9.19024695 29.87431193 48.13705373 75.37183161

99.95167952 URU_W_1

5 1983 2.012138623 9.19024695 29.87431193 48.13705373 75.37183161

99.95167952 URU_W_1

5 1984 2.23553617 6.933916505 19.52767855 37.35843747 57.70723503

77.94684232 URU_W_1

5 1985 1.63595958 7.183761157 18.72282573 35.8894967 64.39758144

85.60973585 URU_W_1

5 1986 2.006627425 9.925274164 29.22378195 42.55465872 65.66471953

86.4733192 URU_W_1

5 1987 1.950330738 10.03803071 17.59936596 41.75805764 65.22453247

99.95675667 URU_W_1

5 1988 1.918352573 8.583370879 22.86241855 33.93633177 60.52697032

101.5160604 URU_W_1

5 1989 1.90275685 8.583370879 22.86241855 33.93633177 60.52697032

117.7579105 URU_W_1

5 1990 1.91054391 8.583370879 22.86241855 33.93633177 60.52697032

96.9636826 URU_W_1

5 1991 1.592527991 8.583370879 22.86241855 33.93633177 60.52697032

89.44144805 URU_W_1

6 1992 1.544628216 8.583370879 22.86241855 33.93633177 60.52697032

86.97085992 URU_W_2

6 1993 1.596154508 8.583370879 22.86241855 33.93633177 60.52697032

89.83395485 URU_W_2

6 1994 1.50953168 8.583370879 22.86241855 33.93633177 60.52697032

88.60403152 URU_W_2

6 1995 0.925775938 13.62599329 35.98170177 44.16854508 65.72504616

91.26990314 URU_W_2

6 1996 0.925775938 13.62599329 35.98170177 44.16854508 65.72504616

88.61582085 URU_W_2

6 1997 0.925775938 13.62599329 35.98170177 44.16854508 65.72504616

88.93559656 URU_W_2

6 1998 0.925775938 13.62599329 35.98170177 44.16854508 65.72504616

85.13942127 URU_W_2

6 1999 1.619827712 8.007818573 15.10824529 44.2042027 65.72504616

85.29396422 URU_W_2

6 2000 1.51126859 12.04059164 21.52251194 45.60437005 79.54187662

86.40834996 URU_W_2

6 2001 1.571608252 4.235666562 25.12110946 29.62076852 63.2351177

85.69356885 URU_W_2

6 2002 1.071150277 11.15151819 20.49302018 28.30090753 61.26701046

83.07478666 URU_W_2

6 2003 1.665774336 7.34115269 21.19106694 29.008286 62.7147385

84.02187665 URU_W_2

6 2004 1.525239932 8.219029797 21.59802355 33.47069 61.30077674

83.92503333 URU_W_2

6 2005 1.488514355 11.93041693 28.43617026 37.67952602 49.72665263

82.68568663 URU_W_2

6 2006 1.441871646 9.375694024 18.25279066 35.66529096 53.12826118

99.62198957 URU_W_2

6 2007 1.504095956 3.440087851 26.95771303 31.23149343 72.21622258

75.19466028 URU_W_2

6 2008 1.373760239 3.936538782 23.18700271 43.84614827 63.63635515

86.86293192 URU_W_2

688

6 2009 2.604312745 6.509971206 12.6177647 43.14230522 57.43686829

87.67475505 URU_W_2

6 2010 1.366104695 8.341263041 26.2130243 46.55259734 63.9406514

85.94175904 URU_W_2

9 1976 1.311419709 9.308221416 23.43467844 40.31367443 65.58445171

90.59814529 Japan_W_76_14

9 1977 1.50953168 9.340806417 25.65343629 41.99267058 65.9644435

88.75930586 Japan_W_76_14

9 1978 2.447901162 8.415350108 25.16400478 42.56213439 64.04129722

85.65468535 Japan_W_76_14

9 1979 1.616080916 9.252561619 26.01347013 38.98193435 60.58763181

92.01144403 Japan_W_76_14

9 1980 1.041184077 9.606183949 22.78264407 48.35906249 63.41732846

86.32162155 Japan_W_76_14

9 1981 0.484261511 11.1075362 28.05537354 42.90715796 67.08952605

89.67565716 Japan_W_76_14

9 1982 1.723923504 8.624632731 29.0407903 42.1939837 66.76730757

86.92333875 Japan_W_76_14

9 1983 2.103609679 7.882934205 25.23955005 45.71779749 66.26485511

92.66975 Japan_W_76_14

9 1984 1.842943906 10.33982499 26.26318232 43.21288114 61.27571548

87.88128277 Japan_W_76_14

689


9 1985 1.513359854 9.205515369 25.95853566 45.90731525 63.76499155

83.93982191 Japan_W_76_14

9 1986 1.665774336 8.540989081 24.55873329 41.81320067 59.60080693

88.20181533 Japan_W_76_14

9 1987 1.525239932 10.4130485 25.24310657 43.63309826 59.63053234

80.72949627 Japan_W_76_14

9 1988 1.128210597 10.05773481 25.3787498 42.28664951 62.23745653

84.27439505 Japan_W_76_14

9 1989 1.441871646 11.07847554 28.93535048 45.02681392 63.7231679

85.75255982 Japan_W_76_14

9 1990 1.4076023 9.678178322 23.30974909 39.855435 65.37961125

86.5788786 Japan_W_76_14

9 1991 1.4076023 10.60472253 25.72464488 44.75333247 61.81452929

82.29200378 Japan_W_76_14

9 1992 1.290574319 6.668256842 26.83241401 39.04440744 64.48832696

90.27417679 Japan_W_76_14

9 1993 1.366104695 7.781915365 26.86368284 44.31472451 63.00233348

86.46167237 Japan_W_76_14

9 1994 1.43997376 11.24761927 25.54690273 46.82004104 64.10601274

85.12430788 Japan_W_76_14

9 1995 1.46968186 9.221110985 22.29091391 42.74105917 61.52144111

83.0818621 Japan_W_76_14

9 1996 1.593975346 10.74612537 25.74751087 47.45449177 64.93449892

88.78194305 Japan_W_76_14

9 1997 1.507946945 5.63216785 21.50513446 46.92545175 64.22919218

84.69770003 Japan_W_76_14

9 1998 1.513588381 7.241173412 28.84547643 46.43289785 64.17678695

85.0828148 Japan_W_76_14

9 1999 1.477780495 12.5866449 24.68863238 44.77400688 62.01116318

84.25903628 Japan_W_76_14

9 2000 1.536599247 12.09367618 29.34291752 46.95667007 63.78189227

86.34992467 Japan_W_76_14

9 2001 1.411511122 10.29490695 28.21126341 43.48481419 59.25762396

83.90299102 Japan_W_76_14

9 2002 1.742220338 9.805259032 27.3004745 45.32225846 62.01427016

87.16844213 Japan_W_76_14

9 2003 1.460622082 11.49717989 31.19887228 46.56215124 60.64580907

87.01477199 Japan_W_76_14

9 2004 1.49724534 1.49724534 22.31769437 45.91509895 63.70083651

85.24985265 Japan_W_76_14

9 2005 1.492740059 11.29016116 22.39556335 45.97344477 62.44302268

82.40380004 Japan_W_76_14

9 2006 1.4076023 11.03874093 21.21433516 48.79682779 63.66674092

80.87071425 Japan_W_76_14

9 2007 1.41947233 12.50340381 26.20104348 40.62048072 62.73281998

83.86321884 Japan_W_76_14

9 2008 1.287850123 12.6632111 22.56652402 45.37007163 64.11126106

89.97638123 Japan_W_76_14

9 2009 1.301204633 10.76859673 27.1894791 46.34100182 64.84777795

85.80147044 Japan_W_76_14

9 2010 1.4076023 11.64313625 19.90368828 38.10032055 63.57609516

85.18258843 Japan_W_76_14

9 2011 1.338012307 6.891657812 21.70225727 50.73681213 69.16832473

90.31161334 Japan_W_76_14

9 2012 1.302616371 7.866349482 25.18009152 45.41805659 63.16280083

85.67968273 Japan_W_76_14

9 2013 1.299336101 6.740322781 28.20826712 40.60558113 60.1888606

84.36593505 Japan_W_76_14

9 2014 1.339928965 11.62654402 30.92378783 46.29530617 63.41800063

83.5498678 Japan_W_76_14

-1

#______________________________________X_______________________________________

# NOW ENTER IN THE FECUNDITY AT AGE FOR THE SPAWNING STOCK BIOMASS (row=year, col=age)

#______________________________________X_____________________________________

#year Age 0 Age 1 Age 2 Age 3 Age 4 Age 5

1970 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1971 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

690

1972 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1973 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1974 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1975 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1976 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1977 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1978 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

691


1979 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1980 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1981 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1982 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1983 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1984 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1985 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1986 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1987 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1988 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1989 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1990 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1991 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1992 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1993 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1994 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1995 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1996 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1997 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1998 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

1999 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2000 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2001 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2002 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2003 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2004 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2005 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2006 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2007 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2008 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2009 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2010 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2011 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2012 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2013 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

2014 1.033843215 2.165536911 9.323119309 32.09984769 59.95088744 87.82638841

-1

692

Table 2. VPA parameter specifications. Not modified after the assessment workshop.

# The methods of estimation include:

# 0 fixed constant at value given for best estimate

# 1 estimate as a 'frequentist' parameter

# -n fix to the same value as parameter n (whether it is estimated or not)

# -0.1 fix to the value of the previous estimated parameter

# 0.1 estimate as a random walk (a lognormal random deviation with given std. dev. and prior expectation equal to

the previous parameter

# 0.2 estimate as a lognormal random deviation with given std. dev. and prior expectation equal to the nearest

previous constant or frequentist parameter)

# 0.3 estimate as a lognormal random deviation with given std. dev. and prior expectation equal to the input best

estimate)

# TERMINAL F PARAMETERS: (lower bound, best estimate, upper bound, indicator, reference age)

#______________________________________X_______________________________________

$ 1 0 0.1 2 1 0.1 first age (AGE 0 in this case)

$ 1 0 0.5 2 1 0.1

$ 1 0 0.25 2 1 0.1

$ 1 0 0.35 2 1 0.1

$ 1 0 0.4 2 1 0.1 next to last age

#______________________________________X_______________________________________

# F-RATIO PARAMETERS F{oldest}/F{oldest-1} (lower bound, best estimate, upper bound, indicator, std. dev. of prior)

# one parameter (set of specifications) for each year

#______________________________________X_______________________________________

$ 1 0.1 0.2 5 1 0.2 1970 estimated

$ 44 0.1 0.2 5 3 0.2 1971-2010 random walk

#_____________________________________X_______________________________________

# NATURAL MORTALITY PARAMETERS: (lower bound, best estimate, upper bound, indicator, std. dev. of prior)

# one parameter (set of specifications) for each age

#______________________________________X_______________________________________

$ 1 0 1.588 1 0 0.1

$ 1 0 1.194 1 0 0.1

$ 1 0 0.748 1 0 0.1

$ 1 0 0.550 1 0 0.1

$ 1 0 0.476 1 0 0.1

$ 1 0 0.4321 1 0 0.1

#______________________________________X_______________________________________

# VARIANCE SCALING PARAMETER (lower bound, best estimate, upper bound, indicator, std. dev.)

# this parameter scales the input variance up or down as desired

# In principal, if you estimate this you should obtain more accurate estimates of the

# magnitude of the parameter variances-- all other things being equal.

# (1 parameter so 1 set of specifications)

#______________________________________X_______________________________________

$ 1 0 1.0 3.0 0 0.1 # 1

$ 1 0 1.0 3.0 1 0.1 # 2

$ 7 0 1.0 3.0 -0.1 0.1 # 3-9

693

Table 3. VPA control file. The assessment report and alternative runs used identical controls except the maximum

iterations was raised to 500 from 200, and the number of consecutive restarts was raised to 7 from 5 for the

alternative runs (see highlighted changes). #-----------------------------------------------------------------------------

# INSTRUCTIONS: the control options are entered in the order specified.

# Additional comment lines may be inserted anywhere in this

# file provided they are preceded by a # symbol in the FIRST

# column, otherwise the line is perceived as free-format data.

#-----------------------------------------------------------------------------

# TITLES AND FILE NAMES (MUST BE PLACED WITHIN SINGLE QUOTES)

#-----------------------------------------------------------------------------

'YFT RUN CLUSTER 1' TITLE OF RUN

'yft2016.dat' DATA FILE NAME (INPUT)

'yft2016.par' PARAMETER SPECIFICATION FILE (INPUT)

'yft2016.r01' RESULTS FILE NAME (OUTPUT)

'yft2016.est' PARAMETER ESTIMATE FILE NAME (OUTPUT)

'yft2016.spd' SPREADSHEET FRIENDLY RESULTS (OUTPUT)

'none' TAGGING DATA FILE (INPUT)

#-----------------------------------------------------------------------------

# MODEL TYPE OPTIONS

#-----------------------------------------------------------------------------

1 NUMBER OF ZONES (1 OR 2)

1 MODEL_TYPE (1=DIFFUSION, 2=OVERLAP)

#-----------------------------------------------------------------------------

# TAGGING DATA SWITCH

#-----------------------------------------------------------------------------

# tagging data switch (0=do not use tagging data, 1=use tagging data)

# | weighting factor for modifying importance of tagging data in objective function

# | | tag timing factors

# | | |

0 1.0 0 0 TAGGING MODEL CONTROLS

#-----------------------------------------------------------------------------

# SEARCH ALGORITHM CONTROLS

#-----------------------------------------------------------------------------

-911 RANDOM NUMBER SEED

500 MAXIMUM NUMBER OF AMOEBA SIMPLEX SEARCH RESTARTS

7 NUMBER OF CONSECUTIVE RESTARTS THAT MUST VARY BY LESS THAN 1% TO STOP SEARCH 0.35 PDEV (standard deviation controlling vertices for Initial simplex of each restart)

#-----------------------------------------------------------------------------

# INDEX WEIGHTING CONTROLS

#-----------------------------------------------------------------------------

1 SCALE (DIVIDE INDEX VALUES BY THEIR MEAN)- ANY VALUE > 0 = YES

0 INDEX WEIGHTING:(0)INPUT CV's, (+)DEFAULT CV, (-)DEFAULT STD. DEV., (999)MLE

0 (0) MULTIPLICATIVE VARIANCE SCALING FACTOR or (1) ADDITIVE VARIANCE SCALING FACTOR

#-----------------------------------------------------------------------------

# CONSTRAINT ON Vulnerability (PARTIAL RECRUITMENT)

#-----------------------------------------------------------------------------

# apply this penalty to the last N years (SET N = 0 TO IGNORE)

# | standard deviation controlling the severity of the penalty

# | | first age affected

# | | | last age affected

# | | | |

3 0.4 1 5 LINKS THE VULNERABILITIES IN THE LAST N YEARS

#-----------------------------------------------------------------------------

# CONSTRAINTS ON RECRUITMENT

#-----------------------------------------------------------------------------

# apply this penalty to the last N years (SET N = 0 TO IGNORE)

# | standard deviation controlling the severity of the penalty

4 0.4 LINKS THE RECRUITMENTS IN THE LAST N YEARS

0 0.1 1 LINKS THE RECRUITMENTS OF THE TWO STOCKS

# |

# ratio of stock (sex) 1 to stock (sex) 2 {a value of 1 means a 1:1 ratio}

#-----------------------------------------------------------------------------

# CONSTRAINT ON SPAWNER-RECRUIT RELATIONSHIP

#-----------------------------------------------------------------------------

# PDF of spawner-recruit penalty: 0=none, 1=lognormal, 2=normal (-)=estimate sigma by MLE

# | first and last years to use in fitting (in terms of recruits)

# | |

0 0 0 PENALIZES DEPARTURES FROM BEVERTON AND HOLT STOCK-RECRUIT CURVE

# (note: check the parameter file to make sure you are estimating the S/R parameters when pdf not 0, or not estimating them when pdf=0))

#-----------------------------------------------------------------------------

# PARAMETER ESTIMATION OPTIONS

#-----------------------------------------------------------------------------

1 OPTION TO USE (1) F'S OR (2) N'S AS TERMINAL YEAR PARAMETERS

-1 ESTIMATE Q IN (+) SEARCH or (<0) by concentrated MLE's

#-----------------------------------------------------------------------------

# BOOTSTRAP ANALYSES

#-----------------------------------------------------------------------------

# Number of bootstraps to run (negative value = do a parametric bootstrap)

# | Use Stine correction to inflate bootstrap residuals (0=NO)

# | |

500 1 1 BOOTSTRAP OPTION

#-----------------------------------------------------------------------------

# RETROSPECTIVE ANALYSES (CANNOT DO RETROSPECTIVE ANALYSES AND BOOTSTRAPS AT SAME TIME)

#-----------------------------------------------------------------------------

0 NUMBER OF YEARS TO GO BACK FOR RETROSPECTIVE ANALYSES

@@EOF@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

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Table 4. Management references for assessment workshop VPA Cluster 1.

Table 5. Management references for assessment workshop VPA Cluster 2.

Table 6. Management references for alternative VPA Cluster 1.

Table 7. Management references for alternative VPA Cluster 2.

MEASURE LOWER CL MEDIAN UPPER CL AVERAGE DETERMINISTIC STD. DEV.

F at MSY 0.92 1.01 1.11 1.01 0.93 0.05

MSY 98,040 101,849 110,026 102,778 96,300 3,303

Y/R at MSY 0.51 0.53 0.56 0.53 0.52 0.01

S/R at MSY 0.48 0.55 0.60 0.55 0.53 0.03

SPR AT MSY 0.13 0.15 0.16 0.15 0.14 0.01

SSB AT MSY 92,276 105,642 117,473 105,749 97,911 6,399

F CURRENT 0.52 0.71 0.84 0.70 0.58 0.08

SSB 2014 117,700 127,650 171,600 131,900 160,800 16,027

SSB 2014/SSB MSY 1.21 1.64

F CURRENT/F MSY 0.70 0.62


F at MSY 1.00 1.01 1.11 1.02 1.12 0.04

MSY 100,907 102,056 110,026 103,824 113,706 3,292

Y/R at MSY 0.52 0.53 0.56 0.53 0.54 0.01

S/R at MSY 0.49 0.55 0.60 0.55 0.53 0.03

SPR AT MSY 0.13 0.15 0.16 0.15 0.14 0.01

SSB AT MSY 102,073 105,642 117,473 106,729 113,436 4,606

F CURRENT 0.64 0.71 0.84 0.73 0.82 0.06

SSB 2014 109,500 124,850 145,300 125,690 113,300 9,728

SSB 2014/SSB MSY 1.18 1.00



F at MSY 0.74 0.79 0.84 0.79 0.79 0.04

MSY 118,164 121,763 125,345 121,667 121,980 2,764

Y/R at MSY 0.64 0.66 0.67 0.66 0.66 0.01

S/R at MSY 0.78 0.79 0.81 0.79 0.80 0.01

SPR AT MSY 0.20 0.20 0.21 0.20 0.20 0.00

SSB AT MSY 144,022 146,518 149,962 146,739 147,861 2,313

F CURRENT 0.63 0.77 0.91 0.77 0.77 0.11

SSB 2014 103,200 126,600 153,900 128,440 128,400 20,275

SSB 2014/SSB MSY 0.86 0.87



F at MSY 0.82 0.92 1.01 0.92 0.93 0.07

MSY 131,171 136,326 140,680 136,103 137,379 3,736

Y/R at MSY 0.63 0.66 0.68 0.65 0.66 0.02

S/R at MSY 0.65 0.66 0.67 0.66 0.67 0.01

SPR AT MSY 0.17 0.17 0.17 0.17 0.17 0.00

SSB AT MSY 134,771 137,535 139,746 137,351 138,720 1,945

F CURRENT 0.57 0.84 1.09 0.83 0.85 0.20

SSB 2014 73,220 108,800 176,800 118,424 109,100 42,832

SSB 2014/SSB MSY 0.79 0.79


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Assessment Workshop VPA Base 1 Cluster 1 Assessment Workshop VPA Base 2 Cluster 2

Figure 1. The trajectories of SSB and F relative to the level that produces MSY in equilibrium illustrate a problem

noted by the assessment working group. In some years, the median and deterministic results were located far from

the center of the 80% confidence intervals, indicating a significant skew in estimates of one or more parameters

(e.g. F, SSB, SSBMSY and FMSY) during those years.

Cluster 1 Cluster 2

Figure 2. Cluster 1 and 2 indices of abundance.

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Cluster 1 Cluster 2

Figure 3. The parameters of the Beverton and Holt spawner-recruit relationship were estimated from the VPA

model results, then fixed for projections.

Figure 4. The length-at-age and weight-at-age derived from a von Bertalannfy growth curve developed to mimic

the Gascuel et al (1992) growth function for ages 5+.

LINF 174.8

k 0.566012

t0 -0.01459

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Assessment Workshop VPA Base 1 Cluster 1 Alternative VPA Base 1 Cluster 1

Figure 5. Fits to the CPUE series for the Cluster 1 assessment workshop (left) and alternative (right) VPA model

runs.

698


Figure 6. Fits to the CPUE series for the Cluster 2 assessment workshop (left) and alternative (right) VPA model

runs.

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Figure 7. Retrospective pattern in fishing mortality-at-age for the CLUSTER 1 assessment workshop (left) and

alternative (right) VPA model runs.

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Figure 8. Retrospective pattern in numbers-at-age for the CLUSTER 1 assessment workshop (left) and alternative

(right) VPA model runs.

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Figure 9. Retrospective pattern in spawning stock biomass and recruitment for the CLUSTER 1 assessment

workshop (left) and alternative (right) VPA model runs.

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Figure 10. Retrospective pattern in fishing mortality-at-age for the CLUSTER 2 assessment workshop (left) and

alternative (right) VPA model runs.

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Figure 11. Retrospective pattern in numbers-at-age for the CLUSTER 2 assessment workshop (left) and alternative

(right) VPA model runs.

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Figure 12. Retrospective pattern in spawning stock biomass and recruitment for the CLUSTER 2 assessment

workshop (left) and alternative (right) VPA model runs.

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Figure 13. Abundance at age for CLUSTER 1 assessment workshop (left) and alternative (right) VPA model runs.


Figure 14. Abundance at age for CLUSTER 2 assessment workshop (left) and alternative (right) VPA model runs.

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Figure 15. Fishing mortality at age, apical F, terminal F at age and the F for CLUSTER 1 assessment workshop

(left) and alternative (right) VPA model runs.

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Figure 16. Fishing mortality at age, apical F, terminal F at age and the F for CLUSTER 2 assessment workshop

(left) and alternative (right) VPA model runs.

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Figure 17. Spawning stock biomass (mt), recruits (age 0) and the spawner-recruit estimates for CLUSTER 1

assessment workshop (left) and alternative (right) VPA model runs.

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Figure 18. Spawning stock biomass (mt), recruits (age 0) and the spawner-recruit estimates for CLUSTER 2

assessment workshop (left) and alternative (right) VPA model runs.

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Figure 19. Spawning stock biomass (mt) and fishing mortality, with the relevant MSY reference level for

CLUSTER 1 assessment workshop (left) and alternative (right) VPA model runs.


Figure 20. Spawning stock biomass (mt) and fishing mortality, with the relevant MSY reference level for

CLUSTER 2 assessment workshop (left) and alternative (right) VPA model runs

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ALTERNATIVE VPA CLUSTER 1

ALTERNATIVE VPA CLUSTER 2

Figure 21. Histograms of bootstrap estimates of SSBMSY, FMSY, SSB2014/SSBMSY and F/FMSY with cumulative

frequency distribution. The bin containing the median estimate is shaded in green.

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Figure 22. Annual stock status estimates adjusted for annual changes in selectivity (alternative VPA models).

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Figure 23. Projection results for the assessment workshop Cluster 1 VPA run.

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Figure 24. Projection results for the assessment workshop Cluster 2 VPA run.

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Figure 25. Projection results for the alternative Cluster 1 VPA run.

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Figure 26. Projection results for the alternative Cluster 2 VPA run.

ALTERNATIVE VPA ASSESSMENTS OF ATLANTIC ......SCRS/2016/207 Collect. Vol. Sci. Pap. ICCAT, 73(2):...

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