Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean : final report.
Date: 2022-09-19
Institutions: European Climate, Infrastructure and Environment Executive Agency
Subjects: Indian Ocean, catch by species, conservation of resources, evaluation of resources, fishery management, fishery research, report, sea fish, sustainable fisheries

Specific Contract No 14 

FRAMEWORK CONTRACT 

EASME/EMFF/2016/008 

Study to improve the current 
assessment and scientific 
advice for yellowfin tuna 
(YFT), bigeye tuna (BET) and 
skipjack (SKJ) in the Indian 
Ocean 

FINAL REPORT 

 
  
 
 
 
 
 
 
     
 
 
 
 
 
 
 
 
 
 
 
 
Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

EUROPEAN COMMISSION 

European Climate, Infrastructure and Environment Executive Agency 
Unit D.3 — Sustainable Blue Economy 

Contact: CINEA EMFAF CONTRACTS 

E-mail:  cinea-emfaf-contracts@ec.europa.eu  

European Commission 
B-1049 Brussels 

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Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

Study to improve the current 
assessment and scientific 
advice for yellowfin tuna 
(YFT), bigeye tuna (BET) and 
skipjack (SKJ) in the Indian 
Ocean 

Specific Contract No 14 

FRAMEWORK CONTRACT: EASME/EMFF/2016/008 

FINAL REPORT 

3 

 
 
 
 
 
 
 
Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

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Luxembourg: Publications Office of the European Union, 2022 

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Reproduction is authorised provided that the source is acknowledged. 

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Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

Table of contents 

LIST OF FIGURES .......................................................................................................... 8 

LIST OF TABLES .......................................................................................................... 10 

EXECUTIVE SUMMARY .................................................................................................. 11 

RÉSUMÉ EXECUTIF ...................................................................................................... 14 

RESUMEN EJECUTIVO .................................................................................................. 18 

INTRODUCTION .......................................................................................................... 22 

General introduction to the Specific Contract ........................................................... 22 

Objective and structure of the Draft Final Report ..................................................... 25 

1.  UNCERTAINTY IN DATA ........................................................................................ 26 

1.1.  Catch data: ................................................................................................ 26 

1.2.  Tagging data:............................................................................................. 38 

1.2.1. 

Evaluate how raw data is modified for the assessment ....................... 40 

1.2.2. 

Investigation on key parameters...................................................... 42 

1.2.3.  Sensitivity of tagging information in stock assessment ....................... 42 

1.3.  Size data ................................................................................................... 49 

1.4.  Catch per unit of effort (CPUE) ..................................................................... 52 

1.4.1.  Options for alternative indices ......................................................... 52 

1.4.2.  Advanced protocol for joint cpues iterations ...................................... 55 

1.4.3.  CPUEs for the entire indian ocean and subregions .............................. 58 

2.  MODEL UNCERTAINTY .......................................................................................... 59 

2.1.1.  Use of alternative assessment models and parameter settings............. 59 

2.1.2.  Development of diagnostics in support of WPPT decisions ................... 61 

2.1.3.  Review of yellowfin assessment (and some aspects of bigeye) ............. 63 

2.1.4. 

Protocol for statistical uncertainty .................................................... 70 

2.1.5.  Alternative key parameters ............................................................. 72 

3. 

PROPOSAL FOR A NEW STOCK ASSESSMENT OF YELLOWFIN IN 2019 ........................ 73 

3.1.  The way forward of the yellowfin workplan after the 2019 WPTT ....................... 78 

3.2. 

Improvements to the yellowfin stock assessment during 2020 .......................... 80 

TASKS AND DELIVERABLES: PROGRESS AND OUTCOMES ................................................ 85 

Milestones ........................................................................................................... 85 

Difficulties ........................................................................................................... 88 

REFERENCES .............................................................................................................. 90 

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Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

LIST OF ACRONYMS 

BET 

BMSY 

Bigeye 

Biomass at Maximum Sustainable Yield 

CAPAM 

Center for the Advancement of Population Methodology 

CECOFAD 

Catch, Effort and ecosystem impacts of FAD-fishing 

CPC 

CPUE 

Contracting Parties and Co-operating Non-contracting Parties 

Catch per Unit of Effort 

DG MARE 

EU Directorate-General for Maritime Affairs and Fisheries 

EASME 

Executive Agency for Small and Medium-sized Enterprises 

EU 

FAD 

FMSY 

GLMM 

IATTC 

ICCAT 

IOTC 

IOTTP 

IRD 

JABBA 

LWR 

MSE 

MSY 

Mpb 

NOAA 

q 

European Union 

Fish Aggregating Device 

Fishing Mortality at Maximum Sustainable Yield 

Generalized linear mixed model 

Inter American Tropical Tuna Commission 

International Commission for the Conservation of Atlantic Tunas 

Indian Ocean Tuna Commission 

Indian Ocean Tuna Tagging Programme 

Institut de Recherche pour le Développement 

Just Another Bayesian Biomass Assessment  

Length-weight relationship 

Management Strategy Evaluation 

Maximum Sustainable Yield 

Biomass Dynamic Model 

National Oceanic and Atmospheric Administration 

Catchability 

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Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

RFMO 

RTTP 

SC 

SKJ 

Regional Fisheries Management Organization 

Regional Tuna Tagging Project 

Scientific Committee 

Skipjack 

SS or SS3 

Stock Synthesis  

ToR 

US 

USSR 

VPA 

WPM 

WPTT 

YFT 

Terms of Reference 

United States 

Union of Soviet Socialist Republics 

Virtual Population Analysis 

Working Party on Methods 

Working Party on Tropical Tunas 

Yellowfin 

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Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

LIST OF FIGURES 

Figure 1. Contribution of the three tropical tuna species under the IOTC mandate to the total catches 
of IOTC species in the Indian Ocean, over the period 1950-2018 (a. Top left: total catch; b. Top right 
percentage, same colour key as Fig. 1a); Bottom: Contribution of each tropical tuna species to the 
total combined catches of tropical tunas (c. Bottom left: nominal catch of each species, 1950-2019; 
d. Bottom right: share of tropical tuna catch by species, 2014-2018).  

Figure  2.  All  tropical  tunas:  average  catches  in  the  Indian  Ocean  over  the  period  2014  –  18,  by 
country. Countries are ordered from left to right, according to the importance of catches of tropical 
tunas reported. The dark line indicates the (cumulative) proportion of catches of tropical tunas for 
the countries concerned, over the total combined catches of species reported from all countries and 
fisheries. 

Figure 3. Average catches in the Indian Ocean over the period 2013–17, by country. Countries are 
ordered from left to right, according to the importance of catches of yellowfin reported. The red line 
indicates the (cumulative) proportion of catches of yellowfin for the countries concerned, over the 
total combined catches of this species reported from all countries and fisheries. Data as of September 
2018 (IOTC, 2018b).  

Figure 4. Amount of good (green), fair (light green), poor (light brown) or very poor (brown) data in 
the total yellowfin nominal catch in the period 1977-2017. Red dashed line is the percentage of fully 
to partially reported nominal catch data (IOTC, 2018b). 

Figure 5. Total catches by tuna species and fishing mode according to 3 estimation methods for the 
2010-2018 period. Logbook declaration: dashed lines, T3 process: dotted line and Random Forest 
model: solid line (results from Duparc et al. 2019). 

Figure 6. Estimated trends for total biomass and fishing mortality relative to MSY. 

Figure 7. The values of the estimated MSY for Indian Ocean yellowfin for each of the underreporting 
scenarios. This figure shows how underreporting can produce a notable impact on reference points. 

Figure 8. Densities of releases (red) and recoveries  (blue). Upper (bigeye), middle (skipjack) and 
bottom (yellowfin). 

Figure 9. Estimated MSY for yellowfin down weighing (blue) and not down weighing (red) tagging 
data. 

Figure  10.  Estimated  MSY  for  bigeye  for  different  weightings  of  tagging  data  (no  weight  (blue), 
medium weight (purple), full weight (red)). 

Figure 11. Stock status of yellowfin for two weightings of tagging data (down weighed (blue) and not 
down weighed (light blue)). 

Figure 12. Stock status of bigeye for three weightings of tagging data (no weight (blue), medium 
weight (purple) and full weighting (light blue)). 

Figure 13. Stock and fishing mortality trajectories relative to MSY benchmarks for 2018 reference 
case (red) and idem but with tag data downweighted (blue). 

Figure 14. Estimated productivity, maximum sustainable yield (MSY) for reference case (with tags, 
io_h80_q1_tm30_dw1) and idem without tags (io_h80_q1_tm30_dw2) scenarios from the 2018 grid 
of scenarios. Dashed lines represent the last nominal catch of the assessment (409,150 tons). 

Figure  15.  Estimated  productivity  for  the  grid  of  24  scenarios  considered  in  the  2018  stock 
assessment of yellowfin. Note that h is steepness (h70=0.7, h80=0.8 and h90=0.9), q is catchability 
(constant=q1,  split  in  two  periods=q2)  and  tag-release  mortality  parameter  (tm30=30%  and 
tm10=10%). Dashed line is the 2017 catch. 

Figure 16. Estimated biomass trajectories for a 10% catch reduction with two scenarios with tags 
(io_h80_q1_tm30_dw1, red line) and without tagging data (io_h80_q1_tm30_dw2, blue line). 

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Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

Figure 17. Distribution of fishing effort for the Japanese longline fleet (aggregated for 5-year periods). 
X-axis is longitude and y-axis is latitude. 

Figure 18. Proportion of yellowfin smaller and larger than 100 cm from Chinese Taipei longline fleets. 

Figure  19.  Indices  of  CPUE  at  annual  scale  for  the  four  regions  considered  in  the  Indian  Ocean 
yellowfin assessment as estimated in the Joint Longline CPUE index workshop (IOTC, 2019b). 

Figure  20.  Indices  of  CPUE  at  annual  scale  for  the  one  and  two  area  configurations  of  the  Indian 
Ocean yellowfin assessment as estimated in the Joint Longline CPUE index workshop (IOTC, 2019b). 

Figure 21. Bigeye stock assessment using Stock Synthesis (left) and JABBA (right) (IOTC, 2019a). 

Figure 22. Left: Preliminary run using the Bayesian biomass dynamic model JABBA submitted by the 
external  reviewer  to  the  2018  WPTT  (Winker  et  al  2018).  Right:    the  estimated  trajectory  by  the 
Reference  model  of  the  2018  stock  assessment.  Purple  dots  indicate  the  trajectory  of  the  point 
estimates for the B/BMSY and F/FMSY ratios for each year from the reference case model (1950–2017) 
and  white  dots  indicate  the  terminal  estimates  from  each  of  the  24  grid  models.  The  grey  point 
represents the weighted median of the 24 model options with associated 80% confidence interval. 

Figure  23.  Projections  of  the  2018  reference  case  with  constant  catch  after  2017  (black  line)  and 
with a reduction to 80% of current catch (dashed line). Left is relative fishing mortality (F/Fmsy) and 
right is relative biomass (B/Bmsy). In blue is the projection period. 

Figure  24.  Spatial  stratification  of  the  Indian  Ocean  for  the  four-region  assessment  model  for 
yellowfin.  The  black  arrows  represent  the  configuration  of  the  movement  parameterisation  of  the 
base assessment model. Note that there are two sub regions for area R1 (R1a and R1b, which were 
separate regions of previous versions of the model). 

Figure 25. Spatial stratification of the Indian Ocean bigeye for the four-region assessment model. 

Figure 26. Areas used in two-area spatial structure model for skipjack (Fu, 2017). 

Figure  27.  Exploratory  analysis  of  the  statistical  uncertainty  associated  to  the  24  Stock  Synthesis 
model runs used in 2018 to provide stock status of  yellowfin. In different colors the deterministic 
stock status estimates for each scenario. 

Figure 28. Exploration of statistical uncertainty for Indian Ocean bigeye (IOTC, 2019a). 

Figure 29. Jittering analysis for the 3 area configurations tested. X-axis are quarters of year of the 
model. The model starts in 1950 and ends in 2017. 

Figure 30. Residuals to size frequency data for the longline fleets. 

Figure 31. Retrospective fits with the proposed new reference model. The fits by omitting 1 year (4 
quarters),  2  years,  3  years,  4  years  and  5  years  show  continuity  and  suggest  that  there  is  no 
retrospective pattern, which adds reassurance on the quality of the model fit. X-axis are the quarters 
of year for the period 1950-2017. 

Figure 32. Projection of the model io_h80_tm30_dw1 model with a 10% catch reduction. X-axis are 
the quarters of year for the period 1950-2017. 

Figure 33. The spawning biomass with time per area. X-axis are the quarters of year for the period 
1950-2017 and projected forward after quarter 284. 

Figure  34.  The  proportion  of  recruitment  per  area.  X-axis  are  the  quarters  of  year  for  the  period 
1950-2017 and projected forward after quarter 284. 

Figure 35: The red dots are the proportion in recruitment in area 1 and the continuous black line is 
the  estimated  loglinear  state  space  model  and  the  density  function  of  the  relative  change  in 
proportion of recruitment in area 1. 

Figure 36. a) the fraction of unfished biomass with time and b) the total catches and per fishery with 
time. 

Figure 37. Sensitivity test by increasing the proportion of recruitment in area 1 for the forecast. 

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Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

LIST OF TABLES 

Table 1. Timing agreed for this Specific Contract 14 (after review in April 2020). Note that after the 
review to the draft final report, a new version is submitted in January 20202. 

Table 2. Reporting category of total catch and % of catch of the three tropical tuna stocks in 2019.  

Table 3. A summary of tagging data processing for yellowfin, bigeye, and skipjack assessments (Fu, 
2020).  The  four  rows  summarise  the  total  number  of  releases  and  recoveries  from  the  RTTO-IO 
program, the number after error filtering and the final processed number included the assessment. 

Table  4.  Parameter  values  of  the  length-weight  relationship  used  in  the  T3  process.  These  values 
correspond to the last update of the tRFMOs for purse seine fisheries. The length-weight relationship 
enables conversion of the length (L) in round weight (RND) according to the equation RND = a*L^b. 
Estimated parameters are a and b (see official tRFMOs documents for details). 

Table  5.  Key  parameters  included  in  the  structural  uncertainty  grids  of  the  most  recent  stock 
assessment models. (*) Model developed in 2019 but not used for advice. 

Table 6. Deliverables of the Specific Contract 14. 

Table 7. Milestones expected for this Specific Contract.  

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bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

EXECUTIVE SUMMARY 

This Specific Contract aimed at improving the current assessment and scientific advice for 
the sustainable fishing of the three principal tropical tunas of the Indian Ocean, in close 
coordination with the IOTC secretariat. This final report outlines the key developments, as 
foreseen in the Terms of Reference (ToR) and the objectives that have not been achieved. 
This report also informs on the difficulties encountered and the alternative paths followed 
to address these. 

The assessments of the three tuna stocks (yellowfin, bigeye and skipjack) face common 
problems and complexities, ranging from uncertainty in the data (observation errors) and 
model  specification  (process  errors)  to  reliable  estimation  of  parameters  (estimation 
errors), that are increasingly affecting the capacity of the Indian Ocean Tuna Commission 
(IOTC)  Scientific  Committee  (SC)  to  provide  robust  management  advice.  This  was 
highlighted in the 2018 stock assessment of yellowfin, one of the most important stocks 
for the IOTC. According to the SC, many difficulties were identified in the stock assessment 
model, which indicated a low predictive power. To address the issues encountered, the SC 
adopted a work plan that included intersessional tasks, aimed at providing a new, improved 
advice  on  the  stock  status  of  yellowfin  and  on  the  management  actions  to  ensure  its 
sustainable harvest.  

Tropical tuna fisheries are a substantial part of EU fishing interests in the Indian Ocean. 
The EU, in close cooperation with IOTC, supports this workplan with a view to have, already 
in 2019-2020, an improved and robust scientific advice in support of sustainable tropical 
tuna fisheries. 

The  workplan adopted  by the  IOTC-SC identifies a  series  of specific tasks to reduce the 
current uncertainty in models and data used in the recent stock assessments of yellowfin. 
Many of these tasks will also contribute to improve the advice on the three tropical tuna 
stocks. The following list enumerates the two main tasks and fifteen subtasks scheduled in 
this contract (numbering as defined by the ToRs): 

1. UNCERTAINTY IN DATA 

1.1. Catch data 

1.2. Tagging data 

1.2.1.  Evaluate how raw data is modified for the assessment 

1.2.2.  Investigation on key parameters 

1.2.3.  Sensitivity of tagging information in stock assessment 

1.3. Size data 

1.4. Catch per unit of effort (CPUE) 

1.4.1.  Options for alternative indices 

1.4.2.  Advanced protocol for joint CPUEs iterations 

1.4.3.  CPUEs for the entire Indian Ocean and subregions 

2. MODEL UNCERTAINTY 

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Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

2.1.1.  Use of alternative assessment models and parameter settings 

2.1.2.  Development of diagnostics in support of WPPT decisions  

2.1.3.  Review of yellowfin assessment (and some aspects of bigeye) 

2.1.4.  Protocol for statistical uncertainty 

2.1.5.  Alternative key parameters 

The first task (Uncertainty in Data) aims at reviewing the four sources of data used in the 
yellowfin, bigeye, and skipjack stock assessments. These include catch, tagging data, size 
data and catch per unit of effort (CPUE). Task 1 aims to identify the main problems with 
the data used and when possible, provide alternatives for improvement. For the review of 
the  data  issues,  we  have  based  our  work  on  the  statistical  data  compiled  by  the  IOTC 
Secretariat  and  expanded  the  tasks  by  describing  the impact  of  the  different  sources  of 
data on the  outcome and quality of the stock  assessments. For the  catch data we have 
explored the impact of different reporting values on model outputs. For the tagging data 
we highlighted the large impact of these on the outcomes of the assessments and described 
the assumptions and concerns on the use of these data into stock assessment models. For 
the size frequency data, we highlighted the current problems with longline size data and 
the  state  of  the  revision  of  these  data  by  an  external  contractor.  For  the  CPUE  we  also 
described  the  indices  used  in  the  assessments,  discussed  their  problems,  and  provided 
alternatives for improving stock assessments.   

The second task (Model Uncertainty) is designed to explore alternative configurations and 
model  options  that  could  provide  a  better  estimate  of  the  productivity  and  historical 
trajectories of yellowfin and the two other tropical tuna stocks. For all sub-tasks, a larger 
effort has been put for the data and models used for yellowfin. However, most of the tasks 
and subtasks include reviews on bigeye and skipjack. For this section, we used the reviews 
of two stock assessments experts to check the yellowfin stock assessment model. The first 
was the external reviewer that was invited to the WPTT in 2018 and the second was the 
main developer of Stock Synthesis, the stock assessment software used in the assessments 
of  the  three  tropical  tuna  stocks.  We  used  both  reviews  to  describe  the  problems  and 
explore  potential  paths of  improvements.  Specifically, in  this  contract  we  have  explored 
simpler stock assessment models, contributed to the development of diagnostics of model 
fits, for the incorporation of statistical uncertainty on the stock assessments and explored 
alternative (simpler) spatial configurations of the stock assessment model, while exploring 
alternative biological parameters.  

One of the main objectives of the SC-workplan and of this Specific Contract was to produce 
an improved  stock  assessment for  yellowfin in the 2019  IOTC Working Party of Tropical 
Tunas (WPTT). For this aim, during 2019, different tasks of the workplan were addressed, 
some under this Specific Contract and others in collaboration with European experts, the 
IOTC and other international colleagues. The description of the proposed new assessment 
submitted  to  the  2019  WPTT  is  presented  as  a  third  main  task  of  this  report,  with  two 
additional subtasks: 

3. PROPOSAL FOR A NEW STOCK ASSESSMENT OF YELLOWFIN IN 2019 

3.1. 

The way forward of the yellowfin workplan after the 2019 WPTT 

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Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

3.2. 

Improvements to the yellowfin stock assessment during 2020 

In 2019, the WPTT agreed that the progress on the workplan was insufficient to provide a 
new management advice. The main reasons for this were the complexity of the endeavour, 
the lack of agreement on key model aspects and time constrains during the WPTT meeting, 
where the main focus  was on the assessment of bigeye.  However, the WPTT also noted 
the  substantial  work  conducted  to  address  the  workplan.  After  the  2019  WPTT  several 
opportunities were identified (subsection 3.1) but only some of them were possible during 
2020  due  to  the  Covid-19  pandemic.  During  2020,  the  work  done  in  2019  has  been 
revisited and two main improvements were made on the yellowfin assessment (subsection 
3.2).  These  consist  of  (1)  adapting  the  current  configuration  of  the  yellowfin  stock 
assessment  that  considers  quarters  as  years  to  a  more  orthodox  annual  model  that 
includes seasons and, (2) to use diagnostic tools to select a small group of models from 
the  grid  partially  agreed  by  the  WPTT  in  2019  based  on  their  plausibility.  The  resulting 
probabilistic estimate of stock status and catch advice built upon the reduced number of 
model runs was presented to the 22nd WPTT (19-24 October 2020). Also, in the last days 
of the 2020 WPTT a problem with the projections of the 2018 model were encountered, 
which  motivated  additional  works  ahead  of  the  2020  Scientific  Committee  meeting.  A 
description  of  the  problem  with  the  projections  and  the  discussions  between  the  key 
analysts working on the yellowfin assessment is also provided in sub-section 3.2.  

In a separate section, we provide a summary of the submission of deliverables, working 
documents and other milestones achieved during this contract.  

The main achievement of this Specific Contract has been a new stock assessment proposal 
for  the  Indian  Ocean  yellowfin  (Urtizberea  et  al,  2019),  but  as  stated  earlier,  further 
analyses were necessary before being agreed by the WPTT. A new proposal was submitted 
in 2020 (Urtizberea et al, 2020). Also, the project has allowed notable improvements on 
the yellowfin stock assessment and has extensively contributed to the IOTC workplan to 
improve the tropical tunas stock assessment and management advice in general. This has 
been achieved by producing eleven working documents for the WPTT, by supporting the 
organization,  attendance,  and  participation  of  EU  scientists  to  the  WPTT  and  two 
intersessional workshops and, by facilitating the coordination of scientists from the EU and 
elsewhere  towards  improving  the  stock  assessment  models.  The  progress  made  in  this 
Specific Contract has allowed the contribution of the EU scientists to the IOTC workplan for 
improvement of the scientific advice of IOTC’s WPTT.  

In this final report, we describe the main difficulties encountered and the alternative paths 
followed to address these.  In particular, we  explain that despite not achieving the main 
objective of providing a new advice on yellowfin in 2019, several aspects  of the routine 
work  of  the  WPTT  have  been  improved  thanks  to  this  contract.  Also,  we  planned  the 
attendance of one or two external experts to the 2019 WPTT. These participations were 
not possible due to last minutes rejections from the appointed experts.  

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Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

RÉSUMÉ EXECUTIF 

Ce contrat spécifique visait à améliorer l'évaluation et les conseils scientifiques actuels pour 
une pêche durable des trois thonidés tropicaux de l'océan Indien. Pour cela, le contrat a 
soutenu la contribution des scientifiques de l'UE, en étroite coordination avec le secrétariat 
de la CTOI et les scientifiques d'autres CPC, pour améliorer le travail du Groupe de Travail 
sur les Thons Tropicaux (GTTT) de la CTOI. Le présent rapport final décrit les principaux 
développements prévus dans le cahier des charges de ce contrat et les objectifs qui n'ont 
pas été atteints. Ce rapport informe également sur les difficultés rencontrées et les voies 
alternatives suivies pour y remédier. 

Les évaluations des trois stocks de thon (albacore, thon obèse et listao) sont confrontées 
à  des  problèmes  et  à  des  complexités  communes,  allant  de  l'incertitude  des  données 
(erreurs  d'observation)  et  de  la  spécification  des  modèles  (erreurs  de  processus)  à 
l'estimation fiable des paramètres (erreurs d'estimation), qui affectent de plus en plus la 
capacité du comité scientifique (SC) de la Commission des thons de l'océan Indien (CTOI) 
à fournir des avis de gestion solides. Cela a été mis en évidence dans l'évaluation faite en 
2018 du stock d'albacore, l'un des stocks les plus importants pour la CTOI. Selon le CS, de 
nombreuses  difficultés  ont  été  identifiées  dans  le  modèle  d'évaluation  du  stock,  ce  qui 
indique un faible pouvoir prédictif. Pour résoudre les problèmes rencontrés, le comité de 
surveillance  a  adopté  un  plan  de  travail  comprenant  des  tâches  intersessions,  visant  à 
fournir un nouvel avis amélioré sur l'état du stock d'albacore et sur les mesures de gestion 
à prendre pour garantir sa pêche durable.  

Les pêcheries de thons tropicaux représentent une part importante des intérêts de pêche 
de l'UE dans l'océan Indien. L'UE, en étroite coopération avec la CTOI, soutient ce plan de 
travail afin de disposer, dès 2019-2020, d'un avis scientifique amélioré et solide en faveur 
de pêcheries durables de thons tropicaux.   

Le plan de travail adopté par le comité scientifique de la CTOI définit une série de tâches 
spécifiques visant à réduire l'incertitude actuelle des modèles et des données utilisés dans 
les  récentes  évaluations  du  stock  d'albacore.  Nombre  de  ces  tâches  contribueront 
également  à  améliorer  les  avis  sur  les  trois  stocks  de  thons  tropicaux.  La  liste  suivante 
énumère  les  deux  tâches  principales  et  les  quinze  sous-tâches  prévues  dans  ce  contrat 
(numérotation définie par le cahier des charges) : 

1. INCERTITUDE DES DONNÉES 

1.1. Données de captures 

1.2. Données de marquage 

1.2.1.  Évaluer comment les données brutes sont modifiées pour l'évaluation 

1.2.2.  Recherche sur les paramètres clés 

1.2.3.  Sensibilité  des  informations  de  marquage  dans  l'évaluation  des 

stocks 

1.3. Données relatives à la taille 

1.4. Captures par unité d'effort (CPUE) 

1.4.1.  Options pour les indices alternatifs 

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1.4.2.  Protocole avancé pour les itérations communes de CPUE 

1.4.3.  CPUE pour l'ensemble de l'océan Indien et les sous-régions 

2. INCERTITUDE DU MODÈLE 

2.1.1.  Utilisation de modèles d'évaluation et de paramétrage alternatifs 

2.1.2.  Développement de diagnostics à l'appui des décisions du WPPT   

2.1.3.  Examen de l'évaluation de l'albacore (et de certains aspects du thon obèse) 

2.1.4.  Protocole pour l'incertitude statistique 

2.1.5.  Autres paramètres clés 

La  première  tâche  (Incertitude  des  données)  vise  à  examiner  les  quatre  sources  de 
données utilisées dans les évaluations des stocks d'albacore, de thon obèse et de listao. Il 
s'agit des prises, des données de marquage, des données de taille et des prises par unité 
d'effort  (CPUE).  La  tâche  1  vise  à  identifier  les  principaux  problèmes  liés  aux  données 
utilisées et, si possible, à proposer des alternatives pour les améliorer. Pour l'examen des 
problèmes  liés  aux  données,  nous  avons  basé  notre  travail  sur  les  données  statistiques 
compilées par le secrétariat de la CTOI et avons élargi les tâches en décrivant l'impact des 
différentes sources de données sur le résultat et la qualité des évaluations des stocks. Pour 
les  données  sur  les  captures,  nous  avons  étudié  l'impact  des  différentes  valeurs  de 
déclaration sur les résultats des modèles. Pour les données de marquage, nous avons mis 
en évidence l'impact important de celles-ci sur les résultats des évaluations et décrit les 
hypothèses et les préoccupations relatives à l'utilisation de ces données dans les modèles 
d'évaluation  des  stocks.  Pour  les  données  sur  la  fréquence  des  tailles,  nous  mettons  en 
évidence les problèmes actuels concernant les données sur la taille des palangres et l'état 
de la révision de ces données par un contractant externe. Pour les CPUE, nous décrivons 
également  les  indices  utilisés  dans  les  évaluations,  discutons  de  leurs  problèmes  et 
proposons des alternatives pour améliorer les évaluations des stocks.   

La  deuxième  tâche  (incertitude  du  modèle)  vise  à  explorer  d'autres  configurations  et 
options  de  modèle  qui pourraient  éventuellement  fournir  une  meilleure  estimation  de  la 
productivité et des trajectoires historiques de l'albacore et des deux autres stocks de thons 
tropicaux. Pour toutes les sous-tâches, un effort plus important a été fait pour les données 
et  les  modèles  utilisés  pour  l'albacore.  Toutefois,  la  plupart  des  tâches  et  sous-tâches 
comprennent des examens sur le thon obèse et le listao. Pour cette section, nous utilisons 
les  examens  de  deux  experts  en  évaluation  des  stocks  pour  le  modèle  d'évaluation  des 
stocks d'albacore. Le premier est l'examinateur externe qui a été invité au GTTT en 2018 
et le second  est le principal développeur de Stock Synthesis, le logiciel d'évaluation des 
stocks utilisé dans les évaluations des trois stocks de thon tropical. Nous utilisons les deux 
examens pour décrire les problèmes  et explorer les pistes d'amélioration possibles. Plus 
précisément,  dans  le  cadre  de  ce  contrat,  nous  avons  exploré  des  modèles  d'évaluation 
des  stocks  plus  simples,  contribué  au  développement  de  diagnostics  d'ajustement  des 
modèles,  à  l'incorporation  de  l'incertitude  statistique  dans  les  évaluations  des  stocks  et 
exploré d'autres configurations spatiales (plus simples) du modèle d'évaluation des stocks 
en même temps que d'autres paramètres biologiques.  

L'un des principaux objectifs du plan de travail du SC et du présent contrat spécifique était 
de produire une évaluation améliorée du stock d'albacore dans le cadre du groupe de travail 
sur  les  thonidés  tropicaux  (GTTT)  de  la  CTOI  en  2019,  si  possible.  Pour  cela,  en  2019, 

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différentes tâches du plan de travail ont été abordées, certaines dans le cadre de ce contrat 
spécifique  et  d'autres  en  collaboration  avec  des  experts  européens,  la  CTOI  et  d'autres 
collaborateurs internationaux. La description de la nouvelle évaluation proposée soumise 
au  GTTT  2019  est  présentée  comme  une  troisième  tâche  principale  de  ce  rapport,  avec 
deux sous-tâches supplémentaires : 

3. PROPOSITION D'UNE NOUVELLE ÉVALUATION DU STOCK D'ALBACORE EN 2019 

3.1. Voie à suivre pour le plan de travail relatif à l'albacore après le GTTT de 2019 

3.2. Améliorations de l'évaluation du stock d'albacore en 2020 

En 2019, le GTTT a convenu que les progrès du plan de travail étaient insuffisants pour 
fournir un nouvel avis de gestion en 2019. Les principales raisons en sont la complexité de 
la tâche, l'absence d'accord sur les aspects clés du modèle et les contraintes de temps lors 
de la réunion du GTTT, où l'accent a été mis sur l'évaluation du thon obèse. Toutefois, le 
GTTT  a  également  pris  note  du  travail  considérable  effectué  pour  répondre  au  plan  de 
travail.  Après  le  GTTT  de  2019,  plusieurs  opportunités  ont  été  identifiées  (sous-section 
3.1), mais seules certaines d'entre elles ont été possibles en 2020 en raison de la pandémie 
de  Covid-19.  En  2020,  le  travail  effectué  en  2019  a  été  revu  et  deux  améliorations 
principales  ont  été  apportées  à  l'évaluation  de  l'albacore  (sous-section  3.2).  Elles 
consistent à (1) adapter la configuration actuelle de l'évaluation du stock d'albacore qui 
considère les trimestres comme des années à un modèle annuel plus orthodoxe qui inclut 
les  saisons  et,  (2)  utiliser  des  outils  de  diagnostic  pour  sélectionner  un  petit  groupe  de 
modèles  dans  la  grille  partiellement  convenue  par  le  GTTT  en  2019  en  fonction  de  leur 
plausibilité.  L'estimation  probabiliste  de  l'état  des  stocks  et  les  avis  de  capture  qui  en 
résultent, basés sur le nombre réduit de passages du modèle, ont été présentés au 22e 
GTTT (19-24 octobre 2020). De plus, dans les derniers jours du GTTT 2020, un problème 
avec  les  projections  du  modèle  2018  a  été  rencontré,  ce  qui  a  motivé  des  travaux 
supplémentaires  avant  la  réunion  du  Comité  scientifique  2020.  Une  description  du 
problème des projections et des discussions entre les principaux analystes travaillant sur 
l'évaluation de l'albacore est également fournie dans la sous-section 3.2.  

Dans  une  section  distincte,  nous  fournissons  un  résumé  de  la  présentation  des  produits 
livrables, des documents de travail et des autres étapes franchies au cours de ce contrat.  

La principale réalisation de ce contrat spécifique a été une nouvelle proposition d'évaluation 
des stocks d'albacore de l'océan Indien (Urtizberea et al, 2019), mais comme nous l'avons 
dit  précédemment,  des  analyses  supplémentaires  ont  été  nécessaires  avant  d'être 
approuvées par le GTTT. Une nouvelle proposition a été soumise en 2020 (Urtizberea et 
al, 2020). En outre, le projet a permis d'améliorer considérablement l'évaluation du stock 
d'albacore  et  a  largement  contribué  au  plan  de  travail  de  la  CTOI  visant  à  améliorer 
l'évaluation du stock de thon tropical et les avis de gestion en général. Cela a été réalisé 
en  produisant  onze  documents  de  travail  pour  le  GTTT,  en  soutenant  l'organisation,  la 
présence et la participation de scientifiques de l'UE au GTTT et à deux ateliers intersessions 
et en facilitant la coordination des scientifiques de l'UE et d'ailleurs en vue d'améliorer les 
modèles d'évaluation des stocks. Les progrès réalisés dans le cadre de ce contrat spécifique 
ont  permis  la  contribution  des  scientifiques  de  l'UE  au  plan  de  travail  de  la  CTOI  pour 
l'amélioration des avis scientifiques du GTTT de la CTOI.  

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Nous décrivons également les principales difficultés rencontrées et les voies alternatives 
suivies pour y remédier. En particulier, nous expliquons que, bien que l'objectif principal 
de fournir un nouvel avis sur l'albacore en 2019 n'ait pas été atteint, plusieurs aspects du 
travail de routine du GTTT ont été améliorés grâce à ce contrat. Nous décrivons également 
comment nous avons prévu un atelier spécifique pour discuter des avancées avant le GTTT 
de 2019, mais que cela n'a pas été possible en raison du manque de dates appropriées 
entre  l'achèvement  du  nouveau  modèle  d'évaluation  des  stocks  proposé  en  2019  et  le 
GTTT. Au lieu de cela, avec ce contrat, nous avons soutenu la participation de scientifiques 
de  l'UE  à  deux  ateliers,  l'un  sur  les  CPUE  des  palangriers  (28  avril-3  mai  2019,  San 
Sebastian,  Espagne)  et  l'autre  sur  l'élaboration  d'un  indice  de  CPUE  pour  la  flotte  de 
senneurs de l'UE (27-30 mai 2019, San Sebastian, Espagne). Nous avons également prévu 
la participation d'un ou deux experts externes au GTTT 2019. Ces participations n'ont pas 
été  possibles  en  raison  de  refus  de  dernière  minute  des  experts  désignés.  Le  budget 
initialement prévu pour l'atelier et la participation des experts internationaux a été utilisé 
pour augmenter la participation des  scientifiques de l'UE au  GTTT 2019 (21-26 octobre, 
San  Sebastian,  Espagne).  En  outre,  en  2020,  la  pandémie  de  Covid-19  a  réduit  les 
possibilités identifiées de participer aux ateliers internationaux et au Congrès mondial de 
la pêche en 2020. 

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RESUMEN EJECUTIVO 

Este  contrato  específico  ha  tenido  como  objetivo  mejorar  la  evaluación  y  el  consejo 
científico actuales para la pesca sostenible de los tres atunes tropicales del Océano Índico. 
Para  ello,  el  contrato  ha  apoyado  la  contribución  de  científicos  de  la  UE,  en  estrecha 
coordinación  con  la  Secretaría  de  la  Comisión  del  Atún  del  Océano  Índico  (IOTC)  y  los 
científicos de otras CPC, para mejorar la labor del Grupo de Trabajo de Túnidos Tropicales 
de  la  IOTC  (WPTT).  En  el  presente  informe  final  se  exponen  las  principales  novedades 
previstas en los Términos de Referencia (ToRs) de este contrato y los objetivos que no se 
han alcanzado. Este informe también informa sobre las dificultades encontradas y las rutas 
alternativas seguidos para abordarlas. 

Las  evaluaciones  de  las  tres  poblaciones  de  atún  tropical  (rabil,  patudo  y  listado)  se 
enfrentan a problemas y complejidades comunes, que van desde la incertidumbre en los 
datos (errores de observación) y la especificación del modelo (errores de proceso) hasta 
la estimación fiable de los parámetros (errores de estimación), que afectan cada vez más 
a la capacidad del Comité Científico (SC) de la IOTC para proporcionar un consejo robusto 
en materia de ordenación. Esto fue puesto en relieve en la evaluación de la población de 
rabil (yellowfin) en 2018, una de las poblaciones más importantes para la IOTC. Según el 
SC, se identificaron problemas en el modelo de evaluación de poblaciones que indicaban 
un bajo poder de predicción. Para abordar estos problemas, el Comité Directivo adoptó un 
plan de trabajo que incluía tareas intersesionales, destinadas a proporcionar un nuevo y 
mejor consejo sobre la situación de la población de rabil y sobre las medidas de ordenación 
para garantizar su explotación sostenible.  

La pesca del atún tropical constituye una parte sustancial de los intereses pesqueros de la 
Unión  Europea  en  el  Océano  Índico.  La  UE,  en  estrecha  cooperación  con  la  IOTC,  ha 
apoyado este plan de trabajo con miras a disponer, ya en 2019-2020, de un asesoramiento 
científico mejorado y sólido en apoyo de la pesca sostenible del atún tropical.   

En el plan de trabajo adoptado por la IOTC-SC se definen una serie de tareas concretas 
para  reducir  la  actual  incertidumbre  en  los  modelos  y  datos  utilizados  en  las  recientes 
evaluaciones  de  las  poblaciones  de  rabil.  Muchas  de  esas  tareas  contribuirán  también  a 
mejorar  el  consejo  de  las  tres  poblaciones  de  atún  tropical.  En  la  siguiente  lista  se 
enumeran las dos tareas principales y las quince subtareas programadas en este contrato 
(numeración definida por los ToRs): 

1. INCERTIDUMBRE EN LOS DATOS 

1.1. Datos de captura 

1.2. Datos de programas de marcado 

1.2.1.  Evaluar cómo se tratan los datos originales para la evaluación 

1.2.2.  Investigar parámetros clave 

1.2.3.  Sensibilidad  de  las  evaluaciones  de  poblaciones  a  los  datos  de 

marcado. 

1.3. Datos de talla 

1.4. Captura por Unidad de Esfuerzo (CPUE) 

1.4.1.  Opciones de índices alternativos 

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1.4.2.  Protocolo avanzado para las iteraciones de CPUE conjuntas 

1.4.3.  CPUEs para todo el Océano Índico y sus subregiones 

2. INCERTIDUMBRE EN EL MODELO 

2.1.1.  Utilización de modelos de evaluación alternativos y ajustes de parámetros 

2.1.2.  Desarrollo de diagnósticos en apoyo de las decisiones del WPTT  

2.1.3.  Examen de la evaluación de rabil (y algunos aspectos del patudo) 

2.1.4.  Protocolo para caracterizar la incertidumbre estadística 

2.1.5.  Parámetros alternativos 

La primera tarea (Incertidumbre en los datos) tiene por objeto examinar las cuatro fuentes 
de datos utilizadas en las evaluaciones de las poblaciones de rabil, patudo y listado. Éstas 
incluyen la captura, los datos de marcado, los datos de talla y la captura por unidad de 
esfuerzo (CPUE). La Tarea 1 tiene por objeto identificar los principales problemas con los 
datos utilizados y, cuando sea posible, ofrecer alternativas para mejorarlos. Para el examen 
de  las  cuestiones  relativas  a  los  datos,  hemos  basado  nuestro  trabajo  en  los  datos 
estadísticos compilados por la Secretaría de la IOTC y ampliado las tareas describiendo el 
impacto de las diferentes fuentes de datos en el resultado y la calidad de las evaluaciones 
de las poblaciones. Para los datos de captura hemos explorado el impacto de los diferentes 
valores de notificación en los resultados del modelo. Para los datos de marcado destacamos 
el gran impacto de éstos en los resultados de las evaluaciones y describimos los supuestos 
y dudas sobre el uso de estos datos en los modelos de evaluación de poblaciones. Para los 
datos de frecuencia de talla, destacamos los problemas actuales con los datos de talla de 
las flotas de palangre y el estado de la revisión de estos datos a través de un externo. Para 
la  CPUE  también  describimos  los  índices  usados  en  las  evaluaciones,  discutimos  sus 
problemas y proporcionamos alternativas para mejorar las evaluaciones.   

La segunda tarea (Incertidumbre en el modelo) tiene por objeto explorar configuraciones 
alternativas y opciones de modelos que podrían proporcionar una mejor estimación de la 
productividad y las trayectorias históricas del rabil y de las otras dos poblaciones de atún 
tropical. En todas las subtareas se ha hecho un mayor esfuerzo en relación con los datos 
y los modelos utilizados para el rabil. Sin embargo, la mayoría de las tareas y subtareas 
incluyen  análisis  sobre  el  patudo  y  el  listado.  En  esta  sección  se  utilizan  las  revisiones 
proporcionadas por dos expertos en evaluación de poblaciones del modelo de evaluación 
del rabil. El primero fue el revisor invitado al WPTT en 2018 y, el segundo es el principal 
desarrollador de Stock  Synthesis, el programa informático de  evaluación de poblaciones 
utilizado  en  las  evaluaciones  de  las  tres  poblaciones  de  túnidos  tropicales.  Utilizamos 
ambos  exámenes  para  describir  los  problemas  y  explorar  opciones  de  mejora. 
Específicamente, en este contrato hemos explorado modelos de evaluación de poblaciones 
más  sencillos,  contribuido  al  desarrollo  de  diagnósticos  de  los  ajustes  del  modelo,  a  la 
incorporación  de  la  incertidumbre  estadística  en  las  evaluaciones  de  las  poblaciones  y 
explorado  configuraciones  espaciales  alternativas  (más  sencillas)  del  modelo  de 
evaluación. Además, hemos explorado parámetros biológicos alternativos para los modelos 
de evaluación.  

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Uno de los principales objetivos del plan de trabajo del SC y de este Contrato Específico 
fue  producir  una  evaluación  mejorada  de  la  población  de  aleta  amarilla  en  el  Grupo  de 
Trabajo de Atún Tropical (WPTT) de la IOTC en 2019. Para ello, durante 2019, se abordaron 
diferentes  tareas  del  plan  de  trabajo,  algunas  bajo  este  Contrato  Específico  y  otras  en 
colaboración con expertos europeos, la IOTC y otros colegas internacionales. La descripción 
de la nueva evaluación propuesta presentada al WPTT 2019 se presenta como una tercera 
tarea principal de este informe, con dos subtareas adicionales: 

3. PROPUESTA DE UNA NUEVA EVALUACIÓN DE LA POBLACIÓN DE RABIL EN 2019 

3.1.  El camino a seguir en el plan de trabajo rabil después del WPTT de 2019 

3.2.  Mejoras en la evaluación de la población de aleta amarilla durante 2020 

En 2019, el WPTT convino en que los avances en el plan de trabajo eran insuficientes para 
proporcionar un nuevo consejo de gestión en 2019. Las principales razones de ello fueron 
la  complejidad  de  la  tarea,  la  falta  de  acuerdo  sobre  aspectos  clave  del  modelo  y  las 
limitaciones de tiempo  durante la reunión del WPTT, que se centró principalmente en la 
evaluación del patudo. Sin embargo, el WPTT también tomó nota de la importante labor 
realizada en el plan de trabajo. Después de la reunión del WPTT de 2019 se identificaron 
varias oportunidades (subsección 3.1), pero sólo algunas de ellas fueron posibles durante 
2020 debido a la pandemia de Covid-19. Durante 2020 se revisó la labor realizada en 2019 
y se introdujeron dos mejoras principales en la evaluación del rabil (subsección 3.2). Éstas 
consisten en: 1) adaptar la configuración actual de la evaluación de la población de aleta 
amarilla  que  considera  los  trimestres  como  años  a  un  modelo  anual  más  ortodoxo  que 
incluye estaciones y, 2) utilizar herramientas de diagnóstico para seleccionar un pequeño 
grupo de los modelos acordados por el WPTT en 2019 sobre la base de su verosimilitud. 
La estimación probabilística resultante de la situación de las poblaciones y el consejo sobre 
capturas, basada en el reducido número de aplicaciones del modelo, se presentó en la 22ª 
reunión del WPTT (19 a 24 de octubre de 2020). Además, en los últimos días del WPTT de 
2020 se encontró un problema con las proyecciones del modelo de 2018, lo que motivó 
trabajos adicionales antes de la reunión del Comité Científico de 2020. En la subsección 
3.2  se  describe  también  el  problema  con  las  proyecciones  y  las  discusiones  entre  los 
principales analistas que trabajan en la evaluación del rabil.  

En una sección independiente, se ofrece un resumen de la presentación de los resultados, 
documentos de trabajo y otros hitos alcanzados durante este contrato.  

El principal logro de este contrato específico ha sido una nueva propuesta de evaluación 
de poblaciones de aleta amarilla del Océano Índico (Urtizberea et al., 2019), pero como se 
dijo anteriormente, el WPTT consideró que fue insuficiente para que el WPTT lo aprobara. 
En 2020 se presentó una nueva propuesta (Urtizberea et al, 2020). Además, el proyecto 
ha permitido mejoras notables en la evaluación de las poblaciones de aleta amarilla y ha 
contribuido ampliamente al plan de trabajo de la IOTC para mejorar la evaluación de las 
poblaciones de atunes tropicales y el consejo en materia de ordenación en general. Esto 
se ha logrado mediante la elaboración de once documentos de trabajo para  el WPTT,  el 
apoyo a la organización, la asistencia y la participación de científicos de la UE en el WPTT 
y dos talleres intersesionales y la facilitación de la coordinación de científicos de la UE y de 
otros lugares para mejorar los modelos de evaluación de las poblaciones. Los progresos 
realizados en este contrato específico han permitido la contribución de los científicos de la 
UE al plan de trabajo de la IOTC para mejorar el consejo científico del WPTT. 

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También  describimos  las  principales  dificultades  encontradas  y  los  caminos  alternativos 
seguidos para abordarlas. En particular, explicamos que, a pesar de no haber logrado el 
objetivo  principal  de  proporcionar  un  nuevo  consejo  sobre  el  rabil  en  2019,  se  han 
mejorado varios aspectos del trabajo rutinario del WPTT gracias a este contrato. Asimismo, 
describimos  cómo  programamos  un  taller  específico  para  discutir  los  avances  antes  del 
WPTT de 2019, pero  esto no fue posible debido a la falta de fechas disponibles entre la 
finalización del nuevo modelo de evaluación de poblaciones propuesto en 2019 y el WPTT. 
En su lugar, con este contrato apoyamos la participación de los científicos de la UE en dos 
talleres, uno el taller conjunto de CPUE de palangre (28 de abril a 3 de mayo de 2019, San 
Sebastián, España) y el otro el taller para desarrollar un índice de CPUE para la flota de 
cerco de la UE (27 a 30 de mayo de 2019, San Sebastián, España). Además, planeamos la 
participación de uno o dos expertos externos al WPTT de 2019. Estas participaciones no 
fueron  posibles  debido  a  los  rechazos  de  última  hora  de  los  expertos  designados.  El 
presupuesto  inicialmente  previsto  para  el  taller  y  la  participación  de  los  expertos 
internacionales se utilizó para aumentar la participación de los científicos de la UE en el 
WPTT de 2019 (21-26 de octubre, San Sebastián, España). Además, en 2020 la pandemia 
de  Covid-19  ha  reducido  las  oportunidades  identificadas  de  participar  en  los  talleres 
internacionales y en el Congreso Mundial de Pesca de 2020. 

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bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

INTRODUCTION 

General introduction to the Specific Contract 

The  assessment  of  the  three  most  important  Indian  Ocean  tropical  tuna  stocks  faces 
common  problems  and  complexities,  ranging  from  uncertainty  in  the  data  (observation 
errors)  and  model  specification  (process  errors)  to  reliable  estimation  of  parameters 
(estimation errors), that are increasingly affecting the capacity of the Indian Ocean Tuna 
Commission (IOTC) Scientific Committee (SC) to provide robust management advice. 

A  recent  example  is  the  scientific  advice  provided  during  the  21th  SC  of  the  IOTC 1 
(December  2018).  Although  the  SC  concluded  that,  the  yellowfin  stock  is  currently 
overfished and  subject  to overfishing, they were not able to provide scientific advice on 
the catch limits that would recover the stock back to sustainable levels. For this, it would 
have  been  necessary  to  estimate  the  probability  of  recovery  of  the  stock  for  a  range  of 
catch limits using model projections. According to the SC, the projections produced in 2018 
did not adequately reflect the known sources of uncertainty due to a series of issues with 
data and model performance.  

In  order  to  address  the  detected  shortfalls  in  the  2018  yellowfin  assessment  and  for 
improving  the  stock  assessment  and  the  consequent  scientific  advice  to  be  provided  in 
2019,  the  IOTC-SC  considered  it  necessary  to  carry  out  a  series  of  intersessional  tasks 
during 2019. For this, the IOTC-SC adopted a  preliminary workplan which needed to be 
carried out through short-term supporting actions. 

Tropical tuna fisheries are a substantial part of EU fishing interests in the Indian Ocean. It 
was  therefore  important  that  the  EU,  in  close  cooperation  with  IOTC,  supported  this 
workplan  with  a  view  to  have,  already  in  2019  (or  in  2020),  an  improved  and  robust 
scientific advice in support of sustainable tropical tuna fisheries.   

The  workplan adopted  by the  IOTC-SC identifies a  series  of specific tasks to reduce the 
current uncertainty in models and data used in the recent stock assessments of yellowfin. 
Many of these tasks will also contribute to improving advice for other tropical stocks too.  

This report provides information about the performed activities, achievements, the main 
challenges  encountered  and  a  description  of  how  the  tropical  tuna  advice  has  improved 
with the workplan. 

The main objective of this SC has been to support the contribution of EU scientists, in close 
coordination  with  the  IOTC  Secretariat  and  scientists  from  other  CPCs,  to  the  IOTC 
workplan for yellowfin. The initial schedule of this SC and the yellowfin workplan aimed at 
producing this new assessment for yellowfin in the 2019 IOTC Working Party of Tropical 
Tunas  (WPTT)  and  any  further  refinements  and/or  integrations  were  to  be  presented  to 
the 2020 WPTT.  

For this purpose, during 2019, different tasks of the workplan were addressed, some under 
this  Specific  Contract  and  others  in  collaboration  with  European  experts,  the  IOTC  and 
other international colleagues. However, the 2019 WPTT agreed that the progress on the 
workplan was insufficient to provide a new management advice. The main reasons being 

1 https://iotc.org/meetings/21st-scientific-committee-sc21 

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Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

the complexity of the endeavour, the lack of agreement on key model aspects and time 
constrains during the WPTT meeting. However, the WPTT also noted the substantial work 
conducted to address the workplan.  

In 2020, despite the impact of the COVID-19 pandemic, additional efforts were placed in 
refining the works presented in 2019. The major changes to the model have been (1) to 
adapt the current configuration of the yellowfin stock assessment that considers quarters 
as years to a more orthodox annual model that includes seasons and, (2) to use diagnostic 
tools to select a small group of models from the grid partially agreed by the WPTT in 2019 
based on their plausibility. The  resulting probabilistic estimate of stock status and catch 
advice built upon the reduced number of model runs was presented to the 22nd WPTT (19-
24 October 2020). Also, in 2020, a technical problem with the projections produced in 2018 
was understood and presented to the group. Overall, during this meeting the progress on 
yellowfin tuna was noted but it was seen as work in progress for a better stock assessment 
model in 2021 and not as work that could lead to any change on the current management 
advice. 

As  said,  the  main  objective  of  this  Specific  Contract  has  been  to  contribute  to  SC’s 
endeavour  to  improve  the  latest  yellowfin  assessment  and  to  structurally  improve  the 
assessments  of  the  three  tropical  tuna  stocks.  In  this  line  of  work,  we  have  aimed  at 
proposing  improvements  and  alternatives  to  the  current  stock  assessment  modelling 
approaches.  This  has  included  a  new  stock  assessment  proposal  and  a  series  of 
methodological improvements that have been incorporated to the WPTT work routine. The 
latest includes the use of diagnostics of fits to ensure the quality of the WPTT outputs, a 
method to provide an estimate of the statistical uncertainty inherent to the assessments 
and the identification of common issues in the use of the available information in the IOTC. 

To achieve the objectives of this project, a series of tasks and sub-tasks were designed: 

TASK 0: PROJECT MANAGEMENT 

TASK 1: UNCERTAINTY IN DATA  

1.1. 

Catch  data:  explore  scenarios  of  various  catch  reporting  methods  to 
improve  data  quality  and  provide  their  strengths  and  weaknesses  in  terms  of 
efficiency, effectiveness and feasibility.  

1.2. 

Tagging  data:  review  the  tagging  data  and  reassess  the  scientific 
publications that estimated tag mortality used in the recent yellowfin and skipjack 
assessments.  Critical  outputs  of  the  review  and  justifications  of  possible 
alternative  estimates  shall  be  provided  and  duly  commented  based  on  their 
biological and statistical foundation:  
1.2.1. Evaluate how the raw data from the tagging programs is modified to address 
mixing,  tag loss,  reporting  rates  etc.  in  order  to  make  them  available  for  the 
stock assessment model. If necessary, propose changes to the current practice 
and provide their biological and statistical justifications and interpretations.  
1.2.2. Investigate  key  parameters,  in  particular  tag-release  mortality,  and 
recommend  values  for  the  three  tropical  tuna  stocks:  yellowfin,  bigeye,  and 
skipjack. Their biological and statistical justifications shall be provided.  

1.2.3. Analyse  the  information  provided  from  tagging  data  on  movements  and 
biomass  of  different  regions  of  the  Indian  Ocean  including  the  sensitivity  of 

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bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

tagging  information  on  the  stock  assessment.  The  biological  and  statistical 
justifications and interpretations shall be provided.  

1.3. 

Size  data:  Review  of  size-frequency  data  and  comment  as  adequate  for 

further improvements of their quality and coverage. 

1.4. Catch per Unit of Effort (CPUE):  

1.4.1. Review and explore options for alternative series of CPUE for different fleets: 
purse  seine,  longline  and  other  fishing  gears.  The  biological,  technical  and 
statistical justifications and interpretations shall be provided.  

1.4.2. Develop an advanced protocol for joint CPUE iterations.  
1.4.3. Calculate  CPUEs  for  tuna  in  the  Indian  Ocean  as  well  as  for  relevant  sub-
regions considered in the stock assessment model. The biological, technical and 
statistical justifications and interpretations shall be provided.  

TASK 2: MODEL UNCERTAINTY  

2.1. 

key 

models: 

uncertainty 

Alternative/Complementary 

inputs- 
characterization and control of structural uncertainty integration amongst models. 
Use  alternative  assessment  models  and  parameter  settings  (biomass  dynamic, 
age/size structured, fully integrated) and explore the feasibility of integrating them 
with a view to provide guidance and solutions on how characterizing and managing 
structural  uncertainty  as  well  as  on  how  combining  across  models’  assumptions 
and  outputs.  The  rationale  of  the  different  assessment  models  and  parameter 
settings,  as  well  as  of  their  integration,  shall  be  duly  commented  and  justified 
based on their biological and statistical implications.  

2.2. 

Develop  a  suite  of  diagnostics,  including  graphic  plots,  to  be  used  by  the 
Working Party of Tropical Tunas (WPTT) and support the following (The rationale 
underpinning the different diagnostics shall be provided and duly justified):  
2.2.1. Deciding on SS3 alternatives  
2.2.2. Integrating models ensemble (mpb, JABBA, VPA, SS3 etc.)  
2.2.3. Decision on alternative options 

2.3. Review the current assessment structure for the yellowfin (and if possible, bigeye) 
and identify potential weaknesses and improvements on key uncertainty inputs (e.g. 
spatial  structure,  weighting  data  sources,  key  parameters  inputs  etc.).  Specific 
attention shall be given to the quality check of the SS3 model set-up and assumptions 
therein.  The  weaknesses  and  improvements  shall  be  listed  and  provided  together 
with their biological and statistical justifications.  

2.4. Develop and provide a protocol to explore statistical uncertainty in the model in an 
efficient manner. In that respect, the possibility to use simpler versions of SS3, that 
incorporates only the data/processes with the lowest associated uncertainty, shall be 
considered. 

2.5. Investigate  and  use  alternative  values  for  key  parameters  (e.g.  growth,  natural 
mortality  and  others).  The  alternative  values,  together  with  the  effect  of  their 
changes in the model outputs, shall be duly commented and justified based on their 
biological and ecological justifications.  

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bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

Workplan timings 

The schedule of the work done during the project is detailed in Table 1. It follows the tender 
specifications and the extension agreed in April 2020 with MARE. The initial duration of the 
contract was 12 months until April 2020. The  interim report was submitted on time but 
after  the  (first)  Final  meeting  (through  videoconference)  it  was  agreed  to  postpone  the 
submission of the Final Report to improve the then limited achievements while securing 
the  advances  made  during  2020  and  the  feedback  of  the  WPTT  in  2020.  Therefore,  the 
initial schedule has been modified so that a draft Final Report has been submitted by the 
1st  of  October.  After  feedback  from  DG  MARE  and  the  WPTT,  a  revised  Final  Report  is 
hereby submitted in the requested timeline (January 2021).  

Table 2. Timing agreed for this Specific Contract 14 (after review in April 2020). Note that after the 
review to the draft final report, a new version was submitted in January 2021. 

Tasks

Tasks content

Task 0

Task 1

Task 2

Meetings

Kick-off

Interim

Final

Reports 

Project 
management
Uncertainty
Data
Uncertainty on 
Model

on

Brussels

TBD

Brussels

Inception report

Draft Interim report

Interim Report

Draft Final Report and Executive
Summary

Final Report & Executive Summary

May

Jun

Jul

Aug

Sept

Oct

Nov

Dec

Jan

Feb Mar

April May

2019

2020
Jun

Jul

Aug

Sept

Oct

Nov

 (*)

 (*)

 (*)

15th

15th

Objective and structure of the Draft Final Report 

The  main  objective  of  the  Final  Report  is  to  describe  the  outputs  achieved  through  the 
different tasks and sub-tasks. We also inform about the main difficulties encountered and 
how the projects’ contribution is expected to continue even after its completion. 

This  project  was  organized  in  two  main  tasks  (Uncertainty  on  Data  and  Uncertainty  on 
Models).  Progress  on  each  task  has  been  submitted  to  EASME  in  deliverables  (three  for 
each  task).  These  have  included  reviews  on  the  current  issues  with  data  and  models 
(deliverables 1.1. and 2.1), proposals for improvements for yellowfin in data and models 
(deliverables 2.1 and 2.2), and an overview of data and model issues regarding tropical 
tunas in the IOTC in general (deliverables 1.3 and 2.3). Now, we present a major part of 
the  information  contained  in  the  deliverables  in  this  report  organized,  again,  in  the  two 
tasks of this project. 

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bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

1.  UNCERTAINTY IN DATA 

The objective of this task was to review, and when possible to improve, the current data 
used in the yellowfin stock assessments and the other two tropical tuna stocks (bigeye and 
skipjack). This task includes four types of data used in stock assessments: catch data, size 
frequency data, catch per unit of effort (CPUE), and tagging data. Also, in this section we 
review the current data issues for yellowfin, bigeye and skipjack separately. Furthermore, 
during  the  project  we  proposed  several  improvements  to  the  data  used  in  the  yellowfin 
stock assessment (some of them applicable to other stocks as well). We have organized 
this section in four subtasks (one per each source of data) and we make specific reference 
to the stocks in each of them. 

1.1.  Catch data: 

Catch data known as nominal catch is submitted by each Contracting and Cooperating Non-
Contracting  Party  (CPC)  to  the  IOTC  Secretariat.  Nominal  catches  are  the  total  annual 
retained catches (in live weight) and discards (in live weight and number), estimated per 
fleet, IOTC Area, gear, and year. Tropical tunas represent around 60% of the yearly tuna 
and tuna-like species catches in the IOTC area of competence (Figure 1). In 2018, catches 
from tuna and tuna like species reached an historical record and tropical tunas were very 
close  to  its  historical  highest  levels  too.  In  the  most  recent  years,  a  large  proportion  of 
tropical tunas catch correspond to skipjack (49.4%) and yellowfin (41.2%), while bigeye 
catch represents 9.4%.  

Figure 3. Contribution of the three tropical tuna species under the IOTC mandate to the total catches 
of IOTC species in the Indian Ocean, over the period 1950-2018 (a. Top left: total catch; b. Top right 
percentage, same  colour  key  as  Fig. 1a);  Bottom:  Contribution  of each tropical  tuna  species  to  the 

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bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

total combined catches of tropical tunas (c. Bottom left: nominal catch of each species, 1950-2019; 
d. Bottom right: share of tropical tuna catch by species, 2014-2018).  

With  regards  to  the  contribution  of  individual  countries  to  tropical  tuna  catches,  two 
European  countries (Spain and France) are at  the top  of the list, contributing with 15% 
and 7% of total tropical tuna catches respectively (Figure 2), both from industrial purse 
seine fleets (IOTC, 2019c).  

Figure 4. All tropical tunas: average catches in the Indian Ocean over the period 2014 – 18, by country. 
Countries  are  ordered  from  left  to  right,  according  to  the  importance  of  catches  of  tropical  tunas 
reported.  The  dark  line  indicates  the  (cumulative)  proportion  of  catches  of  tropical  tunas  for  the 
countries  concerned,  over  the  total  combined  catches  of  species  reported  from  all  countries  and 
fisheries. 

IOTC  Statistics  differentiate  catch  from  artisanal  and  industrial  fishing  fleets:  Artisanal 
fisheries  are  defined  by  FAO  as  traditional  fisheries  involving  fishing  households  (as 
opposed to commercial companies), using relatively small amount of capital and energy, 
relatively  small  fishing  vessels  (if  any)  and  making  short  fishing  trips,  close  to  shore. 
Artisanal  fisheries  are  sometimes  referred  to  as  small  scale  fisheries  and  they  can  be 
subsistence  or  also  commercial  fisheries,  providing  generally  for  local  consumption  but 
sometimes also for export. Industrial fisheries use big boats with high level technology and 
investment  and  can  make  longer  fishing  trips.  These  can  include  both  foreign  and  local 
fleets (www.worldfish.org). In the IOTC glossary there is not a specific definition for these 
terms. However, there is a description for Coastal fisheries which are defined as any fishing 
activity undertaken by a fishing vessel, including small-scale fishing for subsistence or local 
markets, that is not required to be registered on the IOTC Record of Authorized Vessels, 
that targets tuna and tuna-like species or catches tuna and tuna-like species as bycatch 
and operates  exclusively in the waters under the jurisdiction  of the flag State, including 
sport fishing (FAO Term Portal, accessed in 2021). 

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General issues with catch data 

Sometimes catch is not reported to the Secretariat adequately, which can potentially cause 
issues  in  the  robustness/reliability  of  the  stock  assessments.  The  IOTC  Secretariat 
summarized  the  main  data  issues  related  to  the  statistics  of  tropical  tuna  species  in  its 
report  submitted  to  the  WPTT  (IOTC,  2019c).  When  catch  data  are  not  reported 
adequately, the Secretariat estimates a total catch from a range of sources which include:  

1) partial catch and effort data;  

2) data in the FAO FishStat database;  

3)  catches  estimated  by  the  IOTC  Secretariat  from  data  collected  through  port 
sampling;  

4) data published through web pages; 

5)  data  reported  by  parties  on  the  activity  of  vessels  under  their  flag  (IOTC 
Resolution 10/08; IOTC Resolution 12/05) or other flags (IOTC Resolution 14/05; 
IOTC Resolution 05/03);  

6)  data  on  imports  of  bigeye  tuna  from  vessels  under  the  flag  concerned  (IOTC 
Resolution 01/06);  

7) data on imports of tropical tunas from canning factories collaborating with the 
International Seafood Sustainability Foundation (IOTC, 2018b).  

Therefore, the catch data that is used in stock assessments to a certain extent is estimated 
or adjusted by the Secretariat. The IOTC report on data and statistics (IOTC, 2019c) scores 
catch data quality with different values between 0 and 8. Scores of 2 to 6 indicating nominal 
catches  associated  to  datasets  partially  reported  (i.e.,  adjusted  by  the  Secretariat)  and 
score 8 indicating catch data associated to a fishery that is fully estimated by the  IOTC 
Secretariat or that is not available. For example, in 2019, the scores of the total catch of 
2019 for the three tropical tuna stocks is shown in Table 2. For yellowfin, 82% of the 2019 
catch is considered fully reported. 

Score

Meaning

0
2
4
6
8

fully reported

partially reported

not available 

tons
51988
11148
15171
0.53
105

BET

SKJ

YFT

%
66%
14%
19%
0.001%
0.134%

tons
489814
17875
79133
78
12549

%
82%
3%
13%
0.01%
2%

tons
374542
20608
27014
18063
17564

%
82%
5%
6%
4%
4%

Table 5. Reporting category of total catch and % of catch of the three tropical tuna stocks in 2019.  

With  regards  to  reported  nominal  catch,  the  WPTT  in  2019  noted  some  problems  with 
bigeye and yellowfin and some, fewer, issues with skipjack catch reporting. These issues 
are further elaborated in the following paragraphs. 

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bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

Overall, the IOTC Secretariat considers that nominal catches are generally well known for 
the major industrial fisheries, with the proportion of catches estimated, or adjusted, by the 
IOTC Secretariat relatively low. However, in the recent years, issues have also been found 
with  purse  seine  and  longline  fleets’  statistics.  With  regards  to  artisanal  fleets,  which 
account for the 50% of the catches, there are still unresolved issues with regards to their 
historical contribution to tropical tuna catches. 

Specific issues with Indian Ocean yellowfin 

In recent years yellowfin catches have been evenly split between industrial and artisanal 
fisheries.  Industrial  fleets  (Purse  seiners  (free  and  associated  schools)  and  longline 
fisheries) still account for around 50% of total catches, while catches from artisanal gears 
– namely handline, gillnet, and pole-and-line – have steadily increased since the 1980s. 
Contrary  to  other  oceans,  the  artisanal  fishery  yellowfin  catch  component  in  the  Indian 
Ocean  is  substantial,  accounting  for  catches  of  over  200,000  t  per  annum  since  2012. 
Moreover, the proportion of yellowfin from artisanal fisheries catches has increased from 
around 30% in 2000 to nearly 50% in recent years (IOTC, 2018b).  

Figure  3.  Average  catches in the  Indian  Ocean  over the  period 2013–17,  by  country.  Countries  are 
ordered from left to right, according to the importance of catches of yellowfin reported. The red line 
indicates the  (cumulative)  proportion  of  catches  of yellowfin  for  the  countries concerned,  over the 
total combined catches of this species reported from all countries and fisheries. Data as of September 
2018 (IOTC, 2018b).  

Overall, for yellowfin (as for all tropical tuna stocks), the IOTC Secretariat considers that 
nominal  catches  are  generally  well  known  for  the  major  industrial  fisheries  catching 
yellowfin (e.g. European and Asian purse seine and longline fleets), with the proportion of 
catches estimated or adjusted by the IOTC Secretariat being relatively low. However, for 
the case of artisanal fleets the IOTC identifies several issues. For example, the catches for 
many coastal fisheries, notably those from Indonesia, Sri Lanka, Yemen, and Madagascar 
are very uncertain. This is also the case for gillnet fisheries from Pakistan and industrial 
purse seine and longline fleets from India.   

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bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

Figure 4. Amount of good (green), fair (light green), poor (light brown) or very poor (brown) data in 
the total yellowfin nominal catch in the period 1977-2017. Red dashed line is the percentage of fully 
to partially reported nominal catch data (IOTC, 2020). 

After the reviews and re-estimates made by the IOTC Secretariat there is still uncertainty 
in current and past catch data, and not only for artisanal fisheries. Historically, there have 
been  many  fleets  from  “non-IOTC  countries”  (mostly  ex-soviet  and  Thai  purse  seiners, 
before they joined the IOTC as CPC) that did not report catch while operating in the Indian 
Ocean between the mid-1980s to late 1990s, which led to large increases of catches that 
required to be estimated for that period. Also,  issues with catch  reporting from longline 
fisheries in the 1990’s were not sufficiently discussed in the 2019 stock assessment, but 
catch reporting coverage was known to be less than 10% in some years for logbook data 
from longlines in the Indian Ocean (Sharma, 2018). All these factors raise questions over 
the reliability of catches estimated for yellowfin tuna during those years (IOTC, 2018b). 
Furthermore,  while  the  number  of  fleets  from  non-IOTC  parties  operating  in  the  Indian 
Ocean has decreased significantly (mostly ex USSR countries), this has been offset by an 
increase in the number of coastal countries’ vessels (i.e. Indonesia, India, I.R. Iran, Kenya, 
Malaysia, Oman, Seychelles, Tanzania and Thailand) and deep-water fishing nations (i.e., 
Belize,  Guinea  and  Senegal),  many  of  which  have  varying  levels  of  quality  for  statistics 
collected. 

In addition to this, the EU purse seine fleet, which is considered to deliver catch reporting 
of high quality, has presented some issues recently (Duparc et al., 2018). The EU purse 
seine  nominal  catch  data  is  processed  using  the  Tropical  Tunas  Treatment  methodology 

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(called  hereafter,  the  T3  processing),  which  was  built  about  30  years  ago  (Pallarés  and 
Petit 1998) in order to correct biases of the logbook data on species composition and to 
provide more accurate catch estimates per species. The T3 process consists of combining 
four sources of data (logbooks that contain catch declarations by set and by commercial 
size category, stowage plans, landing declarations and port sampling reports). First, the 
logbook data of each fishing set is pondered according to the total catch weight of the trip 
estimated with the landing declarations. Thereafter, the sampling storage wells are used 
to correct potential bias on each set species composition. However, the evolution of fishing 
practices,  specifically  the  use  of  Fish  Aggregating  Devices  (FAD)  have  challenged  the 
current methodology on several aspects of the sampling and data processing. In particular, 
the multi-species nature of tropical tuna surface fisheries gives rise to a series of difficulties 
when  estimating  catch  by  species  (Duparc  et  al.,  2018).  In  the  2018  Working  Party  of 
Tropical Tunas, Duparc et al.(2018) presented a summary of the key elements to evaluate 
the potential biases that could occur in the catch estimates for tropical tunas. Overall, by 
comparing catch weights obtained from T3 processing output and from operational landing 
reports produced by fishing companies, Duparc et al., found a potential overestimation of 
catch of small yellowfin classes (i.e., less than 10kg) when using T3. This overestimation 
may  be  due  to  other  factors  such  as  the  historic  large  spatial-temporal  sampling  strata 
used to correct the catches reported in logbooks.  

Furthermore,  the  change  over  time  in length-weight  relationships  (LWR)  used  in  the  T3 
processing could also play a role in the uncertainty in the catch correction (Duparc et al., 
2018).  The  robustness  of  LWR  relied  on  a  biological  sampling  representative  of  the 
yellowfin  population.  Currently,  the  LWR  for  tunas  are  estimated  from  only  purse  seine 
catch data which represent only a fraction of the total catch of Indian Ocean yellowfin.  

Therefore, today there is large uncertainty in the catch data and this can propagate into 
the stock assessment (Sharma, 2018). Figure 4 and Table 2 show that currently, 15% of 
the  nominal  catch  data  is  partially  reported  and  therefore  estimated  by  the  IOTC 
Secretariat.  This  figure  also  shows  the  quantities  of  catch  data  that  can  be  considered 
good, fair, poor or very poor in relation to the quality of their reporting.    

Issues related to bigeye and yellowfin from longline data 

Catches from Taiwanese longline fleets in the Northwest area fell sharply between 2007-
2011, largely due to the decline in the number of longline vessels active in the north-west 
Indian  Ocean  in  response  to  the  threat  of  piracy.  Then,  their  fishing  rate  bounced  back 
sharply and reduced to stabilize. Since 2012 catches have recovered slightly but they are 
still below the values recorded in 2003 and 2004. 

Problems  with  longline  catch  reporting  from  Taiwanese  fleets  have  caused  concern  over 
their  implications  for  the  main  abundance  indices  used  in  the  assessments.  These  were 
discussed  in  deliverables  1.1  and  1.2  (for  yellowfin)  and  essentially  consist  of  a  sudden 
reduction  of  the  CPUE  of  yellowfin  generated  using  longline  data  (possibly  due  to 
misreporting of small fish and due to piracy), a sudden increase after the bounce back and 
reduction for stability (see Figure 19 in section 1.4.3, R2). This is interpreted by the stock 
assessment model as drastic increases of biomass prior to 2001-2004 and reductions of 

31 

 
 
Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

biomass  after,  followed  by  sharp  increases  and  stabilization  which  may  not  have  really 
happened, and may cause problems in the stock assessment results.  

In order to elucidate these issues, in 2019, the IOTC Secretariat funded a review to identify 
the issues with longline data and to make recommendations on how they should be used 
in the stock assessment. This task has been delayed until the first quarter of 2020 due to 
original data lack of availability. However, this review was delayed again and has now been 
rescheduled to March 2021 due to data confidentiality issues between the contractor and 
the longline fleets. 

Issues related to EU purse seine data for bigeye, yellowfin and skipjack 

EU  industrial  purse  seine  catches  of  bigeye  tuna  on  free-schools  have  shown  a  steady 
decline in recent years. In contrast, reported catches on FADs associated schools increased 
in  2018  doubling  the  2017  total  catch.  For  these  reasons,  in  2018  an  unusually  large 
proportion of bigeye relative to total catches was identified. This matter was discussed in 
the 21st WPTT (IOTC, 2019a), specially with regards to its implications for the assessments 
of the three tropical tuna stocks in general and to the implementation of Resolution 19/01 
for  yellowfin  in  particular  (IOTC,  2019d).  The  WPTT  noted  that  the  unusually  large 
proportion of bigeye (and lower than usual of yellowfin) could have beed due to changes 
in the statistical methodology adopted for the production of the final catch statistics by EU-
Spain  in  2018,  or  changes  in  the  fishing  patterns  (increase  of  sets  on  FAD  associated 
schools and reduction of free school sets), or a combination of both. It was requested that 
Spanish scientists clarify this matter. For the purposes of the stock assessments of bigeye 
and yellowfin (during the 2019 WPTT), nominal catch for 2018 for EU-Spain FAD schools 
was  re-estimated  by  the  IOTC  Secretariat  using  the  total  catch  reported  in  2018  and 
species  proportions  from  2017  (IOTC,  2019c).  It  was  noted  that  the  method  for  re-
estimating catch of 2018 and clarifications on the final catch statistics submitted EU-Spain 
purse seiners would have a notable impact in the assessments and therefore, these should 
be investigated. 

Issues related to Coastal states’ data (bigeye, yellowfin and skipjack) 

Problems of lack of reporting from gillnet/longline fisheries from several coastal states (Sri 
Lanka, I.R. Iran, Pakistan, Indonesia, Madagascar, Yemen, Comoros) have been identified. 
Therefore, catches have been estimated by IOTC Secretariat in recent years and the quality 
of these estimates is thought to be very poor due to the lack of information on the fisheries 
operating in these countries. A substantial review of these data is necessary.  

The problem with the uncertainty in nominal catch is that all catch data used in the stock 
assessment model is introduced as known without error, when in fact, some uncertainty 
should be attributed to these data. This means that contrary to other sources of data, the 
model will fit catch exactly as it was perfectly reported and accounted for. A possible way 
forward to characterize the uncertainty in catch is to design alternative scenarios of quality 

32 

 
 
 
 
Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

of  reporting  and  estimating  for  the  25%  of  the  catch  that  is  considered  only  partially 
reported or unreported (Sharma, 2018).  

Proposed catch information for yellowfin in 2019 and discussions during the WPTT 

Nominal catches were reviewed in 2019 and a new data set was made available by IOTC 
Secretariat for the yellowfin stock assessment. The catch data presented in 2019 to the 
WPTT by the Secretariat indicated that the total catch levels for all tropical tuna combined 
in 2018 almost reached the same levels as in pre-piracy years (before 2007, over 1.1 M t) 
and that this was mainly due to an increment in the catch of skipjack tuna, though also 
catches of yellowfin tuna and bigeye tuna had been increasing. According to the documents 
presented by the IOTC Secretariat and the report of the 2019 WPTT, these issues with the 
nominal catches remained unclarified (IOTC, 2019a): 

•  The WPTT NOTED that EU-Italy has not been reporting any information (nominal 
catch,  catch-and-effort,  size-frequency  data,  list  of  active  vessels  etc.)  for  2018, 
although a purse seine vessel flagged to EU-Italy was informally known as actively 
operating in the Indian Ocean during 2018 and 2019. 

•  The WPTT strongly ENCOURAGED all CPCs to report their data in accordance with 
Resolution 15/02, and NOTED that the IOTC Secretariat is liaising with several CPCs 
(e.g.  Pakistan,  Oman  and  I.R.  Iran  among  others)  to  ensure  that  all  information 
available at national level is timely and accurately reported in the future.  

•  The WPTT NOTED that some important revisions to existing nominal catch series 
for  tropical  tunas  still  need  to  be  endorsed  and  incorporated  within  the  IOTC 
database (e.g. reconstructed Pakistan gillnet catch series for yellowfin and skipjack 
tuna) and that the uncertainty caused by this situation might have implications on 
the Management Strategy Evaluation (MSE) processes for all concerned species.   

•  The WPTT NOTED that underreporting was very likely to have occurred in earlier 
years  of  the  time  series  (1950s-1970s)  and  that  confidence  around  older  data  is 
still low for some species and fisheries. Also, the WPTT NOTED that differences in 
reporting  rates  and  accuracy  of  all  information  available  (including  estimates  of 
catches  by  gear  and  species  performed  by  the  IOTC  Secretariat)  could  result  in 
biases  that  may  influence  stock  assessment  outcomes,  and  that  this  uncertainty 
has to be properly quantified and minimized as much as possible by ensuring that 
catch  histories  are  properly  reconstructed  in  recent  years  as  well  as  in  older 
decades. 

With regards to yellowfin data in particular: 

• 

ACKNOWLEDGING  that  the  revised  catch  series  for  yellowfin  tuna  caught  by 
Pakistan gillnets are not yet incorporated in the IOTC database, the WPTT NOTED 
that these could cause an average increase in catches of 7,100 t per year (in the 
years between 1994 and 2018) when compared to the scientific estimates currently 
available to IOTC. 

33 

 
 
 
Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

• 

• 

• 

• 

• 

• 

• 

However, the WPTT NOTED (…) the proposed revisions to species composition for 
the EU purse seine fleet in 2018. This revision refers to the fact that the species 
composition data from the EU-Spain fleet suggests an unusual proportion of bigeye 
vs yellowfin catches, i.e. the proportion of bigeye is markedly higher than previous 
years. The WPTT NOTED this feature and (…) ASKED the EU to clarify this matter. 

the  WPTT  AGREED  a  methodology  to  revise  the  bigeye,  (and  yellowfin  and 
skipjack)  tuna  catches  reported  by  EU-Spain  in  2018  (limited  to  their  log-
associated school component). 

The WPTT REVIEWED an approach to revising the bigeye tuna catch (and yellowfin 
and  skipjack),  which  applied  the  species  composition  recorded  for  the  log-
associated component of EU-Spain purse seine catches in 2017 to the total catches 
(log-associated) reported in 2018 by the same fleet.  

(…)  The  WPTT  NOTED  that  the  relative  species  composition  of  EU-Spain  purse 
seine  log-school  catches  in  2017  (pBET2017(bigeye),  pSKJ2017  (skipjack)  and 
pYFT2017 (yellowfin)) was as follows:  

o  pBETP2017 = BET2017 : Total tropical tunas2017 ≈ 6%  
o  pSKJP2017 = SKJ2017 : Total tropical tunas 2017 ≈ 65%  
o  pYFTP2017 = YFT2017 : Total tropical tunas 2017 ≈ 29%  

with bigeye (BET) catch in 2017 of 7,926 t, skipjack (SKJ) of 83,426 t and yellowfin 
(YFT)  36,583  t,  being  the  reported  catches  by  species  for  EU-Spain  purse  seine 
log-school  in  2017  and  total  tropicals  being  the  total  catches  of  all  tropical  tuna 
species  reported  for  EU-Spain  purse  seine  log-school  in  2017  (Total  tropical 
tunas2017  =  Bigeye2017 + Sipjack2017  +  Yellowfin2017  =  127,936  t)  while  in  2018  it 
was:  

o  pBET2018 = BET2018 : Total tropical tunas 2018 ≈ 12%  
o  pSKJ2018 = SKJ2018 : Total tropical tunas 2018 ≈ 66%  
o  pYFT2018 = YFT2018 : Total tropical tunas 2018 ≈ 22%  

with  BET2018  (24,507  t),  SKJ2018  (132,709  t)  and  YFT2018  (43,652  t)  being  the 
reported  catches  by  species  for  EU-Spain  purse  seine  log-school  in  2018  and 
TROP2018 being the total catches of all tropical tuna species reported for EU-Spain 
purse seine log-school in 2018 (Total tropical tunas2018 = BET2018 + SKJ2018 + YFT2018 
= 200,239 t). 

For this reason, the WPTT ACKNOWLEDGED that by applying the same species 
composition  identified  for  EU-Spain  purse  seine  log-school  in  2017  to  the  total 
catches  reported  by  the  same  fleet  component  in  2018  (200,239  t  =  BET2018 + 
SKJ2018 + YFT2018), the resulting revised total catches by species for the fleet and 
fishing mode in 2018 would be (TROP=Total tropical tunas):  

o  BET2018_R = pBET2017 * TROP2018 = BET2017 * TROP2018 / TROP2017 = 12,405 t 
o  SKJ2018_R = pSKJ2017 * TROP2018 =  SKJ2017 * TROP2018 / TROP2017 = 130,575 

t 

o  YFT2018_R = pYFT2017 * TROP2018 =  YFT2017 * TROP2018 / TROP2017 = 57,259 t 

34 

 
Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

• 

• 

with TROP2017 = BET2017 + SKJ2017 + YFT2017 and TROP2018 = BET2018 + SKJ2018 + 
YFT2018, in lieu of the currently reported: 

o  BET2018 = 24,507 t,  
o  SKJ2018 = 132,079 t and  
o  YFT2018 = 43,652 t. 

The WPTT AGREED on using catch estimated through this method instead of the 
reported catch for yellowfin, bigeye and skipjack for the year 2018. 

Furthermore,  the  WPTT  NOTED  with  concern  that  several  fleets  not  subject  to 
Resolution 18/01 (e.g., Indonesia and Mauritius purse seines, China and Seychelles 
longlines,  India,  Oman  and  Pakistan  gillnets,  I.R.  Iran  and  Sri  Lanka  coastal 
longlines, Oman handlines) have sharply increased their catches of yellowfin tuna 
with  respect  to  the  theoretical  baseline  year  (2014)  as  well  as  with  respect  to 
previous  year  (2017)  therefore  reducing  the  effectiveness  of  the  measures 
introduced by Resolution 18/01. 

With regards to issues with the EU purse seine data, a revision of T3 methodology was 
presented to the WPTT (Duparc, 2019). The results of this analysis can be summarized 
as follows: 

•  The potential bias pointed out in the 2018 WPTT (Duparc et al. 2018) due to the 
use in the T3 process of too large spatial areas was investigated in 2019 and results 
were presented at the 2019 WPTT (Duparc, 2019). Species composition by fishing 
mode  are  considered  similar  among  all  sets  within  a  given  area.  This  strong 
hypothesis is challenged in several large areas covering coastal and high sea zones 
of the fishing ground. Therefore, this article proposed a new statistical approach to 
estimate  the  species  composition  of  catches  based  on  models  which  account  for 
catch spatial and temporal structure and aimed to replace the current large strata 
in the T3 process. Based on predictive performance indicators, the random forest 
model  (machine  learning  modelling  technique)  was  the  most  accurate  model  at 
estimating  the  species  composition  compared  to  other  candidate  models  (spatial 
kriging and general additive models).  

•  Regarding  the  IOTC  yellowfin  tuna,  first  models  conducted  by  fishing  mode  for 
2015-2018 had good predictive performance for the species composition of the sets 
(4 and 5 % of Mean Absolute Error  for associated and free school sets respectively). 
Furthermore,  yearly  total  catches  estimated  by  fishing  mode  were  compared  to 
those estimated with the logbooks declaration and the current T3 process for 2015-
2018 (Figure 5). The estimated catches of the three candidate models for the free 
school sets were very similar which strengthened the assumption of good quality 
data of the logbook declaration for this fishing mode. On the contrary, regarding 
associated school sets, yellowfin tuna catches seemed to be largely underestimated 
by  logbook  declaration.  The  article  clearly  demonstrated  the  biases  of  the 
declaration in logbook reporting, e.g., bad reporting of bigeye tuna, strengthening 
the need of estimation of the catches for scientific purposes.  

35 

 
 
Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

•  The  article  also  deals  with  the  necessity  to  account  for  vessel  variability  and 
reporting.  The  accuracy  of  the  prediction  could  be  improved  by  adding  fishing 
variables. This point is particularly important because vessels, companies or fleets 
could  have  different  fishing  strategies  leading  to  different  species  composition  of 
their catches. 

•  This  new  approach  has  an  additional  advantage:  The  estimation  of  the  species 
composition of sets, which enables the estimation of the catch spatial distribution 
by species and the possibility to calculate associated errors (confidence intervals). 
The reported catches are considered perfectly known, even if they are estimated by 
crew  members.  The  computation  of  a  confidence  interval  allows  for  a  better 
understanding of the catch precision. 

•  For all these reasons and if the predictive performance in the Indian Ocean was up 
to  scientific  requirements,  this  new  approach  will  be  recommended  after  full 
validation to estimate the tropical tuna catch species composition in the purse seine 
fishery for stock assessments under the T3 methodology. 

Figure 5. Total catches by tuna species and fishing mode according to 3 estimation methods for the 
2010-2018  period.  Logbook  declaration:  dashed  lines,  T3  process:  dotted  line  and  Random  Forest 
model: solid line (results from Duparc et al. 2019). 

One of the objectives of the IOTC workplan for yellowfin and this SC014 is to characterize 
the  potential  impact  of  the  uncertainty  on  catches  by  exploring  alternative  scenarios  of 
catch for yellowfin. One study presented to the 21st WPTT (Merino et al., 2019a), carried-
out  an  exploratory  analysis  of  the  potential  impact  of  misreporting  of  artisanal  fisheries 
catch of yellowfin. In this work, authors generate three scenarios of underreporting from 
the catch series available in 2018 and re-run the reference case configuration of the stock 

36 

 
 
 
 
Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

assessment model of 2018. In this paper, a range of exploratory values for inflating the 
catch that is considered poor and very poor to all artisanal gears are used (50%, 100% 
and  150%  inflation).  With  this,  the  three  scenarios  of  catch  are  used  in  the  stock 
assessment of yellowfin. The 2018 stock assessment was carried out using SS3 which uses 
catch data, length frequency data, abundance indices and tagging information. In order to 
generate catch reporting scenarios, one of the SS3 input files (data.ss) was modified. This 
file  contains  (among  other  information),  one  catch  record  with  a  date  field  per  fishery. 
These catch records were increased for artisanal fleets (gears other than longline and purse 
seine) to account for the potentially unreported catch. The stock assessment model was 
run with the new input files and outputs compared with special attention to estimated time 
series and stock productivity (estimated Maximum Sustainable Yield (MSY)). 

These  results  suggest  that  the  current  uncertainty  in  the  catch  information  of  artisanal 
fisheries does not produce a noticeable impact on the estimates of stock status, but it does 
produce changes in the estimated productivity of the stock (Figures 6 and 7). The main 
driver of the stock abundance trends is the Joint Longline CPUE, which is not affected by 
the catch reporting of artisanal fisheries. 

Figure 6. Estimated trends for total biomass and fishing mortality relative to MSY. 

37 

 
 
 
Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

Figure 7. The values of the estimated MSY for Indian Ocean yellowfin for each of the underreporting 
scenarios. This figure shows how underreporting can produce a notable impact on reference points. 

With regards to the potential implications of this study, the WPTT noted the following 
(IOTC, 2019a): 

• 

• 

The  WPTT  NOTED  that  the  yellowfin  assessment  relative  abundance  estimates 
appeared  to  be  very  robust  to  consistent  underreporting  biases  in  the  artisanal 
fleets, though MSY estimates scaled in a predictable manner. The WPTT NOTED 
that trends in catch misreporting are likely to create more complicated biases, and 
such scenarios may be difficult to derive. 

The WPTT NOTED that some assessment models (including SS), can treat catches 
as  observations  with  error.  However,  it  was  further  noted  that  this  can  be  a 
dangerous  feature to use, because it may result in a significant distortion of the 
input catch series, to fit some other data which might be very unreliable. 

1.2.  Tagging data: 

One way to investigate fish population and fishery dynamics is tagging fish. Fish are tagged 
and  released  to  obtain  information  on  their  movements  and  biological  features  such  as 
growth  or  natural  mortality.  In  theory,  fish  tagging  information  can  contribute  to  more 
accurate estimates of fish population dynamics and a more effective management of fish 
stocks.  

A considerable amount of tagging data was available for inclusion in the assessments of 
tropical tunas in the IOTC. The data used consisted of tuna tag releases and returns from 

38 

 
 
 
 
Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

the Indian Ocean Tuna Tagging Programme (IOTTP), and mainly from its main phase, the 
Regional  Tuna  Tagging  Project-Indian  Ocean  (RTTP-IO)  conducted  during  2005-2009.  A 
total of 54,688 yellowfin tuna, 78,333 skipjack tunas and 34,572 bigeye tuna were tagged 
during the IOTTP, and were released within the western equatorial region, between May 
2005 and September 2007 (IOTC, 2019c) (Figure 8 and Table 3).  

Released

YFT

Recovery 
(number)

Recovery 
(%released
)

BET

SKJ

Released

Recovery 
(number)

Recovery 
(%released)

Released

Recovery 
(number)

Recovery 
(%released)

2005-07

Data period
Total number
           54,688               9,921 
Error filtering            54,392               9,822 

2005-15

Processed 
number

           54,392             10,474 

2005-07

2005-15

2005-07

2005-09

18%
18%

19%

           34,572          5,720 
           34,475          5,679 

           24,104          6,578 

17%
16%

27%

        78,333            10,458 
        77,893            10,248 

        58,420            11,642 

13%
13%

20%

Table 3. A summary of tagging data processing for yellowfin, bigeye, and skipjack assessments (Fu, 
2020).  The  four  rows  summarise  the  total  number  of  releases  and  recoveries  from  the  RTTO-IO 
program, the number after error filtering and the final processed number included the assessment. 

How  the  tagging  data  is  improving  the  three  tropical  tuna  stocks’  assessments  remains 
unclear, due to conflicts with other datasets (Sharma, 2018). In other words, it is not clear 
that the tagging information is helping or not a better knowledge on Indian Ocean tropical 
tuna  stocks’  dynamics  and  this  is  because  there  is  contradictory  information  on  fish 
dynamics from tagging and other  sources  of data. During the 2019  WPTT, the potential 
problems associated with these data were discussed (IOTC, 2019a). It was highlighted that 
the tagging data has a very strong influence in biomass estimates and if tags are wrongly 
assumed to be randomly mixed over the areas considered in the stock assessment, they 
may provide misleading regional biomass scaling information. This is because if fishing is 
concentrated in the release area, then the fishing mortality is overestimated and thus, the 
biomass and productivity estimates tend to be negatively biased. For this reason, tagging 
data should be modelled at fine spatial scale, which is not the case currently. A proposed 
idea discussed in the WPTT was to analyse tag data separately and to introduce the results 
into the model rather than introducing the tagging data into the model as it is currently 
done.  These  discussions  highlight  the  relevance  of  analysing  tagging  data  appropriately 

39 

 
 
Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

and suggest that these data will need to 
be  conveniently  analysed  to  improve 
the  current  stock  assessments  of  the 
three tropical tuna stocks.  

this 

The  IOTC  workplan  for  yellowfin  and 
this SC014 contain a task to review the 
current  use  of  tagging  data  in  the 
yellowfin  stock  assessment.  However, 
unfortunately 
task  was  not 
completed before the WPTT. There are 
different reasons for this: At one initial 
stage  experts  from  outside  the  IOTC 
were contacted for this task but due to 
compatibility issues with their employer 
(NOAA,  US)  they  were  not  able  to 
complete this task. Another expert from 
the IOTC was also contacted (Dr Simon 
Hoyle) but he had no time availability. 
The possibility of carrying out this study 
in 2021 remains open and if completed, 
it will improve the use  of tagging data 
in  all  the  assessments  of  the  three 
tropical  tuna  stocks  in  the  Indian 
Ocean.  It  is  included  in  the  Working 
Party of Tropical Tunas workplan for the 
coming years. 

1.2.1.  Evaluate how raw data is modified 
for the assessment 

Figure  8.  Densities  of  releases  (red)  and  recoveries 
(blue). Upper (bigeye), middle (skipjack) and bottom 
(yellowfin). 

Following the parts of the workplan that 
were not completed in 2019, the IOTC 
Secretariat  prepared  the  document 
the 
IOTC-2020-WPTT22(DP)-10, 
2020 Data Preparatory meeting of the WPTT. In this document, the procedures used for 
processing  the  tag  data  in  the  most  recent  IOTC  tropical  tuna  stock  assessments  were 
summarized. Some important aspects of the use of tagging data include data extraction, 
error  filtering,  fishery  assignment,  age  assignment,  initial  tag  induced  mortality  (and 
chronic tag loss) and estimating reporting rates. There is a tendency to unify the treatment 
to  tagging  data  in  the  three  tropical  tuna  stock  assessments  but  still,  there  are  some 
differences that needed to be explained.   

for 

In  2003,  the  IOTC  initiated  the  implementation  of  the  RTTP-IO  program,  a  project  fully 
funded by the EU. Between 2005 and 2009 over 150,000 tropical tuna were tagged and 
released  from  Tanzania,  Seychelles  and  Mozambique  Channel  under  this  program.  In 
addition to this program, a series of pilot and small-scale tagging projects were developed 

40 

 
 
Study to improve the current assessment and scientific advice for yellowfin tuna (YFT), 
bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

from 2002 and 2009. All these were supervised by the IOTC under the framework of the 
Indian Ocean Tuna Tagging Programme (IOTTP).  

Tropical tuna assessments have integrated the tagging data to inform population size and 
movement  dynamics  and  these  data  significantly influence  the  estimates  of  stock  levels 
and  reference  quantities  (Secretariat,  2020).  The  integration  of  tagging  data  in  the 
assessment  model  is  a  complicated  process.  The  number  of  recaptured  tags  conveys 
information  on  the  size  of  fish  population,  and  the  rate  at  which  the  recapture  decline 
informs mortality (Secretariat, 2020). There are various processes that can produce bias 
on  the  stock  assessments  estimates  and  this  is  one  of  the  reasons  why  the  2019-2020 
workplan  for  yellowfin  included  a  review  of  how  this  data  is  being  processed  in  the 
assessments. The most influential processes include filtering of erroneous/dubious records, 
adjustment  for  potential  tag-loss  and  tag  induced  mortality,  and  correction  for  under-
reporting of recaptures. Here, we explain the assumptions and criteria being applied in the 
recent assessments for yellowfin, bigeye and skipjack (Secretariat, 2020). 

(i) 

(ii) 

(iii) 

(iv) 

(v) 

Data  extract:  The  IOTC  tagging  data  is  maintained  in  a  Microsoft  Access 
database.  The  IOTC  Secretariat  prepares  an  extract  of  the  tagging  data  from 
this database including all the essential information (date, location, size etc. for 
each  release  and  recovered  tag)  before  each  assessment.  These  data  are 
processed  by  the  assessment  scientists  into  a  format  suitable  for  the  stock 
assessment model (Stock Synthesis). The most recent extracts were conducted 
in  2015  for  yellowfin  (used  in  the  2015,  2016,  2018  and  2019),  in  2016  for 
bigeye (used in 2016 and 2019) and in 2011 for skipjack (used in 2011, 2014 
and 2017). The tag releases are aggregated by year, season and length class, 
with  associated  recoveries  by  location,  gear,  year  and  season.  These  original 
data  extracts  contained  54,688,  34,572  and  78,333  releases  for  yellowfin, 
bigeye and skipjack respectively, with 9,921, 5,720 and 10,048 recoveries made 
for each species respectively.  

Error  filtering:  These  data  were  filtered  to  remove  errors  such  as  absence  or 
incorrect  date/location,  length  at  release  or  other  information.  This  initial 
filtering  discarded  20%  of  the  total  number  of  records.  In  the  skipjack 
assessment,  the  recoveries  that  were  made  by  the  same  release  vessel  were 
also removed. 

Fishery assignment: Each tag recovery observation needs to be assigned to a 
fishery/fleet defined in the stock assessment model. For all tropical tuna species, 
the  majority  of  tags  were  recovered  by  the  EU  purse  seine  fleet.  These  are 
separated into associated sets or free school sets based on their expected size 
of fish at the time of recapture. 

Age  assignment:  Tag  releases  are  aggregated  in  release  groups  defined  by 
region,  time,  period  of  release  and  age  class  using  species  growth  curves  to 
convert the size measured at the release and recovery into age classes.  

Initial  tag  induced  mortality:  The  mortality  induced  by  tagging  effectively 
reduces  the  number  of  tag  releases,  and  therefore  biases  the  estimates  of 
fishing mortality and abundance if they are not properly accounted for. The rate 

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bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

of  tag  induced  mortality  has  been  a  matter  of  intense  discussions  during  the 
recent WPTT sessions, in 2019 the group agreed to use the estimates of a study 
on  covariates  of  release  mortality  and  tag  loss  in  large-scale  tuna  tagging 
experiments  (Hoyle  et  al.,  2015)  as  the  best  estimates  for  these  rates. 
Therefore, the values of 27.5% and 30.5% of tag mortality have been assumed 
for  yellowfin  and  bigeye  respectively  in  2019  and,  the  value  of  25%  was 
assumed for skipjack in 2020.  

(vi) 

Chronic  tag  loss:  Similarly,  tag  shedding  reduces  the  size  of  the  tagged 
population over time and can have a significant impact on model estimates. The 
model will overestimate the biomass if tag loss is not accounted for. This value 
was estimated to be 20% at 2000 days at liberty.  

(vii)  Reporting rate: Reporting rates can also produce bias on model estimates. Low 
reporting rates can underestimate fishing mortality and therefore it is important 
to  correct  raw  data  with  reporting  estimates.  In  the  IOTTP,  estimates  of 
reporting are only available for the purse seine fishery. Recent experiments have 
revealed  considerable  temporal  variability  in  reporting  (Hillary  et  al.,  2008). 
Therefore, reporting rate is included in the tagging data processing with different 
rates for each year. Further adjustment is made concerning the proportion of 
purse seine catches that are not landed in Seychelles.   

1.2.2. Investigation on key parameters 

One aspect of the treatment of the tagging data that was included in the workplan and that 
was addressed during the WPTT, is the most adequate values of tag-release and tag-loss 
mortality. In the previous assessments, discussions were raised during the WPTT meetings 
on the most adequate values. In 2019 this matter was clarified after the methodology of a 
recent scientific document was presented (Hoyle et al., 2015). In this regard: 

•  The WPTT AGREED that the values of release mortality and tag loss for the three 
tropical tuna species included in this paper should be adopted for the assessment 
of the tuna stocks in the Indian Ocean. It was further NOTED that new studies are 
being initiated to re-consider how to utilise tagging data, and the outcomes of this 
may result in revisions to these values in the future, but the values provided in this 
paper are the best available estimates currently. The value now in use for yellowfin 
after this in the WPTT is 27.5%.  

1.2.3. Sensitivity of tagging information in stock assessment 

The following analyses illustrate the impact of tagging data on the outcome of the stock 
assessments of yellowfin and bigeye (for skipjack, runs without the influence of the tagging 
data were not included in the model runs used to provide advice): 

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Figure 9 and 10 show the estimated maximum productivity (MSY of yellowfin and bigeye) 
for different treatments of tagging data. In the case of yellowfin, in the 2018 assessment, 
it was considered to keep runs with and without the influence of tagging data. For the case 
of bigeye, a range of influence was considered for different levels of the weight of tagging 
data on model fits (TAG 001 (no influence), TAG 01 (moderate influence), TAG 1 (normal 
influence)). It was observed that tagging data scales down the productivity of the stock 
and  model  configurations  that  downweigh  these  data  tend  to  estimate  a  larger  fish 
population productivity. 

Figures 11 and 12 show the estimated stock status at the time of the assessments (2018 
for yellowfin and 2019 for bigeye) for the same tagging data weightings. For both stocks, 
the larger the influence or weight of the tagging data the more pessimistic the assessment 
outcome (lower biomass and higher fishing mortality).  

These  figures  help  to  understand  the  influence  of  tagging  data  from  the  IOTC  tagging 
programs on the tropical tuna assessments. At this moment, this influence is treated as a 
factor of the structural uncertainty stock assessments grid, which are used to characterize 
the  uncertainty  across  the  different  data  sources  and  modelling  assumptions.  However, 
improving the understanding of these data and finding the most appropriate configuration 
and use in the models would help reducing the overall stock state uncertainty. This would 
also help providing better advice for management.  

Figure 9. Estimated MSY for yellowfin downweighting (blue) and not downweighting (red) tagging 
data. 

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Figure  10.  Estimated  MSY  for  bigeye  for  different  weightings  of  tagging  data  (no  weight  (blue), 
medium weight (purple), full weight (red)). 

Figure 11. Stock status of yellowfin for two weightings of tagging data (downweighted (blue) and not 
downweighted (light blue)). 

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Figure  12.  Stock  status  of  bigeye  for  three  weightings  of  tagging  data  (no  weight  (blue),  medium 
weight (purple) and full weighting (light blue)). 

Tagging data available for yellowfin before the 2019 WPTT 

In total, 10,842 specimens (16% of releases for this species), have been recovered and 
reported to the IOTC Secretariat. More than 86% of these recoveries were by the purse 
seine fleets operating in the Indian Ocean, while around 9% were made by pole-and-line 
and less than 1% by longline vessels (IOTC, 2018b). The area where most of the samples 
were recovered is also the western equatorial region, where purse seine and longline fleets 
operate. Therefore, most of the recoveries could not inform on the fish movements across 
the different  regions considered in the stock assessment. Note that the  Indian Ocean is 
geographically  divided in  areas  for  stock  assessment  and  tagging is  supposed  to  inform 
the  stock  assessment  model  on  fish  movement  across  areas.  The  recaptures  by  fishery 
also inform the assessment model on fishing mortality and abundance.  

The  impact  of  tagging  data  on  yellowfin  stock  assessment  and  its  potential  problems  is 
further illustrated by evaluating two scenarios of the 2018 stock assessment of yellowfin:  

-  Scenario  “io_h80_q1_tm30_dw1”  (with  tags):  This  scenario  was  considered  the 
reference  case  of  the  2018  stock  assessment.  The  code  means  steepness  of  0.8 
(h8), constant catchability (q1), tag-release mortality of 30% (tm30) and tagging 
data included (dw1). 

-  Scenario  “io_h80_q1_tm30_dw2”  (without  tags):  Equal  to  previous  but  tagging 
data downweighed (dw2), which means that the model will not use tagging data to 
estimate stock dynamics, nor biological parameters. 

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Figure  13.  Stock  and  fishing  mortality  trajectories  relative  to  MSY  benchmarks  for  2018  reference 
case (red) and idem but with tag data downweighted (blue). 

As seen in Figure 13, the tagging data has a minimum influence on the estimate of stock 
and fishing mortality (relative to MSY) historical trends. For both scenarios the 2018 stock 
status was overfished (B/BMSY <1) and  subject to  overfishing (F/FMSY  >1).  However, the 
tagging data influences very significantly the estimated stock productivity (Figure 9). The 
stock productivity is described by the MSY, the maximum amount of fish that can be fished 
from  a  population  without  endangering  its  future  sustainability.  The  scenario  that  uses 
tagging  data  estimates  a  productivity  on  average  15%  lower  than  the  scenario  without 
tagging  data  downweighed.  Significantly,  the  scenario  without  tagging  data  estimates  a 
MSY value larger than the catch in the last year (C2017=409.150 thousand tons), but the 
reference case (with tagging data) estimates a MSY 16% lower than that catch in 2017.  

This has significant implications to the scientific advice derived from the stock assessments, 
as for scenarios with tagging data, recent catches are well above the maximum productivity 
while those that do not use tagging data estimate current catch nearby MSY. This applies 
to the two examples shown in Figure 14 but overall, to the entire grid of 24 scenarios used 
in the 2018 stock assessment (Figure 15).  

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Figure 14. Estimated productivity, maximum sustainable yield (MSY) for reference case (with tags, 
io_h80_q1_tm30_dw1) and idem without tags (io_h80_q1_tm30_dw2) scenarios from the 2018 grid 
of scenarios. Dashed lines represent the last nominal catch of the assessment (409,150 tons). 

Figure 15. Estimated productivity for the grid of 24 scenarios considered in the 2018 stock assessment 
of  yellowfin.  Note  that  h  is  steepness  (h70=0.7,  h80=0.8  and  h90=0.9),  q  is  catchability 

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(constant=q1,  split  in  two  periods=q2)  and  tag-release  mortality  parameter  (tm30=30%  and 
tm10=10%). Dashed line is the 2017 catch. 

Figure  16  shows  the  estimated  biomass  trend  for  the  reference  case  with  and  without 
tagging  data  for  a  10%  reduction  from  current  catch.  It  is  evident  that  productivity 
differences between both scenarios could lead to two very different recommendations for 
catch limits. The scenario without tags would estimate that a 10% reduction from current 
catch  would  allow  stock  recovery  to  levels  above  BMSY  but  the  scenario  with  tags  would 
estimate that more drastic reductions would be necessary. 

Figure  16.  Estimated  biomass  trajectories  for  a  10%  catch  reduction  with  two  scenarios  with  tags 
(io_h80_q1_tm30_dw1, red line) and without tagging data (io_h80_q1_tm30_dw2, blue line). 

In this section we have described the tagging information used in the yellowfin assessment 
and  described  its  potential  implications.  In  order  to  improve  the  yellowfin  stock 
assessment, the conflicts of tagging data with other sources of data are yet to be analysed 
and elucidated (Sharma, 2018), thus requiring further studies on the tagging information.  
Some tasks improving available tagging data use are identified and will be included in the 
Terms  of  References  for  potential  contracts  launched  for  this  purpose  by  the  IOTC 
Secretariat: 

•  Technical peer review of the current processing of tagging data for Stock Synthesis 
(SS), the stock assessment model used for yellowfin. This is currently being done 
under a contract with the IOTC by a non-EU expert. 

•  Document the list of data assumptions in processing, which was already covered 

within this project (see section 1.2.1). 

•  Provide a list of  recommendations for best data practices in SS. This is currently 

being done under a contract with the IOTC by a non-EU expert. 

•  Document  base  assumptions  of  tagging  parameters  in  the  model.  This  includes 
parameters such as tag-release mortality, tag-loss or tag reporting rates. This has 
partially been covered within this project (see section 1.2.2). 

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•  Construct  a  tagging  simulation  model  based  on  life  history  parameters  and 
assumptions based on the SS assessment (Lauretta and Goethel, 2017). This has 
not been done yet. 

•  Run  simulated  data  in  SS  to  evaluate  the  influence  of  known  data.  This  has  not 

been done yet. 

•  Evaluate uncertainty grid in the Indian Ocean tag integrated SS model. This has not 

been done yet. 

1.3.  Size data 

In the IOTC, size data are length frequency data, which are individual body lengths of IOTC 
species  per  fleet,  year,  gear,  type  of  school,  month  and  5  degrees  square  areas  (IOTC, 
2018b). The IOTC Secretariat has identified a number of issues with these data (directly 
taken from the Review of Statistical data and fishery trends for tropical tunas (IOTC-2019-
WPTT21-08)): 

• 

Japan  and  Taiwan,  China  (longline  fisheries):  These  follow  the  issues  with  catch 
statistics.  In  2010,  the  IOTC  Scientific  Committee  identified  several  issues 
concerning  the  size  frequency  statistics  available  for  Japan  and  Taiwan,  China, 
which  remain  unresolved.  In  2013  the  IOTC  Secretariat  presented  a  working 
document  to  the  WPTT-15  describing  the  current  data  quality  issues  and 
inconsistences between the length frequency data and catch-and-effort reported by 
Taiwan, China since the mid-2000s. A consultancy was planned for 2019 to work 
directly with the individual national fisheries organizations concerned to resolve the 
current issues with longline, but this work has been delayed until the first quarter 
of 2021.  
In  addition,  the  number  of  specimens  sampled  for  length  on-board  longliners 
flagged in Japan in recent years remains below the minimum 1 fish per metric ton 
of catch recommended by the IOTC.  
I.R.  Iran  and  Pakistan  (gillnet):  data  have  not  been  reported  by  area  and  the 
number of samples are below the minimum sample size recommended by the IOTC 
(1 fish for metric ton of catch) and above recalled. This refers to all tropical tunas. 
•  Sri Lanka (gillnet-longline): sampling coverage is below recommended levels and 

• 

• 

• 

lengths are not available by gear type or fishing area.  
Indonesia  (longline):  size  frequency  data  have  been  reported  for  its  fresh-tuna 
longline fishery in previous years (e.g., 2002-2003), however samples cannot be 
fully broken down by fishing area (i.e., 5° degree grid) and they refer exclusively 
to longliners based in ports in those countries.  

•  To  date,  some  other  countries  have  not  reported  size  frequency  data  for  their 

fisheries:  

o  Longline: India, Oman and the Philippines;  
o  Coastal fisheries: India, Indonesia and Yemen.  

•  Problems with processing purse seine catches: The products of the T3 methodology 
are catch and effort data and size data provided to the IOTC in full compliance of 
Resolutions  15/01  and  15/02  of  the  IOTC  (Duparc  et  al.,  2018).  With  regards  to 
potential problems with size data (in general, not only in the IOTC), three bias could 

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occur  in  the  T3  process.  First  it  is  acknowledged  that  overlaps  between  the 
commercial categories used in logbook declarations (e.g., 3-10 kg, 6-20 kg and 10-
30 kg) may be producing uncertainties in the catch at size estimates. Indeed, these 
weight categories could vary over time among fleets, companies and tuna canneries 
without  any  link  to  the  biology  of  the  species  (e.g.,  the  reproduction  status). 
Therefore,  the  standardisation  of  these  categories  is  necessary  to  estimate  large 
and  small  fish  catches.  Thus,  it  is  recommended  that  the  tRFMO  and  CPCs 
administrations  encourage  companies  to  adopt  common  categories  in  their 
declarations.  Second,  the  local  fish  market  (also  called  “Faux  poisson”),  which  is 
mainly composed of small fishes regarding the major tropical tunas, could truncate 
the lower length category in the catch at size, because it is not included in the T3 
process (however, the  faux poisson  case  for the  estimates does not apply to the 
Indian  Ocean).  Third,  it  is  recommended  that  length-weight  relationships  are 
regularly updated (Table 4). Indeed, the length-weight relationship is an important 
element in the T3 process, because it is directly linked to the species composition 
of the sample when length distribution is converted in weight. Yet, parameters of 
the  length-weight  relationship  became  outmoded  (mostly  in  ICCAT),  and  no 
standardized  procedure  was  established  to  evaluate  new  parameters  as  early  as 
their validity period. Furthermore, estimation of such parameters relies on biological 
data  representative  of  the  fishery,  i.e.,  on  a  long-term  and  large-scale  biological 
sampling which is not always available.  

Table  4.  Parameter  values  of  the  length-weight  relationship  used  in  the  T3  process.  Values 
corresponding  to  the  tRFMOs  purse  seine  fisheries  last  update.  The  length-weight  relationship 
enables  conversion  of  the  length  (L)  in  round  weight  (RND)  according  to  the  equation  RND  = 
a*L^b. Estimated parameters are a and b (see official tRFMOs documents for details). 

tRFMO 

Species 

a 

b 

IOTC 

ICCAT 

Yellowfin tuna 

0.00002459 

2.96670 

Bigeye tuna 

0.00002217 

3.01211 

Skipjack tuna 

0.00000497 

3.39292 

Albacore 

0.0000569070 

2.75140 

Yellowfin tuna 

0.00002153 

2.976 

Bigeye tuna 

0.00002396 

2.9774 

Skipjack tuna 

0.00000748 

3.25256 

Albacore 

0.00001339 

3.1066 

Size frequency issues with particular implications for bigeye and yellowfin 

•  Average fish weight: trends in average weight can be assessed for several industrial 
fisheries but they are very incomplete or of poor quality for some artisanal fisheries, 

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bigeye tuna (BET) and skipjack (SKJ) in the Indian Ocean 

namely hand lines (Yemen, Comoros,  Madagascar), trolling lines (Indonesia) and 
many gillnet fisheries. 

•  Catch-at-Size (Age) table: the estimates are more uncertain in some years and for 

some fisheries due to:  

o  size  data  not  being  available  from  important  coastal  fisheries,  notably 
Yemen, Pakistan, Sri Lanka and Indonesia (lines and gillnets) and Comoros 
and Madagascar (lines). 
the  paucity  of  size  data  available  from  industrial  longlines  from  the  late-
1960s up to the mid-1980s, and in recent years (Japan and Taiwan, China). 
the  paucity  of  catch  by  area  data  available  for  some  industrial  fleets  (NEI 
fleets, I.R. Iran, India, Indonesia, Malaysia).  

o 

o 

Size frequency issues with particular implications for yellowfin 

Some  of  the  issues  explained  in  the  review  made  by  the  external  reviewer  to  the  2018 
WPTT (Sharma, 2018) may have a large influence on the yellowfin assessment. This is the 
case  for  the  potential  misreporting  of  small  size  fish  by  industrial  longlines  since  2004 
(Sharma, 2018). 

•  Potential misreporting of small size fish by industrial longlines: In 2004, after the 
evidence found by ICCAT and IOTC that catch from the Atlantic was being reported 
as catch from the Indian Ocean, Taiwan, China, authorities imposed catch limits to 
industrial longlines. These restrictions combined with market incentives for larger 
fish potentially led to discarding of small yellowfin fish. Size data from the 2004-
2018 period for Taiwanese fleets was not used in the assessment. These data will 
be  reviewed  by  an  external  contractor  to  the  IOTC  ahead  of  the  2021  stock 
assessment.  As  explained  in  the  section  1.4.2,  the  misreporting  of  discards  may 
have had a strong influence on the longline abundance indices. 

Size frequency issues with particular implications for skipjack 

•  Average fish weight: trends in average weights cannot be assessed before the mid-
1980s and are also incomplete for most artisanal fisheries, namely hand lines, troll 
lines and many gillnet fisheries (e.g., Indonesia). 

•  Catch-at-Size  (Age)  table:  the  estimates  are  more  uncertain  in  some  years  and 

some fisheries due to:  

o  general lack of size data before the mid-1980s, for all fleets/fisheries. 
o 

lack of size data available for some artisanal fisheries and many commercial 
gillnet fisheries. 

Progress of new contract on IOTC size data review 

The review of size data from large-scale tuna longline fisheries in the Indian Ocean was 
one of the objectives of the IOTC workplan for yellowfin. The IOTC launched a project for 
this  which  has  the  following  specific  objectives  (from  Hoyle,  2020,  IOTC-2020-
WPTT22(DP)-INF07): 

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•  Review the procedures used for the collection and processing of size data. 

o  To review data types, sources, validation, and processing. 

o  Address quality concerns 

  Compare data from different sources. 

  Explore reasons for discrepancies, in particular the marked decrease 

in small fish from the Taiwanese fleet. 

  Consider the effects on the stock assessments. 

  Where  needed,  recommend  changes  to  size  data  collection  and 

processing. 

During the WPTT Data Preparatory meeting (June 2020) the current status of the project 
was  presented  (Hoyle,  2020).  However,  by  late  September  2020,  the  project  was  only 
starting because the longline fleet data access agreement had only been received by mid-
September, and project completion has been delayed to the 31st March 2021. Therefore, 
this task of the workplan will not be completed by the WPTT in 2020. 

1.4.  Catch per unit of effort (CPUE) 

Catch  and  fishing  effort  can  be  treated  together  when  producing  data  for  stock 
assessments.  Both  provide  indices  related  to  fishing  mortality  and  the  density  of  the 
exploited  stock  (FAO,  2019).  In  the  IOTC,  catch  and  effort  data  refers  to  the  fine-scale 
data – usually from logbooks – reported in aggregated format: per fleet, year, gear, type 
of school, month, spatial grid and species. Information on the use of FADs and activity of 
vessels that assist industrial purse seiners to locate tuna schools (supply vessels) is also 
collected (IOTC, 2019b). The CPUE series used for the most recent yellowfin and bigeye 
assessments are the Joint Longline CPUE index (Hoyle et al., 2018), while for skipjack, the 
Maldivian pole and line index (Medley et al., 2017) and European FAD purse seine index 
(Katara et al., 2017) were used. Each index has its positive and negative aspects and for 
the three stocks there are other alternative abundance indices that have been developed 
recently. 

1.4.1. Options for alternative indices 

For the 2019 stock assessment of yellowfin two CPUE indices (Joint CPUE longline and EU 
purse seine free school) and one fishery-independent index (Buoy Abundance Index) were 
produced.  This  means  that  potentially,  two  other  abundance  indices  could  be  used  in 
combination with the joint longline index: 

  Standardization of catch per unit of effort for yellowfin tuna caught in free 

schools by the EU purse seine during the 1991-2017 period 

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In previous years, the standardization of this CPUE has been presented to IOTC (Katara et 
al., 2018). In those attempts, a delta-lognormal GLMM approach was developed with two 
sub-models:  a  binomial  GLMM  that  standardises  the  probability  of  a  positive  set,  and  a 
lognormal GLMM that standardises catch conditional on the set being positive. In the 2019 
standardization (Guéry et al., 2019b), a positive set is taken to be a set with catch of the 
target species, so null sets are implicitly taken to indicate the absence of fish. However, 
bearing in mind the fact that null sets generally correspond to the presence of a fish school 
that simply avoided the net and that the abundance of large yellowfin may also be related 
to the number of free schools detected per unit of search time, a new modelling approach 
adopted for the Atlantic yellowfin stock assessment was used (Guéry et al., 2019a) based 
on a Delta-lognormal GLMM with 3 components: (i) a Poisson first component modelling 
the density of free schools based on the number of sets (positive and null) per unit of boat 
searching  time,  (ii)  a  binomial  second  component  modelling  the  fraction  of  free  schools 
possessing large yellowfin based on the fraction of positive sets (i.e.,  sets with catch of 
any  species;  non-null  sets)  with  catch  of  large  yellowfin,  and  (iii)  a  lognormal  third 
component modelling the size of free schools possessing large yellowfin based on catch of 
large yellowfin positive sets. Along with the commonly used covariates relating to vessel 
characteristics  and  spatiotemporal  variability,  the  originality  of  this  work  consists  in  the 
inclusion  of (i)  null  sets,  considered  as  presence  of  yellowfin  in  free  schools,  (ii)  fishing 
days without set, considered as absence of free schools, (iii) EU fishing agreement in the 
exclusive economic zones driving EU purse seine fleet presence in these areas, (iv) time 
spent  by  centroid  cell  by  boat  by  day  to  constrain  detectability,  (v)  the  Gulland’s  index 
(Gulland, 1956) of fishing effort concentration to measure the extent to which a fleet has 
concentrated its fishing effort in areas with higher than average catch rates and, (vi) piracy 
as a presence absence variable. Standardized CPUE for free schools was thus defined as 
the product of the number of sets (positive and null) per unit search time, the proportion 
of positive (non-null) sets containing large yellowfin (> 10 kg) and the catch per sets of 
large yellowfin. The production of the EU purse index was supported by the SC14 through 
funding the travel of EU scientists to the standardization workshop that was held in AZTI 
(Spain, May 27-30, 2019) but which was an objective of a different SC project (CECOFAD). 

With  regards  to  this  index,  the  WPTT  asked  for  clarifications  and  suggested  ways  of 
improvement (IOTC, 2019a): 

• 

• 

• 

The WPTT NOTED that purse seine free school catch rates are very high for several 
years around the 2006, so called “golden years”. This period was associated with 
unusual oceanographic conditions, that potentially could have increased abundance 
and/or  purse  seine  catchability.    The  inclusion  of  the  Gulland  index  in  the 
standardization interprets the peak as a catchability effect. 

The group NOTED that Somalian piracy should have a smaller effect on the purse 
seine CPUE than longline CPUE because security personnel reduced the impact of 
piracy on purse seine operations.  

The WPTT NOTED that purse seine free school sets are difficult to define and Fish 
Aggregating  Devices  (FAD)  might  have  an  effect  on  free-school  operations  and 
free-school searching behaviour. The authors informed the WPTT that free-school 
sets  occur  opportunistically  between  FAD  sets,  and  the  FAD  effect  on  the  index 

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should be minimized, because the CPUE index focuses only on large fish that do 
not associate with FADs. 

• 

• 

The WPTT REQUESTED standard CPUE “influence plots” to illustrate the effects of 
the individual co-variates over time. 

The  WPTT  NOTED  that  the  effect  of  technological  improvements  needs  to  be 
considered carefully. The authors informed the WPTT that most of the technological 
development in the purse seine fishery is thought to be aimed at improving FAD 
fishing;  however,  the  Spanish  purse  seine  fleet  prefers  the  more  valuable  fish 
associated with free schools.  

The  WPTT  agreed  to  explore  the  impact  of  this  index  in  the  stock  assessment  model  of 
yellowfin as a sensitivity test (IOTC, 2019a) but this was not done during the WPTT due to 
the lack of time. 

  A novel index of abundance of juvenile yellowfin tuna in the Indian Ocean 

derived from echosounders. 

The  collaboration  with  the  Spanish  vessel-owner  associations  and  the  buoy-provider 
companies, has allowed to recover the information recorded by the satellite linked GPS 
tracking  echosounder  buoys  used  by  the  Spanish  tropical  tuna  purse  seiners  and 
associated  fleet  in  the  Indian  Ocean  since  2010  (Santiago  et  al.,  2019b).  These 
instrumented buoys inform fishers remotely in real-time about the accurate geolocation 
of  the  FAD  and  the  presence  and  abundance  of  fish  aggregations  underneath  them. 
Apart from its unquestionable impact in the conception of a reliable CPUE index for the 
tropical purse seine tuna fisheries using FADs, echosounder buoys can potentially be 
privileged  observation  platforms  (Moreno  et  al.,  2016)  to  evaluate  tuna  abundances 
and  non-tuna  species  using  catch-independent  data.  Current  echosounder  buoys 
provide a single acoustic value without discriminating species or size composition of the 
fish  underneath  the  FAD.  Therefore,  it  has  been  necessary  to  combine  echosounder 
buoy data with fishery data, species composition and average size, to obtain a specific 
indicator. The working document by Santiago et al, (2019b) presents a novel index of 
abundance for juvenile yellowfin tuna in the Indian Ocean derived from echosounder 
buoys for the period 2010-2018, with the aim of contributing to the 2019 assessment 
of this stock. This index was used in the 2019 stock assessment of the Atlantic yellowfin 
(Santiago et al., 2019a). 
It is important to note that this index is not a result of SC14 but we consider that as it 
is an alternative index presented  for the  IOTC WPTT  we  should report on its nature, 
potential and current use and the feedback received by the WPTT.  

The WPTT encouraged further work on this promising new abundance index.  Potential 
avenues for further exploration included (IOTC, 2019a): 

• 

• 

Calibration of signal strength with biomass estimated from catch. 

Further comparisons by buoy brand and model. 

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• 

Sampling from time windows other than industry preferred set times. 

Consideration  of  alternative  time  windows  to  define  “virgin”  attraction 

• 
dynamics. 

Abundance indices used in the 2017 assessment of skipjack 

In the 2017 skipjack assessment two CPUE series were used, the Maldivian pole and line 
fisheries standardized data (2004-2015) (Medley et al., 2017) and the EU purse seine fleet 
CPUE (Katara et al., 2017). The grid of models used to provide scientific advice included 
both series in half of the simulation models and the Maldivian pole and line CPUE in the 
other half of the model grid. Both indices were presented and discussed in the 2017 WPTT 
and for both there is room for improvement. Noting that a new assessment of skipjack is 
scheduled for 2020 there are a number of aspects that could be addressed to improve the 
indices (IOTC, 2017): 

  Maldivian pole and line: 

•  Results suggest that the fishing power of the fleet has decreased, and this is unlikely 

because generally fishing power increases over time. 

•  The  WPTT  noted  a  potential  bias  in  the  index  due  to  an  unbalanced  design  with 

fishing effort not distributed across all the fishing area evenly.  

  European purse seine fishing on FADs: 

•  The standardized CPUE and the nominal CPUE are similar, which may suggest that 
the standardization may have not effectively incorporated the effects of technology 
change, which for purse seiners is believed to be large in recent years. To improve 
this,  it  was  suggested  that  covariates  relating  to  fishing  efficiency  be  effectively 
incorporated into the time series. 

•  The  WPTT  also  suggested  that  alternative  units  of  effort  could  be  explored  to 

improve this index. 

In the 2017 WPTT, there was one more CPUE presented but it was not used in the grid of 
models used to provide scientific advice, the Sri Lankan gillnet CPUE (Haputhandri, 2017). 
This was the first attempt towards a CPUE standardization by Sri Lanka, and also for gillnet 
fisheries.  For  this  reason,  the  WPTT  encouraged  to  continue  refining  and  improving  the 
standardization  with  a  view  to  incorporate  it  to  the  assessments,  ideally  using  detailed 
logbook data available since 2014 

1.4.2. Advanced protocol for joint cpues iterations 

The recent assessments of bigeye (2019) and yellowfin (2018) used a combined longline 
index (also named Joint longline index) only in the group of model runs used to provide 
scientific advice on stock status. This means that  stock status estimations and bigeye and 
yellowfin historical trends have relied only in the longline index. In the SC14 we have not 
produced something that could be called an advanced protocol for joint CPUEs iterations. 
Instead,  we  describe  how  the  joint  longline  index  was  produced  in  2019  and  discuss 

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potential  issues  with  it.  This  index  is  the  result  of  a  collaborative  study  conducted  by  a 
contracted expert with the IOTC Secretariat and experts from the CPCs with longline fleets 
operating  in  the  Indian  Ocean.  These  include  fleets  from  Japan,  Korea,  Seychelles  and 
Taiwan (Hoyle et al., 2018). This method has been used to produce joint longline indices 
for  other  stocks  in  the  IOTC  and  other  tuna  RFMOs.  This  study  aimed  to  validate  and 
improve  the  current  methods  for  developing  indices  of  abundance  for  the  main  IOTC 
species and used data provided by key industrial fleets. Data for the four national longline 
fleets were standardized for each region considered in the stock assessment. The resulting 
indices were produced after exploring several modelling and data transformation methods 
to reduce uncertainty and to improve model fits.  

However, this standardized method appears to present a number  of problems (Sharma, 
2018):  

First, the longline fleets’ spatial coverage has been reduced notably in recent years due to 
piracy  in  the  fishing  areas  off  Somalia  (Western  Indian  Ocean).  Figure  17  shows  that 
Japanese longline fleet effort concentrated in waters off Somalia (R2) but its activity has 
been almost negligible in the last decade. This is the most productive area for yellowfin, 
and it may be influencing the CPUE reduction since 2007.  

Figure 17. Distribution of fishing effort for the Japanese longline fleet (aggregated for 5 year periods). 
X-axis is longitude and y-axis is latitude. 

By reducing the data coverage in recent years, the standardization of the joint index may 
be underestimating yellowfin abundance and needs further exploration. This matter was 
again raised in the meeting for the development of joint longline indices for 2019 (IOTC, 
2019b) but there is still uncertainty on the representativeness of the index to characterize 
fish abundance dynamics in the recent years. 

The second concern on the longline indices is the potential impact of unreported discards 
of  small fish on these fleets.  This practice is known as high-grading and if confirmed, it 

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may lead to underestimating catches and introduce bias in the abundance index. Figure 18 
shows the change in the size proportion for longline fleets from Taiwan since 1979 (light 
and dark blue lines). This figure suggests that after 2004, the proportion of small fish is 
almost negligible. This date corresponds to a national regulation that imposed catch limits 
to  Taiwanese  longlines  and,  due  to  market  conditions,  could  have  created  incentives  to 
retain larger, high-value fish and discard smaller fish. However, this absence of small fish 
comes in contradiction with length data collected by observers in recent years (2010-17), 
for all fleets combined, which contain small fish (light and dark green lines). Considering 
that  lengths  and  numbers  of  fish  are  collected  by  longline  capitains,  it  is  assumed  that 
underreporting  of  lengths  might  lead  to  underreporting  of  fish  numbers,  meaning  that 
some  of  the  fish  caught  by  longlines  may  not  be  recorded  in  logbooks.  Note  also  that 
discards  are  not  included  in  the  logbooks  and  that  this  could  cause  problems  in  the 
standardization of the joint index. For instance, there could be problems if the discard rate 
changes  over  time,  meaning  the  size  selectivity  used  to  standardize  the  index  could  be 
biased.  However,  the  extent  to  which  such  underreporting  has  affected  fish  number 
reporting in logbooks is still to be elucidated and needs further exploration. Chinese Taipei 
scientists have also acknowledged discards in longline fleets since 2017, when catch limits 
for  yellowfin  were  established  as  part  of  the  rebuilding  plan  for  this  stock  (Resolution 
16/01), which have indeed been included in the standardization of the joint longline index 
prepared for the 2019 stock assessment (IOTC, 2019b).  

In  summary,  unaccounted  discards  can  introduce  bias  to  length  samples  and  nominal 
catches and undermine the interpretation of CPUE since 2004.  

Figure 18. Proportion of yellowfin smaller and larger than 100 cm from Chinese Taipei longline fleets. 

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1.4.3. CPUEs for the entire indian ocean and subregions 

The  CPUEs  for  the  different  regions  of  the  Indian  Ocean  were  calculated  in  the  Joint 
Longline  workshop  (IOTC,  2019b)  for  the  four  areas  included  in  the  initial  model 
configuration  (Figure  19).  These  indices  were  also  calculated  for  2  and  1  area 
configurations  to  investigate  alternative  spatial  configurations  of  the  yellowfin  stock 
assessment model (Figure 20). 

Figure  19.  Indices  of  CPUE  at  annual  scale  for  the  four  regions  considered  in  the  Indian  Ocean 
yellowfin assessment as estimated in the Joint Longline CPUE index workshop (IOTC, 2019b). 

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Figure 20. Indices of CPUE at annual scale for the one and two area configurations of the Indian Ocean 
yellowfin assessment as estimated in the Joint Longline CPUE index workshop (IOTC, 2019b). 

2.  MODEL UNCERTAINTY 

The  objective  of  this  task  is  to  review  and  when  possible  improve  the  current  stock 
assessment model of the Indian Ocean yellowfin and also to review the stock assessment 
specifications of the two other tropical tuna stocks (bigeye and skipjack). This task includes 
five subtasks in relation to the assessment of yellowfin carried out between 2018 and 2019 
(included in the subtask 2.1 of the ToRs): (2.1.1) Use of alternative assessment models, 
(2.1.2) Develop diagnostics in support of WPPT decisions on: SS3 alternatives (2.1.2.1), 
integrating models ensembles (2.1.2.2.), deciding on alternative options (2.1.2.3.), (2.1.3) 
Review  the  spatial  structure  of  yellowfin,  (2.1.4)  Protocol  for  exploring  statistical 
uncertainty  and  (2.1.5)  investigate  alternative  values  of  key  parameters.  We  have 
completed the report in this order but there are specific points that are addressed in more 
than one section. Furthermore, the main objective of the SC14 was to produce a new stock 
assessment proposal for yellowfin for the 2019 WPTT and the proposed model for 2019 is 
also described. 

2.1 Alternative/complementary models 

2.1.1. Use of alternative assessment models and parameter settings 

One  of  the  objectives  of  this  specific  contract  was  to  propose  a  new  stock  assessment 
model  configuration  for  yellowfin  in  the  2019  WPTT.  This  new  proposal  is  described  in 
section  3  and  here  we  discuss  the  options  different  from  Stock  Synthesis  to  assess  the 
yellowfin stock.  

Two stock assessment analyses different from the Stock Synthesis were presented to the 
2019 WPTT, only one of them being accomplished as part of this SC14:  

-  First, as part of this SC14, the biomass dynamic model mpb (Kell, 2016), was used 
to  explore  the  potential  impacts  of  the  existing  uncertainty  on  catch,  abundance 
indices,  population  dynamics  and  the  searching  space  for  parameter  estimation 
(Merino  et  al.,  2019b).  The  results  shown  to  the  WPTT  estimate  a  range  of  very 
different stock status and productivity and corroborate that the existing uncertainty 
on  this  fishery  can  lead  to  opposite  stock  status  estimates.  The  model  was 
accompanied  with  several  diagnostic  tests,  which  suggested  that  the  modelling 
choices  such  as  the  constrains  on  the  searching  space  of  parameters,  initial 
parameter values and, the shape of the production function considered, would be 
more  influential  on  the  stock  status  outcomes  than  the  choices  on  the  available 
datasets. With regards to this study, the WPTT noted the following (IOTC, 2019a): 

•  The  WPTT  NOTED  that  this  work  was  undertaken  as  part  of  the  2018  SC 
request to try and improve the 2018 yellowfin stock assessment advice using 
multiple  models.  However,  the  WPTT  concluded  that  this  model  only 

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accounts  for    observation-error  and  therefore  it  may  not  offer  any  new 
insights to improve the current YFT management advice. 

-  Second,  an  alternative  assessment  using  the  Bayesian  surplus  production  model 
JABBA (Winker et al., 2018) was presented with generic diagnostics of goodness of 
fit (Kell and Sharma, 2019). The objective of this work was to assist the IOTC SC 
in  providing  robust  management  advice  for  yellowfin  by  evaluating  alternative 
methods and scenarios that reflect the uncertainty on the assumptions and datasets 
used in the assessment (Kell and Sharma, 2019). All the scenarios evaluated with 
this model indicate that the stock is overfished and undergoing overexploitation, in 
line with the results of the 2018 stock assessment for yellowfin. An important aspect 
of this study is that it provides a generic set of diagnostics that allows models to be 
validated using prediction residual and prediction skills. Importantly the framework 
allows model performance to be compared across different platforms with different 
model assumptions and datasets. In this regard: 

•  The WPTT NOTED that the paper had some good examples of model diagnostics 
that  might  be  applied  more  generally  in  the  IOTC  stock  assessment  process. 
The models were not intended to provide management advice but rather insight 
into  uncertainty  in  the  IOTC  yellowfin  dynamics.  The  WPTT  NOTED  that  the 
paper provided examples of diagnostics that can be applied more generally in 
the IOTC stock assessment process for model validation, i.e. based on prediction 
skill  and  runs  tests.  The  models,  however,  were  not  intended  to  provide 
management  advice  but  to  provide  insight  about  uncertainty  in  IOTC  YFT 
population dynamics.  

With regards to bigeye and skipjack, models different from SS3 have also been used and 
the results obtained have differed qualitatively in some cases (Figure 21). For example, for 
the 2019 stock assessment of bigeye a JABBA (Winker et al., 2018) was presented to the 
WPTT.  The  WPTT  considered  that  the  JABBA  model  does  not  account  for  the  increased 
proportion  of  purse  seine  catches  of  this  stock,  which  may  have  led  to  a  change  in  the 
overall  selectivity  of  the  stock.  On  the  contrary,  this  change  is  accounted  for  in  Stock 
Synthesis and therefore, it was the only model used for management advice. However, the 
WPTT also noted that surplus production models like JABBA remain particularly relevant 
for  developing  model-based  harvest  control  rules  and  to  test  management  procedures 
(IOTC, 2019a). 

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In 2017, the biomass dynamic mpb model for skipjack presented to the WPTT was  only 
preliminary and it was not considered for advice (IOTC, 2017).  

The use of alternative models in the tropical tuna stock assessments is one of this project’s 
objectives  and  the  WPTT  has  stressed  the  need  to  use  these  models  with  a  full  set  of 
diagnostics and fully analysed so they are potentially included for advice. At this moment, 
they are seldom used for anything more than data exploration and preliminary analyses. 

2.1.2. Development of diagnostics in support of WPPT decisions 

The 2019 WPTT agreed that each stock assessment model would need to be accompanied 
by several goodness of fit diagnostics. The goodness of fit of a model describes how well it 
fits a set of observations. Measures of goodness of fit typically summarize the discrepancy 
between  observed  values  and  the  values  expected  under  the  model  in  question  (as 
described in Wikipedia). In the WPTT, diagnostic of fits are often presented but until 2019 
there  has  not  been  a  protocol.  The  diagnostics  that  will  be  requested  for  each  stock 
assessment meeting include: 

•  Likelihood  profiles:  This  measures  the  goodness  of  fit  of  a  statistical  model  to  a 
sample of data for given values of the unknown parameters. It is formed from the 
joint probability distribution of the sample but viewed and used as a function of the 
parameters only, thus treating the random variables as fixed at the observed values 
(Wikipedia). When the likelihood of the model converges into a marked minimum it 
is considered that the model converged to a solution. Sometimes, the function of 
likelihood is not as sharp or does not have a clear minimum. Then, it is considered 
that  the  model  is  not  converging  adequately.  Usually,  the  shape  of  the  negative 
log-likelihood  becomes  steeper  when  there  is  greater  certainty  in  the  estimated 
parameters. 

• 

Jitter  analysis:  This  is  carried  out  adding  random  noise  to  a  vector  of  numeric 
values, which is used to assess the stability of the parameters obtained in a fit to 
data. If the output parameters with and without the added random noise remain 
constant,  this  indicates  stability  and  robustness  of  the  fits.  When  the  estimated 
parameters are different depending on the random noise then the fits are not stable 
and indicates a poor fit to the data. 

•  Hindcast  analysis:  It  is  a  way  of  testing  a  mathematical  model.  Using  known 
estimated  inputs  for  past  events  into  the  model,  these  are  compared  to  model 
outputs without these data to see how well the output matches the known results 
(adapted from Wikipedia). When new data is consistent with the model prediction 
for that period it is considered a good hindcast and if not, it is considered a poor 
hindcast and therefore, a worse model fit. A good hindcast suggests that the model 
has good predicting power and the confidence in the projections increases.. 

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•  Retrospective analysis: This is a research method that is used when the outcome 
of  an  event  is  already  known.  Known  events  are  compared  to  model  predictions 
without  the  control  variables  for  that  year  (adapted  from  Theclassroom.com). 
Retrospective analyses in fisheries modelling are carried out by producing new fits 
by sequentially removing the last fishery observations (generally CPUE). When the 
model  estimates  trajectories  that  are  very  different  from  the  estimated  with  the 
complete data series the model is considered unstable and depends too much on 
the  latest  data  point.  Therefore,  it  is  considered  that  it  has  a  poor  retrospective 
pattern and further improvements to the model are necessary. 

Many of these diagnostics were included in the stock assessments prepared for the 2019 
WPTT. IOTC’s Working Party on Methods (WPM) and WPTT recommend using these types 
of tests in all the stock assessments of the IOTC. 

2.1.2.1. SS3 alternatives 

A  tool  to  evaluate  model  fits  to  available  data  was  developed  specifically  for  the  stock 
assessment model used for yellowfin and the other two tropical tuna stocks: the ss3diag 
(Winker et al., 2020). This library was used to run diagnostic tests such as:  

i.  the root-mean-square-error that helps evaluating if the residuals of fits to the CPUE 

indices and size frequency data are normally distributed (Likelihood).  

ii.  Retrospective analyses to evaluate the reliability of parameter and reference point 
estimates  and  to  spot  possible  systematic  bias  in  the  estimated  trends.  This 
procedure consists of fitting a stock assessment model to the full data set and then 
do likewise with truncated data sets, where the latest data points are sequentially 
removed.  

iii.  Hindcasting analysis allows to evaluate the model prediction skills. A model is fitted 
to  the  first  part  of  the  time  series  and  then  projected  using  catch  data  over  the 
assessment period. The model prediction skill can be evaluated by comparing the 
predictions from the projection with the observed data.  

The  use  of  the  R  library  ss3diag  (https://github.com/jabbamodel/ss3diags,  Winker  et  al 
2020)  helps  scoring  model  runs  based  on  their  diagnostics  analyses.  The  scores  can  be 
binary  (pass,  fail)  or  quantified  across  analyses  to  sort  the  best  performing  models  and 
keep them as a reference grid of models. For example, these analyses can help deciding 
between quarterly and annual with seasons models to build the grid upon, or they can also 
be  used  to  select  a  reduced  number  of  models  for  scientific  advice  on  stock  status  and 
projections  with  different  catch  levels.  The  tool    has  been  developed  further  to  help 
improving the Indian Ocean yellowfin assessment and it is expected that it will continue to 
do so in 2021. The WPTT in 2020 noted that this is one way to select a range of models to 
provide  advice  but  that  the  WPTT  is  still  learning  how  to  determine  which  models  are 
plausible or not. For this reason, the conditions that a model would need to fulfil will need 
to be defined. For example, what is the minimum, score in the diagnostic tests that would 
make the model acceptable.  

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2.1.2.2. Integrating ensemble models 

The  R  library  ss3diag  can  also  be  used  to  produce  diagnostics  of  model  fits  for  models 
carried out using different platforms. For example, the results of biomass model could be 
combined with the output of SS3. This was the case for Atlantic yellowfin, were the output 
of SS3 was combined with output from JABBA and mpb (two biomass dynamic models (Kell 
et al, 2016; Winker et al, 2018).  

2.1.2.3. Decision on alternative options 

When  making  decisions  on  the  models  that  are  considered  plausible  and  therefore,  the 
models that are used to provide scientific advice, diagnostics can be a useful tool. However, 
it is important to note that there is no perfect  way to select a range  of models and the 
diagnostics also contribute to a better understanding of the model itself and how it is using 
all the data available to estimate biomass and biological parameters. A methodology for 
evaluating  models  through  assessing  their  fit  to  data,  their  prediction  skills  and  other 
analyses will support the selection of models for yellowfin in 2021 and other stocks in the 
WPTT.   

2.1.3. Review of yellowfin assessment (and some aspects of bigeye) 

In 2018, the yellowfin stock status was  estimated using Stock Synthesis (Methot Jr and 
Wetzel, 2013), a highly parameterized age-structured stock assessment software. Several 
issues were identified by the external reviewer to the 2018 Working Party of Tropical Tunas 
(WPTT), another revision submitted (in kind) by the Stock Synthesis model developer (Dr 
Richard  Methot)  with  specific  recommendations  and,  a  summary  of  the  issues  found 
between  the  end  of  the  2018  WPTT  and  the  2018  IOTC  Scientific  Committee,  which 
culminated on the absence of catch recommendations and the establishment of the 2019 
workplan for yellowfin (IOTC, 2018a). This section is relatively complex and technical and 
reference to documents and the stock assessments are frequent. However, we have tried 
to express technical details in a comprehensive manner.  

A number of issues were identified by the external reviewer to the IOTC WPTT (Sharma, 
2018). These can be summarized as follows (directly taken from his report submitted to 
the 2018 Scientific Committee meeting (Sharma, 2018)): 

•  There are conflicting data issues that may create problems in the assessment. These 
issues  have  been  presented  and  duly  commented  in  the  section  1  of  this  report. 
(Uncertainty on data). 

•  Preliminary analysis using hindcasting diagnostic techniques suggest that the model 
has poor predictive power. The predictive power of numerical models is tested with 
this technique. The time span of the estimated trend is shortened, and the resulting 
dynamics  are  projected  forward  using  real  historical  catch  data.  The  predicted 
trajectory is compared with the estimated trajectory of the model fit to the entire 
series of data.  

• 

In  stock  assessments,  special  attention  needs  to  be  put  into  characterizing 
uncertainty. In the 2018 stock assessment approaches dealing with uncertainty and 
projections  were  not  given  due  importance  but  these  are  critical  for  stock  status 

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and  management  advice.  The  2018  model  characterized  uncertainty  only  by 
running the stock assessment model under alternative assumptions on a number of 
parameters  (tag  mortality,  steepness,  dynamic/static  catchability,  and  weight  to 
tagging data). However, resampling techniques used to characterize statistical or 
aleatoric uncertainty were not used in 2018  .   

•  Simplified stock assessment models were not examined in detail in the 2018 WPTT. 
SS3  was  the  only  model  that  was  presented  with  diagnostics  during  the  WPTT. 
Simpler  analyses  such  as  biomass  dynamic  models  would  be  useful  as  data 
exploratory tools. Preliminary runs using the Bayesian biomass surplus production 
model  JABBA  (Winker  et  al.,  2018)  showed  different  estimates  compared  to  the 
main stock assessment (Figure 22).  

Figure 22. Left: Preliminary run using the Bayesian biomass dynamic model JABBA submitted by the 
external  reviewer  to  the  2018  WPTT  (Winker  et  al  2018).  Right:    the  estimated  trajectory  by  the 
Reference  model  of  the  2018  stock  assessment.  Black  dots  indicate  the  trajectory  of  the  point 
estimates for the B/BMSY and F/FMSY ratios for each year from the reference case model (1950–2017) 
and  white  dots  indicate  the  terminal  estimates  from  each  of  the  24  grid  models  and  intermediate 
(1997) and initial years (1950).  

•  Diagnostics  could  be  used  to  determine  which  models  are  more  appropriate  and 
preliminary  results  show  that  model  parsimony  often  provide  better  predictive 
power and follow abundance trends better than over-parameterized models. 

•  Using  a  grid  structure  provides  context,  but  examining  these  in  more  detail  may 
indicate some models initially considered as plausible are not plausible in reality. In 
the 2018 stock assessment the grid structure that included options for steepness, 
tag mortality, weight of tagging data and weight of longline CPUE in two different 
periods was not examined in detail to assess plausibility of different runs.  

•  Arbitrary  decisions  on  which  final  models  to  use  for  advice  in  the  reference  grid 

were developed without a thorough analysis. 

•  Another major issue was that differences between global and local minima were not 
examined  during  the  stock  assessment.  Specific  diagnostics  such  as  jitters  or 
retrospective analyses were not sufficiently analysed. 

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•  No considerations were given to future population status with catch projections. It 
is not “where we were” but “where we are going” that will drive the best scientific 
based management recommendations. Considerations should be given to this, but 
unfortunately no discussion or time was spent on this matter. 

Under this Specific Contract, in line with the IOTC workplan for yellowfin, another review 
of  the  yellowfin  model  configuration  was  requested  to  the  main  developer  of  Stock 
Synthesis  (Methot  Jr,  2019).  The  most  relevant  points  of  this  review  are  listed  below. 
Overall, the recommendations were divided in three categories: (1) comments on the area 
configuration of the model and its support in the tag-recapture data; (2) technical aspects 
of  configuring  the  SS  model;  (3)  comments  on  the  grid  that  constitutes  a  24  model 
ensemble  configuration.  A  summary  of  the  main  issues  and  recommendations  from  the 
external review are listed below: 

•  Area configuration and movement:  

o  The available tagging data do not seem sufficient to estimate all area effects 

and movement included in the model.  

o  Given  problems  with  estimations  of  movement  between  areas,  a  simpler 

geographical structure is proposed with only two areas. 

•  Technical details: 

o  Some problems are identified in the fits from length composition data. This 
means that the estimated length-frequency for the catch of some fleets is 
different  from  the  raw  data.  To  better  understand  and  solve  this,  it  is 
recommended to use mean length-at-age data vectors for some fleets and 
compare it with Stock Synthesis model estimates. 

o  There is a potential need for more time-varying selectivity, as one important 

issue for improving the model. 

o  The  parameterization  of  recruitment  in  the  2018  stock  assessment  is 
overcomplicated.  Two  areas  get  recruitment,  and  the  current  model 
configuration  specifies  a  recruitment  offset  for  each  area,  which  is 
unnecessary.  

o  There  are  too  many  estimated  movement  parameters.  Of  all  the  fish 
currently  in  an  area,  one  set  of  parameters  is  needed  for  each  area  that 
some of these fish move into, but the fish that remain in their original areas 
do not need parameters.  

o  Selectivity is currently set relative to age when it should be relative to length. 
o  The fits to each fleet length composition needs improving. As said earlier, 

this could be done by changing selectivity time blocks. 

•  Grid ensemble: 

o  The rationale for settling the factors of the 2018 model grid is questionable. 
There are factors for which the degree of uncertainty has greater impact on 
assessment  results  than  the  factors  considered  in  2018  which  were 
steepness, tag mortality, weight of tagging data and weight of longline CPUE 

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in two different periods. There should be more attention paid to other fixed 
factors (regional scaling factors, natural mortality, growth, and others).  

o  Two of the factors (steepness and the two periods for longline CPUE) may 
not be a good choice as some may not have much impact on the assessment. 

Finally, we explain the main reason to recommend a workplan to improve the advice given 
based on the yellowfin stock assessment. We do this by showing the forward projections 
of the reference case of the 2018 stock assessment model with current catch and with 80% 
from  current  catch  (Figure  23).  Projections  show  that  in  one  to  two  years  fish  and  the 
fishery crash. This behaviour demonstrates that the model is unstable, which is a major 
concern (as also identified by the external reviewer).  

Figure 23. Projections of the 2018 reference case with constant catch after 2017 (black line) and with 
a reduction to 80% of current catch (dashed line). Left is relative fishing mortality (F/Fmsy) and right 
is relative biomass (B/Bmsy). In blue is the projection period. 

If the model was stable, excessive catches would cause a gradual collapse of the stock but 
this  would  follow  a  declining  trend  towards  total  depletion.  What  we  can  see  in  these 
projections is that there is something in the model that causes a crash. The causes of which 
were not fully understood until 2020 (see section 3.2).  

Summary of external reviews to the assessments of tropical tunas 

The external expert to the WPTT has stressed a number of issues with the stock assessment 
models that are common to the three tropical tuna stocks. For all, conflicting data issues, 
problems  associated  to  purse  seine  fleets’  technological  improvements  and  lack  of 
exploration of alternative weightings to data appear to create problems in the assessments 
(Sharma, 2017, Sharma, 2018, Sharma, 2019). For all, the expert identified issues with 
length  frequency  data  from  longliners,  which  mostly  impact  bigeye  and  yellowfin 

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assessments. Also, the data conflicts identified correspond to the information contained in 
CPUE and tagging data, which lead to contrasting outcomes of the stock assessments. The 
reviewer  indicated  that until  2019,  few  efforts  were  dedicated  to  the  characterization  of 
statistical uncertainty, but he acknowledges that this issue seems solved with the method 
presented and used in the 2019 WPTT. 

With  regards  to  the  positive  aspects  of  the  assessments,  he  evaluates  the  process  as 
“transparent”  and  that  most  of  the  unresolved  issues  were  caused  by  the  lack  of  time 
available to examine all data. In 2020, the WPTT hosted one additional Data Preparatory 
meeting (videoconference, 22-24 June) that has helped improving this aspect in 2020 and 
will continue to do so in 2021, when another Data Preparatory meeting is scheduled. 

Spatial structure 

Spatially  disaggregated  stock  assessment  models  such  as  Stock  Synthesis  adopt  region 
structures  to  account  for  differences  in  biological  characteristics  of  the  species,  regional 
exploitation patterns, or the level of mixing amongst subpopulations (Vincent et al., 2018).  

The  geographic  area  considered  in  the  assessments  of  tropical  tunas  in  the  IOTC  is  the 
Indian Ocean, defined by the coordinates 40ºS-25ºN, 20ºE-150ºE.  

For  yellowfin,  the  reference  case  model  of  the  2018  assessment  adopted  a  four-region 
model structure (Figure 24) and retains two regions that encompass the main year-round 
fisheries  in  the  tropical  area  and  two  austral,  subtropical  regions  where  the  longline 
fisheries occur more seasonally (Fu et al., 2018). This structure was hotly debated in the 
2019 WPTT and options for one, two and three areas where explored using Stock Synthesis 
(Urtizberea et al., 2019). The strengths and weaknesses of the different spatial aggregation 
options were discussed in the WPTT. The WPTT agreed on keeping the two and four area 
configurations to retain the influence of tagging data on the assessment.  

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Figure  24.  Spatial  stratification  of  the  Indian  Ocean  for  the  four-region  assessment  model  for 
yellowfin. The black arrows represent the configuration of the movement parameterisation of the 
base  assessment  model.  Note  that  there  are  two  subregions  for  area  R1  (R1a  and  R1b,  which 
were separate regions of previous versions of the model). 

With  regards  to  bigeye,  the  structure  of  the  Stock  Synthesis  model  also  contained  4 
regions,  but  these  are  slightly  different  to  yellowfin  (Figure  25).  This  structure  was 
designed  to  accommodate  the  distribution  of  fleets,  differences  in  regional  abundance 
trends  and  the  available  tagging  data  (Fu,  2019).  Models  with  a  simplified  regional 
structure (1 area) were also explored: the 1-region model estimated a much lower overall 
biomass than the 4-area model. This illustrated the potential bias that may arise when the 
model fails to account for the incomplete tag mixing via appropriate spatial partitioning of 
the  tagging  data  (Fu,  2019).  Noting  the  impact  that  tagging  data  has  on  the  stock 
assessment outcome (see section 1.2.3), getting the most appropriate spatial structure for 
these models is paramount. Also, the external reviewer to the WPTT has suggested simpler 
spatial structures and reducing the weight of tagging data in the assessments as a way to 
improve current models (Methot Jr, 2019, Sharma, 2018, Sharma, 2019). 

The  3-area  model  configuration  accounts  for  the  potential  lack  of  mixing  of  the  tagging 
data  but  ignores  other  aspects  like  the  diffusion  of  tags  into  the  northern  areas  of  the 

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western equatorial region. The 3 area model results in worse fits to CPUE and length data 
and therefore, the 4 area model was preferred (Fu, 2019).  

Figure 25. Spatial stratification of the Indian Ocean bigeye for the four-region assessment model. 

In the 2017 Stock Synthesis configuration for skipjack was simpler than for yellowfin and 
bigeye. The model used to provide scientific advice contained one area only and the two 
area  model  configuration  was  explored  too  (Fu,  2017).  This  model  examined  the  entire 
Indian Ocean as one unit for skipjack, with the different fisheries operating all on the same 
population. Tagging data suggest that skipjack migrates quickly but the limited distribution 
of  tag  releases  and  the  small  number  of  returns  outside  the  purse  seine  fleets  (mostly 
operating in the western equatorial area of the Indian Ocean) make it difficult to estimate 
large scale movements (Fu, 2017). 

The two-area model explored for skipjack assumed that purse seine fleets are operating in 
the  western  area  and  the  Maldives  pole  and  line  fishery  operates  in  the  eastern  Indian 
Ocean  (Figure  26).  This  exploratory  analysis  concluded  that  there  is  not  enough 
information on the most appropriate structure and the simplest configuration was preferred 
(IOTC, 2017, Fu, 2017). 

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Figure 26. Areas used in two-area spatial structure model for skipjack (Fu, 2017). 

One  of  the  biggest  challenges  of  the  tropical  tuna  stock  assessments  is  to  find  the  best 
way of using the available tagging data and to model explicit movement of fish across the 
Indian  Ocean.  Alternatives  for  both  produce  very  different  outcomes  of  the  stock 
assessment and therefore, the advice provided to the Commission. A potential way forward 
to improve the spatial aggregation of tropical tuna models was the participation in the 2020 
Workshop on Spatial Stock Assessment Methods in the context of the 2020 World Fisheries 
Congress in Adelaide (Australia) (postponed to 2021).  

2.1.4. Protocol for statistical uncertainty 

In  the  stock  assessments  of  tropical  tunas  prior  to  2018,  the  impact  of  statistical 
uncertainty  was  not  explored.  This  is  usually  done  using  statistical  techniques  such  as 
bootstrapping,  Hessian  matrices,  Delta  methods  or  other  re-sampling  methods.  In  the 
2019  WPTT  a  study  presented  an  application  of  a  multivariate  lognormal  approach  to 
estimate statistical uncertainty on stock status estimates and future projections for Indian 
Ocean yellowfin tuna (Winker and Walter, 2019). This method was first used in the 2018 
stock assessment of Atlantic bigeye (ICCAT, 2018). This method was also presented to the 
2019 Working Party on Methods (WPM) to illustrate the statistical uncertainty on the 2018 
assessment of yellowfin (Figure 27). The WPTT noted the importance of this approach and 
recommended  its  use  (IOTC,  2019),  because  it  provides  a  visual  representation  of 
uncertainty within and among models that describes the expected covariance among stock 
status indicators.  

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Figure 27. Exploratory analysis of the statistical uncertainty associated to the 24 Stock Synthesis 
model runs used in 2018 to provide stock status of yellowfin. In different colors the deterministic 
stock status estimates for each scenario. 

Figure  27  shows  that  accounting  for  statistical  uncertainty  would  lead  to  stock  status 
determination of Overfished (B<Bmsy) and Overfishing occurring (F>Fmsy) in 82.4% of 
probability. In 2018, this probability was estimated directly from the deterministic fits and 
was 94%.  

This method was used  for the 2019 stock  assessment of bigeye. From the deterministic 
fits of 18 model configurations (coloured points in Figure 28, top-right), a cloud of points 
is generated (white dots), and from these, the probabilities of different stock  status are 
estimated  (probabilities  of  being  in  the  green/yellow/orange/red  quadrants  of  the  Kobe 
plot, Figure 28 left). This method will be used in future assessments of the WPTT. 

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Figure 28. Exploration of statistical uncertainty for Indian Ocean bigeye (IOTC, 2019a). 

2.1.5. Alternative key parameters 

When making predictions with computer models two sources of structural uncertainty are 
encountered: First, model uncertainty is the uncertainty about the functional relationships 
between inputs and outputs (Strong and Oakley, 2014) and the input uncertainty is that 
arising when there is no certainty about input parameters or the quality of the different 
sources of information. The first is generally characterized using stock assessment models 
developed  under  different  principles  (see  previous  sections)  and  the  second  is 
characterized through alternative scenarios built using different key parameters as input 
and  using  (or  weighting)  alternative  series  of  information.  In  the  2017,  2018  and  2019 
assessments  of  the  WPTT,  the  uncertainty  on  tunas’  biological  characteristics  was 
characterized through options for  each parameter in a structural uncertainty grid (Table 
5). 

Table  5.  Key  parameters  included  in  the  structural  uncertainty  grids  of  the  most  recent  stock 
assessment models. (*) Model developed in 2019 but not used for advice. 

Stock 

Biological parameter (and definitions) 

Yellowfin 

-Tag mortality: Mortality associated with tagging and by tag shedding (Hoyle et al., 
2015). It affects the fishing mortality parameter estimated from tagging data, which 
is  higher  for  higher  values  of  tag-mortality.  The  values  used  in  the  2018  stock 

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assessment were 10% and 27.5%. In 2019, the WPTT agreed to use only the 27.5% 
value based on Hoyle et al (2015). 

- Steepness: The steepness of the stock-recruitment relationship (h) is defined as 
the  fraction  of  recruitment  from  an  unexploited  population  obtained  when  the 
spawners are at 20% of the unexploited level (Lee et al., 2012). The values explored 
are 0.7, 0.8 and 0.9. 

-  Natural  mortality*:  A  technical  term  which  refers  to  the  proportion  of  the  fish 
population  dying  by  any  causes  other  than  fishing.  Two  age-specific  vectors  were 
used, one based on previous assessments and another one 40% larger.  

-  Growth*:  Term  used  to reflect  the  production of fish  after  a certain period.  Two 
different  growth  curves  were  tested  (Dortel  et  al.,  2015,  Fonteneau  and  Gascuel, 
2008) 

Bigeye 

Skipjack 

- Steepness: Values of 0.7, 0.8 and 0.9. 

- Steepness: Values of 0.7, 0.8 and 0.9. 

- Natural mortality: Constant across ages and fixed and estimated with differences 
per age classes. 

With regards to yellowfin, two new factors (growth and natural mortality) were added in 
2019 (see section 3 below). The WPTT noted that in the Atlantic stock of yellowfin a new 
approach was adopted to estimate natural mortality from the maximum age of fish (Then 
et al., 2015). The group decided to use this approach and make natural mortality a factor 
of uncertainty with two options. With regards to growth, the equation used until 2018 was 
the same as estimated by Fonteneau and Gascuel (2008). However, the WPTT discussed 
the potential benefit of using a more updated growth curve from Dortel et al (2015), which 
was considered only for sensitivity analysis in the 2018 stock assessment, and decided to 
keep both options as factors of the uncertainty grid.  

3. PROPOSAL FOR A NEW STOCK ASSESSMENT OF YELLOWFIN IN 2019 

As a result of the data and models review and improvements made during the first four 
months of this project, a new SS3 model configuration for yellowfin was proposed to the 
WPTT (Urtizberea et al., 2019). This new configuration accounted for the recommendations 
of  the  external  expert  to  the  WPTT  in  2018  (Sharma,  2018)  and  by  another  recognized 
expert (Methot Jr, 2019). This new model the result of various improvements made to the 
2018 stock assessment and updates with the most recent information. The most important 
features of the 2018 reference model were: 

−  Temporal stratification: 4 seasons 
−  Period of the assessment: 1950-2017 
−  Age-structured population: Ages from 0 to 7 (0-28 in quarters of a year). 
−  4 areas: R1 (R1a+R1b), R2, R3 and R4. 
−  Recruitment in tropical areas (R1 and R4). 
−  Fish migration between areas: R1-R2, R1-R4, R3-R4 

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−  25 fleets defined including (gillnets, handline, longline, baitboat, purse seine free 
school, purse seine on FADs, troll, distant water longline, and a pool of gears 
named as Others). 

−  4 Joint longline indices (one per region). 
−  Catch data from 1950-2017 
−  Length composition until 2014 (due to low confidence in most recent data) 
−  Tagging data 2005-2007 
−  Steepness: 0.8 (sensitivity of 0.7 and 0.9) 
−  SigmaR: Deviation from the stock-recruitment curve allowed (0.6). 
−  Natural mortality at age (from WCPFC) 
−  Growth from Fonteneau et al (2008) 
−  3 quarters mixing latency period for tagging data (38% of tag-recapture data, 

n=3480.1) 

The most important changes made to the 2018 stock assessment were the following: 

-  The use of the new version of SS3 (v.3.30). For this, the 2018 model features were 
migrated to the new mode version and it was checked that results were equivalent. 

-  Spatial structure: Three spatial configuration options were explored in the proposed 
assessment:  Two  area  model  (West  and  East  regions),  three  area  model 
(aggregating regions 3 and 4 of the 2018 model), and the four-area configuration 
from the 2018 model. Results suggest that the two-area model is the most stable 
and  therefore,  the  two-area  model  configuration  was  proposed  for  the  reference 
model.  This  is  based  on  the  jittering  analysis  shown  in  Figure  29.  For  each  area 
configuration, in the upper left panel we see the likelihood of the model fit when 
the search of the numerical parameters starts from different values. For the 4 and 
3 areas, these values can be very different depending on their starting values. For 
the 2 area models, this is notably improved. Also, in the other figures we see that 
all trajectories (all model fits) are very similar in the 2-area model than in the 3 and 
4 areas. This suggests that the 2-area model configuration is more stable and more 
reliable.  

Figure 29. Jittering analysis for the 3 area configurations tested. X-axis are quarters of year of the 
model. The model starts in 1950 and ends in 2017. 

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-  Reducing or omitting the influence of tagging data. Tagging and environmental data 
do not contain enough information to estimate the movement between the 4 areas 
defined in the 2018 model. These data make the model unstable (as seen in the 3 
and  4  area  models).  The  2-area  model,  which  omits  tagging  data  seems  more 
stable. 

-  Update with new data: The proposed reference model was updated with the data 
available for the 2019  WPTT. The  catch and length-frequency data were updated 
until  2018  and  new  estimates  of  the  joint  longline  index  were  used.  The  2018 
reference  model  was  proven  to  be  very  sensitive  to  the  most  recent  length-
frequency  data  and  a  similar  conclusion  was  raised  from  the  proposed  model  for 
2019.  The  residuals  of  fit  show  that  there  seem  to  be  systematic  changes  in  the 
length composition of longline fleets (Figure 30). This figure shows size frequency 
data residuals for the longline fleets (black are positive and white are negative, with 
the size of the bubble indicating the level of the residuals, the highest the bubble, 
the poorest the data fit). Most residuals (difference between model fits and data) 
are  concentrated  in  the  small  sizes  for  the  period  labelled  (177-215)  which 
corresponds  to  the  period  (1994-2003).  Then,  the  large  gray  bubbles  are 
concentrated  in  the  largest  sized  fish  (150-200  cm)  during  the  period  238-272, 
which corresponds to 2008-2018.  

FLEET 7 

fish 

small 

- In the Joint longline CPUE 
workshop  (Hoyle  et  al., 
2019)  it  was  suggested 
that this could be due to a 
potential  lack  of  reporting 
(high-
of 
grading),    or  a  sudden 
change  in  selectivity.  This 
feature  seems  systematic 
across 
RFMOs 
other 
assessments  of  yellowfin 
tuna  and  therefore  merits 
further  investigation.  For 
this  reason,  in  the  current 
version, it was proposed to 
use length frequency data 
from  longline  fleets  until 
2014  only.  This  adds  to 
the current knowledge of the poor quality of the size frequency data for the period 
2008-2018 (see Uncertainty on Data section).  

Figure 30. Residuals to size frequency data for the longline fleets. 

-  Diagnostics:  Different  diagnostics  were  used  to  help  the  development  of  the 
reference model. These include jittering, assessment of the likelihood components 
and  convergence  of  different  model  configurations,  residuals  distribution  and 
retrospective fits. For example, Figure 31 shows a retrospective analysis of the 2-
area model that omits tagging data and size frequency data from 2014 compared 

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to  the  2018  reference  case  model.  This  figure  suggests  that  the  retrospective 
pattern observed in the 2018 model would be reduced with this new configuration. 

Figure 31. Retrospective fits with the proposed new reference model (left) and the retrospective fit 
in the 2018 model reference case. The fits by omitting 1 year (4 quarters), 2 years, 3 years, 4 years 
and  5  years  show  continuity  and  suggest  that  there  is  no  retrospective  pattern  in  the  proposed 
assessment, which adds reassurance on the quality of the model fit. X-axis are the quarters of year 
for the period 1950-2017. 

Additional  sensitivity  analyses  were  made  to  confirm  the  recommendation  of  omitting 
tagging data, to explore alternative values of sigmaR (which is the constrain to recruitment 
variability), steepness, natural mortality, and growth. However, these analyses were not 
conclusive on the most appropriate values to use and it was proposed to include them in 
the structural uncertainty grid during the WPTT.  

The WPTT noted the work done in this study and noted several aspects (IOTC, 2019a): 

The WPTT NOTED that the paper represents the culmination of a substantial intersessional 
collaborative effort to improve the yellowfin Stock Synthesis stock assessment. This paper 
formed the base for the modelling work that was undertaken during the WPTT, in which a 
36 model grid was defined with the following  alternative assumptions used to represent 
assessment uncertainty:  

o  Spatial structure 1: Four areas defined as in 2018 
i.  3 X steepness options (0.7, 0.8, 0.9) 

ii.  2 X M (The values used in the study and values of Atlantic yellowfin) 

iii.  2 X growth functions (the one used in the 2018 stock assessment and 

Dortel et al (2015)) 

iv.  2 X tag weighting (lambda=0.1 and lambda=1) 

o  Spatial structure 2: Two areas, consisting of merged areas 1&2 and 3&4 

i.  3 X steepness options (0.7, 0.8, 0.9) 

ii.  2 X M (The values used in the study and values of Atlantic yellowfin) 

iii.  2 X growth functions (the one used in the 2018 stock assessment and 

Dortel et al (2015)) 

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o  The two spatial options would be weighted equally in the advice. 
o  The  assessment  was  to  be  based  on  the  alternative  2018  PS  CPUE  catch 

assumption derived by the Secretariat during the meeting.  

The grid was the result of substantial debate among the WPTT participants, in which a 
number of key points were AGREED: 

o  The reported Spanish purse seine catches in 2018 should be treated  as an 
unlikely event (subject to further investigation) and should be replaced with 
a  new  estimate  produced  by  the  Secretariat  based  on  historical  species 
composition estimates. 

o  Tag  induced  mortality  and  shedding  assumptions  were  adopted  from  the 

Hoyle et al. (2015) paper (Hoyle et al., 2015). 

o  Tag  mixing  assumptions  are  not  likely  to  be  met  in  the  4-area  model 
configuration, and hence tag down-weighting options should be included with 
equal or higher importance to full tag weight assumptions. Tags should not 
be included in the 2-area model. 

o  Purse seine CPUE series would be examined as a sensitivity test. 
o  M  assumptions  are  based  on  the  Atlantic  otolith  ageing  (and  radio-isotope 
validation), an oldest age method (Then et al., 2015) and the shape of the 
mortality curve that was used in 2018. 

o  The  Dortel  et  al.  (2015)  growth  equation  2  and  the  ad  hoc  growth  curve 

(Fonteneau and Gascuel, 2008) were used. 

o  A  standard  set  of  diagnostics  should  be  applied  to  the  stock  assessment 
models, or at least a subset of models, including jitter analyses, hindcasting, 
retrospectives and runs tests (or some method evaluating systematic lack-
of-fit).    However,  limitations  of  what  can  be  achieved  with  the  diagnostics 
needs to be recognized – they cannot automate the model selection process. 

The WPTT NOTED the preliminary results generated from the reference grid of models. 
The new grid does not suggest any qualitative difference with the assessment carried 
out in 2018. 

The WPTT RECOGNIZED that the collaborative effort represents an ambitious attempt 
to improve the assessment, and was successful for increasing the number of people that 
have a reasonable understanding of Stock Synthesis, the yellowfin assessment issues, 
and the application of standard model diagnostic techniques. Considerations for future 
assessments include: 

o  Comprehensive  reviews  of  size  composition  data  have  not  yet  been 

completed. 

o  The WPTT NOTED that the Fonteneau (2008) growth curve used in previous 
assessments cannot be validated and SUGGESTED therefore the published 
Dortel et al (2015) growth curve(s) should be investigated.  

o  Use  of  the  RTTP-IO  tagging  data  requires  further  consideration,  especially 
with respect to tag mixing assumption violations. Simplification of the biology 
is the rationale behind the choice of the model without movements (i.e. the 

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2-area  model).  Given  the  defection  of  tagging  design,  the  information 
provided by the tagging data in the model are biased and thus not used in 
the newly developed 2-area model but still in the 4-area model. 

o  Further intersessional yellowfin work  should be undertaken to continue the 

current process. 

The  WPTT  NOTED  the  potential  numeric  instability  during  the  projection  phase  in  SS 
version 3.24 which might cause some of the models to crash during forecast in the 2018 
WPTT. This seems to be an issue with SS 3.24 but not with 3.30 as the same phenomenon 
was not evident in this year’s projections of yellowfin tuna. 
The WPTT NOTED the substantial work conducted to address the yellowfin tuna workplan, 
but  that  there  was  still  work  to  be  completed.  As  such  the  WPTT  REQUESTED  that  the 
authors fully document the work conducted prior to, during as well as the work still to be 
addressed after the meeting, in an information document to be provided to the SC in 2019. 
This work will be coordinated by the chair of the WPTT. This document was submitted to 
the IOTC Scientific Committee in 2019 (IOTC-2019-SC22-INF01). 

In  this  regard,  and  in  relation  to  the  development  of  management  advice  for  yellowfin 
tuna: 

The  WPTT  ACKNOWLEDGED  the  efforts  made  towards  improving  the  2018  stock 
assessment but also AGREED that new management advice could not be provided in 2019 
due  to  the  complexity  of  the  work,  lack  of  agreement  on  key  model  aspects  and  time 
constraints  during  the  meeting.  With  regards  to  the  advice  on  catch  limits,  the  WPTT 
NOTED  that  the  models  available  at  the  end  of  the  WPTT  need  further  analyses  and 
exploration before estimating the K2SM. 

Therefore, the WPTT did not agree on a new reference case nor a grid to change the stock 
status  and  catch  limits  advice  and  recommended  to  continue  with  the  improvements 
foreseen in the yellowfin workplan.  However, the WPTT agreed on exploring alternative 
spatial  configurations,  on  one  value  for  tagging  mortality,  including  options  for  natural 
mortality  and  growth  in  the  reference  grid  and  a  general  procedure  to  evaluate  the 
goodness  of  fits.  The  remaining  work  includes  the  review  of  the  size  frequency  data,  a 
further review of the tagging information and a better understanding on the problems with 
the projections. 

3.1.  The way forward of the yellowfin workplan after the 2019 WPTT 

In 2019 a new reference model using Stock Synthesis (Urtizberea, 2019) was presented 
to the WPTT. However, despite noting the effort and work  conducted, the WPTT did not 
consider that the model had improved enough to justify providing a new advice in 2019. 
Instead,  the  WPTT  identified  future  developments  towards  improving  the  current  model 
and advice. 

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According to the discussions during the 2019 WPTT the biggest challenge is probably the 
treatment to tagging data and to model explicit movement. This has implications on the 
spatial configuration of the model and has a notable impact on the outcome of the stock 
assessment of yellowfin and the other tropical tuna stocks too. Other challenges include 
the systematic changes in length composition of the longline fleets, which may be due to 
high  grading,  a  change  in  selectivity  or  change  in  reporting  (Hoyle  et  al.,  2019).  Other 
aspects  that  require  careful  investigation  are  two  key  biological  parameters,  which 
arenatural mortality and growth.  

A  number  of  opportunities  were  identified  in  2019  to  address  these  issues  under  this 
workplan during 2020:  

·  With regards to the spatial structure, the Indian Ocean yellowfin has been selected 
as one of the two case studies for the Spatial Stock Assessment Methods workshop 
to be held just after the 2020 World Fisheries Congress (Adelaide, Australia, October 
2020). The attendance of the main Stock Synthesis developers and those that have 
been involved in the development of the yellowfin models provides opportunities to 
improve the current spatial structure of the model. 

·  With  regards  to  the  use  of  tagging  data,  the  IOTC  foresees  a  specific  review  to 
analyse and optimize the use of this information in the assessments of the three 
tropical  tuna  stocks.  Details  of  the  contract  are  yet  to  be  clarified  as  well  as  the 
contractor. 

·  The WPTT noted that the results of a consultancy to identify and overcome issues 
inherent with longline length-frequency data will be provided in the first quarter of 
2020.  Also  in  this  regard,  noting  that  this  issue  affects  other  yellowfin  stock 
assessments  in  the  Pacific  and  the  Atlantic,  the  2019  yellowfin  model  analyst 
attended  the  2nd  External  review  of  IATTC  staff’s  stock  assessment  of  yellowfin 
tuna in the eastern Pacific Ocean (La Jolla, US, 2-6th December 2019) and received 
feedback from experts in the IATTC scientific staff on potential improvements. For 
example,  the  problems  encountered  in  the  Eastern  Pacific  yellowfin  stock 
assessment  were  similar,  such  as  the  recruitment  shifts  between  areas  to 
compensate the increase of purse seine catch. This idea was essential to understand 
the issues derived by the recruitment distributions between areas and the problems 
with the projections of the yellowfin stock assessment. 

·  Also, it will be of the utmost importance to elucidate the adequate values for key 
biological parameters. In this regard, the Natural Mortality workshop by the Center 
for the Advancement of Population Assessment Methodology (CAPAM, 23-27 March, 
2020,  postponed  due  to  restrictions  after  the  COVID-19  crisis)  will  be  a  good 
opportunity to discuss this matter with top ranked expert scientists. It is expected 
that the developers of the 2019 Indian Ocean yellowfin stock assessment will attend 
this meeting.  
Further intersessional work will also be required in 2020. The WPTT recommended 
that  a  data  preparatory  meeting  is  scheduled  well  in  advance  of  the  assessment 
meeting  to  discuss  all  data  issues  before  the  assessment.  This  would  also  allow 
intersessional work between the data preparatory and assessment meetings. This 
will happen using teleconference tools due to the restrictions after the COVID-19 
crisis. 

· 

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·  The intersessional collaborative work carried out during 2019 should continue using 
electronic  tools  (project  management  tools,  shared  space  for  model  output  and 
other communication software). This will be coordinated by the Chair of the WPTT 
(as in 2019). 

·  Another aspect that requires intersessional analyses are the options for weighting 
the data series in the model. Further analysis is necessary to investigate alternative 
periods  for  recruitment  deviates,  that  could  enable  better  recruitment  deviates’ 
patterns, especially in recent years, a very important aspect due to is influence in 
the projections made to produce advice of tropical tuna stocks. 

3.2.  Improvements to the yellowfin stock assessment during 2020 

The advances made to the yellowfin stock assessment in 2020 consist of (1) adapting the 
current configuration of the yellowfin stock assessment that considers quarters as years to 
a model that includes years and seasons and, (2) to use diagnostic tools to select a reduced 
number of models from the grid from 2019 WPTT. 

1)  The models used in the grid in the latest assessments of yellowfin are configured with 
seasons as years. In other words, the models include 276 quarters instead of the 69 
years of the assessed period (1950-2018). It is unclear why the initial SS3 models were 
built this way (probably to accommodate tagging data) but this configuration makes 
difficult to interpret the output of the model and overcomplicates the modelling of the 
seasonal  movements  between  years.  A  more  parsimonious  model  is  intended  by 
converting the original models into a yearly timestep model configuration that includes 
four seasons within a year. Then, a number of model fit diagnostics (see section 2.1.2) 
were applied to evaluate if the new model configuration allowed for a better fit to the 
available datasets. In the 2020 WPTT this was considered a promising way to improve 
the stock assessment model in 2021 but premature to adopt by the WPTT at its current 
stage. 

2)  The libraries for estimating diagnostics of fit were used to help scoring model runs. This 
approach  could  be  used  to  select  or  weight  the  models  used  for  providing  scientific 
advice. However, this approach needs further development, and it is unclear yet if the 
2021 stock assessment will use the approach. 

3)  The issue with the projections made in 2018 were spotted: 

In 2018, the IOTC SC encountered several issues with the projections made from the 
grid of models used to characterize stock status that year (section 2.1.3 and Figure 23). 
Among these, one was that some models (even those with good apparent performance 
on the diagnostic tests) could not estimate biomass levels for different catch reductions. 
For  example,  in  Figure  32,  the  model  used  as  a  “Reference  Case”  cannot  continue 
projecting biomass with a 10% catch reduction. The problem affects to all models of the 
grid agreed in 2018. 

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Figure 32. Projection of the model io_h80_tm30_dw1 model with a 10% catch reduction. X-axis are 
the quarters of year for the period 1950-2017. 

During the development of SC14 the cause of these projection problems was investigated 
thoroughly by reviewing the possible influence of data and model configuration. However, 
it was only at the end of the 2020 WPTT where the origin of the problem was identified.  

It  is  important  to  recall  that  the  2018  yellowfin  stock  assessment  model  is  spatially 
disaggregated and that it considers four areas. The problem in the projections is that the 
biomass in region 1 (West Indian Ocean) crashes just after the beginning of the projections 
even with large catch reductions (up to 60% of the catch in 2018). In contrast, biomass in 
regions 3 and 4 increases very fast (Figure 33). 

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Figure 33. The spawning biomass with time per area. X-axis are the quarters of year for the period 
1950-2017 and projected forward after quarter 284. 

The model estimates fish recruitment quarterly with a biomass-recruitment function and 
the variability in recruitment is estimated starting from 1972 as deviates from this stock 
recruitment  relationship.  During  the  model  fit  the  model  shares  this  total  recruitment 
among the two areas where recruitment occurs (areas 1 and 4), the equatorial areas. The 
deviates in the proportion of recruitment are also shared in the model since 1977. Figure 
34 shows the estimated proportion of recruitment per area with time.  

Figure 34. The proportion of recruitment per area. X-axis are the quarters of year for the 
period 1950-2017 and projected forward after quarter 284. 

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Figures  35  (left  and  right)  shows  a  significative  increasing  trend,  estimated  during  the 
model fit (before the projections are run), in the proportion of recruitment in area 1 starting 
in 1996 (index 198 in x-axis, dashed blue line in Figure 35, left) when purse seiner catches 
start to attain the current levels and the density function indicates an increase of 20 % 
from  1977.  In  other  words,  the  model  changes  the  initial  proportion  of  recruits  among 
areas  to  be  able  to  fit  the  catch  increase  after  1996  in  the  western  area  of  the  Indian 
Ocean. In addition, the model does not consider any length composition since 2014 and 
therefore the model only relies on the longline CPUE and catches to estimate recruitment 
afterwards and does not find any other way to explain the recent catch increase in area 1. 
Figure 35 right shows the distribution of the relative change in recruitment allocation. If 
the average is below 1 would mean that there has been a change towards area 4 and if 
the average is above 1 means that there has been a change towards area 1. The figure 
shows that the relative change in recruitment allocation between areas 1 and 4 has shifted 
towards more recruits in area 1 (value > 1) and less towards area 4 (values < 1). We recall 
that the model only considers recruitment in areas 1 (Northwestern Indian Ocean) and 4 
(Southeastern Indian Ocean). 

Figure 35: The red dots are the proportion in recruitment in area 1 and the continuous black line 
is  the  estimated  loglinear  state  space  model  and  the  density  function  of  the  relative  change  in 
proportion of recruitment in area 1. 

The beginning of the trend in the proportion of fish in area 1 happens after 198 index in x-
axis in Figure 35 (blue dashed line). Then, the proportion of recruitment in area 1 increases 
and decreases in area 4. This way, the population is stabilized and the large catch since 
1996 can be fitted into the model.  

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Figure 36. a) the fraction of unfished biomass with time and b) the total catches and per fishery with 
time. 

However,  in  the  projection  (after  x-index  280)  it  is  assumed  that  the  proportion  of 
recruitment is that of the initial condition (Figure 34, 60% area 1 and 40% area 4), so 
it is reduced significantly in area 1 and this implies that there is not enough recruitment 
in comparison to the previous years in area 1 to support current catches, not even a 
decrease of 60% from the catch of 2018. This jump to the initial condition does not have 
any logic and it breaks the clear trend estimated in the recent years. 

To understand this better, a sensitivity analysis was made assuming the proportion in 
recruitment of area 1 of the last year of the assessment in the forecast and in this case 
the biomass in area 1 and 2 increased while in area 3 and 4 crashed (Figure 37, left 
recruitment share between areas 1 and 4 and right, biomass projected in each area of 
the model). This shows that the model is very sensitive to the assumption of recruitment 
shared between areas and that this requires a deeper analysis to understand this trend 
to decide the best approach for the projection. This work will be necessary for a better 
configuration of the yellowfin stock assessment model to be developed in 2021. 

Figure 37. Sensitivity test by increasing the proportion of recruitment in area 1 for the forecast. 

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TASKS AND DELIVERABLES: PROGRESS AND OUTCOMES 

The project has allowed notable improvements on the yellowfin stock assessment and has 
extensively  contributed  to  the  IOTC  workplan  to  improve  the  tropical  tunas  stock 
assessment and management advice. This has been achieved by producing several working 
documents for the WPTT, by supporting the organization, attendance, and participation of 
EU scientists to the WPTT and intersessional workshops and by facilitating the coordination 
of scientists from the EU and elsewhere towards improving the stock assessment models. 
Table  6  describes  the  deliverables,  their  names,  task,  nature,  date  of  submission  and 
current state.  

Table 6. Deliverables of the Specific Contract 14. 

Deliverable 

Name 

Task 

Nature  Date 

State 

D1.1 

D2.1 

D1.2 

D2.2 

D1.3 

D2.3 

Review of data issues 
for yellowfin 

1.Uncertainty on 
Data 

Report 

July 2019 (revised October 
2019) 

Review of yellowfin 
2018 model and plan 
for improvement 

Data revision and 
proposed information 
for yellowfin stock 
assessment 

2.Model 
Uncertainty 

Report 

July 2019 (revised October 
2019) 

1.Uncertainty on 
Data 

Report  November 2019 

Yellowfin stock 
assessment model 
proposal 

2.Model 
Uncertainty 

Report  November 2019 

Review of data issues 
common to all tropical 
tunas 

1. Uncertainty on 
Data 

Report  February 2020 

2.Model 
Uncertainty 

Report  February 2020 

Critical review of the 
tropical tuna stock 
assessments in the 
IOTC 

Milestones 

Delivered and 
revision 
submitted 

Delivered and 
revision 
submitted 

Delivered and 
revision 
submitted 

Delivered and 
revision 
submitted 

Delivered and 
revision 
submitted 

Delivered and 
revision 
submitted 

The progress made in this Specific Contract has allowed the contribution of the EU scientists 
to the IOTC workplan for improving the scientific advice of IOTC’s WPTT. Under this Specific 
Contract, support has been given to two intersessional workshops, to the attendance of EU 
scientists  to  the  WPTT  and  to  the  necessary  research  towards  completing  the  different 
tasks and subtasks of the IOTC workplan. The working documents submitted to the WPPT 
(2019 and 2020) that have been produced under this project are (numbers to be updated): 

•  Urtizberea et al (2020) Preliminary Stock Assessment  for Yellowfin Tuna in the 

Indian Ocean: Hypothesis and Diagnostics (IOTC-2020-WPTT(AS)-21). 

•  Merino, G. (2020) Summary of activities of the yellowfin tuna workplan (IOTC-

2020-WPTT(AS)-INF01) 

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•  Duparc et al (2020) Improved version of the tropical tuna treatment process: new 

perspectives for catch estimates of tropical purse seine fishery  (IOTC-2020-
WPTT(AS)-13). 

•  Guéry et al (2020) Skipjack CPUE series standardization by fishing mode for the 
European purse seiners operating in the Indian Ocean (IOTC-2020-WPTT22(DP)-
12). 

•  Santiago et al (2020) A Novel Index of Abundance of Skipjack in the Indian Ocean 

Derived from Echosounder Buoys (IOTC–2020–WPTT22(DP)–14). 

•  Duparc, A. (2019) Assessment of the species composition of major tropical tunas 

in purse seine catches: a new modelling approach for the Tropical Tuna Treatment 
processing (IOTC-2019-WPTT21-10). 

•  Guéry, L., Kaplan, D., Marsac, F., et al. (2019) Accounting for fishing days without 
set, fishing concentration and piracy in the CPUE standardization of yellowfin tuna 
in free schools for the EU purse seine fleet operating in the Indian Ocean during 
the 1991-2017 period (IOTC-2019-WPTT21-44). Also presented during the IOTC 
Working Party on Methods (WPM). 

•  Merino, G., Fu, D., Geehan, J., Urtizberea, A., Santiago, J., Murua, H. (2019a) 
Evaluation of the potential impact of catch underreporting on yellowfin stock 
assessment using exploratory scenarios of catch history (IOTC-2019-WPTT21-47). 

•  Merino, G., Urtizberea, A., Santiago, J., Murua, H. (2019b) Stock assessment of 
Indian Ocean yellowfin using a biomass production model (IOTC-2019-WPTT21-
49). 

•  Santiago, J., Uranga, J., Quincoces, I., et al. (2019) A novel index of abundance 
of juvenile yellowfin tuna in the Indian ocean derived from echosounder buoys 
(IOTC-2019-WPTT21-45). 

•  Urtizberea, A. (2019) Preliminary Assessment of Indian Ocean Yellowfin Tuna 

1950-2018 (Stock Synthesis, v3.30) (IOTC-2019-WPTT-21-50). Also presented 
during the IOTC Working Party on Methods (WPM). 

The main achievement was a new stock assessment proposal (Urtizberea et al, 2019), but 
as mentioned earlier, further analyses are necessary before being agreed by the WPTT. 

With  regards  to  the  other  specific  milestones,  the  project  progressed  as  scheduled  until 
the last stage and the milestones have been achieved (Table 7).  

Table 7. Milestones expected for this Specific Contract. 

Milestone 
number 

Milestone name 

Task (s) 
involved 

M0.1 

Kick-off meetings 

0 

Expected 
date 
(months) 

22nd May 
2019 

Means of 
verification 

Meeting 
minutes 

M0.2 

Working Party of 
Tropical Tunas 

1,2 

21-26 
October 

Working papers 
to the WPM 
and WPTT 

Achieved? 

Yes 

Yes 

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M0.3 

Interim report 
submission 

0 
(outputs 
from 1-
2) 

20th 
December 
2019 

Interim Report 

M0.4 

Interim meeting 

0 

February 
2020 

Meeting 
minutes 

M0.5 

Draft final report 
submission 

M0.6 

Final project 
meeting 

0 
(outputs 
from 1-
2) 

0 
(outputs 
from 1-
2) 

M0.7 

Final report and 
executive 
summary 
submission 

0 
(outputs 
from 1-
2) 

March 
2020 

(Delayed 
to 1st 
October 
2020) 

April 
2020 

April 
2020 

(Delayed 
to 1st 
November 
2020) 

Draft Final 
Report 

Meeting 
minutes 

Final Report 

M0.8 

New Final project 
meeting 

0 
(outputs 
from 1-
2) 

November 
2020 

Meeting 
minutes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

In  addition  to  these  quantitative  milestones,  this  Specific  Contract  has  contributed  to  a 
notable improvement on the work of the WPTT for different reasons: First, it has been a 
highly collaborative work between European scientists but also between the scientists that 
participate  in  the  WPTT  and  in  other  tuna  and  non-tuna  fisheries  modelling  groups.  We 
have had the participation of the main developer of Stock Synthesis (Dr. Richard Methot), 
and  discussions  with  colleagues  from  ICCAT  and  IATTC.  In  addition  to  short  term 
improvements  for  the  yellowfin  model,  the  SC14  has  contributed  to  structural 
improvements on the assessment and management of the three tropical tuna stocks of the 
IOTC.  With  this  project  we  have  contributed  to  identifying  data  issues,  assessed  their 
impact and suggested paths for improvement. We have also made a thorough review of 

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the model options and configuration alternatives, we have explored sources of uncertainty, 
we  have  helped  improving  the  MSE  of  IOTC  stocks,  contributed  to  the  development  of 
diagnostics for inclusion in the stock assessment process and contributed to improve the 
overall stock assessment process. Also, we expect that the yellowfin assessment in 2021 
will more reliable than the 2018 assessment.  

One aspect that probably deserves reiterating is that the SC14 has a strong collaborative 
component.  Almost  all  task  and  advances  have  been  achieved  in  collaboration  between 
scientists directly funded by this Specific Contract and scientists from the IOTC and other 
RFMOs too. For this reason, it is not easy to specify each of the advances as outputs solely 
of this project.  

Difficulties 

The  difficulties  encountered  in  2019  were  related  to  the  short  period  of  time  before  the 
start of this Specific Contract and the date of the 21st session of the WPTT. In this time, 
many  of  the  subtasks  of  the  project  have  been  completed  or  partially  completed  and  a 
proposal for a new model was submitted to the WPTT. However, due to the lack of time 
we  were  unable  to  coordinate  the  selection  of  an  agreed  model  before  the  WPTT  with 
scientists outside the EU, whoare key participants to the WPTT.  

There are specific SC14 objectives and plans which have not been completely achieved.  

1)  The main objective of this SC was to provide new advice on the stock status and 
recommended catch limits in the 2019 WPTT. For this, different tasks of the SC14 
were developed in only 7 months (the period between the launch of this SC (April, 
2019) and the 2019 WPTT (October, 2019) and a new proposed reference case was 
presented (See section 3.1). However, this work was considered insufficient by the 
WPTT, which noted that additional works were necessary for providing a new advice. 
In our view, this is not a failure of the project, but it is true that the original objective 
has not been achieved.  

2)  In our initial workplan we scheduled a specific workshop under this SC to discuss 
advances on the yellowfin assessment, which has not happened. The main reasons 
for this is our inability to find a suitable date and to have the model for 2019 ready 
for consultation with other scientists of the Specific Contract prior to the 2019 WPTT. 
Also, in the original proposal we proposed to contribute to two additional workshops, 
one in combination with another Specific Contract (CECOFAD) for developing one 
purse seine index and another one for participating in the Joint longline index. At 
the end of this contract, we have participated in both workshops and collaborated 
to both by funding the attendance of EU scientists and by hosting both workshops 
in the AZTI’s premises (Spain).  

3)  In the agreed proposal we also scheduled the collaboration of a renowned expert 
on  stock  assessment  modelling  and  also  (if  possible)  his/her  participation  in  the 
2019 WPTT. We had originally budgeted both components. The first part has been 
achieved, Dr Richard Methot (the Stock Synthesis developer) provided a review on 
the  2018  stock  assessment  model  and  is  participating  on  the  continuous 
development of the model. Also, he has committed to make additional changes in 
the software to sort out the issues encountered in the projections (see section 3.2). 
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Also, his collaboration has been in kind as suggested by him. The second objective 
has  not  been  achieved.  We  invited  Dr  Methot  and  other  colleagues  from  ICCAT, 
IATTC and CCSBT for attending the 2019 WPTT but in the last minute all rejected 
the invitation based on their heavy workload schedule. 

In  2020,  the  COVID-19  pandemic  has  made  things  even  more  difficult  and  some 
opportunities  for  improvement  (workshop  on  natural  mortality,  workshop  on  spatial 
modelling under the World’s Fishery Congress) have not been used. However, the advances 
made have been presented and we expect that the WPTT will be able to submit a better 
stock assessment in 2021. 

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REFERENCES 

Dortel, E., Bousquet, N., Sardenne, F., Rivot, E. (2015) An integrated Bayesian modeling 
approach  for  the  growth  of  Indian  Ocean  yellowfin  tuna.  Fisheries  Research  163, 
69-84. 

Duparc, A. (2019) Assessment of the species composition of major tropical tunas in purse 
seine  catches:  a  new  modelling  approach  for  the  Tropical  Tuna  Treatment 
processing (IOTC-2019-WPTT21-10). 

Duparc, A., Cauquil, P., Depetris, M., et al. (2018) Assessment of accuracy in processing 
purse seine tropical tuna catches with the T3 methodology using French fleet data. 
Working  Party  of  Tropical  Tunas,  Seychelles,  October  2018.  IOTC-2018-WPTT20-
16. 

Fonteneau, A., Gascuel, D. (2008) Growth rates and apparent growth curves, for yellowfin, 
skipjack  and  bigeye  tagged  and  recovered  in  the  Indian  Ocean  during  the  IOTTP 
(IOTC-2008-WPTDA-08). First session of the IOTC Working Party on Tagging Data 
Analysis. 

Fu,  D.  (2017)  Indian  Ocean  skipjack  tuna  assessment  1950-2016  (Stock  Synthesis). 
Working  Party  of  Tropical  Tunas,  Seychelles,  October  2017.  IOTC-2017-WPTT19-
47. 

Fu,  D.  (2019)  Preliminary  Indian  Ocean  bigeye  tuna  assessment  1950-2018  (Stock 
Synthesis). Working Party of Tropical Tunas, Donostia, Spain, October 2019. IOTC-
2019-WPTT20-61. 

Fu, D., Langley, A., Merino, G., Urtizberea, A. (2018) Preliminary Indian Ocean yellowfin 
tuna  assessment  1950-2017  (Stock  Synthesis).  Working  Party  of  Tropical  Tunas, 
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       doi: 10.2926/689705 
       ISBN 978-92-95225-58-9