Scientific, Technical and Economic Committee for Fisheries (STECF) : suitability study of the Bayesian state-space model ‘JARA’ for stock status indicator estimation (STECF-Adhoc-22-02)
Date: 2022-04-29
Institutions: Joint Research Centre
Subjects: Community fisheries, common fisheries policy, conservation of fish stocks, environmental economics, environmental indicator, evaluation of resources, fishery management, research report

Scientific, Technical and Economic 
Committee for Fisheries (STECF) 
- 
Suitability study of the Bayesian 
State-Space model ‘JARA’ for 
stock status indicator estimation 
(STECF-Adhoc-22-02) 

Edited  by  Henning  Winker,  Sven  Kupschus,  Christoph  Konrad, 
Michaël Gras and Allesandro Mannini 

 EUR 28359 EN 

  
 
This  publication  is  a  Science  for  Policy  report  by  the  Joint  Research  Centre  (JRC),  the  European 
Commission’s science and knowledge service. It aims to provide evidence-based scientific support 
to  the  European  policymaking  process.  The  scientific  output  expressed  does  not  imply  a  policy 
position of the European Commission. Neither the European Commission nor any person acting on 
behalf  of  the  Commission  is  responsible  for  the  use  that  might  be  made  of  this  publication.  For 
information on the methodology and quality underlying the data used in this publication for which 
the source is neither Eurostat nor other Commission services, users should contact the referenced 
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expression  of  any  opinion  whatsoever  on  the  part  of  the  European  Union  concerning  the  legal 
status of any country, territory, city or area or of its authorities, or concerning the delimitation of 
its frontiers or boundaries. 

Contact information  

Name: STECF secretariat 

Address: Unit D.02 Water and Marine Resources, Via Enrico Fermi 2749, 21027 Ispra VA, Italy 

E-mail: jrc-stecf-secretariat@ec.europa.eu 

Tel.: +39 0332 789343 

EU Science Hub 

https://ec.europa.eu/jrc 

JRC129283 

EUR 28359 EN 

PDF 

ISBN 978-92-76-52267-6 

ISSN 1831-9424 

doi:10.2760/514979 

STECF 

ISSN 2467-0715 

Luxembourg: Publications Office of the European Union, 2022  

© European Union, 2022 

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How  to  cite  this  report:  Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  – 
Suitability  study  of  the  Bayesian  State-Space  model  ‘JARA’  for  stock  status  indicator  estimation 
(STECF-Adhoc-22-02).  Publications  Office  of  the  European  Union,  Luxembourg,  2022,  EUR  28359 
EN, ISBN 978-92-76-52267-6, doi:10.2760/514979, JRC129283. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Authors: 

STECF advice: 

Abella,  J.  Alvaro;  Bastardie,  Francois;  Borges,  Lisa;  Casey,  John;  Damalas,  Dimitrios;  Daskalov, 
Georgi; Döring, Ralf; Gascuel, Didier; Grati, Fabio; Ibaibarriaga, Leire; Jung, Armelle; Knittweis, 
Leyla;  Ligas,  Alessandro;  Martin,  Paloma;  Motova,  Arina;  Moutopoulos,  Dimitrios;  Nord,  Jenny; 
Prellezo,  Raúl;  O’Neill,  Barry;  Raid,  Tiit;  Rihan,  Dominic;  Sampedro,  Paz;  Somarakis,  Stylianos; 
Stransky, Christoph; Ulrich, Clara; Uriarte, Andres; Valentinsson, Daniel; van Hoof, Luc; Vanhee, 
Willy; Villasante, Sebastian; Vrgoc, Nedo 

EWG-Adhoc-22-02 report:  

Winker H (chair), Kupschus S., Konrad C., Gras M., Mannini A. 

i 

 
 
 
 
 
 
 
 
 
 
 
CONTENTS 

Abstract ………………………………………………………………………………………………………………………………..1 

SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES (STECF) - 

Presentation of the suitability of JARA for stock status indicator estimation 
(STECF-ADHOC-22-02) ...............................................................................2 

Background provided by the Commission ..................................................................2 

Request to the STECF .............................................................................................2 

Summary of the information provided .......................................................................2 

STECF comments ...................................................................................................2 

STECF conclusions ..................................................................................................4 

References ............................................................................................................5 

Contact details of STECF members ...........................................................................5 

Expert Working Group EWG-Adhoc-22-02 report ........................................................9 

1 

1.1 

2 

Introduction ............................................................................................. 10 

Terms of Reference for EWG-Adhoc-22-XX ................................................... 10 

Potential shortcomings of current Generalized Linear Mixed Model (GLMM) ...... 10 

2.1.1 

THE GLMM APPROACH IN CPUE STANDARDISATION ................................................ 10 

2.1.2 

THE GLMM APPROACH TO MODEL THE TIME SERIES IN THE CFP MONITORING ................... 11 

2.1.3 

IMPLEMENTATION OF THE GLMM BOOTSTRAP APPROACH ............................................ 11 

3 

3.1 

3.2 

state-space model approach using `jara` .................................................... 12 

State-space model (SSM) approach for time-series analysis ........................... 12 

Bayesian State-space model implementation ................................................ 12 

3.2.1  MODEL FORMULATION .................................................................................... 13 

3.2.2 

TIME-BLOCKS ON TREND ESTIMATION .................................................................. 13 

3.2.3  OBSERVATION ERROR .................................................................................... 14 

3.2.4  BAYESIAN FRAMEWORK ................................................................................... 14 

3.2.5 

JOINT INDEX ESTIMATION (INDICATOR TREND OVER MULTIPLE STOCKS) .......................... 14 

4 

4.1 

Comparison of JARA with GLMM indicators for F/FMSY and B/B2003 ................ 15 

Material and Methods ................................................................................ 15 

4.1.1  DATA SOURCE ............................................................................................. 15 

4.1.2  COMPARATIVE STATISTICAL ANALYSIS .................................................................. 15 

4.1.3  SENSITIVITY ANALYSES OF MISSING VALUES .......................................................... 16 

4.1.4  CROSS-VALIDATION ...................................................................................... 16 

4.2 

Results .................................................................................................... 17 

4.2.1  RESULTS FOR NORTH EAST ATLANTIC STOCKS IN EU WATERS ..................................... 17 

4.2.2  RESULTS FOR MEDITERRANEAN AND BLACK SEA STOCKS ........................................... 21 

ii 

 
 
 
 
4.2.3  SENSITIVITY ANALYSIS OF MISSING VALUES .......................................................... 26 

4.2.4  CROSS-VALIDATION ...................................................................................... 27 

5 

6 

7 

8 

Combined Indicators ................................................................................. 28 

Discussion ............................................................................................... 29 

References ............................................................................................... 30 

Contact details of EWG-Adhoc-22-02 participants ......................................... 32 

iii 

 
 
Abstract 

Commission  Decision  of  25  February  2016  setting  up  a  Scientific,  Technical  and  Economic 
Committee  for  Fisheries,  C(2016)  1084,  OJ  C  74,  26.2.2016,  p.  4–10.  The  Commission  may 
consult the group on any matter relating to marine and fisheries biology, fishing gear technology, 
fisheries  economics,  fisheries  governance,  ecosystem  effects  of  fisheries,  aquaculture  or  similar 
disciplines.  

This ad hoc report first reviews the technical details of the Generalised Linear Mixed effects Model 
(GLMM)  approach  that  is  applied  to  estimate  model-based  indicators  for  the  performance  of  the 
Common Fisheries Policy. It then presents the results a suitability study of applying the Bayesian 
current  GLMM  approach.
State-Space  model 

‘JARA’  as  an  alternative 

the 

to 

1 

 
 
 
 
SCIENTIFIC,  TECHNICAL  AND  ECONOMIC  COMMITTEE  FOR  FISHERIES  (STECF)  - 
Presentation  of  the  suitability  of  JARA  for  stock  status  indicator  estimation 
(STECF-ADHOC-22-02) 

Background provided by the Commission 

DG MARE intends to request STECF to continue the monitoring of fish stocks with respect to the 
CFP objectives relevant to exploitation of the stocks with respect to maximum sustainable yield. 
In  order  to  inform  effectively  about  this  development  of  the  CFP,  MARE  considers  that  the 
indicators  used  should  be  stable,  reliable  and  informative.  MARE  considers  that  it  is  appropriate 
for  STECF  to  review  and  update  the  indicators  as  necessary  and  appropriate  and  at  STECF’s 
discretion, bearing in mind the need to maintain continuity as much as possible. MARE also recalls 
the need for common indicators that cover stock development in both the Northeast Atlantic and 
the Mediterranean basin. 

Request to the STECF 

On the basis of the 2022 CFP monitoring report (see section 6.8 of this report / STECF-Adhoc 22-
01) and the ad-hoc work done by the JRC, make appropriate methodological recommendation for 
the monitoring of fish stocks in relation to the MSY objectives of the CFP. 

Inform on progress in developing common indicators for all EU waters, i.e. for both the Northeast 
Atlantic stocks and the stocks in the Mediterranean Basin. 

Summary of the information provided 

The STECF was provided with a document entitled “Suitability study of the Bayesian State-Space 
model ‘JARA’ for stock status indicator estimation” that described the work done by the JRC team 
in  an  ad-hoc  expert  working  group  (STECF-Adhoc-22-02)  conducted  in  January  2022.  The 
document  reviews  the  shortcomings  of  the  current  Generalized  Linear  Mixed-effects  Model 
(GLMM)  approach  to  compute  model-based  indicators  and  develops  an  alternative  Bayesian 

State-Space  Model  (SSM)  approach.  Both  methods  are  applied  to  the 
indicators for stocks of EU waters of North-East Atlantic and in the Mediterranean and Black Seas. 
The results are compared in terms of precision, consistency, and interpretability. Finally, based on 
the  SSM  approach,  two  options  to  generate  combined  indices  across  European  waters  are 
explored.  

  and 

STECF comments 

STECF  recalls  that  the  first  version  of  the  Common  Fisheries  Policy  monitoring  protocol  for 
computing  indicators  was  developed  in  2015  (Jardim  et  al.,  2015).  Since  then,  this  protocol  (or 
subsequent  updates)  has  been  applied  annually  in  ad-hoc  expert  groups,  whose  work  has  been 
later  adopted  by  STECF  to  report  on  progress  in  achieving  MSY  objectives  in  line  with  the  CFP. 
The  latest  version  of  the  protocol  (Jardim  et  al.,  2019)  was  adopted  by  the  EWG  STECF-18-15 

2 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
and has been applied in the last four years (2019-2022). See section 6.8 in this plenary report for 
the monitoring conducted in 2022.  

STECF  commends  the  work  conducted  by  JRC  to  improve  the  current  methodology  for  model-
based indicators and to develop common indicators for all EU waters.   

Based  on  the  JRC  document,  a  list  of  potential  methodological  changes  in  the  protocol  were 
identified and are described below. 

Change of GLMM to Bayesian SSM 

The current GLMM approach to compute model-based indicators presents some  shortcomings as 
described  in  detail  in  the  document  provided  by  the  JRC  (STECF-Adhoc-22-02).  The  alternative 
Bayesian  SSM  approach  provides  a  statistically  sound  method  with  published  applications 
(Sherley et al., 2019; 2020; Pacoureau et al., 2021) that might suit better to the characteristics 
of  the  data  derived  from  single-stock  assessment  models.  For  large  datasets  with  no  missing 
values,  the  GLMM  and  the  Bayesian  SSM  approaches  estimated  very  similar  indicator  trends  for 

 and 

both 
. The differences between both approaches were primarily related to the 
occurrence of missing data in the time-series.  While the GLMM  approach had a limited ability to 
account  for  missing  values,  the  SSM  approach  accounts  for  the  partial  dependence  between  an 
estimate at t+1 and the estimate at t and could predict stock specific trends providing robust and 
reliable  estimates  as  shown  in  the  sensitivity  analysis  (section  3.5  of  the  document). 
Furthermore,  STECF  emphasizes  that  contrary  to  the  GLMM  approach,  the  Bayesian  SSM 
approach  produced  unbiased  estimates  of  the  nominal  geometric  mean.  Therefore,  STECF 
considers  the  Bayesian  SSM  approach  entails  improvements  in  several  methodological  aspects 
and could replace the GLMM approach to compute the model-based indicators for monitoring the 
CFP. 

Historical consistency of model-based indicators 

The  comparison  of  the  historical  plots  of  the  CFP  model-based  indicators  based  on  the  GLMM 

showed a tendency to systematically overestimate 
 in the NEA. 
The new Bayesian SSM approach has not been applied yet and its historical performance has not 
been  evaluated.  STECF  considers  it  could  be  useful  to  apply  the  new  approach  some  years 
backwards  to  evaluate  the  sensitivity  of  indicators  to  annual  updating.  The  new  approach  could 
provide  further  insights  into  the  underlying  causes  for  historical  changes  in  the  indicators  by 
disentangling the effects of (1) historical assessment bias, (2) changes in the dataset in terms of 
stock composition and (3) the retrospective bias of the model itself.   

 and underestimate 

Normalised biomass time series 

  from  the  Bayesian  SSM  approach  were 
STECF  notes  that  the  model-based  indicators  for 
similar  regardless  of  whether  the  model  was  fitted  to  absolute  or  to  normalised  biomass  time 
series. However, the confidence intervals estimated from the bootstrap procedure on the absolute 
biomass is substantially inflated compared to normalised biomass. This could be attributed to the 
large  variation  in  absolute  SSB  scales.  Given  that  this  has  no  direct  value  for  inferring  some 
average  stock  status  across  many  different  stocks,  STECF  supports  the  proposed  approach  of 
fitting the model to normalised biomass time series.  

Change in reference year for biomass   

Currently  the  reference  year  for  computing  model-based  biomass  indicators  is  2003  (i.e.  the 

indicator is 
). This poses additional problems, though somehow alleviated by the Bayesian 
SSM  methodology,  as  there  are  missing  values  at  the  start  of  the  time  series  (especially  in  the 
Mediterranean and Black Sea where the time-series are shorter). STECF considers that it may be 
more appropriate to select another reference year without missing values and preferably located 
around the middle of the time series. Candidate reference years could be 2009 as that is the first 
year  where  there  are  no  missing  values  and  is  the  year  before  the  full  entry  into  force  of  the 
Mediterranean  Regulation  (Council  Regulations  EC  1967/2006).  It  could  also  be  2013,  the  year 

3 

 
 
prior to the entry into force of the reformed CFP (Regulation (EU) No 1380/2013), for which there 
is the introduction of Maximum Sustainable Yield objectives, or any year in between. Regardless 
of the selected reference year, STECF notes that the Bayesian SSM can introduce time blocks that 
could ease the comparison of trends between different time periods (e.g. before and after the CFP 
reform).  However,  this  additional  feature  has  not  been  tested  for  CFP  monitoring  and  would 
require additional work to be made operational. 

Recruitment model-based indicators 

STECF notes that the model-based approach was not applied to recruitment indicators and could 
therefore  not  be  discussed.  Nevertheless,  in  principle  the  Bayesian  SSM  approach  is  also 
considered  suitable  for  this  indicator  because  the  recruitment  indicator  is  also  a  model-based 
indicator using a GLMM associated to a bootstrap procedure to estimate its uncertainty.   

Sampling frame 

The  sampling  frame  defining  the  reference  list  of  stocks  to  be  included  in  the  analysis  was 
adopted in 2017. STECF notes that the criteria followed in the NEA and in the Mediterranean and 
Black Sea differ from each other, reflecting differences in management and in stock assessment 
coverage  in  the  two  areas.  Every  year  there  are  though  small  changes  in  the  number  of  stocks 
included  in  the  analysis  due  to  changing  availability  of  assessments,  redefinition  of  stock  limits, 
upgrades/downgrades  of  the  assessment  type,  etc.  STECF  considers  that  the  revision  of  the 
model-based  approach  presents  an  opportunity  to  revisit  the  sampling  frame  and  update  it  if 
deemed relevant.  

STECF conclusions 

In conclusion, STECF, jointly with the JRC team, suggests the following work is conducted inter-
seasonally:  

 

 

 

 

 

Apply the new SSM approach to the final 2022 dataset and produce a report 

with the same structure as the EWG STECF-Adhoc-22-01 report. 

Update  the  protocol  based  on  the  new  SSM  approach  (using  the  means 
standardised  SSB  approach  and  the  new  reference  year  to  be  proposed  by  JRC  as 
preferred option). 

Present  a  document  with  alternate  figures  to  convey  the  results  more 
intuitively and to convey the increased information provided by the SSM approach to aid 
interpretation.  

Conduct  further  analysis  to  evaluate  the  underlying  causes  for  historical 
changes in the indicators by disentangling the effects of (1) historical assessment bias, 
(2) changes in the dataset in terms of stock composition and (3) the retrospective bias 
of the model. 

Analyse the feasibility to update the current sampling frame and present (if 

possible) a comparative analysis of the implications for the model-based indicators.   

This work could be presented for discussion and approval in STECF PLEN-22-03, so that the new 
methodology  could  be  readily  applied  in  2023.  STECF  emphasises  that  any  changes  in  the 
indicators and protocols should continue to be in a transparent and reproducible framework. The 
new  Bayesian  SSM  model 
is  already  available  on  the  open  access  platform  github 
(https://github.com/Henning-Winker/JARA) and accomplishes all these principles. 

4 

 
 
 
 
Other  issues,  like  the  inclusion  of  additional  indicators  to  monitor  the  CFP  initiated  by  EWG 
STECF-18-15,  are  considered  relevant,  but  were  set  aside  due  to  the  lack  of  time  and  STECF 
suggests they could be discussed again in PLEN 22-02.  

References 

Council  Reulation  EC  1967/2006  concerning  management  measures  for  the  sustainable 
exploitation  of  fishery  resources  in  the  Mediterranean  Sea,  amending  Regulation  (EEC)  No 
2847/93 and repealing Regulation (EC) No 1626/94 

Jardim,  E.,  Mosqueira,  I.  Osio,  G.C.  and  Scott,  F.  2015.  Common  Fisheries  Policy  Monitoring 

Protocol for computing indicators. EUR 27566 EN. Doi: 10.2788/560953 

Jardim, E., Mosqueira, I., Vasilakopoulos, P., Mannini, A., Pinto, C. and Konrad, C. 2019. Protocol 

for the monitoring of the Common Fisheries Policy Version 4.0.  

Pacoureau, N., Rigby, C. L., Kyne, P. M., Sherley, R. B., Winker, H., Carlson, J. K., Fordham, S. V, 
et al. 2021. Half a century of global decline in oceanic sharks and rays. Nature, 589: 567–
571. 

Regulation  (EU)  No 1380/2013  of  the  European  Parliament  and  of  the  Council  of  11 December 
2013  on  the  Common  Fisheries  Policy,  amending  Council  Regulations  (EC)  No 1954/2003 
and  (EC)  No 1224/2009  and  repealing  Council  Regulations  (EC)  No 2371/2002  and  (EC) 
No 639/2004 and Council Decision 2004/585/EC 

Sherley, R. B., Winker, H., Rigby, C. L., Kyne, P. M., Pollom, R., Pacoureau, N., Herman, K., et al. 
2019a.  Estimating  IUCN  Red  List  population  reduction:  JARA—A  decision-support  tool 
applied to pelagic sharks. Conservation Letters: 1–10.  

Sherley,  R.  B.,  Crawford,  R.  J.  M.,  de  Blocq,  A.  D.,  Dyer,  B.  M.,  Geldenhuys,  D.,  Hagen,  C., 
Kemper,  J.,  et  al.  2020.  The  conservation  status  and  population  decline  of  the  African 
penguin deconstructed in space and time. Ecology and Evolution, 10: 8506–8516. 

Contact details of STECF members 

1  -  Information  on  STECF  members’  affiliations  is  displayed  for  information  only.  In  any  case, 
Members of the STECF shall act independently. In the context of the STECF work, the committee 
members do not represent the institutions/bodies they are affiliated to in their daily jobs. STECF 
members  also  declare  at  each  meeting  of  the  STECF  and  of  its  Expert  Working  Groups  any 
specific interest which might be considered prejudicial to their independence in relation to specific 
items on the agenda. These declarations are displayed on the public meeting’s website if experts 
explicitly  authorized  the  JRC  to  do  so  in  accordance  with  EU  legislation  on  the  protection  of 
personnel data. For more information: http://stecf.jrc.ec.europa.eu/adm-declarations 

Name 

Affiliation1 

Email 

Abella, J. Alvaro 

Independent consultant 

Bastardie, Francois 

Technical  University  of  Denmark, 
Institute  of  Aquatic 
National 
Resources 
(DTU-AQUA), 
Kemitorvet,  2800  Kgs.  Lyngby, 
Denmark 

aabellafisheries@gmail.co
m 

fba@aqua.dtu.dk 

5 

 
 
 
 
 
 
Name 

Affiliation1 

Email 

Borges, Lisa 

FishFix, Lisbon, Portugal 

info@fishfix.eu 

Casey, John 

Independent consultant 

Damalas, Dimitrios 

Daskalov, Georgi 

Döring, Ralf (vice-chair) 

Gascuel, Didier 

Grati, Fabio 

blindlemoncasey@gmail.c
om 

shark@hcmr.gr 

Georgi.m.daskalov@gmail
.com 

ralf.doering@thuenen.de 

Hellenic  Centre 
for  Marine 
Research,  Institute  of  Marine 
Biological  Resources  &  Inland 
Waters, 
Vouliagmenis 
Avenue,  Argyroupolis,  16452, 
Athens, Greece 

576 

Laboratory  of  Marine  Ecology, 
Institute  of  Biodiversity  and 
Ecosystem  Research,  Bulgarian 
Academy of Sciences 

Thünen  Institute  [TI-SF]  Federal 
Research 
for  Rural 
Institute 
Areas,  Forestry  and  Fisheries, 
Institute 
Fisheries, 
Economic analyses  Herwigstrasse 
31, 
Bremerhaven, 
Germany 

D-27572 

Sea 

of 

AGROCAMPUS  OUEST,  65  Route 
de  Saint  Brieuc,  CS  84215,  F-
35042 RENNES Cedex, France 

Didier.Gascuel@agrocamp
us-ouest.fr 

fabio.grati@cnr.it 

Institute 

National  Research  Council  (CNR) 
Biological 
– 
Marine 
Resources 
Biotechnologies 
(IRBIM),  L.go 
Fiera  della  Pesca,  2,  60125, 
Ancona, Italy 

for 
and 

Ibaibarriaga, Leire 

AZTI.  Marine  Research  Unit. 
Txatxarramendi  Ugartea  z/g.  E-
48395 Sukarrieta, Bizkaia. Spain. 

libaibarriaga@azti.es 

Jung, Armelle 

Knittweis, Leyla 

DRDH,  Techopôle  Brest-Iroise, 
BLP  15  rue  Dumont  d’Urville, 
Plouzane, France 

armelle.jung@desrequinse
tdeshommes.org 

Department 
Biology, 
University  of  Malta,  Msida,  MSD 
2080, Malta 

of 

Leyla.knittweis@um.edu.
mt 

6 

 
 
 
Name 

Affiliation1 

Email 

Ligas, Alessandro 

Martin, Paloma 

Motova, Arina 

Moutopoulos, Dimitrios 

CIBM  Consorzio  per  il  Centro 
Interuniversitario 
Biologia 
Marina  ed  Ecologia  Applicata  “G. 
Bacci”,  Viale  N.  Sauro  4,  57128 
Livorno, Italy 

di 

CSIC  Instituto  de  Ciencias  del 
Mar  Passeig  Marítim,  37-49, 
08003 Barcelona, Spain 

ligas@cibm.it; 
ale.ligas76@gmail.com 

paloma@icm.csic.es 

Sea  Fish  Industry  Authority,  18 
Logie  Mill,  Logie  Green  Road, 
Edinburgh EH7 4HS, U.K 

arina.motova@seafish.co.
uk 

of 

Department 
Animal 
& 
Production, 
Aquaculture, University of Patras, 
Rio-Patras, 26400, Greece 

Fisheries 

dmoutopo@teimes.gr 

Nord, Jenny 

Prellezo, Raúl 

O’Neill, Barry 

Raid, Tiit 

Rihan,  Dominic 
chair) 

Sampedro, Paz 

Somarakis, Stylianos 

The  Swedish  Agency  for  Marine 
and Water Management (SwAM) 

Jenny.nord@havochvatten
.se 

AZTI  -Unidad  de  Investigación 
Marina,  Txatxarramendi  Ugartea 
z/g  48395  Sukarrieta  (Bizkaia), 
Spain 

rprellezo@azti.es 

DTU  Aqua,  Willemoesvej  2,  9850 
Hirtshals, Denmark 

barone@aqua.dtu.dk 

Estonian 
Institute, 
Marine 
University of Tartu, Mäealuse 14, 
Tallin, EE-126, Estonia 

Tiit.raid@gmail.com 

(vice-

BIM, Ireland 

rihan@bim.ie 

paz.sampedro@ieo.es 

somarak@hcmr. gr 

Institute 

Spanish 
of 
Oceanography,  Center  of  A 
Coruña,  Paseo  Alcalde  Francisco 
Vázquez,  10,  15001  A  Coruña, 
Spain 

Institute  of  Marine  Biological 
Resources  and  Inland  Waters 
(IMBRIW),  Hellenic  Centre  of 
Marine 
(HCMR), 
Research 
Thalassocosmos  Gournes,  P.O. 
Box  2214,  Heraklion  71003, 
Crete, Greece 

7 

 
 
Name 

Affiliation1 

Email 

Stransky, Christoph 

Thünen  Institute  [TI-SF]  Federal 
Research 
for  Rural 
Institute 
Areas,  Forestry  and  Fisheries, 
Sea 
Institute 
Fisheries, Herwigstrasse  31,  D-
27572 Bremerhaven, Germany 

of 

christoph.stransky@thuen
en.de 

Ulrich, Clara (chair) 

IFREMER, France 

Clara.Ulrich@ifremer.fr 

Uriarte, Andres 

Valentinsson, Daniel 

van Hoof, Luc 

Gestión 

AZTI. 
pesquera 
sostenible.  Sustainable  fisheries 
Arrantza 
management. 
kudeaketa  jasangarria,  Herrera 
Kaia  -  Portualdea  z/g.  E-20110 
Pasaia – GIPUZKOA (Spain) 

Swedish University of Agricultural 
Sciences  (SLU),  Department  of 
Aquatic Resources, Turistgatan 5, 
SE-45330, Lysekil, Sweden 

Wageningen  Marine  Research 
Haringkade  1, 
Ijmuiden,  The 
Netherlands 

auriarte@azti.es 

daniel.valentinsson@slu.s
e 

Luc.vanhoof@wur.nl 

Vanhee, Willy 

Independent consultant 

wvanhee@telenet.be 

Villasante, Sebastian 

of 

Santiago 

de 
University 
Compostela, 
de 
Compostela,  A  Coruña,  Spain, 
Department of Applied Economics 

Santiago 

sebastian.villasante@usc.
es 

Vrgoc, Nedo 

Institute  of  Oceanography  and 
Fisheries,  Split,  Setaliste  Ivana 
Mestrovica  63,  21000  Split, 
Croatia 

vrgoc@izor.hr 

8 

 
 
 
 
 
 
 
 
 
 
EXPERT WORKING GROUP EWG-ADHOC-22-02 REPORT 

REPORT TO THE STECF 

EXPERT WORKING GROUP ON 
Suitability study of the Bayesian State-Space 
model ‘JARA’ for stock status indicator 
estimation (EWG-Adhoc-22-02) 

Virtual meeting, January 2022 

This report does not necessarily reflect the view of the STECF and the 
European Commission and in no way anticipates the Commission’s 
future policy in this area. 

9 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1 

INTRODUCTION 

As stated in the article 50 of the EU Common Fisheries Policy (REGULATION (EU) No 1380/2013), 
the Scientific, Technical and Economic Committee for Fisheries (STECF) is  requested annually by 
the  European  Commission,  the  Council  and  the  European  Parliament  to  compute  a  series  of 
indicators and report on the performance of the Common Fisheries Policy. A protocol that details 
how  these  indicators  are  derived  was  adopted  by  the  STECF  (STECF,  2019).  Two  types  of 
indicators  are  derived,  the  empirical  and  the  model-based  indicators.  In  recent  years,  some 
shortcomings  in  the  model-based  indicators  have  been  identified  by  the  Expert  Group  reporting 
on these indicators. This ad hoc report reviews the framework used to derive these model-based 
indicators and covers the following tasks: 

  Evaluation of shortcomings of the current Generalized Linear Mixed effects Model (GLMM) 
approach for  estimating the key CFP indicators the time series of  yearly fishing mortality 
) and the time series of the yearly 
level (F) over a fishing mortality reference point (

Spawning Stock Biomass (SSB) levels over a reference year value (

) with respect to 

statistical  assumptions  and  emerging  practical  challenges  with  interpretability,  model 
convergence and treatment of missing values. 

  Development of an alternative State-Space Model (SSM) approach for estimating the trend 
,  considering  recent  ‘best-practice’  in  time-series  analysis  and 

  and 

in 

modelling. 

  Conduct  a  comparative  analysis  of  the  indicators  for 

  and 

  between  the 

current GLMM and an alternative SSM for stocks of EU waters in North-East Atlantic and in 
the Mediterranean and Black Seas in terms of precision, consistency and interpretability. 

  Exploring  options  to  generate  combined  indices  for 

  and 

  across  European 

waters of the North-East Atlantic, Mediterranean and Black Seas using the SSM approach 

1.1 

Terms of Reference for EWG-Adhoc-22-XX 

2  POTENTIAL SHORTCOMINGS OF CURRENT GENERALIZED LINEAR MIXED MODEL (GLMM) 

As agreed by STECF (2018a, 2018b) the trends in fishing pressure (

)  and spawning stock 

biomass  (
)  are  computed  by  fitting  a  Generalised  Linear  Mixed  effects  Model  (GLMM), 
assuming Gamma distribution with a log-link function. The model treats the years as independent 
fixed effect factors and applies a random intercept term to account for variations among stocks. 
GLMMs are applied to a wide variety of statistical problems. In fisheries, we found the application 
to  Catch-Per-Unit-Effort  (CPUE)  data  standardisation  particularly  helpful  to  explain  the 
assumptions and highlight the differences to the CFP monitoring application. 

2.1.1  THE GLMM APPROACH IN CPUE STANDARDISATION 

In  fisheries  this  type  of  GLMM  random  effects  structure  can  be  applied  to  abundance  index 
standardization  based  on  large  data  sets  of  individual  Catch-Per-Unit-Effort  (CPUE)  records 
(Helser et al., 2004). The primary objective of CPUE standardization is to use commercial data to 
derive  an  index  of  abundance.  The  model  is  used  to  retain  the  variation  of  CPUE  due  to  the 
change  in  abundance.  The  variations  of  CPUE  due  to  spatio-temporal  variations  of  effort 
attributed  to  distribution,  diversity  of  gear,  fishing  power  and  fishing  tactics  on  the  CPUE  are 

10 

 
 
 
 
 
 
 
removed by the model (Winker et al., 2013). Once standardized, CPUE can be used as an index of 
abundance in stock assessment models. Analogous to random intercept term for stock in the CFP 
monitoring analysis, a common choice in CPUE standardization models is the inclusion of random 
vessel  effects,  which  provides  an  efficient  way  to  combine  CPUE  recorded  from  various  vessels 
into  a  single,  continuous  CPUE  time-series,  despite  discontinuity  of  individual  vessels  over  the 
time  series.  Treating  vessels  as  a  random  effect  addresses  concerns  that  multiple  CPUE  records 
produced  by  the  same  vessel  lack  independence  resulting  in  an  over  estimation  of  precision 
(Thorson  and  Minto,  2015).  A  meaningful  random  intercept  term  relies  on  the  assumption  that 
the variation for a subordinate factor (e.g. individual vessel) is smaller than the variation among 
a  larger  set  of  levels  (e.g.  fleet).  This  assumption  would  be  violated  if,  for  example,  vessels 
exhibited  heterogeneous  temporal  trends  in  catchability.  The  latter  condition  would  probably 
require an additional random year slope term nested within vessel to be adequately accounted for 
(Zuur et al., 2009). 

2.1.2  THE GLMM APPROACH TO MODEL THE TIME SERIES IN THE CFP MONITORING 

The  application  of  the  GLMM  approach  to  the  dataset  of  time-series  of  biomass  and  fishing 
pressure  as  applied  to  monitor  the  performance  of  the  CFP  differs  substantially  in  both  the 
structure,  the  data  generation  process  and  the  sample  size  from  the  CPUE  standardisation 
described above. This results in the following concerns: 

  Each  time-series  is  generated  independently  from  a  stock  assessment  model,  and  could 
lack in certain cases a common year effects factor across stocks (e.g. the Artic cod trends 
in SSB is likely to be independent from the trajectory of the Iberian anchovy SSB).  
In  the  absence  of  a  common  year-effect  (due  to  a  lack  of  replicate  estimates  of  stock 
specific SSB and F for a given year), the GLMM has very limited ability to account missing 
values. 

 

  For  each  stock,  the  annual  estimates  for  each  fishing  pressure  and  biomass  are  strongly 

correlated and therefore lack independent year effects. 

  Unaccounted  systematic,  yet  multi-directional  annual  trends  across  stock  indicators  (i.e. 
increase,  stable,  noisy,  decrease)  may  inflate  some  variances  within  the  random  stock 
effects and may result in misleading trajectories. 

  Strongly  diverging  trends  can  lead  to  bimodal  random  effects  distributions  violating  the 
assumption  of  normality  and  lead  to  GLMM  convergence  issues  particularly  for  small 
numbers of stocks and for groups of stocks with contrasting trends. 

In general, it is expected that the impact of the above listed inconsistencies between the  GLMM 
assumption  and  the  underlying  data  generation  system  will  be  reduced  when  the  number  of 
stocks  increases  (central limit  theorem)  and  the  amount  of  missing values  decreases.  Emerging 
challenges  with  small,  imbalanced  sample  sizes  is  also  indicated  by  the  difficulties  of  achieving 
sufficiently high convergence rates of non-parametric bootstrap samples to  estimate uncertainty 
about the indicator trends on regional levels, such as for the Western and Central Mediterranean 
Sea.  The  combined  North  East  Atlantic  biomass  fits  to  derive  B/B2003  exhibited  some  signs  of 
convergence issues, which might aggravate in future.  

2.1.3  IMPLEMENTATION OF THE GLMM BOOTSTRAP APPROACH 

An  in-depth  review  of  the  R  code  in  the  current  protocol  revealed  that  the  seemingly  inflated 

confidence  intervals  for  the  biomass  indicator 

  can  be  traced  back  to  a  transformation 

artifact  in  the  bootstrap  procedure.  In  contrast  to  the  ratio  based  indicator  of 
,  biomass 
trends (SSB) are first fitted by the GLMM as absolute values of SSB. The estimated mean biomass 

. 
is only afterwards divided by the mean biomass estimate for 2003 to obtain the ratio of 
In  the  current  implementation  of  the  bootstrap  procedure,  all  bootstrapped  vectors  of  total 

11 

 
 
 
 
biomass  estimates 

  for  stock  ,  year    and  bootstrap  replicate  k  originate  from  randomly 

sampling stock biomass trajectories 

 with replacement. As a result, total biomass scale among 

bootstrap  replicates  becomes  incompatible.  The  current  procedure  first  divides  each 

  by  the 

geometric  mean  of 
intervals in the form of quantiles. 

  before  computing  the  median  trend  of 

  and  the  confidence 

The  objective  is  to  monitor  a  relative  biomass  trend  in  absence  of  a  sufficiently  representative 

 indicator. A possible solution to remove spurious uncertainty inflation of 

 indicator is 
to first normalise the individual stock time series by their means prior to fitting the model. This a 
priori removes the large unexplained variation attributable to total biomass scale, while retaining 
the variation of interest of the underlying trends. The procedure would then be analogous of using 

the ratio 

 as input.  

Another  limitation in  relation  to  the  biomass  indicator  is  the  use  of  2003  as  a  reference  year  to 

show the changes in SSB over time. As opposed to the fishing pressure indicator that uses 
 as 
a target reference point, the biomass indicator does not have a target reference point, such as a 

  to  compare  the  biomass  to.  As  a  result,  the  indicator  does  not  show  an  absolute  state  but 

rather a relative state of the indicator.  

Due  to  the  increasing  difficulties  in  the  implementation  of  the  GLMM  assessment  and  the 
difficulties  in interpreting  the  results  based  on  consistency  and  bias  concerns,  a  Bayesian  state-
space  model  approach  was  used  in  trials,  both  as  a  long-term  solution  and  a  means  of  better 
understanding the issues. Details of the model and some preliminary results are provided in this 
report. 

3 

STATE-SPACE MODEL APPROACH USING `JARA` 

3.1  State-space model (SSM) approach for time-series analysis 

State-space  models  (SSM)  provide  a  general  framework  for  analysing  dynamic  systems  that 
considers  process  noise  (year-to-year  variation)  and  observation  error  (or  reporting) 
simultaneously  (de  Valpine  and  Hastings,  2002;  Kéry  and  Schaub,  2012).  The  model  aims  to 
predict  the  trend  in  each  stock  independently  by  only  considering  past  changes  in  the  specific 
stock. This is consistent with the expected underlying system dynamics in assuming a Markovian 

process,  such  that  the  state  of  a  stock  in  the  next  time  step 

  is  conditioned  on  its  state  at 

time  , but independent of past and future states (Kéry and Schaub, 2012). The possible rate of 
change  in  the  time  step  is  constrained  by  previous  rates  of  change.  The  SSM  approach,  as 
opposed  to  the  GLMM  approach,  does  not  require  an  independent  year  effect  (i.e.  that  all  the 
stocks have common trends in SSB and F).  The expected benefit of the SSM approach, under the 
data  conditions,  greatly  reduces  the  risk  of  violating  the  assumption  of  independence  in 
observation  errors.  Given  these  desirable  properties,  there  has  been  a  rapid  uptake  of  state-
space approaches for modelling population dynamics  (Buckland et al., 2004; Thorson and Minto, 
2015;  Winker  et  al.,  2020),  including  more  recent  applications  of  quantifying  population  decline 
(Sherley  et  al.,  2019a,  2020;  Pacoureau  et  al.,  2021)  and  range  shift  indicators  (Currie  et  al., 
2019). State-space models have also been specifically applied for analysing global trends in stock 

status with respect to 

 and 

 (Hilborn et al., 2021). 

3.2  Bayesian State-space model implementation 

The state-space approach for  estimating joint indicator from multiple time-series (‘mixed-trend` 
option’  was  implemented  in  the  R  package  ‘JARA`  (Winker  et  al.,  2020),  which  is  publically 

12 

 
 
 
 
available  on  GitHub  (https://github.com/henning-winker/jara).  JARA  has  been  originally 
developed to provide a generalized Bayesian state-space decision-support tool for trend analysis 
of  abundance  indices  with  direct  applications  to  IUCN  Red  List  assessments  (Sherley  et  al., 
2019a, 2020; Winker  et al., 2020; Pacoureau  et al., 2021). JARA enables the analysis of one or 
multiple abundance indices simultaneously, where each index can have a different scale, contain 
missing years and span different periods. JARA provides the options for fitting relative abundance 
indices  to  estimate  a  mean  trend  (Sherley  et  al.,  2019a),  or  absolute  abundance  indices  (from 
e.g.  different  stock)  to  produce  a  summed  population  trend  for  the  total  population  (Sherley  et 
al.,  2019b,  2020).  To  predict  a  joint  mean  indicator  independent  time-series  of  multiple  stocks, 
JARA was extended by ‘mixed-trend’ option for this analysis. 

JARA  is  run  from  the  statistical  environment  R  and  executed  in  JAGS  (Plummer,  2003),  using  a 
wrapper function from the R library ‘r2jags’. The Bayesian posterior distributions are estimated by 
means of Markov Chain Monte Carlo (MCMC) simulation. In JAGS, all estimable hyper-parameters 
are  assigned  to  a  prior  distribution.  JARA  uses  vague  (uninformative)  prior  distributions 
throughout, so all inferences are drawn from the information in the data. 

3.2.1  MODEL FORMULATION 

A  central  assumption  of  the  state-space  approach  is  that  trend  of  index 

  in  year    follows  a 

Markovian  process,  such  that  will  be  conditioned  on  the 
.  It  is 
assumed  that  the  underlying  population  trend  follows  a  conventional  exponential  growth  model 

  in  the  previous  year 

,  where 

  is  the  growth  rate  in  year 

  can  vary  annually  to 
accommodate  fluctuations  in  recruitment,  fishing  mortality  and  other  time-varying  latent 
(unobservable) effects. For the new ‘mixed-trend’ option, it is assumed that the indicator for each 
stock may increase or decline independently from other stocks. The process equation is therefore 

.  The  growth  rate 

formulated as 

 so that 

, where 

 is the year-to-year rate of 

change  specific  to  index    that  is  assumed  to  vary  around 

  to  represent  the  underlying  mean 

rate of change for each stock  , plus a process variance 

that is common to all subpopulations: 

.  Because  the  process  noise  is  lognormally  distributed,  we  adjusted 

  for 

lognormal bias by subtracting half the process variance at each time step: 

. 

The  corresponding  observation  equation  is  given 

,  so  that  expected  indicator 

trend 

 and the error terms 

 are specific to stock i. 

3.2.2  TIME-BLOCKS ON TREND ESTIMATION 

JARA  facilitates  the  implementation  of  time-blocks  to  allow  changes  in  the  rate  of  change  not 
previously experienced such as might be caused by strong management intervention in fisheries 
or  systematic  changes  in  the  environmental  conditions.  Time  blocks  can  also  be  introduced  to 
conduct  short-term  forecasts  or  make  predictions  over  some  missing  values  of  some  indices  in 
the  last  data  year  (caused  by  the  unavailability  of  stock  assessment  results  in  the  MED&BS). 
into  process  equation  as 
is 
When 

time  block  effect 

implemented 

invoked, 

the 

,  where 

  denotes  the  difference  in  the  rate  of  change  form 

  (i.e. 

). 

13 

 
 
 
 
 
3.2.3  OBSERVATION ERROR 

The total observation variance 

 can be separated into three components: (1) externally derived 

standard errors 

 for each abundance index i, (2) a fixed input variance 

 and (3) estimable 

, where the prior for 

 assumes an uninformative inverse-gamma distribution with 
variance 
both scaling parameters set to 0.001. All three variance components are additive in their squared 
is: 
form,  so 

total  observation  variance 

i  and  year  y 

for  abundance 

index 

the 

.  Each  variance  component  can  be  individually  switched  on  or  off  by  the 

user.  Setting  a  minimum  plausible  observation  error  in  this  way  informs  the  estimate  of  the 
process  variance  as  a  portion  of  the  total  variance.  The  ratio  is  then  used  to  constrain  the 
parameter space of the MCMC process decreasing the likelihood of confounding the two variance 
components,  therefore  increasing  model  stability  and  convergence  of  state-space  models  when 
compared to the GLMM under similar missing value comparisons. 

3.2.4  BAYESIAN FRAMEWORK 

The initial spawning stock biomass size or Fishing mortality level in the first year 
 is drawn in 
log-space  from  a  normal  distribution  with  the  mean  equal  to  the  log  of  the  first  available  count 

 and a standard deviation of 1000. Priors for the process variance (

) can be either fixed or 
estimated. If estimated (default), the process variance prior is implemented via a vague inverse-

gamma  distribution  by  setting  both  scaling  parameters  to  0.001: 
,  which 
yields an approximately uniform prior on the log scale. The estimation of annual rate of change in 

biomass or fishing mortality rate deviates 

,) is implemented through hierarchical priors, where 

 is informed by the stock 

. Vague normal priors with a mean of 0 and variance of 1000, are 

assumed for   ~ 

. 

3.2.5  JOINT INDEX ESTIMATION (INDICATOR TREND OVER MULTIPLE STOCKS) 

Calculation  of  a  joint  index  is  simply  the  averaging  of  the  most  probable  reconstructed  stock 
trends (i.e. all stocks over the same period). However, in cases where means and variances differ 
between  stocks  this  can  produce  biased  estimates  in  the  indicator.  A  non-parametric  bootstrap 
procedure is applied after the MCMC to provide unbiased estimate of the population uncertainty of 
the indicator from the sample available of stock indicators. This procedure follows in principle the 
non-parametric procedure of the current protocol, but is highly efficient as it can make use of the 
MCMC  iterations  (here  10.500)  and  therefore  does  not  require  refitting  JARA  many  times.  The 
bootstrap algorithm comprises the following steps: 

(1) For  each  MCMC  iteration 

  resample  with  replacement  from  the  stock  indicators 

  to 

create a random bootstrap sample for  

. 

(2) To  obtain  a  joint index   

,  for  each  bootstrap  replicate  ,  compute  the  geometric  mean 

across all stocks   as 

, where 

 is the number of stocks. 

(3) Repeat  steps  (1)  and  (2)  over  all  replicates  k  to  obtain  the  posterior  distribution  for  the 

joint index   

Consistent with the current approach, the expected value 
posterior iterations and  uncertainty is represented by the 25th and 75th (50% CIs) and the  2.5th 
and 97.5th (95% CIs) percentiles. 

 is taken as the median across all 

14 

 
 
 
 
 
 
4  COMPARISON OF JARA WITH GLMM INDICATORS FOR F/FMSY AND B/B2003 

4.1  Material and Methods 

4.1.1  DATA SOURCE  

For  the  comparative  analysis  of  the  indicators  for 
  between  the  GLMM  and 
JARA, the new datasets (terminal year of 2020) of the North-East Atlantic (category 1-2 stocks in 
EU  waters)  and  the  Mediterranean  and  Black  Seas  Stocks  were  included.  These  datasets  are 
considered  preliminary  because  at  the  time  this  analysis  was  completed  some  final  checks  in 
preparation  for  the  2022  ad-hoc  report  on  the  Monitoring  of  the  performance  of  the  Common 
Fisheries Policy were still underway. 

  and 

For  the  analysis  of  EU  waters  of  the  North-East  Atlantic,  51  category  1  and  2  stocks  with 

 and 54 category 1 and 2 stocks with estimates of biomass for deriving 

estimates of 
were  included.  Three  stocks  (cod.27.6a,  bli.27.5b67  and  dgs.27.nea)  had  missing  values  for 
2020. Following the protocol, the 2020 values were assumed to be equal to the 2019 value in the 
GLMM analysis. In the CFP monitoring report, it is mentioned that cod.27.6a was at the origin of 
non-convergence issues in the B/B2003 indicator. The only solutions available were to remove the 
stock from the dataset or to change the method to estimate 2020 SSB. The latter solution would 
require a change in the protocol and would need to be applied to the 3 stocks that do not have a 
2020 estimate output from an analytical model.  

For  the  analysis  of  the  Mediterranean  and  Black  Seas,  34  stocks  were  included  to  estimate  the 

 indicator and 33 stocks to estimate 

, which represents a decrease by one stock for 

  and  an  increase  by  1  stock  for 

  compared  to  previous  CFP  STECF  report  (STECF, 
2021). The datasets for the Mediterranean and Black Seas included missing values for 19 stocks, 
where  for  13  of  the  stocks  observations  were  missing  at  the  start  of  the  time  series  and  6  with 
data missing in the terminal year (for details see  STEFC, 2022). 

4.1.2  COMPARATIVE STATISTICAL ANALYSIS 

Following the current protocol (STECF 2021, Annex I), the GLMM approach was used to estimate  

 and 

. The Northeast Atlantic data set span over the years 2003-2020. Three stocks 
cod.27.6a,  bli.27.5b67,  and  dgs.27.nea  did  not  have  estimates  for  2020.  These  values  were 
assumed to be the same as in 2019. The Mediterranean and Black Seas data covered years 2003-
2019 because 38% of stocks from this area had missing values for 2020. The bootstrap approach 
for  estimating  indicator’s  uncertainty  was  only  applied  to  the  North  East  Atlantic  and 
Mediterranean and Black Sea consistently with STECF (2021). GLMM’s uncertainty estimates were 

not derived for individual ecoregions. The input data for the GLMM were time series of 
total biomass by stock. Three analysis were performed: 

 and 

  For  a  direct  comparison,  the  same  data  ranges  were  used  in  both  the  GLMM  and  JARA 
analyses.  However,  3  stocks  in  the  Northeast  Atlantic  were  missing  estimates  for  2020. 
Two  different  methods  were  used  to  fill  the  gap.  In  the  GLMM,  the  2020  value  was 
considered  to  be  the  same  as  in  2019.  In  the  JARA  approach,  the  2020  values  were 
estimated by JARA. The Mediterranean and Black Sea data set ranged from 2003 to 2019. 
Absolute  biomass  estimates  were  derived  by  both  methods  and  subsequently  divided  by 
the 2003 biomass estimate as in the current procedure.  

  An alternative approach to using absolute biomass with its inherent uncertainty of scale in 
many  fisheries  assessments  was  to  normalise  the  trend  in  SSB  therefore  removing  the 

15 

 
 
 
 
 
 
large unexplained variation that is attributable  to the total biomass  scale, while retaining 
the variation of interest of the underlying trends (see Section 2.2).  

  For  the  Mediterranean  and  Black  Seas,  the  missing  values  in  2020  were  predicted  using 
the state-space process of JARA. These predictions were made by implementing the JARA 
time-block option in 2016 (Section 3.2), so that a prediction of a missing value for a stock 
indicator  in  2020  is  forecasted  based  on  the  expected  rate  of  change  that  is  estimated 
from  the  observations  in  2016-2019.  In  contrast  to  the  GLMM,  uncertainty  is  implicitly 
estimated by JARA in the form of MCMC posterior, and could therefore also be represented 
for the ecoregion indicator estimates. 

For all analyses with JARA, the fixed input variance 

 was set so to correspond to a CV of 5% 

and estimable variance 

 was assumed to be common for all stocks, such that total observation 

is  given   
  (see  Section  3.2).  The  MCMC  simulations  were  run  using  three  MCMC 
chains,  each  with  9,000  iterations,  of  which  the  first  2,000  were  discarded  as  ‘burn-in’  and 
thereafter  every  second  iteration  was  saved  (thinning).  This  yielded  a  total  of  10,500  MCMC 
iterations to represent the posteriors’ distributions. 

4.1.3  SENSITIVITY ANALYSES OF MISSING VALUES 

A  sensitivity  experiment  was  designed  to  compare  the  performance  of  the  current  GLMM 
approach  and  JARA  in  accounting  for  large  proportion  of  missing  values  at  the  start  of  the  time 
series, such is the case of the Mediterranean and Black Seas indicator analysis. For this purpose, 

the  indicator  estimates  of 
  for  EU  waters  in  the  North  East  Atlantic  (without 
missing  values)  were  taken  as  control  runs.  Subsequently,  20  stocks  of  the  Northeast  Atlantic 
dataset (c. 35% of the dataset) were selected for their strong positive log-linear regression slopes 

  and 

for 
. For these 20 stocks, the first 3 years of the time series were replaced by 
missing  values.  The  GLMM  and  JARA  were  then  applied  to  these  ‘treatment’  datasets.  Derived 

 and 

indicators 
‘control’ datasets. 

  and 

4.1.4  CROSS-VALIDATION 

  were  compared  to  the  indicators  estimates  from  the  complete 

Finally, a hindcast cross-validation approach (Carvalho et al., 2021; Kell et al., 2021) was applied 
to  evaluate  the  forecast  accuracy  of  JARA  in  cases  where  the  final  data  year  comprises  large 
proportion  of  missing  values.  Here,  hindcasting  describes  the  process  of  sequentially  removing 
the  observations  of  the  terminal  years  and  using  the  reduced  data  set  to  make  predictions  to 
compare  to  the  known  data  in  the  removed  year  and  cross  validated  with  the  model  results  for 
the entire time-series. The JARA fits to the truncated Mediterranean and Black Seas dataset until 
2019 was used as reference run, where 2019 did not contain missing values. Next, missing values 
were sequentially introduced from 2019 back to 2015 (i.e. 5 peels) and JARA was refitted to each 
of  the  five  new  datasets.  Similar  to  Mohn’s  retrospective  analysis  bias  (Hurtado-Ferro  et  al., 
2015),  the  forecast  bias  was  then  quantified  as  mean  relative  error  between  the  indicator 
predictions  for  next  year  that  contained  the  missing  values  and  the  indicator  estimate  for  the 
same year from reference run, such that: 

16 

 
 
 
 
 
 
where 

  is  the  predicted  indicator  based  on  forecasts  of  missing  observations  in  year  , 

  the 

corresponding  estimate  for  year 
observations and h denotes the number hindcasting time steps. 

  from  the  reference  model  that  fitted  with  complete 

4.2  Results 

4.2.1  RESULTS FOR NORTH EAST ATLANTIC STOCKS IN EU WATERS 

A  comparison  of  the  estimated  fishing  pressure  indicator 
  from  the  GLMM  and  JARA  for 
stocks  in  the  North  East  Atlantic  are  shown  in  Figure  1.  The  JARA  estimates  closely  follow  the 
nominal  geometric  mean  of  the  observations  (white  dots),  whereas  the  GLMM  estimates  follow 
the same trend but are positively biased. The 95% confidence intervals from the JARA posteriors 
are slightly wider than those provided by the GLMM bootstrap procedure.  

Figure 1. Comparison of estimates of 
 and associated 95% confidence intervals for stocks (n = 51) in 
EU waters of the North East Atlantic from the GLMM and JARA. The points represent the geometric means of 

the observations for comparison to the nominal 

. 

  are  shown  in  Figure  2.  The  trends  estimate  from  JARA  and  GLMM 
The  estimates  for 
closely  follow  the  ‘nominal’  geometric  mean  of  the  observations  (dots).  There  is  a  small  but 
consistent  negative  bias  in  the  GLMM  trends  in  all  but  the  3  most  recent  years  with  a  steeper 
increase in B/B2003, where three missing values were replaced with previous values in the GLMM 

analysis  (i.e.  different  data).  The  medians  of  the  bootstrapped  JARA  posteriors  for 
produced  the  same  results  irrespective  of  whether  absolute  or  normalised  biomass  time  series 
were fitted by the model. The confidence intervals estimated from the bootstrap procedure on the 
absolute biomass is substantially inflated compared to normalised biomass and comparable to the 
GLMM  also  using  absolute  estimates.  This  large  decrease  in  the  precision  when  using  absolute 

17 

 
 
 
 
 
 
 
 
 
biomass  can  be  attributed  to  large  additional  variation  introduced  when  resampling  stocks  that 
vary widely in absolute biomass scale (see differences between Figure 3 bottom left and Figure 
3 bottom right) rather than a difference between the GLMM and JARA approaches. 

Figure 2. Comparison of estimates of  
 and associated 95% confidence intervals for stocks (n = 54) 
in EU waters of the North East Atlantic from the GLMM using absolute biomass as input and from JARA using 
either  absolute  biomass  (“abs”)  or  normalised  (“norm”)  biomass  as  input.  The  points  represent  the 
geometric means of the absolute biomass observations (GLMM and JARA.abs) and normalised observations, 

which are divided by the 2003 estimate of the respective model run to create the nominal 

. 

18 

 
 
 
 
 
 
 
 
Figure  3.  Comparison  of  JARA  estimates  of 
  and  associated  50%  and  95%  confidence  intervals  for 
stocks (n = 54) in EU waters of the North East Atlantic using either absolute biomass or normalised biomass 
as input (Top) and the fits to absolute and normalized biomass time-series, including the 95% CIs (Bottom). 

Subregional indicators – North East Atlantic 

 and 

The JARA trend estimates for the 
 indicators and associated confidence intervals 
are  shown  for  each  of  the  five  North  East  Atlantic  ecoregions  in  Figure  4.  The  JARA  estimates 
(black  lines)  closely  follow  the  geometric  mean  of  the  observations,  except  in  years  of  missing 
data (2020 for the Celtic Seas and Widely distributed stocks). The effect was most pronounced in 
the Widely distributed stocks, where two of the usual five stocks lacked information. The regional 
estimates from the GLMM (red lines) also followed the same trajectory as the geometric mean of 
the  observations  but  exhibited  a  bias.  The  scale  of  the  bias  for  the  GLMM  indicator  estimates  is 
always  positive  for  F  and  negative  for  SSB.  The  overall  trend  varies  by  region.  The  JARA 

estimates  for 
shown for combined North East Atlantic dataset of EU stocks in Figure 5.  

  with  their  associated  50%  and  95%  confidence  intervals  are 

  and 

19 

 
 
 
 
 
 
 with 50% and 95% CIs for the Baltic Sea,  Bay of Biscay 
Figure  4. JARA estimates for 
and  Iberia,  Celtic  Seas,  Greater  North  ecoregions  and  for  Widely  distributed stocks  .  The  red  dashed  lines 
denote  the  indicator  estimates  from  GLMM.  The  colour-shading  of  the  nominal  geometric  mean  of  the 
observations (dots) indicate the number of available observations in each year.  

 and 

  with  50%  and  95%  CIs  for  the  combined  North  East 
Figure  5.  JARA  estimates  for 
Atlantic dataset for EU waters. The red dashed lines denote the indicator estimates from GLMM. The colour-

  and 

20 

 
 
 
 
 
 
 
 
shading  of  the  nominal  geometric  mean  of  the  observations  (dots)  indicate  the  number  of  available 
observations in each year.  

4.2.2  RESULTS FOR MEDITERRANEAN AND BLACK SEA STOCKS 

A  comparison  of  the  estimated  fishing  pressure  indicator 

  from  the  GLMM  and  JARA  for 

stocks in the Mediterranean and Black Seas are shown on Figure 6. JARA predicted lower 
values  compared  to  the  geometric  mean  observations  for  the  initial  years  (2003-2008).  Those 
years  included  a  large  proportion  of  missing  observations.  In  the  period  2009-2019  the  JARA 
estimates  closely  tracked  observations.  The  GLMM  estimates  were  also  lower  than  the 
observations, however  the  negative  bias was  not as strong as the JARA bias. In the intervening 
period  2009-2018,  observations  and  both  estimates  were  consistent  until  the  final  year  where 
observations  and  the  JARA  model  indicated  a  steeper  decline  in  F.  Similar  to  the  North  East 
Atlantic  results,  the  95%  confidence  intervals  from  the  JARA  posteriors  are  slightly  wider  than 
those from the GLMM bootstrap procedure.  

 and associated 95% confidence intervals for stocks (n = 34) in 
Figure 6. Comparison of estimates of 
the Mediterranean and Black Seas from the GLMM and JARA. The points represent the geometric means of 

the observations to for comparison to the nominal 

. 

The  estimates  for 

  are  shown  in  Figure  7.  The  medians  of  the  bootstrapped  JARA 

 could be seen over two different periods, 2003-2008 and 2009-2019. Over 
posteriors for 
the  first  period  during  which  the  dataset  includes  missing  values,  the  JARA  estimates  do  not 
exhibit  the  same  trajectories.  An  increase  is  seen  until  2006  for  the  normalised  estimates  and 
until  2007  for  the  absolute  estimate.  A  decrease  until  the  start  of  the  second  period  is  then 
observed. Over the second period, both estimates followed the same trajectory and did not differ 
in  absolute  values.  The  GLMM  trend  was  similar  to  the  one  exhibited  by  both  JARA  estimates. 

21 

 
 
 
 
 
 
 
 
However,  the  early  increase  in  the  biomass  indicator  was  steeper,  as  was  the  recent  rise.  This 
demonstrates that all models followed the geometric mean over the period 2009-2019, diverging 
from  the  geometric  mean  where  data  are  missing.  In  contrast  the  GLMM  follows  the  geometric 
mean  over  the  entire  period  including  those  periods  where  the  observations  are  known  to  have 
been  biased  by  systematically  missing  data,  suggesting  it  made  little  adjustment  to  the  central 
tendency. The  contrast in trajectories between JARA and the GLMM appear to be largely a result 
of the difference in the biomass estimates for the reference year 2003, which is clearly sensitive 
to  presence  of  missing  values.  Comparable  to  the  North  East  Atlantic  results,  the  confidence 
intervals  estimated  from  the  bootstrap  procedure  on  the  absolute  biomass  was  substantially 
inflated compared to normalised biomass and comparable to the GLMM. Large additional variation 
of absolute biomass among is contrasted with the trends in the normalized time-series in (Figure 
8). 

Figure 7. Comparison of estimates of  
 and associated 95% confidence intervals for stocks (n = 33) 
in the Mediterranean and Black Seas from the GLMM using absolute biomass as input and from JARA using 
either  absolute  biomass  (“abs”)  or  normalised  (“norm”)  biomass  as  input.  The  points  represent  the 
geometric means of the absolute biomass observations (GLMM and JARA.abs) and normalised observations, 

which are divided by the 2003 estimate of the respective model run to create the nominal 

. 

22 

 
 
 
 
 and associated 50% and  95% confidence intervals for 
Figure 8. Comparison of JARA estimates of 
stocks (n = 33) in the Mediterranean and Black Seas using either absolute biomass or normalised biomass 
as input (Top) and the fits to absolute and normalized biomass time-series, including the 95% CIs (Bottom). 

Subregional indicators – Mediterranean and Black Seas 

The  JARA  trend  estimates  for  the  subregional  indicators  of 
  and  associated 
confidence  intervals  are  shown  for  each  of  the  two  Mediterranean  ecoregions  through  2020  in 
Figure 9. Again, the JARA estimates closely follow the geometric mean for years with no missing 
values  (2009-2019).  In  the  Western  Mediterranean  indicators,  there  is  a  more  pronounced 
departure  between  the  models  than  in  the  Central  Mediterranean  (Figure  9).  This  result 
contrasts with the NE Atlantic results. In the GLMM, fishing mortality estimates are highest at the 
beginning and end of the time-series reaching their lowest values in 2011 while the JARA models 
indicate largely constant F with a slight decrease in recent years. The GLMM predicts SSB to have 
risen  in  the  period  2010-2019,  while  the  JARA  index  suggest  an  almost  flat  trajectory  over  the 
same period. 

  and 

23 

 
 
 
 
 
 
 
 
 
Figure  9.  JARA  estimates  for 
  indicators  with  50%  and  95%  CIs  for  the  Central  and 
Eastern  Mediterranean  ecoregions  including  the  predictions  for  2020  (blue-shaded).  The  red  dashed  lines 
denote the indicator estimates from GLMM through 2019. The colour-shading of the nominal geometric mean 
of the observations (dots) indicate the number of available observations in each year.  

  and 

Figure  10. JARA estimates for 
 with 50% and 95% CIs for the combined Mediterranean 
and Black Seas datasets including the predictions for 2020 (blue-shaded). The red dashed lines denote the 

 and 

24 

 
 
 
 
 
indicator  estimates  from  GLMM.  The  colour-shading  of  the  nominal  geometric  mean  of  the  observations 
(dots) indicate the number of available observations in each year.  

25 

 
 
4.2.3  SENSITIVITY ANALYSIS OF MISSING VALUES 

An  important  advantage  of  JARA  over  the  GLMM  approach  is  that  its  state-process  can  make 
stock specific predictions of indicator trends when data is missing based on the individual stock’s 
underlying  trend  while  maintaining  the  ability  to  propagate  the  uncertainty  in  the  estimates 
(Figure 11). 

Figure  11.  Depicting  the  JARA  fits  by  stock  with  95%  CIs  to  time-series  of 
  for  Mediterranean  and 
Black  Seas  (n  =  34),  The  13  stocks  with  missing  values  are  easily  identifiable  by  the  large  confidence 
intervals at the beginning of the period. Also visible are the stocks which due to missing recent assessments 
have missing values in the most recent year (2020). 

To  test  this  ability,  a  sensitivity  analysis  was  conducted  that  entailed  removing  the  first  three 
years  of  data  from  a  random  set  of  20  North  East  Atlantic  stocks  for  both  the  GLMM  and  JARA 
models.  The  results  suggested  the  JARA  estimates  were  robust  to  the  introduction  of  missing 
values (Figure 12), as evidenced by the similarity of the JARA fits and of the complete data set 
(control) with those that were missing at the beginning of the time series. In contrast, the GLMM 
model  fitted  to  a  reduced  dataset  differed  markedly  in  its  estimation  of  indicator  trends 

26 

 
 
 
 
 
 
 
suggesting higher initial 
 during the first three years and lower SSB subsequent to this. The 
magnitude of bias caused by the missing values can be inferred from the difference over the first 
three  years  in  the  geometric  means  of  control  and  treatment  dataset.  In  contrast  to  JARA,  the 
GLMM was highly sensitive to the occurrence of missing values in the initial years. In the case of 

, the GLMM estimates were even stronger than implied by the geometric means in the first 
three  years  (2003-2005),  but  this  bias  did  not  affect  years  with  complete  observations. 
Nevertheless, the results somewhat distort the initial evaluation of the fishing pressure indicator. 

By  contrast,  the  GLMM  indicator  of 

  GLMM  failed  to  recover  from  the  biased  estimate  of 

 initial year. In this hypothetical examples, this would result in a misleading interpretation of 
the  trend  in  the  North  East  Atlantic,  implying  that  instead  of  achieving  an  average  biomass 
increase  by  some  25%  since  2003,  biomass  would  have  just  rebuilt  to  the  2003  levels  (Figure 
12). 

Figure 12. Results of the sensitivity experiment showing the effects of systematically introducing 20 missing 
values  in  the  first  three  years  2003-2005  on  GLMM  and  JARA  (+NAs)  compared  to  the  control  with  the 
complete  dataset  for  the  North  East  Atlantic.  Solid  circles  (Control)  and  triangles  (NAs)  indicate  the 
magnitude of introduced bias on the nominal geometric mean. 

4.2.4  CROSS-VALIDATION 

To  examine  the  predictive  behaviour  of  the  new  model  for  the  most  recent  year  where 
information  is  frequently  missing  in  the  Mediterranean  and  Black  Sea  assessment,  the  hindcast 

  estimators  indicate  high 
cross-validation  approach  was  applied.  Both   
predictive performance over the last five years even with considerable portions of missing values. 
Variance is small and bias is minimal. The one-step ahead predictions closely followed the trend 
of  the  JARA  reference  model  that  was  fitted  to  the  complete  data  through  2019  indicating  the 
model  is  robust  to  the  frequent  delay  in  information  availability  which  has  hampered  the  GLMM 
approach  where  the  protocol  in  these  cases  was  only  able  to  provide  information  with  a  2-year 
lag.  

  and 

27 

 
 
   
 
 
 
 
 
   
Figure  13.  Hindcasting  cross-validation  analysis  applied  to  the  Mediterranean  and  Black  Seas 

  and 

 dataset (2003-2019), showing the reference model run (ref) fitted to the complete data in 2019 and 
one year-ahead predictions for years with sequentially added missing values (terminal points) for the same 
13  stocks  that  missing  values  in  2020.  The  hindcast  was  performed  using  one  reference  model  and  five 
hindcast  model  runs.  The  grey  shaded  areas  show  the  50%  and  95%  confidence  interval.  Bias  (%)  is 
expressed as mean relative error between the one-step ahead predictions and the corresponding reference 
run estimates. 

5  COMBINED INDICATORS 

  and 

Two  approaches  were  explored  for  estimating  combined  indicators  of 
  for 
European  waters.  The  first  approach  simply  fits  all  available  stocks  from  the  North  East  Atlantic 
and the Mediterranean and Black Seas in one JARA model. This approach can be characterised as 
a  “naïve”  non-stratified  bootstrap  design  where  each  stock  is  treated  as  an  independent  time 
series  without  any  regional  differences  in  the  trends.  The  second  “Joint”  approach  resembles  a 
stratified (blocked) bootstrap design. It is implemented by fitting separate JARA models to stocks 
of North East Atlantic on the one hand and the Mediterranean and Black Seas on the other. The 
bootstrap posteriors for the regions are then combined to create a joint posterior representative 
of  trends  across  European  waters.  The  weighting  by  region  is  currently  implicitly  based  on 
variance  estimates  associated  with  each  region,  but  other  weightings  such  as  number  of  stocks 
per  region,  volume  of  catches,  or  biomass  could  be  considered  dependent  on  objectives.  The 
analysis  was  run  through  2020,  thereby  making  use  of  JARA’s  ability  to  forecast  over  missing 
values. 

The  results  for  combined  indicators  of 
  for  European  waters  are  presented  in 
  and 
Figure  14.  The  naïve  approach  showed  clear  evidence  for  overestimated  precision  exhibited  by 
poor  confidence  interval  coverage  between  the  combined  trends  and  regional  trends.  In 

particular, the confidence intervals of  
 for the Mediterranean Sea showed no overlap with 
combined  trend.  The  joint  posterior  approach  resulted  into  very  similar  median  trends,  but  was 
able  to  capture  the  uncertainty  about  the  combined  trends  much  more  adequately.  The  large 

uncertainty associated to 
 arises from the contrast in fishing pressure intensity and trends 
between  the  North  East  Atlantic  and  Mediterranean  and  Black  Seas,  and  thus  interpreted  the 
more plausible representation of reality.                     

28 

 
 
 
 
Figure  14.  Combined indicators (black line) of 
  for stocks in European  waters  with  50% 
and 95% Cis (grey shaded areas), using the a “Naïve” non-stratified bootstrap approach from a single JARA 
to all stocks (top panel) and a “Joint” posterior approach combined from regional model fits (bottom panel). 
Regional trends for North East Atlantic (NEA) and Mediterranean and Black Seas (Med&BS) with 95% CIs are 
presented for comparison.    

  and 

6  DISCUSSION 

This  report  presented  a  suitability  study  of  the  Bayesian  State-Space  model  ‘JARA’  as  an 
alternative to the current GLMM approach for stock status indicator monitoring. The initial results 
confirmed  that  for  larger  datasets  with  no  missing  values  both  models  estimated  very  similar 

.  In  this  case,  JARA  produced  unbiased  estimates  of 
indicator  trends  for  both 
the  geometric  mean,  whereas  the  GLMM  showed  some  smaller  yet  systematic  divergences  in 
some indicators. These divergences are not trivial to interpret and explain. For example, for North 

  and 

East Atlantic the GLMM indicator for 
 is consistently higher than the geometric, whereas for 
Mediterranean it closely follows the geometric mean over the complete time series period 2009 to 
2017, but divergences during the last two years 2018 and 2019 for no discernible reason. 

More pronounced differences in the trend estimates between JARA and the GLMM can be linked to 
the  occurrence  of  missing  values  in  the  time  series.  In  the  case  of  the  ratio-based  indicator 

29 

 
 
 
 
 
 
 
,  the  contrast  between  models  is  mostly  limited  to  years  with  missing  observations, 

whereas  for 
 missing values at the start of the time series cause systematic distortion of 
the overall trend that persists until the final data year. This highlights one of the weaknesses of 

 indicator in that its interpretation strongly depends on a consistent and reliable estimate 

of 

  for  reference  year  2003.  The  second  weakness  relates  to  its  lack  of  a  meaningful  scale 

with respect to the distance to target reference point, such as 

 The seemingly inflated GLMM 

  were  identified  to  mostly  reflect  the  large  variation  in  absolute 
confidence  intervals  for 
SSB  scale,  which  contains,  however,  no  direct  value  of  information  for  inferring  some  average  
stock status across many different stocks. As the objective is to monitor a relative biomass trend 

in absence of a sufficiently representative 
 indicators, the here proposed solution is to first 
normalise the individual stock time series by their means  prior to fitting the model, which could 
be  readily  implemented  in  JARA.  The  results  suggest  that  the  underlying  trends  in  biomass  are 
much more precise than estimated by the current GLMM bootstrap procedure. 

The  sensitivity  experiment  on  the  impact  of  missing  values  provided  clear  evidence  that  the 
GLMM approach has limited ability  to  account for missing values. By contrast, JARA was able to 

produce unbiased indicator estimates for both 
 under the same treatment that 
introduced  large  proportions  of  missing  values  in  the  first  years  of  the  time  series.  The  current 
protocol prescribes to set the few missing values in 2020 to the equal previous year, whereas the 
estimation  of  the  Mediterranean  and  Black  Seas  indicators  is  currently  limited  to  2019  due  to 
concerns  about  the  impact  of  the  large  proportion  of  missing  values  in  the  most  recent 
assessment  year.  JARAs  prediction  skills  based  on  the  hindcast  analysis  will  likely  increase  the 
robustness of the indicators and allow for a common approach to treating missing values and end 
the questionable practice of assuming constancy for missing data.   

 and 

JARA  can  provide  an  efficient  and  flexible  approach  for  monitoring  stock  status  indicators.  The 
availability  of  the  JARA  source  code  as  R  package  on  the  open  access  platform  github  and 
published applications in the peer-reviewed literature (Sherley et al., 2019a, 2020; Pacoureau et 
al.,  2021)  should  further  aid  to  ensure  reproducibility,  transparency  and  documentation  of  the 
method. Furthermore, JARA’s flexibility, including various variance weighting options, as suits of 
advance  model  diagnostic  tool  could  become  beneficial  for  the  development  of  improved 
indicators.  

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doi:10.2760/329345, JRC111761 

STECF 2018b. CFP Monitoring – expansion of indicators (STECF-18-15). Publications Office of the 
European  Union,  Luxembourg,  2018,  ISBN  978-92-79-79398-1,  doi:10.2760/211585, 
JRC114754 

STECF  2019.    Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  –  Monitoring 
the performance of the Common Fisheries Policy (STECF-Adhoc-19-01). Publications Office of the 
European Union, Luxembourg, 2019, ISBN 978-92-76-02913-7, doi:10.2760/22641, JRC116446 

STECF 2021. Scientific, Technical and Economic Committee for Fisheries (STECF) – Monitoring the 
performance  of  the  Common  Fisheries  Policy  (STECF-Adhoc-21-01).  EUR  28359  EN, 
Publications  Office  of  the  European  Union,  Luxembourg,  2021,  ISBN  978-92-76-36155-8, 
doi:10.2760/26195, JRC124906Thorson, J. T., and Minto, C. 2015. Mixed effects: a unifying 
framework for statistical modelling in fisheries  biology.  ICES Journal of Marine Science, 72: 
doi:10.1093/icesjms/fsu213. 

Winker,  H.,  Kerwath,  S.  E.,  and  Attwood,  C.  G.  2013.  Comparison  of  two  approaches  to 
standardize catch-per-unit-effort for targeting behaviour in a multispecies hand-line fishery. 
Fisheries Research, 139. 

Winker,  H.,  Pacoureau,  N.,  and  Sherley,  R.  B.  2020.  JARA:  Just  Another  Red-List  Assessment. 
Laboratory. 

bioRxiv: 
https://www.biorxiv.org/content/early/2020/06/17/672899. 

Harbor 

Spring 

Cold 

42. 

Zuur, A. F. F., Ieno, E. N. N., Walker, N. J., Saveliev, A. A. A., Smith, G. M. M., Zuur AF, Leno EN, 
Walker  NJ,  Saveliev  AA,  S.  G.,  Zuur,  A.  F.  F.,  et  al.  2009.  Mixed  effects  models  and 

31 

 
 
extensions in ecology with R. Springer, New York. 572 pp. 

8 

CONTACT DETAILS OF EWG-ADHOC-22-02 PARTICIPANTS 

1  -  Information  on  EWG  participant’s  affiliations  is  displayed  for  information  only.  In  any  case, 
Members of the STECF, invited experts, and JRC experts shall act independently. In the context of 
the  STECF  work,  the  committee  members  and  other  experts  do  not  represent  the 
institutions/bodies  they  are  affiliated  to  in  their  daily  jobs.  STECF  members  and  experts  also 
declare  at  each  meeting  of  the  STECF  and  of  its  Expert  Working  Groups  any  specific  interest 
which  might  be  considered  prejudicial  to  their  independence  in  relation  to  specific  items  on  the 
agenda.  These  declarations  are  displayed  on  the  public  meeting’s  website  if  experts  explicitly 
authorized the JRC to do so in accordance with EU legislation on the protection of personnel data. 
For more information: http://stecf.jrc.ec.europa.eu/adm-declarations 

JRC experts 

Name 

Affiliation1 

Email 

Henning Winker 

Sven Kupschus 

Christoph Konrad 

Michaël Gras 

Alessandro Mannini 

European  Commission, 
Joint 
Research  Centre,  Unit  D.02 
Water and Marine Resources, Via 
Enrico  Fermi  2749,  21027  Ispra 
(VA), Italy 

Joint 
European  Commission, 
Research  Centre,  Unit  D.02 
Water and Marine Resources, Via 
Enrico  Fermi  2749,  21027  Ispra 
(VA), Italy 

European  Commission, 
Joint 
Research  Centre,  Unit  D.02 
Water and Marine Resources, Via 
Enrico  Fermi  2749,  21027  Ispra 
(VA), Italy 

European  Commission, 
Joint 
Research  Centre,  Unit  D.02 
Water and Marine Resources, Via 
Enrico  Fermi  2749,  21027  Ispra 
(VA), Italy 

European  Commission, 
Joint 
Research  Centre,  Unit  D.02 
Water and Marine Resources, Via 
Enrico  Fermi  2749,  21027  Ispra 
(VA), Italy 

henning.winker@ec.europa.eu 

sven.kupschus@ec.europa.eu 

christoph.konrad@ec.europa.eu 

michael.gras@ec.europa.eu 

alessandro.mannini@ec.europa.eu 

32 

 
 
 
 
 
 
 
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The Scientific, Technical and 
Economic Committee for 
Fisheries (STECF) has been 
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doi:10.2760/514979 

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