Scientific, Technical and Economic Committee for Fisheries (STECF) : review of the technical measures regulation (STECF-22-19).
Date: 2023-05-05
Institutions: Joint Research Centre, Scientific, Technical and Economic Committee for Fisheries
Subjects: Community fisheries, conservation of fish stocks, environmental economics, evaluation of resources, fishery management, research report, technical regulations, territorial waters

ISSN 1831-9424 

Scientific, Technical and Economic 
Committee for Fisheries (STECF) 
- 
Review of the Technical Measures 
Regulation  
(STECF-22-19) 

Edited by Daniel Valentinsson, Paris Vasilakopoulos & Michael Gras 

2023 

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 
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Address: Unit D.02 Water and Marine Resources, Via Enrico Fermi 2749, 21027 Ispra VA, Italy 
Email: jrc-stecf-secretariat@ec.europa.eu 
Tel.: +39 0332 789343 

EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC133589 

EUR 28359 EN 

PDF 

ISBN 978-92-68-03432-3 

ISSN 1831-9424 

doi:10.2760/335552  KJ-AX-23-007-EN-N 

STECF 

ISSN 2467-0715 

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How to cite this report: 'Scientific, Technical and Economic Committee for Fisheries (STECF) - Review of the Technical Measures Regulation (STECF-
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Authors: 

STECF advice:  

Bastardie, Francois; Borges, Lisa; Casey, John; Coll Monton, Marta; Daskalov, Georgi; Döring, Ralf; 
Drouineau, Hilaire; Goti Aralucea, Leyre; Grati, Fabio; Hamon, Katell; Ibaibarriaga, Leire; Jardim, 
Ernesto;  Jung,  Armelle;  Ligas,  Alessandro;  Mannini,  Alessandro;  Martin,  Paloma;  Moore,  Claire; 
Motova, Arina; Nielsen, Rasmus; Nimmegeers, Sofie; Nord,  Jenny; Pinto, Cecilia; Prellezo, Raúl; 
Raid,  Tiit;  Rihan,  Dominic;  Sabatella,  Evelina;  Sampedro,  Paz;  Somarakis,  Stylianos;  Stransky, 
Christoph;  Ulrich,  Clara;  Uriarte,  Andres;  Valentinsson,  Daniel;  van  Hoof,  Luc; Velasco  Guevara, 
Francisco; Vrgoc, Nedo. 

EWG-22-19 report:  

Valentinsson, D. (Chair); Vasilakopoulos, P. (Chair); Browne, D.; De Carlo, F.; Gras, M.; Hommik, 
K.; Jung, A.; Kalogirou, S.; Kovsars, M.; Ligas, A.; Mantopoulou Palouka, D.; Mantzouni, E.; Moore, 
C.; Raid, T.; Rihan, D.; Thasitis, I.; Ticina, V.; Viva, C. 

i 

i 

 
 
 
 
 
 
 
 
 
 
CONTENTS 

Abstract ................................................................................................................................... 1 

SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES (STECF) - 
Review of the Technical Measures Regulation (STECF-22-19) ............ 2 

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

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

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

References .............................................................................................................................. 4 

Contact details of STECF members .............................................................................. 4 

Expert Working Group EWG-22-19 report ................................................................. 8 

1 

Introduction ............................................................................................................. 9 

1.1 

Terms of Reference for EWG-22-19 ............................................................. 10 

2 

2.1 

2.2 

3 

General Approach to the Terms of Reference .......................................... 11 

Concepts ................................................................................................................. 11 

Approach ................................................................................................................. 11 

Materials and Methods ....................................................................................... 12 

3.1 

Data .......................................................................................................................... 12 

3.1.2  DATASET FOR THE VON BERTALANFFY PARAMETER ESTIMATION . 14 

3.2 

Methods ................................................................................................................... 15 

3.2.1  VON BERTALANFFY PARAMETER ESTIMATION IN ICES STOCKS ..... 15 

3.2.2  SELECTIVITY ANALYSIS .................................................................................... 16 

4 

Results ..................................................................................................................... 18 

4.1 

Von Bertalanffy parameter estimates ......................................................... 18 

4.1.1  NE ATLANTIC STOCKS ....................................................................................... 18 

4.1.2  MEDITERRANEAN STOCKS ............................................................................... 19 

4.2 

Identify the ages and sizes at which fish would need to be caught to 
optimise yield and reduce the catches of juveniles (ToR 1)............... 20 

4.2.1  OPTIMISATION UNDER CURRENT FISHING MORTALITY ..................... 20 

4.2.2  OPTIMISATION UNDER VARYING FISHING MORTALITY ...................... 23 

4.3 

4.4 

Prioritisation of stocks with the highest potential gains (ToR 1) ...... 40 

Identify the fishing gears corresponding to the optimum ages and sizes 
(ToR 2) ..................................................................................................................... 42 

4.4.1  NORTH SEA (ANNEX V OF TMR) .................................................................... 43 

4.4.2  NORTH WESTERN WATERS (ANNEX VI OF TMR) .................................... 43 

4.4.2.1 STOCK SPECIFIC SELECTIVITY – COD.27.7E-K .................................... 44 

ii 

ii 

 
 
4.4.2.2 STOCK SPECIFIC SELECTIVITY – WHG.27.7A ....................................... 45 

4.4.3  SOUTH WESTERN WATERS (ANNEX VII OF TMR) .................................. 45 

4.4.4  BALTIC SEA (ANNEX VIII OF TMR) ............................................................... 46 

4.4.5  MEDITERRANEAN SEA (ANNEX IX OF TMR) ............................................. 46 

4.5 

Case studies of NE Atlantic priority stocks (ToR 1 & ToR 2) .............. 47 

4.5.1  COD (GADUS MORHUA) IN SUBDIVISIONS 22–24 (WESTERN BALTIC) 

(COD.27.22-24) ................................................................................................... 48 

4.5.2  COD (GADUS MORHUA) IN SUBAREA 4, DIVISION 7.D, AND SUBDIVISION 

20 (NORTH SEA, EASTERN ENGLISH CHANNEL, SKAGERRAK) 
(COD.27.47D20) .................................................................................................. 52 

4.5.3  COD (GADUS MORHUA) IN DIVISION 6.A (WEST OF SCOTLAND) 

(COD.27.6A) .......................................................................................................... 56 

4.5.4  COD (GADUS MORHUA) IN DIVISIONS 7.E–K (WESTERN ENGLISH 

CHANNEL AND SOUTHERN CELTIC SEAS) (COD.27.7E-K) ................. 59 

4.5.5  WHITING (MERLANGIUS MERLANGUS) IN DIVISION 7.A (IRISH SEA) 

(WHG.27.7A) ......................................................................................................... 62 

4.5.6  SAITHE (POLLACHIUS VIRENS) IN SUBAREAS 4 AND 6, AND IN DIVISION 

3.A (NORTH SEA, ROCKALL AND WEST OF SCOTLAND, SKAGERRAK AND 
KATTEGAT) (POK.27.3A46) ............................................................................. 64 

4.6 

Case studies of Mediterranean priority stocks (ToR 1 & ToR 2) ....... 68 

4.6.1  HAKE (MERLUCCIUS MERLUCCIUS) IN GSAS 1, 5, 6 AND 7 (NORTHERN 

ALBORAN SEA, BALEARIC ISLANDS, NORTHERN SPAIN, GULF OF LIONS) 
(HKE.01-05-06-07) ............................................................................................. 68 

4.6.2  HAKE (MERLUCCIUS MERLUCCIUS) IN GSAS 8, 9, 10 AND 11 (CORSICA, 

LIGURIAN SEA, TYRRHENIAN SEA, SARDINIA) (HKE.08-09-10-11)72 

4.6.3  HAKE (MERLUCCIUS MERLUCCIUS) IN GSAS 17 AND 18 (ADRIATIC SEA) 
(HKE.17-18) .......................................................................................................... 77 

4.6.4  HAKE (MERLUCCIUS MERLUCCIUS) IN GSA 19 (WESTERN IONIAN SEA) 

(HKE.19) ................................................................................................................. 80 

4.6.5  HAKE (MERLUCCIUS MERLUCCIUS) IN GSA 20 (EASTERN IONIAN SEA) 

(HKE.20) ................................................................................................................. 82 

4.7 

4.8 

5 

5.1 

5.2 

6 

Identify possible operational changes needed to realise the transition to 
higher yields (ToR 3).......................................................................................... 85 

Identify the technical support required to assess at the regional level, the 
potential socio-economic implications of fisheries-based transition plans 
for improving yields (ToR 3) ........................................................................... 87 

Discussion ............................................................................................................... 88 

Caveats of the analysis ..................................................................................... 88 

Next steps .............................................................................................................. 89 

Conclusions ............................................................................................................ 90 

iii 

iii 

 
7 

8 

9 

Contact details of EWG-22-19 participants ............................................... 91 

List of Annexes ..................................................................................................... 94 

References ............................................................................................................ 106 

10 

List of Background Documents .................................................................... 112 

iv 

iv 

 
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 report presents the findings of the STECF Expert Working Group 22-19: Review of the Technical 
Measures Regulation, from the meeting held from 23rd to 27th January 2023 at JRC Ispra. The 33 
stocks  analysed  in  this  report  with  regards  to  their  selectivity  correspond  to  those  that  were 
identified to have age-structured information available, in accordance with Annex XIV of Regulation 
(EU) No 2019/1241: Species for selectivity performance indicators. Eleven stocks were examined 
in more detail in specific cases studies. The report of the EWG-2219 was reviewed by the STECF 
during its March 2023 Plenary Meeting and subsequently released.

     1 

1 

 
 
 
SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES (STECF) - Review 
of the Technical Measures Regulation (STECF-22-19) 

Request to the STECF 

STECF is requested to review the report of the STECF Expert Working Group meeting, evaluate the 
findings and make any appropriate comments and recommendations. 

STECF comments  

EWG 22-19 was held at the JRC in Ispra, Italy, 23-27 January 2023. The meeting was attended by 
18 experts in total, including 5 STECF members and 2 JRC experts. STECF considers that the EWG 
adequately addressed the TORs and has the following specific comments on the ToRs addressed by 
EWG 22-19. 

ToR  1  -  Identify  the  ages  and  sizes  at  which  fish  (as  per  Annex  XIV  of  the  Technical 
Measures Regulation 2019/1241) would need to be caught to optimise yield and reduce 
the catches of juveniles as far as possible, building upon the relevant work of STECF-21-
07. Prioritise stocks where the highest gains can be achieved. 

STECF notes that the analysis carried out by EWG 22-19 has identified the potential gains that can 
be made in terms of single stock catches (yield) and spawning stock biomass (SSB) by increasing 
gear  selectivity,  both  under  current  and  varying  fishing  mortality  (F).  These  gains  are  typically 
accompanied with improvements in the protection of juveniles, except for early maturing species. 

STECF notes that the work conducted by this EWG covered 33 stocks relevant to Annex XIV of the 
TMR and observes that stocks which show the highest potential gains in terms of yield and/or SSB 
through  improving  selectivity,  are  mainly  long-lived,  late  maturing  roundfish  stocks,  and  also 
currently fished above FMSY. As such, STECF notes that EWG 22-19 identified 11 ‘priority stocks’ 
for  such  highest  gains:  cod.27.22-24,  cod.27.47d20,  cod.27.6a,  cod.27.7e-k,  whg.27.7a, 
pok.27.3a4  (in  the  NorthEast  Atlantic)  and,  HKE.01-05-06-07,  HKE.08-09-10-11,  HKE.17-18, 
HKE.19, HKE.20 (in the Mediterranean Sea) 

STECF observes that a large increase in L50 (length corresponding to 50% probability that a fish 
from the population is captured) of priority stocks would be required to reach the identified optimal 
yields under current F patterns. Therefore, to achieve optimal yield, stocks fished far above FMSY 
would require a combination of improved selectivity with a decrease in F.  

STECF  notes  that  optimal  L50  estimates,  both  under  current  F  and  variable  F,  are  uncertain,  as 
they are based on the current population characteristics and biological parameters of the stocks, 
which are expected to change if stock sizes increase substantially. 

ToR 2 - Identify the fishing gears corresponding to the optimum age and size of each of 
the stocks in (1). 

STECF observes that for priority stocks there is limited availability of  gear selectivity studies  for 
static gears compared to the availability of gear selectivity studies for active gears. This hindered 
a  thorough  evaluation  of  the  potential  impact  of  static  gears.  However,  based  on  the  limited 
information available, combined with the partial selectivity of fleet segments inferred from the stock 
assessments (where available), static gears (GNS, GTR, LLS) generally seem to capture fish closer 
to their optimal size than active gears (OTB, TBB). The analysis by the EWG demonstrates also that 
there are cases where modifications of active gears may result in large gains in yield. 

STECF  notes  that  the  observed  selectivity  of  the  OTB  fleet  segment  was  worse  (i.e.,  shifted  to 
smaller  fish)  than  expected  from  baseline  codends  in  all  cases  expect  for  cod.27.6.a,  where 
observed selectivity was fractionally better (~2cm) then the available gear studies. STECF notes 

     2 

2 

 
 
 
 
 
 
 
 
 
 
that the observed selectivity of two stocks - cod.27.47d20 and pok.27.3a46 - was found to be the 
same  as  the  baseline  codend.  Therefore,  STECF  observes  that  gear  selectivity  is  not  always  a 
reliable predictor of population selectivity. 

ToR 3 - If feasible, identify possible operational changes needed to realise the transition 
to higher yields. Identify the technical support required to assess at the regional level, 
the  potential  socio-economic  implications  of  fisheries-based  transition  plans  for 
improving yields. 

STECF observes that any transition in gear selectivity comes with implementation challenges and 
short term economic losses, which are greater in the case of gear change (e.g., from active to static 
gears) than in the case of gear modification (e.g., codend mesh increase in trawlers). By contrast, 
based on the available information, the potential gains in yield and protection of juveniles seem to 
be typically greater for gear changes than for gear modifications. 

STECF observes that technological change is complex and challenging to achieve due to the inherent 
uncertainty  and  the  underlying  perception  of  the  fishing  industry  that  such  changes  will  lead  to 
significant capital outlay and economic loss. 

STECF observes that analyses of socio-economic implications of fisheries-based transitions depend 
on  the  availability  of  data  and  bio-economic  models  for  specific  fisheries.  To  ensure  meaningful 
results these models would need to account for: short term (1-2 years) losses and longer term (5-
10 years) benefits; incorporate target, bycatch and PET species; and include relevant fleets (to be 
able to investigate the socio-economic consequences) and metiers (with explicit selectivity). 

Several models have been developed to analyse socio-economic impacts of management measures 
(STECF 2018, Nielsen et al. 2017), including (but not limited too) FLBEIA (Celtic Sea, Bay of Biscay 
and  North  Sea  (under  development)),  SIMFISH  (Flatfish  fishery  North  Sea),  FishRent  (demersal 
and pelagic versions for North Sea),  BEMTOOL  and IAM (Western Med). STECF notes that these 
models would need effort to update to make them fit for purpose. 

STECF observes the current gap in quantitative analysis on the socio-economic impact of technical 
measures. For such an impact assessment to be conducted this year, STECF plenary proposes the 
following four-step process which may provide some insights in the socio-economic implications of 
fisheries-based transitions: 

1-  Define  questions  &  scenarios  (responsibility  of  DGMARE)  –  For  this  process  to 
succeed, there would need to be a clearly defined list of questions and scenarios provided 
to STECF (preferably by end of April) specifying a shortlist of priorities for the EWG to explore 
as  test  cases.  These  questions  would  outline  the  combination  of  stocks,  gears,  technical 
measures, and areas to explore.  

2-  Scoping  exercise  (responsibility  of  STECF)  –  A  dedicated  subgroup  could  be  formed 
during  PLEN  23-02,  focusing  on  defining  the  test  cases  for  socio-economic  assessments 
which would then be conducted during the follow-up EWG planned for later in 2023 (EWG 
23-15).  This  sub-group  would  identify  data  needs,  available  models,  skills  and  people 
required at the EWG meeting. 

3-  Synthesis of current knowledge (conducted by ad hoc contract) – An ad hoc contract 
in  advance  of  EWG  23-15  could  be  used  to  conduct  a  literature  review  on  the  current 
knowledge  of  the  socio-economic  implications  of  changes  in  technical  measures  (e.g., 
Simons  et  al.  2015).  This  review  would  provide  context  and  support  for  the  analysis  to 
ensure meaningful conclusions can be drawn from the findings of the model applications in 
EWG 23-15. It is also a fallback option in case a limited number of test cases can be analysed 
in the EWG.  

4-  Implementation of test case (responsibility of EWG 23-15) - The experts attending 
the EWG 23-15 (economists, mixed fisheries stock assessors, and modellers) will use the 
data and apply models identified by the scoping exercise, to provide test case(s) of fisheries-
based  transition  plans  to  inform  future  research  goals  and  advice  needs.  The  literature 
review will also allow putting the model results in a broader context of implementation of 
technical measures. 

     3 

3 

 
 
 
STECF conclusions  

STECF concludes that improved selectivity is more likely to lead to higher gains in yield and/or SSB 
for  stocks  which  are  long  lived,  late  maturing,  and  currently  fished  far  above  FMSY,  such  as 
Northeast Atlantic cod stocks and Mediterranean hake stocks.  

STECF concludes that improved gear selectivity is only one aspect of optimising yield and reducing 
juveniles  catches.  As  a  result,  the  gear  studies  considered  by  the  EWG  would  be  more  likely  to 
provide higher gains in terms of yield and SSB, if priority stocks were fished at or below FMSY.  

STECF concludes that additional gear selectivity studies are required to provide robust estimates 
of selectivity for static gears (GNS, GTR, LLS); and additional selectivity options for active gears 
(OTB)  that  provide  a  gear  selectivity  shifted  closer  to  the  optimal  fish  lengths  identified  in  the 
analysis.  

STECF  concludes  that  differences  in  gear  selectivity  coming  from  gear  trials  and  the  realised 
population selectivity of the OTB fleets are observed in some priority stocks. This could be driven 
by  the  operational  reality  of  how  gears  are  used  and  effected  by  external  forces  such  as  fisher 
behaviour, season, or be indicative of a higher availability of smaller fish to the commercial trawlers.  

STECF concludes that mixed fisheries (target, bycatch and PET species) bio-economic models would 
provide valuable information on the trade-offs involved for reaching the optimal yield goals. Several 
models  already  exist  that  can  account  for  the  population  dynamics,  fisher  behaviour,  and 
seasonality of selectivity patterns of multiple stocks combined.  

STECF concludes fisheries-based transition plans for modified or alternative gears require not only 
technical  trials,  but  to  be  also  supported  by  assessments  of  socio-economic  impacts.  STECF 
recognises that substantial work will be required to develop such assessments, which would need 
to be tailored to specific regions and fisheries. To achieve this, STECF proposes a four-step process 
should be followed to ensure favourable outcomes of the EWG 23-15. 

References 

Nielsen JR, Thunberg E, Holland DS, Schmidt JO, Fulton EA, Bastardie F, Punt AE, Allen JI, Bartelings 
H, Bertignac M, Bethke E, Bossier S, Buckworth R, Carpenter G, Christensen A, Christensen V, 
Da-Rocha  JM,  Deng  R,  Döring  R,  Simons  SL,  et  al.  2018.  Integrated  ecological-economic 
fisheries  models  -  Evaluation,  review  and  challenges  for  implementation.  Fish  and  Fisheries 
19(1):1-29 

Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  2018  –  Economic  impact  of 
mixed fisheries options (STECF-18-05). Publications Office of the European Union, Luxembourg 
g, ISBN 978-92-79-79388-2, doi:10.2760/9962, JRC112753 

Simons  SL,  Döring  R,  Temming  A.  2015.  Modelling  fishers'  response  to  discard  prevention 
strategies: the case of the North Sea saithe fishery. ICES Journal of Marine Science 72(5):1530-
1544 

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 

     4 

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Name 

Affiliation1 

Email 

Bastardie, Francois  

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

fba@aqua.dtu.dk  

Borges, Lisa 

FishFix, Lisbon, Portugal 

info@fishfix.eu 

Casey, John 

Independent consultant 

Coll Monton, Marta 

Daskalov, Georgi 

Döring, Ralf 

de 
Consejo 
Investigaciones  Cientificas,  CSIC, 
Spain 

Superior 

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, 
Fisheries, 
Institute 
Economic analyses  Herwigstrasse 
31, 
Bremerhaven, 
Germany 

D-27572 

Sea 

of 

blindlemoncasey@gmail.c
om  

mcoll@icm.csic.es 

Georgi.m.daskalov@gmail
.com 

ralf.doering@thuenen.de 

Drouineau, Hilaire  

Inrae, France 

hilaire.drouineau@inrae.fr  

Goti Aralucea, Leyre  

Grati, Fabio  

leyre.goti@thuenen.de 

fabio.grati@cnr.it  

Research 

Thünen  Institute  of  Sea  Fisheries 
Fisheries 
- 
Economics,  Herwigstrasse  31,  D-
27572 Bremerhaven, Germany 

Unit 

Institute 

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

for 
and 

Hamon, Katell 

Wageningen  Economic  Research, 
The Netherlands 

katell.hamon@wur.nl 

Ibaibarriaga, Leire  

Jardim, Ernesto 

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

Marine  Stewartship  Council  MSC, 
Fisheries  Standard  Director  FSD, 
London 

libaibarriaga@azti.es  

ernesto.jardim@msc.org 

     5 

5 

 
 
 
Name 

Affiliation1 

Email 

Jung, Armelle 

Ligas, Alessandro  

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

armelle.jung@desrequinse
tdeshommes.org  

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

di 

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

Mannini, Alessandro 

CNR  IRBIM  Ancona,  Largo  Fiera 
della Pesca, 260125 Ancona ITALY 

alessandro.mannini@irbim.
cnr.it  

Martin, Paloma  

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

paloma@icm.csic.es 

Motova -Surmava, Arina 

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

arina.motova@seafish.co.
uk 

Moore, Claire 

Marine Institute, Ireland 

claire.moore@marine.ie 

Nielsen, Rasmus 

of 

Copenhagen, 
University 
Section 
for  Environment  and 
Natural  Resources,  Rolighedsvej 
23, 
Frederiksberg  C, 
Denmark 

1958 

rn@ifro.ku.dk 

Nimmegeers, Sofie  

Flanders  research  institute  for 
agriculture,  fisheries  and  food, 
Belgium 

Sofie.Nimmegeers@ilvo.vl
aanderen.be  

Pinto, Cecilia (vice-chair)  Università  di  Genova,  DISTAV  - 
Dipartimento  di  Scienze  della 
Terra,  dell'Ambiente  e  della  Vita, 
Corso Europa 26, 16132 Genova, 
Italy 

cecilia.pinto@edu.unige.it 

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

rprellezo@azti.es 

Raid, Tiit  

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

Tiit.raid@gmail.com  

Rihan, Dominic (chair) 

BIM, Ireland 

rihan@bim.ie  

     6 

6 

 
 
Name 

Affiliation1 

Email 

Sabatella, Evelina Carmen  National  Research  Council  (CNR) 
– 
for  Research  on 
Population  and  Social  Policies 
(IRPPS),  Corso  S.  Vincenzo 
Ferreri,  12,  84084  Fisciano, 
Salerno, Italy 

Institute 

evelina.sabatella@cnr.it  

Sampedro, Paz 

Somarakis, Stylianos  

Stransky, Christoph 

Institute 

of 
Spanish 
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 

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

of 

paz.sampedro@ieo.csic.es  

somarak@hcmr. gr 

christoph.stransky@thuen
en.de 

Ulrich, Clara 

IFREMER, France 

Clara.Ulrich@ifremer.fr  

Uriarte, Andres 

Valentinsson, Daniel  

van Hoof, Luc  

Gestión 

AZTI. 
pesquera 
sostenible.  Sustainable  fisheries 
management. Arrantza 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 
Ijmuiden,  The 
Haringkade  1, 
Netherlands 

auriarte@azti.es 

daniel.valentinsson@slu.s
e 

Luc.vanhoof@wur.nl 

Velasco 
Francisco 

Guevara, 

Spanish Insitute of Oceanography 
- National Research Council, Spain 

francisco.velasco@ieo.csic
.es  

Vrgoc, Nedo  

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

vrgoc@izor.hr 

     7 

7 

 
 
 
 
EXPERT WORKING GROUP EWG-22-19 REPORT 

REPORT TO THE STECF 

EXPERT WORKING GROUP ON  
Review of the Technical Measures Regulation 
(EWG-22-19) 

JRC Ispra, 23-27 January 2023 

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 

     8 

8 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1 

INTRODUCTION 

The entry into force of the “Technical Measures Regulation” (TMR)1, introduced the obligation for 
the Commission to report to the European Parliament and to the Council on the implementation of 
the  Regulation.  This  reporting  obligation  is  aimed  at  assessing  the  “extent  to  which  technical 
measures both at regional and Union level have contributed to achieving the objectives set out in 
Article 3 and reaching the targets set out in Article 4” (Article 31.1). The Regulation does not set 
legally  binding  targets;  the  indicators  are  monitoring  tools  which  may  inform  deliberations  or 
decisions at regional level. 

Measuring progress in achieving the objectives set out in Article 3 and in reaching the targets set 
out in Article 4 is vital to check whether technical measures put in place are adequate and fit for 
purpose, and consequently, to assess where and how changes should be made. 

In  2020,  a  dedicated  STECF  EWG  (EWG  20-02;  STECF,  2020a)  was  tasked  to  evaluate  the 
performance  of  technical  measures  in  line  with  the  above.  The  main  requirement  needed  for 
structuring  the  evaluation  exercise  and  the  associated  EWG  was  to  establish  and  agree  on  a 
methodology and the appropriate indicators that can be used routinely to carry out the evaluation 
required by the Regulation.  

Following EWG 20-02, and given that STECF will be requested to undertake an evaluation of the 
performance of the technical measures every three years thereby advising the Commission on their 
triannual  reporting  obligation,  some  options  on  how  to  proceed  in  the  future  were  provided  and 
discussed during STECF PLEN 21-01 and EWG 21-02. Taking into consideration that a key objective 
of the TMR is to optimise exploitation patterns (Article 3(2a) of the TMR), discussions during STECF 
PLEN 21-01 and EWG 21-02 highlighted the necessity  to assess not only  whether there are any 
changes in selectivity but also how far stocks lie from the optimal selectivity that would offer the 
highest possible yields. For these reasons, a stepwise approach was suggested and followed, with 
a dedicated STECF EWG (EWG 21-07; STECF, 2021) acting as a first step. Specifically, EWG 21-07 
assessed the population selectivity-at-age of species listed in Annex XIV of the TMR and compared 
it to the optimal one.  

Following EWG 21-07, STECF PLEN 22-01 and STECF PLEN 22-03 suggested that the second step 
of the process, assigned to EWG 22-19, should be the evaluation of the population selectivity-at-
length of species listed in Annex XIV of the TMR, in order to be able to link it with actual size-based 
fishing  gear  selectivity  and  offer  optimisation  solutions.  Specifically,  the  Commission  requested 
STECF  to  make  available  information  concerning  the  optimal  sizes  and  ages  at  which  each 
commercial  fish  species,  taken  individually,  should  be  caught  if  these  species  were  fished  in 
separate, clean fisheries, as well as the types and technical definitions of fishing gear that would 
be appropriate to catch fish of these sizes. This information will assist Member States in identifying 
the  direction  and  the  potential  gains  to  be  achieved  concerning  each  fish  stock  individually. 
Recognising  that  many  EU  fisheries  interact  simultaneously  with  multiple  species  during  their 
operation,  Member  States  will  also  need  technical  support  and  operational  tools  to  identify  the 
costs, benefits and feasibility of transitioning from current fishing gears to the direction of optimal 
yields. At this stage, STECF was requested to  identify the necessary  operational changes and to 
identify the technical support necessary to  inform Member States and other parties of the costs 
and benefits of various transition options.  

The main mechanism for adjusting the permitted structure of fishing gear in order to help optimise 
exploitation patterns, protect juveniles and help achieve MSY is through amendments of annexes 
of  the  TMR,  following  the  regionalisation  procedure  set  out  in  Article  15  of  that  Regulation,  i.e. 
based  on  joint  recommendations  from  Member  States  in  accordance  with  Article  18  of  the  CFP 
(Regulation (EU) No 1380/2013). It is anticipated that at a later stage, further work will be needed 
to develop advanced technical support tools, most likely tailored to specific regions and fisheries. 

1 Regulation (EU) 2019/1241 of the European Parliament and of the Council of 20 June 2019 on the conservation of fisheries 
resources and the protection of marine ecosystems through technical measures. 

     9 

9 

 
 
 
 
                                                 
1.1 

Terms of Reference for EWG-22-19 

Following discussions after EWG 20-02 and EWG 21-07, EWG 22-19 is requested to: 

1) Identify the ages and sizes at which fish (as per Annex XIV of the Technical Measures Regulation 
2019/1241) would need to be caught to optimise yield and reduce the catches of juveniles as far 
as possible, building upon the relevant work of STECF-21-07. Prioritise stocks where the highest 
gains can be achieved. 

2) Identify the fishing gears corresponding to the optimum age and size of each of the stocks in 
(1). 

3) If feasible, identify possible operational changes needed to realise the transition to higher yields. 
Identify the technical support required to assess at the regional level, the potential socio-economic 
implications of fisheries-based transition plans for improving yields. 

    10 

10 

 
 
 
 
2 

GENERAL APPROACH TO THE TERMS OF REFERENCE 

2.1 

Concepts 

Fisheries  selectivity  describes  the  ability  to  target  and  capture  fish  by  size  and  species  during 
harvesting operations, allowing bycatch of juvenile fish and non-target species to escape unharmed 
(Garcia, 2009). Accordingly, fisheries selectivity may either refer to (un)desirable species (species 
selectivity)  or  sizes  (size  selectivity).  Species  selectivity  typically  refers  to  the  avoidance  of 
unwanted species (e.g., endangered species, choke species, non-commercial species), while size 
selectivity  refers  to  the  avoidance  of  specific  sizes  (e.g.,  juveniles,  individuals  below  Minimum 
Conservation Reference Size - MCRS) of a given species. Size selectivity is the focus of this report. 

Following  Millar  &  Fryer  (1999),  three  different  types  of  selectivity  can  be  defined  in  order  to 
describe adequately the population selectivity related to size of a fish  l. The following definitions 
and equation are taken from equations (1) & (2) of the aforementioned paper and are an adaptation 
of them in order to define population selectivity in a compact and hierarchical way: 

•  v(l):  The  probability  of  fish  of  length  l  is  available  to  the  gear  (i.e.  part  of  the  vulnerable 
population) 

• c(l): The probability of fish of length l contacts the gear given that is available to the gear (i.e. 
probability of non-avoidance) 

• r(l): The probability that a fish of length l is captured given that it contacted the gear (i.e. gear 
selectivity). 

These three types of selectivity are related to population selectivity s(l) as: 

s(l) ∝ v(l) × c(l) x r(l) 

               (1) 

where s(l) is the population selectivity defined as the probability that a fish of length l from  the 
population is captured. In a more schematic form: 

(availability probability) → (contact probability) → (retention probability) 

where each probability holds if and only if its preceding holds. 

Consequently, population selectivity is the result of fish availability, fish avoidance behaviour and 
gear selectivity. Assuming that the non-avoidance probability is adequately captured by the gear 
trials and is reflected by gear selectivity, it can be inferred that population selectivity describes the 
differential vulnerability to fishing of the demographic components of an entire fish population, as 
a result of both the gear used (e.g., active or passive gear, mesh shape and size) and availability 
(e.g., due to the choice of time and place to fish) (Millar & Fryer, 1999; Scott & Sampson, 2011). 

In age-structured stock assessments, population selectivity is inferred as: 

s(a) = F(a)/max(F(a))  

     (2) 

where F(a) is the fishing mortality at age a and maxF(a) is the maximum fishing mortality observed 
at any age-classs, which is also known as apical F (Fapical) (Scott & Sampson 2011). 

This report deals both with population selectivity, gear selectivity and availability, as well as with 
the interplay between them. Within the report, ‘selectivity’ refers to ‘population selectivity’, unless 
otherwise specified (e.g. as ‘gear selectivity’). Accordingly, ‘A50’/’L50’ are used for population age-
at-50%-selectivity  and  population length-at-50%-selectivity,  respectively,  while  ‘Ag50’/‘Lg50’  are 
used for gear age-at-50%-selectivity and gear length-at-50%-selectivity, respectively.   

2.2 

Approach 

For ToR 1, EWG 22-19 revisited the age-based work on selectivity done by EWG 21-07, which  had 
estimated the age-at-50%-selectivity (A50) for the current selectivity and two versions of optimised 
selectivity  (‘crank’  and  ‘shift’,  explained  later),  obtained  by  projections  to  equilibrium  under  the 
current fishing mortality (F) and different selectivity curves, for a set of stocks corresponding to 
the Annex XIV of Regulation (EU) No 2019/1241. EWG 22-19 extended that work by calculating 
the  length-at-50%-selectivity  (L50)  corresponding  to  the  age-based  estimations  of  EWG  21-07, 

    11 

11 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
and then identified a set of 11 ‘priority stocks’ based on the highest estimated gains in terms of 
both yield and protection of juveniles. 

In  order  to  transform  age-based  estimates  to  length-based  ones,  EWG  22-19  implemented  a 
deterministic conversion using the stock-specific parameters of the von Bertalanffy growth equation 
(Linf, k and t0). These were taken from the respective STECF stock assessment reports in the case 
of  Mediterranean  stocks.  For  ICES  stocks,  where  von  Bertalanffy  parameters  were  not  readily 
available, these were estimated by fitting the von Bertalanffy equation over survey data taken from 
DATRAS. 

EWG 21-07 had also carried out projections for each stock under both varying F and selectivity-at-
age,  to  construct  three-dimensional  graphs  (‘isopleths’)  of  the  equilibrium  yield  and  SSB  from 
different combinations of F and selectivity, to identify the combinations that lead to higher yields 
at lower levels of stock depletion. EWG 22-19 extended that work by exchanging selectivity-at-age 
with selectivity-at-length and rescaling the isopleths.  

For  ToR  2,  EWG  22-19  reviewed  and  compiled  available  gear  selectivity  parameters  from  both 
peer-reviewed and grey literature for as many stocks (by region as listed in Annex XIV of Regulation 
(EU) No 2019/1241) and gears as possible. Priority was however given to the 11 stocks with the 
highest expected gains of increased selectivity from ToR 1 (see later). The aim was to identify and 
list  size  selectivity information  and  technical  specifications,  both  for  the  regional  baseline  gears, 
and for other relevant alternative gears by stock and region. Special focus was to find gear trials 
reporting particularly high size selectivity to match the request of ToR 2 as far as possible.  

EWG 22-19 then focused on the 11 priority stocks (six ICES stocks and five Mediterranean ones) 
with the highest potential gains as separate case studies in order to examine in more detail: (a) 
how the current selectivity curve (both age-based and length-based) compared with the optimal 
one, (b) how the selectivity-at-length curves of different fleet segments (where available from the 
stock assessments) compared between them and with the optimal one and (c) how different gear 
selectivity curves (typically one curve  for the default/baseline gear and 1-3 curves  for improved 
gears) compared both between them, to the selectivity curve of the respective fleet segment and 
to  the  optimal  selectivity  curve.  The  L50  of  different  gears  (Lg50)  was  also  plotted  on  the 
stock-specific  isopleths  of  the  equilibrium  yield  and  SSB  from  different  combinations  of  F  and 
selectivity  to identify  if improved  selectivity  would  require  smaller  changes  in  F  to  approach  the 
optimal yields, and if improved selectivity would be associated with lower levels of stock depletion. 

EWG 22-19 split ToR 3 in two parts. In the first part, EWG 22-19 described the operational changes 
(i.e. gear modifications or gear shifts) that could be made to transition fisheries to higher yields. 
The  second  part  considers  the  technical  support,  information  and  data  needed  to  assess  the 
socio-economic implications of making these transitions. Both parts were addressed qualitatively 
by illustrating challenges and lessons learnt based on relevant case studies in the literature, and 
also from work by EWG 21-07. The work done is intended as a potential first step to assist DGMARE 
in  discussing  and  designing  transition  plans  with  Member  States  and  other  stakeholders  going 
forward. 

3 

MATERIALS AND METHODS 

3.1 

Data 

3.1.1  Stock dataset 

To maintain consistency, the stocks analysed in this report were the same as the ones analysed by 
EWG 21-07 (STECF, 2021), and correspond to those that were identified to have age-structured 
information available, among the stocks listed in Annex XIV of Regulation (EU) No 2019/1241. In 
total, 20 ICES stocks and 13 Mediterranean ones were considered (Table 3.1.1.1). 

    12 

12 

 
 
 
 
 
 
Table 3.1.1.1: Summary table of stocks by region and area that were considered in this report. The column 
‘Fleet  Data’  indicates  the  availability  of  partial  F-at-age  data  of  different  fleet  segments.  NEA:  Northeast 
Atlantic,  MED:  Mediterranean  Sea;  BS:  Baltic  Sea,  NS:  North  Sea;  NWW:  Northwestern  Waters;  SWW: 
Southwestern Waters; WM: Western Mediterranean; CEM: Central and Eastern Mediterranean.  

Region  Area 

Stock 

Species 

Assessment 

Fleet Data 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

BS 

BS 

NS 

NS 

NS 

NS 

NS 

NS 

cod.27.22-24 

Gadus morhua 

ICES, SAM, 2021 

ple.27.21-23 

Pleuronectes platessa 

ICES, SAM, 2021 

cod.27.47d20 

Gadus morhua 

ICES, SAM, 2021 

had.27.46a20 

Melanogrammus aeglefinus 

ICES, TSA, 2021 

ple.27.420 

Pleuronectes platessa 

ICES, AAP, 2021 

ple.27.7d 

Pleuronectes platessa 

ICES, AAP, 2021 

pok.27.3a46 

Pollachius virens 

ICES, SAM, 2021 

whg.27.47d 

Merlangius merlangus 

ICES, SAM, 2021 

NEA 

NWW 

cod.27.6a 

Gadus morhua 

ICES, SAM, 2021 

NEA 

NWW 

cod.27.7e-k 

Gadus morhua 

ICES, SAM, 2021 

NEA 

NWW 

had.27.6b 

Melanogrammus aeglefinus 

ICES, XSA, 2021 

NEA 

NWW 

had.27.7a 

Melanogrammus aeglefinus 

ICES, ASAP, 2021 

NEA 

NWW 

had.27.7b-k 

Melanogrammus aeglefinus 

ICES, SAM, 2021 

NEA 

NWW 

ple.27.7a 

Pleuronectes platessa 

ICES, SAM, 2021 

NEA 

NWW 

whg.27.7a 

Merlangius merlangus 

ICES, ASAP, 2021 

NEA 

NWW 

whg.27.7b-ce-k 

Merlangius merlangus 

ICES, SAM, 2021 

NEA 

SWW  hke.27.3a46-8abd 

Merluccius merluccius 

ICES, SS3, 2021 

NEA 

SWW 

ldb.27.8c9a 

Lepidorhombus boscii 

ICES, XSA, 2021 

NEA 

SWW  meg.27.7b-k8abd 

Lepidorhombus whiffiagonis 

ICES, Bayesian, 2021 

NEA 

SWW 

meg.27.8c9a 

Lepidorhombus whiffiagonis 

ICES, XSA, 2021 

MED  WM  HKE.01_05_06_07 

Merluccius merluccius 

STEFC, a4a, 2020 

MED  WM  HKE.08_09_10_11 

Merluccius merluccius 

STEFC, a4a, 2020 

MED  WM 

MUR.05 

Mullus surmuletus 

STEFC, a4a, 2020 

MED  WM 

MED  WM 

MED  WM 

MED  WM 

MED  WM 

MUT.01 

MUT.06 

MUT.07 

MUT.09 

MUT.10 

Mullus barbatus 

STEFC, a4a, 2020 

Mullus barbatus 

STEFC, a4a, 2020 

Mullus barbatus 

STEFC, a4a, 2020 

Mullus barbatus 

STEFC, a4a, 2020 

Mullus barbatus 

STEFC, a4a, 2020 

MED 

CEM 

HKE.17_18 

Merluccius merluccius 

STEFC, SS3, 2020 

MED 

CEM 

MED 

CEM 

HKE.19 

HKE.20 

Merluccius merluccius 

STEFC, a4a, 2020 

Merluccius merluccius 

STEFC, a4a, 2020 

MED 

CEM 

MUT.17_18 

Mullus barbatus 

STEFC, a4a, 2020 

MED 

CEM 

MUT.22 

Mullus barbatus 

STEFC, a4a, 2020 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

No 

Yes 

Yes 

No 

Yes 

No 

No 

No 

No 

Yes 

No 

Yes 

Yes 

Yes 

No 

No 

Yes 

Yes 

Yes 

No 

No 

No 

No 

No 

    13 

13 

 
 
 
For all stocks, EWG 22-19 used the same stock assessment outputs that had been used by EWG 
21-07 in the form of ‘FLStock’ objects. For some of the stocks, partial F-at-age by fleet segment in 
the form of ‘FLQuant’ objects were also available and analysed. More details on data sources and 
data preparation can be found in STECF, 2021. 

3.1.2  DATASET FOR THE VON BERTALANFFY PARAMETER ESTIMATION 

For the 20 ICES stocks in Table 3.1.1.1, survey-collected length-at-age data were used to estimate 
the von Bertalanffy growth curve parameters. This data was extracted from DATRAS (ICES), which 
is an online database of trawl surveys with access to standard data products, which provide quality 
assured  survey  data.  DATRAS  stores  data  collected  primarily  from  bottom  trawl  fish  surveys 
coordinated by ICES expert groups. The surveys used in this analysis are highlight in Table 3.1.2.1, 
and  were  based  on  the  available  information  in  advice  sheets.  All  data,  before  entering  the 
database, have to pass an extensive quality check. This data was extracted using the ‘icesDatras’ 
package in R (Millar et al., 2022). 

Table 3.1.2.1 Summary of surveys used by stock, information taken from ICES advice sheets. BS: Baltic Sea, 
NS: North Sea; NWW: Northwestern Waters; SWW: Southwestern Waters. 

Area 

Stock 

Species 

Surveys used for assessment 

BS 

cod.27.22-24 

Gadus morhua 

BS 

ple.27.21-23 

Pleuronectes platessa 

Baltic International Trawl Survey (BITS)  

Kattegat Cod Survey (FEJUCS) 

North Sea International Bottom Trawl (NS-IBTS) 

Baltic International Trawl Survey (BITS)  

NS 

cod.27.47d20 

Gadus morhua 

North Sea International Bottom Trawl (NS-IBTS) 

NS 

had.27.46a20 

Melanogrammus aeglefinus 

NS 

ple.27.420 

Pleuronectes platessa 

North Sea International Bottom Trawl (NS-IBTS) 

Scottish West Coast Bottom Trawl Survey (up to 2010) SWC-IBTS 

Scottish West Coast Groundfish Survey (ScoGFS-WIBTS)  

Scottish West Coast Groundfish Survey (UK-SCOWCGFS)  

North Sea International Bottom Trawl (NS-IBTS)  

Irish Ground Fish Survey (IGFS-WIBTS)  

Trident 

BTS - Isis, Common sole (Isis) 

Belgica 

Solea 

Sole Net Survey (SNS) 

ple.27.7d 

Pleuronectes platessa 

North Sea International Bottom Trawl (NS-IBTS)  

Beam Trawl Survey - North Sea, Irish Sea and Western Channel (UK-BTS) 

French Channel Groundfish Survey (FR-GFS) 

pok.27.3a46 

Pollachius virens 

North Sea International Bottom Trawl (NS-IBTS) 

whg.27.47d 

Merlangius merlangus 

North Sea International Bottom Trawl (NS-IBTS)  

NS 

NS 

NS 

NWW 

cod.27.6a 

Gadus morhua 

Scottish West Coast Groundfish Survey (UK-SCOWCGFS)  

Scottish West Coast Groundfish Survey (ScoGFS-WIBTS)  

Irish Ground Fish Survey (IGFS-WIBTS)  

    14 

14 

 
 
 
 
Area 

Stock 

Species 

Surveys used for assessment 

NWW 

cod.27.7e-k 

Gadus morhua 

Irish Ground Fish Survey (IGFS-WIBTS)  

French Southern Atlantic Bottom Trawl Survey (EVHOE-WIBTS)   

NWW 

had.27.6b 

Melanogrammus aeglefinus 

Scottish Rockall Bottom Trawl Survey (Rock-WIBTS) 

NWW 

had.27.7a 

Melanogrammus aeglefinus 

The Northern Ireland MIK Survey (NI MIK) 

Northern Ireland Ground Fish Survey (NIGFS-WIBTS)  

NWW 

had.27.7b-k 

Melanogrammus aeglefinus 

NWW 

ple.27.7a 

Pleuronectes platessa 

NWW 

whg.27.7a 

Merlangius merlangus 

NWW  whg.27.7b-ce-k 

Merlangius merlangus 

SWW 

hke.27.3a46-
8abd 

Merluccius merluccius 

SWW 

ldb.27.8c9a 

Lepidorhombus boscii 

UK Fishery Science Partnership (UKFSPW) 

Irish Ground Fish Survey (IGFS-WIBTS)   

French Southern Atlantic Bottom Trawl Survey (EVHOE-WIBTS)   

UK (E&W)-BTS  

Northern Ireland Ground Fish Survey (NIGFS-WIBTS)  

Northern Ireland Ground Fish Survey (NIGFS-WIBTS)  

The Northern Ireland MIK Survey (NI MIK)  

Irish Ground Fish Survey (IGFS-WIBTS)   

French Southern Atlantic Bottom Trawl Survey (EVHOE-WIBTS)   

Spanish North Coast Bottom Trawl Survey (SP-NSGFS)  

Irish Ground Fish Survey (IGFS-WIBTS)   

Irish Anglerfish and Megrim Survey (IE-IAMS)  

French surveys in the Bay of Biscay (FR-RESSGAC) 

Spanish North Coast Bottom Trawl Survey (SP-NSGFS)  

Spanish North Coast Bottom Trawl Survey (SP-NSGFS)  

SWW 

meg.27.8c9a 

Lepidorhombus whiffiagonis 

Spanish North Coast Bottom Trawl Survey (SP-NSGFS) 

SWW  meg.27.7b-k8abd 

Lepidorhombus whiffiagonis 

Irish Ground Fish Survey (IGFS-WIBTS)  

Spanish Porcupine Bottom Trawl Survey (SP-NSGFS)  

French Southern Atlantic Bottom Trawl Survey (EVHOE-WIBTS)   

The von Bertalanffy growth curve parameters for the 13 Mediterranean stocks of Table 3.1.1.1 were 
extracted from the respective STECF stock assessment reports (STECF, 2020b; 2020c). 

3.2 

Methods 

3.2.1  VON BERTALANFFY PARAMETER ESTIMATION IN ICES STOCKS  

The FSA package in R (Ogle et al., 2020) was used to determine the starting values Ford-Walford  
(vbStarts{FSA}) and a Von Bertalanfy growth function (VBGF) was fit to DATRAS survey data for 
each stock, by bootstrapping a nonlinear regression (nls{stats}(R Core 2022)). The most common 
version of the VBGF was used to calculate these values. Within the FSA package this is refered to 
as the ‘typical’ version of the VBGF and is represented by:  

E[L|t] = Linf (1 − e−k(t−t0))    

               (3)  

where E[L|t] is the expected or average length at time (or age) t,  Linf is the asymptotic average 
length, k is the body growth rate coefficient (units are y−1), and t0 represents the time or age when 
the average length was zero (Ogle, 2016).  

    15 

15 

 
 
 
 
 
 
 
 
 
 
 
 
It should be noted that Linf is the asymptote for the model of average length-at-age (Francis, 1988). 
As with any average, it does not correspond to the maximum length of the animal. Some individuals 
will be larger than average; thus, some animals will be larger than Linf.  

3.2.2  SELECTIVITY ANALYSIS 

For the analysis of the impact of varying age-based selectivity, the R package FLSelex was used, 
that  is  available  on  https://github.com/Henning-Winker/FLSelex.  This  package  is  used  in  FLR 
(Fisheries Library in R; Kell et al., 2007) and its development was led by the co-chair of EWG 21-
07, Henning Winker (JRC). For the analysis of varying length-based selectivity by EWG 22-19, a 
length-based  version  of  FLSelex,  FLSelexLen,  was  developed  by  Michael  Gras  (JRC),  with  the 
support of the EWG 22-19 expert Danai Mantopoulou-Palouka (Aristotle University of Thessaloniki). 
FLSelexLen is available on https://github.com/michaelgras/FLSelexLen. 

FLSelex  and  FLSelexLen  include  a  range  of  age-based  and  length-based  functions,  respectively, 
that allow fitting selectivity curves, varying selectivity by ‘cranking’ or ‘shifting’ the selectivity curve 
(Fig 3.2.2.1, 3.2.2.2), estimating the equilibrium optimisation of selectivity, as well as forecasting. 
They also include a range of relevant plotting functions. More details on  FLSelex can be found in 
the  ‘FLSelex  Handbook’  available  on  https://github.com/Henning-Winker/FLSelex  and  in  STECF, 
2021. 

It  should  be  noted  that  spawner-recruit  relationships  (SRRs)  were  integrated  into  yield 
maximisations at equilibrium. These SRRs were the same ones used by EWG 21-07 (STECF, 2021). 
The SRRs were Beverton-Holt for all stocks except cod.27.6a, where a segmented regression with 
a breakpoint at Blim was used, and for the Mediterranean hake stocks where the geometric mean 
of  recruitment  was  used.  Therefore,  for  Mediterranean  hake  stocks  equilibrium  yield-per-recruit 
(YPR) and SSB-per-recruit (SBR) have been estimated rather than equilibrium yield and SSB. More 
details on the SSR fitting and selection can be found in STECF, 2021. 

For practical reasons and simplicity, EWG 22-19 used the age-at-50%-selectivity (A50) and length-
at-50%-selectivity (L50) inferred from the ascending part of the selectivity curve as the common 
‘currency’ to quantify selectivity. This approximation works well for logistic selectivity curves, but 
may  be  less  accurate  for  selectivity  curves  of  other  types  (e.g.  dome-shaped;  saddle-shaped), 
which are often encountered in fish stocks (Sampson & Scott, 2012). 

    16 

16 

 
 
 
 
Figure 3.2.2.1 ‘Cranking’ the ascending slope of the estimated selectivity curve by varying  A50, shown for 
examples  from  the  Northeast  Atlantic  (cod.27.22-24, cod.27.6a, pok.27.3a46)  and  the  Mediterranean  Sea 
(HKE.08-09-10-11). 

Figure  3.2.2.2  ‘Shifting’  the  estimated  selectivity  curve,  shown  for  examples  from  the  Northeast  Atlantic 
(cod.27.22-24, cod.27.6a, pok.27.3a46) and the Mediterranean Sea (HKE.08-09-10-11). 

    17 

17 

 
 
 
 
 
4 

RESULTS 

4.1 

Von Bertalanffy parameter estimates 

4.1.1  NE ATLANTIC STOCKS  

The VBGF parameter estimates extracted by fitting the VBGF over survey data from DATRAS for 
the  20  NE  Atlantic  stocks  are  presented  in  Table  4.1.1.1,  together  with  a  summary  of  the  data 
used. 

Table 4.1.1.1 Von Bertalanffy parameter estimates (Linf in cm, k, t0) for each of the 20 ICES stocks analysed 
here. Par: parameter; Est: estimate; Q: quarter.  

Stock 

Par 

Est 

Std. error  Median 

2.50% 

97.50%  Max age  Max length 

Sample size  Q 

Sex 

Linf 

125 

Value not estimated – fixed value 

cod.27.22_24 

k 

t0 

0.148 

0.0007 

0.148 

0.146 

0.149 

10 

125 

13723 

1  M/F/U 

-0.051 

0.012 

-0.051 

-0.075 

-0.027 

Linf 

41.7 

3.25 

41.7 

41.1 

42.4 

ple.27.21-23 

k 

t0 

0.258 

0.005 

0.258 

0.247 

0.269 

17 

560 

18406 

1 

F 

-0.340 

0.027 

-0.340 

-0.400 

-0.286 

Year 
range 

2003 - 
2022 

2003 -
2022 

Linf 

138.1 

23.0 

138.1 

134.0 

142.6 

cod.27.47d20 

k 

t0 

0.177 

0.165 

Linf 

45.4 

had.27.46a20 

k 

t0 

0.400 

0.005 

0.013 

0.815 

0.002 

0.177 

0.168 

0.186 

13 

1260 

11303 

1  M/F/U 

- 

0.164 

0.139 

0.191 

45.4 

45.2 

45.6 

0.400 

0.395 

0.404 

17 

92 

90393 

3  M/F/U 

-0.693 

0.006 

-0.693 

-0.703 

-0.681 

Lin 

40.8 

0.86 

40.8 

40.6 

40.9 

ple.27.420 

k 

0.249 

0.019 

0.249 

0.245 

0.253 

32 

67 

68283 

3  M/F/U 

T0 

-1.117 

0.0164 

-1.117 

-1.149 

-1.086 

Linf 

57.1 

1.18 

57.1 

54.9 

59.5 

ple.27.7d 

k 

t0 

0.125 

0.005 

0.124 

0.114 

0.136 

19 

60 

7232 

3 

F 

-2.218 

0.078 

-2.216 

-2.374 

-2.061 

Linf 

146.0 

1.89 

146.0 

142.6 

150.1 

pok.27.3a46 

k 

t0 

0.075 

0.0016 

0.075 

0.114 

0.136 

20 

115 

22992 

3  M/F/U 

-1.446 

0.032 

-1.446 

-1.512 

-1.386 

Linf 

48.5 

0.77 

48.5 

47.9 

49.2 

whg.27.47d 

k 

t0 

0.263 

0.002 

0.263 

0.254 

0.272 

11 

58 

25095 

3 

F 

-1.237 

0.005 

-1.237 

-1.195 

-1.283 

Linf 

137.6 

26.8 

131.4 

105.3 

205.2 

cod.27.6a 

k 

t0 

0.159 

0.039 

0.160 

0.082 

0.233 

4 

79 

649 

4  M/F/U 

-1.179 

0.157 

-1.166 

-1.521 

-0.907 

Linf 

56.3 

0.3 

56.3 

55.7 

56.8 

had.27.7b_k 

k 

t0 

0.312 

0.0046 

0.312 

0.303 

0.320 

10 

820 

27171 

4  M/F/U 

-1.178 

0.016 

-1.178 

-1.209 

-1.146 

Linf 

128.8 

3.2 

128.7 

123 

135.4 

cod.27.7e_k 

k 

t0 

0.287 

0.014 

0.287 

0.261 

0.314 

7 

1100 

2318 

4  M/F/U 

-0.295 

0.029 

-0.294 

-0.352 

-0.239 

had.27.6b 

Linf 

53.7 

0.2 

53.7 

53.3 

54.2 

k 

0.331 

0.004 

0.331 

0.323 

0.340 

13 

810 

13024 

3  M/F/U 

    18 

18 

2010-
2022 

2010-
2022 

2010-
2021 

2010-
2022 

2010-
2022 

2003 - 
2012 

2004 - 
2021 

2003 - 
2021 

2013 - 
2021 

 
 
 
 
  
Stock 

Par 

Est 

Std. error  Median 

2.50% 

97.50%  Max age  Max length 

Sample size  Q 

Sex 

t0 

-0.882 

0.014 

-0.882 

-0.910 

-0.855 

Linf 

63.1 

0.8 

63.1 

61.5 

64.8 

had.27.7a 

k 

t0 

0.246 

0.006 

0.246 

0.234 

0.259 

9 

753 

12606 

1  M/F/U 

-0.326 

0.019 

-0.326 

-0.363 

-0.289 

Linf 

43.3 

1.6 

43.2 

40.6 

46.9 

ple.27.7a 

k 

t0 

0.147 

0.014 

0.147 

0.120 

0.175 

18 

537 

1916 

1 

F 

-1.853 

0.220 

-1.838 

-2.345 

-1.464 

Linf 

43.6 

0.5 

43.6 

42.7 

44.6 

whg.27.7a 

k 

t0 

0.374 

0.009 

0.374 

0.357 

0.394 

7 

494 

17051 

1  M/F/U 

-0.247 

0.017 

-0.247 

-0.280 

-0.214 

Linf 

51.2 

0.3 

51.1 

50.5 

51.8 

whg.27.7b_ce_k 

k 

t0 

0.334 

0.006 

0.334 

0.323 

0.345 

8 

650 

20316 

4  M/F/U 

-1.221 

0.016 

-1.221 

-1.253 

-1.189 

Linf 

108.3 

2.5 

108.2 

103.6 

113.2 

hke.27.3a46-
8abd 

k 

t0 

0.105 

0.004 

0.105 

0.098 

0.113 

11 

912 

3203 

4  M/F/U 

-1.257 

0.035 

-1.256 

-1.327 

-1.191 

Linf 

41.9 

2.7 

41.5 

37.9 

48.2 

lbd.27.8c9a 

k 

t0 

0.113 

0.017 

0.113 

0.080 

0.146 

9 

400 

4975 

4 

F 

-4.332 

0.446 

-4.309 

-5.258 

-3.551 

Linf 

52.6 

2.8 

52.2 

48.3 

58.9 

meg.27.8c9a 

k 

t0 

0.115 

0.014 

0.115 

0.088 

0.142 

11 

540 

4435 

4 

F 

-3.795 

0.311 

-3.769 

-4.427 

-3.253 

Linf 

73.8 

3.8 

73.4 

67.2 

82.4 

meg.27.7b-
k8abd 

k 

t0 

0.075 

0.007 

0.0752 

0.062 

0.088 

12 

590 

7463 

4 

F 

-2.049 

0.140 

-2.041 

-2.336 

-1.799 

Year 
range 

2008 - 
2021 

2009 - 
2017 

2008 - 
2021 

2004 - 
2021 

2003 - 
2008 

2003 – 
2020 

2003 - 
2020 

2003 - 
2021 

4.1.2  MEDITERRANEAN STOCKS 

The  VBGF  parameter  estimates  for  the  13  Mediterranean  stocks  were  extracted  from  the 
Mediterranean stock assessment reports of the STECF EWG 20-09 and EWG 20-15 (STECF, 2020b; 
2020c), in order to be in line with the stock objects used in the corresponding analysis. These VBGF 
parameters are used for age-slicing of the EU DCF length data prior to the stock assessments. In 
some cases, where sex-specific VBGF estimates were available, STECF EWG 20-09 and EWG 20-15 
performed age-slicing by sex during the FLR stock object preparation. Nevertheless, STECF EWG 
22-19 opted for using only one set of VBGF parameters for each stock to convert ages into lengths; 
either a ‘sexes combined’ one, or the ‘female’ one (Table 4.1.2.1). 

Table 4.1.2.1 Summary of the VBGF parameters used by STECF EWG 20-09 and EWG 20-15 to age-slice EU 
DCF length data to prepare the FLR stock objects for stock assessment. The VBGF parameters highlighted in 
bold are the ones used by STECF EWG 22-19 to derive lengths from ages. WM: Western Mediterranean; CEM: 
Central and Eastern Mediterranean; C: sexes combined; F: Females; M: Males. 

Area 

Stock 

Assessment 

sex  Linf (cm) 

k 

t0 

Comments 

WM  HKE.01_05_06_07  STEFC, a4a, 2020  C 

110 

0.178  -0.005 

WM  HKE.08_09_10_11  STEFC, a4a, 2020 

F 

M 

95 

60 

0.16 

-0.06 

0.265 

-0.06 

WM 

WM 

MUR.05 

STEFC, a4a, 2020  C 

33.4 

0.43 

-0.1 

MUT.01 

STEFC, a4a, 2020  C 

34.5 

0.34 

-0.14 

    19 

19 

 
 
 
 
  
  
  
  
  
Area 

WM 

Stock 

Assessment 

sex  Linf (cm) 

k 

t0 

Comments 

MUT.06 

STEFC, a4a, 2020  C 

34.5 

0.34 

-0.14 

ALKs used to convert length data into ages 
for the assessment done by EWG 20-09; 
fitted VBGF used by EWG 22-19 

t0 +0.5 correction  
(original t0 = -0.33) 

t0 +0.5 correction  
(original t0 = -0.33) 

WM 

MUT.07 

STEFC, a4a, 2020  C 

26.25 

0.5 

-0.55 

WM 

MUT.09 

STEFC, a4a, 2020 

WM 

MUT.10 

STEFC, a4a, 2020 

CEM 

HKE.17_18 

STEFC, a4a, 2020 

CEM 

HKE.19 

STEFC, a4a, 2020 

F 

26.56  0.545  0.17 

M 

21.55 

0.56 

0.17 

F 

M 

F 

M 

F 

M 

30 

26 

0.243  0.62 

0.237 

0.9 

111 

0.1 

-0.717 

73 

0.15 

-0.741 

111 

0.1 

-0.6 

73 

0.15 

-0.73 

CEM 

HKE.20 

STEFC, a4a, 2020  C 

104 

0.12 

-0.01 

CEM 

MUT.17_18 

STEFC, a4a, 2020 

F 

29.185  0.247  -0.768 

M  22.725  0.328  -0.816 

CEM 

MUT.22 

STEFC, a4a, 2020  C 

32.6 

0.17 

-1.78 

4.2 

Identify the ages and sizes at which fish would need to be caught to optimise 
yield and reduce the catches of juveniles (ToR 1) 

4.2.1  OPTIMISATION UNDER CURRENT FISHING MORTALITY 

The optimal age/size that fish of each stock need to be caught, i.e. age-at-50%-selectivity (A50) 
and length-at-50%-selectivity (L50), for yield to be maximised under current fishing mortality are 
shown in Tables 4.2.1.1 and 4.2.1.3 for ICES and Mediterranean stocks, respectively. ‘Current’ F 
and  selectivity  refer  to  the  average  of  the  last  three  years  available  from  the  assessments 
(2018-2020 for ICES stocks, 2017-2019 for Mediterranean stocks). 

In  the  NE  Atlantic,  the  optimal  ‘shift’  type  of  selectivity  change  (Fig.  3.2.2.2)  generates  higher 
yields than the optimal ‘crank’ type of selectivity change (Fig. 3.2.2.1) for 12 stocks, and vice versa 
for 8 stocks (Table 4.2.1.1, Table 4.3.1). Meanwhile, the respective gains in protection of juveniles 
are higher for the ‘shift’ than the ‘crank’ type for 13 of the 20 stocks (Table 4.3.1). Due to unrealistic 
estimates  of  length  from  the  age-to-length  conversion  for  cod.27.6a,  possibly  owing  to  the  low 
estimated value of t0 (Table 4.1.1.1), VBGF parameters from the neighbouring cod.27.47d20 were 
used for the age-to-length conversion of cod.27.6a. 

In the Mediterranean, the optimal ‘shift’ type of selectivity change generates higher yields than the 
optimal  ‘crank’  type  of  selectivity  change  for  11  of  the  13  stocks  (Table  4.2.1.3,  Table  4.3.2). 
Meanwhile, the respective gains in protection of juveniles are higher for the ‘shift’ than the ‘crank’ 
type for 6 stocks, while in the 7 remaining stocks juveniles consist less than 0.3% of the catch for 
both the ‘crank’ and the ‘shift’ type (Table 4.3.2). It should be noted that selectivity typically peaks 
at  earlier  ages/sizes  in  Mediterranean  stocks  than  NE  Atlantic  ones  (Fig.  3.2.2.1),  leaving  little 
space for ‘crank’ to improve selectivity compared to ‘shift’. 

To visualise which fleet segments are closer and which further from the optimal selectivity, the A50 
and  L50  inferred  from  the  partial  F-at-age  curves  (partial  selectivity)  of  fleet  segments  were 
examined,  wherever  these  were  available.  Typically,  trawlers  (OTB,  TBB)  were  found  to  select 

    20 

20 

 
 
  
  
  
  
  
  
  
  
  
  
  
 
 
younger/smaller  fish  than  passive  gears  (e.g.  GNS,  GTR,  LLS),  both  in  the  NE  Atlantic  (Table 
4.2.1.2) and the Mediterranean (Table 4.2.1.4). 

Table 4.2.1.1 Selectivity estimates for NE Atlantic stocks summarizing the age-at-50%-selectivity (A50) and 
length-at-50%-selectivity  (L50)  for  the  current  selectivity  (‘Current’)  and  the  optimised  selectivity  curves 
(‘Crank’, ‘Shift’). In addition, the Amat/Lmat corresponding to age/length-at-50%-maturity (‘Maturity’), the Aopt/ 
Lopt at which the total biomass of an unfished cohort (F = 0) attains a maximum (‘Max Biomass’), as well as 
the Aplusgroup/Lplusgroup of the maximum age/length assumed in the assessment (‘Plusgroup’) are provided for 
reference. Ages are as reported by EWG 21-07 and lengths were derived from ages using VBGF parameters 
estimated on the basis of DATRAS data. All ages are given in y and all lengths in cm. Bold font indicates the 
A50/L50 producing the highest gains in yield (see also Table 4.3.1). 

Region  Area 

Stock 

A50 

L50  A50 

L50  A50 

L50  Amat  Lmat  Aopt 

Lopt 

Aplusgroup  Lplusgroup 

Current 

Crank 

Shift  Maturity  Max Biomass 

Plusgroup 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

BS 

BS 

NS 

NS 

NS 

NS 

NS 

NS 

cod.27.22-24 

2.7  40.7  4.2  59.6  6.0  80.4  1.9 

30 

ple.27.21-23 

2.3  20.6  3.6  26.6  6.0  33.6  2.0  18.9 

cod.27.47d20 

1.9  36.5  2.8  51.5  4.4  72.9  3.0  54.5 

had.27.46a20 

1.9  29.3  1.3  24.9  1.3  24.9  2.6  33.2 

ple.27.420 

1.5  19.6  2.0  22  7.2  35.7  2.5  24.2 

ple.27.7d 

1.9  22.9  2.0  23.3  2.0  23.3  2.9  26.9 

pok.27.3a46 

3.8  47.4  4.7  53.9  8.1  74.6  4.7  53.9 

whg.27.47d 

2.3  29.4  1.6  25.5  1.6  25.5  1.3  23.6 

NEA  NWW 

cod.27.6a* 

2.2  41.8  3.8  65.8  4.0  68.1  1.9  36.5 

NEA  NWW 

cod.27.7e-k 

1.1  42.5  1.8  58.2  4.6  97.2  2.0  62.1 

NEA  NWW 

had.27.6b 

2.1  33.7  3.3  40.3  5.0  46.1  2.6  36.8 

NEA  NWW 

had.27.7a 

0.9  16.4  0.6  12.9  0.6  12.9  1.6  23.8 

NEA  NWW 

had.27.7b-k 

3.2  41.9  2.7  39.5  2.7  39.5  1.6  32.6 

NEA  NWW 

ple.27.7a 

1.8  18  1.3  16.1  3.8  24.5  2.9  21.8 

NEA  NWW 

whg.27.7a 

0.5  10.6  1.2  18.2  1.7  22.6  1.6  21.8 

NEA  NWW  whg.27.7b-ce-k 

3.0  38.7  2.1  34.3  2.1  34.3  0.9 

26 

NEA 

SWW  hke.27.3a46-8abd  1.8  29.8  3.5  42.7  3.5  42.7  2.6  36.2 

NEA 

SWW 

ldb.27.8c9a 

3.4  24.4  5.2  27.6  5.6  28.2  1.0  18.9 

NEA 

SWW  meg.27.7b-k8abd  3.2  24  2.7  22.1  2.8  22.5  2.8  22.5 

NEA 

meg.27.8c9a 
*length coversions were made using the VBGF parameters of cod.27.47d20 

SWW 

3.2  29  5.8  35.1  6.0  35.5  1.2  22.9 

6 

6 

5 

7 

9 

4 

9 

4 

4 

5 

5 

4 

7 

7 

4 

6 

5 

6 

5 

6 

80.4 

33.6 

79.5 

43.3 

37.5 

30.8 

79.2 

36.3 

68.1 

100.6 

46.1 

41.4 

51.9 

31.6 

34.7 

46.6 

52.3 

28.8 

30.3 

35.5 

7 

7 

6 

8 

10 

7 

10 

8 

7 

7 

7 

5 

8 

8 

6 

7 

15 

7 

10 

7 

91.2 

35.4 

89 

44 

38.3 

39 

84 

44.3 

97.0 

113 

49.8 

46.1 

53 

33.2 

39.4 

47.9 

88.8 

30.2 

43.9 

37.3 

Table 4.2.1.2 Selectivity estimates for the ICES stocks summarizing the current age-at-50%-selectivity (A50; 
y) and length-at-50%-selectivity (L50; cm) by fleet segments using different gear types. OTB: bottom otter 
trawls;  DRB:  Towed  dredges;  GNS:  Set  gillnets;  GTR:  Trammel  nets;  LLS:  Set  longlines;  MWT:  Midwater 
Trawl;  PAS:  Passive  gears;  SEI:  Seine  nets;  TBB:  Beam  trawls.  Ages  are  as  reported  by  EWG  21-07  and 
lengths were derived from ages using VBGF parameters estimated on the basis of DATRAS data. 

Area 

Stock 

A50 

L50  A50 

L50  A50 

L50  A50  L50  A50 

L50  A50 

L50  A50 

L50  A50 

L50  A50 

L50 

OTB 

DRB 

GNS 

GTR 

LLS 

MWT 

PAS 

SEI 

TBB 

BS 

BS 

cod.27.22-24 

2.38  36.5 

3.17  46.8 

ple.27.21-23 

2.26  20.4 

3.14  46.4 

3.25  25.2 

    21 

21 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
  
 
 
 
 
 
 
 
 
 
  
 
Area 

Stock 

A50 

L50  A50 

L50  A50 

L50  A50  L50  A50 

L50  A50 

L50  A50 

L50  A50 

L50  A50 

L50 

OTB 

DRB 

GNS 

GTR 

LLS 

MWT 

PAS 

SEI 

TBB 

NS 

NS 

NS 

NS 

NS 

NS 

cod.27.47d20 

1.86  35.8 

had.27.46a20 

1.97  29.7 

ple.27.420 

2.03  22.2 

2.41  45.3 

5.19  41.1 

ple.27.7d 

1.89  22.9  2.13  23.9 

2.1  23.8 

pok.27.3a46 

3.7  46.7 

7.4  70.7 

whg.27.47d 

2.64 

31 

NWW 

cod.27.6a* 

2.11  40.6 

NWW 

had.27.6b 

1.72 

31 

NWW 

had.27.7a 

0.9  16.4 

NWW 

ple.27.7a 

1.73  17.8 

SWW  meg.27.7b-k8abd  3.32  24.5 

*length coversions were made using the VBGF parameters of cod.27.47d20 

2.29  43.3 

2.08  39.7  1.57  30.4 

1.88  29.2 

1.95  21.8  1.28  18.4 

2.08  23.7  2.26  24.4 

3.7  35.3  1.16  22.7 

2.86  34.3 

2.52  31.8 

1.8 

18 

3.25  24.2 

Table 4.2.1.3 Selectivity estimates for Mediterranean stocks summarizing the age-at-50%-selectivity (A50) 
and length-at-50%-selectivity (L50) for the current selectivity (‘Current’) and the optimised selectivity curves 
(‘Crank’, ‘Shift’). In addition, the Amat/Lmat corresponding to age/length-at-50%-maturity (‘Maturity’), the Aopt/ 
Lopt at which the total biomass of an unfished cohort (F = 0) attains a maximum (‘Max Biomass’), as well as 
the Aplusgroup/Lplusgroup of the maximum age/length assumed in the assessment (‘Plusgroup’) are provided for 
reference. Ages are as reported by EWG 21-07 and lengths were derived from ages using VBGF parameters 
used and reported by EWG 20-09 and EWG 20-15.  All ages are given in y and all lengths in cm. Bold font 
indicates the A50/L50 producing the highest gains in yield (see also Table 4.3.2). 

Region  Area 

Stock 

A50 

L50  A50 

L50  A50 

L50  Amat  Lmat  Aopt 

Lopt  Aplusgroup  LPlusGroup 

Current 

Crank 

Shift  Maturity  Max Biomass 

Plusgroup 

MED  WM  HKE.01_05_06_07  0.6  11.2  1.3  22.8  4.0  56.1  1.5  25.9 

MED  WM  HKE.08_09_10_11  0.2  3.9  0.4  6.7  5.5  56 

1.4  19.8 

MED  WM 

MUR.05 

1.0  12.6  0.8  10.7  0.7  9.7  0.6  8.7 

MED  WM 

MUT.01 

1.6  15.4  2.4  20  3.0  22.6  0.6  7.7 

MED  WM 

MUT.06 

1.1  11.9  1.3  13.4  3.0  22.6  0.6  7.7 

MED  WM 

MUT.07 

0.8  12.9  1.3  15.8  1.8  18.1  0.6  11.5 

MED  WM 

MUT.09 

1.0  9.7  1.4  13  1.7  15 

0.6  5.5 

MED  WM 

MUT.10 

1.1  3.3  1.5  5.8  3.0  13.2  0.6  NA 

4 

6 

4 

3 

3 

3 

3 

3 

56.1 

59 

27.7 

22.6 

22.6 

21.8 

20.9 

13.2 

5 

7 

5 

4 

4 

4 

4 

4 

MED  CEM 

HKE.17_18 

1.2  19.4  2.4  29.7  7.0  59.7  3.1  35.2 

19 

95.5 

20 

MED  CEM 

MED  CEM 

HKE.19 

HKE.20 

0.2  8.5  1.7  22.8  5.6  51.3  1.7  22.8 

0.9  10.8  1.6  18.3  2.8  29.8  1.4  16.2 

MED  CEM 

MUT.17_18 

1.1  10.8  1.5  12.5  2.5  16.2  0.6  8.4 

MED  CEM 

MUT.22 

0.5  10.5  0.4  10.1  1.1  12.6  0.3  9.7 

6 

3 

3 

2 

53.6 

31.5 

17.7 

15.5 

7 

4 

4 

5 

64.9 

64.3 

29.7 

26.1 

26.1 

23.6 

23.3 

16.8 

97 

59.1 

39.7 

20.2 

22.3 

    22 

22 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
  
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
  
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
  
 
  
 
 
 
  
 
 
 
 
 
 
 
 
 
  
 
  
 
 
 
  
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 4.2.1.4 Selectivity estimates for Mediterranean stocks summarizing the current age-at-50%-selectivity 
(A50;  y)  and  length-at-50%-selectivity  (L50;  cm)  by  fleet  segments  using  different  gear  types.  GNS:  Set 
gillnets; GTR: Trammel nets; LLS: Set longlines; OTB: bottom otter trawls. Ages are as reported by EWG 21-
07 (except for LLS of HKE which was calculated for EWG 22-19) and lengths were derived from ages using 
VBGF parameters used and reported by EWG 20-09. 

GNS 

GTR 

LLS 

OTB 

Area 

Stock 

A50 

L50 

A50 

L50  A50 

L50 

A50 

L50 

WM  HKE.01_05_06_07  1.14   20.3 

2.9   44.4  0.39   7.5 

WM  HKE.08_09_10_11  2.1   27.8  0.97   14.4  3.97  45.2  0.15   3.1 

WM 

WM 

WM 

WM 

MUT.01 

MUT.07 

MUT.09 

1.94   17.5 

3.24   21.6 

MUT.10 

3.01   13.2  1.2   3.9 

1.48   14.6 

0.77   12.7 

1.01   9.8 

1.19   3.9 

4.2.2  OPTIMISATION UNDER VARYING FISHING MORTALITY 

The constructed isopleths (Fig. 4.2.2.1 - 4.2.2.6) illustrate the trade-offs between selectivity and F 
with respect to relative yield and SSB. They also allow to infer the optimal selectivity for different 
levels of F. The isopleths suggest that heavily overfished stocks (e.g. cod.27.22-24; cod.27.7e-k; 
HKE.01-05-06-07) would require a greater change in selectivity (increase in A50/L50) to approach 
the area of optimal yields, compared to those exploited closer to FMSY. For almost all stocks, except 
for  some  of  the  ones  currently  exploited  below  FMSY  (e.g.  whg.27.47d,  had.27.7a,  MUT.22),  any 
increase in A50/L50 would mean higher equilibrium yields at lower levels of stock depletion. 

    23 

23 

 
 
 
 
   
  
   
  
 
  
   
  
   
  
 
  
   
  
 
  
 
  
 
 
 
 
 
 
 
 
 
Figure 4.2.2.1 Isopleths by stock in the Western Baltic Sea. Equilibrium yield (left) and SSB (right) under 
varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; bottom) and 
fishing mortality (Fapical). The black dot represents the estimate under current selectivity and Fapical. 

    24 

24 

 
 
 
 
 
 
Figure 4.2.2.2  Isopleths by stock in the Greater North Sea.  Equilibrium yield (left) and SSB (right)  under 
varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; bottom) and 
fishing mortality (Fapical). The black dot represents the estimate under current selectivity and Fapical. 

    25 

25 

 
 
 
 
 
 
Figure 4.2.2.2 (cont.) Isopleths by stock in the Greater North Sea. Equilibrium yield (left) and SSB (right) 
under varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; bottom) 
and fishing mortality (Fapical). The black dot represents the estimate under current selectivity and Fapical. 

    26 

26 

 
 
 
 
 
 
Figure 4.2.2.2 (cont.) Isopleths by stock in the Greater North Sea. Equilibrium yield (left) and SSB (right) 
under varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; bottom) 
and fishing mortality (Fapical). The black dot represents the estimate under current selectivity and Fapical. 

    27 

27 

 
 
 
 
 
 
Figure 4.2.2.3 Isopleths by stock in the North Western Waters. Equilibrium yield (left) and SSB (right) under 
varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; bottom) and 
fishing mortality (Fapical). The black dot represents the estimate under current selectivity and Fapical. 

    28 

28 

 
 
 
 
 
 
Figure  4.2.2.3  (cont.)  Isopleths  by  stock  in  the  North  Western  Waters.  Equilibrium  yield  (left)  and  SSB 
(right) under varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; 
bottom)  and  fishing  mortality  (Fapical).  The  black  dot  represents  the  estimate  under  current  selectivity  and 
Fapical. 

    29 

29 

 
 
 
 
 
 
Figure  4.2.2.3  (cont.)  Isopleths  by  stock  in  the  North  Western  Waters.  Equilibrium  yield  (left)  and  SSB 
(right) under varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; 
bottom)  and  fishing  mortality  (Fapical).  The  black  dot  represents  the  estimate  under  current  selectivity  and 
Fapical. 

    30 

30 

 
 
 
 
 
 
Figure  4.2.2.3  (cont.)  Isopleths  by  stock  in  the  North  Western  Waters.  Equilibrium  yield  (left)  and  SSB 
(right) under varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; 
bottom)  and  fishing  mortality  (Fapical).  The  black  dot  represents  the  estimate  under  current  selectivity  and 
Fapical. 

    31 

31 

 
 
 
 
 
 
Figure 4.2.2.4 Isopleths by stock in the South Western Waters. Equilibrium yield (left) and SSB (right) under 
varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; bottom) and 
fishing mortality (Fapical). The black dot represents the estimate under current selectivity and Fapical. 

    32 

32 

 
 
 
 
Figure  4.2.2.4  (cont.)  Isopleths  by  stock  in  the  South  Western  Waters.  Equilibrium  yield  (left)  and  SSB 
(right) under varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; 
bottom)  and  fishing  mortality  (Fapical).  The  black  dot  represents  the  estimate  under  current  selectivity  and 
Fapical. 

    33 

33 

 
 
 
 
 
Figure 4.2.2.5 Isopleths by stock in the Western Mediterranean Sea. Equilibrium yield (left) and SSB (right) 
under varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; bottom) 
and fishing mortality (Fapical). The black dot represents the estimate under current selectivity and Fapical. 

    34 

34 

 
 
 
 
 
Figure 4.2.2.5 (cont.) Isopleths by stock in the Western Mediterranean Sea. Equilibrium yield (left) and SSB 
(right) under varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; 
bottom)  and  fishing  mortality  (Fapical).  The  black  dot  represents  the  estimate  under  current  selectivity  and 
Fapical. 

    35 

35 

 
 
 
 
 
 
Figure 4.2.2.5 (cont.) Isopleths by stock in the Western Mediterranean Sea. Equilibrium yield (left) and SSB 
(right) under varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; 
bottom)  and  fishing  mortality  (Fapical).  The  black  dot  represents  the  estimate  under  current  selectivity  and 
Fapical. 

    36 

36 

 
 
 
 
 
 
Figure 4.2.2.5 (cont.) Isopleths by stock in the Western Mediterranean Sea. Equilibrium yield (left) and SSB 
(right) under varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, cm; 
bottom)  and  fishing  mortality  (Fapical).  The  black  dot  represents  the  estimate  under  current  selectivity  and 
Fapical. 

    37 

37 

 
 
 
 
 
 
Figure 4.2.2.6 Isopleths by stock in the Central and Eastern Mediterranean Sea. Equilibrium yield (left) and 
SSB (right) under varying (‘shifting’) age-at-50%-selectivity (A50, y; top) or length-at-50%-selectivity (L50, 
cm; bottom) and fishing mortality (Fapical). The black dot represents the estimate under current selectivity and 
Fapical. 

    38 

38 

 
 
 
 
 
 
Figure 4.2.2.6 (cont.) Isopleths by stock in the Central and Eastern Mediterranean Sea. Equilibrium yield 
(left)  and  SSB  (right)  under  varying  (‘shifting’)  age-at-50%-selectivity  (A50,  y;  top)  or  length-at-50%-
selectivity (L50, cm; bottom) and fishing mortality (Fapical). The black dot represents the estimate under current 
selectivity and Fapical. 

    39 

39 

 
 
 
 
 
 
Figure 4.2.2.6 (cont.) Isopleths by stock in the Central and Eastern Mediterranean Sea. Equilibrium yield 
(left)  and  SSB  (right)  under  varying  (‘shifting’)  age-at-50%-selectivity  (A50,  y;  top)  or  length-at-50%-
selectivity (L50, cm; bottom) and fishing mortality (Fapical). The black dot represents the estimate under current 
selectivity and Fapical. 

4.3 

Prioritisation of stocks with the highest potential gains (ToR 1) 

To prioritise the stocks with highest potential gains, EWG 22-19 built on the outcomes of EWG 21-
07  (STECF,  2021).  EWG  21-07  provided  for  each  stock  a  total  of  five  deterministic  projection 
scenarios over a time horizon of 50 years to attain equilibrium quantities for (a) yield and (b) the 
proportion of juveniles in the catches under current F. As ‘crank’ and ‘shift’ projections (Fig. 3.2.2.1, 
3.2.2.2) provided the maximum potential gains in yield (%), EWG 22-19 decided to focus on these 
two scenarios as basis for prioritisation. Differences between the  outcomes of current selectivity 
and ‘crank’ or ‘shift’ projections were calculated to explore the effect on equilibrium yields and the 
proportion of juveniles in the catches. 

Six stocks were shortlisted in the NE Atlantic using this rationale: four on the basis of the highest 
predicted  yield  change  (cod.27.22-24;  cod.27.7e-k,  cod.27.6a,  cod.27.47d20)  and  another  two 
based on the reduction in juveniles in the catch of more than 50% (whg.27.7a, pok.27.3a46) (Table 
4.3.1). 

Five stocks were shortlisted in the Mediterranean: four on the basis of the highest predicted yield 
change  (HKE.01-05-06-07;  HKE.08_09_10_11,  HKE.19,  HKE.20)  and  another  one  based  on  the 
highest reduction in juveniles in the catch (HKE.17_18) (Table 4.3.2). 

These six NE Atlantic and five Mediterranean stocks were more closely examined in dedicated case 
studies chapters (Chapters 4.5-4.6) with regards to their selectivity and options to improve it. 

    40 

40 

 
 
 
 
 
 
 
 
Table  4.3.1  Prediction  for  NE  Atlantic  stocks  summarizing  the  yield  change  (%)  relative  to  the  current 
selectivity under current F and the associated change in the percentage of juvenile fish in the catch, for the 
scenarios of fishing at the optimised ‘crank’ and ‘shift’ selectivity. The stocks with highest predicted gains in 
yield are  marked in green and the stocks with higher  reductions in juveniles in the catch of the remaining 
stocks are marked in blue. 

Area 

Stock 

Yield change (%)  Change in % juveniles in catch 

Crank 

Shift 

Crank 

BS 

BS 

NS 

NS 

NS 

NS 

NS 

NS 

cod.27.22-24 

322.9 

402.1 

ple.27.21-23 

cod.27.47d20 

had.27.46a20 

ple.27.420 

ple.27.7d 

pok.27.3a46 

whg.27.47d 

14.9 

44.5 

22 

1.3 

0 

17.6 

43.9 

28.3 

92.4 

17.3 

29.8 

0.8 

43.5 

28.9 

NWW 

cod.27.6a 

362.1 

315.2 

NWW 

cod.27.7e-k 

160.9 

375.3 

33 

2.4 

1.1 

0.6 

NWW 

NWW 

had.27.6b 

had.27.7a 

NWW 

had.27.7b-k 

NWW 

NWW 

ple.27.7a 

whg.27.7a 

106.5 

NWW  whg.27.7b-ce-k 

20.1 

SWW  hke.27.3a46-8abd 

22.8 

SWW 

ldb.27.8c9a 

SWW  meg.27.7b-k8abd 

4.4 

0.5 

44.2 

0.9 

2 

7.6 

98.1 

14.1 

16.9 

11.1 

-0.1 

SWW 

meg.27.8c9a 

62.8 

64 

-26.3 

-13.1 

-35.3 

28.8 

-9.1 

-1.8 

-30 

35.1 

-36.1 

-36.2 

-36.2 

18.9 

6.7 

6.4 

-76.7 

24.4 

-54.4 

-5.4 

10.4 

-8.9 

WM  HKE.01_05_06_07 

88.3 

294 

WM  HKE.08_09_10_11 

WM 

WM 

WM 

WM 

WM 

MUR.05 

MUT.01 

MUT.06 

MUT.07 

MUT.09 

6.1 

0.6 

35.3 

24.8 

28.8 

6.2 

129.7 

1 

43.9 

67.8 

27.3 

5 

-67.8 

-19.9 

0.2 

0 

0 

-13.2 

-2.9 

    41 

41 

Shift 

-29.1 

-20.2 

-58.3 

23.6 

-37.9 

-1.4 

-51.8 

19.5 

-26.5 

-73.5 

-35.7 

14.3 

5.1 

-18.8 

-64 

14.3 

-29.8 

-4.7 

5.9 

-7.1 

Shift 

-86.1 

-78.6 

0 

0 

0 

-13 

-2.8 

Table  4.3.2  Prediction for  Mediterranean  stocks summarizing the yield  change (%)  relative to the current 
selectivity under current F and the associated change in the percentage of juvenile fish in the catch, for the 
scenarios of fishing at the optimised ‘crank’ and ‘shift’ selectivity. The stocks with highest predicted gains in 
yield are marked in green and the stock with higher reductions in juveniles in the catch is marked in blue. 

Area 

Stock 

Yield change (%)  Change in % juveniles in catch 

Crank 

Shift 

Crank 

 
 
 
 
Area 

Stock 

WM 

CEM 

CEM 

CEM 

CEM 

CEM 

MUT.10 

HKE.17_18 

HKE.19 

HKE.20 

MUT.17_18 

MUT.22 

Yield change (%)  Change in % juveniles in catch 

Crank 

Shift 

Crank 

25.1 

16.7 

17.9 

49.4 

17.3 

0.4 

47.5 

34.9 

166.6 

99.3 

21.6 

0.6 

-1.3 

-49.6 

-23.5 

-52.3 

-2.3 

0.2 

Shift 

-1.3 

-89.6 

-77.6 

-61.1 

-2.3 

-1.9 

4.4 

Identify the fishing gears corresponding to the optimum ages and sizes (ToR 2) 

Based  on  the  stocks  listed  in  annex  XIV  of  the  TMR,  i.e.  ‘species  for  selectivity  performance 
indicators’, a sub-group of experts at EWG 22-19 were assigned to review and compile information 
on gear selectivity by size (hereby referred to as ‘gear selectivity’) per stock and region. To prioritize 
the available time and resources, main focus was to collect and compile gear selectivity data for 
the  stocks  where  the  highest  gains  can  be  achieved  (Table  4.3.1  &  4.3.2).  This  means  that  the 
number of listed gear options is not equally complete for all stocks. A multi-directional approach of 
searches  was  implemented  in  scientific  data  bases,  personal  expertise,  and  direct  contacts  with 
other experts to find information in both peer-reviewed and grey literature. The aim was to identify 
and list gear selectivity information and technical specifications, both for the regional baseline gears 
(gears  for  which  no  catch  composition  rules  apply  in  the  TCM  regional  annexes)  and  for  other 
relevant alternative gears in each region. The compilation of gear selectivity information per stock 
and region is presented in the Annex (Annex 1). 

Special interest was given to identify gear trials which reported particularly high size selectivity to 
better match the request of ToR 2. However this proved to be challenging as for most stocks/regions 
the  range  of  mesh  sizes/size  selectivity  parameters  in  published  studies  is  rather  narrow  and 
centred around current and historical, often smaller, mesh sizes and/or mesh configurations. This 
means that it is difficult to find documented examples of gears with a selectivity high enough to 
correspond to some of the suggested optimal sizes (Tables 4.2.1.1, 4.2.1.3), at least under current 
fishing mortalities. In some cases, EWG 22-19 therefore choose to list and use examples of gears 
with higher size selectivity for a species from an adjacent area (with similar fisheries). Characteristic 
examples  are  two  studies  with  large-meshed  codends  for  cod  and  saithe  from  the  Barents  Sea 
(Sistiaga et al., 2009; Brinkhof et al., 2022), which were used as examples of more optimal gears 
in terms of size selectivity for cod and saithe in the North Sea and West of Scotland (see Annex 1). 
Listing these higher selectivity gear options from adjacent areas as options for these stocks does 
not mean that they are necessarily directly applicable in similar EU fisheries, but merely meant as 
examples of what can possibly be achieved in terms of gear selectivity. 

Another  observation  from  the  literature  review  exercise  is  that  most  research  on  absolute  gear 
selectivity has been done on demersal bottom trawls and seines (more correctly on cod-ends). By 
contrast,  gear  selectivity  data  for  gillnets,  longlines  and  other  passive  fishing  gears  are  much 
scarcer, often lacking entirely for many species/stocks in most regions. More specifically, although 
some studies on passive gears report other kind of gear selectivity/catch information (for example 
mean/modal size of catch plus some measure of variability), this information was seldom presented 
in  a  way  that  could  be  directly  used  by  EWG  22-19  (i.e.  the  function  that  describes  the  gear 
selectivity  by  length  was  not  reported  or  it  was  not  easy  to  be  calculated  from  the  reported 
information). There were also examples of trawl trials with similar issues of applicability for EWG 
22-19, i.e. in cases where the gear selectivity function (and parameters) were not clearly reported. 
All in all, this means that the compilation of gear selectivity by stock and region presented in Annex 
1 is far from complete but shall be seen as an initial shortlist of gear options for optimizing gear 
selectivity.  More  effort  is  needed  if  a  more  comprehensive  list  of  gear  alternatives  by  stock  and 
region is requested. 

    42 

42 

 
 
 
 
4.4.1  NORTH SEA (ANNEX V OF TMR) 

The North Sea region comprises Union waters in ICES divisions 2a and 3a and ICES sub-area 4. 
Annex  V  of  the  TMR  stipulates  that  the  North  Sea  baseline  mesh  size  in  towed  gear  is  120mm, 
except for division 3a (Skagerrak and Kattegat) where 90mm (with a 270mm diamond mesh panel) 
is allowed. Baseline mesh size for nets is 120mm. Smaller mesh sizes can be used in certain directed 
fisheries but only if by-catches of cod, haddock and saithe do not exceed 20% of the total catch 
weight. Examples of major demersal trawl fisheries that uses mesh sizes smaller than the baseline 
are fisheries for flatfishes like plaice (100mm), sole (80mm), Nephrops and whiting (80mm) and 
Pandalus (35mm). Many of these smaller mesh gears must be fitted with a square mesh panel or 
a grid. 

Additional technical measures are in place as part of remedial measures for cod in the North  Sea 
(Art 16 of Council Regulation (EU) 2022/109). These include the following gear measures in the 
northern part of the region (the Northern North Sea and the Skagerrak): 

For TR1 trawls and seines >100mm: 

i) 
ii) 
iii) 

belly trawls with a minimum belly mesh size of 600mm 
raised fishing line (0.6 m) 
horizontal separating panel with large mesh escape panel 

For TR2 trawls and seines 70-100mm: 

(i) 

(ii) 
(iii) 

horizontal  sorting  grid  with  maximum  50mm  bar  spacing  separating  flatfish  and 
roundfish, with an unblocked fish outlet for roundfish 
SELTRA panel with 300mm square-mesh size 
sorting grid with maximum 35mm bar spacing, with an unblocked fish outlet; 

These gear options stem from an EU/Norway meeting in 2020 where many potential alternatives 
to reduce cod bycatches in trawl fisheries were presented and discussed (Graham & Olsen, 2020). 
As most of these gears were studied using catch comparison trials, i.e. trials comparing catches 
between  a  new/alternative  gear  and  a  baseline  gear  to  estimate  catch  equivalence,  EWG  22-19 
understands that information about absolute selectivity parameters (Lg50 and SRg) are lacking for 
some  of  the  listed  gears.  The  remedial  measures  gears  with  reported  selectivity  estimates  were 
included in the Annex (Annex 1). 

Notably, exemptions from the remedial gear measures listed above are possible if one fishes under 
a so called national cod avoidance plan. EWG 22-19 is not aware of any evaluation of the degree 
of implementation of the North Sea cod remedial gear measures but understands that some fleets 
continuously  use  the  baseline  trawl  gears  under  national  plans.  Because  of  the  uncertain 
implementation, EWG 22-19 choose to focus on the selectivity of the TMR baseline gears instead 
of the remedial measures’ gears in ToR 3. 

4.4.2  NORTH WESTERN WATERS (ANNEX VI OF TMR) 

The North Western Waters region comprises Union waters of sub-areas 5,6 and 7. The TMR lays 
down a baseline mesh size of 100mm in ICES divisions 7b-7k and 120mm in the rest of the region.  

Like  in  the  North  Sea  region,  smaller  mesh  sizes  can  be  used  in  certain  directed  fisheries  if 
by-catches of cod, haddock and saithe do not exceed 20% of the total catch weight. For instance, 
an 80mm codend may be used in directed Nephrops fisheries if combined with either a square mesh 
panel of at least 120mm or sorting grid with a maximum bar spacing of 35mm. 

Additional technical measures are in place for vessels targeting Nephrops  using bottom trawls or 
seines in EU  waters  of  the West  of Scotland, Irish Sea and parts  of the Celtic Sea. The  survival 
exemption to the landing obligation for Nephrops in sub-area 7 is linked to the use of the Irish Sea 
and Celtic Sea technical measures which effectively widens their application. Of these measures, a 
300mm square mesh panel is most commonly used. 

Remedial measures have been implemented in parts of the Celtic Sea to ensure the rapid return of 
cod and whiting stocks to levels capable of producing MSY. These gears include:  

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1.  110mm cod-end with 120mm square-mesh panel 
2.  100mm T90 cod-end 
3.  120mm cod-end 
4.  100mm with 160mm square-mesh panel 

In addition to the above measures, a raised fishing line (1m spacing between the fishing line and 
ground  gear)  has  been  introduced  to  reduce  catches  of  cod,  and  must  be  used  by  vessels  with 
catches consisting of at least 20% of haddock.  

The  majority  of  the  selectivity  studies  carried  out  on  the  above  gears  have  used  the  catch 
comparison  method  as  it  is  an  accepted  method  of  demonstrating  equivalent  selectivity  with 
existing gears, a requirement for the implementation of additional technical measures in the TMR. 
This method does not provide estimates of Lg50 and SRg for each gear. 

STECF PLEN 20-01 provided estimates of the Lg50 and SRg of the above gears using a model of 
selectivity developed by Madsen & Ferro (2003). The results of the analyses with the raised fishing 
line are included in Annex 1. When considering these results, EWG 22-19 notes the following caveat 
from  STECF  PLEN  20-01:  STECF  claimed  that  most  of  the  analyses  presented  below  have  been 
performed during the limited time of the written procedure and with only limited preparatory work, 
and cannot thus cover all aspects of relevance for the request. STECF underlined that more careful 
and in-depth analyses would need to be performed in the frame of dedicated research studies. 

BIM  in  Ireland  have  extensively  tested  a  range  of  codend  and  square  mesh  panel  mesh  size 
combinations and the resulting Lg50s and SRgs are included in the table.  

4.4.2.1 STOCK SPECIFIC SELECTIVITY – COD.27.7E-K 

Since  2019  the  TAC  for  this  stock  has  been  for  bycatches  only.  The  majority  of  catches  are 
attributed to otter trawl fleets (ICES, 2022a). Under the TMR a wide range of technical measure 
options are in place for bottom trawlers and depend on catch composition. Otter board fleets in this 
area typically target either fish or Nephrops with a bycatch of cod. 

Under  the  TMR  the  baseline  mesh  size  for  bottom  trawls  and  seiners  in  ICES  divisions  7b-7k  is 
100mm but within the Celtic Sea Protection Zone remedial measures have been implemented to 
protect whiting and cod: 

1.  110mm cod-end with 120mm square-mesh panel 
2.  100mm T90 cod-end 
3.  120mm cod-end 
4.  100mm with 160mm square-mesh panel 

An additional cod protection measure is in place for bottom trawlers. Vessels with catches consisting 
of at least 20% haddock are required to use a raised fishing line, consisting of 1m spacing between 
the fishing line and ground gear of the trawl.  

The  TMR  includes  a  derogation  from  the  above  measures  for  bottom  trawlers  and  seiners  with 
catches consisting of at least 55 % of either whiting or a combination of hake, monk and megrim:  

  100mm cod-end with a 100mm square mesh panel;  
  T90 100mm cod-end and extension. 

The codend mesh size and square mesh panel combinations above are highly selective for whiting. 
The raised fishing line is more selective for larger, faster growing cod.  

For vessels targeting Nephrops, with catches consisting of more than 30 %, a derogation to the 
100mm baseline is also in place within the Celtic Sea Protection Zone: 

  300mm squared mesh panel; vessels below 12 meters in length over all may use a 

200mm square mesh panel; 

  SELTRA panel; 
  Sorting grid with a 35mm bar or a similar Netgrid selectivity device; 
  100mm cod-end with a 100mm square mesh panel; 

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  dual cod-end with the uppermost cod-end constructed with T90 mesh of at least 90mm 

and fitted with a separation panel with a maximum mesh size of 300mm. 

Use  of the above gears is a condition of the survival exemption for Nephrops in sub-area 7  and 
their use is widespread across 7b-7k. The most commonly used gear is likely to be an 80mm codend 
with 300mm square mesh panel as a square mesh panel has been required in the fishery for some 
time.  

Trials  conducted  by  BIM  in  the  Irish  Sea  during  2017  compared  catches  from  a  300mm  square 
mesh panel with those from a SELTRA 300, fitted with a 300mm square mesh panel closer to the 
codline, and found that the SELTRA reduced cod catches by 81 % (BIM, 2017). Reductions in cod 
catches  from  this  study  are  in  line  with  those  of  Madsen  &  Valentinsson  (2010)  who  tested  the 
SELTRA 300 in the Kattegat and Skagerrak using the covered codend method. The sorting grid with 
35mm  bar  spacing  reduces  catches  of  cod  too  large  to  pass  through  the  bars  but  losses  of 
commercial catches of Nephrops have been reported, as well as handling difficulties.  

4.4.2.2 STOCK SPECIFIC SELECTIVITY – WHG.27.7A 

ICES advises that the majority of Irish Sea whiting (98 %) are bycatches in the Nephrops fishery 
and are below MCRS (27 cm) (ICES, 2021). Under the TMR the baseline mesh size in the Irish Sea 
is 120mm but vessels with catches comprising > 30 % Nephrops and fishing with more than one 
trawl must use a codend mesh size of at least 80mm combined with additional technical measures 
to reduce catches of finfish: 

  300mm square mesh panel; vessels below 12 meters in length over all may use a 200mm 

squared mesh panel; 

  SELTRA panel; 
  Sorting grid with 35mm bar spacing; 
  CEFAS Netgrid; 
  Flip-flap trawl. 

The majority of whiting catches in the Irish Sea are below MCRS with a large component of  the 
stock  measuring  <  20  cm.  BIM  and  the  Irish  Fishing  Industry  have  tested  a  range  of  gear 
modifications aimed at reducing catches of whiting, a major potential choke species in the Irish Sea 
Nephrops  fishery.  Gears  such  as  the  300mm  square  mesh  panel  and  SELTRA  achieve  large 
reductions  in  catches  of  whiting  and  haddock  but  are  ineffective  for  whiting  <  20  cm  which  can 
form a major component of the catch (Browne et al., 2018). The Swedish grid can be effective in 
this regard but is also associated with handling difficulties (Graham & Fryer, 2006) and losses of 
marketable  Nephrops.  Increasing  codend  mesh  size  from  80  to  90mm  is  effective  at  reducing 
catches of whiting < 20 cm but with losses of marketable Nephrops.  

Increasing  codend  mesh  size  in  the  Irish  Sea  Nephrops  fishery  may  not  optimise  exploitation 
patterns, protect juveniles and help achieve MSY for whiting as Sangster et al., (1996) found that 
most whiting and haddock < 20 cm died following escape through codends with mesh sizes ranging 
between 70 and 110mm. They  found that  survival of  smaller codend escapees was much worse 
than for larger whiting (or haddock) and suggest that survival may be a complex function of fish 
length. 

Measures which permit escapement of < 20 cm whiting earlier on in the capture process are likely 
to  improve  survival  rates.  BIM  have  recently  developed  and  tested  modified  multi-rig  sweep 
configurations with some success for larger fish and elasmobranchs but not small whiting. Northern 
Irish work in the same fishery has focused on modifying the forward part of twin-rigged Nephrops 
trawls by removing the cover which has successfully reduced catches of < 20 cm whiting.  

4.4.3  SOUTH WESTERN WATERS (ANNEX VII OF TMR) 

For towed gears (trawl and seines) in South Western Waters (SWW) (i.e., ICES sub-areas 8, 9 and 
10 (Union waters) and CECAF zones (24) 34.1.1, 34.1.2 and 34.2.0 (Union waters), the baseline 
mesh  size  according  to  Annex  VII  is  at  least  70mm  (at  least  55mm  in  ICES  division  9a  east  of 

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longitude  7°23′  48″  W).  Baseline  mesh  size  for  static  nets  is  100mm  or  at  least  80mm  in  ICES 
division 8c and ICES sub-area 9.  

None  of  the  participating  experts  at  the  EWG  22-19  meeting  had  in  depth  knowledge  of  gear 
selectivity trials in SWW fisheries. Therefore the completeness of the list of studies listed in Annex 
1 is probably the lowest among the regions.  

In short and with regards to trawls, the selectivity estimates for 55mm and 70mm diamond mesh 
trawls have been selected and compared to the estimates for 70mm T90 and 80mm diamond mesh 
codends. For gillnets, of the analysis for hake in divisions in 8 a,b,d, the selectivity data from NWW 
for 100mm was selected and compared against the selectivity information for 120mm and 140mm.  
For  division  8c  and  sub-area  9,  the  selectivity  data  for  80mm  and  90mm  from  selectivity 
experiments in 9a were used, along with the estimates for 100mm from NWW. There is only very 
limited  selectivity  data  for  hake  caught  with  longlines,  emanating  from  one  trial  dating  back  to 
2001. As there is no current legislation for hook size in the fishery, the largest and smallest hook 
sizes have been selected for the analysis. 

4.4.4  BALTIC SEA (ANNEX VIII OF TMR) 

Baltic Sea (BS) here covers union waters in ICES divisions 3b, 3c and 3d. Annex VIII of the TMR 
specifies that the baseline mesh size for towed gears in the Baltic region is 120mm T90 or at least 
105mm  fitted  with  a  Bacoma  exit  window  of  120mm.  The  baseline  mesh  size  for  static  nets  is 
110mm. Smaller mesh  sizes are allowed in certain directed fisheries  if by-catches  of  cod do not 
exceed 10% of the total catch weight. 

The TAC for both Baltic cod  stocks are  for bycatches only,  since 2019 for the  eastern and  since 
2022 for the western stock. This has meant that cod catches and bottom trawl effort have shown 
major reductions in recent years (ICES, 2022b; ICES, 2022c). Nowadays Baltic cod is caught as 
bycatch in fisheries for flatfish species (mainly flounder, plaice and sole).  

Overall, selectivity in Baltic Sea cod fisheries is well studied. EWG 22-19 identified and used two 
reviews as the main sources of information to populate Annex 1. These reviews summarise most 
of the central work that has been done over the years: Madsen (2007) reviewed and summarised 
studies between 1970s-mid 2000s, while Stepputtis et al. (2020), and references therein, provided 
information about most of the work done since the Madsen review. The list of gears includes the 
two baseline gears (120mm T90 codend and 105mm codend with a 120mm Bacoma square mesh 
window)  and  various  codend  alternatives  with  improved  larger  mesh  sizes  of  alternative  mesh 
configurations (based on trials or modelled results).  

EWG 22-19 is aware that the regional group of member states for the Baltic Sea (Baltfish) in 2021 
submitted a joint recommendation (JR) that proposed to replace the TMR baseline trawls with three 
trawl alternatives that the JR claimed were better suited for targeting flatfish and with reduced cod 
bycatch rates (scientifically underpinned by Stepputtis et al., 2020). The proposed gear alternatives 
are a 125mm square mesh codend, a two panel 125mm T90 codend with stabilizing lastridge ropes 
and  a  composite  gear  design  called  Nemos  and  Roofless.  STECF  PLEN  21-03  reviewed  these 
alternatives with focus on equivalence of the proposed gears compared to the TMR baseline gears 
and not absolute gear selectivity per se (the focus on equivalence follows TMR art 15 about regional 
technical  measures).  Most  focus  in  Stepputtis  et  al.  (2020)  was  therefore  on  catch  comparison 
results and not on absolute selectivity. Hence, gear selectivity estimates were not available and the 
three proposed alternative trawls from the Baltfish JR were not included in Annex 1. 

4.4.5  MEDITERRANEAN SEA (ANNEX IX OF TMR) 

The Mediterranean Sea is here the waters of the Mediterranean to the East of line 5°36′ W. Annex 
IX of the TMR specifies that the baseline mesh size for towed gears in the Mediterranean Sea is 
40mm square mesh codend (or 50mm diamond mesh codend). The baseline mesh size for static 
nets is 16mm. Also, the mesh size of surrounding nets, purse seines and hook numbers for long 
lines are specified in Annex IX of the TMR. 

EWG 22-19 recognised that according to Annex XIV of the TMR, species for selectivity performance 
indicators in the Mediterranean fishing zones are red mullet and hake. Following the relevant work 

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of EWG 21-07 and ToR 1 of the current report, highest priority was assigned to identify and compile 
selectivity  information  about  Mediterranean  hake  stocks.  Due  to  their  widespread  occurrence 
among different depths and habitats, hake stocks are mainly exploited by bottom trawls, bottom-
set gillnets and bottom-set longlines.  

The group noted that in the Mediterranean Sea most of the fisheries are multispecific and target 
more than one species in one single fishing operation/trip. The most important gears in the region, 
in  terms  of  landings,  are  'towed  gears'  (e.g.  trawls,  beam  trawls,  dredges)  where  a  range  of 
different  mesh  sizes  are  used  in  order  to  optimize  and  adapt  the  gear  to  different  biological 
characteristics  and  behaviour  of  targeted  marine  organisms.  Static  fishing  gears,  such  as  nets, 
longlines, traps, exhibit lower landings compared to towed gears. The mesh sizes and hook sizes 
used  in  different  gears  are  generally  designed  to  catch  the  most  valuable  species  in  the  fishery 
(e.g. Nephrops, hake, shrimps, cephalopods, flatfish, etc.).  

The current review on studies/selectivity estimates (summarised in Annex 1) was mostly based on 
reviews in recent projects (e.g., IMPLEMED) or publications (Lucchetti et al., 2020; 2021; Sbrana, 
2021). An attempt was made to identify parameters for all hake stocks in the Mediterranean Sea. 
When  more  than  one  selectivity  study  was  available,  the  most  recent  study  was  chosen. 
Unfortunately, hake selectivity data are not available for all Mediterranean GSAs. In cases where 
no selectivity data of a hake stock appears in the table, data from a nearby stock or region can be 
used as best estimate. Depending on the experimental design adopted, some studies were excluded 
because  they  were  based  on  catch  comparison  analysis  and  then  it  was  not  possible  to  obtain 
selectivity parameters. 

EWG 22-19 notes that more favourable exploitation and selectivity pattern of the hake stocks could 
be obtained by use of passive/static fishing gears (Annex 1), which appear to have  lower impact 
on the seabed compared to  towed gears (e.g. see EWG 22-12 report; STECF, 2022). The group 
also notes that the otter trawl fishery is one of the most important fisheries in the Mediterranean 
and  a  potential  gear  change  to  passive  gears  such  as  gillnets  or  long  lines  will  have  significant 
socio-economic  and  ecological  consequences.  Therefore,  most  of  the  research  effort  has  been 
focusing to improve trawl fisheries by changing/increasing trawl codend mesh sizes. This may lead 
to  improvement  of  selectivity  pattern  of  hake  stock  exploitation  with  the  aim  to  advice  on  new 
possible technical measures. Considering that all assessed hake  stocks in the Mediterranean  are 
overexploited  (in  terms  of  SSB)  and  that  overexploitation  (F  above  FMSY)  is  taking  place  (GFCM, 
2022), changes in trawl codend mesh  sizes and/or type may  contribute to reducing current  F in 
hake stocks. However, a whole set of tools, mainly awareness campaigns and economic incentives 
towards more selective fishing (e.g., selective licensing, eco-labelling) are totally absent or have 
very limited application in the Mediterranean Sea. 

Regarding the bottom trawl regulation with 40mm square mesh codend (40SM), one aspect that 
could be of interest for further investigations is the influence of the codend length (which is currently 
not  specified  in  the  legislation).  Literature  data  report  many  investigations  on  the  codend 
circumference  and  twine  tickness  (Sala  &  Lucchetti,  2010;  2011;  Sala  et  al.,  2016;  2018),  but 
probably no research has been conducted on the influence of codend length on selectivity and even 
current legislation misses a clear specification of the codend section characteristics and its minimum 
allowed length. EWG 22-19 considers that the lack of a minimum codend length in Regulation (EU) 
2019/1241 may undermine the intentions of the current legislation. This is due to current legislation 
still allowing fishers to use 40mm diamond meshes in the rear section of the extension piece; then, 
if  the  minimum  40SM  codend  length is  not  regulated,  the  use  of  short  codends,  can  deteriorate 
selectivity to levels like that in Council Regulation (EC) No 1626/94, since the rear section of the 
extension piece could effectively work as codend. 

4.5 

Case studies of NE Atlantic priority stocks (ToR 1 & ToR 2) 

Below, six case studies of the priority stocks from the NE Atlantic are presented. Each case study 
looks  at  an  individual  stock,  detailing  fleet  characteristics,  landings  and  discards,  stock  status, 
selectivity studies and the outcomes of the selectivity scenarios in terms of age and length. As in 
Chapter 4.2.1‚ ‘current’ F and selectivity refer to the average of the last three years available from 
the assessments (2018-2020). 

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4.5.1  COD (GADUS MORHUA) IN SUBDIVISIONS 22–24 (WESTERN BALTIC) (COD.27.22-24) 

Western Baltic cod is exploited by a mixed commercial–recreational fishery. Traditionally however, 
commercial fisheries largely dominated the catches. Trawlers have been responsible for the main 
landings  of  cod  in  the  western  Baltic  (63%),  followed  by  gillnetters  (37%)  (ICES,  2021a).  As  a 
consequence  of  reduced  commercial  fishing  opportunities,  the  proportion  of  the  stock  fished  by 
recreational fisheries has increased. In 2020, recreational catches constituted 1,311 tonnes, 46% 
of the total catches, and were included in the stock assessment (ICES, 2021a). The commercial 
fishery targeting cod has changed substantially in the latest years: from being the main targeted 
species in the demersal fishery, directed fishing for cod is banned since 2021 and cod is nowadays 
a  bycatch  species  in  fisheries  targeting  flatfish.  The  stock  is  mainly  fished  by  Denmark  and 
Germany, with smaller amounts caught by Sweden and Poland.  

The total commercial landings for human consumption in the management area  sudivisions (SD) 
22-24 (including the eastern Baltic stock component) in 2020 were 3,329 t (including BMS), with a 
quota utilization of 83% (4,000 t). In the last 10 years, slightly more than half of the total western 
Baltic area landings have been fished in SD 24, in 2019 this changed and was 20% in 2020. This 
change  is  due  to  a  management  regulation  in  place  since  mid-2019,  prohibiting  a  directed  cod 
fishery in SD 24 where the western cod stock intermixes with the eastern Baltic cod stock (ICES, 
2021a).  

Discard data from at-sea observer programs are available from Germany, Sweden and Denmark 
for SD 22–24. The discard rate in 2020 was estimated to be 5% (ICES, 2021a).  

Based on the assessment of 2021, fishing pressure on the stock is above FMSY and between Fpa and 
Flim; spawning-stock biomass is below MSYBtrigger, Bpa and Blim (ICES, 2021a) (Fig. 4.5.1.1). 

The fishing mortality estimated from the assessment has remained high and the SSB considerably 
lower than was predicted. Such a pattern suggests that processes other than fisheries and assumed 
natural  mortality  are  affecting  the  SSB  of  the  western  Baltic  cod  stock.  The  sources  for  the 
presumably additional unaccounted mortality are presently unclear but could involve e.g. increased 
natural  mortality  (due  to  increased  predation,  hypoxia,  decreased  condition,  increased  water 
temperatures)  and  unreported  catches.  The  stock  is  currently  exploited  as  bycatch  in  targeted 
fisheries for other species (mainly flatfish). 

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Figure  4.5.1.1  Cod  in  subdivisions  22–24,  western  Baltic  stock  (cod.27.22-24).  Summary  of  the  stock 
assessment (ICES, 2021a). 

The selectivity analysis indicates that the western Baltic cod stock may produce higher equilibrium 
yields under current F by shifting A50 by 3.3 y or L50 by 39.7 cm (Fig. 4.5.1.2; Table 4.2.1.1). 

Figure 4.5.1.2 Cod in subdivisions 22–24, western Baltic stock(cod.27.22-24). Fitted ‘Current’ and ‘Optimal’ 
(shifted)  selectivity  by  age  (left)  and  by length  (right)  under  current  F,  plotted  together  with  the  maturity 
ogive. Points represent the observed selectivity. 

Of  the  fleets  that  exploit  the  stock,  OTB  exhibits  the  lowest  selectivity  by  selecting  fish  with  an 
average size of 36.5 cm, i.e. 43.9 cm smaller than the optimal size. By contrast, gillnets and other 
passive  gears  exhibit  higher  selectivity  by  selecting  fish  with  an  average  size  of  around  46  cm, 
which is still 34 cm lower that the optimal size (Fig. 4.5.1.3; Table 4.2.1.2). 

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Figure 4.5.1.3 Cod in subdivisions 22–24, western Baltic stock (cod.27.22-24). Fitted ‘Current’ and ‘Optimal’ 
(shifted) selectivity by length and fitted selectivity by  fleet segment. GNS: Set gillnets; OTB:  bottom otter 
trawls; Passive: other passive gear. 

The gear selectivity parameters for the two current baseline codends (Lg50 = 42.3 cm, SRg = 6.7 
cm for the 120mm T90 alternative and Lg50 = 38.7 cm, SRg = 8.0 cm for the 105mm with 120mm 
Bacoma  alternative;  Annex 1) were derived  from Wienbeck  et al. (2014), Herrmann (2008) and 
Herrmann et al. (2009). A more selective alternative codend corresponding to a full square mesh 
codend of 140mm from Steputtis et al. (2020) was also considered (Lg50 = 50.0 cm, SRg = 7.2 
cm).  Gear  selectivity information for GNS were found in the literature  but EWG 22-19 could not 
reproduce a selectivity function from the information provided there. 

Although the T90 baseline according to the literature appears to exhibit somewhat better selectivity 
than the Bacoma baseline, both baseline trawl alternatives exhibit  an Lg50 far lower (38-41 cm) 
than the optimal L50. The 140mm square mesh modification is an alternative with improved cod 
size selectivity, but it would still be likely to catch fish on average 30 cm smaller than the optimal 
size for this stock (Fig. 4.5.1.4, Annex 1). 

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Figure  4.5.1.4  Cod  in  subdivisions  22–24,  western  Baltic  stock  (cod.27.22-24).  Fitted  ‘Optimal’  (shifted) 
selectivity by length, fitted selectivity by the OTB fleet segment (‘OTB’), gear selectivity curves for the baseline 
120 mm T90 (‘T90 baseline’), the baseline 105 mm with 120 mm square mesh window (‘Bacoma baseline’) 
and a more selective alternative: 140 mm square mesh codend (‘square mesh 140mm’). 

Selectivity analysis under varying F, confirms that lowering F would allow selectivity improvements 
to  be  more  effective  in  taking  the  stock  closer  to  MSY  (Fig.  4.5.1.5).  When  comparing  current 
selectivity with the gear selectivity of the baseline and improved codend (140mm square mesh), it 
can be inferred that under current Fapical the stock would still lie far from the highest equilibrium 
yields  with  either  of  the  gears  examined,  although  the  140mm  square  mesh  would  double  both 
yield and SSB per recruit. Close-to-optimal yields (90% of maximum) would be extracted by shifting 
L50 to ca. 62 cm, while fishing at a Fapex ca. 1/3 of current (Fig. 4.5.1.5). However, there is the 
caveat of isopleths and current selectivity referring to population selectivity, which is different than 
gear  selectivity,  as  the  former  is  the  result  of  all  gears  acting  on  the  fishery,  while  also  being 
affected by availability (see Chapter 2.1).  

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Figure  4.5.1.5  Isopleths by length for western  Baltic  cod  (cod.27.22-24). Equilibrium  yield (left) and SSB 
(right) under varying (‘shifting’) length-at-50%-selectivity (L50; cm) and fishing mortality (Fapical). Plotted are 
the current L50 and Fapical (•), the Lg50 of the 105/120 mm Bacoma codend baseline (◊), the Lg50 of the 120 
mm  T90 baseline (□) and  for a more selective codend  alternative: 140  mm  square  mesh codend (x).  Also 
plotted is the combination of lowest L50 and Fapical producing an equilibrium MSY at 90% of the maximum (*). 

4.5.2  COD (GADUS MORHUA) IN SUBAREA 4, DIVISION 7.D, AND SUBDIVISION 20 (NORTH SEA, 

EASTERN ENGLISH CHANNEL, SKAGERRAK) (COD.27.47D20) 

Landings  data  reported  to  ICES  show  that  this  stock  is  predominantly  landed  by  demersal  otter 
trawlers and seine netters utilising nets with mesh >100m (75%), followed by gillnets (10.5%), 
demersal trawlers using mesh 70 – 99mm (5.5%), beam trawls (4.5%), and other gears accounting 
for the smallest fraction (4.5%) (ICES, 2021b). All countries around the greater North Sea exploit 
this stock (ICES, 2021b) but largest catches are taken by UK, Denmark and Norway. 

Recreational catches are estimated to account for 3.6–8.9% of total removals between 2010–2019, 
but values are provisional and not included in the assessment due to unknown age structure and 
uncertainty (ICES, 2021b). 

Total  landings  for  cod  in  Subarea  4,  Division  7.d,  and  Subdivision  20  have  varied  around  25-35 
thousand tonnes during the period 2009 to 2018 and decreased markedly in 2020 and 2021. In 
2020  discards  were  estimated  to  be  4,701  tonnes,  which  corresponds  to  a  discard  rate  of  19% 
(ICES, 2021b). 

Fishing pressure  on the stock is above FMSY but below Fpa and Flim; spawning-stock  size is below 
MSYBtrigger,  Bpa,  and  Blim  (Fig.  4.5.2.1)  (ICES,  2021b).  The  highest  abundances  of  recruits  are 
normally found in the Skagerrak and Kattegat with another hotspot of recruitment appearing east 
of Scotland over the last 10 years.  

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Figure 4.5.2.1 Cod in Subarea 4, Division 7.d, and Subdivision 20 (cod.27.47d20). Summary of the stock 
assessment (ICES, 2021b). 

The selectivity analysis indicates that the North Sea cod stock may produce the highest equilibrium 
yields under current F by shifting A50 by 2.5 y or L50 by 36.4 cm (Fig. 4.5.2.2; Table 4.2.1.1). 

Figure  4.5.2.2  Cod  in  Subarea  4,  Division  7.d,  and  Subdivision  20  (cod.27.47d20).  Fitted  ‘Current’  and 
‘Optimal’  (shifted)  selectivity  by  age  (left)  and  by  length  (right)  under  current  F  plotted  together  with  the 
maturity ogive. Points represent the observed selectivity. 

Of the fleet segments exploiting the stock, TBB exhibits the worst selectivity followed by OTB. These 
fleets select fish on average 42.5 cm and 37.1 cm, respectively, smaller than the optimal size (Fig. 

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4.5.2.3; Table 4.2.1.2). GNS is the fleet with highest average selectivity, exhibiting an average L50 
of 45.3 cm, i.e. still 27.6 cm lower than optimal size. 

Figure  4.5.2.3  Cod  in  Subarea  4,  Division  7.d,  and  Subdivision  20  (cod.27.47d20).  Fitted  ‘Current’  and 
‘Optimal’  (shifted)  selectivity  by  length  and  fitted  selectivity  by  fleet  segment.  GNS:  Set  gillnets;  LLS:  Set 
longlines; OTB: bottom otter trawls; SEI: seine nets 

The gear selectivity parameters for the two baseline codends (Lg50 = 39.4 cm, SRg = 8.4 cm for 
the Subarea 4 baseline [120mm codend] and Lg50 = 33.8 cm, SRg= 39.1 cm for the subdivision 20 
(Skagerrak) baseline [90mm with a 270mm panel]; Annex 1) were derived from Madsen & Ferro 
(2003), STECF PLEN 20-01 and from Krag et al. 2016. Due to the lack of studies of trawl/codend 
designs for the stock with selectivity parameters closer to the optimal size for North Sea cod (72.9 
cm), two more selective codend alternatives were obtained and used in the analysis: a 145mm T90 
codend (T90_145mm) (Lg50 = 55.1 cm, SRg = 12.1 cm) and a 155mm codend (Lg50 = 61.3 cm, 
SRg = 8.7 cm). These results stem  from trials in the Barents Sea by  Brinkhof  et al. (2022)  and 
Sistiaga et al. (2009). 

The estimated current selectivity of the OTB segment is somewhat lower than the gear selectivity 
of the Subarea 4 120mm baseline gear alone (Fig. 4.5.2.4). EWG 22-19 consider that this can be 
partly explained by the lower selectivity of the subdivision 20 (Skagerrak) baseline gear (90mm 
with 270 mmwindow) and the by the fact that cod is also caught as bycatch in trawls with smaller 
mesh  sizes  targeting  other  species  (TR2  trawls  for  e.g.  Nephrops,  whiting  etc.).  Both  baseline 
codends exhibit selectivity far lower than the optimum, with Lg50s 34 and 39 cm lower than the 
optimal L50 for the North Sea and the Skagerrak baseline respectively. Noticeable is that the two 
more  selective  codend  alternatives  (T90_145mm  and  155mm)  would  bring  trawl  selectivity 
markedly closer to the optimal selectivity (Fig. 4.5.2.4, Annex 1). 

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Figure 4.5.2.4 Cod in Subarea 4, Division 7.d, and Subdivision 20 (cod.27.47d20). Fitted ‘Optimal’ (shifted) 
selectivity by length (‘Optimal’), fitted selectivity by the OTB fleet segment (‘OTB’), gear selectivity curves for 
the  baseline  120mm  (‘120mm_baseline’),  the  Skagerrak  baseline  90mm  with  270mm  window 
(‘90/270_baseline’) and two more selective alternatives: 155mm codend (‘155mm’) and 145mm T90 codend 
(‘T90_145mm’). 

Selectivity analysis under varying F, confirms that lowering F would allow selectivity improvements 
to  be  more  effective  in  taking  the  stock  closer  to  MSY  (Fig.  4.5.2.5).  When  comparing  current 
selectivity  with  the  gear  selectivity  of  the  baseline  and  improved  codends  (155mm  and  145mm 
T90), it can be inferred that under current Fapical the stock would be close to the highest equilibrium 
yields with the improved gears examined (especially the 155mm codend). Close-to-optimal yields 
(90% of maximum) would be extracted by shifting L50 to ca. 63 cm, while fishing at a Fapical around 
0.5  (Fig.  4.5.2.5).  However,  there  is  the  caveat  of  isopleths  and  current  selectivity  referring  to 
population selectivity, which is different than gear selectivity, as the former is the result of all gears 
acting on the fishery, while also being affected by availability (see Chapter 2.1).  

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Figure 4.5.2.5 Isopleths by length for North Sea cod (cod.27.47d20). Equilibrium yield (left) and SSB (right) 
under varying (‘shifting’) length-at-50%-selectivity (L50; cm) and fishing mortality (Fapical). Plotted are  the 
current L50 and Fapical (•), the L50 of the Skagerrak 90/270 mm baseline (◊), the L50 of the North Sea 120 
mm baseline (□) and for two more selective codend alternatives: 155 mm (x) and 145 mm T90 codend (+) 
and the combination of lowest L50 and Fapical producing an equilibrium MSY at 90% of the maximum (*). 

4.5.3  COD (GADUS MORHUA) IN DIVISION 6.A (WEST OF SCOTLAND) (COD.27.6A) 

This  stock  receives  bi-annual  advice,  therefore  this  advice  and  assessment  used  in  this  analysis 
and referenced in this summary was completed in 2020 (ICES, 2020). Since 2012 there has been 
no directed fishery for cod in ICES division 6.a, with ICES advice of zero catch and a minimisation 
of bycatch and discards since 2003.  

Landings  are  from  bycatch  in  the  mixed  bottom  trawl  fishery  targeting  haddock,  saithe,  hake, 
anglerfish and megrim. The majority of the landings are taken by trawls targeting demersal finfish 
(92%), with those targeting Nephrops accounting for < 1%, gillnets < 2%, and other fishing gears 
accounting for around 4%. Area-misreported landings are a feature of this fishery (ICES, 2020). 
Estimated  area-misreported  landings  (catches  taken  in  Division  6.a,  but  reported  elsewhere) 
account for over 40% of the total landings in recent years (average percentage 2017–2019). ICES 
has noted that measures to reduce area misreporting should be introduced (ICES, 2020). 

Discarding has been considered high since 2007, with a peak of 3,867 tonnes in 2011. However, in 
2019,  there  was  a  significant  decrease  in  the  proportion  of  discards  due  to  an  increase  in  TAC 
compared to recent years. For the period of 2016-2018 the average discard rate of cod was 75%. 
However, in 2019 the discard rate rapidly decreased to 9% (ICES, 2020) 

Fishing pressure on the stock is above FMSY, Fpa and Flim; spawning-stock size is below MSYBtrigger, 
Bpa, and Blim (ICES, 2020). Stock structure remains an issue for cod in Division 6.a. The issues of 
multiple  stocks  in  Division  6.a  and  connectivity  with  the  North  Sea  stock  remain  sources  of 
uncertainty (ICES, 2020). Management measures taken so far have not resulted in a recovery of 
the stock. Even though fishing mortality declined between 2009 and 2016, it has shown an increase 
since.  It  is  not  known  whether,  and  to  what  extent  this  increase  is  associated  with  the 
discontinuation  of  the  days-at-sea  regulation  in  2017,  which  was  part  of  the  cod  recovery  plan 
(ICES, 2020). 

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Figure 4.5.3.1 Cod in Division 6.a (West of Scotland) (cod.27.6a). Summary of the stock assessment (ICES, 
2020). 

The  selectivity  analysis  indicates  that  the  West  of  Scotland  cod  stock  may  produce  the  highest 
equilibrium  yields  under  current  F  by  cranking  A50  by  1.6  y  or  L50  by  24  cm.  Almost  as  high 
equilibrium yield and maximum protection of juveniles can be achieved by shifting A50 by 1.8 y or 
L50 by 26.3 cm (Fig. 4.5.3.2; Table 4.2.1.1). 

Figure 4.5.3.2 Cod in Division 6.a (West of Scotland) (cod.27.6a). Fitted ‘Current’ and ‘Optimal’ (shifted and 
cranked) selectivity by age (left) and by length (right) under  current  F,  plotted together with  the  maturity 
ogive. Points represent the observed selectivity. 

The selectivity of the OTB fleet is very similar to the total selectivity of all fleets (‘Current’). Both 
are however far lower than the optimal selectivity (Fig. 4.5.3.3; Table 4.2.1.2). 

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Figure 4.5.3.3 Cod in Division 6.a (West of Scotland) (cod.27.6a). Fitted ‘Current’ and ‘Optimal’ (shifted and 
cranked) selectivity by length and fitted selectivity for the OTB fleet segment. 

Figure  4.5.3.4  Cod  in  Division  6.a  (West  of  Scotland)  (cod.27.6a).  Fitted  ‘Optimal’  selectivity  by  length 
(‘Optimal shift’ and ‘Optimal crank’), fitted selectivity by the OTB fleet segment (‘OTB’), gear selectivity curves 
for the baseline 120mm (‘120mm_baseline’), 120mm in a trawl with 1m raised fishing line (‘120_RFL’) and a 
more selective alternative: 145mm T90 codend (‘T90_145mm’). 

The gear selectivity parameters of the baseline codend (Lg50 = 39.4 cm, SRg = 8.4 cm; Annex 1) 
was derived from Madsen and Ferro (2003),  and the parameters of the 120mm codend with 1m 
raised fishing line (Lg50 = 42 cm, SRg = 9 cm; Annex 1) is from STECF PLEN 20-01. Like for the 
North  Sea  cod  case  study,  there  is  a  lack  of  studies  of  trawl/codend  designs  for  the  stock  with 
selectivity parameters closer to the optimal size for the  West  of Scotland cod (66–68 cm;  Table 
4.2.1.1). Therefore, a more selective codend from a gear trial in adjacent waters was used in the 

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analysis: 145mm T90 codend (T90_145mm) with Lg50 = 55.1 cm, SRg = 12.1 cm). These results 
stem from gear experiments in the Barents Sea by Brinkhof et al. (2022). 

Selectivity analysis under varying F, confirms that lowering F would allow selectivity improvements 
to  be  more  effective  in  taking  the  stock  closer  to  MSY  (Fig.  4.5.3.5).  When  comparing  current 
selectivity with the gear selectivity of the improved codend, it can be inferred that under current 
Fapical  the  stock  would  come  close  to  the  highest  equilibrium  yields  with  the  145mm  T90  codend 
(Fig. 4.5.3.5). Close-to-optimal yields (90% of maximum) would be extracted by shifting L50 to 
ca. 50 cm, while more than halving the Fapical compared to current (Fig. 4.5.3.5). However, there is 
the caveat of isopleths and current selectivity referring to population selectivity, which is different 
than gear selectivity, as the former is the result of all gears acting on the fishery, while also being 
affected by availability (see Chapter 2.1).  

Figure  4.5.3.5  Isopleths  by  length  for  West  of  Scotland  cod  (cod.27.6a).  Equilibrium  yield  (left)  and  SSB 
(right) under varying (‘shifting’) length-at-50%-selectivity (L50; cm) and fishing mortality (Fapical). Plotted are 
the current L50 and Fapical (•), the L50 of the 120 mm baseline codend (◊), the L50 of 120 mm with 1 m raised 
fishing line (+) and for a more selective codend alternative: 145 mm T90 (x). and the combination of lowest 
L50 and Fapical producing an equilibrium MSY at 90% of the maximum (*). 

4.5.4  COD  (GADUS  MORHUA)  IN  DIVISIONS  7.E–K  (WESTERN  ENGLISH  CHANNEL  AND 

SOUTHERN CELTIC SEAS) (COD.27.7E-K) 

Celtic Sea cod is mainly exploited by otter trawlers, seine nets and beam trawlers. Cod is mainly 
caught  in  area  27.7.g,  followed  by  areas  27.7.h,  27.7.e  and  27.7.j.  No  landings  are  reported  in 
27.7.k and few in 27.7.j2 (ICES, 2021c). 

Landings data reported to ICES show that Celtic Sea cod is mainly landed by otter trawlers (71%), 
seine nets (11%), beam trawls (9%), landings of gillnets and longlines accounting for the smallest 
fraction  (<  9%)  (ICES,  2021c).  In  2020  this  stock  was  landed  by  four  countries:  Ireland  (487 
tonnes, 53%), France (371 tonnes, 40%), UK (44 tonnes, 5%), and Belgium (18 tonnes, 2%), with 
other countries landing < 1% (ICES, 2021d). 

Due to the rapid growth of cod in this area, discards are mostly composed of one and two-year old 
fish (ICES, 2021c). Since 2011, quotas were not restrictive, and the discard rate has been stable 
around 10– 15%. However, following the recent TAC reductions, TAC is now restrictive for most 
countries. The discards estimate for 2020 is 231 t, which corresponds  to a discards rate of 20%, 
i.e.  around  the  average  of  recent  years  (ICES,  2021d).  Fleets  discarding  cod  are  otter  trawlers 
(63%), seine nets (10%) and beam trawls (21%). Discards from gillnets and longlines account for 
the smallest fraction (< 6%) (ICES, 2021d). 

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Celtic  Sea  cod  is  currently  assessed  using  SAM  which  is  a  state-space  stock  assessment  model. 
Fishing pressure on the stock is above FMSY, Fpa, and Flim; spawning-stock size is below MSYBtrigger, 
Bpa, and Blim (ICES, 2021d). Mixed-fisheries issues could be responsible for maintaining F at high 
level, as fishing opportunities of other gadoids are higher. In this context, cod is no longer a target 
species  but  can  be  considered  as  bycatch  in  the  fleet  targeting  haddock,  whiting  and  Nephrops 
(ICES, 2021c). 

Figure  4.5.4.1  Cod  in  divisions  7.e–k  (western  English  Channel  and  southern  Celtic  Seas)  (cod.27.7e-k). 
Summary of the stock assessment (ICES, 2021d). 

The selectivity analysis indicates that this stock may produce the highest equilibrium yields under 
current F by shifting A50 by 3.5 y or L50 by 54.7 cm, in which case higher protection of juveniles 
would be also ensured (Fig. 4.5.4.2; Table 4.2.1.1 and Table 4.3.1). 

Partial selectivity by fleet was not available for this stock, so some examples of gear selectivity of 
relevant trawl codends is also plotted in Fig. 4.5.4.2. Gear selectivity parameters corresponding to 
the current baseline codend mesh size (‘100mm_baseline’; Lg50 = 32.7 cm, SRg = 7.0 cm) were 
derived from Madsen and Ferro (2003) (Annex 1). The other three gears plotted in Fig 4.5.4.2 are 
part of the remedial measures for the Celtic Sea cod (see section 4.4.2.1). These gears are 100mm 
T90 (Lg50 = 39 cm, SRg = 7.4 cm), 120mm (Lg50 = 42 cm, SRg = 9 cm) and 110mm with 120mm 
square mesh panel (Lg50 = 44 cm, SRg = 9.2 cm), all mounted in trawls with a 1m raised fishing 
line. The parameters for the three latter gears were all derived from STECF PLEN 20-01. Like for 
the other cod stocks, studies of trawl alternatives with size selectivity closer to the optimum size is 
lacking also for the Celtic Sea cod stock. Due to that Fig. 4.5.4.2 already shows four examples of 
gear selectivity, no codend alternative with a much higher size selectivity is shown here. Examples 
of such more selective trawl codends can be found in the other cod case studies and in Annex 1. 

Current  selectivity  is  slightly  higher  than  the  selectivity  of  the  baseline  gear,  and  around  the 
selectivity of the three  codend alternatives introduced as part of the remedial measures (i.e.  an 
Lg50 slightly above 40 cm, Fig 4.5.4.3), which is however around 55 cm lower than the optimal size 
(Fig 4.5.4.2). 

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Figure  4.5.4.2  Cod  in  divisions  7.e–k  (western  English  Channel  and  southern  Celtic  Seas)  (cod.27.7e-k). 
Fitted ‘Current’ and ‘Optimal’ (shifted) selectivity by age (left) and by length (right) under current F, plotted 
together with the maturity ogive. Points represent the observed selectivity. Also shown on the right are gear 
selectivity  curves  for  the  baseline  100mm  (‘100mm_baseline’)  and  for  three  codend  alternatives  with  1m 
raised fishing line (RFL):  A 100mm T90 alternative (‘T90_100mm_RFL’), a 120mm diamond mesh alternative 
(‘120mm_RFL’) and a 110mm codend with 120 mm square mesh panel (‘110mm_120mmSMP_RFL’). 

Selectivity analysis under varying F, confirms that lowering F would allow selectivity improvements 
to  be  more  effective  in  taking  the  stock  closer  to  MSY  (Fig.  4.5.4.3).  When  comparing  current 
selectivity with the gear selectivity of the baseline and improved trawl/codends, it can be inferred 
that under current Fapical the stock would still lie far from the highest equilibrium yields with either 
of  the  gears  examined  (Fig.  4.5.4.3).  Close-to-optimal  yields  (90%  of  maximum)  would  be 
extracted by shifting L50 to ca. 80 cm, while fishing at a Fapical similar or lower than the current 
(Fig.  4.5.4.3).  However,  there  is  the  caveat  of  isopleths  and  current  selectivity  referring  to 
population selectivity which is different than gear selectivity, as the former is the result of all gears 
acting on the fishery, while also being affected by availability (see Chapter 2.1).  

Figure 4.5.4.3 Isopleths by length for cod in divisions 7.e–k (western English Channel and southern Celtic 
Seas)  (cod.27.7e-k).  Equilibrium  yield  (left)  and  SSB  (right)  under  varying  (‘shifting’)  length-at-50%-
selectivity (L50; cm) and fishing mortality (Fapical). Plotted are the current L50 and Fapical (•), the Lg50s of the 
120mm  baseline  codend  (◊);  the  T90_100mm_RFL  (+)  alternative;  the  120mm_RFL  alternative  (x)  the 
110mm_120mmSMP_RFL alternative (✴), and the combination of L50 and Fapical producing an equilibrium MSY 
at 90% of the maximum (*). 

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4.5.5  WHITING (MERLANGIUS MERLANGUS) IN DIVISION 7.A (IRISH SEA) (WHG.27.7A) 

Landings  data  reported  to  ICES  show  that  Irish  Sea  whiting  is  mainly  landed  by  otter  trawlers 
targeting  demersal  finfish  (94.5%),  some  from  otter  trawlers  targeting  Nephrops  (0.3%),  while 
landings from other gears account for a minor fraction (5.2%) (ICES, 2021e). In 2020 this stock 
was  landed  mainly  by  three  countries:  Ireland  (56  tonnes,  54%),  UK  (42  tonnes,  41%),  and 
Belgium (5 tonnes, 5%), with other countries landing < 1% (ICES, 2021e). 

As reported by ICES (2021f), the majority of whiting catches in the Irish Sea have not been landed 
but discarded in the last couple of decades. These discards are mostly associated with the Nephrops 
directed fleets of Ireland and the UK. Despite recent increased sampling levels, discard information 
remains very imprecise. This has contributed to the highly fluctuating fishing pressure estimates in 
recent years. The main fleets landing whiting are finfish directed fleets from Ireland and Northern 
Ireland. In recent years, landings were submitted for the PTM_SPF metier. These are likely from 
trips targeting herring where whiting was a bycatch. Total discard estimate was 1030 t in 2020, 
resulting in a discards rate of 92% (ICES, 2021e). Otter trawlers targeting Nephrops account for 
98%  of  discards,  therefore  the  effort  in  the  Nephops  fishery  is  the  main  driver  of  discarding  for 
Irish Sea whiting.  

Most  of  the  whiting  caught  are  discards  in  the  Nephrops  fishery  and  are  below  the  minimum 
conservation  reference  size  (MCRS).  The  introduction  of  further  highly  selective  gears  to  reduce 
finfish catch and discards in the Nephrops fishery appears to have reduced whiting catches in the 
last three years. Discard rate however remain high relative to the total catches (ICES, 2021e) 

The Irish Sea whiting is currently assessed using ASAP (Age-Structured Assessment Programme; 
NOAA toolbox) (ICES, 2021f). Fishing pressure on the stock is above FMSY, Fpa, and Flim; spawning-
stock size is below MSYBtrigger, Bpa, and Blim (ICES, 2021e). Mixed-fisheries issues are responsible 
for maintaining F at high level. In this context, whiting in the Irish sea is not considered a target 
species but a bycatch in the fleet targeting Nephrops (ICES, 2021e). 

Figure  4.5.5.1  Whiting in Division 7.a (Irish Sea) (whg.27.7a).  Summary of  the stock assessment  (ICES, 
2021f) 

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The selectivity analysis indicates that this stock may produce the highest equilibrium yields under 
current F by cranking A50 by 0.7 y or L50 by 7.6 cm. Almost as high potential equilibrium yield 
with similar protection of juveniles can be achieved by shifting A50 by 1.2 y or L50 by 12 cm (Fig. 
4.5.5.2; Table 4.2.1.1 and Table 4.3.1). 

Partial  selectivity  by  fleet  was  not  available  for  this  stock,  so  some  examples  of  gear  selectivity 
estimates  of  relevant  trawl  codends  is  also  plotted  in  Fig.  4.5.5.2.  Gear  selectivity  parameters 
corresponding to the current baseline codend mesh size (‘120mm_baseline’; Lg50 = 38.7 cm, SRg 
= 14.5 cm) and an 80mm codend with 120mm square mesh panel, the baseline mesh gear in the 
Nephrops fishery,  i.e. the fishery that contributes to the majority of the discards  (‘80_120SMP’; 
Lg50 = 32.9 cm, SRg = 9.4 cm), are shown. Additional Nephrops measures are in place under the 
TMR but the main gear used in the Nephrops fishery, an 80 mm codend with 300 mm square mesh 
panel, was outside the parameters of the model of Madsen & Ferro (2003). Selectivity parameters 
for both gears were derived from Madsen & Ferro (2003) (Annex 1). 

Current estimated population selectivity (10.6 cm) is lower than the optimal size (18.2-22.6 cm) 
and,  somewhat  surprisingly,  much  lower  than  the  gear  selectivity  of  both  the  120mm_baseline 
codend (38.7 cm) and the 80_120SMP codend (32.9 cm) (Fig. 4.5.5.2, 4.5.5.3). Whiting in 7a is 
smaller at age than in other areas (ICES, 2023), resulting in many small sized fish interacting with 
the commercial fleet which is very much a bycatch fishery as the majority of the stock is taken by 
discards in the Nephrops fishery. The failure of selective gears to improve population selectivity of 
Irish  Sea  whiting  may  be  due  to  the  relatively  small  size  of  whiting  in  the  Irish  Sea  leading  to 
reduced  swimming ability and fatigue or different behaviour, i.e.  staying low rather than exiting 
escape panels like larger whiting. It should be also noted that the gear selectivity parameters of 
the 80_120SMP codend coming from Madsen & Ferro (2003) could be outdated; current population 
selectivity estimates refer to 2018-2020, biological parameters used to estimate optimal selectivity 
come from the 2021 assessment (ICES, 2021e) and von Bertalanffy parameter estimates used for 
the age-to-length conversion refer to 2008-2021 (Table 4.1.1.1). 

Figure  4.5.5.2  Whiting  in  Division  7.a  (Irish  Sea)  (whg.27.7a).  Fitted  ‘Current’  and  ‘Optimal’  (shifted) 
selectivity by age (left) and by length (right) under current F, plotted together with the maturity ogive. Points 
represent the observed selectivity. Also shown on the right are gear selectivity curves for the baseline 120mm 
(‘120mm_baseline’) and for the 80mm codend with 120mm square mesh panel that is the main gear in the 
Nephrops directed fishery (‘80mm_120mmSMP’). 

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Figure 4.5.5.3 Isopleths by length for whiting in Division 7.a (Irish Sea) (whg.27.7a). Equilibrium yield (left) 
and SSB (right) under varying (‘shifting’) length-at-50%-selectivity (L50; cm) and fishing mortality (F apical). 
Plotted are the current L50 and Fapical (•), the Lg50 of 80mm codend with 120mm square mesh panel (◊) and 
the combination of L50 and Fapical producing an equilibrium MSY at 90% of the maximum (*). 

4.5.6  SAITHE (POLLACHIUS VIRENS) IN SUBAREAS 4 AND 6, AND IN DIVISION 3.A (NORTH SEA, 
ROCKALL AND WEST OF SCOTLAND, SKAGERRAK AND KATTEGAT) (POK.27.3A46) 

Saithe in ICES divisions 27.3a, 27.4 and 27.6 is fished commercially by 14 countries, but mainly 
Norway, Germany, and France (ICES, 2021h). Landings have varied between 63 and 88 thousand 
tonnes during the last decade. The stock is mainly landed by otter trawlers (84%), gillnets (6%) 
and other gears (10%) (ICES, 2021h).  Discarding is  estimated to be around 4% of the catch.  

The stock is currently assessed using age-based analytical assessment SAM that uses catches in 
the model and in the forecast (ICES, 2021h). Fishing pressure on the stock is above FMSY but below 
Fpa and Flim; spawning-stock size is below MSYBtrigger and between Bpa and Blim (ICES, 2021g). 

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Figure  4.5.6.1 Saithe in subareas 4 and 6, and in Division 3.a (North Sea, Rockall and West of Scotland, 
Skagerrak and Kattegat) (pok.27.3a46). Summary of the stock assessment (ICES, 2021h). 

The selectivity analysis indicates that the saithe stock may produce the highest equilibrium yields 
under current F by shifting A50 by 4.3 y or L50 by 27.2 cm (Fig. 4.5.6.2; Table 4.2.1.1). 

Figure  4.5.6.2 Saithe in subareas 4 and 6, and in Division 3.a (North Sea, Rockall and West of Scotland, 
Skagerrak and Kattegat) (pok.27.3a46). Optimal selectivity scenarios (‘shift’ scenario) by age (left) and by 
length  (right)  under  current  F,  plotted  together  with  the  maturity  ogive.  Points  represent  the  observed 
selectivity. 

OTB largely dominates the current selectivity on the stock, which is indicated by the very similar 
shapes of the total (‘Current’) and OTB selectivity curves in Fig. 4.5.6.3. The OTB fleet selects fish 

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at an average size of 46.7 cm (i.e. 27.9 cm smaller than the optimal size). The GNS fleet show an 
average selectivity close to the optimal size (70.7 cm, i.e. only 3.9 cm below optimum, Fig. 4.5.6.3; 
Table 4.2.1.2) 

Figure  4.5.6.3 Saithe in  subareas 4 and 6, and in Division 3.a (North  Sea, Rockall and West of Scotland, 
Skagerrak and Kattegat) (pok.27.3a46). Fitted ‘Current’ and ‘Optimal’ (shifted) selectivity by length and fitted 
selectivity by fleet segment. GNS: Set gillnets; OTB: bottom otter trawls. 

Gear selectivity parameters (Lg50 = 46.4 cm, SRg = 9.3 cm), corresponding to the current baseline 
codend  mesh  size  (120mm  diamond)  were  derived  from  gear  trials  for  the  stock  reported  by 
Graham et al. (2004) (Annex 1). Due to the lack of studies of trawl/codend designs from the area 
with selectivity parameters closer to the optimal size (74.6 cm), a more selective codend alternative 
was used in the analysis (145mm T90 codend, with a Lg50 = 56.2 cm, SRg = 8.5 cm). The selectivity 
of the T90_145 was estimated from trials in the Barents Sea by Brinkhof et al. (2022).  

The  T90_145  gear  modification  came  closer  to  the  optimal  selectivity  than  the  baseline  120mm 
diamond  mesh  codend  and  the  current  selectivity  of  the  OTB  fleet  segment,  but  would  still  be 
catching fish on average 18 cm before their optimal size (Fig. 4.5.1.4). 

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Figure  4.5.6.4 Saithe in subareas 4 and 6, and in Division 3.a (North Sea, Rockall and West of Scotland, 
Skagerrak and Kattegat) (pok.27.3a46). Fitted  ‘Optimal’ (shifted) selectivity by length, fitted selectivity by 
the OTB fleet segment (‘OTB’), gear selectivity curves for the baseline 120mm (‘120mm_baseline’) and a more 
selective alternative 145mm T90 codend (‘T90_145mm’). 

Selectivity  analysis  under  varying  F,  confirms  that  lowering  F  somewhat  would  allow  selectivity 
improvements to be more  effective in taking the stock  closer to MSY  (Fig.  4.5.6.5). The  current 
selectivity and the gear selectivity of the baseline is very similar which can likely be explained by 
that  the  fishery  on  the  stock  is  dominated  by  120mm  trawls  (i.e.  baseline  trawls).  The  more 
selective  alternative  codend  (145mm  T90)  would  improve  equilibrium  yield  (to  around  80%  of 
maximum) at current F. Close-to-optimal yields (90% of maximum) would be achieved by shifting 
L50 to ca. 65 cm, while fishing at a Fapical around 0.5 (Fig. 4.5.6.5). However, there is the caveat 
of isopleths and current selectivity referring to population selectivity which is different than gear 
selectivity, as the former is the result of all gears acting on the fishery, while also being affected 
by availability (see Chapter 2.1). 

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Figure 4.5.6.5 Isopleths by length for saithe in subareas 4 and 6, and in Division 3.a (North Sea, Rockall and 
West  of  Scotland,  Skagerrak  and  Kattegat)  (pok.27.3a46).  Equilibrium  yield  (left)  and  SSB  (right)  under 
varying (‘shifting’) length-at-50%-selectivity (L50; cm) and fishing mortality (Fapical). Plotted are the current 
L50 and Fapical (•), the Lg50 of the 120mm codend baseline (◊) and one more selective codend alternative: 
145mm T90 codend (x), and the combination of L50 and Fapical producing an equilibrium MSY at 90% of the 
maximum (*). 

4.6 

Case studies of Mediterranean priority stocks (ToR 1 & ToR 2) 

Below, five case studies of the priority stocks from the Mediterranean Sea are presented. Each case 
study looks at an individual stock, detailing fleet characteristics, landings and discards, stock status, 
selectivity studies and the outcomes of the selectivity scenarios in terms of age and length. As in 
Chapter 4.2.1‚ ‘current’ F and selectivity refer to the average of the last three years available from 
the assessments (2017-2019). Note that in the Mediterranean hake stocks the geometric mean of 
recruitment was used in all projections to estimate optimal selectivity. Therefore, for Mediterranean 
hake  stocks  equilibrium  yield-per-recruit  (YPR)  and  SSB-per-recruit  (SBR)  have  been  estimated 
rather than equilibrium yield and SSB. 

4.6.1  HAKE  (MERLUCCIUS  MERLUCCIUS)  IN  GSAS  1,  5,  6  AND  7  (NORTHERN  ALBORAN  SEA, 

BALEARIC ISLANDS, NORTHERN SPAIN, GULF OF LIONS) (HKE.01-05-06-07)  

This stock is mainly exploited by trawlers operating on the continental shelf and slope as part of a 
mixed fisheries, and polyvalent small-scale fisheries using long lines, gill nets and trammel nets. 
In GSA 5, hake exploitation is attributed exclusively to bottom trawlers, as catches from gillnets 
and longlines are negligible, while total landings fluctuate considerably along the time series.  In 
the Gulf of Lions (GSA 7), hake is exploited by two countries’ fleets, namely French trawlers, French 
gillnetters, Spanish trawlers and Spanish longliners.  

DCF data made available to EWG 20-09 (STECF, 2020b) illustrate that in GSAs 1, 5, 6 and 7, most 
of the hake landings come from otter trawls. The combined contribution of set nets and longlines 
to the total value of landing is approximately 8% (roughly 4% for each of the two fleets). Discards 
data,  also  available  through  the  DCF,  show  that  the  highest  discard  rates  were  observed  at  the 
bottom trawl fishery; for the other gears the discard rate is negligible. 

The stock assessment carried out by EWG 20-09 suggested that hake in GSAs 1, 5, 6 and 7 has 
been overexploited for the entire time series (Fig. 4.6.1.1). Fishing mortality in 2019 (F2019 = 1.58) 
was four times higher compared to the reference point F0.1, which is the proxy of FMSY (F0.1= 0.388). 
SSB has been increasing in the last 3 years of the assessment (Fig. 4.6.1.1). 

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Figure 4.6.1.1 HKE.01-05-06-07. Summary of the stock assessment (STECF, 2020b). 

The selectivity analysis suggested that this stock may produce the highest equilibrium yields under 
current F by shifting A50 by 3.4 y or L50 by 45 cm (Fig. 4.6.1.2; Table 4.1.2.3). 

    Figure  4.6.1.2  HKE.01-05-06-07.  Fitted  ‘Current’  and  ‘Optimal’  (shifted)  selectivity  by  age  (left)  and  by 
length  (right)  under  current  F,  plotted  together  with  the  maturity  ogive.  Points  represent  the  observed 
selectivity. 

Of the fleet segments exploiting the stock, OTB exhibits the worst selectivity pattern, catching fish 
on average 48.6 cm smaller than the optimal size (Fig. 4.6.1.3; Table 4.1.2.4). By contrast, LLS is 
the fleet segment operating closest to the optimal selectivity,  catching fish on average just 11.7 
cm under their optimal size. 

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Figure  4.6.1.3  HKE.01-05-06-07.  Fitted  ‘Current’  (2019)  and  ‘Optimal’  (shifted)  selectivity  by  length  and 
fitted selectivity by fleet segment. GNS: Set gillnets; GTR: Trammel nets; LLS: Set longlines; OTB: bottom 
otter trawls. 

Gear selectivity parameters (Lg50 = 17.64 cm, SRg = 7.4 cm), corresponding to the current baseline 
codend mesh size (40mm square mesh – 40SM) were derived from gear trials in GSA 6 performed 
in the framework of the IMPLEMED project (Sbrana, 2021) (Annex 1). A more selective set of gear 
selectivity parameters (Lg50 = 22.19 cm, SRg = 5.03 cm) corresponding to a square mesh size of 
50mm (50SM) coming from the same project was also considered. No gear selectivity parameters 
were found in the literature for non-OTB gears in the area. 

The 50SM gear modification came closer to the optimal selectivity, than both the default 40SM gear 
and the current selectivity of the OTB fleet segment, but would be still catching fish on average 33 
cm before their optimal size (Fig. 4.6.1.4).  

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Figure 4.6.1.4 HKE.01-05-06-07. Fitted ‘Optimal’ (shifted) selectivity by length, fitted selectivity by the OTB 
fleet segment (‘OTB’), gear selectivity curves for the baseline 40 mm square mesh trawler (‘40 SM’) and the 
50 mm square mesh codend trawler (‘50 SM’) and presumed relative ‘Availability’ of different sizes (inferred 
as the ratio ‘OTB’/’Gear 40 SM’ scaled to 1), assuming total and unbiased implementation of the 40 SM by the 
OTB fleet segment.  

It is notable that the baseline 40SM gear exhibits better gear selectivity for smaller fish  than the 
partial  population  selectivity  of  the  OTB  fleet  segment,  which  is  based  on  partial  F-at-age  (Fig. 
4.6.1.4).  This  discrepancy  could  be  due  to  technical  tweaks  (e.g.  using  a  shorter  codend  –  see 
Chapter  4.4.5)  and/or  operational  choices  by  the  fishers  that  would  result  in  different  gear 
selectivity  of  the  40SM  than  that  observed  in  the  gear  trials.  For  example,  selectivity  trials  are 
usually  performed  under  experimental  conditions  (e.g.,  shorter  tow  duration,  standard  towing 
speed at 3 knots, etc.), while commercial fishing is characterized by long tow durations (up to 3-4 
hours on the shelf or at the shelf break). This could determine the filling of the codend, reducing 
the selectivity of the net. Another explanation could be that there are other gears included within 
the OTB fleet segment selecting more juvenile hake than the 40SM. Assuming that the OTB fleet 
segment is dominated by the 40SM gear and that the 40SM gear selectivity in the field is similar to 
the one observed in gear trials, another explanation for the discrepancy between the 40SM gear 
selectivity  and  the  partial  population  selectivity  of  OTB  could  be  the  differential  availability  of 
different  fish  sizes.  Based  on  equation  (1)  (Chapter  2.1),  assuming  that  the  non-avoidance 
probability has been adequately captured by the gear trials and is reflected in gear selectivity, it 
can be inferred that availability to the baseline trawling gear is much higher for smaller fish than 
bigger ones compared to the gear trials (Fig. 4.6.1.4). This could be related to the place/time that 
trawling takes place and could hint towards spatiotemporal measures needed to improve selectivity, 
which  could  be  easier  to  implement  than  securing  gear  specifications  compliance  on  board.  For 
example, commercial trawling on the shelf and shelf break is potentially performed close to hake 
nursery  areas  when  targeting  deep-water  rose  shrimp,  thus  increasing  the  availability  of  small 
hakes  to  the  trawl  net.  Additionally,  a  FRA  has  been  established  in  GSA  7  to  protect  big  hakes 
(spawners)  (REC.CM-GFCM/33/2009/1),  therefore  decreasing  the  availability  of  big  fish  to  OTB 
gears. 

Selectivity analysis under varying F, confirms that lowering F would allow selectivity improvements 
to  be  more  effective  in  taking  the  stock  closer  to  MSY  (Fig.  4.6.1.5).  When  comparing  current 
selectivity with the gear  selectivity of the baseline and improved trawler, it can be inferred that 
under current Fapical the stock would still lie far from the highest equilibrium yields with either of the 
gears  examined  (Fig.  4.6.1.5).  However,  there  is  the  caveat  of  isopleths  and  current  selectivity 

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referring to population selectivity, which is different than gear selectivity, as the former is the result 
of all gears acting on the fishery, while also being affected by availability (see Chapter 2.1). Close-
to-optimal yieds (90% of maximum) would be extracted by shifting L50 to ca. 48 cm, while fishing 
at a Fapical ca. three times lower than current (Fig. 4.6.1.5).  

Figure  4.6.1.5  Isopleths  by  length  for  HKE.01-05-06-07.  Equilibrium  yield-per-recruit  (left)  and  SSB-per 
recruit  (right)  under  varying  (‘shifting’)  length-at-50%-selectivity  (L50;  cm)  and  fishing  mortality  (Fapical). 
Plotted are the current L50 and Fapical (•), the Lg50 of the 40mm square mesh baseline trawler (◊), the Lg50 
of  the  50mm  square  mesh  trawler  (x)  and  the  combination  of  the  lowest  L50  and  Fapical  producing  an 
equilibrium yield at 90% of the maximum (*).  

4.6.2  HAKE (MERLUCCIUS MERLUCCIUS)  IN  GSAS 8, 9, 10 AND 11 (CORSICA, LIGURIAN  SEA, 

TYRRHENIAN SEA, SARDINIA) (HKE.08-09-10-11) 

European hake represents the most important  target species in this region in terms of landings, 
income, vessels involved and socioeconomic synergies. In GSAs 9 and 10, it is exploited mainly by 
trawlers  on the shelf and slope as part of a mixed fishery, but some considerable quantities are 
also landed by small-scale fisheries using set nets (gillnets and trammel nets) and bottom longlines. 
In Sardinia (GSA 11), although hake is not actively targeted by the fleets operating in the area, it 
is considered one of the most important species in terms of biomass landed coming as a bycatch 
in the above mentioned gears. In Corsica (GSA 8), few small bottom trawlers are active producing 
negligible hake bycath quantities together with some even smaller quantities coming from small-
scale fisheries. It should be noted that due to the bottom bathymetry trawlers can only operate at 
the eastern part of Corsica. 

Based  on EWG 20-09 (STECF,  2020b), in GSAs 9, 10 and 11, the majority of the hake landings 
come from otter trawls. In GSAs 9 and 10, set nets contribute around 35% of the landings, while 
in GSA 10 longlines contribute around 17% of the landings. In GSA 11, hake landings come solely 
from bottom trawl fishery. Discard rates vary greatly among GSAs, although in GSA 9, 10 and 11 
for some years such data were not available. On average, in GSA 9, discard rate of hake is 18%, 
in GSA 10 it is around 4% and in GSA 11 it is 32%. 

Fishing mortality in 2019, estimated as the average F over age-classes 1-3, was F2019 = 0.57, which 
was more than three times higher than F0.1 (F0.1 = 0.17). This indicates that the hake stock in GSAs 
8, 9, 10 and 11 is overexploited (Fig. 4.6.2.1).  

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Figure 4.6.2.1 HKE.08-09-10-11. Summary of the stock assessment (STECF, 2020b). 

The selectivity analysis suggested that this stock may produce the highest equilibrium yields under 
current F by shifting A50 by 5.3 y or L50 by 52.1 cm, in which case high protection of juveniles 
would be also ensured (Fig. 4.6.2.2; Table 4.1.2.3). 

    Figure  4.6.2.2  HKE.08-09-10-11.  Fitted  ‘Current’  and  ‘Optimal’  (shifted)  selectivity  by  age  (left)  and  by 
length  (right)  under  current  F,  plotted  together  with  the  maturity  ogive.  Points  represent  the  observed 
selectivity. 

Of  the  fleet  segments  exploiting  the  stock,  OTB  exhibits  the  worst  selectivity,  selecting  fish  on 
average 52.9 cm smaller than the optimal size (Fig. 4.6.2.3; Table 4.1.2.4). By contrast, LLS is the 
fleet  segment  operating  closest  to  the  optimal  selectivity,  selecting  fish  on  average  just  11  cm 
under their optimal size. 

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Figure  4.6.2.3  HKE.08-09-10-11.  Fitted  ‘Current’  and  ‘Optimal’  (shifted)  selectivity  by  length  and  fitted 
selectivity by fleet segment.  GNS: Set gillnets; GTR: Trammel nets; LLS: Set longlines; OTB: bottom otter 
trawls. 

Gear  selectivity  parameters  (Lg50  =  17.62  cm,  SRg  =  6.35  cm),  corresponding  to  the  current 
baseline codend mesh size (40mm square mesh – 40SM) were derived from gear trials in GSA 9 
(Brčić et al. 2018) (Annex 1). Due to the lack of recent selectivity studies in the area  on codend 
meshes higher than 40SM or 50DM, a more selective set  of  gear  selectivity parameters (Lg50 = 
20.46 cm, SRg = 8 cm) corresponding to a square mesh size of 55mm (55SM) was obtained from 
a meta-analysis by Lucchetti et al. (2021).  

The 55SM gear modification came closer to the optimal selectivity, than both the default 40SM gear 
and the current selectivity of the OTB fleet segment, but would be still catching fish on average 
35.5 cm smaller than their optimal size (Fig. 4.6.2.4). It is notable that, as in the case of HKE.01-
05-06-07, the baseline 40SM gear exhibits a lower gear selectivity for smaller fish than the partial 
population selectivity of the OTB fleet segment (which is based on the partial F-at-age). Possible 
reasons for this discrepancy are discussed in detail in the previous Chapter 4.6.1. Assuming total 
and  unbiased  implementation  of  the  40SM  by  OTB  in  the  area  and  that  the  non-avoidance 
probability is reflected in gear selectivity, it can be inferred that availability to the baseline trawling 
gear is much higher for smaller fish than  bigger ones compared to the gear trials (Fig. 4.6.2.4). 
This  could  be  related  to  the  place/time  that  trawling  takes  place  and  could  hint  towards 
spatiotemporal measures needed to improve selectivity. Some of the most important nursery areas 
of hake are found in the Tyrrhenian and Sardinian Seas (Bartolino et al., 2008; Colloca et al., 2009, 
2015; Ligas et al., 2015). This can further increase the availability of juvenile hakes to the  OTB 
fisheries in this area.  

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Figure 4.6.2.4 HKE.08-09-10-11. Fitted ‘Optimal’ (shifted) selectivity by length, fitted selectivity by the OTB 
fleet segment (‘OTB’), gear selectivity curves for the baseline 40mm square mesh trawler (‘40 SM’) and the 
55mm square mesh codend trawler (‘55 SM’) and presumed relative ‘Availability’ of different sizes (inferred 
as the ratio ‘OTB’/’40 SM’ scaled to 1), assuming total and unbiased implementation of the 40SM by the OTB 
fleet segment. 

Additionally,  gear  selectivity  parameters  were  obtained  from  Sbrana  et  al.  (2007)  for  gillnets 
operating in GSA 9, with 53mm mesh size (baseline – 33cm modal length) and 82mm mesh size 
(improved  –  51cm  modal  length).  These  gear  selectivity  parameters  were  then  plugged  to  the 
bi-modal selectivity function obtained by Millar & Fryer (1999). 

The 82mm gillnet modification came closer to the optimal selectivity than both the baseline 53mm 
gillnet gear and the current  selectivity of the  GNS fleet segment,  and  would be catching fish on 
average just 13 cm smaller than their optimal size (Fig. 4.6.2.5). 

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Figure 4.6.2.5 HKE.08-09-10-11. Fitted optimal (‘shift’) selectivity by length (‘Optimal’), fitted selectivity by 
the GNS fleet segment (‘GNS’), gear selectivity curves for the baseline 53mm gillnet (‘53mm’) and a more 
selective 82mm gillnet (’82 mm’). 

Figure 4.6.2.6 Isopleths by length for HKE.08-09-10-11. Equilibrium yield-per-recruit (left) and SSB-per-
recruit (right) under varying (‘shifting’) length-at-50%-selectivity (L50; cm) and fishing mortality (Fapical). 
Plotted are the current L50 and Fapical (•), the Lg50 of the 40mm square mesh baseline trawler (◊), the Lg50 
of the 55mm square mesh trawler (x), the Lg50 of the 53mm baseline gillnet (□), the Lg50 of the 82mm 
gillnet (+), and the combination of the lowest L50 and Fapical producing an equilibrium yield at 90% of the 
maximum (*). 

When comparing current selectivity with the gear selectivity of the baseline and improved trawler, 
it can be inferred that under current Fapical the stock would come closer to the highest equilibrium 
yields  but  not  quite  there,  with  either  of  the  trawlers  examined  (Fig.  4.6.2.6).  Close-to-optimal 
yieds (90% of maximum) would be extracted by shifting L50 to ca. 45 cm, while fishing at similar 
levels with current Fapical (Fig. 4.6.2.6). Notably, the 82mm gillnet offers an Lg50 very close to that 
level (Lg50 = 43 cm). However, there is the caveat of isopleths and current selectivity referring to 

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population selectivity which is different than gear selectivity, as the former is the result of all gears 
acting on the fishery, while also being affected by availability (see Chapter 2.1).  

4.6.3  HAKE (MERLUCCIUS MERLUCCIUS) IN GSAS 17 AND 18 (ADRIATIC SEA) (HKE.17-18)  

Fleets exploiting hake in the Adriatic are trawlers, gillnetters and longliners. This stock is exploited 
in a mixed fishery together with other important stocks (red mullet, mantis shrimp and sole). Based 
on  genetic  results  of  the  MAREA  StockMed  project,  hake  exploited  in  GSAs  17  and  18  consist  a 
distinct sub-population in the Adriatic Sea (Fiorentino et al. 2015). 

EWG 22-19 notes that, in accordance with Articles 17 and 21 of the TMR, a fishery restricted area 
(FRA)  in  the  Jabuka/Pomo  Pit  region  in  the  Adriatic  Sea  has  been  established  by  GFCM  (Rec 
GFCM/41/2017/3 and Rec GFCM/44/2021/2). This FRA was established with the aim to contribute 
to the protection of vulnerable marine ecosystems and essential fish habitats (i.e. spawning and 
nursery grounds) for important demersal stocks exploited by Adriatic trawl fishery such as European 
hake and Norway lobster. Establishment of this FRA can be viewed as a technical measure that, in 
synergy with others, contributes to the objectives of CFP, in particular protection of juveniles and 
spawning aggregations of hake and other demersal stocks in the Adriatic Sea, reduction of catches 
of undersized hake specimens, minimization of the negative environmental fishing impact on the 
seabed habitat within Jabuka/Pomo Pit FRA, and eventually improving the overall selectivity of the 
trawl  fishery.  EWG  22-19  notes  that  more  studies  are  needed  to  demonstrate  the  effects  of 
Jabuka/Pomo Pit FRA, and additional measures aimed to improve trawl codend selectivity are likely 
to  act  in  synergy  with FRAs  in  order  to  contribute  in  optimising  the exploitation  pattern  of  hake 
stocks in the Adriatic and beyond. 

A  recent  study  (Chiarini  et  al.,  2022)  evaluated  the  early  effects  of  Pomo/Jabuka  Pit  FRA  as  a 
technical measure (based on abundance indices), analysing CPUE changes in the period 2012-2019 
for the five commercially and/or ecologically important species (Norway lobster, hake, deep water 
rose shrimp, blue whiting and Munida spp.) in three different FRA strata. In general, it seems that 
in case of hake FRA  effects are mainly positive, but due to large  confidence intervals these first 
results  should  be  interpreted  with  caution,  suggesting  the  need  to  continue  with  FRA  effects 
monitoring.  

Most of the landings in GSAs 17 and 18 come from the bottom trawler fleet, followed by longlines 
and to a lesser extent gillnet fishery and ‘rapido’ trawls only in the case of GSA 17 (STECF, 2020c). 
In earlier years, there were no discard estimates available and as a result only a deterministic mean 
discard ratio could be applied. The majority of discards come from the OTB fleet.  

The relevant stock assessment by EWG 20-15 (STECF, 2020c) is considered to include a complete 
stock  unit  as  it  includes  both  commercial  data  (landings  and  discards)  from  the  DCF  and  data 
provided by Albania and Montenegro coming from the GFCM framework. Fishing is well above the 
reference points, with the 2019 level of fishing mortality (F2019 = 0.41) calculated to exceed the 
reference point FMSY (FMSY = 0.179), suggesting that the stock is being overexploited (Fig. 4.6.3.1). 

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Figure 4.6.3.1 HKE.17-18. Summary of the stock assessment (STECF, 2020c).  

The selectivity analysis suggested that this stock may produce the highest equilibrium yields under 
current F by shifting A50 by 5.8 y or L50 by 40.3 cm, in which case high protection of juveniles 
would be also ensured (Fig. 4.6.3.2; Table 4.1.2.3). 

The partial selectivity by fleet segment was not available for this stock, so the gear selectivity of 
two gears has been also plotted on Fig. 4.6.3.2. Gear selectivity parameters (Lg50 = 15.7 cm, SRg 
= 7.56 cm) corresponding to the current baseline codend mesh size (40mm square mesh – 40SM) 
were  derived  from  gear  trials  (Sala  &  Lucchetti,  2010)  (Annex  1).  Due  to  the  lack  of  recent 
selectivity studies in the area on codend meshes higher than 40SM or 50DM, a more selective set 
of gear selectivity parameters (Lg50 = 20.46 cm, SRg = 8 cm) corresponding to a square mesh size 
of 55mm (55SM) was obtained from a meta-analysis by Lucchetti et al. (2021). 

Current selectivity was slightly better than the gear selectivity of the baseline 40SM trawler, owing 
to overall selectivity incorporating also more selective gears than trawlers ((Fig. 4.6.3.2). It should 
be also noted that the establishment of a FRA in GSA 17 probably reduced the availability of small 
hake  to  the  OTB  gear.  The  55SM  gear  modification  came  only  slightly  closer  to  the  optimal 
selectivity, than both the default 40SM gear and the current selectivity, but it would be still catching 
fish on average 39.2 cm smaller than their optimal size (Fig. 4.6.3.2). 

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    Figure  4.6.3.2  HKE.17-18.  Fitted  ‘Current’  and  ‘Optimal’  (shifted)  selectivity  by  age  (left)  and  by  length 
(right) under current F plotted together with the maturity ogive. Points represent the observed selectivity. 
Also shown on the right are the gear selectivity curves for the baseline 40mm square mesh trawler (‘40 SM’) 
and the 55mm square mesh trawler (‘55 SM’)  

Selectivity analysis under varying F, confirms that lowering F would allow selectivity improvements 
to  be  more  effective  in  taking  the  stock  closer  to  MSY  (Fig.  4.6.3.3).When  comparing  current 
selectivity with the gear selectivity of the improved trawler, it can be inferred that under current 
Fapical the stock would come only marginally closer to the highest equilibrium yields (Fig. 4.6.3.3). 
However, there is the caveat of isopleths and current selectivity referring to population selectivity, 
which is different than gear selectivity, as the former reflects the effect of all the gears acting on 
the fishery, while also being affected by availability (see Chapter 2.1). Close-to-optimal yieds (90% 
of maximum) would be extracted by shifting L50 to ca. 37 cm, while halving the Fapical compared to 
current (Fig. 4.6.3.3).  

 Figure  4.6.3.3  Isopleths  by  length  for  HKE.17-18.  Equilibrium  yield-per-recruit  (left)  and  SSB-per-recruit 
(right) under varying (‘shifting’) length-at-50%-selectivity (L50; cm) and fishing mortality (Fapical). Plotted are 
the current L50 and Fapical (•), the Lg50 of the 40mm square mesh baseline trawler (◊), the Lg50 of the 55mm 
square mesh trawler (x) and the combination the lowest L50 and Fapical producing an equilibrium yield at 90% 
of the maximum (*).  

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4.6.4  HAKE (MERLUCCIUS MERLUCCIUS) IN GSA 19 (WESTERN IONIAN SEA) (HKE.19) 

Hake in GSA 19 is mainly exploited by trawlers, longlines, gillnets and trammel nets. On average, 
catches from longlines (LLS) represent approximately 20% of total hake landings, from gillnets and 
trammel nets combined (GNS and GTR) approximately 20%, and from trawlers 60% (OTB). 

The data from DCF analysed by the EWG 20-07 (STECF, 2020c) showed that total catch, landings 
and  discards  have  been  decreasing  since  2004.  While  the  landings  are  reported  for  all  years, 
discards are missing from 2002 to 2005 and from 2007 to 2008, as collection of discard data was 
not foreseen by DCF. Discard data were subsequently reconstructed for the missing years. Catches 
after a peak in 2004 decreased to low values during the last 8 years (Fig. 4.6.4.1). Latest (2019) 
landings  of  approximately  700  tons  were  approximately  2.3  times  lower  than  landings  levels  in 
2004 (1630 tons). Discards also followed a decreasing trend.  

The  bulk  of  catches  are  taken  by  bottom  otter  trawls  (OTB)  and  longlines  (LLS)  for  the  landing 
fraction, and by OTB for the discard component. Discards varied from year to year and were about 
1.5-6%  of  landings.  Taking  into  account  the  fleets  targeting  hake,  the  decrease  in  landings  in 
bottom trawlers is contrasted by the increasing of landings in longlines and nets. 

Length frequency distributions from catches show that hake is dominated by individuals up to 30 
cm and a variation in size selectivity between gears is evident.  

The 2020 stock assessment indicated that the hake stock in GSA 19 was overexploited, given that 
F2019 = 0.325, higher than F0.1 (F0.1 = 0.135). STECF EWG 20-15 advised that fishing mortality in 
2021 should be no more than 0.135 and corresponding catches of hake in 2021 to deliver this value 
should not exceed 379 tonnes. It is noted that the SSB is increasing after 2016, as a result of a 
decreasing fishing mortality (Fig. 4.6.4.1). 

Figure 4.6.4.1 HKE.19. Summary of the stock assessment (STECF, 2020c).  

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The selectivity analysis suggested that this stock may produce the highest equilibrium yields under 
current F by shifting A50 by 5.4 y or L50 by 42.8 cm, in which case high protection of juveniles 
would be also ensured (Fig. 4.6.4.2; Table 4.1.2.3). 

The partial selectivity by fleet segment was not available for this stock, so the gear selectivity of 
two gears has been also plotted on Fig. 4.6.4.2. For this area, it was not possible to find any recent 
studies on gear selectivity curves, on either the baseline or an improved trawler. Therefore, gear 
selectivity  parameters  (Lg50  =  14.88  cm,  SRg  =  4.5  cm),  corresponding  to  the  current  baseline 
codend  mesh  size  (40mm  square  mesh  –  40SM)  and  a  more  selective  set  of  gear  selectivity 
parameters (Lg50 = 20.46 cm, SRg = 8 cm) corresponding to a square mesh size of 55mm (55SM) 
were obtained from a meta-analysis by Lucchetti et al. (2021). 

Current selectivity was slightly worse than the gear selectivity of the baseline 40SM trawler (Fig. 
4.6.4.2),  but  this  could  be  due  to  the  absence  of  gear  trials  in  the  GSA  19,  or  due  to  reasons 
discussed in detail in Chapter 4.6.1. The 55SM gear modification came slightly closer to the optimal 
selectivity than both the default 40SM gear and the current selectivity, but would be still catching 
fish on average 30.8 cm smaller than their optimal size (Fig. 4.6.4.2). 

     Figure 4.6.4.2 HKE.19. Fitted ‘Current’ and ‘Optimal’ (shifted) selectivity by age (left) and by length (right) 
under current F plotted together with the maturity ogive. Points represent the observed selectivity. Also shown 
are the gear selectivity curves for the baseline 40mm square mesh trawler (‘40 SM’) and the 55mm square 
mesh trawler (‘55 SM’). 

When  comparing  current  selectivity  with  the  gear  selectivity  of  the  improved  trawler,  it  can  be 
inferred that under current F the stock would come slightly closer to the highest equilibrium yields 
(Fig. 4.6.4.3). Close-to-optimal yieds (90% of maximum) would be extracted by shifting L50 to ca. 
42  cm,  with  and  Fapical  between  0.60  and  0.70  (Fig.  4.6.4.3).  However,  there  is  the  caveat  of 
isopleths  and  current  selectivity  referring  to  population  selectivity,  which  is  different  than  gear 
selectivity, as the former reflects the effect of all the gears acting on the fishery, while also being 
affected by availability (see Chapter 2.1). 

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Figure 4.6.4.3 Isopleths by length for HKE.19. Equilibrium yield-per-recruit (left) and SSB-per-recruit (right) 
under varying (‘shifting’) length-at-50%-selectivity (L50; cm) and fishing mortality (Fapical). Plotted are  the 
current L50 and Fapical (•), the Lg50 of the 40mm square mesh baseline trawler (◊), the Lg50 of the 55mm 
square mesh trawler (x) and the combination of the lowest L50 and Fapical producing an equilibrium yield at 
90% of the maximum (*).  

4.6.5  HAKE (MERLUCCIUS MERLUCCIUS) IN GSA 20 (EASTERN IONIAN SEA) (HKE.20)  

In GSA 20 (Eastern Ionian Sea), hake mainly inhabits muddy substrates at depths ranging from 50 
to 600 m. Hake is mainly targeted by the bottom trawl fishery as well as fisheries with nets (gillnets 
and trammel nets) and longlines. The gill nets are set at depths ranging from 80 to 300 m and the 
mesh size used is usually 48 to 64  mm. This fishery is mainly carried out during summer, when 
bottom trawl fishery is prohibited. Longline fishery for hake also operates mainly during the summer 
at deeper waters, down to 500 m. 

The official landings of hake in GSA 20 are recorded by the Hellenic Statistical Authority (ELSTAT) 
and the same values are also reported in the FAO/GFCM databases. However, the structure of the 
dataset changed after 2015 and includes the landings of an extra small-scale coastal fleet of 10,000 
vessels (Tsikliras et al., 2020). To account for these additional landings that inflated the landings 
time  series  after  2016,  a  correction  of  the  hake  landings  from  1982  to  2015  was  implemented 
multiplying  by  1.31,  which  is  the  difference  of  hake  with  and  without  the  extra  fleet.  The  DCF 
dataset contains too many missing points and is inconsistent in terms of landings as the landings 
reported for 2003-2006 are very high, probably owing to a raising factor error. Towards the end of 
the time series, the DCF dataset seems to converge with the official one. 

The bottom trawl fishery in Greece is described as mixed, operating 24hr per day. Bottom trawl 
fishing targeting hake takes place mainly during daytime over muddy bottoms at depths ranging 
from 80 to 400 m. Other important targeted species in the hake fishery are shrimps, anglerfish, 
blue whiting, and red mullet. After an increase during the period from 2000 to 2008, the official 
landings of hake have been continuously declining, with a slight increase during the last three years 
(Fig. 4.6.5.1). 

According to the Greek DCF, the discards of haκe in GSA 20 were over 750t in the mid-2000s and 
have been declined to negligible values (< 15t) for OTB since 2013, while GNS and GTR produced 
discards that exceed 30t after 2016. The highest proportion of total discards (88% in 2018) is no 
longer attributed to OTB but to nets, which is strange as nets with large mesh size do not usually 
discard much fish. 

The stock was overexploited in 2019, although decreasing in the last six years of the time-series, 
as Fbar (F2019 = 0.74) was above F0.1 (F0.1 = 0.36) (Fig. 4.6.5.1). SSB shows an increasing trend in 
the last four years of the assessment period. 

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Figure 4.6.5.1 HKE.20. Summary of the stock assessment (STECF, 2020c).  

The selectivity analysis suggested that this stock may produce the highest equilibrium yields under 
current F by shifting A50 by 1.9 y or L50 by 19 cm, in which case high protection of juveniles would 
be also ensured (Fig. 4.6.5.2; Table 4.1.2.3). 

The partial selectivity by fleet segment was not available for this stock, so the gear selectivity of 
two  gears  has  been  also  plotted  on  Fig.  4.6.5.2.  For  hake  in  GSA  20,  as  in  GSA  19,  it  was  not 
possible to find any recent studies on gear selectivity curves, either the baseline or an improved 
trawler. Therefore, gear selectivity parameters (Lg50 = 14.88 cm, SRg = 4.5 cm), corresponding to 
the current baseline codend mesh size (40mm square mesh – 40SM) and a more selective set of 
gear selectivity parameters (Lg50 = 20.46 cm, SRg = 8 cm) corresponding to a square mesh size 
of 55mm (55SM) were obtained from a meta-analysis by Lucchetti et al. (2021). 

Current selectivity was worse than the gear selectivity of the baseline 40SM trawler (Fig. 4.6.5.2), 
but this could be due to the absence of gear trials in GSA 20, or due to reasons discussed in detail 
in Chapter 4.6.1. The 55SM gear modification came closer to the optimal selectivity than both the 
default 40SM gear and the current selectivity, and would be catching fish on average just 9.3 cm 
smaller than their optimal size (Fig. 4.6.5.2). 

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    Figure 4.6.5.2 HKE.20. Fitted ‘Current’ and ‘Optimal’ (shifted) selectivity by age (left) and by length (right) 
under  current  F,  plotted  together  with  the  maturity  ogive.  Points  represent  the  observed  selectivity.  Also 
shown are the gear selectivity curves for the baseline 40mm square mesh trawler (‘40 SM’) and the 55mm 
square mesh trawler (‘55 SM’). 

When  comparing  current  selectivity  with  the  gear  selectivity  of  the  improved  trawler,  it  can  be 
inferred that under current F the stock would come close to the MSY (Fig. 4.6.5.3). Close-to-optimal 
yieds (90% of maximum) would be extracted by shifting L50 to ca. 26 cm, with a Fapical of ca. 1.4 
(Fig.  4.6.9.3).  However,  there  is  the  caveat  of  isopleths  and  current  selectivity  referring  to 
population selectivity which is different than gear selectivity, as the former reflects the effect of all 
the gears acting on the fishery, while also being affected by availability (see Chapter 2.1). 

Figure 4.6.5.3 Isopleths by length for HKE.20. Equilibrium yield-per-recruit (left) and SSB-per-recruit (right) 
under varying (‘shifting’) length-at-50%-selectivity (L50; cm) and fishing mortality (Fapical). Plotted are  the 
current L50 and Fapical (•), the Lg50 of the 40mm square mesh baseline trawler  (◊), the Lg50 of the 55mm 
square mesh trawler (x) and the combination of the lowest L50 and Fapical producing an equilibrium yield at 
90% of the maximum (*).  

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4.7 

Identify possible operational changes needed to realise the transition to higher 
yields (ToR 3) 

According to Haasnoot et al. (2016), it is entirely possible that fishers can positively influence their 
impact  on  the  sustainability  of  fish  stocks  through  operational  changes.  However,  technological 
change leading to a shift of fishing gears or fishing operations that improve sustainability is complex 
and challenging to achieve. This is due to the inherent uncertainty and the underlying perception 
of the fishing industry that such changes will lead to significant capital outlay and economic loss.  

EWG 22-19 has identified three types of operational change that could be implemented to improve 
selectivity and increase yields as follows: 

a)  Gear modifications (e.g., through increases in mesh size or use of a selectivity device)  
b)  Gear changes (e.g., moving from active to static gears) 
c)  Spatiotemporal measures (e.g., through closed areas or seasons) 

As the focus of EWG 22-19 is on gear selectivity and gear shifts, spatiotemporal measures to avoid 
or limit fishing activity in specific areas or at specific times of the year are not considered further 
in  this  ToR.  However,  EWG  22-19  notes  that  such  measures  can  constitute  another  method  to 
improve selectivity and lead to increased yields over time. By way of an example, EWG 22-19 refers 
in particular to the assessment of the closures under the Western Mediterranean MAP as reported 
by EWGs 20-13, 21-01, 21-13, 22-01 and 22-11. 

a) Gear modifications. Such modifications are among the simplest and common operational changes 
used  to  improve  yields  via  increased  selectivity.  They  can  be  in  the  form  of  simple  increases  in 
codend mesh size and changes in mesh configuration, or the mandatory/voluntary use of selectivity 
devices such as square mesh panels or sorting grids. They can be used in specific fisheries where 
the  relevant  species  may  be  a  target  species  or  an  important  bycatch.  The  widespread  use  of 
increasing mesh size or a selectivity device over time will potentially increase yield depending on 
uptake and compliance.  However, biological benefits are not easy to evaluate (Suuronen & Sardà, 
2007) and may not systematically lead to the desired increased size selectivity (Yang & Herrmann, 
2022).  

Gear  modifications  can  be  introduced  over  several  years  using  a  stepwise  approach,  ‘slow  and 
steady increase’ (e.g., change in mesh size) or immediately when the state of the stock dictates 
urgent action (e.g., mandatory use of a selection device such as a grid).  

As described in EWG 21-07, the capital cost implications for making such changes are generally 
reasonably  low  (e.g.,  €1,000-€10,000).  However,  the  short-term  loss  of  earnings  as  a  result  of 
reduced  catches  of  marketable  fish  may  be  significant  and  depending  on  the  gear  modification 
made, may never return the vessel to the level of earnings post-change. For example, moving to 
significantly larger codend mesh size (140mm) in a mixed gadoid fishery targeting cod would result 
in  the  almost  total  and  permanent  loss  of  whiting  catch in  the  fishery,  due  to  the  differences  in 
selectivity and potential size of the two species. 

Introducing gear modifications is usually based on technical trials that demonstrate the benefits of 
such modifications in terms of reduced catches of juveniles, as well as the corresponding losses (if 
any) of marketable catch associated with them. In the past, such modifications have usually met 
resistance from industry and unless introduced into legislation, uptake has been slow. Where gear 
changes  have  been  introduced  in  a  stepwise  process,  change  has  been  easier  (e.g.,  under 
Regulation (EU) 1241/2019 fishers were allowed two years to transition to the baseline mesh size 
of 120mm in North Western waters).  

According to STECF EWG 12-20, the successful introduction of gear modifications is related to the 
incentives  that  are  in  place  to  support  the  change,  particularly  when  it  will  result  in  short-term 
losses, as well as acceptance that the measures are needed. While support through grant aid to 
offset some of the modification costs is useful and often necessary, it is usually not always sufficient 
to encourage the smooth transition to any new gear.  

Co-management  or  stakeholder  integrative  approaches  are  now  commonly  accepted  as  being 
alternatives to compulsory measures and provide positive ecological, social and economic benefits 
in various fisheries over the world, mainly for SSF (Chuenpagdee & Jentoft, 2007; Ratana & Jentoft, 
2007; Macher et al., 2018; Smallhorn-West et al., 2022). These strategies can represent additional 

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tools,  especially  in  a  context  of  growing  social  society  demand  for  natural  common  resource 
management.  

During  the  Horizon  2020  project  DiscardLess,  numerous  measures  to  reduce  unwanted  or 
undersized catches were tested and modelled over a wide range of EU fishing fleet segments (i.e., 
a  related  exercise  to  that  in  ToR  3  of  the  current  report).  The  DiscardLess  project  produced 
guidelines and a toolkit for fishers, scientists and managers to assess the validity and the economic 
consequences of future selective gears to be developed and experimented by the fishing industry. 
These guidelines could be useful in helping to develop methodologies for assessing the impacts of 
operational change.   

In the example of the trawl fishery for hake in the western Balearic a simulation study showed a 
positive result as a result of operational change. The   scenario of avoiding immature hake (<30 
cm) implied increases in yield and SBB as well as income, crew wages and profit with a resulting 
significant  reduction  in  fishing  mortality.  Similar  results  were  obtained  for  Spanish  trawlers 
operating in the Bay of Biscay targeting hake when the mesh size was increased from 100 to 120 
mm (Prellezo et al., 2017).  

Nonetheless, a gradual increase in gear selectivity is no guarantee for successful implementation 
and  improvements  in  yield.  Suuronen  et  al.  (2007)  showed  that  no  obvious  change  in  fleet 
selectivity, nor decreased discarding of young fish, took place in spite of several gradual regulatory 
changes intended to increase size selectivity in the Baltic cod trawl fishery in the 1990s and early 
2000s. The authors suggested that the regulatory changes had been nullified by circumvention of 
the measures introduced based on an unwillingness to accept short-term economic losses and the 
lack of acceptance of the decision-making process. 

b)  Gear  changes.  As  described  by  Suuronen  et  al.  (2012),  transitioning  from  one  gear  type  to 
another  is  seldom  easy  or  practical.  The  size  and  design  of  existing  fishing  vessels  and  their 
machinery,  vessel  layout and equipment often limit the possibilities of changing fishing method. 
Additionally,  fishing  gears,  fishing  vessels,  operations,  and  practices  have  evolved  over  a 
considerable period of time, around  specific fishing operations and  fishing patterns. Accordingly, 
the evolved fishing gear and practices are “tailor-made” to catch specific target species or species 
groups  in  a  manner  that  is  often  perceived  to  be  optimised  to  the  best  technical  and  economic 
scenarios.  Furthermore,  where  fishing  practices  are  rooted  in  tradition,  there  is  often  a  strong 
resistance  to  change.  Therefore,  such  changes  require  considerable  lead-in  time  to  achieve, 
depending on the scale of the gear shift.  

Gear  changes also imply significant investment in both the gear and the vessel. As described in 
EWG 21-07, this could be in the region of €250,000 - €500,000.  Additionally, there will be losses 
due to downtime needed to convert the vessel, as well as likely short-term loss of earnings as a 
result  of  reduced  catches  of  marketable  fish  as  the  crew  adapt  to  the  new  fishing  method. 
Depending on the gear change, earnings may never return the vessel to the level of earnings post-
change  due  to  the  lower  efficiency  of  the  gear.  Such  reductions  may  be  partially  offset  by  the 
increased  value  per  kg  of  the  catch  from  a  larger  proportion  of  bigger  fish  in  the  catch  or  from 
reduced operational costs such as fuel.  

Moving to alternative gears generally needs a detailed economic and technical assessment of costs 
to change and likely revenues once converted, as fishers need a level of assurance that the new 
gear will have benefits in the long-term. Additionally, such changes are probably only an option for 
small  discrete  fisheries  involving  a  relatively  small  number  of  vessels  as  converting  significant 
number of  vessels is likely to lead to extremely large capital investment. Large-scale shifts may 
also lead to effort displacement and potentially have market implications. Nevertheless, the case 
studies described in Chapters 4.5 & 4.6 indicate that for some of the priority stocks it is the change 
of gear (from active to passive), rather than modifications of existing active gears, that would bring 
stocks closer to the optimal selectivity regimes. 

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4.8 

Identify  the  technical  support  required  to  assess  at  the  regional  level,  the 
potential  socio-economic  implications  of  fisheries-based  transition  plans  for 
improving yields (ToR 3) 

There  are  certain  barriers  to  transitioning  to  modified  or  alternative  fishing  gears  (Glass  et  al., 
2007; Jennings & Revill, 2007; Suuronen et al., 2007, Gascoigne & Willsteed, 2009). These include:  

Incompatibility of vessels with alternative gear;  

  Lack of familiarity with cost-effective and practical alternatives;  
  A fear to lose a level playing field among countries/fleets; 
  Availability of technologies;  
 
  Risk of losing marketable catch;  
  Additional work for crew making recruitment and retention difficult;  
  Concerns with safety at sea by using unfamiliar gears or strategies;  
  High investment costs;  
  Lack of capital or restricted access to capital;  
 
 

Ineffective technology infrastructure support; and  
Inflexible fisheries management systems. 

In order to achieve successful transition, Suuronen et al. (2007) described a range of factors and 
steps  that  need  to  be  taken  to  affect  change.  Research  and  development  priorities  should  be 
established with work undertaken to support development and uptake of modified or alternative 
fishing. These include: 

  Promoting  and  funding  studies  of  cost-effective  gear  designs  and  fishing  operations  to 
commercialize  economically  viable,  practical  and  safe  alternatives  to  current  fishing 
methods; 

  Analysis and review of best practice operations across fisheries, both at EU and globally;  
  Provide training to improve the technical ability among fishers to adapt to alternative gears; 
  Establishment  of  appropriate  incentives,  and  execution  of  robust  but  flexible  fishery 
management policies that support the transition to modified or alternative technologies;  
  Close cooperation between the fishing industry, scientists, managers and other stakeholders 

to enable development and introduction. 

The DiscardLess project developed a three-step process to assess operational changes (O’Neill & 
Noble, 2017):  

1.  Assess the difference in key outcome measures between the benchmark (baseline) gear and 

the experimental gear for the trial;  

2.  Estimate the annual implications of adopting the experimental gear based on results of the 

trial; 

3.  Indicate whether the trial gear has met specific targets or goals for the vessel operator. It 
should  be  noted  that  this  method  focuses  on  short  to  medium  term  implications  of  gear 
change for the vessel operator. 

To  measure  the  socio-economic  implications,  in  addition  to  technical  trials  and  estimates  of  the 
improvement in yield, data to estimate short-term and long-term costs and potential revenues, as 
well as estimates of capital costs for conversion/modifications of gear and vessels are needed. The 
impact on operational costs (e.g., crew retention, labour costs and fuel) also need to be factored 
into  any  assessment  (e.g.,  it  is  likely  that  converting  from  active  to  static  gears  would  have  a 
positive impact on fuel consumption but may lead to an increase in crew required as static gears 
are more labour intensive).  

In all cases, technical trials to establish whether the operational changes are viable are needed. 
The  results  of  these  trials  should  inform  the  trade-off  between  long-term  viability  against 
short-term  losses  and  feed  into  model  simulations  to  support  the  transition.  There  are  several 
bio-economic  models  available  that  could  be  used  to  project  the  potential  economic  outcomes. 
These include models such as FLBeia, MEFISTO, BEMTOOL. Particularly for conversion to alternative 
gears,  technical  trials  to  support  such  initiatives  are  not  necessarily  straightforward  and  would 

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require much longer trials to demonstrate practicality and viability, compared to gear modifications 
that are easier to assess.  

In relation to change management in fisheries, Pol & Eayrs (2019) suggest that fishers, scientists 
and managers are reluctant to change and they identify deficits in information and motivation as 
the drivers of this reluctance. They suggest changing management strategies to improve voluntary 
uptake of fishing gears that promote sustainability, while mandatory implementation of selective 
gears  was  identified  as  one  means  of  successfully  implementing  change.  The  TMR  lays  down 
mandatory  baseline  mesh  sizes  with  the  facility  for  alternative  gears  to  be  implemented  on  the 
basis of equivalent selectivity. Since the TMRs introduction in 2019 Member States have therefore 
been motivated to recommend the addition of multiple alternative gears to the TMR.  

The CFP goal that all stocks are managed at or above levels capable of producing MSY remains a 
challenge  for  certain  stocks.  Regional  multi-annual  plans  include  safeguards  that  require  the 
implementation of remedial measures to reduce fishing mortality where stocks are assessed to be 
below certain targets (e.g. Blim). These measures should ensure the rapid return of a given stock 
to  MSY  and  their  implementation  should  be  based  on  the  best  scientific  evidence  available. 
Gear-based remedial measures have been implemented for cod and whiting in parts of the Celtic 
Sea and for cod in the North Sea. These measures aims to drive changes in the selectivity of the 
fishery but the basis for their implementation is not always defined in terms of the shift in fishing 
mortality.  To  evaluate  the  effects  of  the  measures  are  further  complicated  by  the  fact  that  the 
practical implementation level is unclear. An example is that in the North Sea cod case (art. 16 of 
Council Regulation (EU) 2022/109), member  states  can avoid the implementation of the stricter 
remedial gear measures by so called national cod avoidance plans containing other (gear or non-
gear) measures to limit cod catches. 

The current process (EWG 22-19) addresses this deficit in information and provides fishers, gear 
technologists  and  managers  with  clear  goals  in  respect  of  fishing  mortality.  It  also  provides 
motivation to question whether currently implemented selective gears are likely to achieve target 
fishing mortality and identify or develop of gears that are more likely to reach targets. For instance, 
issues such as unaccounted/ cryptic mortality for fishing gear escapees (Gilman et al., 2013) which 
likely effects fish < MCRS to a greater extent than larger fish (Sangster et al., 1996), are driving 
development and implementation of gears that provide escape opportunities earlier in the catching 
process (Krag et al., 2010; Melli et al., 2018; McHugh et al., 2017; 2019; Browne et al., 2022). 
The development process takes time and resources. By addressing deficits in information such as 
target fishing mortality and providing motivation EWG 22-19 is promoting change.  

5 

DISCUSSION 

The work carried out by EWG 22-19, extends the work carried out by EWG 21-07 to facilitate the 
implementation of the TMR by offering analytical tools (FLSelex; FLSelexLen) and approaches to 
assess selectivity. The work combines the typically rather theoretical focus of stock assessments 
and projections with the more practical elements of gear trials and gear selectivity, and packages 
these two elements together in a rather hands-on tool (the isopleth diagrams) to assess trade-offs 
of  different  management  options.  This  work  illustrates  the  potential  of  improved  selectivity  in 
conjunction with changes in F to realise higher yields at lower level of stock depletion and maps 
relevant pathways to that end, thus serving results-based management. Additionally, the approach 
followed for the selectivity analysis presented in the case studies of Chapters 4.5  - 4.6 is readily 
transferable to the stock assessment process and could contribute to enhance our understanding 
of  the  state  and  yield  potential  of  fish  stocks.  This  can  be  seen  as  a  first  step  towards  the 
establishment of harvest control rules and catch advice in which the catch option tables would not 
only feature fishing mortality options, but also alternative options of selectivity and combinations 
thereof. 

5.1 

Caveats of the analysis  

The analysis conducted by EWG 22-19 to infer the optimal age/size that fish should be caught to 
optimise yields is based on projections that often lead to stock sizes that have not been observed 

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in  recent  history,  especially in  the  case  of  the  priority  stocks.  As  with  any  extrapolation  beyond 
available  observations,  this  comes  with  large  uncertainty.  Projections  are  subject  to  the 
assumptions on processes such as the spawner-recruit relationship (SRR), growth, natural mortality 
(M) etc. Such assumptions are based  on the average population state in most  recent years and 
may  be  appropriate  for  assessments  and  short-term  forecasts,  but  may  not  necessarily  hold  for 
long-term forecasts. Moreover, potential changes in the ecosystems, such as regime shifts, may 
largely affect the population dynamics of the exploited stocks, e.g. through a distortion in the SRR. 
In such cases, a change in fishing mortality or selectivity could have minor influence and might not 
automatically lead to an expected recovery of the stock (e.g. as is the case for the eastern Baltic 
cod  stock;  ICES,  2022c).  Therefore,  the  results  presented  here  should  interpreted  with  caution; 
they are informative about the direction of change in yield, juvenile catches etc., but the absolute 
changes may be under- or over-estimated.  

Optimisation  of  selectivity  under  both  current  and  varying  F  and  the  associated  optimal  L50s 
estimated  here  refer  to  population  selectivity.  When  comparing  population  selectivity  (L50) with 
gear selectivity (Lg50) it should be noted that these are different; population selectivity reflects the 
effect of all the gears acting on the fishery, while also being affected by availability (see Chapter 
2.1). Hence, such comparisons between L50 and Lg50 should be interpreted with caution. 

The use of A50/L50 as a ‘selectivity currency’ in this report should be treated with care, considering 
its limitations to quantify the underlying selectivity pattern across all ages. A50 is only one of the 
five  parameters  used  to  construct  selectivity  curves  in  FLSelex  (STECF,  2021),  and  while  it  is 
typically  the  most  influential  parameter  of  a  selectivity  curve  with  regards  to  yield  outcomes 
(Vasilakopoulos et al., 2016), different equilibrium yields could still emerge from potential changes 
in  the  shape  of  the  selectivity  curve  that  would  leave  A50/L50  unchanged.  In  actual  stocks, 
population selectivity curves can take many different shapes that may also differ from year to year 
(Sampson & Scott, 2012). For some of these curves (e.g. saddle-shaped ones), A50/L50 could be 
a  poor  indicator  of  selectivity.  A50/L50  was  deemed  adequate  to  summarize  selectivity  in  the 
context of the simulated projections to equilibrium employed here, which were designed to illustrate 
how far different stocks lie from an optimal selectivity. However, it would be precarious to only use 
A50/L50  to  track  interannual  changes  in  the  actual  selectivity,  as  estimated  from  annual  stock 
assessments.  For  monitoring  purposes,  an  F-based  selectivity  metric  would  be  more  adequate 
(Vasilakopoulos et al., 2020; STECF, 2020a).  

Another  challenge  for  this  analysis  was  the  absence  of  relevant  or  recent  gear  trials  to  produce 
representative gear selectivity curves for some of the stocks examined. In some cases, the results 
of  gear  trials  performed  a  long  time  ago  (e.g.  whg.27.7a)  and/or  in  other  areas  (e.g.  HKE.19; 
HKE.20) had to be used, with their applicability being questionable. Moreover, while static gears 
often exhibited the greatest potential for optimising selectivity of the priority stocks, there was a 
general  scarcity  of  published  gear  selectivity  parameters  for  static  gears  that  would  allow  us  to 
study their effect in more detail.  

5.2 

Next steps 

There  is  need  to  systematically  tackle  and  follow-up  the  implementation  of  the  TMR  in  order  to 
identify potential needs for increased selectivity. The work by EWG 21-07 and now EWG 22-19 has 
identified data needs, an analytical framework, and a tool to evaluate trade-offs in fishing mortality 
and  selectivity.  Furthermore,  EWG  22-19  offered  an  outline  and  a  qualitative  assessment  of  the 
operational changes needed to realise the transition to higher yields and associated socio-economic 
implications in response to ToR 3. This work needs to be extended in order to facilitate that the 
TMR works in practice. A natural next step would be to work through one or a few more concrete 
case  studies/examples  of  how  increased  selectivity  through  the  adoption  of  new/modified  gears 
could be implemented, including an operational transition plan and assessment of socio-economic 
consequences. 

The TMR stipulates (paragraphs 38-39) that every three years: “the Commission should report to 
the  European  Parliament  and  to  the  Council  on  the  implementation  of  this  Regulation.  (…)  That 
report should assess the extent to which technical measures (…) have contributed to achieving the 
objectives  and  reaching  the  targets  of  this  Regulation.  For  the  purpose  of  that  report,  adequate 
selectivity indicators (…) could be used as a reference tool to monitor progress over time towards 

    89 

89 

 
 
 
the CFP objective of minimising unwanted catches”. This suggest that there is a need to develop 
selectivity indicators to track the implementation of the TMR. Soon after the TMR came into force, 
a JRC-led study tested the suitability of several candidate metrics, shortlisting F-based indicators 
(such as Frec/Fbar) as the most suitable to track changes in selectivity (Vasilakopoulos et al., 2020). 
EWG 20-02 applied Frec/Fbar to a large number of stocks to track changes in selectivity, identifying 
the cases (e.g. stocks, stock assessment types) where this indicator worked better than others and 
the  reasons  for  that.  EWG  20-02  concluded  that  there  were  still  open  issues,  such  as  the  exact 
form  that  the  F-based  indicator  should  take,  the  need  to  link  the  selectivity  indicator  to  a 
threshold/reference point and the need for a better understanding of how changes in the indicator 
are  related  to  specific  technical  measures.  EWG  21-07  and  EWG  22-19,  while  not  focusing  on 
tracking selectivity changes, advanced some of these issues; e.g. EWG 21-07 proposed Fjuv/Fapical 
as  an  alternative  to  Frec/Fbar  and  both  EWGs  systematised  a  selectivity  optimisation  approach 
through FLSelex that could be used as the base for inferring selectivity thresholds. In any case, the 
work on selectivity indicators initiated at EWG 20-02 needs to be continued in order to facilitate the 
implementation of the TMR.  

6 

CONCLUSIONS 

The main conclusions of EWG 22-19 can be summarised as follows: 

  EWG 22-19 concludes that improvements in selectivity would most strongly benefit long-living, 
late-maturing  stocks  characterized  by  greater  growth  overfishing  (e.g.,  Mediterranean  hake 
stocks, NE Atantic cod stocks). 

  EWG 22-19 concludes that generally stocks that are heavily overfished would gain substantially 
in both yield and SSB if selectivity is increased together with a decrease in F. The heavier the 
overfishing the greater the distance from optimal selectivity; lowering F would allow selectivity 
improvements to be more effective in taking the stocks closer to MSY. 

  EWG  22-19  concludes  that  in  the  case  of  the  priority  stocks,  i.e.  the  ones  with  the  highest 
potential gains in yield and/or juvenile protection from a selectivity increase, no known gear 
can  readily  produce  optimal  yields  under  current  fishing  mortality.  Only  exception  was 
whg.27.7a,  due  to  the  biological  particularities  of  the  stock  and  possibly  outdated  gear 
selectivity estimates. 

  EWG 22-19 notes that partial population selectivity of OTB is sometimes worse (i.e. shifted to 
smaller fish) than expected from the published gear selectivity of the  baseline  codends. This 
could be due to technical tweaks or operational choices taken during commercial fishing that 
change  the  actual  gear  selectivity,  the  catch  of  juveniles  in  smaller-meshed  trawl  fisheries 
targeting other species, or it could be indicative of a higher availability of smaller than larger 
fish to the commercial trawlers. Nevertheless, there are also stocks that are largely caught with 
the  baseline  gear  only,  where  the  partial  population  selectivity  of  OTB  is  very  similar  to  the 
selectivity of the baseline gear, as expected (e.g. pok.27.3a46). In any case, gear selectivity 
alone is not always a reliable predictor of population selectivity.  

  EWG 22-19 concludes that in the case of priority stocks, the often scarce information available 
suggests that static gears (GNS, GTR, LLS) generally capture fish closer to their optimal size 
than active gears (OTB, TBB).  

  EWG 22-19 notes the limited availability of gear selectivity studies for static gears compared to 
the  availability  of  gear  selectivity  studies  for  active  gears,  which  hindered  a  more  thorough 
evaluation of the potential impact of static gears. 

  EWG 22-19 suggests there is a need for more gear selectivity studies estimating the selectivity 
curves of static gears. More studies of active gears with a selectivity closer to the optimal sizes 
identified here would also be valuable. 

    90 

90 

 
 
 
  EWG  22-19  concludes  that  any  transition  in  gear  selectivity  comes  with  implementation 
challenges and short-term economic losses, which are greater in the case of gear change (e.g., 
from active to static gears) than in the case of gear modification (e.g., codend mesh increase 
in  trawlers).  By  contrast,  the  potential  gains  in  yield  and  protection  of  juveniles  are  usually 
greater for gear changes than for gear modifications. 

  EWG 22-19 concludes that the results from any trial testing gear modifications or gear changes 
are case specific, and any operational change would require a detailed cost-benefit study prior 
to  the  introduction  of  technical  modifications,  as  well  as  a  transition  plan  based  on  an 
assessment of the impacts to address any issues with implementation.  

  EWG 22-19 notes that technological change is complex and challenging to achieve due to the 
inherent uncertainty and the underlying perception of the  fishing industry that  such changes 
will lead to significant capital outlay and economic loss. Moving to modified or alternative gears 
requires  not  only  technical  trials,  but  also  an  accompanying  socio-economic  and  technical 
assessment,  as  fishers  need a level of assurance that the new gear will have benefits in the 
long-term. 

  EWG 22-19 suggests that further work will be needed to develop advanced technical support 

tools, most likely tailored to specific regions and fisheries, at a later stage. 

7  CONTACT DETAILS OF EWG-22-19 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 

STECF members 

Name 

Affiliation1 

Email 

Ligas, Alessandro  

CIBM  Consorzio  per  il  Centro 
Interuniversitario  di  Biologia 
Marina ed Ecologia Applicata “G. 
Bacci”, Italy  

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

Moore, Claire 

Marine Institute, Ireland 

claire.moore@marine.ie 

Raid, Tiit  

Estonian 
University of Tartu, Estonia 

Marine 

Institute, 

Tiit.raid@gmail.com  

    91 

91 

 
 
 
 
 
Rihan, Dominic  

BIM, Ireland 

rihan@bim.ie  

Valentinsson, Daniel 

Swedish 
Agricultural  Sciences 
Sweden 

University 

of 
(SLU), 

daniel.valentinsson@slu.se 

Invited experts 

Name 

Affiliation1 

Email 

Browne, Daragh 

Bord Iascaigh Mhara 

daragh.browne@bim.ie 

De Carlo, Francesco 

APLYSIA  Soc.  Coop. 
a r. l. 

fradecarlo@gmail.com 

Hommik, Kristiina 

Eesti  Mereinstituut 
Tartu Ülikool 

kristiina.hommik@ut.ee 

Jung, Armelle 

DRDH 

armelle@desrequinsetdeshommes.org 

Kalogirou, Stefanos 

Agricultural 
University of Athens 

stefanos.kalogirou@aua.gr 

Kovsars, Maksims 

Mantopoulou Palouka, Danai 

Mantzouni, Eirini 

Thasitis, Ioannis 

Institute  of 
Food 
safety, Animal Health 
and 
Environment 
"BIOR" 

Aristotle  University 
of Thessaloniki 

Hellenic  Ministry  of 
Rural  Development 
and Food 

of 
Department 
Fisheries  and  Marine 
Research  

maksims.kovsars@bior.lv 

danaim@hcmr.gr 

emantzo@gmail.com 

ithasitis@dfmr.moa.gov.cy 

    92 

92 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Ticina, Vjekoslav 

Institute 
of 
Oceanography  and 
Fisheries 

ticina@izor.hr 

Viva, Claudio 

Freelance biologist 

viva@cibm.it 

JRC experts 

Name 

Affiliation1 

Email 

Gras, Michael (focal) 

JRC Ispra 

michael.gras@ec.europa.eu 

Vasilakopoulos, Paris 

JRC Ispra 

paris.vasilakopoulos@ec.europa.eu 

European Commission 

Name 

Affiliation1 

Email 

Gras, Michael 

JRC Ispra 

jrc-stecf-secretariat@ec.europa.eu 

Observers 

Name 

Affiliation1 

Email 

Piron, Marzia 

Mediterranean Advisory Council 

segreteria@med-ac.eu 

    93 

93 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8  LIST OF ANNEXES  

Annex 1.  Gear  specifications and gear selectivity  curve  parameters by stock. ‘Plotted’ refers to gear selectivity curves being plotted  in the case studies (i.e. 
Chapters 4.5-4.6) SM: square mesh codend; DM: diamond mesh codend; SMP: square mesh panel; RFL: raised fishing line; OTB: bottom otter trawls; GNS: Set 
gillnets; LLS: Set longlines 

Plotted  Region  Area  Stock 

Gear 
type 

Mesh 
size 

Gear 
Specification 

Baseline  Lg50 (cm) 

SRg 
(cm) 

Reference 

Notes 

Y 

NEA 

BS 

cod.27.22-24 

OTB 

120 

Bacoma 

NEA 

BS 

cod.27.22-24 

OTB 

146 

Bacoma 

NEA 

BS 

cod.27.22-24 

OTB 

120 

SM 

NEA 

BS 

cod.27.22-24 

OTB 

130 

SM 

Y 

Y 

NEA 

BS 

cod.27.22-24 

OTB 

140 

SM 

NEA 

BS 

cod.27.22-24 

OTB 

120 

T90 

NEA 

BS 

cod.27.22-24 

OTB 

130 

T90 

NEA 

BS 

cod.27.22-24 

OTB 

140 

T90 

NEA 

NEA 

NEA 

BS 

BS 

BS 

cod.27.22-24 

OTB 

120 

2x4 mm 

cod.27.22-24 

OTB 

130 

2x4 mm 

cod.27.22-24 

OTB 

140 

2x4 mm 

NEA 

BS 

cod.27.22-24 

GNS 

110 

1.5x4; 
ratio 50% 

hanging 

NEA 

BS 

ple.27.21-23 

OTB 

120 

Bacoma 

    94 

94 

Y 

N 

N 

N 

N 

Y 

N 

N 

N 

N 

N 

Y 

Y 

38.7 

8 

45.2 

10.3 

42.4 

7.2 

46.1 

7.2 

50 

7.2 

42.3 

6.7 

44.5 

6.7 

48 

6.7 

Wienbeck  et  al., 
2014 

Wienbeck  et  al., 
2014 

Wienbeck  et  al., 
2014 

2008; 
Herrmann, 
Herrmann  et  al., 
2009 

Herrmann, 
2008; 
Herrmann  et  al., 
2009 

Herrmann, 
2008; 
Herrmann  et  al., 
2009 

2008; 
Herrmann, 
Herrmann  et  al., 
2009 

Herrmann, 
2008; 
Herrmann  et  al., 
2009 

35.6 

40.3 

44.9 

45.5 

9.3 

Madsen, 2007 

10 

Madsen, 2007 

10.8 

Madsen, 2007 

Madsen, 2007 

21.4 

2 

Wienbeck  et  al., 
2014 

modal 
shaped 
curve 

size, 

bell 
selection 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Plotted  Region  Area  Stock 

Gear 
type 

Mesh 
size 

Gear 
Specification 

Baseline  Lg50 (cm) 

SRg 
(cm) 

Reference 

Notes 

NEA 

BS 

ple.27.21-23 

OTB 

146 

Bacoma 

NEA 

BS 

ple.27.21-23 

OTB 

120 

SM 

NEA 

BS 

ple.27.21-23 

OTB 

130 

SM 

NEA 

BS 

ple.27.21-23 

OTB 

140 

SM 

NEA 

BS 

ple.27.21-23 

OTB 

120 

T90 

NEA 

BS 

ple.27.21-23 

OTB 

130 

T90 

NEA 

BS 

ple.27.21-23 

OTB 

140 

T90 

NEA 

BS 

ple.27.21-23 

OTB 

90 

120 mm SMP 

NEA 

BS 

ple.27.21-23 

OTB 

120 

NEA 

BS 

ple.27.21-23 

OTB 

130 

NEA 

BS 

ple.27.21-23 

OTB 

140 

NEA 

NS 

cod.27.47d20 

OTB 

90 

NEA 

NS 

cod.27.47d20 

OTB 

120 

Y 

Y 

4 mm, 100 meshes 
around, 300 kg 

4 mm, 100 meshes 
around, 300 kg 

4 mm, 100 meshes 
around, 300 kg 

270  mm  escape 
window (SELTRA) 

NEA 

NEA 

NS 

NS 

cod.27.47d20 

cod.27.47d20 

OTB 

OTB 

120 

135 

and 0.6 m  RFL 

1x8 mm 

    95 

95 

N 

N 

N 

N 

Y 

N 

N 

Y 

N 

N 

N 

Y 

Y 

N 

N 

24.9 

4.3 

20.8 

22.3 

24.4 

3 

3 

3 

24.3 

2.1 

26 

29 

2.1 

2.1 

18.8 

5.1 

Wienbeck  et  al., 
2014 

Wienbeck  et  al., 
2014 

Herrmann, 
2008; 
Herrmann  et  al., 
2009 

2008; 
Herrmann, 
Herrmann  et  al., 
2009 

Herrmann, 
2008; 
Herrmann  et  al., 
2009 

Herrmann, 
2008; 
Herrmann  et  al., 
2009 

Herrmann, 
2008; 
Herrmann  et  al., 
2009 

Frandsen 
2009 

et 

al., 

26.4 

2.6 

O´Neill et al., 2020 

28.3 

2.6 

O´Neill et al., 2020 

30.2 

2.6 

O´Neill et al., 2020 

33.8 

39.1 

Krag et al., 2016 

Baseline  trawl  gear  in 
3a (SD 20+21) 

39.4 

8.4 

Madsen  & 
Ferro 
2003,  STECF  PLEN 
20-01 

Baseline  trawl  gear  in 
4 and 7d 

42.0 

52.14 

9.0 

STECF PLEN 20-01 

5.64 

Sistiaga et al., 2009 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Plotted  Region  Area  Stock 

Gear 
type 

Mesh 
size 

Gear 
Specification 

Baseline  Lg50 (cm) 

SRg 
(cm) 

Reference 

Notes 

Y 

NEA 

NS 

cod.27.47d20 

OTB 

155 

1x8 mm 

NEA 

NS 

cod.27.47d20 

OTB 

Flexigrid 

NEA 

NS 

cod.27.47d20 

OTB 

Sort-V 

NEA 

NS 

cod.27.47d20 

OTB 

Flexigrid 

NEA 

NS 

cod.27.47d20 

OTB 

135 

Y 

NEA 

NS 

cod.27.47d20 

OTB 

145 

NEA 

NS 

cod.27.47d20 

OTB 

Grid 

NEA 

NS 

cod.27.47d20 

SDN 

124 

55  mm  grid/  135 
mm codend 

55  mm  grid/  135 
mm codend 

55  mm  grid/  130 
mm codend 

T90 

T90 

35 mm grid/90 mm 
codend 

2x4  mm, 
meshes 

97 

NEA 

NS 

had.27.46a20 

OTB 

90 

120 mm SMP 

NEA 

NS 

had.27.46a20 

OTB 

120 

2x4  mm; 
meshes 

100 

NEA 

NS 

had.27.46a20 

OTB 

120 

and 0.6 m  RFL 

NEA 

NEA 

NS 

NS 

had.27.46a20 

OTB 

135 

1x8 mm 

had.27.46a20 

OTB 

155 

1x8 mm 

NEA 

NS 

had.27.46a20 

OTB 

Flexigrid 

NEA 

NS 

had.27.46a20 

OTB 

Flexigrid 

NEA 

NS 

had.27.46a20 

OTB 

Sort-V 

55  mm  grid/  135 
mm codend 

55  mm  grid/  130 
mm codend 

55  mm  grid/  135 
mm codend 

NEA 

NEA 

NS 

NS 

had.27.46a20 

had.27.46a20 

OTB 

OTB 

135 

145 

T90 

T90 

    96 

96 

N 

N 

N 

N 

N 

N 

Y 

N 

Y 

Y 

N 

N 

N 

N 

N 

N 

N 

N 

61.26 

8.67 

Sistiaga et al., 2009 

Barents Sea study but 
included for contrast 

Barents Sea study but 
included for contrast 

(bell  shaped  selection 
curve:  19.63;  5.32; 
0.44) 

50.45 

11.27 

Sistiaga et al., 2009 

52 

10.93 

Sistiaga et al., 2009 

52.7 

50.2 

55.1 

19.63 

8.1 

Brinkhof et al., 2022 

9.7 

Brinkhof et al., 2022 

12.1 

Brinkhof et al., 2022 

Frandsen 
2009 

et 

al., 

41.6 

12.6 

Noack et al., 2017 

43.8 

18.6 

35.9 

35.9 

6 

6 

Frandsen 
2009 

et 

al., 

Madsen  &  Ferro, 
2003,  STECF  PLEN 
20-01 

Madsen  &  Ferro, 
2003,  STECF  PLEN 
20-01 

45 

6.62 

Sistiaga et al., 2009 

56.36 

6.46 

Sistiaga et al., 2009 

46.67 

11 

Sistiaga et al., 2009 

51.9 

9.2 

Brinkhof et al., 2022 

49.22 

9.61 

Sistiaga et al., 2009 

49 

51.6 

8.8 

8.5 

Brinkhof et al., 2022 

Brinkhof et al., 2022 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Plotted  Region  Area  Stock 

Gear 
type 

Mesh 
size 

Gear 
Specification 

Baseline  Lg50 (cm) 

SRg 
(cm) 

Reference 

Notes 

NEA 

NS 

had.27.46a20 

OTB 

Nephrops 
grid 

35 mm grid/90 mm 
codend 

NEA 

NS 

ple.27.420 

SDN 

124 

2x4  mm, 
meshes 

97 

NEA 

NS 

ple.27.420 

OTB 

90 

120 mm SMP 

NEA 

NS 

ple.27.420 

OTB 

100 

NEA 

NS 

ple.27.420 

OTB 

110 

NEA 

NS 

ple.27.420 

OTB 

120 

NEA 

NS 

ple.27.420 

OTB 

130 

NEA 

NS 

ple.27.420 

OTB 

140 

NEA 

NS 

ple.27.7d 

OTB 

100 

NEA 

NS 

ple.27.7d 

OTB 

110 

NEA 

NS 

ple.27.7d 

OTB 

120 

NEA 

NS 

ple.27.7d 

OTB 

130 

NEA 

NS 

ple.27.7d 

OTB 

140 

Y 

NEA 

NS 

pok.27.3a46 

OTB 

120 

4 mm, 100 meshes 
around, 300 kg 

4 mm, 100 meshes 
around, 300 kg 

4 mm, 100 meshes 
around, 300 kg 

4 mm, 100 meshes 
around, 300 kg 

4 mm, 100 meshes 
around, 300 kg 

4 mm, 100 meshes 
around, 300 kg 

4 mm, 100 meshes 
around, 300 kg 

4 mm, 100 meshes 
around, 300 kg 

4 mm, 100 meshes 
around, 300 kg 

4 mm, 100 meshes 
around, 300 kg 

2x5  mm, 
meshes 

100 

NEA 

NS 

pok.27.3a46 

OTB 

90 

120 mm SMP 

NEA 

NS 

pok.27.3a46 

OTB 

Flexigrid 

55  mm  grid/  130 
mm codend 

NEA 

NS 

pok.27.3a46 

OTB 

135 

T90 

    97 

97 

Y 

Y 

Y 

Y 

N 

Y 

N 

N 

N 

N 

Y 

N 

N 

Y 

Y 

N 

N 

16.26 

Frandsen 
2009 

et 

al., 

(bell  shaped  selection 
curve:  16.26;  5.56; 
0.15) 

29.1 

2.2 

Noack et al., 2017 

18.8 

5.1 

Frandsen 
2009 

et 

al., 

22.6 

2.6 

O´Neill et al., 2020 

Baseline gear south of 
57.30 

24.5 

2.6 

O´Neill et al., 2020 

26.4 

2.6 

O´Neill et al., 2020 

Baseline gear north of 
57.30 

28.3 

2.6 

O´Neill et al., 2020 

30.2 

2.6 

O´Neill et al., 2020 

22.6 

2.6 

O´Neill et al., 2020 

24.5 

2.6 

O´Neill et al., 2020 

26.4 

2.6 

O´Neill et al., 2020 

28.3 

2.6 

O´Neill et al., 2020 

30.2 

2.6 

O´Neill et al., 2020 

46.4 

9.3 

Graham et al., 2004 

37.1 

1.8 

Frandsen 
2009 

et 

al., 

54.5 

50.8 

7.3 

Brinkhof et al., 2022 

7.7 

Brinkhof et al., 2022 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Plotted  Region  Area  Stock 

Gear 
type 

Mesh 
size 

Gear 
Specification 

Baseline  Lg50 (cm) 

SRg 
(cm) 

Reference 

Notes 

N 

Y 

N 

Y 

N 

N 

Y 

NEA 

NS 

pok.27.3a46 

OTB 

145 

T90 

NEA 

NS 

whg.27.47d 

OTB 

120 

2x4  mm; 
meshes 

100 

NEA 

NS 

whg.27.47d 

OTB 

120 

and 0.6 m  RFL 

Y 

Y 

Y 

Y 

Y 

Y 

Y 

NEA 

NWW  cod.27.6a 

OTB 

120 

NEA 

NWW  cod.27.6a 

OTB 

120 

And 1 m  RFL 

NEA 

NWW  cod.27.6a 

OTB 

145 

T90 

NEA 

NWW  cod.27.7e-k 

OTB 

100 

T90 and 1 m (RFL)  N 

NEA 

NWW  cod.27.7e-k 

OTB 

110 

120 mm SMP and 1 
m RFL 

NEA 

NWW  cod.27.7e-k 

OTB 

120 

1 m  RFL 

NEA 

NWW  cod.27.7e-k 

OTB 

100 

2x4  mm; 
meshes 

100 

NEA 

NWW  had.27.6b 

OTB 

120 

NEA 

NWW  had.27.6b 

OTB 

110 

NEA 

NWW  had.27.6b 

OTB 

110 

NEA 

NWW  had.27.6b 

OTB 

120 

110mm 
12m 

110mm 
12m 

120mm 
12m 

SMP/9-

SMP/9-

SMP/9-

N 

N 

Y 

Y 

N 

N 

N 

56.2 

41.7 

8.5 

Brinkhof et al., 2022 

11.9 

Madsen  &  Ferro, 
2003 

41.7 

11.4 

39.4 

8.4 

Madsen  &  Ferro, 
2003,  STECF  PLEN 
20-01 

Madsen  &  Ferro, 
2003,  STECF  PLEN 
20-01 

9 

STECF PLEN 20-01 

12.1 

Brinkhof et al., 2022 

Barents Sea study but 
included for contrast 

7.4 

STECF PLEN 20-01 

9.2 

STECF PLEN 20-01 

42 

55.1 

39 

44 

42 

32.7 

9 

7 

STECF PLEN 20-01 

Madsen  &  Ferro, 
2003 

Madsen  &  Ferro, 
2003 

32.1 

6.1 

33.13 

7.67 

BIM, 2009 

34.03 

13.81 

BIM, 2009 

38.07 

10.07 

BIM, 2009 

NEA 

NWW  had.27.6b 

OTB 

100 

120mm SMP/6-9m  N 

39.17 

10.83 

BIM, 2009 

NEA 

NWW  had.27.7a 

OTB 

120 

NEA 

NEA 

NWW  had.27.7b-k 

NWW  had.27.7b-k 

OTB 

OTB 

80 

90 

NEA 

NWW  had.27.7b-k 

OTB 

90 

Y 

N 

N 

N 

33.3 

6.6 

Madsen  &  Ferro, 
2003 

24.23 

27.67 

11.2 

BIM, 2010 

13.05 

BIM, 2010 

34.47 

18.3 

BIM, 2010 

120mm 
12m 

SMP/9-

    98 

98 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Plotted  Region  Area  Stock 

Gear 
type 

Mesh 
size 

Gear 
Specification 

Baseline  Lg50 (cm) 

SRg 
(cm) 

Reference 

Notes 

NEA 

NEA 

NEA 

NWW  had.27.7b-k 

NWW  had.27.7b-k 

OTB 

OTB 

100 

120 

NWW  had.27.7b-k 

OTB 

80 

NEA 

NWW  had.27.7b-k 

OTB 

80 

NEA 

NEA 

NWW  had.27.7b-k 

NWW  had.27.7b-k 

OTB 

OTB 

110 

100 

NEA 

NWW  had.27.7b-k 

OTB 

100 

NEA 

NWW  had.27.7b-k 

OTB 

110 

120mm 
12m 

SMP/9-

T90 and 1 m RFL 

160 mm SMP and 1 
m RFL 

120 mm SMP and 1 
m RFL 

NEA 

NEA 

NWW  had.27.7b-k 

OTB 

120 

And 1 m  RFL 

NWW  ple.27.7a 

OTB 

120 

NEA 

NWW  whg.27.6a 

OTB 

110 

NEA 

NWW  whg.27.6a 

OTB 

110 

NEA 

NWW  whg.27.6a 

OTB 

120 

110mm 
12m 

110mm 
12m 

120mm 
12m 

SMP/9-

SMP/9-

SMP/9-

Y 

N 

N 

N 

N 

N 

N 

N 

N 

Y 

N 

N 

N 

27.95 

42.73 

21.33 

11.86 

BIM, 2010 

9.02 

BIM, 2010 

11.57 

BIM, 2010 

28.21 

14.84 

BIM, 2010 

32.39 

32.9 

19.45 

BIM, 2010 

5.8 

STECF PLEN 20-01 

38.8 

15.2 

STECF PLEN 20-01 

36.4 

35.9 

26.4 

8.6 

STECF PLEN 20-01 

6 

STECF PLEN 20-01 

2.6 

STECF PLEN 20-01 

38.67 

8.9 

BIM, 2009 

36.17 

10.19 

BIM, 2009 

47.2 

12.09 

BIM, 2009 

NEA 

NWW  whg.27.6a 

OTB 

100 

120mm SMP/6-9m  N 

48.72 

17.5 

BIM, 2009 

Y 

Y 

NEA 

NWW  whg.27.7a 

OTB 

120 

NEA 

NWW  whg.27.7a 

OTB 

80 

120 mm SMP 

Y 

N 

NEA 

NWW  whg.27.7b-ce-k 

OTB 

100 

T90 and 1 m (RFL)  N 

NEA 

NWW  whg.27.7b-ce-k 

OTB 

100 

NEA 

NWW  whg.27.7b-ce-k 

OTB 

110 

160 mm SMP and 1 
m RFL 

120 mm SMP and 1 
m RFL 

NEA 

NEA 

NWW  whg.27.7b-ce-k 

OTB 

120 

And 1 m  RFL 

NWW  whg.27.7b-ce-k 

OTB 

90 

N 

N 

N 

N 

38.7 

11.1 

Madsen  &  Ferro, 
2003 

32.9 

38.4 

52.2 

9.4 

Madsen  &  Ferro, 
2003 

10 

STECF PLEN 20-01 

13.5 

STECF PLEN 20-01 

42.3 

11.9 

STECF PLEN 20-01 

41.7 

31.06 

11.4 

STECF PLEN 20-01 

9.57 

BIM, 2010 

    99 

99 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Plotted  Region  Area  Stock 

Gear 
type 

Mesh 
size 

Gear 
Specification 

Baseline  Lg50 (cm) 

SRg 
(cm) 

Reference 

Notes 

NEA 

NWW  whg.27.7b-ce-k 

OTB 

90 

NEA 

NEA 

NWW  whg.27.7b-ce-k 

OTB 

NWW  whg.27.7b–ce–k 

OTB 

100 

110 

NEA 

SWW  hke.27.3a46-8abd  OTB 

55 

120mm 
12m 

SMP/9-

NEA 

SWW  hke.27.3a46-8abd  OTB 

70 

T90 

NEA 

SWW  hke.27.3a46-8abd  OTB 

55 

NEA 

SWW  hke.27.3a46-8abd  OTB 

70 

NEA 

NEA 

SWW  hke.27.3a46-8abd  OTB 

SWW  hke.27.3a46-8abd  OTB 

80 

60 

NEA 

NWW  hke.27.3a46-8abd  OTB 

110 

NEA 

NWW  hke.27.3a46-8abd  OTB 

120 

110mm 
12m 

120mm 
12m 

SMP/9-

SMP/9-

N 

Y 

N 

Y 

N 

Y 

Y 

N 

N 

N 

N 

37.24 

9.01 

BIM, 2010 

35.96 

37.43 

14.04 

BIM, 2010 

17.97 

BIM, 2010 

19.77 

12.84 

Valerias et al., 2014 
(DESCARSEL) 

Baseline  mesh  size  in 
9a 

22.45 

8.26 

Valerias et al., 2014 
(DESCARSEL) 

13.5 

23.1 

15.68 

23.49 

Anon., 2022 

Anon., 2022 

Baseline  mesh  size  in 
9a 

Baseline  mesh  size 
except in 9a 

7.92 

Cuende et al., 2022 

4.36 

Cuende et al., 2022 

30.6 

33.96 

BIM, 2009 

50.07 

29.68 

BIM, 2009 

NEA 

NWW  hke.27.3a46-8abd  OTB 

100 

120mm SMP/6-9m  N 

37.4 

21.6 

BIM, 2009 

NEA 

NWW  hke.27.3a46-8abd  OTB 

90 

NEA 

NWW  hke.27.3a46-8abd  OTB 

90 

90+120 mm SMP 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NWW  hke.27.7e-k 

OTB 

110 

NWW  hke.27.7e-k 

GNS 

94 

NWW  hke.27.7e-k 

GNS 

106 

NWW  hke.27.7e-k 

GNS 

115 

NWW  hke.27.7e-k 

GNS 

126 

NWW  hke.27.7e-k 

GNS 

155 

NEA 

NWW  hke.27.7e-k 

GNS 

80 

N 

N 

N 

N 

N 

N 

N 

N 

Y 

    100 

100 

30.68 

12.87 

BIM, 2010 

32.3 

38.11 

65.9 

74.3 

80.6 

88.3 

108.6 

17.8 

BIM, 2010 

BIM, 2010 

Anon., 1993 

Anon., 1993 

Anon., 1993 

Anon., 1993 

Anon., 1993 

56,28±9,3 

BIM, 2011 

Baseline  mesh  size  in 
8c and 9 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Plotted  Region  Area  Stock 

Gear 
type 

Mesh 
size 

Gear 
Specification 

Baseline  Lg50 (cm) 

SRg 
(cm) 

Reference 

Notes 

NEA 

NWW  hke.27.7e-k 

GNS 

100 

NEA 

NEA 

NWW  hke.27.7e-k 

GNS 

120 

NWW  hke.27.7e-k 

GNS 

140 

NEA 

NWW  hke.27.7e-k 

GNS 

80 

NEA 

NWW  hke.27.7e-k 

GNS 

100 

NEA 

NEA 

NEA 

NEA 

NWW  hke.27.7e-k 

GNS 

120 

NWW  hke.27.7e-k 

GNS 

140 

SWW  hke.27.8c9a 

SWW  hke.27.8c9a 

OTB 

OTB 

55 

60 

DM 

DM 

NEA 

SWW  hke.27.9a 

GNS 

70 

NEA 

SWW  hke.27.9a 

GNS 

80 

NEA 

SWW  hke.27.9a 

GNS 

90 

NEA 

SWW  hke.27.9a 

NEA 

SWW  hke.27.9a 

NEA 

SWW  hke.27.9a 

NEA 

SWW  hke.27.9a 

LLS 

LLS 

LLS 

LLS 

NEA 

NEA 

NEA 

NEA 

NWW  meg.27.7b-k8abd  OTB 

NWW  meg.27.7b-k8abd  OTB 

NWW  meg.27.7b-k8abd  OTB 

100 

110 

110 

NWW  meg.27.7b-k8abd  OTB 

90 

NEA 

NWW  meg.27.7b-k8abd  OTB 

90 

    101 

101 

Hook size 10 

Hook size 9 

Hook size 7 

Hook size 5 

90mm SMP/6-9m 

90mm SMP/6-9m 

120mm SMP/6-9m  N 

120mm 
12m 

SMP/9-

N 

N 

Y 

N 

N 

Y 

Y 

N 

N 

N 

N 

N 

Y 

N 

N 

N 

N 

N 

N 

N 

70,36±11,63 

BIM, 2011 

84,43±13,95 

98±16,06 

BIM, 2011 

BIM, 2011 

54±8,2 

Revill et al., 2007 

Baseline  mesh  size 
except in 8c and 9 

Baseline  mesh  size  in 
8c and 9 

Baseline  mesh  size 
except in 8c and 9 

63±7,3 

76±8,7 

88±10,2 

15.9 

17.4 

40,1±2,42 

46,7±2,82 

51,1±3,08 

45,6±4,98 

45,6±4,71 

44,4±4,61 

45,3±4,97 

28.55 

31.02 

32.04 

33.75 

Revill et al., 2007 

Revill et al., 2007 

Revill et al., 2007 

3 

Campos et al., 2003 

3.8 

Campos et al., 2003 

Dos  Santos  et  al., 
2001 

Dos  Santos  et  al., 
2001 

Baseline  mesh  size  in 
8c and 9 

Dos  Santos  et  al., 
2001 

Dos  Santos  et  al., 
2001 

Dos  Santos  et  al., 
2001 

Dos  Santos  et  al., 
2001 

Dos  Santos  et  al., 
2001 

12.77 

BIM, 2008 

7.35 

BIM, 2008 

9.74 

BIM, 2008 

17.38 

BIM, 2010 

33.2 

14.58 

BIM, 2010 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Plotted  Region  Area  Stock 

Gear 
type 

Mesh 
size 

Gear 
Specification 

Baseline  Lg50 (cm) 

SRg 
(cm) 

Reference 

Notes 

NEA 

NEA 

NWW  meg.27.7b-k8abd  OTB 

NWW  meg.27.7b-k8abd  OTB 

100 

120 

NEA 

NWW  meg.27.7b-k8abd  OTB 

80 

NEA 

NWW  meg.27.7b-k8abd  OTB 

100 

NEA 

NEA 

NWW  meg.27.7b-k8abd  OTB 

110 

NWW  hke.27.3a46-8abd  OTB 

110 

MED 

WM 

HKE.01_05_06_07  OTB 

40 

MED 

WM 

HKE.01_05_06_07  OTB 

40 

MED 

WM 

HKE.01_05_06_07  OTB 

50 

MED 

WM 

HKE.01_05_06_07  OTB 

52 

MED 

MED 

MED 

MED 

WM 

HKE.01_05_06_07  OTB 

WM 

HKE.01_05_06_07  OTB 

WM 

HKE.01_05_06_07  OTB 

WM 

HKE.01_05_06_07  OTB 

40 

50 

50 

40 

MED 

WM 

HKE.01_05_06_07  OTB 

40 

MED 

MED 

MED 

WM 

HKE.08_09_10_11  OTB 

WM 

HKE.08_09_10_11  OTB 

WM 

HKE.08_09_10_11  OTB 

40 

50 

60 

MED 

WM 

HKE.08_09_10_11  GNS 

53 

Y 

Y 

Y 

120mm 
12m 

120mm 
12m 

SMP/9-

SMP/9-

20 mm grid/40 mm 
codend 

20 mm grid/40 mm 
codend 

DM 

DM 

SM 

SM 

SM 

SM 

SM 

SM 

DM 

DM 

DM 

MED 

WM 

HKE.08_09_10_11  GNS 

62.5 

DM 

Y 

N 

N 

N 

N 

N 

N 

N 

Y 

N 

Y 

N 

N 

Y 

Y 

Y 

Y 

N 

N 

N 

34.61 

41.24 

11.03 

BIM, 2010 

9.53 

BIM, 2010 

29.09 

15.59 

BIM, 2010 

38.8 

16.16 

BIM, 2010 

38.53 

38.11 

14.74 

BIM, 2010 

7.56 

BIM, 2010 

18.9 

3.4 

Massutì et al., 2009 

16.8 

4.1 

Sardà et al. 2004 

14.5 

15.2 

17.64 

22.19 

20.9 

17.2 

16 

17.62 

13.71 

15.5 

29 

34 

NA 

NA 

Larraneta  et  al., 
1969 

Larraneta  et  al., 
1969 

7.4 

Sbrana et al., 2022 

5.03 

Sbrana et al., 2022 

5.1 

2.4 

4.8 

Sbrana et al., 2022 

Baro et al., 2007 

Bahamon 
2006 

et 

al., 

6.35 

Brcic et al., 2018 

3.37 

Brcic et al., 2018 

4.8 

Lembo et al., 2002 

Sbrana et al., 2007 

Sbrana et al., 2007 

estimated by applying 
the  bi-modal  function 
SELECT method, 

estimated by applying 
the  bi-modal  function 
SELECT method, 

    102 

102 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Plotted  Region  Area  Stock 

Gear 
type 

Mesh 
size 

Gear 
Specification 

Baseline  Lg50 (cm) 

SRg 
(cm) 

Reference 

Notes 

MED 

WM 

HKE.08_09_10_11  GNS 

70 

DM 

MED 

WM 

HKE.08_09_10_11  GNS 

82 

DM 

N 

N 

38 

43 

Sbrana et al., 2007 

Sbrana et al., 2007 

MED 

ALL 

HKE.08_09_10_11  OTB 

40 

SM 

Y* 

14.88 

4.5 

Lucchetti et al., 2021 

Y 

MED 

ALL 

HKE.08_09_10_11  OTB 

55 

MED 

CEM 

HKE.17_18 

OTB 

60 

Y 

MED 

CEM 

HKE.17_18 

OTB 

40 

MED 

CEM 

HKE.17_18 

OTB 

56 

MED 

CEM 

HKE.17_18 

OTB 

54 

SM 

DM 

SM 

DM 

T90 

N* 

20.46 

8 

Lucchetti et al., 2021 

N 

Y 

N 

N 

16.6 

15.7 

4.6 

Soldo, 2004 

8.68 

Sala  &  Lucchetti, 
2010 

16.25 

7.56 

Sala  &  Lucchetti, 
2011 

21.26 

7.02 

Petetta et al., 2020 

Y 

MED 

CEM 

HKE.17_18 

OTB 

55 

SM 

N* 

20.46 

8 

Lucchetti et al., 2021 

MED 

CEM 

HKE.19 

OTB 

40 

SM 

MED 

CEM 

HKE.19 

OTB 

50 

DM 

Y 

Y 

14.9 

5.9 

Aydin  &  Tosunoglu, 
2010 

14.4 

6.3 

Dereli & Aydin, 2016 

MED 

ALL 

HKE.19 

OTB 

40 

SM 

Y* 

14.88 

4.5 

Lucchetti et al., 2021 

MED 

ALL 

HKE.19 

OTB 

55 

SM 

N* 

20.46 

8 

Lucchetti et al., 2021 

MED 

ALL 

HKE.20 

OTB 

40 

SM 

Y* 

14.88 

4.5 

Lucchetti et al., 2021 

Y 

Y 

Y 

estimated by applying 
the  bi-modal  function 
SELECT method, 

estimated by applying 
the  bi-modal  function 
SELECT method, 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

L50 and SR From GSA 
22  paper  cited 
in 
Lucchetti et al. 2021 

L50 and SR From GSA 
22  paper  cited 
in 
Lucchetti et al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

    103 

103 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Plotted  Region  Area  Stock 

Gear 
type 

Mesh 
size 

Gear 
Specification 

Baseline  Lg50 (cm) 

SRg 
(cm) 

Reference 

Notes 

Y 

MED 

ALL 

HKE.20 

OTB 

55 

SM 

N* 

20.46 

8 

Lucchetti et al., 2021 

MED 

ALL 

HKE.20 

OTB 

40 

SM 

MED 

ALL 

HKE.20 

OTB 

50 

DM 

MED 

EM 

HKE.22 

OTB 

50 

MED 

MED 

EM 

EM 

HKE.22 

HKE.22 

OTB 

OTB 

50 

40 

MED 

ALL 

HKE.22 

OTB 

55 

DM 

DM 

SM 

SM 

Y 

Y 

Y 

Y 

Y 

14.9 

5.9 

Aydin  &  Tosunoglu, 
2010 

14.4 

6.3 

Dereli & Aydin, 2016 

11.4 

14.4 

15.5 

4.6 

6.3 

4.7 

Tosunoglu  et  al., 
2008 

Dereli & Aydin, 2016 

Özbilgin et al., 2012 

N* 

20.46 

8 

Lucchetti et al., 2021 

MED 

EM 

HKE.22 

LLS 

MED 

EM 

HKE.22 

LLS 

MED 

EM 

HKE.22 

LLS 

MED 

EM 

HKE.22 

LLS 

Hook  VMC  brand  - 
size 6 

Hook  VMC  brand  - 
size 7 

Hook  VMC  brand  - 
size 8 

Hook  VMC  brand  - 
size 9 

NA 

60.09 

Öztekin et al., 2020 

NA 

51.45 

Öztekin et al., 2020 

NA 

46.43 

Öztekin et al., 2020 

NA 

40.11 

Öztekin et al., 2020 

MED 

EM 

HKE.28 

GNS 

28 

DM 

MED 

EM 

HKE.28 

GNS 

30 

DM 

MED 

EM 

HKE.28 

GNS 

32 

DM 

N 

N 

N 

30.61 

Deniza et al., 2020 

32.8 

Deniza et al., 2020 

34.99 

Deniza et al., 2020 

    104 

104 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

L50 and SR From GSA 
22  peper  cited 
in 
Lucchetti et al. 2021 

L50 and SR From GSA 
22  peper  cited 
in 
Lucchetti et al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

estimated by applying 
the  bi-modal  function 
SELECT method, 

estimated by applying 
the  bi-modal  function 
SELECT method, 

estimated by applying 
the  bi-modal  function 
SELECT method, 

estimated by applying 
the  bi-modal  function 
SELECT method, 

estimated by applying 
the  bi-modal  function 
SELECT method, 

estimated by applying 
the  bi-modal  function 
SELECT method, 

estimated by applying 
the  bi-modal  function 
SELECT method, 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Plotted  Region  Area  Stock 

Gear 
type 

Mesh 
size 

Gear 
Specification 

Baseline  Lg50 (cm) 

SRg 
(cm) 

Reference 

Notes 

MED 

ALL 

HKE 

OTB 

40 

MED 

ALL 

HKE 

OTB 

40 

MED 

ALL 

HKE 

OTB 

40 

MED 

ALL 

MUT 

OTB 

40 

MED 

ALL 

HKE 

OTB 

50 

MED 

ALL 

MUT 

OTB 

50 

MED 

ALL 

HKE 

OTB 

50 

MED 

ALL 

MUT 

OTB 

50 

MED 

ALL 

HKE 

OTB 

55 

MED 

ALL 

MUT 

OTB 

55 

MED 

ALL 

HKE 

OTB 

60 

MED 

ALL 

MUT 

OTB 

60 

MED 

ALL 

HKE 

OTB 

70 

MED 

ALL 

MUT 

OTB 

70 

MED 

ALL 

HKE 

OTB 

75 

MED 

ALL 

MUT 

OTB 

75 

DM 

DM 

SM 

SM 

DM 

DM 

SM 

SM 

SM 

SM 

DM 

DM 

DM 

DM 

DM 

DM 

N* 

N* 

Y* 

Y* 

Y* 

Y* 

N* 

N* 

N* 

N* 

N* 

N* 

N* 

N* 

N* 

N* 

10.86 

3 

Lucchetti et al., 2021 

10.18 

Lucchetti et al., 2021 

14.88 

4.5 

Lucchetti et al., 2021 

12.67 

Lucchetti et al., 2021 

13.58 

5 

Lucchetti et al., 2021 

12.73 

Lucchetti et al., 2021 

18.6 

7 

Lucchetti et al., 2021 

15.84 

Lucchetti et al., 2021 

20.46 

8 

Lucchetti et al., 2021 

17.42 

Lucchetti et al., 2021 

16.29 

6 

Lucchetti et al., 2021 

15.27 

Lucchetti et al., 2021 

19.01 

6.5 

Lucchetti et al., 2021 

17.82 

Lucchetti et al., 2021 

20.36 

7 

Lucchetti et al., 2021 

19.09 

Lucchetti et al., 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

SR  estimated 
from 
Fig.  1  in  Lucchetti  et 
al. 2021 

* Hypothetical Lg50 scenario based exclusively on the mean Selection Factor (SF) calculated using the data and mesh size and geometry obtained from the review. 

    105 

105 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9  REFERENCES 

Anon., 1993. Selectivity of Fishing Gears in Irish Waters. EU Contract BIO/ECO 1993/11. 160pp + 
Appendices. 

Anon.,  2022.  Experimental  selectivity  trials  on  board  research  vessel:  Selectivity  trials  on  board 
commercial  bottom  trawl  OTB_DEF>=55.  Project  SELECTLUGO2021:  In  Annex  C  to  SWW  Joint 
Recommendation:  De Minimis Exemption Consolidation Request of 5% for several species for 2023 
Onwards Proposed From Spain For Trawlers in the Bay of Biscay and Iberian Waters (ICES 8abd 
and 8c9a). 76pp 

Aydın,  C.,  Tosunoglu,  Z.  2010.  Selectivity  of  diamond,  square  and  hexagonal  mesh  codends  for 
Atlantic  horse  mackerel  Trachurus  trachurus,  European  hake  Merluccius  merluccius,  and  greater 
forkbeard Phycis blennoides in the eastern Mediterranean. Journal of Applied Ichthyology 26, 71-
77. 

Bahamon,  N.,  Sardà,  F.,  Suuronen,  P.  2006.  Improvement  of  trawl  selectivity  in  the  NW 
Mediterranean demersal fishery by using a 40 mm square mesh codend.  Fisheries Research, 81, 
15-21. 

Baro, J., Muñoz de los Reyes, I. 2007. Comparación de los rendimientos pesqueros y la selectividad 
del  arte  de  arrastre  empleando  mallas  cuadradas  y  rómbicas  en  el  copo.  Informes.  Técnicos. 
Instituto Español de Oceanografía. 188. 23 pp. 

Bartolino, V., A. Ottavi, F. Colloca, G. D. Ardizzone, G. Stefánsson. 2008. Bathymetric preferences 
of juvenile European hake (Merluccius merluccius). ICES Journal of Marine Science 65, 963-969. 

Beverton,  R.J.H.,  Holt,  S.J.  1957.  On  the  dynamics  of  exploited  fish  populations,  Fisheries 
Investigations (Series 2), volume 19. United Kingdom Ministry of Agriculture and Fisheries, 533 pp. 
3, 4 

BIM,  2008.  Enhanced  data  collection  programme  for  the  Rockall  fishery.  PROJECT  07.MT.130 
report. 

BIM, 2009. Summary report of Gear Trials in Demersal Fisheries VIa Funded under NDP Supporting 
Measures  for  Sea  Fisheries  Development  Project  09.  SM.T1.01.  Bord  Iascaigh  Mhara  (BIM). 
September 2009. 18pp.  

BIM, 2010. Effect of Vessel Horsepower on the Selectivity of Demersal Trawls. BIM Project Report 
10.MT.01. 

BIM, 2011. Gillnet selectivity trials in the hake fishery to the South and West of Ireland in ICES 
areas VIIb,g and j. PROJECT 11.SM.T1.01 Bord Iascaigh Mhara (BIM) April-July 2011. 12pp.  

Brčić, J., Herrmann, B., Sala, A., 2018. Can a square-mesh panel inserted in front of the cod end 
improve size and species selectivity in Mediterranean trawl fisheries? Canadian Journal of Fisheries 
and Aquatic Sciences 75, 704–713. 

Brinkhof, J., Larsen, R.B., Herrmann, B. 2022. Make it simpler and better: T90 codend improves 
size  selectivity  and  catch  efficiency  compared  with  the  grid-and-diamond  mesh  codend  in  the 
Northeast Atlantic bottom trawl fishery for gadoids. Ocean and Coastal Management 217.  

Browne, D., McHugh, M., Murphy, S., Minto, C., Oliver, M., Cosgrove, C. 2022. Testing of modified 
rigging towards reduction of unwanted catches in the Nephrops fishery. Irish Sea Fisheries Board 
(BIM), Fisheries Conservation Report, August 2022. 12 pp. 

Browne, D., Oliver, M., McHugh, M., Minto, C., Cosgrove, R.2018. Assessment of a SELTRA sorting 
box with 90 mm codend mesh size in the Irish Sea Nephrops fishery. y. Irish Sea Fisheries Board 
(BIM), Fisheries Conservation Report, February 2018. 10 pp. 

Campos, A., Fonseca, P., Erzini, K. 2003. Size selectivity of diamond and square mesh cod ends for 
four by-catch species in the crustacean fishery off the Portuguese south coast.  Fisheries Research 
60, 79–97. 

Chiarini, M., Guicciardi, S., Angelini, S., Tuck, I.D., Grilli, F., Penna, P., et al. 2022. Accounting for 
environmental  and  fishery  management  factors  when  standardizing  CPUE  data  from  a  scientific 

106 

106 

 
 
 
    
survey: A case  study for Nephrops norvegicus in the Pomo Pit area (Central Adriatic Sea).  PLoS 
ONE 17, e0270703. 

Chuenpagdee,  R.,  Jentoft,  S.,  2007.  Step  zero  for  fisheries  co-management:  what  precedes 
implementation. Marine Policy 31, 657–668. 

Colloca,  F.,  Garofalo,  G.,  Bitetto,  I.,  Facchini,  M.T.,  Grati,  F.,  Martiradonna,  A.,  et  al.  2015.  The 
Seascape of Demersal Fish Nursery Areas in the North Mediterranean Sea, a First Step Towards the 
Implementation of Spatial Planning for Trawl Fisheries. PLoS ONE 10, e0119590. 

Cuende,  E.,  Sistiaga,  M.,  Herrmann,  B.,  Arregi,  L.  2022.  Optimizing  size  selectivity  and  catch 
patterns  for  hake  (Merluccius  merluccius)  and  blue  whiting  (Micromesistius  poutassou)  by 
combining square mesh panel and codend designs. PLOS ONE 17, e0262602. 

Deniza, T., Göktürka, D., Ateşb, C. 2020. Selectivity parameters of European hake gillnets for target 
and  by-catch  species  with  a  perspective  on  small-scale  fisheries  management  in  the  Sea  of 
Marmara, Turkey. Regional Studies in Marine Science 33. 

Dereli, H., Aydin, C., 2016. Selectivity of Commercial and Alternative Codends for Four Species in 
the  Eastern  Mediterranean  Demersal  Trawl  Fishery.  Turkish  Journal  of  Fisheries  and  Aquatic 
Sciences 16, 971-992. 

Dos Santos, M.N., Erzini, K.,  Gaspar, M.B., Monteiro, C.C.  Sá, R., Bentes, L., Gonçalves, J.M.C., 
Lino, P.G., Ribeiro, J. 2001. Comparison of Long-line and Monofilament Gill Net Selectivity for Hake 
(Merluccius  merluccius)  in  the  Algarve  (Southern  Portugal).  NAFO  SCR  Doc.  01/96.  Scientific 
Council meeting – September 2001. 7pp. 

Dos Santos, M.N., Gaspar, M., Monteiro, C.C. and Erzini, K. 2003. Gill net selectivity for European 
hake Merluccius merluccius from southern Portugal: implications for fishery management. Fisheries 
Science 69: 873-882. 

Eayrs, S., Pol, M. 2019. The myth of voluntary uptake of proven fishing gear: investigations into 
the challenges inspiring change in fisheries. ICES Journal of Marine Science 76, 392-401. 

Fiorentino, F., Massutì, E., Tinti, F., Somarakis, S., Garofalo, G., Russo, T., Facchini, M.T., et al.  
2015.  Stock  units:  Identification  of  distinct  biological  units  (stock  units)  for  different  fish  and 
shellfish  species  and  among  different  GFCM-GSA.  STOCKMED  Deliverable  03:  FINAL  REPORT. 
January 2015, 310 pp. 

Francis,  R.I.C.C.  1988.  Are  growth  parameters  estimated  from  tagging  and  age-length  data 
comparable? Canadian Journal of Fisheries and Aquatic Sciences 45, 936-942. 

Frandsen, R.P., Holst, R, Madsen, N., 2009. Evaluation of three levels of selective devices relevant 
to management of the Danish Kattegat–Skagerrak Nephrops fishery. Fisheries Research 97, 243-
252. 

Garcia, S.M. (Comp.). 2009. Glossary. In Cochrane, K. and S.M. Garcia. (Eds). A fishery managers’ 
handbook. FAO and Wiley-Blackwell: 473-505. 

Gascoigne, J., Willsteed, E., 2009. Moving Towards Low Impact Fisheries in Europe. Policy Hurdles 
& Actions. MacAlister and Partners Ltd., Seas at Risk, 103 pp 

GFCM, 2022. Report of the twenty-third session of the Scientific Advisory Committee on fisheries, 
2022 
FAO 
(https://www.fao.org/3/cc3109en/cc3109en.pdf) 

headquarters, 

Rome, 

21–24 

Italy, 

June 

Gilman,  E.,  Suuronen,  P.,  Hall,  M.,  Kennelly,  S.  2013.  Causes  and  methods  to  estimate  cryptic 
sources of fishing mortalitya. Journal of Fish Biology 83, 766-803. 

Glass, C.W., Walsh, S.J., van Marlen, B., 2007. Fishing technology in the 21st century: integrating 
fishing and ecosystem conservation. ICES Journal of Marine Science 64, 1499–1502. 

Graham, N., Fryer, R.J., 2006. Separation of fish from Nephrops norvegicus into a two-tier cod-end 
using a selection grid. Fisheries Research 82, 111-8. 

Graham, N., O’Neill, F.G., Fryer, R.J., Galbraith, R.D., Myklebust A. 2004. Selectivity of a 120 mm 
diamond cod-end and the effect of inserting a rigid grid or a square mesh panel. Fisheries Research 
67, 151-161. 

107 

107 

 
 
 
    
Graham,  N.,  Olsen,  E.  (Editors)  2020.  Report  of  the  EU-Norway  Technical  Group  Meeting  on 
additional technical measures aimed at the protection of both juvenile and adult cod. 122pp. 

Haasnoot, T., Kraan, M., Bush, S.R. 2016. Fishing gear transitions: lessons from the Dutch flatfish 
pulse trawl. ICES Journal of Marine Science 73, 1235-1243. 

Herrmann, 
https://doi.org/10.13140/2.1.3055.0086  

2008. 

user 

B. 

A 

guide 

to 

the 

FISHSELECT 

software 

tool. 

Herrmann, B., Krag, L.A., Frandsen, R.P., Madsen, N., Lundgren, B., Stæhr, K.-J., 2009. Prediction 
of selectivity from morphological conditions: Methodology and a case study on cod (Gadus morhua). 
Fisheries Research 97, 59–71. 

ICES, 2020. Cod (Gadus morhua) in Division 6.a (West of Scotland). In Report of the ICES Advisory 
Committee, 2020. ICES Advice 2019, cod.27.6a. https://doi.org/10.17895/ices.advice.6106  

ICES, 2021a. Cod (Gadus morhua) in subdivisions 22-24, western Baltic stock (western Baltic Sea). 
In  Report  of  the  ICES  Advisory  Committee,  2021.  ICES  Advice  2021,  cod.27.22-24. 
https://doi.org/10.17895/ices.advice.5942 

ICES,  2021b.  Cod  (Gadus  morhua)  in  Subarea  4,  Division  7.d,  and  Subdivision  20  (North  Sea, 
eastern English Channel, Skagerrak). In Report of the ICES Advisory Committee, 2021. ICES Advice 
2021, cod.27.47d20. https://doi.org/10.17895/ices.advice.7746  

ICES, 2021c. Working Group for the Celtic Seas Ecoregion (WGCSE). ICES Scientific Reports 3:56. 
1505 pp. https://doi.org/10.17895/ices.pub.8139 

ICES, 2021d. Cod (Gadus morhua) in divisions 7.e-k (eastern English Channel and southern Celtic 
Seas).  In  Report  of  the  ICES  Advisory  Committee,  2021.  ICES  Advice  2021,  cod.27.7e–k. 
https://doi.org/10.17895/ices.advice.7751 

ICES, 2021e. Benchmark Workshop on North Sea Stocks (WKNSEA). ICES Scientific Reports 3:  25. 
756 pp. https://doi.org/10.17895/ices.pub.7922 

ICES,  2021f.  Whiting  (Merlangius  merlangus)  in  Division  7.a  (Irish  Sea).  In  Report  of  the  ICES 
whg.27.7a. 
Committee, 
Advisory 
https://doi.org/10.17895/ices.advice.7887. 

Advice 

2021, 

2021. 

ICES 

ICES,  2021g.  Iberian  waters  –  mixed-fisheries  considerations.  In  Report  of  the  ICES  Advisory 
Committee, 2021. ICES Advice 2021. http://doi.org/10.17895/ices.advice.9183. 

ICES, 2021h. Saithe (Pollachius virens) in subareas 4 and 6, and in Division 3.a (North Sea, Rockall 
and West of Scotland, Skagerrak and Kattegat). In Report of the ICES Advisory Committee, 2021. 
ICES Advice 2021, pok.27.3a46. https://doi.org/10.17895/ices.advice.7827.  

ICES, 2022a. Cod (Gadus morhua) in divisions 7.e-k (eastern English Channel and southern Celtic 
Seas).  In  Report  of  the  ICES  Advisory  Committee,  2022.  ICES  Advice  2022,  cod.27.7e–  k. 
https://doi.org/10.17895/ices.advice.19447898. 

ICES, 2022b. Cod (Gadus morhua) in subdivisions 22–24, western Baltic stock (western Baltic Sea). 
In  Report  of  the  ICES  Advisory  Committee,  2022.  ICES  Advice  2022,  cod.27.22–24. 
https://doi.org/10.17895/ices.advice.19447868. 

ICES, 2022c. Cod (Gadus morhua) in subdivisions 24–32, eastern Baltic stock (eastern Baltic Sea). 
In Report of the ICES Advisory Committee, 2022. ICES Advice 2022, cod.27.24–32, https://doi.org/ 
10.17895/ices.advice.19447874. 

ICES, 2023. Report of the Second Workshop on the Impact of Ecosystem and Environmental Drivers 
on Irish Sea Fisheries Management (WKIrish2), 26–29 September 2016, Belfast, Northern Ireland. 
ICES CM 2016/BSG:02. 199 pp. 

Jennings, S., Revill, A.S., 2007. The role of gear technologists in supporting an ecosystem approach 
to fisheries. ICES Journal of Marine Science 64, 1525–1534. 

Kell, L.T., Mosqueira, I., Grosjean, P., Fromentin, J-M., Garcia, D., Hillary, R., Jardim, E., Mardle, 
S., Pastoors, M.A., Poos, J.J., Scott, F., Scott, R.D. 2007. FLR: an open-source framework for the 
evaluation and development of management strategies. ICES Journal of Marine Science 64, 640-
646. 

108 

108 

 
 
 
    
Krag, L.A., Herrmann, B., Feekings, J., Karlsen, J.D. 2016. Escape panels in trawls – a consistent 
management tool? Aquatic Living Resources 29, 306. 

Krag,  L.A.,  Holst,  R.,  Madsen,  N.,  Hansen,  K.,  Frandsen,  R.P.  2010.  Selective  haddock 
(Melanogrammus aeglefinus) trawling: Avoiding cod (Gadus morhua) bycatch. Fisheries Research 
101, 20-26. 

Larraneta, M.G., Suau, P., San Feliu., J.M. 1969. Experiencias de selectividad en la pesquería de 
arrastre en el levante español. Investigación Pesquera 33, 15-53. 

Lembo, G., Carbonara, P., Silecchia, T., Spedicato, M.T. 2002. Prove di pesca a strascico con rete 
a  doppio  sacco  per  la  valutazione  della  selettività  dell'attrezzo  e  della  qualità  del  prodotto.  I 
quaderni scientifici della Lega Pesca 5, 47. 

Ligas, A., Colloca, F., Lundy, M.G., Mannini, A., Sartor, P., Sbrana, M., Voliani, A., Belcari, P. 2015. 
Modeling  the  growth  of  recruits  of  European  hake  (Merluccius  merluccius)  in  northwestern 
Mediterranean Sea with generalized additive models. Fishery Bulletin 113: 69-81. 

Lucchetti, A., Virgili, M., Petetta, A., Sartor, P. 2020. An overview of gill net and trammel net size 
selectivity in the Mediterranean Sea. Fisheries Research 230, 105677. 

Lucchetti, A., Virgili, M., Vasapollo, C., Petetta, A., Bargione, G., Li Veli, D., Brcic, J., Sala, A. 2021. 
An overview of bottom trawl selectivity in the Mediterranean Sea.  Mediterranean Marine Science 
22, 566-585. 

Macher, C., Bertignac, M.,  Guyader, O., Frangoudes, K., Frésard, M.,  Le  Grand, C., Merzéréaud, 
M., Thébaud, O., 2018. The role of technical protocols and partnership engagement in developing 
a  decision  support  framework  for  fisheries  management.  Journal  of  Environmental  Management 
223, 503-516. 

Madsen, N., 2007. Selectivity of fishing gears used in  the Baltic Sea cod fishery. Reviews in Fish 
Biology and Fisheries 17, 517-544. 

Madsen,  N.,  Ferro,  R.S.T.  2003.  STECF  Meeting  on  cod  assessment  and  technical  measures. 
Brussels, 28 April-7 May 2003. 127 pp. Appendix 5. 

Madsen, N., Valentinsson, D., 2010. Use of selective devices in trawls to support recovery of the 
Kattegat cod stock: a review of experiments and experience. ICES Journal of Marine Science 67, 
2042–2050. 

McHugh, M., Browne, D., Oliver, M., Minto, C., Cosgrove, R. 2019. Staggering the fishing line: a 
key  bycatch  reduction  option  for  whitefish  trawlers.  Irish  Sea  Fisheries  Board  (BIM),  Fisheries 
Conservation Report, June 2019. 8 pp. 

Melli,  V.,  Karlsen,  J.D.,  Feekings,  J.P.,  Herrmann,  B.,  Krag,  L.A.,  2018.  FLEXSELECT:  counter-
herding device to reduce bycatch in crustacean trawl fisheries. Canadian Journal of Fisheries and 
Aquatic Sciences 75, pp.850-860. 

Millar,  C.,  Large,  S.,  Magnusson,  A.  2022.  icesDatras:  DATRAS  Trawl  Survey  Database  Web   
Services_. R package version 1.4.0, https://CRAN.R-project.org/package=icesDatras  

Millar, R.B., Fryer, R.J. 1999. Estimating the size selectivity curves of towed gears, traps, nets and 
hooks. Reviews in Fish Biology and Fisheries 9, 89–116. 

Noack,  T.,  Frandsen,  R.P.,  Krag,  L.A.,  Mieske,  B.,  Madsen,  N.  2017.  Codend  selectivity  in  a 
commercial Danish anchor seine. Fisheries Research 186, 283–291. 

O’Neill,  F.G.,  Fryer,  R.J.,  Frandsen,  R.P.,  Herrmann,  B.,  Madsen,  N.,  Mieske,  B.  2020.  A  meta-
analysis of plaice size-selection data in otter trawl codends. Fisheries Research 227, 105558. 

O’Neill,  F.G.,  Noble,  S.  2017.  Report  on  meta-analyses  of  gear  selectivity  data  in  terms  of  gear 
design parameters (Horizon 2020 DiscardLess Report D3.2) Zenodo doi: 10.5281/zenodo.1203984  

Ogle, D.H., 2016. Introductory to fisheries analysis in R. CRC press 

Ogle, D.H., Wheeler, P., Dinno, A. 2020. FSA: Fisheries Stock Analysis. R package version 0.8.31, 
https://github.com/droglenc/FSA. 

109 

109 

 
 
 
    
Özbilgin, H., Tokac, A., Kaykac, H. 2012. Selectivity of commercial compared to larger mesh and 
square mesh trawl codends for four fish species in the Aegean Sea. Journal of Applied Ichthyology 
28, 51–59. 

Öztekin, A., Özekinci, U., Ayaz, A. 2020. Determining the hook selectivity of bottom longline used 
for European hake (Merluccius merluccius, L. 1758) in Saros Bay (northern Aegean Sea, Turkey). 
Iranian Journal of Fisheries Sciences 19, 2608-2617. 

Petetta, A., Herrmann, B., Virgili, M., De Marco, R., Canduci,G., Li Veli, D., Bargione, G., Vasapollo, 
C., Lucchetti, A. 2020. Estimating selectivity of experimental diamond (T0) and turned mesh (T90) 
codends in multi-species Mediterranean bottom trawl. Mediterranean Marine Science 21, 545–557. 

Prellezo, R., Carmona, I., García, D., Arregi, L., Ruiz, J., Onandia, I. 2017. Bioeconomic assessment 
of  a  change  in  fishing gear  selectivity:  the  case  of  a  single-species  fleet  affected  by  the  landing 
obligation. Scientia Marina 81, 371-380. 

R  Core  Team,  2022.  R:  A  language  and  environment  for  statistical  computing.  R  Foundation  for 
Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. 

Ratana,  C.,  Jentoft,  S.  2007.  Step  zero  for  fisheries  co-management:  What  precedes 
implementation. Marine Policy 31, 657-668. 

Revill,  A.,  Cotter,  J.,  Armstrong,  M.,  Ashworth,  J.,  Forster,  R.,  Caslake,  G.,  Holst,  R.  2007.  The 
selectivity  of  the  gill-nets  used  to  target  hake  (Merluccius  merluccius)  in  the  Cornish  and  Irish 
offshore fisheries. Fisheries Research 85, 142-147. 

Sala, A., Herrmann, B., De Carlo, F., Lucchetti, A., Brčić, J. 2016. Effect of Codend Circumference 
on the Size Selection of Square-Mesh Codends in Trawl Fisheries. PLoS ONE 11, e0160354. 

Sala, A., Herrmann, B., Lucchetti, A., Notti, E. 2018. Service tender for the provision of services to 
conduct  and  implement  a  scientific  study  to  improve  trawl  gear  selectivity  for  fisheries  research 
unit.  Final  report  for  the  Maltese  department  of  fisheries  and  aquaculture,  Tender  Ref. 
CT3012/2017, 70 pp 

Sala,  A.,  Lucchetti,  A.  2010.  The  effect  of  mesh  configuration  and  codend  circumference  on 
selectivity in the Mediterranean trawl Nephrops fishery. Fisheries Research 103, 63-72. 

Sala, A., Lucchetti, A.,  2011. Effect of mesh size and codend circumference  on selectivity in the 
Mediterranean demersal trawl fisheries. Fisheries Research 110, 252–258. 

Sampson, D.B., Scott, R.D. 2012. An exploration of the shapes and stability of population-selectivity 
curves. Fish and Fisheries 13, 89–104. 

Sangster,  G.I.,  Lehmann,  K.,  Breen,  M.  1996.  Commercial  fishing  experiments  to  assess  the 
survival of haddock and whiting after escape from four sizes of diamond mesh cod-ends. Fisheries 
Research 25, 323-345. 

Sardà, F., Molí, B., Palomera, I. 2004. Preservation of juvenile hake (Merluccius merluccius, L.) in 
the western Mediterranean demersal trawl fishery by using sorting grids. Scientia Marina 68, 435-
444. 

Sbrana, M. 2021. Improving the selectivity of trawl gears in the Mediterranean Sea to advance the 
sustainable  exploitation  pattern  of 
fisheries  (IMPLEMED).  Specific  Contract  No. 
EASME/EMFF/2019/1.3.2.6/01/  SI2.818717.  Final  Report.  ISBN  978-92-95225-54-1.  doi: 
10.2926/194244 

trawl 

Sbrana,  M.,  Belcari,  P.,  De  Ranieri,  S.,  Sartor,  P.  2007.  Comparison  of  the  catches  of  European 
hake (Merluccius merluccius, L. 1758) taken with experimental gillnets of different mesh sizes in 
the northern Tyrrhenian Sea (western Mediterranean). Scientia Marina 71, 47-56. 

Sbrana, M., De Carlo, F., Ligas, A., Massaro, A., Musumeci, C., Rossetti, I., Sartini, M., Vasapollo, 
C.,  Viva,  C.,  Sartor,  P.,  Pretti,  C.  2022.  Testing  experimental  devices  in  the  extension  piece  to 
increase the selectivity of bottom trawl in the NW Mediterranean.  Frontiers  of Marine Science 9, 
1017766. 

Scott, R.D., Sampson, D.B. 2011. The sensitivity of long-term yield targets to changes in fishery 
age-selectivity. Marine Policy 35, 79–84. 

110 

110 

 
 
 
    
Sistiaga,  M.,  Herrmann,  B.,  Grimaldo,  E.,  Larsen,  B.R.,  2009.  Investigation  of  the  paired-gear 
method in selectivity studies. Fisheries Research 97, 196-205.  

Smallhorn-West, P., Cohen, P.J., Phillips, M., Jupiter, S.D., Govan, H., Pressey, R.L., 2022. Linking 
small-scale fisheries co-management to U.N. Sustainable Development Goals. Conservation Biology 
e13977. 

Soldo,  A.  2004.  Construction,  technical  characteristics  and  selectivity  of  bottom  trawls  in  the 
Adriatic. PhD thesis (In Croatian) 

STECF, 2020a. Review of technical measures (part 1) (STECF-20-02). EUR 28359 EN, Publications 
Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-27161-1, doi:10.2760/734593, 
JRC123092. 

STECF, 2020b. Stock Assessments: demersal stocks in the western Mediterranean Sea (STECF-20-
09).  EUR  28359  EN,  Publications  Office  of  the  European  Union,  Luxembourg,  ISBN  978-92-76-
27165-9, doi:10.2760/286667, JRC122993 

STECF, 2020c. Stock Assessments in the Mediterranean Sea  – Adriatic, Ionian and Aegean Seas 
(STECF-20-15).  EUR  28359  EN,  Publications  Office  of  the  European  Union,  Luxembourg,  2020, 
ISBN 978-92-76-27168- 0, doi:10.2760/877405, JRC122994. 

STECF, 2021. Review of the Technical Measures Regulation (STECF-21-07). Publications Office of 
the  European  Union,  Luxembourg,  2021,  EUR  28359  EN,  ISBN  978-92-76-45890-6, 
doi:10.2760/790781, JRC127718 

STECF, 2022. Validation of selected sustainability indicators and underlying methodologies for the 
revision of the EU marketing standards for fisheries products (STECF-22-12). Publications Office of 
the European Union, Luxembourg, 2022 

Stepputtis,  D.,  Santos,  J.,  Zimmermann,  C.  2020.  Technical  approaches  to  avoid  cod  catches  in 
Baltic Sea trawl fisheries. Thünen Institute of Baltic Sea Fisheries, Rostock, Germany 37 pp. 

Suuronen, P., Chopin, F., Glass, C., Løkkeborg, S., Matsushita, Y., Queirolo, D., Rihan, D. 2012. 
Low impact and fuel-efficient fishing - Looking beyond the horizon.  Fisheries Research 119-120, 
135-146. 

Suuronen, P., Sardà, F. 2007. The role of technical measures in Euro- pean fisheries management 
and how to make them work better. ICES Journal of Marine Science 64, 751-756.  

Suuronen,  P.,  Tschernij,  V.,  Jounela,  P.,  Valentinsson,  D.,  Larsson,  P-O.  2007.  Factors  affecting 
rule compliance with mesh size regulations in the Baltic cod trawl fishery. ICES Journal of Marine 
Science 64, 1603-1606. 

Tosunoğlu,  Z.,  Aydın,  C.,  Özaydın,  O.  2008.  Selectivity  of  a  50-mm  diamond  mesh  knotless 
polyethylene codend for commercially important fish species in the Aegean Sea. Journal of Applied 
Ichthyology 24, 311-315. 

Tsikliras,  A.C.,  Dimarchopoulou,  D.,  Pardalou,  A.  2020.  Artificial  upward  trends  in  Greek  marine 
landings: a case of presentist bias in European fisheries. Marine Policy, 117, 103886 

Valeiras, J., Pérez, N., Araujo, H., Salinas, I., Bellido, J.M. 2014. Atlas de los descartes de la flota 
de  arrastre  y  enmalle  en  el  caladero  nacional  Cantábrico‐Noroeste.  Instituto  Español  de 
Oceanografía. 122pp 

Vasilakopoulos, P., Jardim, E., Konrad, C., et al. 2020. Selectivity metrics for fisheries management 
and advice. Fish and Fisheries 21, 621-638.  

Vasilakopoulos,  P.,  O'Neill,  F.  G.,  Marshall,  C.  T.  2016.  The  unfulfilled  potential  of  fisheries 
selectivity to promote sustainability. Fish and Fisheries 17, 399-416.  

Wienbeck, H.,  Herrmann, B., Feekings, J.P., Stepputtis, D., Moderhak, W., 2014. A comparative 
analysis of legislated and modified Baltic Sea trawl codends for simultaneously improving the size 
se-lection of cod (Gadus morhua) and plaice (Pleuronectes platessa). Fisheries Research 150, 28-
37. 

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Yang, B., Herrmann, B. 2022. Effect of codend mesh sizes on the size selectivity and exploitation 
pattern of cocktail shrimp (Trachypenaeus curvirostris) in shrimp trawl fishery of the South China 
Sea. Frontiers of Marine Science 9, 928906. 

10  LIST OF BACKGROUND DOCUMENTS  

Background documents are published on the meeting’s web site on:  
https://stecf.jrc.ec.europa.eu/ewg2219 

List of background documents: 

EWG-22-19 – Doc 1 - Declarations of invited and JRC experts (see also section 7 of this report – 
List of participants) 

112 

112 

 
 
 
    
 
 
 
 
 
 
 
 
 
 
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Scientific, 

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1