Review of the technical measures regulation (STECF-21-07).
Date: 2021-12-15
Institutions: Joint Research Centre, Scientific, Technical and Economic Committee for Fisheries
Subjects: common fisheries policy, conservation of fish stocks, fishery management, fishery research, fishing regulations, performance measurement, quantity of fish landed, report, technical regulations

Scientific, Technical and Economic 
Committee for Fisheries (STECF) 
- 
Review of the Technical Measures 
Regulation  
(STECF-21-07) 

Edited by Sarah B. M Kraak, Henning Winker & Zeynep Hekim 

 EUR 28359 EN 

 
This  publication  is  a  Science  for  Policy  report  by  the  Joint  Research  Centre  (JRC),  the  European 
Commission’s science and knowledge service. It aims to provide evidence-based scientific support 
to  the  European  policymaking  process.  The  scientific  output  expressed  does  not  imply  a  policy 
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Contact information  

Name: STECF secretariat 

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

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

Tel.: +39 0332 789343 

EU Science Hub 

https://ec.europa.eu/jrc 

JRC127718 

EUR 28359 EN 

PDF 

ISBN 978-92-76-45890-6 

ISSN 1831-9424 

doi:10.2760/790781 

STECF 

ISSN 2467-0715 

Luxembourg: Publications Office of the European Union, 2021  

© European Union, 2021 

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How  to  cite  this  report:  Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  – 
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 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Authors: 

STECF advice:  

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

EWG-21-07 report:  

Brĉić, J.; Cardinale, M.; Gascuel, D.; Gerritsen, H.; Haase, S.; Hekim, Z.; Hommik, K.; Kraak, S. 
B. M.; Ligas, A.; Mehault, S.; Outrequin, T.; Rihan, D.; Sala, A.; Scarcella, G.; Sys, K.; Valeiras, 
J.; Vasilakopoulos, P.; Winker, H. 

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TABLE OF CONTENTS 

SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES 
(STECF) - Review of the Technical Measures Regulation (STECF-
21-07) ........................................................................................................................ 1 

Request to the STECF ........................................................................................................ 1 

Background provided by the Commission ................................................................. 1 

STECF comments ................................................................................................................. 1 

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

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

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

Expert Working Group EWG-21-07 report ................................................................. 9 

1 

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

1.1 

Terms of Reference for EWG-21-07 ............................................................. 11 

2 

3 

3.1 

3.2 

3.3 

3.4 

3.5 

3.6 

3.7 

General Approach to the Terms of Reference .......................................... 12 

Material and Methods ......................................................................................... 13 

Data .......................................................................................................................... 13 

Software used ....................................................................................................... 16 

Using apical F as a standardized metric for evaluating selectivity .. 16 

Selectivity-at-age ................................................................................................ 16 

The Selex function .............................................................................................. 17 

Varying selectivity-at-age ................................................................................ 18 

Equilibrium optimisation and forecasting of selectivity scenarios 
(ToRs 1 and 2) ...................................................................................................... 20 

3.7.1  STOCK RECRUITMENT RELATIONSHIP (SRR) .......................................... 21 

3.7.2  PROJECTION SCENARIOS................................................................................. 23 

3.8 

Evaluating impacts of fleet-specific selectivity (ToR 3)........................ 23 

3.8.1  ONE FLEET AT A TIME ....................................................................................... 23 

3.8.2  JACKKNIFE ............................................................................................................. 24 

3.9 

Isopleths (ToR 4) ................................................................................................. 24 

4 

4.1 

Results ..................................................................................................................... 24 

Potential gain in terms of yield and proportion juveniles in the catch 
from optimising selectivity at current F (ToR 1 & 2) ............................. 24 

4.1.1  CASE STUDY EXAMPLES ................................................................................... 25 

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4.1.1.1  West of Scotland Cod (cod.27.6a) ............................................................ 25 

Assessment .......................................................................................................................... 25 

State of the stock .............................................................................................................. 25 

Selectivity scenarios ......................................................................................................... 26 

Projections ............................................................................................................................ 27 

4.1.1.2  Northern Hake (hke.27.3a46-8abd) ........................................................ 28 

Characterisation of the stock ........................................................................................ 28 

State of the stock .............................................................................................................. 29 

Selectivity scenarios ......................................................................................................... 29 

Projections ............................................................................................................................ 30 

4.1.1.3  Red Mullet in GSA 09 (MUT09) .................................................................. 32 

Characterisation of the stock ........................................................................................ 32 

Assessment .......................................................................................................................... 32 

State of the stock .............................................................................................................. 32 

Selectivity scenarios ......................................................................................................... 33 

Projections ............................................................................................................................ 34 

4.1.1.4  EUROPEAN HAKE IN THE ADRIATIC SEA (GSAs 17-18) .................. 35 

Characterisation of the stock ........................................................................................ 35 

Assessment .......................................................................................................................... 36 

State of the stock .............................................................................................................. 36 

Selectivity scenarios ......................................................................................................... 37 

Projections ............................................................................................................................ 37 

4.1.2. RESULTS FOR ALL STOCKS .............................................................................. 38 

4.2 

Potential gain in terms of yield and proportion juveniles in the catch 
from optimising selectivity at current F, by fleet (ToR 3) ................... 45 

4.2.1.1.  Stocks with ICES advice .............................................................................. 45 

4.2.1.2  Stocks with STECF-GFCM advice ............................................................... 47 

4.2.2  JACKKNIFE ANALYSES ....................................................................................... 49 

4.2.3.  SUMMARY ToR 3 .................................................................................................. 54 

4.3 

4.4 

Combined effects of fishing mortality rate and selectivity pattern on 
yield and SSB (ToR 4 a) ................................................................................... 55 

Practical issues regarding the attainment of the biologically optimal 
selection pattern in the context of mixed fisheries and multi-gear 
fisheries (ToR 4b) ................................................................................................ 65 

4.4.1  PRACTICAL ISSUES IN MIXED FISHERIES – QUALITATIVE 

CONSIDERATIONS .............................................................................................. 65 

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4.4.2  PRACTICAL ISSUES IN MIXED FISHERIES – QUANTITATIVE 

CONSIDERATIONS .............................................................................................. 68 

5 

5.1 

5.2 

5.4 

5.5 

5.6 

5.7 

6 

7 

8 

9 

DISCUSSION ......................................................................................................... 70 

Caveats of the approach for interpreting the analysis ......................... 70 

General practical issues regarding the attainment of the biologically 
optimal selection pattern .................................................................................. 72 

Three main management tools to improve selectivity ......................... 75 

A gradual change? ............................................................................................... 75 

Results-based management ............................................................................ 76 

Progress in evaluating the technical measures regulation and future 
steps ......................................................................................................................... 77 

Conclusions ............................................................................................................ 78 

References .............................................................................................................. 80 

Contact details of EWG-21-07 participants ............................................................... 84 

List of Annexes .......................................................................................................... 87 

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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  evaluates  the  performance  of  technical  measures  to  conserve  fishery 
resources  and  protect  marine  ecosystems  in  accordance  with  Article  31  of  Regulation  (EU) 
1241/20.  The  report  presents  the  findings  from  STECF  Expert  Working  Group  21-07:  Review  of 
the  Technical  Measures  Regulations  from  the  meeting  held  remotely  from  11th  to  15th  October 
2021. The 33 stocks analysed in this report 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. The report of the EWG-2107 was reviewed by the 
STECF during its November 2021 Plenary Meeting and subsequently released. 

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SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES (STECF) - Review 
of the Technical Measures Regulation (STECF-21-07) 

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. 

Background provided by the Commission 

The EWG 21-07 was requested to address the following Terms of Reference: 

1.  Calculate  the  respective  selectivity-at-age  that  (a)  predict  the  highest  yield  at  current 
fishing  mortality  rates  or  harvest  rates,  and  (b)  provide  the  greatest  protection  of 
juveniles. 

2.  Compare  the  optimised  selectivity-at-age  predicted  under  (1)  with  current  selectivity-at-
age estimates for the stocks concerned in terms of both (a) yield gains and (b) protection 
of juveniles 

3.  Compare  the  optimised  selectivity-at-age  predicted  under  (1)  with  current  selectivity-at-
age  estimates  by  fleet,  gear  and  area,  which  should  be  analysed  to  the  most 
disaggregated level that is feasible in terms of both yield gains and protection of juveniles.  
4.  For regional case studies, explore trade-offs between fishing pressure and selectivity with 
a view to minimising impacts and maximizing catches under different scenarios for catch, 
fishing  mortality  and  in  relation  to  fisheries  reference  points.  STECF  is  further  asked  to 
comment on practical issues regarding the attainment of the biologically optimal selection 
pattern in the context of mixed fisheries and multi-gear fisheries. 

STECF comments 

EWG  21-07  was  a  follow-up  to  the  EWG  20-02  (October  2020).  The  expert  working  group  met 
online  from  the  11th  to  the  15th  October  2021.  The  meeting  was  attended  by  15  experts, 
including  four  STECF  members  and  three  JRC  experts.  Three  DG  MARE  representatives  and  one 
observer also attended the meeting. 

STECF notes that all the ToRs were addressed by the EWG. A dataset of 33 stocks (20 in north-
eastern Atlantic and 13 in the Mediterranean)  was provided to the EWG. ToR 1 and  ToR 2  were 
tackled and presented together; ToR 3 was addressed by extending the approach for ToR 1 and 2 
to  compare  fleet-specific  selectivity  patterns  to  the  optimised  selectivity  pattern  derived  under 
ToR 1 in terms of both yield and the protection of juveniles. The analysis for ToR 3 was limited to 
a subset of stocks for which fleet disaggregated data are available. 
ToR 4 was dealt with in two parts. In the first part (“4a”), for each stock the combined effects of 
varying  fishing  mortality  rate  and  selectivity  patterns  were  explored  in  terms  of  minimising 
impacts on spawning stock biomass (SSB) and maximizing catches. 
The  second  part  of  ToR  4  (“4b”)  was  interpreted  as  a  request  to  comment  on  practical 
implications of trying to attain optimal selectivity in the context of mixed fisheries and multi-gear 
fisheries from the point of view of changes in gear technology. STECF comments follow this order.  

STECF  notes  that  throughout  this  document,  ‘selectivity’  refers  to  ‘population  selectivity’  to 
describe 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). An increase in selectivity thus 

1 

1 

 
 
 
 
 
 
 
 
 
 
 
 
means  here  an  increase  in  age  at  50%  selection  (S50)  towards  the  optimal  age  at  first  catch 
Lcopt at population level. 

STECF notes that the analyses performed under  ToRs 1 to 4a are solely based  on mathematical 
computations involving varying population age structure dynamics and selectivity in a single stock 
approach,  and  do  not  contain  any  socio-economic  consideration.  Economic  and  management 
considerations in a mixed-fisheries context are discussed in ToR 4b.  

STECF also notes that all the analyses (except ToR 4b) were performed with the R/FLR package 
FLSelex,  which  was  developed  by  JRC  specifically  for  this  EWG  and  is  available  on 
https://github.com/Henning-Winker/FLSelex. 

Stock-specific  results  and  findings  are  given  in  the  relevant  sections  of  the  EWG  report  and  are 
not  reproduced  here.  Nevertheless,  based  on  the  findings  presented  in  the  report,  a  number  of 
general observations can be made in relation to the ToRs as follows. 

ToRs 1 and 2 

For  each  stock  of  the  Annex,  current  selectivity  (ToR  2)  and  optimised  selectivity  (ToR  1, 
following different scenarios for optimising yield, explained below) were quantified and the stock 
dynamics  were  projected  forward  for  each  of  these  selectivity  patterns  until  equilibrium  was 
reached. At equilibrium, (a) yield and (b) proportion of juveniles in the catch were quantified for 
each of these selectivity patterns. For each stock, the results were summarized as a comparison 
of  (a)  yield  and  (b)  proportion  of  juveniles  in  the  catch  between  current  selectivity  and  each 
optimised  selectivity  pattern  (ToR  2).  The  two  optimisation  scenarios  (ToR  1)  represent  (i)  a 
situation where young fish can be avoided, e.g. through spatial-temporal closure of nursery areas 
or  through  exclusion  devices  applied  to  fishing  gears  (“crank”)  and  (ii)  increased  mesh  size 
(“shift”). The results show which stocks benefit in terms of yield and/or protection of juveniles by 
improving selectivity and stocks that are currently already fished close to optimal selectivity. 

Four case studies (two in Atlantic waters, two in the Mediterranean) were selected to present and 
discuss  the  methods  and  results  in  detail.  The  selected  case  studies  were  West  of  Scotland  cod 
(cod.27.6a), which is heavily over-exploited and below Blim, northern hake (hke.27.3a46-8abd), 
which  is  exploited  at  FMSY  and  is  above  Blim,  red  mullet  in  GSA  09  (MUT09),  which  is  over-
exploited  but  with  high  biomass,  and  hake  in  the  Adriatic  Sea  (HKE.17_18),  which  is  over-
exploited  with  biomass  around  70  percent  of  the  precautionary  biomass  Bpa.  The  results  for  all 
the stocks are summarized in tables and plots available in the EWG 21-07 report and in Appendix 
3 to the EWG report. 

Regarding ToRs 1 and 2, STECF notes that: 

-  Any increase in selectivity will lead to a reduction in the proportion of juveniles in the 

catches;  

-  Benefits of increased selectivity include a decrease of growth overfishing, lower risk of 
recruitment overfishing, greater proportion of large spawners, and increased stock 
biomass; 

-  Stocks which are subject to a higher level of growth overfishing, which are typically large-

- 

bodied and late-maturing (e.g., hake stocks in the Mediterranean, cod), will (in the long 
term) benefit the most; 
Increase in long-term yields are linked with increase in selectivity for the vast majority of 
the stocks investigated. For a few exceptions only (some haddock and whiting stocks in 
the Northeast Atlantic), which are currently under-exploited, optimisations in yield are 
associated with a decrease in S50, leading to an increased proportion of juveniles in the 
catch; 

2 

2 

 
 
 
 
 
 
 
 
 
 
 
-  Similar to a reduction in F towards FMSY, an increase in selectivity towards optimisation is 

associated with a short-term loss in yield depending on the magnitude of change and the 
biological characteristics of the stock. In general, those short-term losses will be more 
than compensated by the long-term gains;  

-  The “shift” scenario (mimicking an increase of mesh size) typically comes with higher yield 

gains compared to the “crank” scenario (mimicking nursery areas closure or use of 
exclusion devices), with only few exceptions, such as red mullet in GSA07 and GSA09, cod 
in 6a, hake in SWW, and some whiting and haddock stocks;  

-  The “crank” scenario generally gives better results in terms of reduction of juvenile 

catches, with only few exceptions, such as cod in 6a, whiting in 7a, and hake and megrim 
in SWW. 

ToR 3 

The analyses foreseen under ToR 3 were performed on a subset of stocks (13 in ICES areas, 6 in 
the  Mediterranean)  for  which  fleet  disaggregated  data  were  available.  Two  different  approaches 
were  taken.  In  the  first  approach,  the  results  show  for  each  fleet  (within  each  stock)  the 
comparison  of  projected  (a)  yield  and  (b)  proportion  of  juveniles  in  the  catch  between  current 
fleet  selectivity  and  the  optimised  selectivity  patterns  quantified in  ToR  1  as  well  as  the  current 
selectivity for all fleets. These results indicate, for each stock, fleets with selectivity patterns far 
from  optimal  selectivity  and  fleets  with  selectivity  patterns  closer  to  optimal  selectivity.  The 
second approach is a so-called “Jackknife” approach, in which projections are run with one fleet 
excluded  at  a  time  and  the  fishing  mortality  scaled  to  current  level  of  fishing  mortality.  In  this 
way, it can be seen what the gains are if a particular fleet would be excluded (again, within each 
stock). 

Regarding ToR 3, STECF notes that: 

-  Active gears in general perform worse in terms of protection of juveniles and yield for the 

analysed stocks; 

-  Current otter bottom trawl (OTB) selectivity leads to lower yield when compared to current 

- 

selectivity of all fleets combined; 
In the Northeast Atlantic, beam trawls (TBB) often have high proportions of juveniles in 
the catch, with the exception of plaice in the eastern English Channel (ICES area 7d); 
-  Current OTB selectivity gives rise to a high proportion of juveniles (80% in numbers) in 

the catches of Mediterranean hake; 

-  The Jackknife analysis indicates that compared to the current situation, excluding OTB 

selectivity would give the greatest improvement in the protection of juveniles and yield to 
stocks of larger-bodied demersal species, such as cod and hake; 

-  The results from ToR 3 suggest that increases in yield and improvements in protection of 
juveniles can be obtained through various mechanisms; i) increasing the selectivity for 
OTB, ii) allocating less effort to fishing with such gears and/or iii) shifting the effort to 
areas/seasons where the impacts on juveniles can be minimized. 

ToR 4a 

In  ToR  4a,  for  each  stock,  the  combined  effects  of  varying  fishing  mortality  rate  and  selectivity 
pattern  were  explored  in  terms  of  yield  and  SSB,  as  well  as  indicating  the  values  of  FMSY  as  a 
function of selectivity. The results are summarized as “isopleths” plots, which illustrate how yield 
and SSB can be increased by either reducing fishing mortality (shown on the X.axis) or increasing 
selectivity pattern (Y-axis). 

3 

3 

 
 
 
 
 
 
 
 
 
 
 
As concerns ToR 4a, STECF notes that: 

-  All the stocks that are overexploited would gain in both yield and SSB if selectivity is 

increased simultaneously with decreased fishing mortality; the greatest gain would be for 
those stocks that are most heavily overexploited; 

-  Simultaneously increasing the selectivity and decreasing F would require smaller changes 
compared to manipulating only one parameter. This may increase the incentive (or rather 
decrease the disincentive) for change;  
Increased selectivity has often proportionally larger long-term effects on yield than on 
SSB. In many cases, a decrease in F in combination with increased selectivity is needed to 
see marked increases in SSB; 

- 

-  Small-bodied and fast-growing species, which are commonly assumed to have high natural 
mortality of younger age classes (e.g., whiting stocks), have less to benefit in terms of 
yield from the increase in selectivity at current fishing mortality. In some cases, increased 
selectivity would lead to decreasing yield, but it would always result in larger SSB; 

-  Stocks that are currently underexploited (F<FMSY) (e.g., North Sea whiting and Irish Sea 
plaice) would not produce higher yield with increasing selectivity and/or decreasing F.  

ToR 4b 

STECF notes that EWG 21-07 responded to the 2nd part of ToR 4 (“4b”) with a detailed discussion 
of  the  short-term  effects  of  selectivity  changes  in  selected  mixed  fisheries  (qualitatively)  and  a 
summary  of  a  case  study  of  the  North  Sea  on  selectivity  changes  in  mixed  fisheries  that  was 
carried out under a different project (quantitatively using FLBEIA) (Outrequin, 2021; Outrequin et 
al., in prep.). 

As concerns ToR 4b, STECF notes that: 

- 

In mixed-fishery situations, technical measures are often compromises that tend to 
increase short-term costs for the industry, through short-term losses, re-designing of 
vessels and/or equipment costs;  

-  The mixed-fisheries multi-gear examples demonstrate the complexity in improving 

selection patterns. Possible solutions differ case-by-case and include a combination of 
gear-based and spatial/temporal measures and reductions in fishing effort;  

-  Simulations showed that for a given level of fishing mortality implementation of larger 

mesh sizes in the North Sea, at least for the main gears, would result in the long-term in 
larger landings, larger remaining biomass and less unwanted catches. 

STECF conclusions 

STECF concludes that the EWG 21-07 fully addressed all of the ToRs. 

STECF  concludes  that  the  approach  taken  by  the  EWG  is  scientifically  sound.  The  data  used  are 
the  best  available,  and  are  sufficient  to  support  the  methods  and  findings.  While  the  EWG 
discusses  some  caveats  relating  to  the  interpretation  of  results  in  Section  5  of  the  report,  the 
outcomes  are  reliable  and  informative.  However,  while  the  data  and  methods  used  by  the  EWG 
are appropriate, the outputs from simulations and projections for each stock are deterministic and 
hence the precision of the results cannot be quantified.  

4 

4 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
STECF concludes that increasing selectivity contributes to reaching some of the current objectives 
of the CFP, especially if applied together with reductions in fishing mortality. Advantages of such 
an approach include:  

- 

reaching the current FMSY (i.e. maximum sustainable yield exploitation rate, defined as the 
target of fisheries management in Article 2.2 of the 2013 CFP basic regulation) with less 
overall reduction in fishing pressure, in particular for stocks that are currently heavily 
overfished.  

-  ensuring a higher protection of juveniles by improved exploitation patterns, as required in 

- 

Article 3.2a of the current TMR. 
improved compliance with the landing obligation due to reduced incentives to underreport 
catches <MCRS (Article 15 of 2013 CFP basic regulation). 

-  discard reduction due to lower catches of individuals below MCRS (Article 2.5a and Article 

- 

4.1a of the TMR regulation).  
reducing the impact of fishing on exploited fish stocks, according to Article 2.3 of the 2013 
CFP Basic Regulation which stipulate that “The CFP shall implement the ecosystem-based 
approach to fisheries management so as to ensure that negative impacts of fishing 
activities on the marine ecosystem are minimized”. In particular, improving selectivity 
together with reducing fishing pressure towards Fmsy would lead to higher biomass than by 
reducing fishing pressure alone. This means that a given level of catches would be 
achieved with comparatively less effort, implying thus fewer greenhouse gas emissions, 
habitats impacts and bycatches of sensitive species.  

STECF  concludes  that  further  work  is  still  needed  to  progress  along  the  review  of  the  Technical 
Measures Regulation, as discussed in ToR 7.3 of this PLEN 21-03 report. 

References 

Outrequin  T.  (2021)  La  modification  des  engins  de  pêche  peut-elle  réduire  l'impact  de  la  pêche 

sur les ressources halieutiques? Mémoire de master, UMR ESE Institut Agro, Rennes.  

Outrequin  T.,  Gascuel  D.,  Vermard  Y.  (in  prep.)  Landings  more  impacting  less:  a  simulation  of 

mesh sizes increase in the North Sea mixed demersal fisheries. 

Contact details of STECF members 

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

Name 

Affiliation1 

Email 

Abella, J. Alvaro 

Independent consultant 

aabellafisheries@gmail.co
m 

5 

5 

 
 
 
 
 
 
 
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 

Catchpole, Thomas 

Damalas, Dimitrios 

Daskalov, Georgi 

Döring, Ralf (vice-chair) 

Gascuel, Didier  

Grati, Fabio 

blindlemoncasey@gmail.c
om  

thomas.catchpole@cefas.c
o.uk  

shark@hcmr.gr 

Georgi.m.daskalov@gmail
.com 

ralf.doering@thuenen.de 

CEFAS 
Pakefield 
Suffolk, UK, NR33 0HT 

Lowestoft 
Road, 

Laboratory, 
Lowestoft, 

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

576 

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

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

D-27572 

Sea 

of 

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

Didier.Gascuel@agrocamp
us-ouest.fr 

fabio.grati@cnr.it  

Institute 

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

for 
and 

Ibaibarriaga, Leire  

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

libaibarriaga@azti.es  

Jung, Armelle 

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

armelle.jung@desrequinse
tdeshommes.org  

6 

6 

 
 
Name 

Affiliation1 

Email 

Knittweis, Leyla 

Kraak, Sarah  

Ligas, Alessandro 

Martin, Paloma  

Motova, Arina  

Moutopoulos, Dimitrios 

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

of 

Leyla.knittweis@um.edu.
mt  

Thünen  Institute  of  Baltic  Sea 
Fsheries,  Alter  Hafen  Süd  2, 
18069 Rostock, Germany.  

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

di 

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

sarah.kraak@thuenen.de 

ligas@cibm.it 

paloma@icm.csic.es 

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

arina.motova@seafish.co.
uk 

of 

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

Fisheries 

dmoutopo@teimes.gr 

Nord, Jenny  

Prellezo, Raúl  

O’Neill, Barry 

Raid, Tiit  

Rihan,  Dominic 
chair) 

Sampedro, Paz 

The  Swedish  Agency  for  Marine 
and Water Management (SwAM)  

Jenny.nord@havochvatten
.se 

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

rprellezo@azti.es 

DTU  Aqua,  Willemoesvej  2,  9850 
Hirtshals, Denmark 

barone@aqua.dtu.dk  

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

Tiit.raid@gmail.com  

(vice-

BIM, Ireland 

rihan@bim.ie  

Institute 

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

paz.sampedro@ieo.es  

7 

7 

 
Name 

Affiliation1 

Email 

Somarakis, Stylianos  

Stransky, Christoph  

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, 
Sea 
Institute 
Fisheries, Herwigstrasse  31,  D-
27572 Bremerhaven, Germany 

of 

somarak@hcmr. gr 

christoph.stransky@thuen
en.de 

Ulrich, Clara (chair) 

IFREMER, France 

Clara.Ulrich@ifremer.fr  

Uriarte, Andres 

Valentinsson, Daniel 

van Hoof, Luc 

Gestión 

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

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

Wageningen  Marine  Research 
Haringkade  1, 
Ijmuiden,  The 
Netherlands 

auriarte@azti.es 

daniel.valentinsson@slu.s
e 

Luc.vanhoof@wur.nl 

Vanhee, Willy  

Independent consultant 

wvanhee@telenet.be 

Villasante, Sebastian 

of 

Santiago 

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

Santiago 

sebastian.villasante@usc.
es 

Vrgoc, Nedo 

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

vrgoc@izor.hr 

8 

8 

 
 
EXPERT WORKING GROUP EWG-21-07 REPORT 

REPORT TO THE STECF 

EXPERT WORKING GROUP ON  
Review of the Technical Measures Regulation 
(EWG-21-07) 

Virtual meeting, 11-15 October 2021 

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

9 

9 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1 

INTRODUCTION 

According  to  Article  31  of  Regulation  (EU)  2019/1241  on  the  conservation  of  fishery  resources 
and protection of marine ecosystems through technical measures, the Commission is required to 
report every third year, following evaluation by STECF, on the extent to which technical measures 
both  at  regional  level  and  at  European  Union  level  have  contributed  to  achieving  the  objectives 
set  out  in  Article  3  and  reaching  the  targets  set  out  in  Article  4  of  Regulation  (EU)  2019/1241. 
The  first  report  was  due  to  be  submitted  on  the  31  December  2020  and  it  appeared  with  some 
delay due to covid-19 in September 2021 (COM, 2021a,b). The next report is due 31 December 
2023.  To  facilitate  this  reporting,  STECF  is  requested  by  the  Commission  to  evaluate  the 
performance of technical measures to conserve fishery resources and protect marine ecosystems. 
For the first report the STECF had set up EWG 20-02 for the evaluation. The Terms of Reference 
for  EWG  20-02  (STECF,  2020a)  included  two  tasks  that  were  explicitly  concerned  with  the 
fisheries  selectivity  on  commercial  species  in  relation  to  the  objectives  and  targets  set  out  in 
Article  3.2(a)  and  Article  4.1(a)  respectively  of  the  regulation:  the  objective  to  “optimise 
exploitation  patterns  to  provide  protection  for  juveniles  and  spawning  aggregations  of  marine 
biological  resources”  and  the  target  that  “catches  of  marine  species  below  the  minimum 
conservation  reference  size  are  reduced  as  far  as  possible”.  Other  tasks  addressed  impacts 
arising  from  innovative  gear  and  effects  on  sensitive  species,  habitats  and  marine  ecosystems. 
Here, we are concerned with the former: fisheries selectivity on commercial species, namely the 
species listed in Annex XIV of Regulation (EU) 2019/1241. 

In  October  2020  it  was,  of  course,  not  possible  to  evaluate  the  impact  of  the  new  Regulation, 
because it had only been in force since a bit more than one year (since August 2019). EWG 20-02 
(STECF, 2020a) decided to look at a longer historical period of time and compare time series of a 
selectivity indicator with the timing of the introductions of various technical measures in the past. 
The  indicator  chosen  was  the  ratio  between  the  fishing  mortality  rate  on  the  fish  at  the  age  of 
recruitment to the fishery and the mean fishing mortality rate over a range of adult ages (the so-
called  Fbar  used  in  stock  assessments  and  advice):  Frec/Fbar.  In  a  comparison  with  a  set  of 
alternative  indicators,  this  indicator  had  been  identified  as  the  most  promising,  being  robust  to 
variation  in  recruitment  and  changes  in  fishing  mortality  (Vasilakopoulos  et  al.,  2020). 
Nevertheless, EWG 20-02 (STECF, 2020a) listed a range of caveats to the use of this indicator for 
the  purpose  of  this  analysis  and,  overall,  did  not  identify  any  obvious  correlations  between 
changes  in  the  selectivity  indicator  and  specific  changes  in  technical  measures  for  the  species 
investigated. STECF PLEN 20-03 (STECF, 2020d) concluded that the ToR that had been asked of 
EWG 20-02 was extremely wide-ranging in scope and that to address it explicitly and provide an 
informed,  meaningful  response,  would  have  required  far  more  time  and  expertise  than  that 
afforded  to  EWG  20-02.  STECF  PLEN  20-03  (STECF,  2020d)  also  concluded  that,  although 
temporal trends in selectivity were found, the extent to which such changes can be attributed to 
implementation of technical measures cannot be deduced. 

Following  this,  STECF  and  the  Commission  considered  how  to  proceed  to  be  ready  for  the  next 
evaluation,  which is  due  in  2023.  After  long  deliberations,  and  the  identification  by  STECF  PLEN 
21-01  (STECF,  2021a)  of  the  mid-term  need  to  establish  and  agree  on  a  methodology  and  the 
appropriate  indicators  that  can  be  used  to  perform  the  evaluation  of  the  regulation,  it  was 
decided to take a step-by-step approach and explore the merits of some partial approaches one 
by one. In the early summer of 2020, DG MARE proposed a first version of the current ToRs 1-3 
and  these  were  discussed  by  STECF  PLEN  21-02  (STECF,  2021b).  STECF  PLEN  21-02  (STECF, 
2021b) proposed some slight modifications and the addition of ToR 4. STECF PLEN 21-02 (STECF, 
2021b)  considered  the  ToRs  1-3  to  be,  at  least  in  theory,  straightforward  and  feasible  to  be 
tackled  by  EWG  21-07.  There  were  some  concerns  that  the  ToRs  1-3  would  be  only  a  very 
simplistic and partial step towards a more fully comprehensive evaluation, but it was decided that 
a  small  step  would  be  more  feasible  than  a  (too)  large  step.  In  order  to  accommodate  the 
possibility that time would allow for more in-depth analyses, STECF PLEN 21-02 (STECF, 2021b) 
proposed to add a 4th ToR in which the specific scientific expertise of the EWG participants could 
be  used  in  case  studies  towards  a  more  comprehensive  evaluation.  Finally,  the  Commission 

10 

10 

 
 
 
issued the current ToRs 1-4, slightly modified from the proposal by STECF PLEN 20-02 (of which 
ToRs  1-3  were  based  on  the  proposal  by  DG  MARE)  (STECF,  2021b).  In  particular,  the  second 
part of ToR 4 was added by the Commission at a late stage: the request to comment on practical 
issues  regarding  the  attainment  of  the  biologically  optimal  selection  pattern  in  the  context  of 
mixed fisheries and multi-gear fisheries. 

1.1 

Terms of Reference for EWG-21-07 

Following discussions after STECF EWG 20-02, STECF 21-01 and STECF 21-02, the EWG 21-07 is 
requested to: 

1. 

2. 

3. 

4. 

Calculate the  respective selectivity-at-age that  (a) predict the highest  yield 
at current fishing mortality rates or harvest rates, and (b) provide the greatest protection 
of juveniles. 

Compare  the  optimised  selectivity-at-age  predicted  under  (1)  with  current 
selectivity-at-age estimates for the stocks concerned in terms of both (a) yield gains and 
(b) protection of juveniles 

Compare  the  optimised  selectivity-at-age  predicted  under  (1)  with  current 
selectivity-at-age estimates by fleet, gear and area, which should be analysed to the most 
disaggregated level that is feasible in terms of both yield gains and protection of juveniles.  

For  regional  case  studies,  explore  trade-offs  between  fishing  pressure  and 
selectivity  with  a  view  to  minimising  impacts  and  maximizing  catches  under  different 
scenarios for catch, fishing mortality and in relation to fisheries reference points. STECF is 
further asked to comment on practical issues  regarding the attainment of the biologically 
optimal selection pattern in the context of mixed fisheries and multi-gear fisheries. 

11 

11 

 
 
 
 
 
 
 
2 

GENERAL APPROACH TO THE TERMS OF REFERENCE 

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).  Therefore,  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),  while  size  selectivity 
refers to the avoidance of specific sizes (usually small ones) of a given species. Size selectivity is 
the focus of this report. 

There are three types of size selectivity (Millar & Fryer, 1999): contact selectivity (often referred 
to  as  ‘gear  selectivity’),  which  is  the  differential  retention  probability  of  fish  that  encounter  a 
gear; available selectivity, which expresses the differential availability of different fish sizes to a 
gear; and population selectivity, which is the combination of the two (i.e. gear selectivity plus fish 
availability). Consequently, 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).  Population  selectivity 
differs  from  gear  selectivity  in  that  it  is  the  product  of  all  gears  acting  upon  a  stock  and  of  the 
spatio-temporal  allocation  of  both  fish  and  fishers.  Within  this  report,  ‘selectivity’  refers  to 
‘population selectivity’, unless otherwise specified. 

It should be noted that, because this document uses population selectivity, the metric of S50 (age 
at  which  50%  is  selected)  cannot  directly  be  related  to  mesh  size.  Through  selectivity 
experiments, length-based selectivity parameters (such as L50, length at which 50% is retained) 
can  be  related  to  mesh  size  of  individual  gears;  with  age-length  information  these  can  be 
transformed into gear selectivity at age (an exercise that was done in the Annex of this report). 
But, as explained above, population selectivity results from the combination of gears in use and 
the spatio-temporal allocation of fish and fishers. 

The approach to the Terms of Reference is as follows:  

ToR 1 and ToR 2 are presented together (Results section 4.1.). For each stock of the Annex (see 
Table  3.1.1),  the  current  selectivity  (ToR  2)  is  described  by  the  best  fit  of  a  flexible  selectivity 
function on the F-at-age curve, and the resulting selectivity curve is then varied according to two 
alternative scenarios (“cranking” and “shifting”, explained below, ToR  1). The maximum yield at 
equilibrium is  computed  for  each  of  the  so  generated  new  selectivity  curves,  and  the  selectivity 
curve  that  attained  the  maximum  equilibrium yield  across  all  variations  is  selected  as  optimized 
selectivity  for  each  scenario.  The  so  optimised  selectivity  patterns  are  considered  and  the  age-
structured stock dynamics are then projected forward under the current fishing mortality (Fcur) for 
each of these selectivity patterns until equilibrium is reached (50 years). At this equilibrium point, 
the yield and the percentage of juvenile fish in the catch are quantified for each of the selectivity 
patterns and compared to the equilibrium values attained  from projections based on the current 
selectivity  for  Fcur  and,  in  addition,  based  on  the  scientific  advice  for  F  (ICES  /  GFCM).  This 
procedure  addresses  both  ToRs  1  and  2  in  that  the  optimised  selectivity  patterns  (ToR  1)  are 
evaluated  in  terms  of  (a)  yield  and  (b)  protection  of  juveniles;  and  then  a  comparison  is  made 
with  the  current  selection  pattern  (ToR  2)  in  terms  of  (a)  yield  and  (b)  protection  of  juveniles. 
Thus,  it  can  be  assessed  which  stocks  have  the  largest  potential  gains  in  terms  of  yield  and 
protection of juveniles by improving selectivity, and which stocks are currently already fished with 
close  to  optimal  selectivity.  The  EWG  defines  juveniles  as  immature  individuals  (following  the 
maturity-at-age values used in the official stock assessment model). 

ToR  3  (Results  section  4.2)  is  addressed  by  extending  the  approach  described  above  for  ToR  1 
and  2  to  compare  fleet-specific  selectivity  patterns  to  the  optimised  selectivity  pattern  derived 
under  ToR  1  in  terms  of  both  yield  and  the  protection  of  juveniles.  The  analysis  for  ToR  3  is 
limited to a subset of stocks for which fleet disaggregated data are available (Table 3.1.1). In this 
case,  two  approaches  are  followed  to  evaluate  fleet-specific  selectivity.  In  the  first  approach, 
fleet-specific selectivity curves are fitted to available partial fleet F-at-age data for each stock that 
could be included in the analysis. The age-structured stock dynamics are then projected forward 

12 

12 

 
 
for  one  fleet-specific  selectivity  curve  at  a  time  under  Fcur  and  outcomes  are  compared  to  the 
current (combined fleet) and the optimised selectivity projections from ToRs 1 and 2 in terms of 
the equilibrium (a) yield and (b) proportion of juveniles in the catch. Therefore, for each fleet, the 
comparison  is  made  between  fishing  under  the  fleet’s  selectivity  against  what  the  optimised 
selectivity would result in. In this way it can be assessed, for each stock, which fleet’s selectivity 
pattern  is  far  from  optimal  selectivity  and  which  fleet  may  be  closer  to  optimal  selectivity.    The 
second approach is a so-called Jackknife approach, in which projections are  made for exclusions 
of  one  fleet  at  a  time  and  the  partial  changes  of  fishing  mortality  are  scaled  to  current.  The 
Jackknife  analysis  therefore  presents  evaluation  of  the  partial  selectivity  impact  for  each  of  the 
excluded fleets by predicting the potential gains in terms of yield and protection of juveniles if a 
particular fleet were to be excluded.  

With respect to ToR 4,  the EWG interprets the first part  (“4a”) as follows (Results section 4.3.). 
Firstly,  for  each  stock  the  combined  effects  of  varying  fishing  mortality  rate  and  selectivity 
patterns  are  explored  in  terms  of  minimising  impacts  on  spawning  stock  biomass  (SSB)  and 
maximizing  catches.  To  map  the  optimal  exploitation  regimes  (i.e.  combinations  of  F  and 
selectivity)  that  can  produce  high  yields  at  high  levels  of  SSB,  isopleths  were  constructed  by 
computing  the  equilibrium  states  of  all  F  and  selectivity  combinations  for  each  stock.  This 
‘isopleth approach’ allows to identify which combinations lead to high catches and high SSB, and 
to visualise by contrast the unfulfilled potential (if any) of selectivity to promote higher yields at 
lower  levels  of  stock  depletion.  It  thus  facilitates  the  choice  of  pathways  of  change  in  F  and/or 
selectivity  to  move  towards  more  sustainable  exploitation  regimes  and  had  been  proposed  as 
analytic  tool  for  this  purpose  in  previous  STECF  EWGs  on  Technical  Measures  (STECF  2013; 
2015).  To  evaluate  the  relation  to  fisheries  reference  points,  non-stationary  FMSY  values  (or 
proxies of it), which vary as function of selectivity, are depicted in the isopleth plots.  

The second part of ToR 4 (“4b”, Results section 4.4.) is interpreted as a request to comment on 
practical  implications  of  trying  to  attain  optimal  selectivity  in  the  context  of  mixed  fisheries  and 
multi-gear  fisheries  from  the  point  of  view  of  changes  in  gear  technology.  Here  the  EWG  (i) 
discusses the short-term effects of selectivity changes in selected case studies (qualitatively), (2) 
and  (ii)  presents  a  synthesis  of  main  results  from  a  simulation  study  on  selectivity  changes  in 
North Sea. This study on mixed fisheries  with FLBEIA was recently carried  out under a different 
project. 

3 

MATERIAL AND METHODS 

3.1 

Data 

The stocks analysed in this report (Table 3.1.1) 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. 

For the ICES region of the Northeast Atlantic, stock assessment outputs in the form of ‘FLStock’ 
objects  based  on  2020  benchmark  were  received  timely  before  to  the  meeting  as  requested.  In 
addition,  a  new  dataset  of  ‘FLStock’  objects  for  the  most  recent  2021  assessment  became 
available  as  part  of  the  preparation  for  the  ICES  WKREF1  workshop  (2-4th  Nov,  2021),  which 
were  prepared  in  a  joint  effort  of  ACOM,  ICES  assessment  working  group  and  M.  Cardinale 
(Expert)  and  H.  Winker  (Co-Chair),  who  are  also  the  Co-Chairs  of  WKREF1.  EWG  agreed  to  use 
the  most  recent  2021  ‘FLStock’  data  for  the  analysis,  also  considering  that  it  undergone  very 
thorough consistency checks against the ICES 2021 advice. For the Mediterranean Sea, available 
‘FLStock’ objects were extracted from the electronic annexes of the STEFC Stock Assessments in 
the  Mediterranean  Sea  (STECF-20-09,  STECF-20-16)  and  prepared  prior  to  the  meeting  by  the 
JRC  (Table  3.1.1).  Only  those  stock  assessments  were  used  that  formally  passed  the 
benchmarking  process.  Although  the  assessments  output  vary  arguably  in  quality,  they  were 
considered to represent the best available information.   

13 

13 

 
 
 
 at  fleet/gear  were  available for  ICES  stocks (2017-2019) from 
Partial fishing mortality at age 
the 2020 Review of technical measures (STECF, 2020a). In addition, fleet information in the form 
of  ‘FLFleet’  objects  from  the  ICES  Working  Group  on  mixed  fisheries  advice  were  received  from 
ICES as requested.  However, on  closer inspection by the EWG, disaggregated catch-at-age  data 
were not available for key such as North Sea and North Western waters, and the EWG therefore 

agreed  to  use  the  partial 
  fleet  dataset  (level  4)  from  STECF-20-02  (STECF,  2020a)  for  the 
analysis  (ToR  3).  For  this  purpose,  the  data  set  was  compiled  in  the  form  of  a  list  ‘FLQuants’ 

objects, which containing the partial 

 vectors by fleet by the JRC.  

For  Mediterranean  stocks  partial 
  by  fleet  segment  were  only  available  for  selected  Western 
Mediterranean  stocks  in  the  STEFC  EWG-21-01  report  on  the  West  Med  assessments  (Table 
3.1.1):  conversion  factors,  closures,  effort  data  and  recreational  fisheries.  The  data  extracted 
from  the  Tables  presented  in  EWG  21-01  and  a  dataset  was  compiled  in  the  form  of  a  list 

‘FLQuants’ objects, which containing the partial 

 vectors by fleet before the meeting by the JRC.  

14 

14 

 
Table 3.1.1: Summary Table of stocks by region and area that were used for the analysis in this report. The 
column  ‘Fleet  Data’  indicates  the  subset  of  stocks  for  fleet  data  were  available.  NEA:  Northeast  Atlantic, 
MED: Mediterranean Sea; BS: Baltic Sea, NS: North Sea; NWW: Northwestern Waters; SWW: Southwestern 
Waters.  

Region 

Area 

Stock 

Species 

Assessment 

Fleet Data 

ICES, AAP, 2021 

ICES, AAP, 2021 

ICES, SAM, 2021 

ICES, SAM, 2021 

ICES, SAM, 2021 

ICES, SAM, 2021 

ICES, SAM, 2021 

ICES, ASAP, 2021 

ICES, SAM, 2021 

ICES, SS3, 2021 

ICES, XSA, 2021 

cod.27.22-24 

ple.27.21-23 

Gadus morhua 

Pleuronectes platessa 

cod.27.47d20 

Gadus morhua 

ICES, SAM, 2021 

ICES, SAM, 2021 

ICES, SAM, 2021 

had.27.46a20 

Melanogrammus aeglefinus 

ICES, TSA, 2021 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

BS 

BS 

NS 

NS 

NS 

NS 

NS 

NS 

ple.27.420 

ple.27.7d 

pok.27.3a46 

whg.27.47d 

Pleuronectes platessa 

Pleuronectes platessa 

Pollachius virens 

Merlangius merlangus 

NEA 

NWW 

cod.27.6a 

NEA 

NWW 

cod.27.7e-k 

Gadus morhua 

Gadus morhua 

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 

NEA 

NWW  whg.27.7a 

Merlangius merlangus 

NEA 

NWW  whg.27.7b-ce-k 

Merlangius merlangus 

SWW 

hke.27.3a46-8abd 

Merluccius merluccius 

SWW 

ldb.27.8c9a 

Lepidorhombus boscii 

NEA 

NEA 

NEA 

NEA 

SWW  meg.27.7b-k8abd 

Lepidorhombus whiffiagonis 

ICES, Bayesian, 2021 

SWW  meg.27.8c9a 

Lepidorhombus whiffiagonis 

ICES, XSA, 2021 

MED 

WM 

HKE.01_05_06_07 

Merluccius merluccius 

MED 

WM 

HKE.08_09_10_11 

Merluccius merluccius 

MED 

WM  MUR.05 

Mullus surmuletus 

STEFC, a4a, 2020 

STEFC, a4a, 2020 

STEFC, a4a, 2020 

MED 

WM  MUT.01 

Mullus barbatus barbatus 

STEFC, a4a, 2020 

MED 

WM  MUT.06 

Mullus barbatus barbatus 

STEFC, a4a, 2020 

MED 

WM  MUT.07 

Mullus barbatus barbatus 

STEFC, a4a, 2020 

MED 

WM  MUT.09 

Mullus barbatus barbatus 

STEFC, a4a, 2020 

MED 

WM  MUT.10 

Mullus barbatus barbatus 

STEFC, a4a, 2020 

MED 

CEM 

HKE.17_18 

Merluccius merluccius 

MED 

CEM 

HKE.19 

Merluccius merluccius 

MED 

CEM 

HKE.20 

Merluccius merluccius 

STEFC, SS3, 2020 

STEFC, a4a, 2020 

STEFC, a4a, 2020 

MED 

CEM  MUT.17_18 

Mullus barbatus barbatus 

STEFC, a4a, 2020 

MED 

CEM  MUT.22 

Mullus barbatus 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 

15 

15 

 
 
 
3.2  Software used 

For the analyses of the impact of varying fisheries selectivity patterns, the R package FLSelex was 
produced and is available on https://github.com/Henning-Winker/FLSelex. This package is used in 
FLR and it requires the stock data as FLR  objects (Fisheries  Library in R: Kell et al., 2007). The 
Co-chair  Henning  Winker  (JRC)  developed  tested  the  package  on  the  stock  data  prior  to  the 
meeting  with  the  support  of  the  FLR  Core  Team  Members  Iago  Mosqueira  (Wageningen 
University) and Laurence Kell (Imperial College). 

3.3  Using apical F as a standardized metric for evaluating selectivity 

Comparing the impacts of alternative selectivity pattern requires setting the instantaneous rate of 

fishing mortally   at comparable constant levels. For this purpose, it is important to consider that 

the definition of selectivity differs across regions. In Europe, it common to use 

 as a measure of 

annual 

,  whereas  in  many  other  regions  (e.g. US  West  Coast,  South  Africa,  Australia  and  New 

Zealand) the so called apical 

 = 

 is used as a standard metric. 

  has  the  undesirable  property  that  its  scale 
With  regards  to  isolating  the  selectivity  effect, 
depends on the pre-specified age range across which 
 is set to 
ages 2-4 to represent the dominant age classes under the current selectivity regime, but the goal 
is  to  evaluate  the  effect  of  selecting  fish  only  at  age-5,  a  common 
  would  result  in 
disproportionately  high 
  is  computed  for  age  ranges  that  are 
 as is used in FLR. For this reason, and consistent 
hardly selected for the definition 
with previous studies (e.g. Sampson and Scott 2011), the 
 as the standardized 
quantity  to  compare  stock  responses  across  selectivity  pattern  in  FLSelex.  To  implement  this  in 
FLR,  the 
  range  determined  by  fbarmin  and  fbarmax  is  dynamically  adjusted  in  the  FLStock 
object to the 

 under each selectivity scenario under equilibrium conditions. 

  on  ages  5+.  This  is  because 

 is averaged. For example, if 

 is used as 

 = 

/

3.4  Selectivity-at-age 

The starting point for the FLSelex analysis are the FLStock objects based on the most recent age-
structured assessments (e.g. a4a, SAM, SS3) conducted by ICES and STECF (Table 1.1). Due to 
the age-structured assessment model outputs, selectivity is expressed as selectivity-at-age (Sa), 
such that: 

where 

 is the instantaneous rate of fishing mortality at age (e.g. Sampson and Scott 2011). 

A common assumption is that 
 follows a logistic curve in the form of an ogive. However, initial 
exploration  of  FLStock  objects  based  on  recent  ICES  benchmark  assessments  indicate  the  a 
logistic  is  the  exception  than  the  norm  (c.f.  Sampson  and  Scott,  2011).  The  North  Sea  plaice 

ple.27.420 provides an example where the observed fishery selectivity 
 pattern estimates, and 
thus the population selectivity diverges clearly from a logistic selectivity assumption (Figure 3.1). 

16 

16 

 
 
 
 
 
 
 
Figure 3.1: Observed 

 = 

/

 for North Sea plaice ple.27.420 over the recent 3 years 

3.5  The Selex function 

A  variety  of  dome-shaped  selectivity  curves  can  arise  because 
  is  estimated  on 
combined  fleet  level,  combining  multiple  fleet  (gear)  segments  that  fish  over  a  wide  range  of 
different  areas.  In  fact,  Sampson  and  Scott  (2011)  demonstrated  that  a  logistic  population 
selectivity  pattern  would  require  that  all  age-classes  would  be  equally  distributed  in  space  and 
time and harvested with the gear that is associated with a logistic selectivity. 

  and  thus 

To  accommodate  a  wide  variety  of  selectivity  curves,  FLSelex  provides  a  flexible  5-parameter 
parametric  selex()  function  (cf. Huynh  et  al.,  2018),  which  comprises  the  following  three 
compounds: 

1.  A 

 describing the ascending limb of the selectivity curve 

where 

 and 

 are the ages where 

 corresponds to 0.5 and 0.95. 

2.  An adjustable 

 decribing the descending 

where 

  denotes  a  normal  probability  density  distribution,  S_{max}  corresponds  to  the 

mean of the normal distribution where 

 peaks, 

 determines the slope of the descending limb 

with the standard deviation of the normal given by the product 
mimimum the descending slope (height). 

, and 

 determines the 

17 

17 

 
 
 
 
 
 
The expected 

 is then defined as a piece-wise function of the form (Figure 3.2): 

Figure 3.2: Observed 
function.  logis:  logistic  of  acending  curve  (
descending curve, height: adjusted height of the halfnormal (

 and fitted 

, 

 for North Sea plaice illustrating the compounds of the piece-wise selex 
)  for  the 

),  hnorm:  unadjusted  halfnormal  (

, 

) 

3.6  Varying selectivity-at-age 

The FLSelex package provides three inbuilt options to vary the five  estimated selex parameters, 
of which the EWG agreed to explore the following two: 

1.  The option 

 sequentially changes 

, thereby changing ascending slope of the curve, 
. This change in selectivity pattern is intended to represent a 
given an upper bound at 
situation  where  targeting  of  young  fish  can  be  minimized,  e.g. through  spatial-temporal 
closure  of  nursery  grounds  or  gear  through  exclusion  devices.  The  range  of  selectivity 
variations  through  “cranking”  is  shown  for  illustrative  examples  from  the  Northeast 
Atlantic (cod.27.6a, hke.27.3a46-8abd) and the Mediterranean Sea (MUT.09, HKE.17_18) 
in Figure 3.3. 

18 

18 

 
 
 
   
 
  
Figure  3.3:  “Cranking”  the  ascending  slope  of  the  estimated  selectivity  curve  by  varying 
,  shown  for 
examples from the Northeast Atlantic (cod.27.6a, hke.27.3a46-8abd) and the Mediterranean Sea (MUT.09, 
HKE.17_18) 

2.  The  option 

, 

  and 

  sequentially  changes 

  thereby  shifting  the  selectivity 
curve, while retaining the shape unchanged. The default upper bound is to theoretical age 
at 
 where an unfished cohort attains its maximum biomass (Froese et al. 2008; Froese 
et  al. 2016).  Change  in  the  selectivity  pattern  through  “shifting”  is  intended  to 
approximate situations where (1) a reduction  of small specimens in the catch is achieved 
through  larger  mesh  sizes  of  active  gears,  thereby  reducing  drag  associated  with  higher 
catchability of larger and faster fish, (2) a shift of fishing effort to areas where larger fish 
have  higher  densities  or  (3)  a  combination  of  (1)  and  (2).  The  range  of  selectivity 
variations through “shifting” is illustrated for the four example stocks in Figure 3.4. 

19 

19 

 
 
 
  
Figure 3.4: “Shifting” the estimated selectivity curve in its unchanged shape by changing 
simultaneously,  shown  for  examples  from  the  Northeast  Atlantic  (cod.27.6a,  hke.27.3a46-8abd)  and  the 
Mediterranean Sea (MUT.09, HKE.17_18) 

 and 

, 

3.7  Equilibrium optimisation and forecasting of selectivity scenarios (ToRs 1 and 2) 

As  outlined  in  the  “General  Approach  to  the  Terms  of  Reference”,  addressing  ToRs  1,  2  and  3 
entails  the  following  principle  steps.  Firstly,  the  maximum  yield  at  equilibrium  is  computed  for 
each of the “cranked” and “shifted” selectivity curves, and the selectivity curve that attained the 
maximum equilibrium yield across all iterations is selected as optimized selectivity curve for the 
respective scenario.  In this way, optimised selectivity patterns are then projected forward under 

the  current  fishing  mortality  (
reached (50 years). 

)  for  each  of  these  selectivity  patterns  until  equilibrium  is 

The yield optimisation at equilibrium in  FLSelex is computed using the C++ optimisation routine 
in the FLR package FLBRP, which estimates reference points and values of SSB, recruitment, yield 
and  catch  at  equilibrium  for  the  selectivity  curves  generated  through  “cranking”  and  “shifting” 

over a wide range of   values (see Isopleths ToR 4), including the 

 (ToRs 1,2 and 3). 

Forecasting  over  a  range  of  selectivity  pattern  in  FLSelex  is  conducted  with  the  FLR  package 
FLasher. All forecasts assume deterministic recruitment, so at long-term forecasts are equivalent 
to the equilibrium estimates. While forecasting is computational more demanding as thus limited 
to  a  specified 
),  it  provides  the  increased  flexible  for  computing  additional 
quantities of interest from the output in the form of a FLStocks objects. To address ToRs 1, 2 and 

  value  (default 

20 

20 

 
 
 
 
3 (b) the percentage of juveniles in the catches is computed as ratio of the number immature to 
the total number of fish in the catch in addition to (a) the relative change yield (%). 

All  equilibrium  optimisations  and  forecast  were  done  based  on  the  average  weight-at-age 
maturity-at-age 
way, 
common practice for forecasting under 

, 
  vector  of  the  three  most  years.  By  the  same 
  based  on  three  years  average,  which  is  consistent  with 

  was  calculated  based  on 

,  natural  mortality  at  age 

 in ICES and STEFC. 

3.7.1 

STOCK RECRUITMENT RELATIONSHIP (SRR) 

The  EWG  discussed  the  importance  of  integrating  spawner  recruit  relationship  (SRR)  into  yield 
maximisations  at  equilibrium  and  projections  under  alternative  selectivity  scenarios.  The  EWG 
agreed that, where possible (and plausible), a Beverton-Holt SRR should be fitted to recruitment 
 estimates from ‘FLStock’ objects given prior information from a recent meta-analysis of 
(Thorson  2020; 

FishLife  R  package 

 and 

all  SSRs 
the 
that  were  extracted 
https://github.com/James-Thorson-NOAA/FishLife). 

from 

The  Beverton-Holt  SRRs  were  fitted  using  the  FLR  library  FLSRTMB  (Winker  and  Mosquiera; 
https://github.com/flr/FLSRTMB). The specification of the Beverton-Holt in FLR is: 

  is  the  number  of  recruits  in  year 

where 
minus minimum age 
 defined for the stock (typically age-0 or age-1). To integrate available 
prior  information  on  the  steepness  of  the  Beverton-Holt  SSR,  the  above  equation  is  re-
parameterised  as  function  of  steepness    and  unfished  spawning  biomass  per-recruit 
,  such 
as used, e.g., in Stock Synthesis (Methot and Wetzel, 2013): 

  is  the  spawning  stock  biomass  in  year 

, 

where  steepness    is  defined  as  the  ratio  of  recruitment  when 

 to the virgin recruitment 

 at 

 and 

  equals  20%  of  the  unfished 
 is treated as non-stationary being function of 

annual quantities of 
The prior distribution for   is generated from a truncated logit distribution (

. Given that   is bounded by definition between 0.2 and 1, 
) of the form 

 and 

, 

such that 

where 
distribution of   from the hierarchical taxonomic FishLife model (Thorson, 2020). 

  correspond  to  the  input  of  species-specific  predictions  for  the 

  and 

The  FLSRTMB  estimates  of 
Beverton-Holt formulation in FLR, such that 

  and    are  then  converted  into  the  parameters 

  and 

  of  the 

21 

21 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
where the reference for 

 to predict   and   was taken the average 

 across all year. 

The  EWG  reviewed  the  fits  of  the  SSRs  for  all  stocks  and  deemed  the  majority  of  fits  adequate 
(see example in Fig. 3.5, Appendix 1). Exceptions were identified in the cases of one ICES stock, 
cod.27.7e-k,  and  the  Mediterranean  hake  stocks,  where  the  Beverton-Holt  model  resulted  in 
.  All  stocks  had  common  that  showed  severely  truncated  age-structure, 
unrealistic  unfished 
short-  or  questionable  (cod.27.7e-k)  historical  time  series  and  typically  showed  a  decreasing 
trend of recruitment, which probability limited a more reliable estimation of the SSR function. In 
the  case  of  cod.27.7e-k,  an  alternative  SSR  was  approximated  in  the  form  of  segmented 
  reference  point.  In  the  absence  of  any  biomass 
regression  by  fixing  the  break  point  to  the 
reference points for Mediterranean hakes, no SSR could be fitted and recruitment was taken the 
geometric  mean  across  all  years.  In  particular,  the  selectivity  analysis  for  Mediterranean  hakes 
can  therefore  only  interpreted  from  per-recruit  perspective,  which  is  likely  to  underestimate 
potential gains through optimising the selectivity patterns. 

Figure  3.5:    Fits  of  the  Beverton-Holt  stock  recruitment  relationships  (SRR)  for  cod  West  of  Scotland 
(cod.27.6a), Northern hake (hke.27.3a46-8abd) and Western Mediterranean red mullet in GSA 9 (MUT-09) 
and geometric mean of constant recruitment for Adriatic hake (HKE.17_18). Estimated steepness values of 
the Beverton-Holt SRR are indicated in brackets. 

22 

22 

 
 
 
 
3.7.2 

PROJECTION SCENARIOS 

For  each  stock,  a  total  of  five  scenarios  were  projected  deterministically  over  a  horizon  of  50 
years  to  attain  equilibrium  quantities  for  (a)  yield  and  (b)  the  proportion  of  juveniles  in  the 
catches (Table 3.7.1). 

Table 3.7.1: Specification of projection scenarios evaluated for ToRs 1-3  

No 

Scenario  Specification 

1 

Cur 
(Reference) 

Projections are made using the current selectivity (best fits to the observed 
selectivity-at-age) and at current F (Fcur), which serves the reference case of 
the status quo situation against which the four following alternative scenarios 
are evaluated 

2 

Crank 

Projections are made at Fcur using “cranked” selectivity curve (Section 3.4) 
that resulted in the maximum yield   

3 

Shift 

Projections are made at Fcur using “shifted” selectivity curve (Section 3.4) 
resulted in the maximum yield. 

4 

Amat 

Projections are made under Fcur using “shifted” selectivity curve (Section 3.4) 
for which the age-at-50%-selectivity (S50) corresponds to age-at-50%-
maturity (Amat50).   

5 

Fadv 

Projections are made using the current selectivity, but at F levels that 
correspond to the scientific advice for FMSY (Fadv) based on the benchmark 
assessments. 

3.8  Evaluating impacts of fleet-specific selectivity (ToR 3) 

3.8.1 

ONE FLEET AT A TIME  

The analysis on impacts of fleet-specific selectivity builds on the projection scenarios described in 
in the previous Section 3.7.2 (Table 3.7.1) for for ToR 1 and 2. It is designed to compare fleet-
specific  selectivity  patterns  to  the  optimised  selectivity  pattern  derived  under  ToR  1  in  terms  of 
both yield and the protection of juveniles. This analysis is limited to a subset of stocks for which 
fleet  disaggregated  data  are  available  (Table  2.1.1.).  For  each  stock,  projections  were  made  at 

  for  one  fleet-based  selectivity  at  the  time  over  a  projection  horizon  of  50  years  to  attain 

equilibrium quantities for (a) yield and (b) the proportion of juveniles in the catches. 

Fleet-based selectivity curves were derived by fitting the 5-parameter selectivity function (Section 
) of the last available data years (ICES: 
3.5) to the average partial F-at-age values for fleet   (
2017-2019;  STEFC:  2019).  The  partial 
  values  are  calculated  according  to  the  following 
formula: 

23 

23 

 
 
 
 
 
 
where 
for all fleets combined. 

 is the catch-at-age for fleet   and 

 is the assessment model estimate of catch-at-age 

To reduce the number of fleets and increase the robustness of the selectivity curves, some fleets 
were  grouped  based  on  expert  knowledge  and  a  visual  inspection  of  the  empirical  selectivity 
patterns. Only those fleets were analyzed of which the cumulative partial F accounted for at least 
95% of the harvest  rate of a  stock.  The  OTB,  OTT  and PTB  fleets were grouped as  otter trawls, 
while the SDN and SSC fleet were grouped as Seines, and the PTM and OTM fleets were grouped 
as Midwater Trawls (see Appendix 2). 

3.8.2  JACKKNIFE  

The  Jackknife  projections  are  based  on  the  selectivity  of  the  fishery  after  removing  individual 
fleets  from  the  fishery  within  a  region.  The  new 
  for  the  fishery  was  calculated  by  multiplying 
 for the last 3 years of each remaining fleet   with a constant multiplier of 
the average partial 
catch-at-age 
  after 
excluding a fleet was equal to the overved 
was  calculated  by  multiplying  all  partial 
multiplier.  This  new 
function (Section 3.5) to make projections for each Jackknife run at 

 of the fishery. Finally, the jackknife vectors for 
  values  of  the  selected  fleets  with  the  smallest 
  was  used  to  calculate  a  selective  curve  using  the  5-parameter  selectivity 

.  This  age  specific  multiplier  was  chosen  so  that  the  sum  of  the  partial 

. 

A  similar  grouping  of  the  fleets  was  done  as  for  the  fleet-  based  analysis  as  described  above. 
Fleets  that  did  not  contribute  to  95%  of  the  harvest  rate  of  a  stock  within  the  region  were 
removed from the analysis. 

3.9  Isopleths (ToR 4) 

For each stock the combined effects of varying fishing mortality rate and selectivity patterns are 

explored  in  terms  of  yield  and 
  in  terms  spawning  stock  biomass  (SSB)  and  maximizing 
catches. To map the optimal exploitation regimes (i.e. combinations of F and selectivity) that can 

,  three-dimensional  (‘F  x  selectivity  x  equilibrium  yield’ 
produce  high  yields  at  high  levels  of 
and  ‘F  x  selectivity  x  equilibrium  SSB’)  isopleths  were  constructed  with  FLSelex.  The  underlying 

equilibrium  values  of  yield  and 

  that  were  computed  for  the  selectivity  curves  generated 

through  “shifting”  over  a  wide  range  of 

  values  (c.f.  Section  X.X).  To  evaluate  the  relation  to 

fisheries  reference  points,  non-stationary 
which vary as function of selectivity, are depicted in the so isopleth plots. 

  values  (or  proxies  of  it in  the  absense  of  a  SRR), 

4 

RESULTS 

4.1  Potential  gain  in  terms  of  yield  and  proportion  juveniles  in  the  catch  from 

optimising selectivity at current F (ToR 1 & 2) 

In this section, four illustrative examples are described extensively, while the summary results for 
all  stocks  are  displayed  in  tables  and  graphs.  The  full  results  for  all  stocks  are  shown  in  the 
Appendix 3. The four examples for illustration were chosen to cover a range of different features: 
two  of  the  examples  are  ICES  stocks  and  two  are  Mediterranean  stocks;  for  each  area  an 
overexploited  and  a  more  sustainably  exploited  stock  were  chosen;  two  of  the  examples  are 
characterised  by  dome-shaped  selectivity  and  two  are  characterised  by  sigmoid  selectivity.  The 

24 

24 

 
 
 
 
 
 
 
four  illustrative  examples  are  cod  in  the  West  of  Scotland  (cod.27.6a),  northern  hake 
(hke.27.3a46-8abd), red mullet in GSA 09 and European hake in the Adriatic Sea (GSAs 17-18). 
In addition to the two scenarios (“crank” and “shift”) of optimized selectivity under current F (ToR 
1), two more scenarios are displayed here: “Amat” refers to a scenario where selectivity is shifted 
so  that  S50=a50  (where  a50  is  the  age  at  which  50%  are  mature);  Fadv  refers  to  a  scenario 
under current selectivity but with F at the value that is advised (often FMSY).  

4.1.1  CASE STUDY EXAMPLES 

4.1.1.1  West of Scotland Cod (cod.27.6a) 

Characterisation of the stock 

Cod in 6a (West  of Scotland) are fast-growing and  start to recruit to  the fishery at age 1.  They 
are  fully  selected  at  age  4.  Cod  in  this  region  mature  relatively  early  (a50=  age  2).  Natural 
mortality is assumed to be relatively high at age 1 (between 0.5 and 0.65) and gradually declines 
to around 0.2 for older fish (ICES, 2021a). This assumption is based on the Lorenzen approach. 

Assessment 

The  stock  is  assessed  with  a  SAM  model.  F-at-age  is  assumed  to  be  the  same  for  ages  4  and 
older. For younger ages, F is assumed to be auto-correlated between ages. This results in a flat-
topped selectivity curve that is close to a logistic curve (sigmoid shape). 

State of the stock 

The  stock  has  been  over-exploited  for  the  entire  available  time  series  and  has  been  well  below 
Blim  since  the  mid  1990s.  Recruitment  appears  to  be  impaired  due  to  the  low  stock  size  (ICES, 
2020a). 

25 

25 

 
 
 
 
 
 
Figure.  4.1.1.1:  West  of  Scotland  cod  (cod.27.6a).  Stock  status  overview  based  on  the  ICES  2021 
benchmark assessment. 

Selectivity scenarios 

The current selectivity pattern is relatively gradual (non-steep): while fish are selected from age 
1 onwards, they are only fully selected at age 4 with a S50 of around age 2. This means that there 
is considerable scope for the “crank” scenario to change selectivity. Under the “crank” option, S50 
of the optimum pattern is around age 3.5, which is a bit below the maximum possible under the 
“crank” option.  

The “shift” scenario gives an optimum S50 of around age 4. At that age, cod are around 50cm in 
length.  

Shifting selectivity to match maturity will result in a slight decrease in S50 to age 2. 

26 

26 

 
 
 
Figure 4.1.1.2: West of Scotland cod (cod.27.6a). Optimal selectivity scenarios. Dots represent the 
observed selectivity-at-age and dashed line represents maturity-at-age from the assessment model. 

Projections 

The  “cranking”  scenario  is  projected  to  allow  the  stock  to  rebuild  in  the  medium  term  under 
current  F  and  it  will  result  in  a  drastic  reduction  in  juvenile  catches  and  increases  in  yield. 
“Shifting”  selectivity  to  age  4  is  projected  to  lead  to  similar  benefits  as  the  “cranking”  scenario, 
but  to  a  lesser  extent.  Also  reducing  F  to  the  advised  value  but  keeping  current  selectivity  has 
similar  benefits,  but  to  an  even  lesser  extent.  Shifting  selectivity  to  match  maturity  leads  to  a 
slight deterioration of the current selection pattern as the selection curve is moved slightly to the 
left.  

27 

27 

 
 
 
Figure  4.1.1.3:  West  of  Scotland  cod  (cod.27.6a).  Optimal  selectivity  scenario  projections  assuming 
Beverton-H  S-R  relationship.  The  dashed  line  represents  the  projection  under  the  current  observed 
selectivity. 

4.1.1.2  Northern Hake (hke.27.3a46-8abd) 

Characterisation of the stock 

The northern hake stock (Biscay, Celtic Seas, North Sea and Baltic) is relatively fast growing and 
fully selected to the fishery around age 4. Hake mature relatively early (a50=2.5 years). Natural 
mortality is assumed to be 0.4 for all length (and age) classes.  

Assessment 

The  stock  is  assessed  with  a  Stock  Synthesis  model  and  uses  length  data  inputs  only  (ICES, 
2021b). The model is fitted with 7 fleets and each fleet has its own selectivity pattern; some have 
a double-normal pattern, others single logistic. Selectivity varies over time for one fleet, while it 

28 

28 

 
 
 
 
 
is  assumed  to  be  constant  for  the  other  six.  The  resulting  selectivity  pattern  of  the  combined 
fleets has a dome shape, which flattens out around age 10. 

State of the stock 

The stock has been exploited around FMSY since 2011 after a long period of over-exploitation that 
led  to  a  period  where  the  stock  was  below  Blim.  Currently  the  stock  size  is  well in  excess  of  Blim 
(ICES, 2021c). 

Figure 4.1.1.4: Northern hake (hke.27.3a46-8abd). Stock overview 

Selectivity scenarios 

Under the current selectivity pattern  S50 is around age 2. “Cranking” can increase this to almost 
age 4. “Shifting” also results in an increase of S50 to age 4. “Shifting” has the result that apical F 
increases to older ages. Shifting the selectivity to match maturity results in a moderate increase 
in S50 to around age 2.5. 

29 

29 

 
 
 
 
Figure 4.1.1.5: Northern hake (hke.27.3a46-8abd). Optimal selectivity scenarios. Dots represent the 
observed selectivity-at-age and dashed line represents maturity-at-age from the assessment model. 

Projections 

Under the current fishing rate, the “crank” scenario is projected to lead to the largest catches and 
SSB  and  the  lowest  percentage  of  juveniles  in  the  catches  after  reaching  equilibrium.  The 
selectivity curve fitted to the current maturity values (“Amat”) and the “shift” scenario would also 
lead to higher catches and SSB and lower percentages of juvenile catches compared to the status 
quo  scenario  (fit  and  obs).  Before  reaching  equilibrium,  short-term  losses  are  to  be  expected. 
Reducing F to the advised value but keeping current selectivity has negligible effects. 

30 

30 

 
 
 
 
Figure  4.1.1.6:  Northern  hake  (hke.27.3a46-8abd).  Optimal  selectivity  scenario  projections  assuming  BH 
S-R relationship. The dashed line represents the projection under the current observed selectivity. 

31 

31 

 
 
 
 
 
 
 
 
 
 
 
 
4.1.1.3  Red Mullet in GSA 09 (MUT09) 

Characterisation of the stock 

Red  mullet  in  the  Ligurian  and  northern  Tyrrhenian  Seas  (FAO-GFCM  GSA09,  western 
Mediterranean)  are  fast-growing  and  short-living,  and  are  almost  fully  selected  by  age  1.  Red 
mullet  in  this  region  matures  relatively  early,  and  they  are  fully  mature  at  age  1.  Natural 
mortality is assumed to be relatively high at age 1 (M=0.87) and gradually declines to M=0.59 for 
older fish. This assumption is based on the Chen-Watanabe approach. 

Assessment 

The stock has been assessed with an a4a model at STECF EWG 20-09 (STECF, 2020b) and GFCM 
WGSAD  2020  (GFCM,  2020).  F-at-age  is  estimated  by  means  of  a  separable  model  with 
smoothers on age and time. F-at-age is estimated independently for each age, and F at the oldest 
ages is bound to F of age 2 (i.e., F at ages 2 and higher are assumed to be the same). The model 
estimates selectivity to be approximately logistic (essentially flat from age 2 onwards). 

State of the stock 

The  stock  has  been  over-exploited  for  the  entire  available  time  series;  in  recent  years  F  has 
shown a decreasing pattern, and F in 2019 is 1.66 times the reference point (F0.1 = 0.51). SSB is 
increasing. 

32 

32 

 
 
 
 
Figure 4.1.1.7: Red mullet in GSA09 (MUT.09). Stock overview. 

Selectivity scenarios 

The current selectivity pattern is already quite steep and  “cranking” can only affect the selection 
of age 1 fish, as age 2 fish are fully selected,  while “shifting” will also affect age 2 fish catches. 
Therefore,  changing  the  selectivity  pattern  by  means  of  both  “cranking”  and  “shifting”  will 
reduces the catch of age 1 fish to zero. Shifting selectivity to match maturity will decrease S50. 

33 

33 

 
 
 
Figure 4.1.1.8: Red mullet in GSA09 (MUT.09). Optimal selectivity scenarios. Dots represent the observed 
selectivity-at-age and dashed line represents maturity-at-age from the assessment model. 

Projections  

The current F projections show that the “crank” and “shift” scenarios perform similarly and would 
produce the highest catches in the long term (in 2030), as well as the lowest catches of juveniles, 
while the “Amat” scenario would produce lower catches and higher catches of juveniles. Reducing 
F  to  the  advised  value  but  keeping  current  selectivity  is  predicted  to  lead  to  increased  SSB, 
slightly reduced catches and slightly reduced juveniles in the catch. 

34 

34 

 
 
 
Figure  4.1.1.9:  Red  mullet  in  GSA09  (MUT.09).  Optimal  selectivity  scenario  projections.  The  dashed  line 
represents the projection under the current observed selectivity. 

4.1.1.4  EUROPEAN HAKE IN THE ADRIATIC SEA (GSAs 17-18) 

Characterisation of the stock 

The stock of European hake was assumed in the boundaries of the whole Adriatic Sea (GSA 17-
18),  as  suggested  by  the  genetic  results  of  the  MAREA  StockMed  project  that  shows  a  common 
sub-population of hake throughout the Adriatic Sea (Fiorentino et al., 2014). In the Adriatic Sea, 
European  hake  spawn  throughout  the  year,  but  with  different  intensities.  Females  attain  larger 
size than males, which grow more slowly after full maturation at the age of three or four years. 
Consequently, the proportion of males in the population is higher in the lower length classes and 
proportion of females is higher for greater lengths. Natural mortality is assumed to be relatively 
high  at  age  1  and  gradually  declines  to  M=0.2  for  older  fish.  This  assumption  is  based  on  the 
average obtained from several approaches using life trait histories. 

35 

35 

 
 
 
 
 
Assessment 

The  stock  has  been  assessed  with  an  SS3  model  during  the  GFCM  benchmark  on  Adriatic  hake 
(GFCM, 2019). The benchmark investigated all available input data and carried out an analysis of 
the performance of two different stock assessment models: a stock synthesis (SS3) model and an 
assessment-for-all (a4a) model, both of them tested with different assumptions and/or input data 
series. Overall, both models provided similar perspectives on the status of the stock, and a final 
advice was prepared on the basis of the outcomes of an SS3 model. In 2020, the assessment has 
been updated both at STECF EWG 20-15 (STECF, 2020c) and GFCM WGSAD 2020 (GFCM, 2020). 

State of the stock 

European  hake  in  GSAs  17–18  was  considered  to  be  in  overexploitation  and  overexploited,  with 
fishing mortality 2.88 times FMSY and with biomass around 70 percent the precautionary biomass 
(STECF, 2020c). 

Figure 4.1.1.10: European hake in the Adriatic Sea (HKE.17_18). Stock overview. 

36 

36 

 
 
 
 
Selectivity scenarios 

The  current selectivity pattern is dome  shaped and is already quite steep  and ages 3 and 4  are 
fully selected. 

Figure 4.1.1.11: European hake in the Adriatic Sea (HKE.17_18). Dots represent the observed selectivity-
at-age and dashed line represents maturity-at-age from the assessment model. 

Projections 

The current F projections show that the “shift” scenario would produce the highest catches in the 
long term (2030), while the “crank” and “Amat" scenarios would produce lower catches than the 
“shift”  scenario  in  a  shorter  period  (2025).  However,  the  changes  of  the  population  selection 
curve are going to produce always higher catches in the medium/long term than the status quo 
scenarios  (fit  and  obs)  as  well  as  lower  impact  on  juveniles  and  clear  increases  of  SSB.  It  is 
important  to  note  that  the  benefits  of  a  larger  SSB  are  most  likely  underestimated  in  the 
simulations as recruitment is assumed to not increase (i.e., average recruitment) when the SSB 
increases. On the other hand, the model does not assume a density dependent mechanism, which 
will have an opposite effect when SSB increases to high levels. In any case, as reference points 
are  generally  updated  every  3-5  years  in  GFCM,  so  that  when  the  SSB  will increase,  knowledge 
on  density  dependent  mechanisms  and  on  shape  of  the  stock  recruitment  relationship  will 
increase  and  assumptions  of  long-term  simulations  can  be  updated.  Reducing  F  to  the  advised 
value but keeping current selectivity has smaller benefits than changing selectivity under current 
F. 

37 

37 

 
 
 
 
 
Figure 4.1.1.12: European hake in the Adriatic Sea (HKE.17_18). Optimal selectivity scenario projections. 
The dashed line represents the projection under the current observed selectivity. 

4.1.2. RESULTS FOR ALL STOCKS 

In analogy to the results elaborated on above, Tables 4.1.2.1 and 4.1.2.3 display, for each stock, 
the percentage gain in yield, compared to the current selectivity, of the four scenarios as well as 
the current percentage of juveniles in the catch and under the same four scenarios; all at the end 
of the projected time series in the year 2070 (when equilibrium has been reached). The level of 
overfishing  (F/FMSY)  is  displayed  as  well.  These  results  are  visualized  in  Figures  4.1.2.1  and 
4.1.2.2.  For  reference,  the  S50  of  each  scenario  as  well  as  the  age  at  50%  mature  (a50)  are  in 
Tables 4.1.2.2 and 4.1.2.4. 

38 

38 

 
 
 
 
 
 
 
Table  4.1.2.1:  Selectivity  estimates  for  Northeast  Atlantic  stocks  summarizing  the  age-at-50%-selectivity 
(S50)  for  the  current  selectivity-at-age  (Cur),  the  optimised  selectivity  curves  (Crank,  Shift)  and  S50 
corresponding to age-at-50%-maturity (Amat). In addition, the age at which the total biomass of an unfished 
cohort  (F  =  0)  attains  a  maximum  (Aopt)  as  well  as  the  plus  group  (=  maximum  age)  assumed  in  the 
assessment model are provided for reference. 

Region  Area 

Stock 

Age-at-50%-Selectivity (S50) 

Cur 

Crank 

Shift 

Amat 

Aopt 

Plus 
Group 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

NEA 

BS 

BS 

NS 

NS 

NS 

NS 

NS 

NS 

cod.27.22-24 

ple.27.21-23 

cod.27.47d20 

had.27.46a20 

ple.27.420 

ple.27.7d 

pok.27.3a46 

whg.27.47d 

NWW 

cod.27.6a 

NWW 

cod.27.7e-k 

NWW 

had.27.6b 

NWW 

had.27.7a 

NWW 

had.27.7b-k 

NWW 

ple.27.7a 

NWW  whg.27.7a 

NWW  whg.27.7b-ce-k 

SWW 

hke.27.3a46-8abd 

SWW 

ldb.27.8c9a 

SWW  meg.27.7b-k8abd 

SWW  meg.27.8c9a 

2.7 

2.3 

1.9 

1.9 

1.5 

1.9 

3.8 

2.3 

2.2 

1.1 

2.1 

0.9 

3.2 

1.8 

0.5 

3.0 

1.8 

3.4 

3.2 

3.2 

4.2 

3.6 

2.8 

1.3 

2.0 

2.0 

4.7 

1.6 

3.8 

1.8 

3.3 

0.6 

2.7 

1.3 

1.2 

2.1 

3.5 

5.2 

2.7 

5.8 

6.0 

6.0 

4.4 

1.3 

7.2 

2.0 

8.1 

1.6 

4.0 

4.6 

5.0 

0.6 

2.7 

3.8 

1.7 

2.1 

3.5 

5.6 

2.8 

6.0 

1.9 

2.0 

3.0 

2.6 

2.5 

2.9 

4.7 

1.3 

1.9 

2.0 

2.6 

1.6 

1.6 

2.9 

1.6 

0.9 

2.6 

1.0 

2.8 

1.2 

6 

6 

5 

7 

9 

4 

9 

4 

4 

5 

5 

4 

7 

7 

4 

6 

5 

6 

5 

6 

7 

7 

6 

8 

10 

7 

10 

8 

7 

7 

7 

5 

8 

8 

6 

7 

15 

7 

10 

7 

BS: Baltic Sea; NS: North Sea, NWW: North Western Waters, SWW: South Western Waters 

39 

39 

 
 
 
 
Table  4.1.2.2:  Prediction  for  ICES  stocks  summarizing  the  yield  change  (%)  relative  to  the  current 
selectivity under current F (blue: positive; red: negative; colour intensity indicates magnitude of effect) and 
the associated percentage of juvenile fish in the catch (%) (from red, via orange and yellow to green: high 
to low values) for the scenarios of fishing according  to scientific advice  under current selectivity (Fadv) and 
the  optimised  selectivity  curves  at  current  F.  For  reference,  over-fishing  (F2020  >  Fadv)  and  “under-fishing” 
(F2020 < Fadv) is also presented (red: overfishing; green: underfishing). 

Area 

Stock 

F2020/ 

Fadv 

Yield Change (%) 

Juveniles in catch (%) 

Fadv 

Crank 

Shift 

Amat 

Cur 

Fadv  Crank  Shift  Amat 

BS 

BS 

NS 

NS 

NS 

NS 

NS 

NS 

cod.27.22-24 

3.39 

277.4 

322.9  402.1 

-98.5 

36.2  20.9 

9.9 

7.1 

50 

ple.27.21-23 

0.94 

1.6 

14.9 

28.3 

-3.9 

24.8  23.6 

11.7 

4.6 

28.1 

cod.27.47d20 

1.6 

56.6 

44.5 

92.4 

61.8 

71.2  64.3 

35.9 

12.9  39.7 

had.27.46a20 

ple.27.420 

ple.27.7d 

pok.27.3a46 

whg.27.47d 

NWW 

cod.27.6a 

1.02 

0.71 

0.87 

1.25 

0.5 

2.5 

-3.7 

-2.7 

1.2 

9.9 

30 

22 

1.3 

0 

17.3 

-7.1 

41.5  41.8 

70.3 

65.1 

13 

29.8 

7.2 

37.9  39.5 

28.8 

0 

20.9 

0.8 

-0.9 

47.1  48.6 

45.3 

45.7  26.9 

17.6 

43.5 

16.2 

52.5 

48 

22.5 

0.7 

31 

43.9 

28.9 

28.9 

25.7  26.2 

60.8 

45.2  45.2 

259.2 

362.1  315.2 

-58.6 

42.7 

32 

6.6 

16.2  46.5 

NWW 

cod.27.7e-k 

3.97 

207.6 

160.9  375.3  191.3  73.6  43.3 

37.4 

0.1 

35.6 

NWW  had.27.6b 

NWW  had.27.7a 

NWW  had.27.7b-k 

NWW  ple.27.7a 

NWW  whg.27.7a 

1.13 

0.27 

0.89 

0.22 

3.65 

15 

18.5 

4.5 

64.9 

33 

2.4 

1.1 

0.6 

44.2 

12 

36.2  22.1 

0 

0.5 

22.5 

0.9 

-0.5 

51.8  61.7 

70.7 

66.1  19.2 

2 

7.6 

1.3 

4.2 

98 

62.6  30.1 

69.3 

67.7  71.2 

23.5  32.6 

29.9 

4.7 

10.8 

78.8 

64 

2.1 

14.8  19.5 

50 

106.5 

98.1 

NWW  whg.27.7b-ce-k 

1.02 

-15.9 

20.1 

14.1 

14.1 

36 

11.7 

60.4 

50.3  50.3 

SWW  hke.27.3a46-8abd 

0.97 

SWW 

ldb.27.8c9a 

0.56 

0.4 

1.5 

SWW  meg.27.7b-k8abd 

0.72 

-0.3 

22.8 

16.9 

10.8 

63.3  62.2 

8.9 

33.5  49.7 

4.4 

0.5 

11.1 

-6.9 

5.4 

4.4 

0 

0.7 

12.2 

-0.1 

-0.1 

16.7  15.6 

27.1 

22.6  22.6 

SWW  meg.27.8c9a 

0.88 

4.6 

62.8 

64 

-52.5 

8.9 

9.9 

0 

1.8 

16.1 

BS: Baltic Sea; NS: North Sea, NWW: North Western Waters, SWW: South Western Waters 

40 

40 

 
 
 
 
 
Table 4.1.2.3: Selectivity estimates for Mediterranean stocks summarizing the age-at-50%-selectivity (S50) 
for the current selectivity-at-age (Cur), the optimised selectivity curves (Crank, Shift) and S50 corresponding 
to age-at-50%-maturity (Amat). In addition, the age at which the total biomass of an unfished cohort (F = 0) 
attains a maximum (Aopt) as well as the plus group (= maximum age) assumed in the assessment model are 
provided for reference. 

Region  Area 

Stock 

MED  WM  HKE.01_05_06_07 

MED  WM  HKE.08_09_10_11 

MED  WM  MUR.05 

MED  WM  MUT.01 

MED  WM  MUT.06 

MED  WM  MUT.07 

MED  WM  MUT.09 

MED  WM  MUT.10 

MED 

CEM  HKE.17_18 

MED 

CEM  HKE.19 

MED 

CEM  HKE.20 

MED 

CEM  MUT.17_18 

MED 

CEM  MUT.22 

MED: Mediterranean Sea  

Age-at-50% Selectivity (S50) 

Cur 

Crank 

Shift 

Amat 

Aopt 

Plus 
Group 

0.6 

0.2 

1.0 

1.6 

1.1 

0.8 

1.0 

1.1 

1.2 

0.2 

0.9 

1.1 

0.5 

1.3 

0.4 

0.8 

2.4 

1.3 

1.3 

1.4 

1.5 

2.4 

1.7 

1.6 

1.5 

0.4 

4.0 

5.5 

0.7 

3.0 

3.0 

1.8 

1.7 

3.0 

7.0 

5.6 

2.8 

2.5 

1.1 

1.5 

1.4 

0.6 

0.6 

0.6 

0.6 

0.6 

0.6 

3.1 

1.7 

1.4 

0.6 

0.3 

4 

6 

4 

3 

3 

3 

3 

3 

5 

7 

5 

4 

4 

4 

4 

4 

19 

20 

6 

3 

3 

2 

7 

4 

4 

5 

41 

41 

 
 
 
 
 
Table  4.1.2.4:  Predictions  for  Mediterranean  stocks  summarizing  the  yield  change  (%)  relative  to  the 
current  selectivity  under  current  F  (blue:  positive;  red:  negative;  colour  intensity  indicates  magnitude  of 
effect) and the associated percentage of juvenile fish in the catch (%) (from red, via orange and yellow to 
green:  high  to  low  values)  for  the  scenarios  fishing  according  to  scientific  advice  under  current  selectivity 
(Fadv) and the optimised selectivity curves at current F. For reference, over-fishing (F2020 > Fadv) and “under-
fishing” (F2020 < Fadv) is also presented (red: overfishing; green: underfishing). 

Area 

Stock 

WM  HKE.01_05_06_07 

WM  HKE.08_09_10_11 

WM  MUR.05 

WM  MUT.01 

WM  MUT.06 

WM  MUT.07 

WM  MUT.09 

WM  MUT.10 

CEM  HKE.17_18 

CEM  HKE.19 

CEM  HKE.20 

CEM  MUT.17_18 

Fadv 

4.2 

3.62 

1.05 

1.66 

4.62 

1.78 

2.32 

1.36 

2.88 

3.31 

2.67 

2.75 

F2019/ 

Yield Change (%) 

Juveniles in catch (%) 

Fadv 

Crank 

Shift 

Amat 

Cur 

Fadv 

Crank 

Shift  Amat 

25.5 

88.3 

294 

67.8 

86.3  65.1 

18.5 

0.2 

44.8 

6.1 

0.6 

129.7 

37.1 

78.6  63.8 

58.7 

1 

1 

0.6 

0.2 

35.3 

43.9 

-23.9 

24.8 

67.8 

-51 

0 

0 

0 

0 

0 

11.7 

-1 

8.4 

19 

5.1 

28.8 

27.3 

-5.7 

13.2  10.1 

-5.8 

6.2 

5 

-12.3 

2.9 

4.7 

8.8 

25.1 

47.5 

-31.5 

1.3 

11.4 

0 

0 

0 

0 

16.1 

0 

0 

0.5 

0.2 

24.6 

0.1 

11.9 

0 

5.1 

42 

0 

0 

0 

0 

0 

10.4 

16.7 

34.9 

18.1 

89.9  73.3 

40.3 

0.3 

20.8 

17.9 

166.6 

54 

78.7  67.3 

55.2 

1.1 

46.2 

28.3 

49.4 

99.3 

31.1 

63.3  47.4 

-2.2 

17.3 

21.6 

-11.8 

2.3 

7.5 

11 

0 

2.2 

0 

38 

7.2 

CEM  MUT.22 
WM: Western Mediterranean Sea CEM: Central Mediterranean Sea 

0.34 

22.3 

0.4 

0.6 

0.4 

15.9  17.9 

16.1 

14 

16.4 

42 

42 

 
 
 
 
 
Figure 4.1.2.1: Summary phase plots for Northeast Atlantic stocks (n = 20) showing the yield change (%) 
of the four scenarios (Crank, Shift, Amat and Fadv) relative to the current selectivity under current F (x-axis) 
and  the  differences to  the  current  percentage  of  juvenile  fish  in  the  catch  (%).  Percentage  values  in  the 
legend denote the  percentage  of  juvenile  fish  in  the  catch  (%)  under  current F and  selectivity  and  symbol 
shapes denote the  four  regions.  The four quadrants  highlight  the  “wins”  and  “losses”. Bottom-  right (win-
win): increase in yield and reduction in juvenile catch; Top-Right (win-loss) increase in yield but increase in 
juvenile catch, Bottom-Left (loss-win) decrease  in yield but  reduction  in juvenile  catch; Top-Left (loss-loss) 
decrease  in yield and increase in juvenile  catch. Arrows show  the predicted direction of  change  under each 
scenario from the status quo (current F and selectivity) at the crosshair (0,0). 

43 

43 

 
 
 
 
 
  
 
Figure 4.1.2.2: Summary phase plots for Mediterranean stocks (n  = 13) showing the yield change (%) of 
the for the four scenarios (Crank, Shift, Amat and Fadv) relative to the current selectivity under current F (x-
axis) and the differences difference to the current percentage of juvenile fish in the catch Percentage values 
in  the  legend denote the  percentage  of  juvenile  fish  in  the  catch  (%)  under  current F and  selectivity  and 
symbol  shapes denote the  four  regions.  The four quadrants  highlight  the  “wins”  and  “losses”. Bottom- 
right (win-win): increase in yield  and reduction  in juvenile  catch; Top-Right (win-loss) increase  in  yield but 
increase  in  juvenile  catch, Bottom-Left  (loss-win)  decrease  in  yield  but  reduction  in  juvenile  catch; Top-
Left (loss-loss)  decrease  in  yield and increase  in  juvenile  catch. Arrows  show  the  predicted  direction  of 
change under each scenario from the status quo (current F and selectivity) at the crosshair (0,0). 

From  the  results  in  the  tables  4.1.2.1-4.1.2.4  and  figures  4.1.2.1-4.1.2.2.  the  following  can  be 
seen: 

- 

In  general,  any  improvement  in  selectivity  will  (in  the  long  term)  most  strongly  benefit 
stocks  characterized  by  greater  growth  overfishing,  which  are  typically  large-bodied  and 
late-maturing (e.g., hake stocks in the Mediterranean, cod); 

-  A selectivity improvement that achieves a higher protection of juveniles is often producing 

a higher increase in yield; 

-  Exceptions  are  some  haddock  and  whiting  stocks  in  the  Northeast  Atlantic,  where 
optimisations  in  yield  were  associated  with  a  decrease  in  S50,  leading  to  increased 

44 

44 

 
 
 
 
 
proportion  of  juveniles  in  the  catch.  Here,  the  potential  yield  optimization  was  typically 
small in terms of both relative and absolute gains in yield when compared to the current 
situation. 

-  Any long-term increase in yields linked to a change in selectivity will imply, in general, a 
short-term  loss  in  yield,  but  this  short-term  loss  is  similar  under  Fadv  scenario.  Those 
short-term  losses  will  be  compensated  by  the  long-term  gains  (i.e.  long  term  gains  are 
larger than short term losses); 

-  The  “shift”  scenario  typically  comes  with  higher  yield  gains  compared  to  the  other  three 
scenarios, with only few  exceptions, such as red mullet in GSA07 and GSA09,  cod in 6a, 
hake in SWW, and some whiting and haddock stocks); 

-  The  “crank”  scenario  allows  to  achieve  better  results  in  terms  of  reduction  of  juvenile 
catches, with only few exceptions, such as cod in 6a, whiting in 7a, and hake and megrim 
in SWW; 
For few ICES stocks, neither the “crank” nor the “shift” scenario leads to a reduction in the 
proportion of juveniles in the catch and these proportions even increase. 

- 

-  The results described above are general considerations, and for specific stocks the reader 
should  refer  to  the  stock-by-stock  results  (in  the  tables  4.1.2.1-4.1.2.4  and  figures 
4.1.2.1-4.1.2.2 and in the Appendix 3). 

4.2  Potential  gain  in  terms  of  yield  and  proportion  juveniles  in  the  catch  from 

optimising selectivity at current F, by fleet (ToR 3) 

This  section  provides  insights  into  the  performance  of  fleet-based  selectivity  scenarios  for  the 
different  stocks  with  fleet  disaggregated  data.  Performance  is  presented  in  terms  of  (a)  yield 
change relative to the current selection pattern, and (b) the percentage of juvenile species in the 
catch. Both metrics are calculated for equilibrium states of the stock. 

4.2.1. COMPARISON OF OPTIMIZED SELECTIVITY WITH SELECTIVITY ESTIMATES BY FLEET 

4.2.1.1. Stocks with ICES advice 

The performance of individual fleets against the optimal selectivity scenarios is provided in Table 
4.2.1.2 (Yield) and Table 4.2.1.3. (% juveniles in the catch). Fishing with only beam trawls would 
lead to higher yields for North Sea whiting than both optimized scenarios and fishing. Current F 
for  North  Sea  whiting  is  50%  of  the  FMSY  and  current  selectivity  (S50  =  3  years)  is  late  when 
compared to the relatively early maturation (a50 = 0.9), so that in this specific case yield could be 
increased by selection with a wider age range and with a different selectivity curve shape that is 
closer to beam trawl, but was not captured by the “crank” or “shift” scenarios. Similarly, trammel 
nets would lead to slightly higher yields for English Channel plaice than both optimized scenarios, 
while  fishing  with  only  set  gillnets  would  lead  to  higher  yields  for  North  Sea  saithe  than  the 
“crank” scenario. Only for megrim in the Southwestern Waters, otter trawl fisheries would lead to 
slightly  higher  yields  compared  to  the  status  quo  scenarios.  In  all  other  cases  OTB  gives  lower 
yields  than  current  selectivity  of  the  all  gears  combined.  By  contrast,  fleets  operating  with 
gillnets, longlines and other passive gear as well as beam trawls result in higher yields for most of 
the stocks considered compared to the status quo selectivity pattern, except for gillnets for North 
Sea haddock and beam trawl for North Sea plaice, where yields are lower. For the seine nets, the 
yield gains and losses are variable.  

In  terms  of  the  proportion  of  juveniles  in  the  catch,  in  two  cases,  North  Sea  haddock  and  Irish 
Sea haddock, the optimized scenarios perform worse than each of the fleet-specific scenarios as 
well as the current all-fleet scenario. In the cases of North Sea plaice and North Sea whiting the 
optimized scenarios result in quite high proportions of juveniles in the catch;  for plaice only OTB 

45 

45 

 
 
 
  
 
performs  worse  and  for  whiting  both  seine  and  beam  trawl  perform  worse.  Compared  to  the 
current selectivity of all fleets combined, OTB performs better for North Sea plaice but worse for 
the two haddock stocks in North-western Waters. For Eastern Channel plaice, beam trawl leads to 
lower proportions of juveniles in the catch than is currently the case for all fleets. Gill nets are a 
large improvement for North Sea saithe compared to the current mixed-fleet situation. 

Table  4.2.1.1:  Selectivity  estimates  for  Northeast  Atlantic  stocks  summarizing  the  age-at-50%-selectivity 
(S50) by Gear type. 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. 

Area  

Stock  

OTB   DRB   GNS   GTR  

LLS   MWT  

PAS  

SEI  

TBB  

cod.27.22-24  

ple.27.21-23  

cod.27.47d20  

had.27.46a20  

ple.27.420  

2.38  

2.26  

1.86  

1.97  

2.03  

3.17  

2.41  

5.19  

ple.27.7d  

1.89   2.13  

2.1  

3.14  

3.25  

2.29  

BS  

BS  

NS  

NS  

NS  

NS  

NS  

NS  

pok.27.3a46  

whg.27.47d  

NWW  

cod.27.6a  

NWW  

had.27.6b  

NWW  

had.27.7a  

NWW  

ple.27.7a  

SWW  

meg.27.7b-
k8abd  

7.4  

3.7  

2.64  

2.11  

1.72  

0.9  

1.73  

3.32  

2.08   1.57  

1.88  

1.95   1.28  

2.08   2.26  

3.7  

1.16  

2.86  

2.52  

1.8  

3.25  

Table 4.2.1.2: Prediction for the Northeast Atlantic stocks summarizing the yield change (%) relative to the 
current  selectivity  under  current  F  (blue:  positive;  red:  negative;  colour  intensity  indicates  magnitude  of 
effect)  comparing  the  optimized  (Crank,  Shift,  Amat)  selectivity  curves  to  the  prediction  for  fleet-specific 
selectivity curves at current F . 

Area 

Stock 

Crank 

Shift 

Amat  OTB  DRB  GNS  GTR  LLS  MWT 

PAS 

SEI 

TBB 

BS 

BS 

NS 

NS 

NS 

NS 

NS 

NS 

cod.27.22-24 

322.9 

402.1 

-98.5 

-48.4 

63.3 

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 

-3.9 

-0.2 

92.4 

61.8 

-1.5 

17.3 

29.8 

-7.1 

7.2 

-0.1 

-1.2 

33.7 

-2.3 

35.5 

0.8 

-0.9 

-11.8 

-7.4 

2.3 

43.5 

16.2 

-0.8 

33.2 

28.9 

28.9 

-6.7 

NWW 

cod.27.6a 

362.1 

315.2 

-58.6 

-6.1 

46 

54.5 

11 

25.6 

25.5 

1.9 

-17.8 

-3.8 

-7.9 

3 

13.5 

57 

46 

 
 
 
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
  
  
  
  
  
  
  
   
   
   
   
   
   
   
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
NWW  had.27.6b 

NWW  had.27.7a 

NWW  ple.27.7a 

SWW 

meg.27.7b-
k8abd 

33 

2.4 

0.6 

0.5 

44.2 

12 

-15.1 

0.9 

7.6 

-0.5 

-19.9 

4.2 

-6.6 

-0.1 

-0.1 

0.3 

-8.1 

-4 

1.2 

4 

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. 

Table  4.2.1.3:  Prediction  for  the  Northeast  Atlantic  stocks  summarizing  associated  percentage  of  juvenile 
fish  in  the  catch  (%)  (from  red,  via  orange  and  yellow  to  green:  high  to  low  values)  under  current  F 
comparing  optimised  selectivity  to  the  predictions  for  the  current  selectivity  (all  fleets)  and  fleet-specific 
selectivity curves. 

Area 

Stock 

Cur 

Crank 

Shift  Amat  OTB  DRB  GNS  GTR  LLS  MWT 

PAS 

SEI 

TBB 

7.1 

4.6 

50 

36.7 

37.2 

28.1 

25.2 

38.6 

15.5 

12.9 

39.7 

72 

65.1 

13 

38.3 

0 

20.9 

27.3 

62.1 

40.7 

66.1 

45.7 

26.9 

51.9 

37.8 

36.9 

0.7 

31 

53.6 

22.4 

67.6 

80.1 

40.7 

35.2 

42.8 

44.4 

32.6 

67.7 

64.2 

cod.27.22-24 

36.2 

9.9 

ple.27.21-23 

24.8 

cod.27.47d20 

71.2 

had.27.46a20 

41.5 

BS 

BS 

NS 

NS 

NS 

NS 

NS 

NS 

ple.27.420 

ple.27.7d 

pok.27.3a46 

whg.27.47d 

NWW 

cod.27.6a 

NWW  had.27.6b 

NWW  had.27.7a 

NWW  ple.27.7a 

SWW 

meg.27.7b-
k8abd 

37.9 

47.1 

52.5 

25.7 

42.7 

36.2 

51.8 

23.5 

11.7 

35.9 

70.3 

28.8 

45.3 

22.5 

60.8 

6.6 

0 

70.7 

29.9 

45.2 

45.2 

28.1 

16.2 

46.5 

42.2 

0.5 

22.5 

46.2 

66.1 

19.2 

60.3 

4.7 

10.8 

25.8 

16.7 

27.1 

22.6 

22.6 

15.4 

2.3 

3.7 

23.3 

18.1 

4.2.1.2  Stocks with STECF-GFCM advice  

The performance of individual fleets against the optimal selectivity scenarios is provided in Table 
4.2.1.5  (Yield)  and  Table  4.2.1.6.  (%  juveniles  in  the  catch).  As  expected,  none  of  the  single 
fleets  would lead to higher yields than the optimized selectivity scenarios.  However, remarkable 
differences exist between fleets. Those difference are mainly related to the S50 values of the fleets 
(Table  4.2.1.4.).  For  none  of  the  stocks  considered  in  this  analysis,  otter  trawl  fisheries  would 
lead  to  higher  yields  compared  to  the  status  quo  scenarios.  In  contrast,  fleets  operating  with 
gillnets  result  in  higher  yields  for  all  of  the  stocks  considered  compared  to  the  status  quo 
selectivity  pattern.  For  the  trammel  netters,  the  yield  gains  and  losses  are  variable,  while  the 
longline fleet would lead to a remarkable gain in the yield of hake.  

Similar trends are found for the ratio of juveniles in the catch. None of the fleet-specific scenarios 
perform  better  than  the  optimized  selectivity  scenarios.  The  otter  trawl  fleet  performs  less  well 
than the status quo scenarios with relatively more juveniles in the catch, especially for the hake 
stocks.  In  contrast,  the  passive  gears  result  in  a  lower  percentage  of  juveniles  in  the  catch 

47 

47 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
compared  to  the  status  quo  scenario.  Only  in  case  of  MUT.10,  the  selectivity  pattern  of  the 
gillnetters is a disadvantage compared to the current selectivity of the fleet.  

Table  4.2.1.4:  Selectivity  estimates  for  Mediterranean  stocks  summarizing  the  age-at-50%-selectivity 
(S50) by Gear type. GNS: Set gillnets; GTR: Trammel nets; LLS: Set longlines; OTB: bottom otter trawls. 

Area  

Stock  

GNS  

GTR  

HKE.01_05_06_07  

HKE.08_09_10_11  

1.14  

2.1  

WM 

WM 

WM 

WM 

WM 

WM 

MUT.01  

MUT.07  

MUT.09  

MUT.10  

LLS  

2.9  

OTB  

0.39  

0.15  

1.48  

0.77  

1.01  

1.19  

0.97  

1.94  

3.24  

3.01  

1.2  

Table  4.2.1.5:  Prediction  for  the  Mediterranean  stocks  summarizing  the  yield  change  (%)  relative  to  the 
current  selectivity  under  current  F  (blue:  positive;  red:  negative;  colour  intensity  indicates  magnitude  of 
effect)  comparing  the  optimised  selectivity  curves  to  the  prediction  for  fleet-specific  selectivity  curves  at 
current F. 

Area 

WM 

WM 

WM 

WM 

WM 

WM 

Stock 

Crank  Shift 

Amat  GNS  GTR  LLS  OTB 

HKE.01_05_06_07 

88.3 

294 

67.8 

35.2 

197 

-11.5 

HKE.08_09_10_11 

6.1 

129.7 

37.1 

56.3 

-28.8 

-13.5 

MUT.01 

MUT.07 

MUT.09 

MUT.10 

35.3 

43.9 

-23.9 

11.5 

28.8 

27.3 

-5.7 

6.2 

5 

-12.3 

-17.3 

25.1 

47.5 

-31.5 

29.2 

5.9 

-5.8 

-0.2 

-0.5 

-9.1 

Table  4.2.1.6: Prediction for the Mediterranean stocks summarizing associated percentage of juvenile fish 
in the catch (%) (from red, via orange and yellow to green: high to low values) under current F comparing 
optimised  selectivity  to  the  predictions  for  the  current  selectivity  (all  fleets)  and  fleet-specific  selectivity 
curves 

Area 

Stock 

Cur  Crank  Shift  Amat  GNS  GTR  LLS  OTB 

WM 

WM 

WM 

WM 

WM 

WM 

HKE.01_05_06_07  86.3  18.5 

0.2  44.8  48.1 

3.1  91.6 

HKE.08_09_10_11  78.6  58.7 

MUT.01 

MUT.07 

MUT.09 

MUT.10 

0 

13.2 

2.9 

1.3 

0 

0 

0 

0 

48 

0 

0 

16.1  16.9  51.8 

82.5 

0 

0.2  24.6 

0.1  11.9 

0 

5.1  30.5  0.1 

13.3 

3.5 

25.7 

48 

 
 
 
 
 
 
 
   
   
   
   
   
   
   
   
   
   
 
  
  
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
  
  
  
  
  
4.2.2  JACKKNIFE ANALYSES 

The Jackknife analysis completes the fleet-specific approach by taking out one fleet at a time by 
subtracting  the  partial  fleet  Fa  from  the  total  Fa  of  all  fleets  and  scaling  up  the  remaining  Fa  to 
match  the  current  F  for  all  fleets.  The  Jackknife  analysis  therefore  presents  evaluation  of  the 
partial  selectivity  impact  for  each  of  the  excluded  fleets.  In  cases  where  a  fishery  is  mainly 
dominated by a single fleet, both catches and percentage juveniles in the catch are similar to the 
status  quo  scenario  if  fleets  other  than  the  dominant  fleet  are  removed.  For  those  cases, 
significant changes in selectivity patterns can only be achieved by removing the dominant fleet. 

Fig. 4.2.2.1: Jackknife of Northeast Atlantic stocks in the Western Baltic Sea under current F. Projections of 
catch (left) and percentage of juveniles in the catch (right) of status quo and when particular fleet segments 
are omitted. 

49 

49 

 
 
 
 
 
 
Fig. 4.2.2.2: Jackknife of Northeast Atlantic stocks in the Greater North Sea under current F. Projections of 
catch (left) and percentage of juveniles in the catch (right) of status quo and when particular fleet segments 
are omitted. 

50 

50 

 
 
Fig.  4.2.2.2:  (cont.):  Jackknife  of  Northeast  Atlantic  stocks  in  the  Greater  North  Sea  under  current  F 
(cont.). Projections of  catch  (left)  and percentage  of  juveniles in  the  catch  (right) of status quo and  when 
particular fleet segments are omitted. 

51 

51 

 
 
 
Fig. 4.2.2.3: Jackknife of Northeast Atlantic stocks in North Western waters under current F. Projections of 
catch (left) and percentage of juveniles in the catch (right) of status quo and when particular fleet segments 
are omitted. 

52 

52 

 
 
 
Fig.  4.2.2.4:  Jackknife  of  Western  Mediterranean  stock  in  GSA  1-7  under  current  F.  Projections  of  catch 
(left) and percentage of juveniles in the catch (right) of status quo and when particular fleet segments are 
omitted. 

53 

53 

 
 
Fig.  4.2.2.5:  Jackknife  of  Western  Mediterranean  stock  in  GSA  8-11  under  current  F.  Projections  of  catch 
(left) and percentage of juveniles in the catch (right) of status quo and when particular fleet segments are 
omitted. 

4.2.3.  SUMMARY ToR 3 

From the results shown in sections 4.2.1 and 4.2.2 the following can be seen: 

  Otter bottom trawl (OTB) selectivity has negative effects in terms of yield loss when compared 

to the status quo selectivity of all fleets combined. 

  Active gears in general appear to perform worse in terms of juvenile catches and SSB. In the 
Northeast  Atlantic,  especially  beam  trawls  often  have  high  proportions  of  juveniles  in  the 

54 

54 

 
 
 
 
catch, with the only exception of plaice in the English Channel. In the Western Mediterranean 
Sea, OTB selectivity is associated with very high proportions (>80%) of juvenile hakes in the 
catches.   

  The  Jackknife  analysis  showed  that  excluding  OTB  would  be  mostly  beneficial  to  stocks  of 
larger bodied cods and hakes in terms of both increase in yield and protection of juveniles.  
  The  negative  partial  impact  on  cods  differed  among  regions  and  was  least  in  the  North  Sea 
and  most  severe  in  the  case  of  cod.27.6a  (West  of  Scotland)  in  North-Western  Waters 
(NWW),  which  is  currently  assessed  to  be  below  Blim  (ToR  3).  In  the  Western  Mediterranean 
Sea,  the  predicted  yield  gains  from  excluding  OTB  for  hakes  would  exceed  the  only  few 
hundred tons long term yield loss of a mullet stock in GSA 9 by several thousand tons. 
In  general,  the  results  from  ToR  3  suggest  that  largest  improvements  in  yield  and  juvenile 
protection can made through optimising the gear selectivity of bottom trawl gears, allocating 
less effort to fishing with those gears or shifting the effort to areas/season where the impacts 
on juveniles can be minimized. 

 

4.3  Combined  effects  of  fishing  mortality  rate  and  selectivity  pattern  on  yield  and 

SSB (ToR 4 a) 

Isopleths show the trade-offs between S50 and F with respect to relative yield and SSB (stocks for 
which  Beverton-Holt  stock-recruitment  relationship  was  assumed)  or  YPR  and  SPR  (stocks  for 
which no specific S-R relationship was assumed).  

From the results in the figures 4.3.1.-4.3.6 the following can be seen: 

 

In  most  cases  it  is  seen  that  improvement  in  selectivity  (increase  in  S50)  would  lead  to 
higher  yield  (or  YPR)  and  SSB  (or  SPR).  All  cod  stocks  in  the  North-East  Atlantic 
(cod.27.22-24, cod.27.47d20, cod.27.6a, cod.27.7e-k) are overfished, and yield values at 
current  fishing  mortality  and  selectivity  are  only  5-20%  of  the  maximum  possible  yield, 
and SSB is <5% of the virgin spawning biomass (Figures 4.3.1-4.3.3). These large-bodied 
species stocks show the largest potential increase in yield from improvement of selectivity. 
Stocks that are heavily overexploited (e.g., cod.27.7e-k) would gain more if selectivity is 
increased  simultaneously  with  decreased  fishing  mortality.  In  addition,  such  an  approach 
would mean that reduction in F could be less severe when simultaneously both selectivity 
and F are manipulated.  

  Optimisation  of  selectivity  has  often  larger  positive  effects  on  yield  than  on  SSB.  For 
example,  for  plaice  in  Baltic  Sea  (ple.27.21-23,  Figure  4.3.1),  cod  in  North  Sea 
(cod.27.47d20,  Figure  4.3.2)  and  haddock  in  North-Western  Waters  (had.27.6bm  Figure 
4.3.3), improvement in selectivity would lead to higher yield but the improvement in SSB 
level is  minor.  Same  can  be  seen  in  the  Mediterranean  hake  stocks,  where  SPR  levels  at 
current  F  and  selectivity  are  <5%  for  HKE.01_05_06_07  and  HKE.17_18,  and  <10%  for 
and  HKE.08_09_10_11  and  HKE.19  (Figures  4.3.5-4.3.6).  Especially  for  the  two  hake 
stocks  that  have  SPR  <5%,  decrease  in  F  with  improved  selectivity  is  needed  to  see 
significant changes in SPR level. In many cases, a decrease in F combined with an increase 
in  the  age-at-selectivity  is  needed  to  see  large  changes  in  SSB.  However,  an  increase  in 
SSB albeit not as large as yields when selectivity is improved, has a multitude of positive 
effect,  both  for  the  fisheries  through  an  increase  in  CPUE  and  average  individual  size  of 
the  fish  caught,  for  the  stock  as  it  increases  its  resilience  to  climate  change  and  for  the 
ecosystem as larger biomass and size diversity in general increase ecosystem functionality 
and services. 

55 

55 

 
 
 
 
 
 
  Small  bodied  and  fast-growing  species  which  are  described  by  high  natural  mortality 
values for young ages (e.g., whiting  and red mullet stocks) have less to benefit from the 
increase  in  selectivity  at  current  fishing  mortality.  For  example,  increasing  selectivity  for 
whiting  in  North-western  waters  (whg.27.7b-ce-k,  Figure  4.3.3)  and  North  Sea 
(whg.27.47d,  Figure  4.3.2)  would  lead  to  decreasing  yield  instead  of  commonly  seen 
increase in yield due to improvement in selectivity (e.g., NEA cod stocks).  

 

Isopleths  for  fast-growing  red  mullets  in  the  Mediterranean  show  that  improvement  in 
selectivity  would  lead  to  higher  yield  (close  to  the  maximum  relative  yield),  and  also 
improve  the  SSB  level  (Figures  4.3.5-4.3.6).  Exceptions  to  this  are  MUR.05  and  MUT.22 
which  are  the  only  red  mullet  stocks  in  the  Mediterranean  that  are  not  overexploited 
(F<FMSY).  In  case  of  MUR.05,  improvement  in  selectivity  would  lead  to  decreased  yield, 
however  when  combining  selectivity  increase  with  minor  F  increase,  max  yield  can  be 
reached. In case of MUT.22 increased selectivity does not affect the yield, for higher yield 
F needs to be increased. 

 

Increase  in  SSB  in  response  to  improved  selectivity  and  decreased  F  is  more  predictable 
and consistent across species compared to responses in yield to changes in selectivity and 
F.  

  Stocks that are underexploited (F<FMSY) e.g., North Sea whitings and Irish Sea plaice 

would not produce higher yield with increasing S50 or decreasing F. 

56 

56 

 
 
 
 
 
Figure 4.3.1: Isopleths for Northeast Atlantic stocks in the Western Baltic Sea. Equilibrium yield (left) and 
SSB  (right)  under  varying  age-at-50%-selectivity  (“shifting”)  and  fishing  mortality  rate  F.  The  black  dot 
represents the estimate under current selectivity and F, and white dots show the dynamic FMSY estimates as 
a function of changes in selectivity. 

Figure 4.3.2: Isopleths for Northeast Atlantic stocks in the Greater North Sea. Equilibrium yield (left) and 
SSB  (right)  under  varying  age-at-50%-selectivity  (“shifting”)  and  fishing  mortality  rate  F.  The  black  dot 
represents  the  estimate  under  current  selectivity  and  F,  and  small  white  dots  show  the  dynamic  FMSY 
estimates as a function of changes in selectivity. 

57 

57 

 
 
 
 
Figure  4.3.2  (cont.):  Isopleths  for  Northeast  Atlantic  stocks  in  the  Greater  North  Sea.  Equilibrium  yield 
(left)  and  SSB  (right)  under  varying  age-at-50%-selectivity  (“shifting”)  and  fishing  mortality  rate  F.  The 
black  dot  represents  the  estimate  under  current  selectivity  and  F,  and  white  dots  show  the  dynamic  FMSY 
estimates as a function of changes in selectivity. 

58 

58 

 
 
  
Figure  4.3.3:  Isopleths for  Northeast  Atlantic stocks in  North  Western  Waters.  Equilibrium  yield  (left)  and 
SSB  (right)  under  varying  age-at-50%-selectivity  (“shifting”)  and  fishing  mortality  rate  F.  The  black  dot 
represents the estimate under current selectivity and F, and white dots show the dynamic FMSY estimates as 
a function of changes in selectivity. 

59 

59 

 
 
 
Figure 4.3.3 (cont.): Isopleths for Northeast Atlantic stocks in the North Western Waters. Equilibrium yield 
(left)  and  SSB  (right)  under  varying  age-at-50%-selectivity  (“shifting”)  and  fishing  mortality  rate  F.  The 
black  dot  represents  the estimate  under  current  selectivity  and  F,  and  small white  dots  show  the  dynamic 
FMSY estimates as a function of changes in selectivity. 

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60 

 
 
 
Figure 4.3.4:  Isopleths for Northeast Atlantic stocks in South  Western  Waters. Equilibrium yield (left) and 
SSB  (right)  under  varying  age-at-50%-selectivity  (“shifting”)  and  fishing  mortality  rate  F.  The  black  dot 
represents  the  estimate  under  current  selectivity  and  F,  and  small  white  dots  show  the  dynamic  FMSY 
estimates as a function of changes in selectivity. 

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61 

 
 
 
Figure  4.3.5:  Isopleths  for  Western  Mediterranean  stocks.    Equilibrium  yield  (left)  and  SSB  (right)  under 
varying  age-at-50%-selectivity  (“shifting”)  and  fishing  mortality  rate  F.  The  black  dot  represents  the 
estimate under current selectivity and F, and small white dots show the dynamic FMSY estimates as a function 
of  changes  in  selectivity.  Note  that  in  the  absence  of  a  stock  recruitment  relationship  for  hakes  Yield  and 
SSB are expressed as per-recruit YPR and SPR, respectively, with white dots representing Fmax. 

62 

62 

 
 
 
Figure 4.3.5 (cont.): Isopleths for Western Mediterranean stocks.  Equilibrium yield (left) and SSB (right) 
under varying age-at-50%-selectivity (“shifting”) and fishing mortality rate F. The black dot represents the 
estimate under current selectivity and F, and small white dots show the dynamic FMSY estimates as a function 
of changes in selectivity. 

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63 

 
 
 
Figure  4.3.6:  Isopleths  for  Eastern  Mediterranean  stocks.  Equilibrium  yield-per-recruit  YPR  (left)  and 
spawning  biomass  per  recruit  SPR  (right)  under  varying  age-at-50%-selectivity  (“shifting”)  and  fishing 
mortality rate F in the  absence  of a stock recruitment  relationship for hakes. The black dot represents the 
estimate  under  current  selectivity  and  F,  and  small  open  circles  show  the  dynamic  Fmax  estimates  as  a 
function of changes in selectivity. 

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Figure  4.3.6  (cont.):  Isopleths  for  Eastern  Mediterranean  stocks.  Equilibrium  yield-per-recruit  YPR  (left) 
and spawning biomass per recruit SPR (right) under varying age-at-50%-selectivity (“shifting”) and fishing 
mortality rate F in the  absence  of a stock recruitment  relationship for hakes. The black dot represents the 
estimate  under  current  selectivity  and  F,  and  small  open  circles  show  the  dynamic  Fmax  estimates  as  a 
function of changes in selectivity. 

4.4  Practical  issues  regarding  the  attainment  of  the  biologically  optimal  selection 

pattern in the context of mixed fisheries and multi-gear fisheries (ToR 4b) 

4.4.1  PRACTICAL ISSUES IN MIXED FISHERIES – QUALITATIVE CONSIDERATIONS 

The majority of mixed species fisheries that exist in EU waters are characterised by having towed 
gear  fisheries  where  a  range  of  mesh  sizes  are  used  to  select  a  range  of  species  often  with 
differing  biologically  characteristics.    Generally,  the  mesh  sizes  are  designed  principally  to  catch 
the  most  valuable  species  in  the  fishery  (e.g.  Nephrops),  and  in  practice,  the  choice  of  gear  is 
often a short-term compromise to discourage the retention of small fish rather than one based on 
biological suitability (e.g. maturity).  

An example of highly mixed fisheries that can be used to illustrate these practical issues is in the 
Celtic Sea.  A range of species are targeted with different gears in the Celtic Sea, many with high 
levels of catches of juveniles combined with a highly depleted cod stock, which is caught in most 
Celtic  Sea  fisheries.  Celtic  Sea  fisheries  can  be  split  into  active  and  passive  gears.  Otter  trawl 
fisheries  take  place  for  mixed  gadoids  (cod,  haddock,  whiting,  hake),  Nephrops,  anglerfish, 
megrim,  rays  as  well  as  cephalopods  (cuttlefish  and  squid).  Beam  trawl  fisheries  target  flatfish 
(plaice,  sole,  turbot),  anglerfishes,  megrim  and  cephalopods  (cuttlefish  and  squid)  while  gillnet 
fisheries target flatfish, hake, pollack, cod, anglerfishes as well as some crustacean species. Beam 
trawling  occurs  for  flatfish  and  skates  and  rays.  The  fisheries  are  mainly  prosecuted  by  French, 

65 

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Irish, and English vessels with additional Belgian beam trawl fisheries and Spanish trawl and net 
fisheries along the shelf edge. 

For  many  years  in  the  Celtic  Sea  there  has  been  large  but  sporadic  recruitment  for  the  gadoid 
stocks  and  high  levels  of  exploitation  resulting  in  significant  fluctuations  in  the  stocks.  More 
recently,  recruitment  has  been  well  below  the  long-term  average  for  cod  and  whiting  while 
haddock  and  hake  have  experienced  above  average  recruitment  in  2018  and  2019.  These 
fluctuations  and  particularly  the  overexploitation  of  the  cod  stock  have  created  issues  for  the 
towed gear  fisheries in the Celtic Sea, particularly for  cod which is caught in many fisheries  but 
which  from  a  gear  perspective  is  a  difficult  species  to  improve  selectivity  for  without  increasing 
mesh  size  to  a  level  that  would  potentially  render  many  of  the  current  towed  gear  fisheries 
economically unviable.  STECF EWG 13-04 indicated that as a general rule for gadoid species, for 
every  3.3  cm  increase  in  S50  a  10mm  increase  in  mesh  size  is  needed.  Such  an  increase  would 
mean  increasing  the  baseline  mesh  size  in  the  Celtic  Sea  in  the  mixed  gadoid  fisheries  in  the 
Celtic  Sea  from  the  current  100mm  to  ~150mm-180mm.  Such  a  mesh  size  would  be 
incompatible in the current fishery, which targets haddock, hake, megrim and anglerfish and even 
increasing  mesh  size  in  a  stepwise  manner  would  result  in  very  high  short-  to-medium-term 
losses.  A  gradual  increase  in  mesh  size  would  reduce  fishing  mortality  to  FMSY  levels  for  cod 
without  losses  for  other  associated  species  such  as  haddock  and  whiting.  In  this  case,  specific 
gear alterations, such as the use of raised fishing line gear can help to reduce the cod catch while 
maintaining  the  catch  of  haddock,  hake  and  whiting  but  would  need  to  be  seen  in  combination 
with other measures such as spatial/temporal closures.  

In  the  Celtic  Sea  Nephrops  fishery,  the  central  issue  remains  that  the  mesh  size  used  in  the 
Nephrops  fishery  is  optimal  for  Nephrops  but  not  for  the  bycatch  species  and  definitely  not  for 
cod. The introduction of selectivity devices such as square mesh panels has helped to reduce the 
catches of juvenile haddock, whiting and hake but not for large-bodied species such as cod. There 
is a readymade solution to this in the form of sorting grids which are effective at releasing most 
of the bycatch species (including cod) from Nephrops trawls but there is a marked reluctance for 
fishermen to use such devices because they exclude most of the bycatch of other species such as 
anglerfish  as  well  as  cod,  which  are  an  economically  valuable  part  of  the  catch.  In  effect,  while 
the fishery targets Nephrops, the bycatch is considered economically valuable enough for fishers 
to resist change and adopt highly selective gears that turn the fishery into a single species fishery 
with little or no bycatch. This is in contrast to the Nephrops fishery in the Skagerrak and Kattegat 
where  due  to  the  poor  state  of  the  cod  stock  in  these  waters,  Swedish  fishers  were  forced  by 
legislation  to  adopt  the  use  of  sorting  grids  in  their  fishery  in  order  to  keep  fishing.  The  fishery 
was  essentially  turned  into  a  single  species  fishery  and  overtime  the  loss  of  bycatch  has 
diminished as an issue. Therefore, in the Celtic Sea Nephrops fishery significant improvements in 
the  selection  patterns  could  be  attained  by  converting  the  fishery  into  a  single  species  fishery 
though the adoption of sorting grids. On the other hand, Nephrops fishery is a relatively modern 
target fisheries, which is a direct consequence of the large historical depletion of top predators as 
gadoids.  In  the  Skagerrak  and  Kattegat,  a  substantial  fishery  for  Nephrops  has  developed  that 
uses baited pots, i.e., creel fishery, which has minimal bycatch of gadoids and other fish or, when 
caught,  bycaught  fish  can  be  released  unharmed.  The  transformation  of  the  current  target 
Nephrops  fishery  with  trawlers  to  alternative  methods  as  creel  can  constitute  a  way  forward  to 
reduce the catches of juvenile gadoids, noting there are undoubtedly barriers to such a transition 
as acknowledged by Suuronen et al., 2012. These include: 

lack of familiarity with cost-effective and practical alternatives; 
incompatibility of vessels with alternative gear; 
risk of losing marketable catch; 

 
 
 
  concerns with safety at sea by using unfamiliar gears or strategies; 
  high investment costs; 
 

lack of capital or restricted access to capital; 

Many of these barriers  are relevant to the Celtic Sea fishery, nonetheless there is scope to give 
serious consideration to such a shift as a partial solution to the mixed fishery issues that exist. 

66 

66 

 
The beam trawl fishery in the Celtic Sea prosecuted by Belgian, Irish and UK vessels, has slightly 
different  practical  issues  centred  around  cod.  The  gear  used  is  designed  to  target  sole  with  a 
small  mesh  codend  of  80mm.  Previous  selectivity  experiments  have  shown  that  increasing  the 
mesh size above 80mm will result in high losses of sole catch.  However, in reality, sole makes up 
only a small but valuable part of the catch with the bycatch of megrim, anglerfish and rays being 
important  components  of  the  overall  catch.    The  beam  trawl  fleet  also  catches  a  small  but  not 
insignificant  amount  of  cod.  The  current  mesh  size  used  is  unselective  for  all  of  these  bycatch 
species.  Unlike  the  Nephrops  fishery,  there  is  no  readymade  gear-based  solution  to  reduce  cod 
catches given the design and operation of beam trawl gear.  Therefore, in this particular fishery, 
with no obvious gear-based solution, improving selection patterns could only be achieved through 
other  technical  measures  such  as  spatial/temporal  closures  or  significant  reductions  in  fishing 
effort. 

Implemented  measures  for  the  recovery  of  cod  stocks  include  closed  seasons,  closed  areas  and 
gear specifications. The selectivity measures, applied especially to the Baltic, North Sea and NWW 
mixed  trawl  fishery  aimed  mainly  at  improving  the  size  selectivity.  Also  spawning  closures  for 
Baltic cod stocks were already implemented since the late 1990s. The reduction in MCRS for Baltic 
cod stocks introduced in 2015 aimed to reduce the volume of cod catches below MLS/MCRS and 
hence  discards.  However,  it  was  not  expected  to  change  the  actual  catch  profile  and  thus  not 
affect  selectivity  for  Baltic  cod.  The  Frec/Fbar  indicator  suggests  a  trend  of  improving  selectivity 
with  less  fishing  pressure  on  juvenile  cod  and  an  improved  exploitation  pattern.  However,  EWG 
20-02  could  not  identify  whether  this  is  related  to  changes  in  technical  measures  or  reflects 
changes in fishing patterns. 

In the South-western waters, there are also multiple mixed fisheries. In these fisheries, European 
hake  is  the  most  valuable  species  landed  by  the  French  and  Spanish  fleets  in  terms  of  tonnes 
(8%)  and  value  (10%).  Cornou  et  al.  (2021)  indicate  that  in  the  Bay  of  Biscay  for  example, 
European  hake  is  targeted  by  demersal  and  pelagic  trawlers  together  with  a  mix  of  other  fish 
species.  For  these  two  fleets,  with  respective  codend  mesh  size  of  70-80mm  and  100mm,  hake 
represents  34%  and  84%  respectively  of  the  total  catch  weight  (among  which  5  and  8%  are 
unwanted  catches  of  juveniles).  This  species  is  also  targeted  by  gill  nets  where  it  represents 
between  42%  and  97%  of  the  total  catch  depending  on  vessel  length  (smaller  or  larger  than 
15m,  respectively).  Among  these  catches,  3%  to  12%  are  unwanted  catches.  Fleets  for  which 
hake is a major component of the catch generate relatively low levels of unwanted catches since 
their  mesh  size  and  fishing  strategies  are  adapted  to  target  individuals  above  MCRS.  In  these 
fisheries, the selection pattern is closer to the optimal.  

Hake  is  also  caught  as  bycatch  with  trammel  nets  targeting  sole  (6%  of  the  total  catch  among 
which between 43% and 60% are unwanted catch) as well as with trawls targeting sole (9 % of 
the total catch weight among which 4% are unwanted catch). Similar to the Celtic Sea, the main 
issue in that mixed fishery is the Nephrops trawl fleet which catches significant quantities of hake 
below  MCRS  since  Nephrops  and  juvenile  of  hake  are  found  on  the  same  fishing  grounds  of  the 
“Grande  Vasière”.  Hake  represent  14%  of  the  Nephrops  trawlers  catch,  of  which  16%  are 
discarded. Nephrops is the target species but bycatch species such as hake constitute significant 
part  of  the  fishers’  gross  income.  In  this  fishery  the  mesh  size  is  set  to  maximise  the  catch  of 
Nephrops  rather  than  hake.  As  in  the  Celtic  Sea  Nephrops  fishery,  there  are  technical  solutions 
available to reduce the level of bycatch but there is a reluctance to uptake the most effective of 
these selectivity devices due to the resulting loss of marketable hake catch.  

Another  way  to  catch  Nephrops  without  hake  by-catch  is  by  using  creels.  However,  in  practice 
such  an  option  faces  technical,  cultural,  economic  and  spatial  issues.  Indeed,  trawl  vessels  are 
designed to tow gear and the equipment, layout and space on board are not suitable to store and 
handle  passive  gear  in  a  secure  way.  Furthermore,  skippers  and  crews  have  no  experience  in 
fishing  for  Nephrops  with  creels.  Culturally,  the  shift  from  a  metier  to  another  cannot  be 
instantaneous. The market in France for Nephrops is structured according to trawl caught and not 
creel caught. Therefore, not only the fishing sector would have to adapt their operations, but the 
processing  sector  would  also  have  to  adapt  (but  see  the  Skagerrak-Kattegat  example  above 
where the switch has happened rather quickly).   

Additionally, the “Grande Vasière” area is exploited by other towed gear metiers such as demersal 
and  pelagic  trawl  targeting  gadoids,  which  generate  spatial  conflicts  between  metiers.  For  all 

67 

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these  reasons,  and  despite  several  creel  tests  in  the  Grande  Vasière,  the  Nephrops  trawl  fleet 
adopted  technical  measures  to  improve  its  selectivity  and  reduce  juvenile  hake  bycatch.  For 
instance,  the  codend  diamond  mesh  size  increased  from  55mm  to  70mm  in  1999,  and  then  in 
2005  a  100mm  square  mesh  panel  aiming  at  hake  juvenile  escapement  was  added  to  the 
legislation (STECF, 2020a). Since 2011 and to keep reducing discards, the Nephrops fleet has to 
choose among 3 selective devices, which are 80mm codend or bottom 62mm square mesh panel 
or a rigid grid. During the last decade, many selectivity projects were carried out to promote the 
adoption of selective trawl devices on a voluntary basis in order to cope with the implementation 
of  the  landing  obligation.  In  mixed  fisheries  such  as  the  hake/Nephrops  one,  the  challenge  of 
reducing discards to reach FMSY is mainly due to the differences of species behaviour, morphology 
and MCRS. Nephrops has a MCRS of 9cm, a passive behaviour and crustacean morphology. Hake 
has a MCRS of 27 cm TL, an active behaviour and a round fish shape. From a technical point of 
view, gear selectivity should consider all these differences to reach the best trade-off leading to 
the highest escapement of juvenile hake while catching Nephrops above MCRS.  

Summarizing:  

 

 

 

As currently applied, technical measures, can in principle, adjust exploitation 
pattern and rate, but it is likely that the anticipated impacts of these  measures have not 
been  fully  realised  due  to  inability  or  unwillingness  to  deploy  as  intended,  the  fact  that 
they are often compromises in mixed fishery situations and due to enforcement difficulties. 
Gear-based measures tend to increase short-term costs, through short-term losses and/or 
equipment  costs  and  they  are  generally  borne  by  the  individual  business  without 
compensation.  This  incentivises  fishers  to  circumvent  technical  measures,  a  response  to 
minimise  short  term  losses.  Even  with  the  new  Technical  Measures  regulation  in  place, 
these practical issues have hindered thus far the attainment of selection patterns closer to 
the optimal patterns. 

The  improvement  of  selectivity  must  include  an  improvement  on  fishing 
strategies and spatial/temporal measures in conjunction with gear selection characteristics 
to improve compliance with the landing obligation under the reformed CFP. To  reach this 
objective  a  collaborative  approach  is  essential,  including  fisher  associations  and  the 
stakeholders. 

The  Celtic  Sea  and  South-western  waters  examples  described  above, 
demonstrate  the  complexity  in  improving  selection  patterns  in  mixed  species  and  multi-
gear  fisheries.  In  each  of  these  mixed  fisheries,  the  possible  solutions  are  different  and 
include a combination of gear-based and spatial/temporal measures along with changes in 
fishing patterns and possible reductions in fishing effort. Making all such changes at once 
might be politically, culturally and economically difficult.   

4.4.2  PRACTICAL ISSUES IN MIXED FISHERIES – QUANTITATIVE CONSIDERATIONS  

A study has been recently conducted at the scale of the North Sea Ecoregion, in order to analyze 
the effect of technical regulations on several stocks at the same time (Outrequin 2021, Outrequin 
et al. in prep.). In this study, the impact of fishing on targeted stocks was analyzed focusing on 
the  case  of  benthic  and  demersal  resources,  and  thus  taking  into  account  the  9  main  stocks 
harvested  by  mixed  fisheries.  The  FLBEIA  model  (Garcia  et  al.,  2017),  which  here  considers  all 
the  fleets  performing  in  the  North  Sea,  was  used  to  run  scenarios  of  modifications  of  the  three 
main fishing gears, namely: the large mesh otter trawl (TR1), the small mesh otter trawl (TR2), 
and  the  small  mesh  beam  trawl  (BT2).  As  the  results  of  this  study  clearly  illustrate  a  way 
selectivity could be improved in practice, we  summarize here the  outputs of a  scenario where a 
large increase in mesh sizes is implemented, in addition to the use of selectivity devices (details 
in Table 4.4.2.1). Such a scenario leads to a large increase of the mean age at first catch (Table 
4.4.2.1.).  On  average  for  all  the  considered  stocks  and  for  the  whole  fishery,  this  increase  is 
around 0.9 year. Gear modification doesn’t change stocks selectivity equally, some stocks benefit 
more than others of such changes.  

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Table 4.4.2.1: Summary of the modification scenario implemented and consequences on the related age at 
first catch for the main stocks. 

Gears modified (mesh 
size & selective 
devices) 

Scenarios 

Current 

Selectivity improved 

TR1 

TR2 

BT2 

100mm 

70mm 

80mm 

140mm 

140mm 

T90 de 100mm and square 
mesh top panel 

Age at first catch (S50) 

Cod.27.47d20 (Cod) 

Had.27.46a20 (Haddock) 

Ple.27.7d (Eastern-Channel Plaice) 

Ple.27.420 (North Sea Plaice) 

s
k
c
o
t
S

Pok.27.3a46 (Saithe) 

Sol.27.4 (North Sea Sole) 

Tur.27.4 (Turbot) 

Whg.27.47d (Whiting) 

Wit.27.3a47d (Witch) 

2.1 

2.0 

2.3 

1.4 

3.4 

1.7 

2.4 

3.2 

4.2 

3.0 

4.3 

2,.6 

2.4 

4.4 

3.3 

2.6 

3.4 

4.7 

Long-term effects of an increase in the selectivity of the main gears used  in the North Sea show 
that, in such a mixed fishery, the mesh size increase may lead to a large reduction of the fishing 
impact, and to a better use of marine resources. 

An important reduction of discards is observed, while landings and biomass are increased (Figure 
4.4.2.1).  In  this  scenario,  on  average  for  the  9  studied  stocks,  the  relative  biomass  B/Bo 
increases from 37% to 52%, landings gain 21%, while discards are reduced by half. The highest 
benefits are observed for cod (biomass increases by 72%, and landings are multiplied by 8) and 
the lowest for whiting (biomass +19%, and a decrease in landings). 

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Simulations  also  showed  that  the  current  level  of  total  landings  could  be  maintained  with larger 
mesh  sizes  and  half  of  the  current  effort,  then  leading  to  a  more  than  doubling  of  the  biomass 
compared  to  the  current  situation.  These  results  are  beneficial  in  an  economic  and  ecologic 
perspective. In particular, the fishing effort is hugely reduced, while more biomass would result in 
higher  CPUE.  Demographic  structures  contain  more  old  individuals,  so  stocks  become  more 
resistant  and  resilient  to  the  environment.  There  is  as  well  a  positive  side  effect  on  fleets  not 
concerned  by  the  modification  implemented.  The  entire  North-Sea  fishery  (mixed-fishery  and 
others)  could benefit from the modification of  the concerned gears (otter trawl, demersal seine, 
and beam trawl). For example, long-liners would catch more fish, especially more cod. 

Figure 4.4.2.1: Catch and biomass at the equilibrium in the two different scenarios for the 9 stocks. Figures 
in the blue bar corresponds to the proportion of landings in catch, green corresponds to discards, and in the 
red bar to the relative biomass B/Bo. Bar on the left corresponds to “current gear” scenario, and on the right 
side to the “modification gear” scenario. Values above  bars correspond to gain (catch and biomass) of  the 
current scenario (left  of  the paired  bars) compared to  the “modification gear” scenario  (right  of  the  paired 
bars).  

5 

DISCUSSION 

5.1  Caveats of the approach for interpreting the analysis 

Throughout the analyses conducted by the EWG, the tacit assumption was that increased SSB is 
proportionally  related  to  increased  reproductive  potential.  This  may  not  always  be  the  case,  for 
example, if adults cannibalise on young individuals (e.g. in hake).  

However,  this  could  also  be  compensated  by  the  fact  that  biomass  of  large  fish  may  contribute 
more to reproductive potential than biomass of smaller (adult) fish (e.g., in cod; BOFFFF effect as 
described by Hixon et al., 2014). Hixon et al. (2014) reviewed the evidence that in numerous ﬁsh 
species,  weight-speciﬁc  annual  fecundity  increases  with  female  age  and/or  size;  egg  quality  or 
larval  quality  may  also  increase  with  female  age  and/or  size.  Furthermore,  older  and/or  larger 
females  may  have  more  extensive  spawning  seasons,  providing  a  bet-hedging  life-history 

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strategy  helping  to  ensure  that  some  larvae  are  spawned  at  times  of  favourable  environmental 
conditions  (Hixon  et  al.,  2014).  Larger  and/or  older  females  may  thus  contribute  more  to 
reproductive potential and hence recruitment. Improved selectivity (increased S50) leads to a size 
structure  in  the  population  with  more  fish  that  are  older  and  thus  larger.  If  larger  fish  have  a 
more  than  proportionally  larger  reproductive  output  than  smaller  adults,  this  contributes  to  an 
increased benefit of improved selection (increased S50). On the other hand, one could argue that 
increased S50 leads to increased fishing pressure on the large individuals of the stock.  There are 
studies suggesting to reduce the fishing pressure on large individuals since they contribute  more 
than  proportionally  to  the  reproduction  success  of  a  stock  (e.g.  Birkeland  and  Dayton,  2005). 
Several  stocks  show  a  dome-shaped  selectivity  pattern  of  the  population.  In  these  cases,  the 
fishing pressure is reduced on the largest individuals of the population. The effects of cannibalism 
and BOFFFFs were not considered in the analyses of the EWG. 

Another effect that was not considered is the “Rosa Lee” effect. This is the effect that, when size-
selective ﬁshing removes faster-growing individuals at higher rates than slower-growing ﬁsh, the 
surviving  populations  will  become  dominated  by  slower-growing  individuals  (Lee,  1912).  When 
this “Rosa Lee” phenomenon is ignored, bias occurs in catch and stock projections. A simulation 
study  based  on  cod-like  population  dynamics  (Kraak  et  al.,  2019a)  showed  that  when  the  L50 
was  increased  from  30  to  40  cm,  the  catches  of  undersized  ﬁsh  were  overestimated  by  150%. 
That means that the actual benefits of increased S50 in terms of reduced proportions of juveniles 
in the catch may be (much) greater than the predictions of our analyses indicate. However, this 
study also found that increased  S50 has the negative side effect that it reduces mean length-at-
age,  especially  among older  ﬁsh  and  especially  in  populations  with  slow  mean  growth  (Kraak  et 
al.,  2019a).  If  the  BOFFFF  effects  relate  to  length  itself  (rather  than  age),  increased  gear 
selectivity would thus result in a negative effect on reproductive potential and hence recruitment. 
Again, these effects were not considered in the analyses of the EWG. 

Projections  are  subject  to  the  assumptions  made  in  the  assessment  model  and  may  be 
particularly sensitive to assumptions on the selection pattern and M. Note that such assumptions 
based on average conditions in most recent years may be appropriate for assessments and short-
term forecasts, but may be not necessarily hold  for long-term forecasts. As such, the long-term 
projections  should  not  be  interpreted  as  likely  future  states,  but  in  the  context  of  estimating 
baselines  analogous  to  reference  point  estimations  in  the  assessment  process.  For  example,  in 
some models the selection of older fish is a-priori assumed to be flat-topped. This may not reflect 
reality  but  there  might  be  insufficient  information  in  the  data  to  allow  the  model  to  estimate 
selection  patterns  for  each  age  independently.  Similarly,  in  the  majority  of  cases,  there  is  no 
empirical  data  available  to  support  the  assumption  of  M.  Often  these  assumptions  are  based  on 
extrapolations from a limited number of stocks for which empirical data are available but there is 
invariably  a  large  amount  of  variation  in  these  data,  resulting  in  large  uncertainty  about  the 
assumptions.  Misspecifications  of  M  and  selectivity  are  well  known  to  introduce  considerable 
uncertainty in stock assessments and  can bias short-term  forecasts.    Similar to reference point 
estimation  (e.g.  FMSY),  such  bias  can  be  aggravated  in  long-term  forecasts  and  may  produce 
unrealistically  high  equilibrium  biomass  if  M  or  selectivity  are  incorrectly  specified.  The  results 
presented  here  should therefore  be  interpreted  with  caution.  In  general,  the  present  results  are 
informative about the direction of change in yield, juvenile catches, etc. but the absolute changes 
may be under- or over-estimated. 

The use of S50 as a ‘selectivity metric’ in this report should be treated  with care,  considering its 
limitations to quantify the underlying selectivity pattern across all ages. S50 is only one of the five 
parameters  used  to  construct  often  dome-shaped  selectivity  curves  in  FLSelex  (the  other  four 
being S95, Smax, Dcv, Dmin), 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 
S50  unchanged.  Moreover,  in  some  cases  (e.g.  saddle-shaped  selectivity  curves;  Sampson  and 
Scott,  2012),  S50  could  be  even  considered  a  poor  indicator  of  selectivity.  S50  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  S50  to  track  interannual  changes  in  the 
actual  selectivity,  as  estimated  from  annual  stock  assessments.  This  is  due  to  the  shape  of  the 

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selectivity  curve  typically  exhibiting  variations  from  year  to  year,  hence  making  the  consistency 
and comparability of the annual S50 values problematic. Of course, this is not an issue for stocks 
assessed  under  a  constant  selectivity  assumption  (e.g.  using  separable  stock  assessment 
models),  but  in  these  cases  it  is  anyway  impossible  to  track  selectivity  changes  based  on  the 
stock  assessment  outputs.  Therefore,  for  monitoring  purposes,  an  F-based  selectivity  metric 
would be more adequate (e.g. Frec/Fbar employed by STECF, 2020a, or Fjuv/Fapical employed here). 

Also, a change in the ecosystem might lead to an SSB-recruit relationship which does not follow 
Beverton-Holt  or  hockey-stick  but  instead  is  characterized  by  several  years  of  very  low 
recruitment  independent  of  SSB.  A  change  in  fishing  rate  or  pattern  might  only  have  minor 
influence in these cases and might not automatically lead to a recovery of the stock as described 
by  the  calculations  done  by  the  EWG.  Examples  for  this  is  the  eastern  Baltic  cod  stock  (ICES, 
2021d). 

In  an  attempt  to  convert  selectivity-at-length  from  gear  experiments  to  selectivity-at-age  (see 
Annex), it was found that this may lead to a distorted view of selectivity. An example is shown for 
50  mm  diamond-mesh  codend  in  Figure  5.1.  The  reason  is  that  selectivity  does  of  course  not 
change in discrete time steps, but instead rather continuously (as a function of size). 

Figure 5.1: Comparison between size selection (red curve) and age selection (black curve). 

5.2  General  practical  issues  regarding  the  attainment  of  the  biologically  optimal 

selection pattern 

As the analyses from Tors 1-3 show, optimal selection patterns have not been achieved for many 
stocks across EU fisheries. This is partially due to challenges of implementing technical measures 
in mixed fisheries where towed gears are the  predominant gears used. There are many reasons 
for this as have been described previously by STECF EWGs 12-20, 13-04 and 15-05.  

Like  other  management  tools  employed  to  manage  fisheries,  technical  measures  suffer  from 
implementation  error  (i.e.  the  expected  benefits  are  not  realised  in  practice  because  they  are 
often  poorly  enforced).  From  a  political  perspective,  technical  measures  often  form  part  of  a 
negotiation strategy, potentially leading to a dilution of the final measures agreed. This can often 
be  driven  by  perceived  negative  impacts  (losses  of  marketable  catches)  in  the  fishery  and  the 
desire  of  managers  to  broker  a  deal,  even  though  the  measures  agreed  may  be  sub-optimal  in 

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terms  of  achieving  longer-term  management  objectives  relating  to  optimising  exploitation 
patterns. 

Different types of technical measures are used in different ways. They have been mainly used to 
protect juveniles and improve the selectivity of fishing gears, reducing the amount of fish that is 
discarded.  Historically,  the  measures  have  focused  on  individual  stocks  but  in  recent  years  they 
have  been  used  to  reduce  the  impact  of  fishing  on  multiple  aspects  of  the  marine  ecosystem, 
including habitats and non-commercial species. In European waters, technical measures changes 
have focused on the bottom trawl fisheries, as most  of the discard  choke situations affect these 
fleets  and  there  are  difficulties  in  improving  selectivity.  Over  the  last  few  decades  commercial 
vessels  have  implemented  more  selective  trawl  gears,  including  a  variety  of  selectivity  devices 
and changes in mesh size and geometry in the codends.  

A  further  implementation  problem  is  that  the  majority  of  gear-related  technical  measures  are 
developed  by  national  laboratories  and  research  groups  on  the  basis  of  relatively  short 
experimental trials. As a consequence, measures tend to be evaluated under idealised conditions 
where the key design features are controlled and monitored. This potentially leads to issues when 
they  are  implemented  into  the  fishery  with  an  over-optimistic  outlook  of  their  potential  benefits 
given  the  multiple  factors  that  affect  selectivity  (e.g.  tow  time,  environmental  conditions,  gear 
and  vessel  construction).  These  experiments  ultimately  can  only  ever  provide  short-term  point 
estimates of selectivity. 

Additionally,  often  selectivity  measures  are  introduced  in  response  to  a  single  species 
conservation issue where the stock prognosis is poor and is typically characterised as having high 
discards.  This  is  often  confounded  by  the  fact  that  the  overexploited  stock  tends  to  have  a 
truncated  age  structure  with  few  old  fish  in  the  stock  and  are  caught  in  mixed  fisheries.  This 
means  that  the  fishery  relies  heavily  on  young  fish,  and  any  increase  in  selectivity  by  whatever 
means  results  in  significant  losses  of  marketable  fish.  The  perception  that  a  proportion  of  catch 
will  be  lost  in  the  short-term  leads  to  be  a  strong  negative  incentive  to  deploy  tactics  that 
mitigate  these  losses  and  has  led  to  a  technological  race  by  the  industry  to  develop  and  deploy 
technical  fixes  to  circumvent  the  regulations  and  minimise  such  losses  (STECF,  2012,  2013).  In 
response,  regulations  are  subsequently  amended  as  a  result,  and  led  to  ever  increasing 
complexity  in  the  regulations,  and  elevated  difficulties  in  terms  of  monitoring,  controlling  and 
enforcement (STECF, 2012, 2013).  

Another  practical  issue  that  has  been  highlighted  in  the  past  by  STECF  is  related  to  the  use  of 
minimum  conservation  reference  sizes  (MCRS)  and  catch  composition  rules.  These  are  and 
continue  to  be  used  widely  in  technical  measures  legislation.  They  are  assumed  to  act  as  a 
coercive  incentive  to  avoid  areas  with  high  concentrations  of  juveniles  or  unwanted  species. 
However, there is no clear evidence to suggest that this is the case. Intuitively these make sense 
as technical measures but in practice they have encouraged discarding where they have not been 
correctly  aligned  (MCRSs  with  gear  selectivity  characteristics  or  catch  composition  with  species 
composition in the fishing ground) or if there is economic advantage to fishing in areas with high 
abundance  of  juveniles  in  order  to  catch  the  larger  individuals  in  the  population.  In  the  past, 
STECF  noted  that  fleets  complied  with  catch  composition  regulations  simply  by  discarding 
components  of  the  catch  in  order  to  balance  the  retained  catch  with  the  composition  rules, 
negating any benefits in terms of controlling fishing mortality. The predominant reaction to both 
catch-composition rules and minimum landing sizes was, before the landing obligation, to comply 
through  discarding,  particularly  if  moving  to  other  areas  would  result  in  a  reduction  in  potential 
revenue (i.e. movement to an area with fewer marketable fish). With the landing obligation, this 
is  less  of  an  issue,  although  the  use  of  catch  composition  rules  is  still  embedded  in  the  current 
Technical Measures Regulation so the incentive to discard has not necessarily diminished. 

5.3 

Three major benefits to convince stakeholders 

Convincing  stakeholders  is  obviously  the  first  step  required  to  move  towards  better  size-
selectivity  patterns  in  European  fisheries.  The  results  presented  here  clearly  demonstrate  that 
improving  selectivity  appears  unavoidable  to  reach  the  current  objectives  of  the  CFP  and  would 
lead to three major types of benefits: 

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1  Improving  selectivity  is  a  powerful  way  to  reach  sustainability  (FMSY)  more  easily.  In 
particular  for  stocks  that  are  currently  strongly  overfished,  decreasing  the  fishing 
pressure to FMSY, based on the current selectivity pattern, is a well-known difficult issue in 
fisheries  management.  It  generally  implies  severe  (and  socially  difficult)  reductions  in 
fleet capacities and may lead to large short-term losses in landings. In contrast,  results 
presented here clearly illustrate how improving selectivity may increase the value of FMSY 
(see  the  above  yield  isopleths  analyses),  making  it  a  less  difficult  target  to  reach. 
Therefore,  for  managers  and  as  well  for  fishers,  fishing  effort  AND  selectivity  should  be 
considered  as  management  tools  that  deserve  to  be  used  to  implement  in  practice  the 
CFP  objective  of  exploiting  all  stocks  according  to  the  MSY  approach.  In  the  medium 
term, this means that scientific advice should consider various options, calculating TACs 
according  to  various  selectivity  patterns  related  to  size-based  technical  regulations.  In 
practice, there is no doubt that for  some  stocks improving selectivity would be the only 
way to reach the FMSY target (for example cod 27.7e-k). 

2  Results obtained during the EWG confirm that protecting juveniles requires more selective 
fisheries.  This  protection  is  a  requirement  of  the  current  TMR  which  stipulate  in  article 
3.2a that “Technical measures shall […] provide protection for juveniles”, specifying that 
this can be achieved based on minimum conservation reference sizes  (article 13), mesh 
size  specifications  (article  15),  closed  or  restricted  areas  (article  17),  and  real-time 
closures  or  moving-on  provisions  (article  19).  Simulations  conducted  during  the  EWG 
clearly demonstrate that the current selectivity patterns lead to high juvenile catches and 
large  mortality  rates  on  juveniles  for  most  of  the  studied  stocks,  while  optimizing  the 
selectivity  according  to  the  scenarios  explored  for  ToRs  1  and  2  would  result  in  a  huge 
drop  for  both  these  parameters.  In  other  words,  to  effectively  implement  the  current 
regulation  there  is  no  alternative  (except  closing  fisheries)  than  increasing  drastically 
ages at first catch.   

3  It is possible to maintain or even increase landings while having much less impact on the 
biomass  of  the  targeted  species,  which  is  also  a  requirement  of  the  CFP,  and  more 
specifically  of  the  ecosystem  approach  to  fisheries  management.  Indeed,  the  basic  CFP 
regulation stipulates, in article 2 dedicated to its objectives, “The CFP shall implement the 
ecosystem-based  approach  to  fisheries  management  so  as  to  ensure  that  negative 
impacts  of  fishing  activities  on  the  marine  ecosystem  are  minimized”.  Obviously,  the 
targeted  stocks  are  part  of  the  ecosystems  and  reducing  their  biomass  affects  the 
structure  and  functioning  of  marine  food  webs.  Implicitly,  the  objective  of  “minimizing 
the  impact  of  fishing”  means  that  we  may  continue  fishing  but  with  as  little  impact  as 
possible. From that point of view, simulations conducted during the EWG clearly showed 
that  the  current  selectivity  patterns  lead  to  large  reductions  in  stock  biomass  and  SSB, 
even  in  the  case  of  the  implementation  of  the  FMSY  approach.  Thus,  residual  SSB  were 
estimated  to  be  between  20%  and  35  %  of  the  unexploited  SSB,  depending  on  stocks, 
which  probably  have  cascading  effects  on  many  other  if  not  all  biological  compartments 
of the ecosystem. Conversely, improving selectivity would lead to a large reduction of the 
fishing  impacts  on  SSB,  which  encompasses  large  fish  which  play  a  key  role  in  the 
functioning of marine ecosystems. Here too, there is no doubt that seriously considering 
the  objectives  to  reduce  adverse  ecosystem  impacts  of  fishing  implies  to  improve  the 
size-based selectivity applied to targeted stocks. From this point of view, the EWG notes 
that  an  optimized  selectivity  leads  to  Y(F)  curves  which  become  very  flat,  with  a  wide 
range of fishing mortality producing very similar yields, but with very different impacts on 
the  stocks  biomass.  In  such  a  situation,  reaching  the  new  FMSY,  associated  with  the 
optimized  selectivity,  may  involve  large  fishing  efforts  and  thus  lead  to  unprofitable 
fisheries, while inducing higher impacts than those required by the ecosystem approach. 
Conversely,  a  lower  fishing  effort,  associated  with  lower  production  costs,  would  allow 
almost equivalent catches and reduced impacts.  

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In other words, from the perspective of an Ecosystem approach of fisheries management (AEFM) 
optimal  selectivity  should  be  associated  with  more  conservative  targets  than  FMSY.  An  Flow  value 
(the  fishing  mortality  which  provides  90%  of  the  MSY)  would  for  example  be  a  much  more 
suitable target for sustainability, as a proxy for FMEY (the fishing mortality allowing the maximum 
economic yield, i.e. say an optimized profitability of fisheries), and as  a powerful  way to  reduce 
the impact of fishing on targeted stocks, compared to the FMSY objective.  

The  EWG  also  underlines  that  rebuilding  healthy  ecosystems,  characterized  by  less  impacted 
stocks,  is  becoming  an  objective  of  particular  importance  in  the  context  of  climate  change.  In 
particular,  large  predator  fish,  that  are  commonly  caught  by  little-selective  bottom  gears,  are 
playing  a  key  role  in  the  stability  and  resilience  of  marine  food  webs.  From  that  point  of  view, 
going beyond the current FMSY target, which is based on little-selective gears or fishing practices, 
and  adopting  more  precautionary  targets  for  fisheries  management  (such  as  e.g.  FMEY  or  proxy) 
while  optimizing  mesh  sizes,  appears  to  be  the  most  effective  pathway  for  adaptation  of  the 
fishing management rules to climate change. 

5.4  Three main management tools to improve selectivity  

Just  increasing  the  current  minimum  conservation  reference  sizes  (MCRS)  would  probably  be 
insufficient  to  improve  fisheries  selectivity,  with  the  high  risk  to  just  lead  to  larger  discards  or 
unwanted catches, without any decrease in small fish catches. In contrast (or in addition), three 
main management tools can be used to improve selectivity. The first two are the regulation of the 
mesh sizes of fishing gears and elimination of particular gears. For example, results of Outrequin 
et  al.  (2021  and  in  prep.)  presented  above  clearly  illustrate  the  point,  demonstrating  that  the 
implementation of larger mesh sizes in the North Sea, at least for the main gears, would result in 
larger landings, larger remaining biomass and less discards (see  section 4.4.2.). More generally, 
the  effects  of  such  a  change  in  mesh  size,  on  yields  and  SSBs,  has  also  been  presented  above 
based on the “shift” simulations.  

The  major  alternative  or  additional  way  to  implement  an  improvement  in  selectivity  would  be 
based  on  spatial  management,  by  closing  definitively  or  temporarily  areas  where  small  fishes 
occur.  This  is  what  “crank”  scenarios  were  assumed  to  simulate.  However,  it  is  important  to 
stress  that  in  spatio-temporal  measures,  important  factors  influencing  the  population-selectivity 
model  are  the  spatial  distribution  of  fishing  mortality  as  well  as  the  movement  of  fish  between 
subpopulations  (Sampson  and  Scott,  2011).  Spatial  measures  applied  to  protect  juveniles  of 
species with high movement rate from protected to non-protected areas would in general be less 
effective.  

5.5  A gradual change? 

Of course, fishers cannot be asked to change their fishing gears progressively from year to year, 
changing mesh sizes step by step, as this would imply to buy new gears each time, thus leading 
to  high  and  probably  unsustainable  costs.  Therefore,  for  a  given  fisher,  any  gear  selectivity 
improvement  has  to  be  envisaged  by  a  large  and  long-term  if  not  unique  step.  From  this 
perspective,  it  is  commonly  admitted  that  all  fishers  have  to  change  their  selectivity  pattern 
simultaneously. Otherwise, the spared fishes issued from large mesh-sized gears would be caught 
by  the  small-mesh  size  ones,  thus  leading  to  a  situation  where  innovative  virtuous  fishermen 
would suffer losses, while conservative fisherman will benefit from additional catches. 

However,  two  mechanisms  can  contradict  such  a  vision,  where  changes  do  not  have  to  be 
implemented  gradually.  The  first  is  the  use  of  spatial  management  where  areas  encompassing 
large quantities of small fish would be progressively protected, thus leading to a gradual change 
that would simultaneously and equally affect all fishers.  The  second  mechanism would be based 
on  a  change  of  the  quota  distribution  system,  in  order  to  incentivize  virtuous  fishermen  who 
would  agree  to  use  larger  mesh  sizes.  In  this  perspective,  article  17  of  the  CFP  regulation 

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stipulates  that  quotas  can  be  distributed  by  MS  among  fishers  taking  into  account  their 
environmental  or  social  performances.  We  can  thus  imagine  a  situation  where  individual  catch 
quotas would at least be maintained for virtuous fishers, while the short-term losses induced by 
overall larger mesh sizes would be supported by the others. 

It  can  also  be  noted  that  in  some  fisheries,  a  gradual  increase  in  2-3  years  should  be  feasible 
because this is the life time of the gear. In such a case, the regulations should define who must 
change first, possibly putting in place accompanying measures, and in particular a complement in 
the allocation of quotas as mentioned above. 

5.6  Results-based management 

Results-based management (RBM) describes a framework where outcomes can be demonstrated 
by industry and verified by managers on behalf of society, thus reversing the burden of proof to 
the industry (Fitzpatrick et al. 2011). In the context of RBM a fishery is managed by its outputs, 
giving more liberty to fishers to choose how, where and when to fish, instead of having detailed 
and  prescriptive  top-down  rules.  In  practice,  there  would  be  still  some  general  objectives  and 
standards  in  place  (e.g.  avoid  overfishing),  but  a  regionalized  approach  would  set  the  specific 
measures in a fishery/area to meet these objectives.  

Previous work on selectivity and technical measures by the dedicated STECF EWGs (STECF 2012; 
2013;  2015;  2017)  has  always  acknowledged  the  need  to  facilitate  RBM.  This  EWG  is  also 
contributing  to  that  direction.  The  inclusion  of  different  selectivity  scenarios  on  a  stock-by-stock 
and fleet-by-fleet basis provides a range of choices to the local industries. Moreover, the isopleths 
parameterized  here  for  specific  stocks  featuring  both  F  and  selectivity  as  explanatory  variables, 
map  the  effects  of  different  management  strategies  on  the  stock  size  and  yield,  illustrating  a 
greater  range  of  possible  exploitation  regimes  (i.e.  combinations  of  F  and  selectivity)  than  the 
current  system  focusing  solely  on  regulating  F.  These  isopleths  also  assist  the  decisions  by  the 
industry  on  the  exact  way  they  want  to  move  towards  regimes  with  higher  yields  through 
changes  in  F  and  selectivity.  Combining  the  information  coming  from  multiple  stock-specific 
isopleths  of  sympatric  stocks  could  further  inform  the  decisions  on  changing  the  exploitation 
regime, by illustrating the gains and losses to be expected for the mixed fishery as a whole. 

The  move  towards  results-based  management,  has,  as  yet,  to  a  large  extent  failed  to  be 
implemented.  The  main  reason  is  that  the  required  comprehensive  monitoring,  control  and 
enforcement  and  quantification  of  the  catch  (e.g.  through  “fully  documented  fisheries”,  FDF  or 
electronic  monitoring,  EM)  have  not  been  widely  established.  Because  the  discard  ban  is  not 
enforced, the costs of overfishing the quotas are still largely external to the fishing business. The 
new TMR is still largely very prescriptive micromanagement but also allows for bottom-up results-
based  management  in  the  form  of  the  explicit  allowances  for  national  management  plans  and 
pilots. Unfortunately, the momentum and drive towards FDF that was present in the first half of 
the past decade, through pilot projects (van Helmond et al. 2015; Needle et al. 2015; Mortensen 
et  al.  2017),  seem  to  have  halted.  Most  joint  recommendations  and  exemption  requests  from 
member  states  are  not  accompanied  by  proposals  for  extensive  monitoring,  control  and 
enforcement  and  quantification  of  the  catch  (e.g.  ToR  6.5  in  STECF,  2021b).  According  to  van 
Helmond et al. (2020), managers and industry are often reluctant to the uptake of EM. Improved 
understanding  of  the  fisher's  concerns,  for  example  intrusion  of  privacy,  liability  and  costs,  and 
better exploration of the benefits may enhance implementation on a larger scale (van Helmond et 
al., 2020). 

Much research has been carried out on bottom-up spatio-temporal approaches. Fishers could use 
digital platforms to be informed on the spatial distribution of wanted and unwanted catches. The 
information in these tools could consist of long-term, scientifically processed data such as in the 
tool  from  Calderwood  et  al.  (2020).  They  could  also  be  based  on  real-time  information  that  is 
shared by the fishers (Bergsson & Plet-Hansen, 2016; Bergsson et al., 2017; Eliasen and Bichel, 
2016;  Needle  et  al.,  2015)  or  sent  from  the  fishers  to  scientists  or  managers  and  back.  This 
information  can  be  used  for  real-time  closures  and  move-on  rules.  Examples  of  success  include 
the science-industry collaborative program that runs since 2010 in the US Georges Bank  scallop 

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fishery  (O'Keefe  &  DeCelles,  2013).  Vessels  share  near  real-time  location  information  about 
flounder  bycatch  amounts.  Scientists  compile  the  information,  identify  bycatch  hotspots,  and 
provide  daily  advisories  to  vessels  on  the  fishing  grounds.  Another  example  is  the  Shorebased 
Whiting  Cooperative  (SWC,  formed  in  2012)  at  the  US  west  coast;  information  on  location  and 
catch  is  processed  and  distributed  back  to  the  members  in  near  real-time,  in  the  form  of  high-
resolution maps, enabling them to make fine-scale decisions (Holland and Martin, 2019; McQuaw, 
2019).  Similarly,  in  the  US  northwest  Atlantic  mid-water  trawl  fishery  targeting  Atlantic  herring 
and  Atlantic  mackerel,  a  voluntary  bycatch  avoidance  program  exists,  as  a  partnership  between 
industry, state government, and university, through near real-time information sharing of catches 
on a spatial grid (Bethoney et al., 2013, 2017). Closer to home, in the Celtic Sea, the UK runs an 
industry-science collaboration project  with a real-time self-reporting scheme to avoid bycatch of 
spurdog, whose zero (or low) TAC potentially chokes the fishery (Hetherington, 2014); here maps 
with grid cells and traffic-light colours are produced and sent back to participating fishers. 

ICES  (2020b)  reviewed  the  current  availability  of  innovative  gear.  For  an  increased  adoption  of 
selective gear, it is necessary to involve the industry. Involvement creates a feeling of ownership 
which  increases  compliance  levels  (Kraak  and  Hart,  2019b).  Fitzpatrick  et  al.  (2019)  found  that 
some  fishers  are  interested  in  more  selective  gears  and  spatial  and  temporal  closures  and  that 
fishers found it important to “integrate fishers’ local ecological knowledge into discard plans”. The 
results-based  setup  of  the  2008-2015  cod  plan  had  promoted  bottom-up  initiatives  aimed  at 
attaining set targets with measures that were more suited to local conditions (Kraak et al., 2013). 
This experience showed that given the right stimulus, the industry can rapidly develop and deploy 
fishing  tactics,  including  gear  and  behavioural  changes,  when  there  are  specific  objectives  and 
strong drivers to do so. Reid et al. (2019) reviewed bottom-up results-based approaches, looking 
at  trials  where  fishers  tried  to  reduce  their  unwanted  catches  by  whatever  (legal)  means  they 
thought  best.  In  some  cases,  they  were  able  to  reduce  unwanted  catches,  in  others  they  were 
less  successful.  O’Neill et  al.  (2019)  reviewed  ways  to  encourage  and  support  fishers  to  design, 
develop  and  test  selective  gears  that  will  avoid  unwanted  catches.  They  also  examined  the 
success  of  science-industry  collaborations  and  emphasised  the  benefits  of  a  flexible  regulatory 
environment.  

Just before the adoption of the new TMR, Eliasen et al. (2019) discussed the role that fishers can 
play  by  actively  participating  in  the  development  of  gears  and  contributing  to  the  scientific 
documentation  of  their  selectivity.  They  concluded  that  “a  more  flexible  system  of  gear 
development  and  evaluation  would  be  possible  by  a)  involvement  of  fishers  in  proposing  gear 
adjustments,  self-sampling  and  documenting  results  according  to  scientific  protocols  […]  and  b) 
[…]  faster  approval  of  gear  use  under  a  regionalised  technical  regulation  regime  with  yearly 
adjustments  of  management  plan”.  The  latter  echoes  the  STECF  (2017)  advice.  Nevertheless, 
appropriate incentives for behavioural change, perhaps in the form of a carrot or a stick, should 
also be established. Else fishers will not adopt behaviour that is costly to them in the short term 
(Hardin, 1968) in terms of reduced catches and higher costs (buying new gear etc.). One way of 
doing this is the internalisation of the costs to the resource and the ecosystem into the individual 
fishing  business;  fishers  will  then  try  to  avoid  these  costs.  And  this  internalisation,  in  turn, 
requires comprehensive monitoring, control and enforcement and quantification of the catch. 

5.7  Progress in evaluating the technical measures regulation and future steps 

Already in 2008, the EC had initiated renewal of the TMR that was in force (EC, 1998) through a 
proposal for a new  regulation (COM, 2008). This proposal called for  clearer,  simpler rules and a 
regional  approach.  However,  the  EC  did  not  follow  through  with  this  proposal.  In  2012,  the  EC 
established a STECF  EWG to assist in the development of technical measures and to investigate 
possible new approaches to regulating technical measures in the context of a reformed CFP. This 
group  met  multiple  times  (STECF,  2012,  2013,  2015,  2017).  Two  lines  of  avenue  that  were 
recommended  by  these  groups  were  (i)  moving  towards  results-based  management  (STECF 
2012,  2013),  and  (ii)  linking  exploitation  pattern  and  yield  through  a  harvest-control-rule  type 
approach (STECF, 2013). Regarding the second point, the EWG (STECF, 2013) started exploring 
how  technical  measures  as  drivers  for  changes  in  selectivity  pattern  can  be  formally  integrated 
into  multiannual  management  plans,  along  with  existing  measures,  such  as  TACs,  whereby 

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positive  adjustments  in  exploitation  pattern  could  result  in  increased  fishing  opportunities.  This 
could potentially be achieved by directly linking exploitation pattern and yield through a harvest-
control-rule  type  approach,  e.g.  in  the  catch  advice,  where  the  fishing  mortality  rate  implying 
maximum sustainable yield (FMSY) is recalculated to consider changes in exploitation pattern. This 
would  have  the  benefit  of  giving  a  transparent  association,  all  else  being  equal,  between 
improving selectivity and improved fishing opportunities thereby creating an obvious incentive to 
improve  selectivity.  STECF  has  also  called  for  the  development  of  suitable  selectivity  metrics  to 
track  the  effect  of  technical  measures  on  exploited  fish  stocks  (e.g.,  STECF,  2015).  Candidate 
metrics  were  tested  in  a  study  (Vasilakopoulos  et  al.,  2020)  and  the  most  suitable  indicator 
appeared  to  be  Frec/Fbar  (the  ratio  between  the  fishing  mortality  rate  on  the  fish  at  the  age  of 
recruitment to the fishery and the mean fishing mortality rate over a range of adult ages). In the 
meanwhile,  the  new  TMR  was  adopted  and  came  into  force  in  2019  (EU,  2019).  In  2020,  to 
advise the EC in the context of their reporting obligation according to article 31 of the regulation, 
the  EWG  (STECF,  2020a)  provided  time  series  of  the  Frec/Fbar  indicator  for  each  of  the  stocks  in 
Annex  XIV  of  the  regulation  and  attempted  to  relate  the  changes  in  the  indicator  to  changes  in 
technical  measures.  STECF  PLEN  20-03  (STECF,  2020d),  however,  concluded  that,  although 
temporal trends in selectivity were found, the extent to which such changes can be attributed to 
implementation  of  technical  measures  cannot  be  deduced.  As  explained  in  the  introduction, 
DGMARE  in  consultation  with  STECF  about  how  to  proceed  to  prepare  for  the  next  evaluation, 
which is due in 2023, decided to request advice from the EWG in a stepwise approach. The step 
for the current EWG is stipulated in the current ToRs. The ToRs 1-3 quantify by stock and also by 
fleet  how  far  removed  the  current  fishing  patterns  are  from  optimal  selectivity  patterns.  ToR  4 
demonstrates  in  which  directions  fisheries  can  move,  in  terms  of  fishing  mortality  rate  and 
selectivity  pattern,  towards  optimal  yield  and  SSB  and  also  discusses  what  the  major  obstacles 
are in practice, especially in the context of mixed fisheries with multiple gears. In mixed fisheries, 
optimized  selectivity  can  obviously  not  be  reached  simultaneously  and  tradeoffs  need  to  be 
defined.  Therefore,  the  EWG  considers  that  the  next  steps  should  focus  on  these  obstacles, 
perhaps  in  dedicated  case  studies  with  priority  on  the  fisheries  that  are  currently  furthest 
removed  from  sustainability.  Work  will  be  needed  including  simulating  and  assessing  various 
scenarios. The current EWG report gives guidance (i) that sustainability can best be achieved by a 
combination  of  reducing  fishing  mortality  and  improving  selectivity,  (ii)  that  three  options  are 
available  to  change  selectivity,  namely  gear/mesh  changes,  elimination  of  particular  gears  and 
spatiotemporal measures; (iii) that the first  two should best be implemented gradually (because 
of  the  cost  of  buying  gear)  but  that  the  latter  can  be  implemented  more  immediately;  and  (iv) 
which  fisheries  and  fleets  are  farthest  removed  from  sustainable  exploitation  (in  terms  of 
differences between current and optimised patterns). Focus should be on how to create the right 
incentives  that  lead  to  the  uptake  of  more  selective  gear  and  spatio-temporal  effort  allocation 
shifts. As noted above, much research has been done of this; these insights need to be linked to 
the  actual  cases  that  require  most  urgent  change.  The  demonstration  through  the  isopleths  in 
ToR  4  of  how  higher  yields  and  SSB  can  be  achieved  through  a  combination  of  reduced  fishing 
mortality rate and improved selectivity, can be seen as a first step towards the establishment of 
harvest  control  rules  and  catch  advice  in  which  the  catch  options  do  not  only  feature  several 
options  of  fishing  mortality  rate  but  also  several  options  of  selectivity  pattern  and  combinations 
thereof. 

6 

CONCLUSIONS 

ToRs 1-3 quantify by stock and by fleet how far the current fishing patterns are removed from the 
optimal  selectivity  patterns.  ToR  “4a”  demonstrates  in  which  direction  fisheries  can  move,  in 
terms of fishing mortality rate and selectivity pattern, to reach optimal yield and SSB. ToR “4b” 
discusses  the  major  obstacles  in  practice,  especially  in  the  context  of  mixed  fisheries  with 
multiple gears. For species-specific and fleet-specific results, the figures and tables in the results 

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section  of  the  report  need  to  be  consulted.  Here,  some  general  conclusions  are  phrased, 
sometimes  mentioning  specific  examples.  For  species-specific  and  fleet-specific  conclusions,  the 
results section has to be consulted. 

 

In general, any improvement in selectivity will (in the long term) most strongly benefit stocks 
characterized  by  greater  growth  overfishing,  which  are  typically  large-bodied  and  late-
maturing (e.g., hake stocks in the Mediterranean, cod) (ToRs 1-2).  

  A  selectivity  improvement  that  achieves  a  higher  protection  of  juveniles  often  produces  a 

higher increase in long-term yield (ToRs 1-2). 

  Exceptions  are  some  haddock  and  whiting  stocks  in  the  Northeast  Atlantic,  where 
optimisations in yield were associated with a decrease in  S50, leading to increased proportion 
of juveniles in the catch (ToRs 1-2). 

  Any long-term increase in yield linked to a change in selectivity will imply, in general, a short-
term  loss  in  yield,  but  this  short-term  loss  is  generally  similar  to  the  short-term  loss  that 
would  result  from  reducing  current  F  to  the  F  levels  in  accordance  with  the  scientific  advice 
under current selectivity. Those short-term losses will be more than compensated by the long-
term gains (ToRs 1-2).  

  The “shift” scenario typically comes with higher yield gains compared to the “crank” scenario, 
with only few exceptions, such as red mullet in GSA07 and GSA09, cod in 6a, hake in SWW, 
and some whiting and haddock stocks (ToRs 1-2).   

  The  “crank”  scenario  generally  gives  better  results in  terms  of  reduction  of  juvenile  catches, 
with  only  few  exceptions,  such  as  cod  in  6a,  whiting  in  7a,  and  hake  and  megrim  in  SWW 
(ToRs 1-2). 

  Active gears in general perform worse in terms of juvenile catches and SSB (ToR 3). 
  Otter bottom trawl (OTB) selectivity has negative effects in terms of yield loss when compared 

 

 

 

 

to the status quo selectivity of all fleets combined (ToR 3). 
In  the  Northeast  Atlantic,  beam  trawls  often  have  high  proportions  of  juveniles in  the  catch, 
with the exception of plaice in the English Channel. 
In  the  Western  Mediterranean  Sea,  OTB  selectivity  is  associated  with  very  high  proportions 
(>80%) of juvenile hakes in the catches (ToR 3).   
Jackknife analysis excluding OTB would be mostly beneficial to stocks of larger bodied gadoids 
in terms of both increase in yield and protection of juveniles (ToR 3). 
In  general,  the  results  from  ToR  3  suggest  that  largest  improvements  in  yield  and  juvenile 
protection can made through optimising the gear selectivity of bottom trawl gears, allocating 
less effort to fishing with those gears or shifting the effort to areas/season where the impacts 
on juveniles can be minimized. 

  Stocks that are heavily overexploited (e.g., all cod and Mediterranean hake stocks) would gain 
substantially  in  both  yield  and  SSB  if  selectivity  is  increased  simultaneously  with  decreased 
fishing mortality (ToR “4a”). 

 

  Simultaneously  increasing  the  selectivity  and  decreasing  F  would  demand  smaller  changes 
compared to only manipulating only one parameter. This may increase the incentive (or rather 
decrease the disincentive) for change (ToR 4).  
Increased selectivity has often larger effects on yield than on SSB. In many cases, a decrease 
in  F  combined  with  increased  selectivity  is  needed  to  see  large  changes  in  SSB  (ToR  “4a”). 
However, an increase in SSB has a multitude of positive effects, both for the fisheries through 
an  increase  in  CPUE  and  average  individual  size  of  the  fish  caught,  for  the  stock  as  it 
increases  its  resilience  to  climate  change  and  for  the  ecosystem  as  larger  biomass  and  size 
diversity in general increase ecosystem functionality, resilience and services. 

  Small-bodied  and  fast-growing  species,  which  are  commonly  assumed  to  have  high  natural 
mortality of younger age classes (e.g., whiting stocks), have less to benefit in terms of yield 
from  the  increase  in  selectivity  at  current  fishing  mortality.  In  some  cases,  increased 
selectivity would lead to decreasing yield, but it would always result in larger SSB (ToR “4a”).  

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  Stocks  that  are  currently  underexploited  (F<  FMSY)  (e.g.,  North  Sea  whiting  and  Irish  Sea 

plaice) would not produce higher yield with increasing selectivity or decreasing F (ToR “4a”).  

  When  selectivity  is  optimised,  the  yield-F  curve  usually  becomes  very  flat.  In  this  case, 
increasing  F  until  FMSY  would  just  add  costs  for  no  additional  catch  (ToR  “4a”).  Thus,  in  this 
case a more conservative target may lead to more profitable and less impacting fisheries. 
In  mixed-fishery  situations,  technical  measures  are  often  compromises  that  tend  to  increase 
short-term  costs  for  the  industry,  through  short-term  losses,  re-designing  of  vessels  and/or 
equipment costs (ToR “4b”).  

 

  The  mixed-fisheries  multi-gear  examples  demonstrate  the  complexity  in  improving  selection 
patterns. Possible solutions differ case-by-case and include a combination of gear-based and 
spatial/temporal measures and reductions in fishing effort. Making these changes is politically, 
culturally and economically difficult (ToR “4b”).   

  For  the  improvement  of  selectivity  in  mixed  fisheries  a  collaborative  approach  with 

stakeholders is essential (ToR “4b”).  

  Simulations  showed  that  implementation  of  larger  mesh  sizes  in  the  North  Sea,  at  least  for 
the main gears, would result in larger landings, larger remaining biomass and less unwanted 
catches (ToR “4b”). 

7 

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8  CONTACT DETAILS OF EWG-21-07 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 

84 

84 

 
 
 
 
 
 
Gascuel, Didier 

Kraak, Sarah 

Ligas, Alessandro 

Rihan, Dominic 

Invited experts 

Name 

Brĉić, Jure 

AGROCAMPUS  OUEST,  65  Route 
De  Saint  Brieuc,  CS  84215, 
F35042 RENNES Cedex, France 

Thünen  Institute  of  Baltic  Sea 
Fsheries,  Alter  Hafen  Süd  2, 
18069 Rostock, Germany.  

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

BIM, 
Laoghaire, Co Dublin, Ireland 

Crofton 

Road, 

Dun 

Didier.Gascuel@agrocampus-ouest.fr 

sarah.kraak@thuenen.de  

ligas@cibm.it 

Dominic.Rihan@bim.ie  

Affiliation1 

Email 

University  of  Split,  Department 
of Marine Studies 

jure.brcic@unist.hr 

Cardinale, 
Massimiliano 

Swedish 
Agricultural Sciences (SLU) 

University 

of 

massimiliano.cardinale@slu.se 

Gerritsen, Hans 

Marine Institute 

hans.gerritsen@marine.ie 

Haase, Stefanie 

Thünen  Institute  of  Baltic  Sea 
Fisheries 

stefanie.haase@thuenen.de 

Hommik, Kristiina 

Estonian 
University of Tartu 

Marine 

Institute 

kristiina.hommik@ut.ee 

Mehault, Sonia 

IFREMER 

sonia.mehault@ifremer.fr 

Outrequin, Thomas 

Agrocampus-Ouest 

thomas.outrequin@agrocampus-
ouest.fr 

Sala, Antonello 

Scarcella, Giuseppe 

Italian National Research Council 

antonello.sala@cnr.it 

CNR-IRBIM  Institute  for  Marine 
Biological 
and 
Biotechnology 

Resources 

giuseppe.scarcella@cnr.it 

Sys, Klaas 

Flanders  Research  Institute  for 
Agriculture, Fisheries and Food 

klaas.sys@ilvo.vlaanderen.be 

Valeiras, Julio 

Instituto 
Oceanografía 

Español 

de 

julio.valeiras@ieo.es 

85 

85 

 
 
 
 
JRC experts 

Name 

Affiliation1 

Email 

Vasilakopoulos, Paris  JRC 

Paris.VASILAKOPOULOS@ec.europa.eu 

Winker, Henning 

JRC 

Henning.WINKER@ec.europa.eu  

European Commission 

Name 

Affiliation1 

Email 

Hekim, Zeynep 

JRC, STECF secretariat 

Hekim.ZEYNEP@ec.europa.eu 
jrc-stecf-secretariat@ec.europa.eu  

Moset 
Maria 

Martinez, 

DG MARE D3 

Maria.MOSET-
MARTINEZ@ec.europa.eu  

Patterson, Kenneth 

DG MARE D3 

Kenneth.PATTERSON@ec.europa.eu 

Observers 

Name 

Affiliation1 

Email 

Piron, Marzia 

Mediterranean Advisory Council 

marzia_piron@hotmail.it  

86 

86 

 
 
 
 
 
 
 
 
 
 
9  LIST OF ANNEXES  

Electronic annexes are published on the meeting’s web site on:  
https://stecf.jrc.ec.europa.eu/reports/tech-measures 

List of electronic annexes documents: 

EWG-21-07 – Electronic Annex 1 – Appendix 1 

EWG-21-07 – Electronic Annex 2 – Appendix 2 

EWG-21-07 – Electronic Annex 3 – Appendix 3 

87 

87 

 
 
 
 
 
 
  
 
 
 
 
 
 
ANNEX:  Conversion  of  selectivity-at-length  from  gear  experiments  to 
selectivity-at-age 

To obtain an average size selection for baseline codends used in the Mediterranean bottom trawl 
fisheries  either  40  mm  square-mesh  (SM40)  or  50  mm  diamond-mesh  (DM50),  for  hake  (HKE) 
and  red  mullet  (MUT),  a  dataset  from  Lucchetti  et  al.  (2021)  was  used  for  the  analysis.  The 
dataset  was  further  updated  with  new  field  results  from  Sala  et  al.  (2018).  See  Table  4  for 
details.  

The resulting dataset consisted of 83 experiments for diamond-mesh and 20 for square-mesh for 
hake  while  for  red  mullet  we  analyzed  79  values  for  diamond-mesh  and  19  for  square-mesh. 
Linear models were found to relate the size selection parameters (L50 and SR) with mesh size for 
each  species  and  mesh  configuration.  The  models  were  used  to  predict  L50  and  SR  values  for 
baseline  codends  (SM40  and  DM50)  used  in  the  Mediterranean  bottom  trawl  fisheries.  The 
estimated coefficients of each model are presented in Table 1.  

Table 1. Size selection coefficients for hake (HKE) and red mullet (MUT) obtained using the data 
collected  from  literature.  L50:  length  of  fish  that  has  a  50%  probability  of  being  retained  or 
escaping  after  entering  the  codend;  SR:  difference  in  length  between  the  fish  that  has  a  75  % 
probability of retention and that with a 25% probability of retention. 

Parameter  Species  MC  a 

b 

L50 

HKE 

DM  -0.135  0.273 

MUT 

DM  2.33 

0.199 

MUT 

SM  3.04 

0.247 

SR 

HKE 

DM  -1.05 

0.115 

MUT 

DM  -4.86 

0.176 

MUT 

SM  -0.626  0.068 

In  case  of  hake,  where  almost  all  studies  were  performed  with  SM40  codends,  instead  of 
producing  simple  linear  model  and  then  predict  size  selection  parameters,  for  square-mesh 
codend average values of L50 and SR were calculated (Figure 1). The models in Table 1 were also 
used  to  predict  size  selection  parameters  for  60  mm  diamond-mesh,  while  values  for  a  50  mm 
square-mesh codend was taken from Sala et al. (2018). For red mullet, the selection parameters 
of all 4 codends (SM40, SM50, DM50, and DM60) were inferred using the models. 

Since stock assessment does not use length but age of fish, the L50 and SR values obtained for 
DM50,  SM40,  DM60  and  SM50  were  converted  into  age  for  each  GSA  separately,  using  the  von 
Bertalanffy growth parameters shown in Table 2.  

Selectivity studies use logit curve to estimate size selection parameters L50 and SR: 

where l is fish length, L50 is the 50% retention length and SR is the difference between the 75% 
retention length and the 25% retention length. 

        (1) 

88 

 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
Following  the  calculation  of  the  age-at-length,  we  used  equation  (1)  to  estimate  retention 
probability for each age class in each GSA separately (Table 3).  

It  is  noteworthy  to  observe  that  providing  information  on  age  selection  (e.g.,  retention  at  age 
class 0, 1, 2, 3, etc.) the curve misrepresents the real size selection.  An example is shown for 50 
mm diamond-mesh codend in Figure 3. 

A 

B 

y=0.273x-0.135 

y=0.115x-1.05 

C 

D 

Figure 1. Linear models used to predict L50 (A) and SR (B) values for hake (HKE) in DM50 (small 
red diamond) and DM60 (large red diamond) codends. Small red squares in (C) and (D) represent 
the mean L50 and SR, respectively for SM40 codend, while large red squares represent L50 and 
SR data for SM50 codend obtained from Sala et al. (2018). Small grey crosses represent L50 or 
SR values obtained in the Med experiments and collated in the paper Lucchetti et al. (2021) and 
Sala et al. (2018).  

89 

 
 
 
 
 
 
A 

C 

B 

y=0.199x+2.33 

y=0.176x-4.86 

D 

y=0.247x+3.04 

y=0.0674x-0.626 

Figure 2. Linear models used to predict L50 (A) and SR (B) values for red mullet  (MUT) in DM50 
(small  red  diamond)  and  DM60  (large  red  diamond)  codends.  Small  red  squares  in  (C)  and  (D) 
represent  the  modelled  L50  and  SR,  respectively  for  SM40  codend,  while  large  red  squares 
represent  L50  and  SR  data  for  SM50  codend.  Small  grey  crosses  represent  L50  or  SR  values 
obtained in the Med experiments and collated in the paper Lucchetti et al. (2021) and Sala et al. 
(2018). 

Table 2. VBGF coefficients (Linf, k,t0) used in the analysis. HKE: Hake, MUT: Red mullet. 

Species  GSA  Sex 

Linf (cm)  k 

t0 

HKE 

MUT 

HKE 

MUR 

HKE 

1 

1 

5 

5 

6 

Combined  110 

0.178  -0.005 

Combined  34.5 

0.34 

0.143 

Combined  110 

0.178  -0.005 

Combined  33.4 

0.43 

-0.1 

Combined  110 

0.178  -0.005 

90 

 
 
 
 
Species  GSA  Sex 

Linf (cm)  k 

t0 

MUT 

HKE 

MUT 

HKE 

HKE 

MUT 

6 

7 

7 

8 

9 

9 

Combined  34.5 

0.34 

0.14 

Combined  110 

0.178  -0.005 

Combined  34.5 

0.34 

0.14 

Females 

95 

0.16 

-0.06 

Females 

95 

0.16 

-0.06 

Females 

26.56 

0.545  0.17 

HKE 

10 

Females 

95 

0.16 

-0.06 

MUT 

10 

Females 

30 

0.243  -0.62 

HKE 

11 

Females 

95 

0.16 

-0.06 

MUT 

17 

Females 

29.185 

0.247  -0.768 

HKE 

17 

Combined  104 

0.12 

-0.01 

HKE 

18 

Combined  104 

0.12 

-0.01 

MUT 

18 

Females 

29.185 

0.247  -0.768 

HKE 

19 

Females 

111 

0.1 

-0.6 

HKE 

20 

Combined  104 

0.12 

-0.01 

MUT 

22 

Combined  32.6 

0.17 

-1.78 

Table 3. HKE and MUL retention for each age class. MC: mesh configuration (DM, diamond-; SM, 
square-mesh);  MS:  mesh  size  in  the  codend;  L50:  length  of  fish  that  has  a  50%  probability  of 
being  retained  or  escaping  after  entering  the  codend;  SR:  difference  in  length  between  the  fish 
that has a 75 % probability of retention and that with a 25% probability of retention; A50: age of 
fish  that  has  a  50%  probability  of  being  retained  or  escaping  after  entering  the  codend;  ASR: 
difference in age between the fish that has a 75 % probability of retention and that with a 25% 
probability of retention; AX_ret: retention at age X. HKE: Hake, MUT: Red mullet. 

Speci
es 

HKE 

HKE 

M
C 

D
M 

D
M 

MS  GS
A 

L50  SR  A5
0 

50  1 

60  1 

13.
53 

16.
27 

4.7
1 

5.8
6 

0.7
3 

0.8
9 

AS
R 

0.2
7 

0.3
5 

A0_r
et 

A1_r
et 

A2_r
et 

A3_r
et 

A4_r
et 

A5_r
et 

A6_r
et 

0.00  0.89 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.66 

1.00  1.00 

1.00  1.00  1.00 

91 

 
 
 
 
Speci
es 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

M
C 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

MS  GS
A 

L50  SR  A5
0 

40  1 

1 

51.
6 

50  5 

60  5 

40  5 

5 

51.
6 

50  6 

60  6 

40  6 

6 

51.
6 

50  7 

60  7 

40  7 

7 

51.
6 

50  8 

60  8 

40  8 

15.
15 

21.
93 

13.
53 

16.
27 

15.
15 

21.
93 

13.
53 

16.
27 

15.
15 

21.
93 

13.
53 

16.
27 

15.
15 

21.
93 

13.
53 

16.
27 

15.
15 

4.3
5 

3.5
1 

4.7
1 

5.8
6 

4.3
5 

3.5
1 

4.7
1 

5.8
6 

4.3
5 

3.5
1 

4.7
1 

5.8
6 

4.3
5 

3.5
1 

4.7
1 

5.8
6 

4.3
5 

0.8
3 

1.2
4 

0.7
3 

0.8
9 

0.8
3 

1.2
4 

0.7
3 

0.8
9 

0.8
3 

1.2
4 

0.7
3 

0.8
9 

0.8
3 

1.2
4 

0.9
0 

1.1
1 

1.0
3 

A0_r
et 

A1_r
et 

A2_r
et 

A3_r
et 

A4_r
et 

A5_r
et 

A6_r
et 

0.00  0.81 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.08 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.89 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.66 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.81 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.08 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.89 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.66 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.81 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.08 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.89 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.66 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.81 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.08 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.65 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.37 

0.98  1.00 

1.00  1.00  1.00 

0.00  0.46 

1.00  1.00 

1.00  1.00  1.00 

AS
R 

0.2
6 

0.2
2 

0.2
7 

0.3
5 

0.2
6 

0.2
2 

0.2
7 

0.3
5 

0.2
6 

0.2
2 

0.2
7 

0.3
5 

0.2
6 

0.2
2 

0.3
6 

0.4
7 

0.3
4 

92 

 
 
Speci
es 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

M
C 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

MS  GS
A 

L50  SR  A5
0 

8 

51.
6 

50  9 

60  9 

40  9 

9 

51.
6 

21.
93 

13.
53 

16.
27 

15.
15 

21.
93 

50  10  13.
53 

60  10  16.
27 

40  10  15.
15 

51.
6 

10  21.
93 

50  11  13.
53 

60  11  16.
27 

40  11  15.
15 

51.
6 

11  21.
93 

50  17  13.
53 

60  17  16.
27 

40  17  15.
15 

51.
6 

17  21.
93 

3.5
1 

4.7
1 

5.8
6 

4.3
5 

3.5
1 

4.7
1 

5.8
6 

4.3
5 

3.5
1 

4.7
1 

5.8
6 

4.3
5 

3.5
1 

4.7
1 

5.8
6 

4.3
5 

3.5
1 

1.5
8 

0.9
0 

1.1
1 

1.0
3 

1.5
8 

0.9
0 

1.1
1 

1.0
3 

1.5
8 

0.9
0 

1.1
1 

1.0
3 

1.5
8 

1.1
5 

1.4
1 

1.3
0 

1.9
6 

AS
R 

0.3
0 

0.3
6 

0.4
7 

0.3
4 

0.3
0 

0.3
6 

0.4
7 

0.3
4 

0.3
0 

0.3
6 

0.4
7 

0.3
4 

0.3
0 

0.4
3 

0.5
6 

0.4
1 

0.3
6 

A0_r
et 

A1_r
et 

A2_r
et 

A3_r
et 

A4_r
et 

A5_r
et 

A6_r
et 

0.00  0.01 

0.95  1.00 

1.00  1.00  1.00 

0.00  0.65 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.37 

0.98  1.00 

1.00  1.00  1.00 

0.00  0.46 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.01 

0.95  1.00 

1.00  1.00  1.00 

0.00  0.65 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.37 

0.98  1.00 

1.00  1.00  1.00 

0.00  0.46 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.01 

0.95  1.00 

1.00  1.00  1.00 

0.00  0.65 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.37 

0.98  1.00 

1.00  1.00  1.00 

0.00  0.46 

1.00  1.00 

1.00  1.00  1.00 

0.00  0.01 

0.95  1.00 

1.00  1.00  1.00 

0.00  0.32 

0.98  1.00 

1.00  1.00  1.00 

0.00  0.16 

0.91  1.00 

1.00  1.00  1.00 

0.00  0.16 

0.97  1.00 

1.00  1.00  1.00 

0.00  0.00 

0.56  1.00 

1.00  1.00  1.00 

93 

 
 
Speci
es 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

HKE 

MUT 

MUT 

MUT 

MUT 

MUT 

M
C 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

MS  GS
A 

L50  SR  A5
0 

50  18  13.
53 

60  18  16.
27 

40  18  15.
15 

51.
6 

18  21.
93 

50  19  13.
53 

60  19  16.
27 

40  19  15.
15 

51.
6 

19  21.
93 

50  20  13.
53 

60  20  16.
27 

40  20  15.
15 

51.
6 

20  21.
93 

50  1 

60  1 

40  1 

50  1 

50  6 

12.
25 

14.
24 

12.
92 

15.
39 

12.
25 

4.7
1 

5.8
6 

4.3
5 

3.5
1 

4.7
1 

5.8
6 

4.3
5 

3.5
1 

4.7
1 

5.8
6 

4.3
5 

3.5
1 

3.9
3 

5.6
8 

2.0
7 

2.7
5 

3.9
3 

1.1
5 

1.4
1 

1.3
0 

1.9
6 

0.7
0 

0.9
8 

0.8
7 

1.6
0 

1.1
5 

1.4
1 

1.3
0 

1.9
6 

1.4
3 

1.7
1 

1.5
2 

1.8
8 

1.4
3 

AS
R 

0.4
3 

0.5
6 

0.4
1 

0.3
6 

0.4
8 

0.6
2 

0.4
5 

0.3
9 

0.4
3 

0.5
6 

0.4
1 

0.3
6 

0.5
2 

0.8
3 

0.2
8 

0.4
2 

0.5
2 

A0_r
et 

A1_r
et 

A2_r
et 

A3_r
et 

A4_r
et 

A5_r
et 

A6_r
et 

0.00  0.32 

0.98  1.00 

1.00  1.00  1.00 

0.00  0.16 

0.91  1.00 

1.00  1.00  1.00 

0.00  0.16 

0.97  1.00 

1.00  1.00  1.00 

0.00  0.00 

0.56  1.00 

1.00  1.00  1.00 

0.04  0.79 

1.00  1.00 

1.00  1.00  1.00 

0.02  0.51 

0.97  1.00 

1.00  1.00  1.00 

0.01  0.65 

0.99  1.00 

1.00  1.00  1.00 

0.00  0.03 

0.90  1.00 

1.00  1.00  1.00 

0.00  0.32 

0.98  1.00 

1.00  1.00  1.00 

0.00  0.16 

0.91  1.00 

1.00  1.00  1.00 

0.00  0.16 

0.97  1.00 

1.00  1.00  1.00 

0.00  0.00 

0.56  1.00 

1.00  1.00  1.00 

0.00  0.12 

0.90  0.99 

1.00  1.00  1.00 

0.00  0.11 

0.68  0.94 

0.99  0.99  1.00 

0.00  0.01 

0.97  1.00 

1.00  1.00  1.00 

0.00  0.00 

0.65  0.99 

1.00  1.00  1.00 

0.00  0.12 

0.90  0.99 

1.00  1.00  1.00 

94 

 
 
Speci
es 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

M
C 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

MS  GS
A 

L50  SR  A5
0 

60  6 

40  6 

50  6 

50  7 

60  7 

40  7 

50  7 

50  9 

60  9 

40  9 

50  9 

14.
24 

12.
92 

15.
39 

12.
25 

14.
24 

12.
92 

15.
39 

12.
25 

14.
24 

12.
92 

15.
39 

50  10  12.
25 

60  10  14.
24 

40  10  12.
92 

50  10  15.
39 

50  17  12.
25 

60  17  14.
24 

5.6
8 

2.0
7 

2.7
5 

3.9
3 

5.6
8 

2.0
7 

2.7
5 

3.9
3 

5.6
8 

2.0
7 

2.7
5 

3.9
3 

5.6
8 

2.0
7 

2.7
5 

3.9
3 

5.6
8 

1.7
1 

1.5
2 

1.8
8 

1.4
3 

1.7
1 

1.5
2 

1.8
8 

1.3
1 

1.5
8 

1.3
9 

1.7
6 

1.5
4 

2.0
3 

1.7
0 

2.3
4 

1.4
4 

1.9
4 

AS
R 

0.8
3 

0.2
8 

0.4
2 

0.5
2 

0.8
3 

0.2
8 

0.4
2 

0.5
1 

0.8
6 

0.2
8 

0.4
5 

0.9
1 

1.5
0 

0.5
0 

0.7
8 

0.9
4 

1.5
6 

A0_r
et 

A1_r
et 

A2_r
et 

A3_r
et 

A4_r
et 

A5_r
et 

A6_r
et 

0.00  0.11 

0.68  0.94 

0.99  0.99  1.00 

0.00  0.01 

0.97  1.00 

1.00  1.00  1.00 

0.00  0.00 

0.65  0.99 

1.00  1.00  1.00 

0.00  0.12 

0.90  0.99 

1.00  1.00  1.00 

0.00  0.11 

0.68  0.94 

0.99  0.99  1.00 

0.00  0.01 

0.97  1.00 

1.00  1.00  1.00 

0.00  0.00 

0.65  0.99 

1.00  1.00  1.00 

0.00  0.19 

0.93  0.99 

1.00  1.00  1.00 

0.00  0.15 

0.73  0.93 

0.97  0.98  0.99 

0.00  0.03 

0.98  1.00 

1.00  1.00  1.00 

0.00  0.01 

0.75  0.99 

1.00  1.00  1.00 

0.01  0.20 

0.74  0.95 

0.99  1.00  1.00 

0.02  0.15 

0.49  0.78 

0.91  0.96  0.98 

0.00  0.03 

0.78  0.99 

1.00  1.00  1.00 

0.00  0.01 

0.27  0.85 

0.98  1.00  1.00 

0.02  0.25 

0.77  0.95 

0.99  1.00  1.00 

0.03  0.18 

0.52  0.79 

0.91  0.96  0.97 

95 

 
 
Speci
es 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

MUT 

M
C 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

D
M 

D
M 

S
M 

S
M 

MS  GS
A 

L50  SR  A5
0 

40  17  12.
92 

50  17  15.
39 

50  18  12.
25 

60  18  14.
24 

40  18  12.
92 

50  18  15.
39 

50  22  12.
25 

60  22  14.
24 

40  22  12.
92 

50  22  15.
39 

2.0
7 

2.7
5 

3.9
3 

5.6
8 

2.0
7 

2.7
5 

3.9
3 

5.6
8 

2.0
7 

2.7
5 

1.6
0 

2.2
7 

1.4
4 

1.9
4 

1.6
0 

2.2
7 

0.9
9 

1.6
0 

1.1
9 

1.9
8 

AS
R 

0.5
2 

0.8
1 

0.9
4 

1.5
6 

0.5
2 

0.8
1 

1.1
4 

1.8
4 

0.6
2 

0.9
4 

A0_r
et 

A1_r
et 

A2_r
et 

A3_r
et 

A4_r
et 

A5_r
et 

A6_r
et 

0.00  0.06 

0.84  0.99 

1.00  1.00  1.00 

0.00  0.02 

0.32  0.86 

0.98  1.00  1.00 

0.02  0.25 

0.77  0.95 

0.99  1.00  1.00 

0.03  0.18 

0.52  0.79 

0.91  0.96  0.97 

0.00  0.06 

0.84  0.99 

1.00  1.00  1.00 

0.00  0.02 

0.32  0.86 

0.98  1.00  1.00 

0.11  0.50 

0.86  0.96 

0.99  1.00  1.00 

0.10  0.32 

0.62  0.82 

0.92  0.96  0.98 

0.01  0.34 

0.94  1.00 

1.00  1.00  1.00 

0.00  0.08 

0.51  0.90 

0.98  1.00  1.00 

96 

 
 
 
 
Figure 3. Comparison between size selection (red curve) and age selection (black curve). 

97 

 
 
 
 
 
 
Table 4. Mediterranean studies on trawl selectivity. Selectivity parameters collated in Lucchetti et 
al.  (2021)  and  Sala  et  al.  (2018).  For  diamond-  (DM)  and  square-mesh  (SM)  codends;  NMS: 
nominal mesh size in the codend;  MMS: measured mesh size in the codend;  L50: length of fish 
that  has  a  50%  probability  of  being  retained  or  escaping  after  entering  the  codend;  SR: 
difference in length between the fish that has a 75 % probability of retention and that with a 25% 
probability of retention.  

References 

Species  MC  NMS  MMS  L50 

SR 

Abella & Serena, 1998 

HKE 

DM  38 

NA 

10.2 

1.8 

Aldebert e Carriers, 1990 

HKE 

DM  NA 

34 

11.4  NA 

Aldebert e Carriers, 1990 

HKE 

DM  NA 

40 

13 

NA 

Aldebert e Carriers, 1990 

HKE 

DM  NA 

50 

16.3  NA 

Aldebert e Carriers, 1990 

HKE 

DM  NA 

60 

19.5  NA 

Aldebert e Carriers, 1990 

HKE 

DM  NA 

40 

12.4  NA 

Aydin & Tosunoglu, 2010 

HKE 

DM  44 

44.7 

10.4 

3.1 

Aydin & Tosunoglu, 2010 

HKE 

SM 

40 

42.4 

14.4 

4.8 

Aydin & Tosunoglu, 2010 

HKE 

HEX  40 

42.6 

11 

4.3 

Aydin & Tosunoglu, 2010 

HKE 

DM  44 

44.7 

10.3 

3.4 

Aydin & Tosunoglu, 2010 

HKE 

SM 

40 

42.4 

14.9 

5.9 

Aydin & Tosunoglu, 2010 

HKE 

HEX  40 

42.6 

10.6 

4.6 

Bahamon et al.,2006 

HKE 

DM  42 

40.3 

10.1 

3.1 

Bahamon et al.,2006 

HKE 

SM 

42 

40.3 

16 

4.8 

Baino, 1998 

HKE 

DM  40 

NA 

8.31 

3.2 

Baro et al., 2007 

HKE 

DM  40 

NA 

8.67 

3.9 

Baro et al., 2007 

HKE 

SM 

40 

NA 

15.21  3.12 

Baro et al., 2007 

HKE 

SM 

40 

NA 

17.16  2.38 

Belcari et al.,2007 

HKE 

DM  40 

NA 

9.17 

2.56 

Belcari et al.,2007 

HKE 

DM  60 

NA 

18.1 

10.62 

Brčić et al., 2018a 

HKE 

DM  50 

51.9 

13.59  2.18 

Brčić et al., 2018a 

HKE 

DM  50 

51.9 

13.1 

2.1 

98 

 
 
 
References 

Species  MC  NMS  MMS  L50 

SR 

Brčić et al., 2018a 

HKE 

DM  50 

51.9 

17.82  6.2 

Brčić et al., 2018a 

HKE 

DM  50 

51.9 

17.33  6.12 

Brčić et al., 2018a 

HKE 

SM 

40 

40.2 

13.88  0.27 

Brčić et al., 2018a 

HKE 

SM 

40 

40.2 

13.39  2.32 

Brčić et al., 2018a 

HKE 

SM 

40 

40.2 

18.11  4.29 

Brčić et al., 2018a 

HKE 

SM 

40 

40.2 

17.62  6.35 

Brčić et al., 2018b 

HKE 

DM  50 

51.9 

13.71  3.37 

Burgaud & Dremiere, 1992   HKE 

DM  44 

NA 

11.7 

4 

Dereli & Aydin, 2016 

HKE 

DM  44 

44.27  12.3 

1.6 

Dereli & Aydin, 2016 

HKE 

DM  50 

50.82  14.4 

6.3 

Dereli & Aydin, 2016 

HKE 

SM 

40 

41.18  14.3 

3.4 

Dremiere, 1979 

HKE 

DM  NA 

35.5 

12.8  NA 

Dremiere, 1979 

HKE 

DM  NA 

34 

10.2  NA 

Dremiere, 1979 

HKE 

DM  NA 

34.9 

11.4  NA 

Ferretti & Froglia, 1975  

 HKE 

DM  NA 

35.5 

9 

3.4 

Ferretti & Froglia, 1975  

 HKE 

DM  NA 

42 

11 

3 

Genç et al., 2018 

 HKE 

T90  44 

45.4 

12.8 

4.6 

Genç et al., 2018 

 HKE 

T90  44 

45.4 

13.2 

4 

Genç et al., 2018 

 HKE 

T90  40 

40.4 

12.1 

1.7 

Gil De Sola Simarro, 1991  

 HKE 

DM  NA 

39.3 

9.26  NA 

Gil De Sola Simarro, 1991  

 HKE 

DM  NA 

36.2 

9.1 

NA 

Gil De Sola Simarro, 1994  

 HKE 

DM  NA 

35 

8.21  NA 

Gil De Sola Simarro, 1994  

 HKE 

DM  NA 

40 

9.11  NA 

Gil De Sola Simarro, 1994  

 HKE 

DM  NA 

50 

12.85  NA 

Guijarro & Massuti, 2006 

 HKE 

DM  40 

NA 

11.6 

0.8 

Guijarro & Massuti, 2006 

 HKE 

SM 

40 

NA 

15.3 

2.2 

99 

 
 
References 

Species  MC  NMS  MMS  L50 

SR 

Larraneta et al.,1969 

 HKE 

DM  NA 

35.5 

9.5 

NA 

Larraneta et al.,1969 

 HKE 

DM  34 

NA 

11.9  NA 

Larraneta et al.,1969 

 HKE 

DM  36 

NA 

11.5  NA 

Larraneta et al.,1969 

 HKE 

DM  40 

NA 

14.1  NA 

Larraneta et al.,1969 

 HKE 

DM  42 

NA 

11.8  NA 

Larraneta et al.,1969 

 HKE 

DM  44 

NA 

11.2  NA 

Larraneta et al.,1969 

 HKE 

DM  48 

NA 

13.1  NA 

Larraneta et al.,1969 

 HKE 

DM  50 

NA 

14.5  NA 

Larraneta et al.,1969 

 HKE 

DM  52 

NA 

15.2  NA 

Lembo et al., 2002  

HKE 

DM  40 

NA 

7.9 

4.1 

Lembo et al., 2002  

HKE 

DM  60 

NA 

15.5 

4.8 

Levi et al.,1971 

HKE 

DM  NA 

35.5 

9.5 

NA 

Lucchetti, 2008 

HKE 

DM  40 

42.8 

7.6 

4.01 

Lucchetti, 2008 

HKE 

SM 

40 

42.8 

12.98  3.65 

M’Rabet, 1994 

HKE 

DM  40 

33.5 

11.11  NA 

M’Rabet, 1994 

HKE 

DM  40 

34.9 

11.48  NA 

M’Rabet, 1998 

HKE 

DM  40 

38.4 

13 

1 

M’Rabet, 1998 

HKE 

DM  48 

44.2 

14.3 

0.6 

Ordines et al., 2006 

HKE 

DM  40 

NA 

10.6 

3.3 

Ozbilgin et al., 2005 

HKE 

DM  40 

NA 

14.28  3.42 

Ordines et al., 2006 

HKE 

SM 

40 

NA 

15.2 

3.3 

Petetta et al., 2020 

HKE 

DM  54 

55.2 

11.26  21.33 

Petetta et al., 2020 

HKE 

T90  54 

55.3 

21.26  7.02 

Petrakis & Stergiou, 1997  HKE 

DM  28 

NA 

4.16 

6.75 

Petrakis & Stergiou, 1997  HKE 

DM  40 

NA 

13.79  7.06 

Petrakis & Stergiou, 1997  HKE 

SM 

40 

NA 

15.1 

5.68 

100 

 
 
References 

Species  MC  NMS  MMS  L50 

SR 

Petrakis et al., 2004 

HKE 

DM  40 

NA 

12.6 

5.16 

Petrakis et al., 2004 

HKE 

DM  40 

NA 

12.32  4.87 

Petrakis et al., 2004 

HKE 

DM  40 

NA 

10.44  4.87 

Sala & Luchetti, 2010 

HKE 

DM  40 

45.2 

8.03 

3.8 

Sala & Luchetti, 2010 

HKE 

DM  40 

45.2 

9.12 

4.72 

Sala & Luchetti, 2010 

HKE 

SM 

40 

43.25  11.97  6.11 

Sala & Luchetti, 2010 

HKE 

SM 

40 

43.25  15.7 

8.68 

Sala & Luchetti, 2010 

HKE 

DM  40 

46.35  10.84  7.15 

Sala & Luchetti, 2010 

HKE 

DM  40 

46.35  9.37 

5.33 

Sala & Luchetti, 2011 

HKE 

DM  48 

46.5 

11.45  5.62 

Sala & Luchetti, 2011 

HKE 

DM  48 

46.5 

10.43  5.87 

Sala & Luchetti, 2011 

HKE 

DM  56 

56.75  16.25  7.56 

Sala & Luchetti, 2011 

HKE 

DM  56 

56.1 

11.99  7.94 

Sala et al.,2007 

HKE 

DM  44 

44.73  9.85 

2.75 

Sala et al.,2007 

HKE 

DM  44 

44.33  7.7 

1.3 

Sala et al.,2008  

HKE 

DM  40 

38.7 

8.26 

1.74 

Sala et al.,2008  

HKE 

SM 

40 

38.65  14.17  3.64 

Sarda et al.,2006  

HKE 

SM 

36 

NA 

18.47  5.07 

Sbrana & Reale, 1994  

HKE 

DM  34 

NA 

7.74 

2.29 

Sbrana et al., 1998  

HKE 

DM  NA 

34 

7.47 

2.29 

Soldo, 2004 

HKE 

DM  48 

NA 

14.28  4.15 

Soldo, 2004 

HKE 

DM  48 

NA 

13.94  5.31 

Soldo, 2004 

HKE 

DM  48 

NA 

13.7 

5.55 

Soldo, 2004 

HKE 

DM  48 

NA 

11.99  7.38 

Soldo, 2004 

HKE 

DM  60 

NA 

16.64  2.96 

Soldo, 2004 

HKE 

DM  60 

NA 

16.62  4.59 

101 

 
 
References 

Species  MC  NMS  MMS  L50 

SR 

Tokaç et al.,2010 

HKE 

DM  40 

42.42  11.59  4.07 

Tosunoglou et al., 2008 

HKE 

DM  50 

49.44  11.4 

4.1 

Tosunoglu et al.,2003 

HKE 

DM  40 

41.9 

10.6 

2.84 

Vives et al.,1966 

HKE 

DM  34 

NA 

10 

NA 

Vives et al.,1966 

HKE 

DM  40 

NA 

16.5  NA 

Vives et al.,1966 

HKE 

DM  60 

NA 

22.5  NA 

Ates et al., 2010 

MUT 

DM  44 

43.46  10.70  2.90 

Ates et al., 2010 

MUT 

SM 

40 

37.55  14.20  3.10 

Aydin et al., 2011 

MUT 

SM 

40 

40.80  14.40  2.50 

Aydin et al., 2011 

MUT 

SM 

40 

40.80  14.30  2.30 

Aydin et al., 2011 

MUT 

DM  50 

50.10  15.20  4.40 

Aydin et al., 2011 

MUT 

DM  50 

50.10  15.30  4.40 

Baino, 1998 

MUT 

DM  40 

NA 

9.02 

1.98 

Baro et al., 2007 

MUT 

DM  40 

NA 

8.07 

2.21 

Baro et al., 2007 

MUT 

SM 

40 

NA 

11.50  1.79 

Brcic et al., 2018b 

MUT 

DM  50 

51.90  10.35  9.11 

Burgaud & Dremiere, 1992  MUT 

DM  32 

NA 

8.40 

1.50 

Burgaud & Dremiere, 1992  MUT 

DM  40 

NA 

9.30 

4.10 

Burgaud & Dremiere, 1992  MUT 

DM  44 

NA 

10.40  2.00 

Cicek, 2015 

MUT 

DM  44 

NA 

9.27 

4.61 

Demirci & Akyurt, 2017 

MUT 

DM  44 

NA 

13.19  2.51 

Demirci & Akyurt, 2017 

MUT 

DM  50 

NA 

17.52  6.94 

Demirci & Akyurt, 2017 

MUT 

SM 

40 

NA 

14.14  3.43 

Dereli & Aydin, 2016 

MUT 

DM  44 

44.27  11.10  2.00 

Dereli & Aydin, 2016 

MUT 

DM  50 

50.82  12.90  2.20 

Dereli & Aydin, 2016 

MUT 

SM 

40 

41.18  12.90  2.00 

102 

 
 
References 

Species  MC  NMS  MMS  L50 

SR 

Dereli & Aydin, 2016 

MUT 

T90  40 

42.42  13.60  3.10 

Ferretti & Froglia, 1975 

MUT 

DM  NA 

38.00  7.70 

1.40 

Ferretti & Froglia, 1975 

MUT 

DM  NA 

35.50  8.50 

1.60 

Ferretti & Froglia, 1975 

MUT 

DM  NA 

42.00  8.30 

1.90 

Ferretti & Froglia, 1975 

MUT 

DM  NA 

42.70  7.80 

2.60 

Ferretti & Froglia, 1975 

MUT 

DM  NA 

42.70  8.50 

2.00 

Ferretti & Froglia, 1975 

MUT 

DM  NA 

42.70  7.00 

2.20 

Ferretti & Froglia, 1975 

MUT 

DM  NA 

41.80  8.80 

2.90 

Larraneta et al.,1969 

MUT 

DM  34 

NA 

9.90  NA 

Larraneta et al.,1969 

MUT 

DM  36 

NA 

10.60  NA 

Larraneta et al.,1969 

MUT 

DM  40 

NA 

12.50  NA 

Larraneta et al.,1969 

MUT 

DM  34 

NA 

10.50  NA 

Larraneta et al.,1969 

MUT 

DM  46 

NA 

10.00  NA 

Larraneta et al.,1969 

MUT 

DM  50 

NA 

14.60  NA 

Larraneta et al.,1969 

MUT 

DM  52 

NA 

12.40  NA 

Lembo et al., 2002 

MUT 

DM  40 

NA 

8.90 

1.80 

Lembo et al., 2002 

MUT 

DM  60 

NA 

13.20  4.10 

Levi et al.,1971 

MUT 

DM  NA 

35.50  8.30  NA 

Livadas, 1988 

MUT 

DM  34 

39.19  9.20 

1.60 

Livadas, 1988 

MUT 

DM  34 

39.19  9.90 

2.20 

Livadas, 1988 

MUT 

DM  34 

39.19  9.80 

2.00 

Livadas, 1988 

MUT 

DM  40 

46.38  14.70  2.90 

Livadas, 1988 

MUT 

DM  40 

46.38  13.90  2.80 

Livadas, 1988 

MUT 

DM  40 

46.38  17.90  2.40 

Lök et al.,1997 

MUT 

DM  44 

NA 

13.68  2.92 

Lök et al.,1997 

MUT 

DM  44 

NA 

15.06  3.24 

103 

 
 
References 

Species  MC  NMS  MMS  L50 

SR 

Lök et al.,1997 

MUT 

DM  44 

NA 

14.32  2.14 

M’Rabet, 1994 

MUT 

DM  40 

33.50  10.98  NA 

M’Rabet, 1994 

MUT 

DM  40 

34.90  9.14  NA 

M’Rabet, 1998 

MUT 

DM  40 

38.40  9.90 

0.90 

M’Rabet, 1998 

MUT 

DM  48 

44.20  13.00  1.10 

Mytilineou et al., 2021 

MUT 

DM  40 

43.20  9.34 

2.00 

Mytilineou et al., 2021 

MUT 

SM 

40 

43.20  13.31  2.23 

Mytilineou et al., 2021 

MUT 

DM  50 

51.10  10.83  4.73 

Özbilgin et al.,2011 

MUT 

DM  40 

42.40  10.06  2.05 

Özbilgin et al.,2011 

MUT 

DM  40 

42.40  11.14  2.14 

Özbilgin et al.,2011 

MUT 

DM  40 

42.40  10.76  2.27 

Özbilgin et al.,2011 

MUT 

DM  40 

42.40  10.95  1.91 

Ozbilgin et al., 2015 

MUT 

DM  44 

44.50  7.10 

6.70 

Ozbilgin et al., 2015 

MUT 

SM 

40 

41.36  14.10  2.60 

Ozbilgin et al., 2015 

MUT 

DM  44 

42.03  8.40 

5.20 

Ozbilgin et al., 2015 

MUT 

DM  50 

51.14  12.10  4.70 

Petetta et al., 2020 

MUT 

DM  54 

55.20  16.70  2.78 

Petetta et al., 2020 

MUT 

T90  54 

55.30  23.10  11.48 

Petrakis et al., 2004 

MUT 

DM  40 

NA 

12.37  2.52 

Sala & Luchetti, 2011 

MUT 

DM  48 

46.50  10.74  4.59 

Sala & Luchetti, 2011 

MUT 

DM  48 

46.50  7.50 

6.61 

Sala & Luchetti, 2011 

MUT 

DM  56 

56.75  12.78  4.63 

Sala & Luchetti, 2011 

MUT 

DM  56 

56.10  9.95 

7.72 

Sala et al., 2007 

MUT 

DM  44 

44.73  8.90 

2.68 

Sala et al. ,2007 

MUT 

DM  44 

44.33  7.12 

1.61 

Sala et al., 2008 

MUT 

DM  40 

38.70  7.76 

1.86 

104 

 
 
References 

Species  MC  NMS  MMS  L50 

SR 

Sala et al., 2008 

MUT 

SM 

40 

38.65  10.91  1.43 

Sala et al., 2015 

MUT 

DM  44 

45.15  8.58 

1.51 

Sala et al., 2015 

MUT 

SM 

44 

45.95  13.20  1.73 

Sala et al., 2015 

MUT 

DM  54 

54.70  11.63  3.41 

Sala et al., 2015 

MUT 

SM 

54 

56.90  17.28  4.30 

Sala et al., 2016 

MUT 

SM 

41 

41.05  13.07  2.34 

Sala et al., 2016 

MUT 

SM 

41 

41.05  12.48  2.40 

Sala et al., 2016 

MUT 

SM 

41 

41.50  10.29  1.43 

Sala et al., 2006 

MUT 

DM  44 

45.00  8.90 

2.68 

Soldo, 2004 

MUT 

DM  48 

NA 

13.49  1.74 

Tokaç et al., 1998 

MUT 

DM  36 

NA 

11.02  1.76 

Tokaç et al., 1998 

MUT 

DM  40 

NA 

12.19  2.15 

Tokaç et al., 1998 

MUT 

DM  44 

NA 

13.50  2.65 

Tokaç et al., 1998 

MUT 

SM 

36 

NA 

11.82  1.58 

Tokaç et al., 1998 

MUT 

SM 

40 

NA 

13.20  1.85 

Tokaç et al., 1998 

MUT 

SM 

44 

NA 

14.67  2.89 

Tokaç et al., 2004 

MUT 

DM  36 

37.00  12.70  1.80 

Tokaç et al., 2004 

MUT 

DM  36 

37.00  12.80  1.80 

Tokaç et al., 2004 

MUT 

DM  40 

41.90  10.70  1.90 

Tokaç et al., 2004 

MUT 

DM  40 

41.90  10.70  1.90 

Tokaç et al., 2014 

MUT 

DM  40 

40.44  9.38 

2.48 

Tokaç et al., 2014 

MUT 

DM  44 

44.33  11.53  2.72 

Tokaç et al., 2014 

MUT 

DM  50 

51.34  15.40  3.15 

Tokaç et al., 2014 

MUT 

T90  40 

40.44  12.65  1.48 

Tokaç et al., 2014 

MUT 

T90  44 

44.33  14.80  1.62 

Tosunoglu et al.,2003b 

MUT 

DM  40 

41.90  10.60  1.71 

105 

 
 
References 

Species  MC  NMS  MMS  L50 

SR 

Voliani & Abella, 1998 

MUT 

DM  38 

NA 

9.30 

1.50 

Sala et al. (2018) 

MUT 

SM  NA 

51.6 

16.22  1.86 

Sala et al. (2018) 

MUT 

DM  NA 

51.65  11.23  3.51 

Sala et al. (2018) 

MUT 

SM  NA 

42.03  14.31  0.54 

Sala et al. (2018) 

HKE 

SM  NA 

51.6 

21.93  3.51 

Sala et al. (2018) 

HKE 

DM  NA 

51.65  7.38 

4.26 

Sala et al. (2018) 

HKE 

SM  NA 

42.03  17.22  3.29 

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