Evaluation of maximum catch limits and closure areas in the Western Mediterranean (STECF-22-01).
Date: 2022-04-21
Institutions: Joint Research Centre, Scientific, Technical and Economic Committee for Fisheries
Subjects: Mediterranean Sea, aquaculture, fishery management, fishing controls, fishing grounds, fishing net, fishing vessel, marine ecosystem, marine life, maritime economy, multiannual financial framework, research report, sea fishing, simulation, stock

Scientific, Technical and Economic 
Committee for Fisheries (STECF) 
- 
Evaluation of maximum catch 
limits and closure areas in the 
Western Mediterranean 
(STECF-22-01) 

Edited by Cecilia Pinto and Alessandro Mannini 

 EUR 28359 EN 

 
This  publication  is  a  Science  for  Policy  report  by  the  Joint  Research  Centre  (JRC),  the  European 
Commission’s science and knowledge service. It aims to provide evidence-based scientific support 
to  the  European  policymaking  process.  The  scientific  output  expressed  does  not  imply  a  policy 
position of the European Commission. Neither the European Commission nor any person acting on 
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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 

JRC129243 

EUR 28359 EN 

PDF 

ISBN 978-92-76-51980-5 

ISSN 1831-9424 

doi:10.2760/657891 

STECF 

ISSN 2467-0715 

Luxembourg: Publications Office of the European Union, 2022  

© European Union, 2022 

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How  to  cite  this  report:  Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  – 
Evaluation  of  maximum  catch  limits  and  closure  areas  in  the  Western  Mediterranean  (STECF-22-
01). EUR 28359 EN,  Publications Office of the European Union, Luxembourg, 2021,  ISBN 978-92-
76-51980-5, doi:10.2760/657891, JRC129243. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Authors: 

STECF advice:  

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

EWG-22-01 report:  

Pinto C., Bastardie F., Bitetto I., Borges L., Certain G., Gourguet S., Grati F., Kupschus S., Leutha 
S., Mannini A.,  Maynou F., Merzereaud  M., Musumeci C.,  Phan T.A., Pierucci A.,  Russo  T., Sala-
Coromina J., Spedicato M.P., Tassetti A.N., Viva C. 

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CONTENTS 

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

SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES 

(STECF) – Evaluation of maximum catch limits and closure areas in 
the Western Mediterranean (STECF-22-01). .............................................. 3 

Request to the STECF ........................................................................................................ 3 

Background provided by the Commission ................................................................. 3 

STECF comments ................................................................................................................. 5 

STECF conclusions ............................................................................................................... 9 

References .............................................................................................................................. 9 

Contact details of STECF members ............................................................................ 10 

Expert Working Group EWG-22-01 report ............................................................... 14 

1 

1.1 

1.2 

2 

Introduction ........................................................................................................... 15 

Terms of Reference for EWG-22-01 ............................................................. 16 

Main findings ......................................................................................................... 19 

Maximum catch limits ........................................................................................ 36 

2.1 

Background ............................................................................................................ 37 

2.1.1  LPUE data by quarter in EMU 1 ...................................................................... 37 

2.1.1.1  EMU1 European hake (passive gears)..................................................... 37 

2.1.1.2  EMU1 European hake (active gears)........................................................ 38 

2.1.1.3  EMU1 Blue and red shrimp (active gears) ............................................. 40 

2.1.2  Data issues in EMU 1 ......................................................................................... 44 

2.1.2.1  FDI Table A (Landings) ................................................................................. 44 

2.1.2.2  FDI Table G (Effort) ........................................................................................ 45 

2.1.3  LPUE data by quarter in EMU 2 ...................................................................... 47 

2.1.3.1  EMU2 European hake (passive gears)..................................................... 47 

2.1.3.2  EMU2 European hake (active gears)........................................................ 48 

2.1.3.3  EMU2 Blue and red shrimp and giant red shrimp (active gears) . 48 

2.1.4  Data issues in EMU 2 ......................................................................................... 55 

2.1.5  References .............................................................................................................. 58 

2.2 

Progress of operational mixed fisheries models ...................................... 59 

2.2.1  IAM in EMU 1 ......................................................................................................... 59 

2.2.1.1  Recall on the main issues and conclusions from EWG 21-13 ........ 59 

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2.2.1.2  Implementation progresses in EWG 22-01 ........................................... 59 

2.2.1.3  Stocks................................................................................................................... 59 

2.2.1.4  Fleet segments ................................................................................................. 61 

2.2.1.5  Economic indicators ........................................................................................ 65 

2.2.2  ISIS-Fish in EMU 1 (GSA 7) ............................................................................ 66 

2.2.2.1  ISIS-Fish implementation for European hake in GSA 7 ................... 66 

2.2.3  BEMTOOL in EMU 2 ............................................................................................. 68 

2.2.3.1  State of completion during EWG 22-01 .................................................. 68 

2.2.3.1  Space and time scale ..................................................................................... 68 

2.2.3.2  Stocks................................................................................................................... 68 

2.2.3.3  Fleets .................................................................................................................... 74 

2.2.4  References .............................................................................................................. 75 

2.3 

Management scenarios and results .............................................................. 76 

2.3.1  IAM in EMU 1 ......................................................................................................... 76 

2.3.1.1  Management scenarios considered ........................................................... 76 

2.3.1.2  Results ................................................................................................................. 81 

2.3.1.3  Discussion ........................................................................................................... 95 

2.3.2  ISIS-fish in EMU 1 (GSA 7) ............................................................................. 96 

2.3.2.1  Management scenarios considered ........................................................... 96 

2.3.2.2  Results ................................................................................................................. 97 

2.3.3  BEMTOOL in EMU 2 ........................................................................................... 102 

2.3.3.1  Baseline Run 2022-2025 ............................................................................ 102 

2.2.4  Runs performed and analysed during EWG 22-01: discussion ....... 104 

3 

Closure areas ...................................................................................................... 124 

3.1 

Background .......................................................................................................... 124 

3.1.1  Implementation of closure areas in EMU 1 ............................................. 124 

3.1.1.1  References ........................................................................................................ 125 

3.1.2  Implementation of closure areas in EMU 2 ............................................. 126 

3.1.2.1  Fishery Restricted Areas in GSA 9 to protect Hake Juveniles, 

according to the Italian Decree of 6 August 2020. .............................. 126 

3.1.2.2  Fishery Restricted Areas in GSA 10 to protect Hake Juveniles, 

according to the Italian Decree of 6 August 2020. .............................. 127 

3.1.2.3  Fishery Restricted Areas in GSA 11 to protect Hake Juveniles, 

according to the Italian Decree of 6 August 2020. .............................. 128 

3.1.2.4  Additional areas restricted to bottom trawling in EMU 2 

implemented before the Italian Decree of 6 August 2020. .............. 129 

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3.1.2.5  References ........................................................................................................ 145 

3.1.3  Evaluation of closure areas in previous working groups.................... 145 

3.1.4  References ............................................................................................................ 147 

3.2 

Development of hotspots’ seascape ........................................................... 147 

3.2.1  Survey data: MEDISEH shapefiles .............................................................. 147 

3.2.2  Commercial data coupled to survey data ................................................ 151 

3.2.3  Define closure areas based on persistent hotspots ............................. 154 

3.2.4  References ............................................................................................................ 158 

3.3 

Progress of operational mixed fisheries models .................................... 159 

3.3.1  ISIS-Fish in EMU 1 (GSA 7) .......................................................................... 159 

3.3.1.1  ISIS-Fish assumptions related to spatial features ........................... 159 

3.3.2  SMART in EMU 2................................................................................................. 159 

3.3.2.1  Background ...................................................................................................... 159 

3.3.2.2  Application of the SMART model to the West Med MAP ................. 160 

3.3.2.3  Space and time scale ................................................................................... 162 

3.3.2.4  Simulated Scenarios ..................................................................................... 163 

3.3.3  References ............................................................................................................ 164 

3.4 

Management scenarios and results ............................................................ 165 

3.4.1  ISIS-fish in EMU 1 (GSA 7) ........................................................................... 165 

3.4.1.1  Management scenarios considered ......................................................... 165 

3.4.2  SMART in EMU 2................................................................................................. 167 

3.4.2.1  Effects on the stocks: Spawning Stock Biomass (SSB) and Fishing 
mortality (F) ........................................................................................................ 167 

3.4.2.2  Effects on Fleets ............................................................................................. 176 

4 

Remaining issues and future developments ........................................... 179 

4.1 

Future development of operational mixed fisheries models ............. 179 

4.1.1  IAM in EMU 1 ....................................................................................................... 179 

4.1.1.1  Proposed changes in IAM for next group ............................................. 179 

4.1.1.2  Fleet segmentation in IAM ......................................................................... 179 

4.1.2  ISIS-Fish in EMU 1 ............................................................................................ 184 

4.1.3  BEMTOOL in EMU 2 ........................................................................................... 185 

4.1.4  SMART in EMU 2................................................................................................. 185 

4.2 

Issues important for the advice and the interpretation of results . 185 

4.2.1  Maximum catch limits ...................................................................................... 185 

4.2.2  Closure areas ...................................................................................................... 186 

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4.2.3  Economic indicators .......................................................................................... 187 

4.2.4  References ............................................................................................................ 187 

5 

6 

7 

Contact details of EWG-22-01 participants ............................................. 189 

List of Annexes ................................................................................................... 192 

List of Background Documents .................................................................... 192 

v 

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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  is  the  eighth  of  a  suite  of  STECF  EWG  reports  dedicated  to  the  evaluation  of  the 
implementation  of  the  Western  Mediterranean  Sea  Multi-Annual  management  Plan  (hereafter, 
MAP), following EWG reports 18-09, 18-13, 19-01, 19-14, 20-13, 21-01 and 21-13.  

The group was requested to implement mixed fisheries bio-economic models to run a number of 
scenarios up to 2025 with varying parameters and up to 2030 with fixed parameters, to evaluate 
existing  closure  areas  implemented  since  2020  and  2022,  maximum  catch  limits  (MCLs) 
implemented since 2022 and draft a mixed fisheries advice. 

For  all  mixed  fisheries  models  applied  during  the  EWG,  the  data  from  the  DCF  official  data  calls 
and  from the western Mediterranean stock assessments, were the same as the ones used during 
STECF EWG 21-13, as there were no updates since 2021. 

The  non-spatially  explicit  mixed  fisheries  models,  IAM  (for  EMU  1)  and  BEMTOOL  (for  EMU  2), 
focused  on  the  scenarios  evaluating  MCLs  on  ARA,  ARS  and  HKE.  Reference  MCLs  for  ARA  and 
ARS where obtained from the Regulation EU 2022/110 and from STECF EWG 21-11, while for HKE 
were obtained solely from EWG 21-11.  

IAM was updated to differentiate between a coastal and a deep-water trawling within the Spanish 
fleet segments, as France does not have a deep-water fishery at present. As the model timescale 
has a yearly resolution, it was not possible to estimate the effect of a monthly MCL. The scenarios 
considered had a MCL for HKE, one for ARA, and one for both HKE and ARA. These scenarios were 
run  for  a  decreasing  MCL  through  time  (forward  scenario:  aims  at  reaching  catch  at  Fmsy  by 
2025)  and  an  increasing  MCL  through  time  (inverse  scenario:  starting  value  is  catch  at  Fmsy) 
accounting  for  the  distribution  of  stocks  by  GSA,  for  a  total  of  10  scenarios  alternative  to  the 
baseline.  None  of  the  scenarios  allowed  reaching  Fmsy  for  all  six  species  targeted  by  the  MAP, 
except when applying a MCL on both ARA and HKE, simultaneously. The results for the HKE stock 
should be taken with caution as the MCL was applied only to trawlers in these scenarios, but this 
species can generally be targeted also by longliners and gillnetters. The economic consequences 
of scenarios accounting for a MCL on HKE, or both ARA and HKE, lead to a massive drop of GVA 
for the Spanish and French trawling fleets, while economic advantages are observed for longliners 
and gillnetters. 

BEMTOOL  was  updated  and  refined  to  consider  the  different  types  of  fishing  activity  exerted  by 
each fleet segment at metier level. The model timescale is set at a monthly resolution, so it was 
possible to run scenarios accounting for a monthly MCL (monthly flexibility was not considered). 
Only scenarios accounting for a MCL either for ARA or ARS where run, but never in combination 
(ARA+ARS),  nor  considering  a  MCL  on  HKE,  for  a  total  of  eight  scenarios:  forward  scenario, 
inverse scenario, monthly forward scenario, monthly inverse scenario, once with an MCL on ARA 
and  once  with  an  MCL  on  ARS.  Implementing  a  MCL  on  the  deep-water  fisheries  suggested  an 
improvement for all stocks except for HKE. ARA and ARS would improve thanks to the control of 
the MCL, while MUT, DPS and NEP would stay within the upper and lower limits of Fmsy, despite 
the reallocation of fishing effort from deep to  coastal fisheries.  The implementation of a  reverse 
MCL  did  not  show  a  recovery  of  the  stocks.  Moreover,  a  MCL  split  by  month  seems  to  have  a 
lower  impact  on  the  catches  of  ARA  and  ARS  in  the  short  term.  The  GVA  shows  an  increase  for 
the  passive  gears  fleets  (i.e.,  gillnetters  and  longliners)  and  a  strong  decrease  for  all  trawling 
fleets in the first two years, with a stable trend over the following years. 

The spatially explicit mixed fisheries model ISIS-Fish also ran scenarios accounting for MCLs, but 
only for HKE in GSA 7, implementing a forward, an inverse and a monthly MCL for a total of four 
scenarios. The forward scenario led to fishing mortalities below Fmsy in 2025 because the value 
defined for MCL did not account for biomass rebuilding. On the other hand, the fishing mortality 

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achieved  using  the  HCR  in  the  inverse  scenario,  never  fell  below  Fmsy,  because  of  the 
unrestricted  catches  of  netters  and  long-liners.  In  terms  of  revenues,  both  MCL  paths  led  to 
strong decreases for trawlers. 

The spatially explicit mixed fisheries models, ISIS-Fish (GSA 7) and SMART (EMU 2), focused on 
the  evaluation  of  closure  areas:  existing  closure  areas,  existing  closure  areas  which  were 
seasonal  to  become  permanent,  existing  closure  areas  extended  to  all  fishing  gears,  additional 
closure  areas  (only  EMU  2),  expansion  of  closure  areas  by  50%  (only  EMU  2)  and  expansion  of 
closure areas by 100% (only EMU 2). 

ISIS-Fish was applied only for HKE in GSA 7 being the first time this model was used within this 
working  group.  The  extension  of  closure  areas  to  all  fishing  gears  (passive  gears  on  top  of 
trawlers) in GSA 7 did not show any improvement, while shifting from a seasonal to a permanent 
closure showed a decrease in F and an increase in SSB. A decrease of catches of juvenile hake of 
20% was observed both with seasonal and permanent closures. Catches of adults increased due 
to  recovery  of  the  stock  and  considering  the  low  level  of  initial  catches.  It  should  be  noted  that 
revenues increased for passive gears but decreased for trawlers. 

SMART  was  updated,  increasing  the  spatial  resolution  of  the  spatial  grid  of  the  model,  to  be  in 
line  with  outputs  of  the  ad-hoc  contracts  preceding  the  EWG  and  with  ISIS-Fish.  None  of  the 
scenarios considered for EMU 2 evaluating spatial closures allowed to reach Fmsy by 2025, except 
for  MUT  in  10  and  NEP  in  9  which  remains  underutilized.  SSB  shows,  nevertheless,  an  increase 
across  years.  None  of  the  scenarios  allowed  to  reduce  catches  by  20%  for  all  species.  All 
scenarios  are  associated  with  a  sharp  decrease  in  revenues;  spatial  closures  not  widened  or 
seasonal would involve lower decrease of the profits than widened and permanent closures. Loss 
of  profits  is  more  evident  for  VL12-18  and  VL18-24,  although  the  loss  is  evident  for  all  fleet 
segments. 

During EWG 22-01 no explicit comparison between the implementation of an effort regime and a 
MCL regime was run. The group advices to do so accounting for the limitations encountered in the 
implementation  of  MCLs  during  EWG  22-01.  It  should  be  noted  that  the  reduction  of  GVA  is 
estimated in the short term (up to 2025), but further tests should be done to estimate the trend 
of GVAs in the mid- and long-term. It is highlighted that given the large number of other species 
exploited  beyond  the  key  ones  included  in  the  management  plan  and  in  the  simulation  models, 
the  actual  socio-economic  impact  of  the  plan  remains  uncertain.  Also,  the  economic  results  are 
presented considering a constant number of vessels, and would differ if the number of vessels is 
reduced. Additionally, it is difficult to evaluate the socio-economic impact of the MAP on the fleets 
as at present no socio-economic reference points are used to compare the results against those. 

2 

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SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES (STECF) – 
Evaluation of maximum catch limits and closure areas in the Western Mediterranean 
(STECF-22-01). 

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 

EWG 22-01 was requested to address the following Terms of References: 

ToR  1.  STECF  is  requested  to  continue  the  development  of  management  models  including 
different levels of maximum catch limits for deep-water  shrimps and for hake in both West  Med 
management units.  

ToR  2.  STECF  is  also  requested  to  develop  mixed-fisheries  spatio-temporal  scenarios  for  all 
demersal fishing gear (e.g. bottom trawls, gillnets, longlines) in EMU1 and EMU2 with simulations 
from 2020 to 2030. The STECF evaluation should be looking at differences in captures reduction 
between  the  2019  situation  (prior  to  closure  adoptions)  by  species  and  by  age-class  and  the 
following scenarios:  

a) Status quo scenario: closures adopted since the implementation start of the West Med MAP by 

the 3 Member States; 

b) Same delineation of  closures areas as in 2020, 2021 and 2022 and all closure areas become 

permanent from 2023 onwards; 

c) Same delineation of closures areas as in 2020, 2021 and 2022 and all closure areas are for all 

fishing gear (e.g. trawlers, longliners, netters);  

d)  10%  of  permanent  closure  areas  in  each  GSA,  taking  into  account  the  different  types  of 
habitats  such  as  for  instance  waters  shallower  than  200m  depth  and  waters  deeper  than 
200m; 

e)  20%  of  permanent  closure  areas  in  each  GSA  with  half  of  it  in  waters  shallower  than  200m 

depth and half of it in waters deeper than 200m;  

f)  30%  of  permanent  closure  areas  in  each  GSA  with  half  of  it  in  waters  shallower  than  200m 

depth and half of it in waters deeper than 200m. 

To provide an order of magnitude of the closures efficiency, it should be aimed at reducing about 
20% of captures of juveniles and spawners of each target species in each GSA. For each GSA, the 
EWG  is  requested  to  propose  recommendations  for  designing  alternative  closures  based  on 
criteria  such  as  but  not  limited  to  bathymetry,  depth,  type  of  substrate,  stock  seasonality, 
establishment of a buffer area, minimal size of the closure area, etc. 

TORs  were  further  detailed  in  agreement  with  DG  MARE  to  give  additional  directions  to  the 
working group experts’: 

As  discussed  during  the  meeting  on  27  January  2022,  the  legal  concept  adopted  in  December 
2021  by  Member  States  to  manage  by  output  the  deep-water  shrimps  in  the  western 
Mediterranean  is  “maximum  catch  limit”.  Contrary  to  the  legal  concept  of  total  allowable  catch 
(TAC),  a  maximum  catch  limit  does  not  involve  a  legal  right  for  the  future  years  and  does  not 
create  a  fixed  relative  stability  (i.e.  a  distribution  of  the  fishing  opportunities  between  the 
concerned Member States). Concretely, it means that there is no legal obligation for a continuity 
of catch levels between 2022, 2023, 2024, onwards. However, to simplify the modelling work, the 

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STECF  experts  are  invited  to  work  with  TACs  and  use  a  reduction  at  percentage  level  through 
time  (e.g.  10%  reduction  of  the  previous  year  catches:  (TAC[year+1]=0.9*TAC[year]))  as  an 
alternative  to  following  the  transition  path  calculated  by  STECF  EWG  21-11  for  each  stock.  The 
model  output  should  be  analysed  in  order  to  determine  whether  such  reductions  allow  to  reach 
MSY by 2025 at the latest. 

Details on TACs scenarios implementation: 

-  TACs  should  be  implemented  both  on  hake  (HKE)  for  the  coastal  metiers  and  on  deep-water 
shrimps  (ARA  and  ARS)  for  the  deep-water  metiers.  If  possible,  there  would  thus  be  3 
scenarios  with  TACs:  a)  hake  only,  b)  deep-water  shrimps  only,  c)  simultaneously  hake  & 
deep-water shrimps. Those  scenarios would  be completed in September 2022 by the  effort 
reductions and additional management measures as done in EWG 21-13. 

-  All  models  should  be  simulating  2  fleets  corresponding  to  the  two  metiers  (deep-water  and 
coastal fisheries). This definition of metiers is unstable as fishing boats can move from one 
metier to the other easily from one day to the next, but changes due to external parameters 
cannot be accounted for in the models therefore the metiers will be treated as two separate 
fleets. 

-  Models  that  can  implement  a  monthly  time  step  (e.g.  BEMTOOL  and  SMART)  will  test  a  first 
scenario where the TACs are implemented      annually and fishers are assumed to follow a 
“run to fish” behaviour and a second scenario where TACs are implemented monthly. When 
TACs  are  implemented  monthly,  if  possible,  the  value  implemented  each  month  should 
change  depending  on  the  seasonality  of  the  fisheries.  To  obtain  information  on  the 
seasonality the experts can rely on experts’ opinions over the different areas and use      DCF 
landings data by quarter, when data by quarter are available. The reference year should be 
2015-2017. 

- All models should account for monthly flexibility but not annual inter-flexibility. When the TAC is 
not consumed within one month, it can be used the next month (except for December).  

-  An  optional  scenario  will  be  accounting  for  an  “inverse  TAC”  system.  Quotas  will  not  be 
decreasing  through  time,  but  the  first  year  of  implementation  will  be  the  hardest  with  an 
increase  of  the  quota  through  time  as  the  stock  recovers.  The  values  could  be  taken  from 
the transition path to Fmsy by 2025 calculated by STECF EWG 21-11. 

- For 2022, the maximum catch limit values for deep-water shrimps can be found in the Fishing 
Opportunities Regulation for the Med and Black Sea: Council Regulation (EU) 2022/110 of 27 
January 2022 fixing for 2022 the fishing opportunities for certain fish stocks and groups  of 
fish stocks applicable in the Mediterranean and Black Seas (OJ L 21, 31.1.2022, p. 165–186) 

- For 2022, there is no maximum  catch values for hake in the EU regulation so modelling would 

start for hake with catch limits in 2023. 

Details on Spatial scenarios implementation: 

- Concerning point b) of TORs: if closures are already permanent within a GSA this point will not 

be tested. 

- Concerning points d), e) and f) the closure of 10%, 20% and 30% of each GSA should be tested 
subtracting  already  existing  closure  areas  from  this  percentage.  Additionally,  the  areas 
>1000m and the <50m or within 3 nautical miles from the coast, should be excluded from 
what is considered the “total area of a GSA”. 

-  Ad-hoc  contracts  ran  prior  to  EWG  22-01  will  produce  shapefiles  with  the  distribution  of 
hotspots of juveniles and spawners of hake and potentially of the five main target species of 
the  MAP.  The  location  of  the  closure  areas  should  take  into  account  the  distribution  of 
hotspots to test the efficiency of the closure areas. 

- As reported in the TORs: “To provide an order of magnitude of the closures efficiency, it should 

aim at reducing about 20% of captures of each target species in each GSA.” 

4 

4 

 
 
The  reduction  of  ~20%  should  be  calculated  in  comparison  to  captures  in  2015-2017,  as  per 
TORs. 

STECF comments 

The Expert Working Group 22-01 met online from 28th February to 4th March 2022. The meeting 
was attended by 20 experts, including three STECF members and two JRC experts. 

STECF  notes  that  this  EWG  is  one  of  several  EWGs  dedicated  to  providing  advice  to  the 
Commission  on  the  Western  Mediterranean  Multiannual  Plan  (Regulation  (EU)  2019/1022, 
referred to as WestMed MAP) since 2018. These EWGs have improved knowledge and evaluated 
various management issues through mixed fisheries modelling.  

Models  

STECF  notes  that  four  different  fleet-based  mixed-fisheries  models  were  used:  two  models  in 
Effort  Management  Unit  (EMU)  1,  i.e.  GSAs  1-2-5-6-7  (though  one  was  applied  to  GSA7  only), 
two  models  in  EMU  2  (GSAs  8-9-10-11).  Three  of  these  models  (IAM  in  EMU  1,  BEMTOOL  and 
SMART  in  EMU  2)  have  been  used  and  developed  since  STECF  EWG  19-01.  The  fourth  model, 
ISIS-fish (Mahevas and Pelletier, 2004; Pelletier et al., 2009), was tested during EWG 22-01 for 
the  first  time,  in  an  attempt  to  implement  a  spatially  explicit  model  for  EMU1.  ISIS-fish  is  a 
deterministic simulation model designed to explore the dynamics of mixed fisheries. It combines 
spatially explicit fish and fleet dynamics at a monthly time step. Fishing mortality results from the 
interaction between the spatial distribution of population abundance and the spatial distribution of 
fishing  effort  for  the  different  métiers.  It  can  evaluate  closures  effect  accounting  for  effort 
reallocation and catch limitations.  

STECF notes that this model was initially presented in EWG 19-01 but its case study application to 
the  Western  Med  was  not  sufficiently  mature  at  that  time  and  could  not  be  used.  STECF 
acknowledges thus that progresses in parameterisation and implementation have been achieved 
since  then  and  that  the  model  could  be  tested  again  this  year.  STECF  notes,  however,  that  the 
ISIS-fish model was only used to analyse the European hake fishery in GSA7. 

Parameterisation of scenarios and main results by EMU and type of scenarios 

Testing proposals for maximum catch limits (MCLs) in EMU1 

The  scenarios  run  by  EWG  22-01  in  EMU1  considered  MCLs  for  European  hake  (GSAs  1-5-6-7) 
and  for  blue  and  red  shrimp  (GSA1  and  GSAs  6-7)  applying  MCLs  for  hake  only,  shrimps  only, 
and  for  both  species  at  the  same  time.  These  scenarios  were  run  either  with  a  decreasing  MCL 
through  time  (forward  scenario,  aimed  at  reaching  Fmsy  by  2025,  where  MCL  is  progressively 
decreased from 2022 to 2025) or with an increasing MCL through time (inverse scenario: where 
MCL is set at Fmsy in 2022, and then gradually increased between 2023 and 2025), for a total of 
10 scenarios compared to the baseline (Table 2.3.1.1.1 of the EWG 22-01 report).  

5 

5 

 
 
 
 
 
 
 
 
 
 
 
 
 
The  IAM  model  (Merzéréaud  et  al.,  2011)  was  parameterised  to  differentiate  between  Spanish 
coastal  and  deep-waters  trawl  fisheries,  while  no  deep-water  fisheries  were  considered  for 
France. STECF notes that since the model timescale has an annual time step, EWG 22-01 did not 
evaluate the effect of monthly MCLs. 

STECF notes that the two scenarios that simulated the implementation of MCL simultaneously on 
European  hake  in  GSAs  1-5-6-7  and  blue  and  red  shrimp  in  GSA  1  and  GSAs  6-7  (forward  and 
inverse  scenarios,  scenarios  i  and  j)  forecast  a  general  increase  of  the  biomass  of  the  exploited 
stocks; however, only the forward scenario foresees exploitation levels in line with the objectives 
of the WestMed plan, or below (Section 2.3.1.2 of the EWG 22-01 report).  

The  implementation  of  a  MCL  only  on  European  hake  (scenario  a)  forecasts  an  increase  in  the 
biomass  of  hake  in  GSAs  1-5-6-7,  red  mullet  in  GSA1,  GSA6  and  GSA7,  and  Norway  lobster  in 
GSA6. This scenario allows the stocks of red mullet in GSA1 and GSA6 to reach Fmsy. However, 
this is associated to an increase of F and a decrease of blue and red shrimp biomass in both GSA1 
and GSAs 6-7 (ARA1 and AR67), due to the reallocation of fishing effort to deeper waters. 

STECF  notes  that,  in  general,  all  scenarios  forecast  bio-economic  impacts  for  the  French  and 
Spanish  trawlers  in  the  short  term  with  a  decrease  in  their  Gross  Value  Added  (GVA).  In  the 
scenarios  where  MCL  is  applied  on  European  hake  only,  all  the  trawl  fleet  segments  are 
economically impacted.  In  contrast,  in  the  scenarios  where  MCL  is  applied  only  on  blue  and  red 
shrimp,  only  the  fleet  segments  involved  in  deep-water  trawling  are  impacted,  (i.e.  Spanish 
trawlers  above  12  meters).  Conversely,  economic  advantages  are  projected  for  vessels  using 
longlines and gillnets (Sections 2.3.1.2 and 2.3.1.3 of the EWG 22-01 report). 

STECF  notes  that  none  of  the  scenarios  result  in  achieving  Fmsy  for  all  stocks,  except  when 
applying MCLs simultaneously on both blue and red shrimp and hake. However, STECF notes that 
the results for hake should be treated with caution as the MCL was applied in the simulations to 
trawlers only, not accounting for the longline and gillnet fisheries which also exploit this stock. 

STECF  notes  that  the  spatially  explicit  mixed  fisheries  model,  ISIS-Fish,  was  also  used  to  run 
scenarios  accounting  for  MCL  for  hake  in  GSA  7.  Forward,  inverse  and  monthly  MCL  scenarios 
were used for a total of four scenarios alternative to the baseline (Table 2.3.2.1.1 of the EWG 22-
01 report). 

Testing proposals for closed areas in EMU1 (GSA7 only here)  

The ISIS-Fish model was also applied to evaluate the effects of area closures on European hake in 
GSA 7. The scenarios run by EWG 22-01 are summarized in the Table 3.4.1.1.1 of the EWG 22-01 
report. 

Testing proposals for maximum catch limits in EMU2 

STECF notes that the BEMTOOL model (Rossetto et al., 2015; Russo et al., 2017) was used to run 
scenarios  simulating  the  implementation  of  MCL  on  blue  and  red  shrimp  and  giant  red  shrimp, 
separately.  No  scenario  accounting  for  a  simultaneous  implementation  of  MCLs  on  the  two 
shrimps was tested. In addition, the simulation of the implementation of MCL on European hake 
in EMU2 was not tested by EWG 22-01 (Section 2.3.3.1 of the EWG 22-01 report). 

6 

6 

 
 
 
 
 
 
 
      
 
 
 
The  BEMTOOL  model  was  updated  to  consider  the  different  types  of  fishing  activity  exerted  by 
each fleet segment at a métier level. The model timescale was set at a monthly time step, so it 
was possible to run scenarios accounting for monthly MCLs. 

STECF notes that implementing a MCL on the deep-water fisheries showed an improvement for all 
the stocks considered, with the only exception of hake. Blue and red shrimp and giant red shrimp 
showed an improved stock status, while red mullet, deep-water rose shrimp and Norway lobster 
remained within the upper and lower limits of Fmsy, despite the reallocation of fishing effort from 
deep-waters to coastal fisheries. 

The  implementation  of  an  inverse  MCL  approach  did  not  show  a  recovery  of  the  stocks.  In 
general,  the  GVA  shows  an  increase  for  small-scale  fisheries  (i.e.,  gillnet  and  longlines),  and  a 
strong decrease for all the trawling fleets. 

Testing proposals for closed areas in EMU2  

The SMART model (Russo et al., 2014; D’Andrea et al., 2020) was updated increasing the spatial 
resolution of the spatial grid of the model in line with ISIS-Fish and with the outputs of the ad-hoc 
contracts preceding the EWG. This allowed improving the quality of the information on the spatial 
distribution of both the spawning and nursery areas.  

STECF  notes  that  EWG  22-01  advises  that  protection  of  stocks  should  not  be  evaluated 
considering  percentages  of  areas  of  protection,  but  through  the  evaluation  of  the  response  of 
stocks to spatial management measures.  

STECF  notes  that  none  of  the  scenarios  considered  for  EMU  2  (Table  3.3.2.4.1  of  EWG  22-01 
report) evaluating spatial closures will achieve Fmsy by 2025, with the exception of red mullet in 
GSA10  and  GSA9,  and  Norway  lobster  in  GSA9.  The  SSB  of  most  of  the  stocks  showed  does 
increase across year-on-year. 

None of the scenarios reduce catches by 20% for all species. All scenarios are associated with a 
sharp  decrease  in  profits  in  the  short-term.  The  loss  of  profits  is  larger  in  the  fleet  segments 
VL12-18 and VL18-24. 

General comments 

STECF  acknowledges  that  all  the  ToRs  have  been  addressed  by  EWG  22-01.  However,  STECF 
notes h that not all the scenarios originally listed in the ToRs could be addressed by EWG 22-01.  

Regarding  ToR  1,  STECF  notes  that  not  all  the  MCL  scenarios  could  be  run  by  EWG  22-01.  In 
particular,  no  monthly  MCL  was  tested  in  EMU1,  while  no  scenario  on  European  hake  MCL  was 
evaluated  in  EMU2.  STECF  notes  that  in  the  absence  of  specifications  on  how  MCLs  would  be 
implemented  and  shared  across  countries  and  fleets,  EWG  22-01  simulated  MCLs  as  TACs  as 
stated  in  the  EWG  ToRs.  STECF  notes  that  the  scenarios  run  by  EWG  22-01  did  not  take  into 
account the adaptation of the MCL to the status of the stock (e.g. Fmsy, SSB) that is expected to 

7 

7 

 
 
 
 
 
 
 
 
 
 
 
 
 
change during the application of management measures. This aspect needs to be further explored 
to accommodate the adaptive setting of MCLs on a yearly basis in future projections. 

Regarding  ToR2,  EWG  22-01  did  not  run  scenarios  accounting  for  10,  20,  and  30%  permanent 
closures in each GSA. STECF notes that EWG 22-01 considered it not feasible to implement those 
scenarios  as  the  EWG  was  not  in  the  position  to  decide  which  areas  should  be  closed.  As  an 
alternative to those scenarios, the increase by 50 and 100% of the surface of the existing closed 
areas was simulated (in EMU2 only).  

In  addition,  STECF  acknowledges  that  no  biomass  reference  points  are  available  for  the  key 
stocks in order to fully test scenarios based on Harvest Control Rules (HCR). 

STECF notes that none of the scenarios tested by EWG 22-01 lead to achieving Fmsy for all the 
targeted  stocks  in  EMU1,  except  when  applying  a  MCL  system  in  parallel  with  the  effort  regime 
reduction on both blue and red shrimp and European hake simultaneously. 

STECF notes that implementing a MCL system on the deep-water fisheries targeting blue and red 
shrimp  and  giant  red  shrimp  in  EMU2  improved  the  exploitation  status  of  all  stocks,  with  the 
exception of European hake. 

STECF  notes  that  applying  a  permanent  area  closure  in  GSA7  would  decrease  the  catches  of 
juvenile hake by 20%, although Fmsy would not be reached by 2025. 

STECF  notes  that  in  EMU2,  none  of  the  area  closure  scenarios  tested  by  EWG  22-01  lead  to 
reductions in the catches of all target species by 20% or achieve Fmsy for all species. 

STECF notes that all scenarios show a sharp decrease in profits. 

STECF  notes  that  given  the  large  number  of  other  species  that  are  exploited  with  the  target 
species  of  the  WestMed  MAP,  the  actual  socio-economic  impact  of  the  simulations  remains 
uncertain. STECF notes that the models provide estimated trends in some economic indicators for 
a  number  of  scenarios.  However,  STECF  cannot  assess  to  which  extent  these  impacts  threaten 
the  economic  sustainability  of  the  fleets  as  no  socio-economic  reference  points  are  available  to 
qualify  the  results.  Additionally,  the  socio-economic  evaluations  assume  a  constant  number  of 
vessels, and results would be different if the number of vessels is reduced through, for example, 
a  decommissioning  scheme,  or  effort  increased  as  inactive  vessels  become  active  when  stocks 
recover.  

STECF  acknowledges  the  efforts  made  to  accommodate  new  types  of  management  measures 
during  the  EWG.  STECF  recognises  that  the  various  models  have  different  abilities  to  simulate 
different  types  of  management  measures  and  acknowledges  that  the  model  implementation  of 
these  new  measures  raised  many  conceptual  and  methodological  questions  for  the  modellers. 
This  is  particularly  true  regarding  the  modelling  of  MCL  management  and  new  area  closures. 
STECF agrees with the EWG that some of these questions may require further elaboration. 

Nevertheless, STECF acknowledges that the models implemented by EWG 22-01 are state-of-the-
art and can be used for the evaluation of management strategies. They allow for a comparison of 
various management strategies in terms of both their likelihood to achieve the objectives of the 
MAP  and  their  relative  impact  on  the  economic  outcomes  for  the  fleets.  STECF  agrees  that  the 

8 

8 

 
 
 
 
 
 
 
 
 
      
 
annual  update  of  these  models  incorporating  the  most  recent  stock  and  fleet  data  allows 
monitoring the ongoing performance of the MAP. 

STECF notes though that models used so far do not account for ecosystems effects of protecting 
sensitive  habitats;  therefore  the  current  simulations  may  underestimate  the  risk  posed  to  the 
stocks by displacement effects from persistent hotspots areas to the surrounding areas.  

STECF  notes  that  it  would  be  worthwhile  to  carry  out  further  investigation  of  potentially 
conflicting  effects  of  cumulating  several  management  measures,  which  may  either  add  up  or 
counteract  each  other,  and  may  even  have  adverse  effects  on  the  stocks  depending  on  effort 
redistribution.  

STECF conclusions 

STECF concludes that EWG 22-01, as the latest of a series of dedicated EWGs, has made further 
progress in assessing the consequences of management measures in the Western Mediterranean. 

STECF  concludes  that  EWG  22-01  ran  scenarios  of  different  management  measures  applying  on 
different fleets in different areas various management measures. STECF acknowledges that there 
has  been  improvement  in  the  modelling  approach  and  in  the  range  of  results  compared  to 
previous  EWGs  reports,  although  sometimes  strong  assumptions  or  limited  data  still  had  to  be 
used  for  the  modelling.  In  particular,  STECF  concludes  that  uncertainty  remains  for  the  full 
evaluation of economic impacts of the management measures since not all species caught by the 
fleets  are  included  in  the  model.  STECF  concludes  thus  that  the  results  need  to  be  interpreted 
with caution. 

STECF  acknowledges  that  the  scenarios  requested  to  EWG  22-01  are  complex  and 
comprehensive.  STECF  notes  that  not  all  scenarios  could  be  run  under  the  existing  models’ 
configuration  and  resolution,  in  spite  of  important  modelling  effort  being  mobilised.  STECF 
concludes that available data, knowledge and manpower limit the level of precision and scale that 
mixed-fisheries  models  can  achieve,  and  thus  advises  that  management  measures  should  not 
operate at a scale finer than the data and knowledge available to  enforce, monitor and evaluate 
their effectiveness with some degree of confidence.  

STECF  concludes  that  most  scenarios  simulated  indicate  that  Fmsy  will  not  be  achieved  for  all 
stocks  by  2025.  The  implementation  of  additional  measures  like  closed  areas  or  MCLs  would, 
however, improve in many cases the stock status compared to the current effort regime alone. 

STECF  concludes  that  all  scenarios  tested  with  mixed  fisheries  models  predict,  as  in  previous 
years,  some  worsening  of  the  economic  performance  of  the  fleets  during  the  first  years  of 
implementation.  Although  for  some  fleets  the  losses  may  be  recovered  later,  the  results 
emphasise  the  difficult  trade-offs  for  between  real  short-term  costs  for  individual  fishers  and 
expected collective long-term gains in the future. 

References  

D’Andrea, L., Parisi, A., Fiorentino, F., Garofalo, G., Gristina, M., Russo, T., Cataudella, S., 2020. 
smartR: a R package for spatial modelling of fisheries and simulation of effort management. 
Methods in Ecology and Evolution 00:1-10. 10.1111/2041-210X.13394 

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9 

 
 
 
 
 
 
 
 
 
 
 
Mahévas,  S.,  Pelletier,  D.,  2004.  ISIS-Fish,  a  generic  and  spatially  explicit  simulation  tool  for 
evaluating the impact of management measures on fisheries dynamics. Ecological Modelling, 
171: 65-84. 

Merzéréaud,  M.,  Macher,  C.,  Bertignac,  M.,  Frésard,  M.,  Le  Grand,  C.,  Guyader,  O.,  Fifas,  S., 
2011.  Description  of  the  impact  assessment  bio-economic  model  for  fisheries  management 
(IAM). Amure Electronic Publications, Working Papers Series D-29-2011, 19 p. 

Pelletier,  D.,  Mahévas,  S.,  Drouineau,  H.,  Vermard,  Y.,  Thebaud,  O.,  Guyader,O.  &  Poussin,  B., 
2009. Evaluation of the bioeconomic sustainability of multi-species multi-fleet fisheries under 
a wide range of policy options using ISIS-Fish. Ecological Modelling, 220(7): 1013-1033. 

Rossetto,  M.,  Bitetto,  I.,  Spedicato,  M.  T.,  Lembo,  G.,  Gambino,  M.,  Accadia,  P.,  et  al.  (2015). 
Multi-criteria  decision-making  for  fisheries  management:  a  case  study  of  Mediterranean 
demersal fisheries. Marine Policy 53, 83–93. doi: 10.1016/j.marpol.2014.11.006 

Russo,  T.,  Parisi,  A.,  Garofalo,  G.,  Gristina,  M.,  Cataudella,  S.,  et  al.,  2014.  SMART:  A  Spatially 
Explicit  Bio-Economic  Model  for  Assessing  and  Managing  Demersal  Fisheries,  with  an 
Application  to  Italian  Trawlers  in  the  Strait  of  Sicily.  PLoS  ONE,  9(1):  e86222. 
doi:10.1371/journal.pone.0086222 

Russo,  T.,  Bitetto,  I.,  Carbonara,  P.,  Carlucci,  R.,  D’Andrea,  L.,  Facchini,  M.T.,  Lembo,  G., 
Maiorano,  P.,  Sion,  L.,  Spedicato,  M.T.,  Tursi,  A.  and  Cataudella,  S.,  2017.  A  Holistic 
Approach to Fishery Management: Evidence and Insights from a Central Mediterranean Case 
Study (Western Ionian Sea). Front. Mar. Sci. 4:193. doi: 10.3389/fmars.2017.00193 

Contact details of STECF members 

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

Name 

Affiliation1 

Email 

Abella, J. Alvaro 

Independent consultant 

Bastardie, Francois 

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

aabellafisheries@gmail.co
m 

fba@aqua.dtu.dk  

Borges, Lisa 

FishFix, Lisbon, Portugal 

info@fishfix.eu 

Casey, John 

Independent consultant 

blindlemoncasey@gmail.c
om  

10 

10 

 
 
 
 
Name 

Affiliation1 

Email 

Damalas, Dimitrios 

Daskalov, Georgi 

Döring, Ralf (vice-chair) 

Gascuel, Didier  

Grati, Fabio 

shark@hcmr.gr 

Georgi.m.daskalov@gmail
.com 

ralf.doering@thuenen.de 

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

576 

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

Thünen  Institute  [TI-SF]  Federal 
Research 
for  Rural 
Institute 
Areas,  Forestry  and  Fisheries, 
Fisheries, 
Institute 
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 
(IRBIM),  L.go 
Biotechnologies 
Fiera  della  Pesca,  2,  60125, 
Ancona, Italy  

for 
and 

Ibaibarriaga, Leire  

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

libaibarriaga@azti.es  

Jung, Armelle 

Knittweis, Leyla 

Ligas, Alessandro 

Martin, Paloma  

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

armelle.jung@desrequinse
tdeshommes.org  

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

of 

Leyla.knittweis@um.edu.
mt  

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

di 

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

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

paloma@icm.csic.es 

11 

11 

 
 
Name 

Affiliation1 

Email 

Motova, Arina  

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

arina.motova@seafish.co.
uk 

Moutopoulos, Dimitrios 

of 

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

Fisheries 

dmoutopo@teimes.gr 

Nord, Jenny  

Prellezo, Raúl  

O’Neill, Barry 

Raid, Tiit  

Rihan,  Dominic 
chair) 

Sampedro, Paz 

Somarakis, Stylianos  

Stransky, Christoph  

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 

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

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

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

of 

paz.sampedro@ieo.es  

somarak@hcmr. gr 

christoph.stransky@thuen
en.de 

Ulrich, Clara (chair) 

IFREMER, France 

Clara.Ulrich@ifremer.fr  

12 

12 

 
 
Name 

Affiliation1 

Email 

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 

University 
de 
de 
Compostela, 
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 

13 

13 

 
 
 
 
 
 
 
 
 
EXPERT WORKING GROUP EWG-22-01 REPORT 

REPORT TO THE STECF 

EXPERT WORKING GROUP ON  
EVALUATION OF MAXIMUM CATCH LIMITS AND 
CLOSURE AREAS IN THE WESTERN 
MEDITERRANEAN 
(EWG-22-01) 

Virtual meeting, 28 of February – 4 of March 2022 

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

14 

14 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1 

INTRODUCTION 

This report is the eighth of a suite of STECF EWG reports dedicated to the fishing effort regime in 
the Western Mediterranean Sea.  

The  first  EWG  in  June  2018  (STECF  18-09)  addressed  a  number  of  issues  related  to  managing 
fisheries with fishing effort regimes. Building on a review of previous  experiences worldwide, the 
report  highlighted  the  main  and  well  known  concern  that  catchability  estimates  (relationship 
between  fishing  effort  and  fishing  mortality)  are  imprecise  and  vary  systematically  since  fishers 
will tend to increase their efficiency in order to maintain their historical catch and revenue levels 
in spite of effort reduction1. This was corroborated by quantitative analyses of differences in catch 
efficiency  between  fishing  trips  using  trip-based  data  from  Italy  and  Spain,  differences  that  are 
only little explained by features such as vessel size or fishing area. Also, a study was presented 
monitoring continuous increase in gear size (width, opening, twin trawl etc) in the Mediterranean, 
highlighting a potential for further increase in fishing efficiency that may counteract the expected 
effect  of  effort  reduction.  Finally,  a  comparison  of  the  completeness  and  consistency  of  the 
various  datasets  on  catch  and  effort  by  fleet  segments  available  at  the  JRC  was  performed, 
highlighting a number of gaps. 

The  second  EWG in October 2018 (STECF 18-14) built further on these  results. The  relationship 
between  fishing  effort  and  fishing  mortality,  aggregated  at  the  level  of  fleet  segment  and  year, 
was analysed for a number of the MAP stocks using the available time series of stock assessment. 
This relationship was shown to be never linear, and in most cases it cannot even be detected in 
the  time  series.  This  means  that  a  reduction  of  fishing  effort  will  not  translate  by  a  similar 
reduction  of  fishing  mortality  at  least  in  the  first  years  of  implementation.  Secondly,  the  trips 
analyses were extended to new data from France, showing similar results as for Italy and Spain. 
Finally,  a  first  review  of  existing  bioeconomic  mixed  fisheries  models  in  the  Western  Med  was 
conducted.  Considering  that  many  models  were  potentially  available  but  that  none  of  them  was 
directly operational for  the purpose of the  MAP, a 2  years road map  was agreed to improve the 
availability and use of such models. 

Accordingly,  the  third  EWG  in  March  2019  (STECF  19-01)  focused  uniquely  on  updating  and 
improving  mixed-fisheries  models.  Several  models  of  various  complexity  were  presented  and 
tested  for  the  two  regions  (EMU1  &  and  EMU2).  Good  progresses  were  achieved  but  the  most 
important  issue  left  was  the  need  to  develop  a  single  combined  model  for  EMU1  including  data 
from  both  Spain  and  France  together,  instead  of  the  existing  models  by  GSA.  In  addition,  the 
EWG  listed  numerous  other  issues  and  future  questions  regarding  data  and  models’  dimensions 
(e.g. stock definition, inclusion of other species than the MAP species etc). 

The  fourth  EWG  in  October  2019  (STECF  19-14)  was  the  continuation  of  this  work,  progressing 
further  on  these  issues  in  order  to  have  models  and  datasets  fully  operational  for  providing 
mixed-fisheries  advice  on  the  MAP.  In  particular,  a  first  version  of  a  combined  IAM  model  for 
EMU1 was presented, including both Spanish and French fleets but including only hake data. Two 
models  were  run  in  parallel  for  EMU  2  (BEMTOOL  and  SMART),  providing  different  insights  on 
future  development.  During  the  EWG  19-14,  specific  focus  was  also  given  to  how  to  simulate 
closed areas in the bioeconomic models to evaluate their potential impact in the medium-term. 

The  fifth  EWG  in  October  2020  (STECF  20-13)  was  largely  an  update  of  STECF  19-14  regarding 
models  and  scenarios  (see  ToRs).  The  models  were  updated  with  the  most  recent  assessment 
data  (from  STECF  EWG  20-09)  and  FDI  effort  data  (from  STECF  20-10)  and  extended  to  cover 
some of the gaps previously identified (mainly for EMU 1), and a number of scenarios were run. 
Additional  issues  were  though  considered.  In  2020,  the  West  Med  MAP  has  been  implemented 
since  January  1st,  through  Regulation  (EU)  2019/1022,  with  fishing  opportunities  in  terms  of 
maximum  allowable  fishing  effort  in  fishing  days  fixed  for  2020  in  Council  Regulation  (EU) 

1 http://www.fao.org/gfcm/fishforum2018/presentations/en/, Theme 1 session 2 

15 

15 

 
 
 
                                                 
2019/2236. The EWG compared the reference levels used for fishing effort quotas and discussed 
the  implications  of  the  sometimes  large  discrepancies  observed  between  scientific  and  policy 
data.  

The  sixth  EWG  in  March  2021  (STECF  EWG  21-01)  explored  the  datasets  on  the  trawl  fleets 
exploiting demersal stocks to  estimate the conversion factors between fleet segments to  ensure 
that  effort  swaps  will  not  lead  to  an  undesirable  increase  in  fishing  mortality.  The  EWG 
highlighted  the  need  to  have  data  at  fishing  trip  (VMS  data)  level  when  estimating  conversion 
factors.  The  impact  of  recreational  fishery  on  the  stocks  covered  by  the  Western  Mediterranean 
Multi-Annual Plan was found to be negligible. The EWG also assessed the proposals for additional 
closure  areas  for  2021  received  from  Spain,  but  had  no  time  nor  data  to  propose  alternative 
closure areas for EMU 1 and 2. 

The seventh EWG held the last week of September 2021 was partially an update of STECF 20-13 
and  partially  an  update  of  STCEF  21-01.  The  models  were  updated  with  the  most  recent 
assessment data (from STECF EWG 21-11) and FDI effort data (from STECF 21-12) and extended 
(compared to last year) to run scenarios accounting for alternative selectivity and introduction of 
TACs.  The  EWG  updated  the  F-E  relationships  and  estimated  conversion  factors  at  metier  and 
stock level. In 2021, the second year of the West Med MAP has been implemented since January 
1st,  through  Regulation  (EU)  2021/90,  setting  fishing  opportunities  in  terms  of  maximum 
allowable  fishing  effort  in  fishing  days  for  2021.  This  year  as  well  the  EWG  compared  the 
reference  levels  used  for  fishing  effort  quotas  and  found  large  discrepancies  between  scientific 
and policy data, the implications were discussed during the EWG. 

This eighth EWG held the first week of Mach 2022 was a technical exercise to improve the mixed-
fisheries  modelling  frameworks  to  in  preparation  of  future  EWGs.  The  EWG  focused  on  the 
evaluation  of  two  specific  management  measures  considered  in  the  western  Mediterranean 
management  plan:  maximum  catch  limits  (MCLs)  and  closure  areas.  In  order  to  evaluate  these 
measures  in  isolation  from  others  considered  in  the  western  Mediterranean  management  plan, 
effort reductions applied in 2022 following Regulation (EU) 2022/110 were not considered during 
EWG  22-01.  MCLs  on  ARA  and  ARS  (following  Regulation  (EU)  2022/110)  and  on  HKE  and 
existing  closure  areas  were  evaluated.  EWG  22-01  evaluated  the  the  possibility  of  defining 
additional  closure  areas  with  the  available  data  and  highlighted  numerous  limitations  in  the 
process. 

1.1 

Terms of Reference for EWG-22-01 

In adopting the western Mediterranean multi-annual management plan (West Med MAP), Member 
States  agreed  to  implement  several  management  measures,  such  as  fishing  effort  reduction, 
closure  areas  and  maximum  catch  limits,  to  secure  the  achievement  of  MSY  by  1  January  2025 
for  all  demersal  stocks  in  the  western  Mediterranean.  The  work  of  the  STECF  expert  working 
group will continue building on the previous evaluations by STECF expert working groups to look 
into (i) the implementation of maximum catch limits for deep-water shrimps and hake as well as 
(ii) the delineation of additional closure areas. 

Regarding maximum catch limits, Article 7.3.b of the West Med MAP states that “The fishing effort 
decrease  may  be  supplemented  with  any  relevant  technical  or  other  conservation  measures 
adopted  in  accordance  with  Union  law,  in  order  to  achieve  the  FMSY  by  1  January  2025.”  The 
setting of a maximum catch limit framework for the most overfished stocks (hake and deep-water 
shrimps)  is  thus  considered.  STECF  EWG  21-11  has  identified  transition  values  for  maximum 
catch limits to achieve MSY by 2025 for all stocks and STECF EWG 21-13 has started developing 
models  to  include  maximum  catch  limits  in  the  evaluation  of  West  Med  management  scenarios. 
Different  levels  of  maximum  catch  limits  for  demersal  species,  in  particular  deep-water  shrimps 
and European hake, in the management unit of the western Mediterranean should be analysed by 
STECF experts to prepare for the evaluation of management scenarios combining maximum catch 
limit and effort reductions.  

Regarding  closure  areas,  Article  11.1,  alternatively  Article  11.2,  aims  at  protecting  juveniles  of 
European hake. All three concerned Member States adopted Article 11.3 and agreed to establish 
additional  closure  areas  by  17  July  2021  and  on  the  basis  of  best  available  scientific  advice, 
where there is evidence of a high concentration of juvenile fish, below the minimum conservation 

16 

16 

 
 
reference size, and of spawning grounds of demersal stocks, in particular for the target stocks of 
the West Med MAP. In addition, France and Spain adopted in December 2020 targets of capture 
reductions  of  demersal  stocks  and  committed  to  reduce  between  15%  and  25%  the  capture  of 
juveniles and spawners in each GSA.  

STECF PLEN 19-03, PLEN 20-01 and STECF EWG 21-01 have reviewed the proposals of closures 
(placement and period) submitted in 2020 and 2021 by the 3 Member States and determine their 
efficiency to protect juveniles of hake (as planned in Article 11.2) and juveniles and spawners of 
all six target demersal species included in the West Med MAP (as planned in Article 11.3).  

However,  in  view  of  Article  11.4,  the  closure  areas  should  be  reviewed  for  Member  States  to 
update the closure areas based on STECF advice. 

 Request to the STECF: 

1)  STECF  is  requested  to  continue  the  development  of  management  models  including  different 
levels  of  maximum  catch  limits  for  deep-water  shrimps  and  for  hake  in  both  West  Med 
management units.  

2) STECF is also requested to develop mixed-fisheries spatio-temporal scenarios for all demersal 
fishing  gear  (e.g.  bottom  trawls,  gillnets,  longlines)  in  EMU1  and  EMU2  with  simulations  from 
2020  to  2030.  The  STECF  evaluation  should  be  looking  at  differences  in  captures  reduction 
between  the  2019  situation  (prior  to  closure  adoptions)  by  species  and  by  age-class  and  the 
following scenarios:  

a) Status quo scenario: closures adopted since the implementation start of the West Med MAP by 
the 3 Member States  

b) Same delineation of  closures areas as in 2020, 2021 and 2022 and all closure areas become 
permanent from 2023 onwards  

c) Same delineation of closures areas as in 2020, 2021 and 2022 and all closure areas are for all 
fishing gear (e.g. trawlers, longliners, netters)  

d)  10%  of  permanent  closure  areas  in  each  GSA,  taking  into  account  the  different  types  of 
habitats such as for instance waters shallower than 200m depth and waters deeper than 200m*  

e)  20%  of  permanent  closure  areas  in  each  GSA  with  half  of  it  in  waters  shallower  than  200m 
depth and half of it in waters deeper than 200m*  

f)  30%  of  permanent  closure  areas  in  each  GSA  with  half  of  it  in  waters  shallower  than  200m 
depth and half of it in waters deeper than 200m*  

*see document attached of priority areas identified for delineation of additional closure areas  

To provide an order of magnitude of the closures efficiency, it should be aimed at reducing about 
20% of captures of juveniles and spawners of each target species in each GSA. For each GSA, the 
EWG  is  requested  to  propose  recommendations  for  designing  alternative  closures  based  on 
criteria  such  as  but  not  limited  to  bathymetry,  depth,  type  of  substrate,  stock  seasonality, 
establishment of a buffer area, minimal size of the closure area etc. 

TORs  were  further  detailed  in  agreement  with  DGMARE  to  give  additional  directions  to  the 
working group experts’: 

As  discussed  during  the  meeting  on  27  January  2022,  the  legal  concept  adopted  in  December 
2021  by  Member  States  to  manage  by  output  the  deep-water  shrimps  in  the  western 
Mediterranean  is  “maximum  catch  limit”.  Contrary  to  the  legal  concept  of  total  allowable  catch 
(TAC),  a  maximum  catch  limit  does  not  involve  a  legal  right  for  the  future  years  and  does  not 
create  a  fixed  relative  stability  (i.e.  a  distribution  of  the  fishing  opportunities  between  the 
concerned Member States). Concretely, it means that there is no legal obligation for a continuity 
of catch levels between 2022, 2023, 2024, onwards. However, to simplify the modelling work, the 
STECF  experts  are  invited  to  work  with  TACs  and  use  a  reduction  at  percentage  level  through 
time  (e.g.  10%  reduction  of  the  previous  year  catches:  (TAC[year+1]=0.9*TAC[year]))  as  an 
alternative  to  following  the  transition  path  calculated  by  STECF  EWG  21-11  for  each  stock.  The 
model  output  should  be  analysed  in  order  to  determine  whether  such  reductions  allow  to  reach 
MSY by 2025 at the latest. 

17 

17 

 
Details on TACs scenarios implementation: 

-  TACs  should  be  implemented  both  on  hake  (HKE)  for  the  coastal  metiers  and  on  deep-
water shrimps (ARA and ARS) for the deep-water metiers. If possible, there would thus be 
3 scenarios with TACs: a) hake only, b) deep-water shrimps only, c) simultaneously hake 
&  deep-water  shrimps.  Those  scenarios  would  be  completed  in  September  2022  by  the 
effort reductions and additional management measures as done in EWG 21-13. 

-  All models should be simulating 2 fleets corresponding to the two metiers (deep-water and 
coastal fisheries). This definition of metiers is unstable as fishing boats can move from one 
metier  to  the  other  easily  from  one  day  to  the  next,  but  changes  due  to  external 
parameters cannot be accounted for in the models therefore the metiers will be treated as 
two separate fleets. 

-  Models  that  can  implement  a  monthly  time  step  (e.g.  BEMTOOL  and  SMART)  will  test  a 
first scenario where the TACs is implemented annually and fishers are assumed to follow a 
“run  to  fish”  behaviour  and  a  second  scenario  where  TACs  are  implemented  monthly. 
When  TACs  are  implemented  monthly,  if  possible,  the  value  implemented  each  month 
should change depending on the seasonality of the  fisheries.  To  obtain  information on 
the seasonality the experts can rely on experts’ opinions over the different areas 
and using DCF landings data by quarter, when data by quarter are available. The 
reference year should be 2015-2017. 

-  All models should account for monthly flexibility but not annual inter-flexibility. When the 
TAC  is  not  consumed  within  one  month,  it  can  be  used  the  next  month  (except  for 
December).  

-  An  optional  scenario  will  be  accounting  for  an  “inverse  TAC”  system.  Quotas  will  not  be 
decreasing through time, but the first year of implementation will be the hardest with an 
increase of the quota through time as the stock recovers. The values could be taken from 
the transition path to Fmsy by 2025 calculated by STECF EWG 21-11. 

- 

- 

For  2022,  the  maximum  catch  limit  values  for  deep-water  shrimps  can  be  found  in  the 
Fishing  Opportunities  Regulation  for  the  Med  and  Black  Sea:  Council  Regulation  (EU) 
2022/110  of  27  January  2022  fixing  for  2022  the  fishing  opportunities  for  certain  fish 
stocks and groups of fish stocks applicable in the Mediterranean and Black Seas (OJ L 21, 
31.1.2022, p. 165–186) 

For  2022,  there  is  no  maximum  catch  values  for  hake  in  the  EU  regulation  so  modelling 
would start for hake with catch limits in 2023. 

Details on Spatial scenarios implementation: 

-  Concerning point b) of TORs: if closures are already permanent within a GSA this point will 

not be tested. 

-  Concerning points d), e) and f) the closure of 10%, 20% and 30% of each GSA should be 
tested  subtracting  already  existing  closure  areas  from  this  percentage.  Additionally,  the 
areas  >1000m  and  the  <50m  or  within  3  nautical  miles  from  the  coast,  should  be 
excluded from what is considered the “total area of a GSA”. 

-  Ad-hoc  contracts  ran  prior  to  EWG  22-01  will  produce  shapefiles  with  the  distribution  of 
hotspots of juveniles and spawners of hake and potentially of the five main target species 
of the MAP. The location of the closure areas  should take into account the distribution of 
hotspots to test the efficiency of the closure areas. 

18 

18 

 
 
-  As  reported  in  the  TORs:  “To    provide    an    order    of    magnitude    of    the    closures  
efficiency,    it    should    aim    at  reducing    about    20%        of    captures    of    each    target  
species  in each  GSA.” 

The  reduction  of  ~20%  should  be  calculated  in  comparison  to  captures  in  2015-2017,  as  per 
TORs. 

1.2  Main findings 

KEY FINDINGS FOR EMU1 (GSAs 1 2 5 6 7) 

In  EMU  1,  several  stocks  are  strongly  overexploited  (STECF  21-11),  including  European  hake 
(HKE) in GSAs 1-5-6-7, red mullet (MUT) in GSA 1 and in GSA 6, and Blue and red shrimp (ARA) 
in GSA 1 and in GSAs 6-7.  

The  scenarios  investigated  with  the  IAM  model  simulated  an  implementation  of  maximum  catch 
limits (MCL) on European hake and/or on Blue and red shrimp. Two scenarios (scenarios i and j) 
simulate  the  implementation  of  MCL  simultaneously  on  European  hake  in  GSAs  1-5-6-7  and  on 
both stocks of Blue and red shrimp in GSA 1 and GSAs 6-7, with for scenario i an implementation 
of  MCL  using  a  transition  period  where  MCL  is  progressively  decreased  from  2022  to  2025;  and 
the  inverse  (where  MCL  is  progressively  increased)  for  scenario  j.  The  inverse  MCL  scenario  are 
those  that  simulate  a  more  restrictive  MCL  in  2022  and  then  a  gradual  increase  in  the  MCL 
between 2023 and 2025.  Although all scenarios predict globally an increase in the biomasses of 
the exploited stocks, only scenario i foresees exploitation levels in lines with the objectives of the 
plan, or below.  

Globally,  all  scenarios  imply  fishing  effort  reduction  up  to  2021  and  foresee  some  bio-economic 
impact  for  French  and  Spanish  trawlers  in  the  short  term  with  a  decrease  in  their  Gross  Value 
Added.  In  the  scenarios  where  MCL  is  applied  on  European  hake  (scenarios  a,  b,  i  and  j),  all 
trawler fleet segments are economically impacted. However in the scenarios where MCL is applied 
only on Blue and red shrimp (scenarios c to h), only the fleet segments using deep water trawling 
are impacted, i.e. the Spanish trawlers above 12 meters.  

The implementation of a MCL only on European hake (scenarios a and b) predicts an increase in 
hake  biomass  (Fig.  1.2.2),  but  also in  Red  mullet  (for  the  three  stocks  MUT1,  MUT6  and  MUT7) 
and  Norway  lobster  (NEP67)  biomasses.  The  effort  reductions  associated  with  these  scenarios 
allow  the  stocks  of  red  mullet  in  GSA1  and  GSA6  to  reach  Fmsy.  However,  those  scenarios  are 
associated with a decrease in  Blue and red shrimp biomasses (ARA1 and AR67) and  an increase 
in their fishing mortalities, due to the reallocation of fishing effort to deep water trawling. 

When  considering inverse MCL  scenarios, the increases in biomass are less  significant than with 
the MCL scenarios, however the negative economic impacts on the trawl fleets are less significant 
in the medium and long term.  

It should be noted, that in the scenarios tested in this report, the MCL is only applied to trawlers, 
therefore  total  landings  of  European  hake  are  higher  than  the  MCL  values,  which  explains  why 
European hake fishing mortality (Fbar) is higher than Fmsy in 2025 with scenarios a and b.  

19 

19 

 
Figure  1.2.1.  EMU  1  (IAM  model).  Predicted  Fishing  mortalities  by  modelled  stock  (in  row)  under  alternative  scenarios  (in  column).  The  stocks  are  as  follow 
(from top to bottom): Hake GSAs1-5-6-7 (HKE1567), red mullet GSA1 (MUT1), red mullet GSA6 (MUT6), red mullet GSA7 (MUT7), Norway lobster GSA6 (NEP6), 
blue and red shrimp GSA1 (ARA1) and blue and red shrimp GSAs6-7 (ARA67). Historical values of Fbar are given in the white areas and simulated values in the 
blue area. Estimated F 0-1, Flower and Fupper from EWG 21-11 stock assessments are represented. Simulations run until 2031 and vertical black lines indicate 
the year 2025. 

20 

 
 
Figure 1.2.2. EMU 1 (IAM model). Predicted Spawning Stock Biomasses by modelled stock (in row) under alternative scenarios (in column). The stocks are as 
follow (from top to bottom): Hake GSAs1-5-6-7 (HKE1567), red mullet GSA1 (MUT1), red mullet GSA6 (MUT6), red mullet GSA7 (MUT7), Norway lobster GSA6 
(NEP6), blue and red shrimp GSA1 (ARA1) and blue and red shrimp GSAs6-7 (ARA67). Historical values of SSBs are given in the white areas and simulated values 
in the blue area. Simulations run until 2031 and vertical black lines indicate the year 2025.  

21 

 
 
Figure 1.2.3. EMU 1 (IAM model). Evolution of the total Gross Value Added (GVA, i.e. proxy for the profit, in K euros) by fleet segment for each alternative 
scenario from 2020 to 2031. Vertical black lines indicate the year 2025. Scenarios are in column and fleet segment in row. The fleet segments are as follow (from 
top to bottom): French demersal trawlers 18-24m, French demersal trawlers >24m, other French vessels <12m and other French vessels >12m, Spanish trawlers 
< 12m, Spanish trawlers 12-18m, Spanish trawlers 18-24m, Spanish trawlers >24m, Spanish netters and Spanish vessels using hooks. 

22 

 
 
 
 
ISIS-Fish  results  provided  further  details  about  scenarios  impacts  on  European  hake  in 
GSA7  including  closure  scenarios.  Existing  closures,  scenario  a,  allowed  a  reduction  of 
the  fishing  mortality  compared  to  the  reference  period  (2015-2017)  of  about  3%. 
Scenario b, when seasonal closures become permanent, provided an additional protection 
and a decrease in F of 7% compared to 2015-2017 particularly for older age classes. On 
the  contrary,  scenario  c,  when  closures  were  applied  to  all  gears,  had  little  effect  on 
fishing mortality compared to scenario a (Fig. 1.2.4). The impact on  SSB of  scenarios a 
and c in comparison to the reference period was negligible, while scenario b provided an 
increase  of  7%  (Fig.  1.2.5).  In  terms  of  gross  revenues  on  hake,  closures  benefited 
Spanish  longliners  which  revenues  increase  compared  to  2015-2017,  but  trawlers 
revenues were reduced by 5 to 8% compared to 2015-2017 (Fig. 1.2.6).  

These  results  were  conditioned  to  the  assumptions  made  in  simulating  fish  and  effort 
distribution  and  effort  report  (see  section  3.3.1).  They  could  be  improved  with  the 
analysis  of  past  behaviour  of  the  fleets  in  response  to  2020-2021  closures  and  new 
results regarding hake hotspots and stock-recruitment relationship. It is expected that in 
view  of  the  current  assumptions,  the  effect  of  closures  was  underestimated  in  terms  of 
benefits for the stock and negative impact for the fleets.  

Maximum catch limits were simulated for hake in GSA7. MCL in GSA7 was set equal the 
MCL  defined  for  EMU1  multiplied  by  the  historical  (average  2018-2020)  proportion  of 
catch in GSA7 compared to EMU1. It was then apportioned to the trawler fleets according 
to  historical  share  (2018-2020).  The  annual  MCL  2022-2025  was  either  set  based  on  a 
transition  path  from  current  fishing  mortality  to  catch  at  Fmsy  (using  a  priori  set  MCL 
values  from  STECF  EWG  21-11)  (SCd  and  f)  or  as  catch  at  Fmsy  from  2022  on 
(accounting  for  biomass  evolution,  HCR-like  scenario)  (SCe  and  g).  An  annual  MCL 
allowing  a  race  for  quota  (SCd  and  e)  and  a  monthly  distributed  MCL  (SCf  and  g)  were 
compared. Because of the very different nature of the ISIS-Fish model compared 
to the assessment model, fishing mortality cannot be compared. An equivalent of 
Fmsy in ISIS was computed as 0.27 and used as a reference for HCR and evaluation of 
objectives.  

Biomass of hake at the end of the  simulations (2030)  accounting for a MCL  were in the 
same  order  of  magnitude  in  all  scenarios  (6  to  7  times  the  average  2015-2017),  but 
trajectories  differed  a  lot  particularly  when  comparing  the  transition  scenario  to  the 
immediate Fmsy scenario. In fact, the transition scenario led to fishing mortalities below 
Fmsy in 2025 because the value defined for MCL did not account for biomass rebuilding. 
On the other hand, the fishing mortality achieved using the HCR, never fell below Fmsy, 
because of the unrestricted catches of netters and longliners.  

In terms of revenues, both MCL paths led to strong decreases for trawlers. In case of the 
transition  path  the  decrease  is  progressive  and  reached  -71%,  -80%  and  -90% 
respectively  for  French  trawlers  of  18-40m,  24-40m  and  Spanish  trawlers  in  2025.  The 
inverse scenario led to a very sharp decrease in 2022, although slightly smaller than the 
transition (-67%,-77%,-89%). However revenues increased again from 2023 onwards to 
higher  levels  and  outperformed  the  reference  period  for  French  fleets.  Spanish  trawlers 
on the other hand only reached 30% to 60% of their historical hake revenues by the end 
of the simulation (2030). The imbalance between fleets is likely to attribute to the catch 
allocation  between  fleets  that  reflects  catch  share  in  2018-2020  while  effort  is 
constrained  to  2015-2017  values.  Finally,  the  previous  results  only  concerned  gross 
revenues on hake. The true consequences may be either lesser, if fleets have the ability 
to  avoid  hake  and  report  on  other  species,  or  worst,  if  they  had  to  completely  stop 
fishing.   

23 

 
 
 
 
 
 
On  the  contrary,  the  re-allocation  of  effort  from  trawlers  highly  benefits  netters  and 
longliners  fleets,  whose  revenues  were  multiplied  respectively  by  3  and  12.  This  latter 
result  should  be  considered  with  caution,  due  to  the  rudimentary  modelling  of  the 
Spanish longliner fleet.    

Monthly  distributed  MCL  reduced  global  F  more  than  the  annual  MCL,  but  increased 
pressure on recruitment. The  explanation lies in the assumption of the model regarding 
the gradual arrival of recruits in the fishery in the course of the year with a first peak in 
June.  Revenues  are  higher  for  all  fleets  with  these  scenarios  compared  to  their  annual 
equivalent.  

Figure  1.2.4  GSA7  (ISIS-Fish  model).  Predicted  Fishing  mortalities  of  hake  under  alternative 
scenarios (in column). Hindcasted values of Fbar are given in the white area and simulated values in 
the  blue  area.  A  fishing mortality  of  0.27  was assessed  as the equivalent of  F0.1  for the  ISIS-Fish 
model. Simulations run until 2030 and vertical black lines indicate the year 2022. 

Figure  1.2.5  GSA7  (ISIS-Fish  model).  Predicted  Spawning  Stock  Biomasses  of  Hake  under 
alternative  scenarios  (in  column).  Hindcasted    values  of  SSBs  are  given  in  the  white  area  and 
simulated  values  in  the  blue  area.  Simulations  are  run  until  2030  and  vertical  black  lines  indicate 
the year 2022. 

24 

 
 
 
 
 
 
 
 
 
Figure 1.2.6 Evolution of the Gross Value (in K euros) associated to hake landings by fleet segment 
for  each  alternative  scenario  from  2015  to  2030.  Vertical  black  lines  indicate  the  year  2022. 
Scenarios  are  in  column  and  fleet  segment  in  row.  The  fleet  segments  are  as  follow  (from  top  to 
bottom):  French  netters,  French  demersal  trawlers  18-24m,  French  demersal  trawlers  >24m, 
Spanish long-liners, Spanish demersal trawlers. 

KEY FINDINGS FOR EMU2 (GSAs 8 9 10 11) 

The  most  overexploited  stocks  in  EMU  2  are  Blue  and  red  shrimp  (ARA)  and  European 
hake  (HKE),  for  which  a  constant  catch  may  lead  to  a  further  decrease  of  biomass, 
especially  for  ARA.  The  setting  of  a  maximum  catch  limit  foreseen  in  the  EU  Regulation 
2022/110 would allow to approach Fmsy for ARA, but not for HKE that is mainly caught 
by  different  metiers,  while  deep-water  rose  shrimp  (DPS) in  GSAs  9-10-11  would  reach 
Fmsy  upper  with  all  scenarios.  ARS,  that  is  the  third  species  most  overexploited  after 
ARA and HKE, would see significantly reduced its fishing mortality below the Fmsy lower 
with all scenarios, except  D (MCL_ARS_REV_monthly) and F (MCL_ARA_REV) scenarios. 
ARS,  ARA  and  NEP  stocks  would  benefit  of  all  the  scenarios,  due  to  the  re-allocation  of 
the  effort  from  the  deep-water  metier  (OTB_DWS  and  OTB_DES)  to  the  demersal 
(OTB_DES).  This reallocation produces an increase in the fishing pressure  on the other 
stocks,  contributing  to  partly  reduce  the  underutilization  of  red  mullet  in  GSA10,  red 
mullet in GSA 9 and Norway lobster in GSA 9.  
Scenarios  B  and  D,  implementing  a  reverse  MCL  on  ARS9-10-11  at  annual  and  at 
monthly level respectively, represent the scenarios showing a performance slightly below 
status  quo  (SQ)  in  terms  of  revenues  and  profitability.  This  is  due  to  the  fact  that  the 
model  predicts    a  decrease  in  the  revenues  of  the  more  valuable  stocks,  partly 
compensated  by  the  increase  in  the  revenues  of  the  other  stocks,  assuming  the  same 
fishing  effort  and  fleet  capacity  of  2021  (and,  thus,  variable  and  fixed  costs).  B  and  D 

25 

 
 
 
 
 
 
 
scenarios  would  produce  an  improvement  in  SSB  values  of  ARS  and  ARA  only  slightly 
smaller than scenarios E and G, setting the MCL on ARA. Moreover, a MCL split by month 
impacts less on the catches of ARA and ARS in the short term, while by about 3-4 years 
the scenarios reach the same catch level.  

It  is  important  to  notice  that  the  scenarios  here  presented  do  not  yet  consider  the 
adaptation  of  the  MCL  to  the  status  of  the  stock  (e.g.  Fmsy,  SSB)  that  is  expected  to 
change during the application of management measures. This aspect needs to be further 
explored and refined to possibly accommodate the adaptive setting of MCL year by year 
in the projections. 

26 

 
 
 
 
 
 
 
Figure 1.2.7. EMU 2 (BEMTOOL model). Predicted Fishing mortalities by modelled stock (in row) under the MAP scenario of effort reduction (in column). The 
stocks are as follow (from top to bottom): Hake GSAs8-9-10-11 (HKE), red mullet GSA10(MUT10), red mullet GSA9 (MUT9),deep-water rose shrimp GDSs9-10-11 
(DPS), Giant red shrimp GSAs9-10-11(ARS), Norway lobster GSA9 (NEP9) and blue and red shrimp GSAs9-10-11 (ARA). Historical values of Fbar are given in the 

27 

 
 
 
 
 
 
white areas and simulated values in the blue area. Estimated F 0-1, Flower and Fupper from EWG 20-09 stock assessments are represented, in orange, blue and 
red horizontal lines, respectively. 

28 

 
 
 
 
 
 
Figure 1.2.8 EMU 2 (BEMTOOL model). Predicted Spawning Stock Biomasses (in row) under the MAP scenario of effort reduction (in column). The stocks are as 
follow  (from  top  to  bottom):  Hake  GSAs8-9-10-11  (HKE),  red  mullet  GSA10  (MUT10),  red  mullet  GSA9  (MUT9),  deep-water  rose  shrimp  GSAs9-10-11  (DPS), 
Giant red shrimp GSAs9-10-11 (ARS), Norway lobster GSA9 (NEP9) and blue and red shrimp GSAs9-10-11 (ARA). Historical values of SSB are given in the white 
areas and simulated values in the blue area. Historical values of SSBs are given in the white areas and simulated values in the blue area. Simulations run until 
2030 and vertical black lines indicate the year 2025. 

29 

 
 
 
 
 
Figure  1.2.6.  Evolution  of  the  total  Gross  Value  Added  (GVA,  i.e.  proxy  for  the  profit,  in  K  euros)  by  fleet  segment  for  each  alternative  scenario  from  2020  to 
2031. Vertical black lines indicate the year 2025. Scenarios are in column and fleet segment in row. 

30 

 
 
 
 
 
 
The  results  of  the  SMART  model  indicate  that  most  of  the  spatial  scenarios  could  significantly  improve  the  conditions  of  some  stocks  both  in 
terms  of  SSB  and  F.  In  particular,  Base  +  extended  New  Closures  scenarios  are  very  promising  for  HKE,  ARA,  ARS  but  for  the  other  stocks 
(DPS, NEP and MUT) the benefits are less evident.   

31 

 
 
 
 
 
 
Figure  1.2.7.  Evolution  of  the  Spawning  Stock  Biomass  (SSB)  by  stock  for  each  alternative  scenario  from  2020  to  2030.  Scenarios  are  in  column  and  fleet 
segment in row. 

32 

 
 
 
 
 
 
 
Figure  1.2.7.  Evolution  of  the  fishing  mortality  (F)  by  stock  for  each  alternative  scenario  from  2020  to  2030.  Scenarios  are  in  column  and  stocks  in  rows.

33 

 
 
 
 
 
 
METHODS AND DATA SOURCES 

Four  different  fleet-based  mixed-fisheries  models  were  used  for  the  two  EMUs  (Two  models  in 
EMU1  (although  one  is  applied  only  to  GSA  7  for  now),  two  models  in  EMU  2).  All  four  models 
present some levels of similarities, but also some differences. The selection of three of the models 
(BEMTOOL,  IAM,  SMART)  was  initially  performed  during  STECF  EWG  19-01  among  existing 
options  in  each  area,  and  the  subsequent  developments  were  progressed  during  STECF  19-14, 
STECF 20-13, EWG 21-13 and for this EWG 22-01. A fourth model (ISIS-fish) was added during 
this EWG 22-01 in order to have a spatial model also for EMU 1. 

In EMU 1, no global model covering all Spanish and French fleets in all GSAs 1,2,5,6,7 did exist 
prior  to  EWG  19-01.  Therefore,  some  work  was  necessary  to  select  the  most  appropriate 
modelling  platform  and  extend  it  to  cover  the  required  stocks  and  fleets.  Progresses  were 
achieved during STECF 19-14, where French and Spanish effort and hake catch data for 10 fleet 
segments  were  brought  together  in  the  IAM  model  (Merzereaud  et  al.,  2011).  This  model  was 
extended  to  all  assessed  MAP  stocks  during  STECF  20-13  and  updated  to  simulate  increased 
selectivity  and  account  for  a  TAC  regime  during  this  EWG  21-13.  IAM  also  accounts  for 
stochasticity in its process now. 

The  second  model  applied  to  EMU1,  ISIS-fish  is  a  deterministic  simulation  model  designed  to 
explore  the  dynamics  of  mixed  fisheries  (Mahevas  and  Pelletier,  2004;  Pelletier  et  al.,  2009).  It 
combines  spatially  explicit  fish  and  fleet  dynamics  at  a  monthly  time  step.  Fishing  mortality 
results  from  the  interaction  between  the  spatial  distribution  of  population  abundance  and  the 
spatial  distribution  of  fishing  effort  for  the  different  métiers.  It  can  evaluate  closures  effect 
accounting for  effort  reallocation and catch limitations. It currently describes the hake  fishery in 
GSA7.  

EMU  2  extends  along  the  coast  of  a  single  Member  State,  and  several  models  already  existed 
prior to STECF 19-01 covering all Italian activities in GSAs 9, 10 and 11. Two models (BEMTOOL 
and SMART) were updated and adapted during EWGs 19-01, 19-14, 20-13, 21-13 and 22-01 for 
the purpose of the MAP evaluation. They differ widely in their scale and purposes. The first model, 
BEMTOOL  is  a  multi-species  multi-gear  bio-economic  simulation  model  for  mixed  fisheries 
simulating  the  effects  of  management  options  on  stocks  and  fisheries  on  a  fine  time  scale 
(month). For the closure areas aimed at reducing the catches of juveniles, the proportion of effort 
reduced  in  the  closed  spatial  subunits  was  used  to  calibrate  the  changes  in  the  exploitation 
patterns, based on an increase of the size at first capture. BEMTOOL includes  19 fleet segments 
and 7 stocks. 

The  second  model  applied  to  EMU  2,  SMART  (Russo  et  al.,  2014)  is  an  individual-based  model 
where each fishing vessel is considered as an independent agent that operates to maximize gains 
(as  difference  between  revenues  and  costs),  which  can  thus  explore  the  potential  effects  of 
different spatial and/or temporal closures taking into account the potential reallocation of fishing 
vessels towards other fishing grounds. 

IMPORTANT ISSUES IN RELATION TO THE ADVICE 

As  discussed  in  STECF  EWG  19-01  and  21-13  the  fishing  effort  regime  described  in  the  Multi-
Annual  management  Plan  in  the  Western  Mediterranean  for  the  fisheries  exploiting  demersal 
stocks,  is  applied  to  all  vessels  fishing  with  trawls  in  the  areas  (GSA  1-2-5-6-7-8-9-10-11), 
targeting  two  main  stock  groups  (the  continental  shelf  and  upper  slope  group  and  the  deep 
waters  group)  and  vessel  length  categories  included  in  the  regulation.  Thus,  each  fishing  trip 
should  be  allocated  in  a  category  formed  by  a  combination  of  area/stock/length  category. 
Although  the  first  and  last  variables  are  easily  determined  (area  being  the  GSA  where  the  boat 
operates,  and  vessel  length  being  part  of  the  vessel  characteristic),  the  allocation  to  a  stock 
group  is  not  as  direct.  This  is  related  to  the  multispecific  nature  of  the  trawl  Mediterranean 

34 

 
 
 
 
 
 
fishery, also characterized by high versatility, as vessels can target both stock groups within the 
same  fishing  day,  working  at  different  depth  strata.  In  order  to  account  for  this  issue  all  four 
mixed-fisheries  bio-economic  models  (except  ISIS-Fish  for  now  as  it  is  applied  only  to  GSA  7 
were no deep-water fisheries is carried out) were updated so to consider for Spanish and Italian 
fleet  segments  a  coastal  and  a  deep-water  fisheries  and  account  for  effort  reallocation  due  to 
Maximum Catch Limits  (MCLs) implementation and follow the fishing days limitations defined by 
the Western Mediterranean MAP. Nevertheless, there are not fixed rules for allocating each fishing 
trip  to  one  of  the  two  stock  groups,  specifically  there  not  fixed  rules  across  countries  and  the 
definition  of  “coastal  metier”  applied  to  all  GSAs  is  considered  to  be  too  coarse  as  fishing 
strategies  in  the  coastal  areas  can  vary  quite  widely,  depending  on  the  GSA  and  the  country 
considered. There this issue is still pending. 

Following Regulation 2022/110 the MCLs for Spain and Italy were considered within the modelling 
frameworks  as  the  reference  catch  value  for  the  deep-water  fisheries  in  EMU  1  and  EMU  2  in 
2022. The MCL for France, instead could not be accounted for due to the lack of data suggesting 
the presence of such a fishery in GSA 7 and 8, and to the lack of stock assessment for ARA and 
ARS  in  GSA  7  and  8.  Therefore,  it  was  not  possible  to  account  for  the  effect  of  a  potential 
increase in the pressure on ARA and ARS stocks if a deep-water fishery started in GSA 7 and 8 by 
a  French  fleet.  A  number  of  revisions  on  how  MCLs  were  implemented  during  EWG  22-01  are 
reported  in  section  4.2.1.  The  main  point  that  experts  highlighted  is  to  consider  Fmsy  as  the 
target for 2025 instead of a  value of catch at  Fmsy. This procedure would allow to evaluate the 
consequences  of  implementing  a  MCL,  as  biomass  response  is  updated.  In  order  to  have  a 
meaningful  update  of  the  biomass  though,  specific  harvest  control  rules  (HCR)  to  be  accounted 
for  should  be  defined.  It  should  than  be  stressed  that  all  stocks  evaluated  in  the  western 
Mediterranean at present lack official biomass reference points such as Bpa or Blim. 

During  the  procedure  followed  to  define  hotspots  that  could  potentially  help  define  additional 
closure  areas,  a  number  of  limitations  due  to  the  availability  of  spatial  data  were  highlighted  in 
section  4.2.1.  One  of  the  main  points  raised  was  the  lack  of  survey  data  for  more  than  one 
quarter  per  GSA  limiting  the  potential  to  test  the  effect  of  seasonal  closures  against  permanent 
closures independently from fishers’ behavior. As highlighted in section 3.2.2 the complete lack of 
information on the distribution and behaviour of fleet segments under 15m is quite limiting when 
defining spatial management measures as it could underestimate the importance of management 
of  coastal  areas  compared  to  high  seas  ones.  These  caveats  should  be  considered  when 
interpreting  results  obtained  from  spatially  explicit  mixed-fisheries  models.  Due  to  time 
limitations  it  was  not  possible  for  either  of  the  spatially-explicit  models  to  explore  the  effects  of 
implementing  both  MCLs  and  closure  areas  which  aim  at  limiting  fishing  mortality  on  the  same 
species.  EWG  22-01  highlights  that  managing  a  resource  by  controlling  multiple  aspects  at  the 
same time can become counterproductive, therefore these kind of scenarios should be explored in 
future EWGs and considered with caution, specifically considering that the Western Mediterranean 
MAP plans to implement effort reductions, closure areas and MCLs at the same time on the same 
fisheries. 

Bioeconomic  models  rely  on  modelling  the  population  dynamics  of  fish  stocks  and  the  economic 
dynamics  of  fleets.  In  the  case  of  multi-species  fisheries,  such  as  the  western  Mediterranean 
demersal  fisheries,  the  number  of  fish  stocks  for  which  there  are  parameters  to  populate  a 
population  dynamics  models  are  typically  few.  For  instance,  in  EMU1  demersal  fisheries  land 
around  60  species  in  significant  quantities,  but  only  6  are  concerned  by  the  Multi-Annual 
Management Plan (see STECF 18-13 and STECF 20-13). These 6 species are, obviously, the main 
species  in  terms  of  landings  and  economic  importance  at  EMU  level,  and  stock  assessments  are 
regularly produced for those. However, they may represent 20% or less (depending on the GSA) 
of the total demersal fisheries production (see STECF 20-13). However, the population dynamics 
of the majority of these other demersal stocks (“secondary species” or commercial bycatch) is not 
well-known and the effect of the effort reduction proposed in the MAP on these secondary species 
cannot be assessed with any accuracy. Similarly, for EMU 2 data of FDI of 2020 confirm that the 

35 

 
 
 
 
 
target  species  of  the  MAP  represent  45%  in  volume  and  56%  in  value,  compared  to  the  total 
landing.  Accounting  for  other  important  species  of  the  trawl  fisheries  in  EMU2  for  both  landing 
volume  and  value  would  bring  the  evaluation  of  the  total  landings  around  59%  in  volume  and 
72%  in  value.  Though,  at  present,  analytical  stock  assessments  are  not  available  for  these 
additional  species.  As  a  consequence,  the  evaluation  of  the  true  economic  effect  of  the  effort 
reduction remains uncertain. 

2 

MAXIMUM CATCH LIMITS 

Under  the  Western  Mediterranean  Multiannual  Plan  (Regulation  (EU)  2019/1022),  fishing  effort 
reduction  could  be  supplemented  (Article  7(3)(b))  with  specific  technical  or  conservation 
measures, where scientific advice shows that remedial measures are needed, in order to achieve 
the  required  FMSY  levels  for  demersal  stocks.  The  additional  measures  can  be  a  combination  of 
changes  in  fishery  selectivity,  temporal  and/or  permanent  closures  or  maximum  catch  limits 
(MCLs), among others. 

MCLs  have  already  been  set  for  sardine  and  anchovy  in  the  Adriatic  Sea  since  2019,  where  a 
maximum  level  of  catches  for  both  species  is  given  limited  to  Croatia,  Italy  and  Slovenia,  but 
without an allocation between the three MSs until 2021, temporal or by fleet (Council Regulation 
2019/2236).  MCLs  have  also  been  established  in  2022  for  blue  and  red  shrimp  and  giant  red 
shrimp, with quota allocated between Italy and Spain (France does not have a fisheries targeting 
blue and red shrimps and giant red shrimp) (Regulation (EU) 2022/110). Examples of local MCLs 
implemented in Mediterranean  fisheries include  boat  seines targeting transparent gobies in Italy 
and Spain (Commission Implementing Regulation (EU) 2018/1634 and Commission Implementing 
Regulation  (EU)  No  2020/1243  &  No  2020/1242,  respectively),  as  well  as  sandeel  in  Catalonia 
(NE Spain, Commission Implementing Regulation (EU) 2021/1713). Perhaps the best documented 
example of successful MCL implementation in Mediterranean fisheries is the co-management plan 
of  the  sandeel  boat  seine  fishery  in  Catalonia,  Spain  where  a  monthly  MCL  is  divided  by  quotas 
allocated  per  vessel  and  day  (Lleonart  et  al.,  2014).  Both  this  fishery  and  the  crystal  goby 
fisheries are carried out by a limited number of small vessels using specific fishing gear and with 
limited effort, so the main output control measure (MCL) is complemented by a number of input 
measures. 

Annually-agreed  Total  Allowable  Catches  (TACs)  for  EU  North  East  Atlantic  stocks  have  been  in 
place  since  the  beginning  of  the  CFP.  TACs  are  shared  among  the  different  MSs  according  to 
‘relative stability’, a system by which MS are allocated a fixed proportion of a stock’ TAC, that was 
assigned to each MS participating in the fishery at the time of joining the EU (Borges, 2018). On 
the other hand, MCLs as currently in force in Mediterranean fisheries are fixed exclusively for one 
year  and  without  prejudice  to  any  other  measures  adopted  in  the  future  and  any  possible 
allocation  scheme  between  Member  States  (Council  Regulation  2019/2236).  Regardless  of  the 
different  EU  legal  definitions  and  requirements,  both  TACs  and  MCLs  constitute  output  control 
fisheries management measures, designed to limit fishing mortality of a particular stock when its 
predetermined  catch  amount  is  exhausted.  In  addition,  both  TACs  and  MCLs  carry  the 
requirement of the Landing Obligation (Art. 15 of Regulation (EU) 2013/1380).  

Lizaso  et  al.  (2020)  and  Bellido  et  al.  (2020)  offer  a  general  discussion  of  the  advantages  and 
disadvantages of input fisheries control measures, and was adapted as follows.  Output limits are 
flexible  and  may  adapt  fishing  mortalities  to  stock  status  but  they  may  be  ineffective  due  to 
several reasons, among them  a rush to  fish, insufficient enforcement  or to discarding.  If fishers 
have exhausted their quota of a given stock but continue fishing for other stocks, they will discard 
or land illegally catches of the  species for which the quota is  exhausted. The  Landing Obligation 
intends to stop this practice but introduces additional problems, namely early closure of a fishery 
due  to  the  exhaustion  of  one  of  its  quotas  (choke  effect),  in  addition  to  new  enforcement 

36 

 
 
 
 
 
 
challenges.  According  to  STECF  21-13  however,  in  an  input  effort  management  system  fishers 
tend to invest in technical improvements to increase their efficiency in catching fish (higher catch 
per unit of effort (CPUE)) as the overall catch is not limited. Calculations by EUROSTAT show an 
increase in efficiency in fishing fleets in the past even in areas without effort management system 
in  place  (EUROSTAT,  2019).  The  consequences  of  an  increase  in  efficiency  would  be  that  the 
effort  must  be  reduced  regularly  to  adjust  for  the  technical  improvements.  In  addition,  the 
definition and measurement of a reasonable unit measure of fishing effort is also a challenge. 

It is important to notice that neither the management of fishing effort or of catch are likely to be 
effective  unless  they  are  correctly  implemented  and  applied  to  all  fishers  (or  at  least  the 
overwhelming  majority)  engaged  in  a  fishery  (adapted  from  Pope,  2002).  In  general,  output  or 
input controls will only be  effective if its overall baselines and ceilings are in line and consistent 
with  the  reality  of  the  fishery  they  are  meant  to  limit  (for  example,  the  landings  of  sardine  and 
anchovy  in  the  Spanish  Mediterranean  purse  seine  fisheries  are  much  lower  than  the  TACs  in 
force  because  of  the  low  biomass  levels  of  these  stocks).  Specifically,  technological  creep  and 
increase  fishing  efficiency  in  an  effort  scheme  needs  to  be  taken  into  account,  while  discarding 
and illegal landings need to be controlled in a catch system. 

2.1 

Background 

2.1.1  LPUE data by quarter in EMU 1  

The Landings per Unit of Effort (LPUE) for species European hake (HKE) and blue and red shrimp 
(ARA) were computed from FDI data for France (FRA) (Tables A and G) and MBS data (DCF 2021, 
tables catch and effort). As explained in the section “Data issues” the data for ESP had numerous 
inconsistencies for the years 2015-2017.  

2.1.1.1  EMU1 European hake (passive gears) 

HKE is exploited by three main types of passive (static) fishing gears in EMU1 by Spain (ESP) and 
FRA  fleets.  Passive  gears  are  operated  by  small  to  medium  sized  vessels  (VL0006  to  VL1218) 
operating  set  longlines  (LLS),  gillnets  (GNS)  and  to  a  lesser  extent  trammel  nets  (GTR).  The 
quarterly  evolution  of  HKE  LPUE  in  GSA1  shows  the  main  contributor  to  LPUE  is  VL1218  using 
GNS, while LLS activity was very low for the years 2019-2020. In this last two years the LPUE of 
HKE in GSA1 comes practically entirely from set nets, except in the last quarter of 2020 when the 
contribution of LLS in VL0612 appears to become significant again. Vessels in the smallest length 
class VL0006 have very low production of HKE (fig. 2.1.1.1.1). 

In GSA5 only the vessel class VL0612 is active on HKE, with most of the LPUE attributed to GNS 
except  in  certain  years  (2016  and  first  half  of  2017;  2019)  when  the  contribution  of  LLS  is 
dominant (fig. 2.1.1.1.1).  

In GSA6 the main vessel length-gear combination responsible for HKE LPUE is VL1218 operating 
LLS,  also  for  some  years  (2015,  2019)  vessels  in  the  medium-size  class  VL1218  are  important. 
The  LPUE  produced  by  vessels  in  classes  VL0612  and  VL1218  operating  GNS  is  low  (20-30%  of 
total  LPUE)  but  consistent  across  all  years  and  quarters.  As  in  GSA1,  vessels  in  the  smallest 
length class VL0006 have sporadic, very low production of HKE (fig. 2.1.1.1.1). 

Two  member  states  operate  passive  gears  in  GSA7:  ESP  and  FRA,  but  only  FRA  has  consistent, 
high production of HKE. The  ESP units operating in GSA7 use practically always LLS,  with some 
quarters  of  inactivity  in  2015,  2016  and  2017  and  are  in  most  cases  attributed  to  the  VL1218 
vessel  length  class.  In  FRA  instead  most  of  the  HKE  LPUE  is  due  consistently  to  units  operating 
GNS  in  the  length  class  VL1218,  with  some  contribution  of  the  smaller  VL0612  class.  Note 
however than in the last quarter of 2020 practically the entire HKE production is attributed to the 
VL0612  vessel  length  class  operating  different  types  of  set  nets  (GNS,  GTR,  GTN)  or  even  LLS, 
which had very low incidence on previous quarters (fig. 2.1.1.1.1). 

37 

 
 
 
 
 
 
Overall  no  significant  changes  in  the  proportions  of  HKE  LPUE  allocated  to  the  different  vessel 
length classes and métier combinations can be appreciated for the period 2015-2020. 

2.1.1.2  EMU1 European hake (active gears) 

HKE is produced by otter bottom trawls (OTB) operated by ESP in GSA1, 5, 6 and 7. In GSA7 the 
FRA trawlers operate increasingly twin and midwater trawls (OTT and OTM).  

In  GSA1,  50%  or  more  of  the  HKE  LPUE  is  consistently  (across  all  quarters)  produced  by  large 
trawlers operating OTB in the vessel length class VL2440, attributed to the main three demersal 
métiers:  DEF,  MDD  and  DWS,  with  predominance  of  DEF.  A  variety  of  mid-size  to  small  vessels 
have  some  contribution  to  LPUE  consistently  each  quarter  of  the  period  2015-2020  in  small 
proportions  each,  mostly  attributed  to  the  DEF  métier.  The  smallest  vessel  length  class 
represented  in  the  data  set  (VL0612)  had  some  contribution  to  LPUE  only  in  the  first  two  years 
(fig. 2.1.1.2.1).  

In GSA5 the contribution of the largest vessel class VL2440 to HKE LPUE has increased from less 
than 20% in 2015 to practically 50% in recent years. Again, HKE LPUE is attributed to the main 
three  demersal  métiers  but  the  contribution  of  DEF  is  more  important.  As  in  GSA1,  units  in 
VL1218  and  VL1824  also  have  consistent  contributions  to  HKE  LPUE  along  the  entire  period, 
distributed among the three demersal métiers (fig. 2.1.1.2.1). 

38 

 
 
 
 
 
Figure 2.1.1.1.1 LPUEs of European hake caught by passive gears in EMU 1 calculated in relation to fishing days, by GSA and fleet segment.

39 

 
 
 
 
 
 
In GSA6, 50% or more of the HKE LPUE is consistently produced by large trawlers operating OTB 
in  the  vessel  length  class  VL2440,  with  the  largest  share  attributed  to  the  DEF  métier  (cf.  with 
GSA1 and GSA5 where the contributions of MDD and DWS where relatively important). Similar to 
GSA1,  vessel  length  classes  VL1218  and  VL1824  produce  consistent  proportions  of  HKE  LPUE 
along the entire period 2015-2020, attributed to the three demersal métiers, with predominance 
of DEF. The contribution of small vessels VL0612 is testimonial (fig. 2.1.1.2.1). 

In GSA 7, HKE LPUE for ESP is split practically equally between the largest vessel class VL2440 
and the medium sized VL1824 and the LPUE is generally attributed to the DEF métier. Note 
however sporadic contribution to HKE LPUE by units in VL1218 by ESP in some quarters of the 
period and in the entire year 2020. In FRA between 60 and 70% of the HKE LPUE is contributed 
by the largest vessel class VL2440 operating OTB, OTM, or OTT generally in the DEF métier. 
Consistent contribution the VL1824 class in the DEF métier is also observed (fig. 2.1.1.2.1). 

Overall no significant changes in the proportions of HKE LPUE allocated to the different vessel 
length classes and métier combinations can be appreciated for the period 2015-2020. 

2.1.1.3  EMU1 Blue and red shrimp (active gears) 

The  Blue  and  red  shrimp  (ARA)  is  produced  exclusively  by  ESP  otter  bottom  trawl  in  EMU1  in 
GSAs 1, 5 and 6. For the purposes of this analysis the relatively small production of ARA in GSA2 
is included in GSA1 and the production of ARA by ESP in GSA7 is combined within GSA6. 

In  GSA1  the  three  vessel  length  classes  VL1218,  VL1824  and  VL2440  produce  similar 
contributions  to  ARA  LPUE.  Most  of  the  production  is  attributed  to  métier  DWS,  but  a  certain, 
non-negligible amount is attributed to MDD (fig. 2.1.1.3.1).  

In  GSA5  the  contribution  of  the  smaller  VL1218  vessel  length  class  to  ARA  LPUE  is  very  low 
across  the  entire  study  period.  The  contribution  of  the  MDD  métier  is  relatively  more  important 
here than in GSA1 and the chart shows that small proportions of ARA LPUE are attributed even to 
the DEF métier for vessel length class VL2440 (fig. 2.1.1.3.1). 

In  GSA6,  similar to GSA1, the three vessel length classes VL1218, VL1824 and VL2440 produce 
comparable  contributions  to  ARA  LPUE.  In  some  quarters  of  the  years  2015-2016  small 
contributions  to  LPUE  by  the  smaller  vessel  length  class  VL0612  are  also  shown.  In  GSA6  ARA 
LPUE is consistently attributed to métiers DWS and MDD. 

Overall  no  significant  changes  in  the  proportions  of  ARA  LPUE  allocated  to  the  different  vessel 
length classes and métier combinations can be appreciated for the period 2015-2020. 

40 

 
 
 
 
 
Figure 2.1.1.2.1 LPUEs of European hake caught by passive gears in EMU 1 calculated in relation to fishing days, by GSA and fleet segment.

41 

 
 
 
 
 
 
42 

 
 
 
 
 
 
Figure 2.1.1.3.1 LPUEs of Blue and red shrimp caught by trawling gears in EMU 1 calculated in relation to fishing days, by GSA and fleet segment.

43 

 
 
 
 
 
2.1.2  Data issues in EMU 1 

The DCF Fisheries Dependent Information (FDI) data made available to EWG 22-01 for GSAs 1, 2, 
5, 6, 7 submitted by ESP had numerous inconsistencies in Tables A (landings) and G (effort) for 
the  years  2015-2017.  These  inconsistencies  were  revelead  by  internal  checks  of  the  quarterly 
data  series  for  the  primary  indicators  of  interest  to  EWG  22-01:  total  landings  for  Table  A,  and 
total  fishing  days  and  total  sea  days  for  Table  G.  Cross  checks  with  the  data  bases  in  the 
Mediterranean  and  Black  Sea  (MBS,  DCF  2021)  and  in  the  Annual  Economic  Report  electronic 
appendix were also made. The data issues identified for the two target stocks (hake:  HKE;  blue 
and red shrimp: ARA) and the demersal fleets were as follows: 

2.1.2.1  FDI Table A (Landings) 

The landings for the years 2015-2017 of HKE and ARA in FDI Table A are consistenly lower (40 to 
50% lower, depending on the year and quarter) than the landings reported in the MBS data set 
(fig.2.1.2.1.1). Figures  2.1.2.1.1-.2  compare the value landings of HKE and ARA in the different 
GSAs  of  EMU1  exploited  by  the  ESP  fleets.  The  MBS  landings  data  (in  grey)  are  systematically 
higher than the FDI data for the years 2015-2017 but coincide exactly in the years 2018-2020. 

Figure  2.1.2.1.1  Landings  per  quarter  of  European  hake  (HKE)  in  the  GSAs  exploited  by  ESP  in  EMU1 
according to the FDI_A data (color) and from the MED_BS data (DCF 2021) (grey). 

44 

 
 
 
 
 
 
 
 
Figure  2.1.2.1.2  Landings  per  quarter  of  blue  and  red  shrimp  (ARA)  in  the  GSAs  exploited  by  ESP  in  EMU1 
according to the FDI_A data (color) and from the MED_BS data (DCF 2021) (grey). 

2.1.2.2  FDI Table G (Effort) 

Immediately prior to EWG 21-13 a new data  set was made  available to the group from the  ESP 
administration  to  correct  from  previous  inconsistencies.  The  group  from  EWG  22-01  notes  that 
this  new  dataset  is  still  showing  inconsistencies  in  the  indicator  total  fishing  days.  Figures 
2.1.2.2.1-.2 show this indicator in the original FDI Table G data set and in the revised FDI Table 
G.  Both  data  sets  are  inconsistent  for  the  years  2015-2017,  specifically  OTB  which  is  the  most 
important gear contributing to the total catches of Western Mediterranean MAP target species in 
EMU1. The total fishing days are largely underreported for all vessel length classes of OTB of ESP 
in  EMU1  for  years  2014-2017  (Fig.  2.1.2.2.1).  Total  fishing  days  in  other  sources,  such  as  the 
electronic  annex  to  the  Annual  Economic  Review  or  the  MED_BS  data  set,  show  a  progressive 
decrease in fishing effort for all vessel length classes, from high values in 2014 to low values in 
2020. This downward trend is correctly reported in the revised FDI Table G data  for  OTB vessel 
classes  VL1218  and  VL1824  (Fig.  2.1.2.2.2)  but  not  for  VL2440  or  VL40XX.  Moreover,  the  data 
series  for  the  latter  ends  in  2017  (Fig.  2.1.2.2.2).  Furthermore,  for  OTB  the  revised  data  series 
adds two vessel length classes (VL0010 and VL1012) with incomplete time coverage. In addition, 
in the revised Table G data set further inconsistencies were introduced in other fishing gear. For 
instance,  compare  LLS,  GNS  and  PS  in  the  figures  2.1.2.2.1-.2).  The  data  series  for  PS,  which 
was correct in the original FDI Table G, shows now in the revised table very large values of effort 
for  the  years  2014-2017,  incosistent  with  2018-2020  for  all  length  classes  (Fig.  2.1.2.2.2). 
Similarly  GNS  and  LLS  show  abrupt  jumps  for  certain  vessel  length  classes  in  the  revised  data 
series (Fig. 2.1.2.2.2). Although not relevant for this EWG, the values of fishing days reported for 
DRB are more than one order of magnitude higher in the revised FDI Table G compared with the 
original.  Note  also  the  very  large  amount  of  effort  reported  for  “NK”  fishing  gear  in  the  revised 
table (Fig. 2.1.2.2.2). 

45 

 
 
 
 
 
 
 
Figure  2.1.2.2.1.  Total  fishing  days  per  vessel  length  and  fishing  gear  for  ESP  fleets  in  EMU1  in  the  original 
(submitted to the official 2021 FDI datacall) FDI Table G data set. 

Figure  2.1.2.2.2.  Total  fishing  days  per  vessel  length  and  fishing  gear  for  ESP  fleets  in  EMU1  in  the  revised 
(submitted prior to EWG 21-13) FDI Table G data set. 

Figure 2.1.2.2.3 shows in more detail the discrepancy between the original FDI Table G effort and 
the MBS Table effort in total fishing days for the main three demersal fishing gears 

46 

 
 
 
 
 
 
 
 
 
Figure 2.1.2.2.3. Fishing effort (total fishing days) in GSAs 1, 2, 5, 6 and 7 exploited by ESP by vessel class 
for the three main fishing gears. Continuous line: effort table from MBS (DCF 2021); broken line: Table G from 
FDI (original table). 

The total fishing days indicator in FDI Table G was compared with the electronic data from the 
Annual Economic Report (STECF 21-08) and partial agreement for certain years and combinations 
gear  /  vessel  length  was  found.  Instead,  the  indicator  total  sea  days  in  FDI  Table  G  (revised) 
and  in  the  electronic  appendix  to  STECF  21-08  coincided  almost  exactly.  For  this  reason,  it  was 
decided to use  total  sea days as proxy for fishing effort by ESP in EMU1 for the present  EWG. 
Furthermore,  as  fishing  days  and  sea  days  are  equivalent  1  to  1  in  ESP,  due  to  the  historical 
limitation of fishing trips to 12 or 15 h per day, the use of the correctly reported  total sea days 
in the present EWG is justified. 

2.1.3  LPUE data by quarter in EMU 2 

Landings  per  Unit  Effort  (LPUE)  for  European  hake  (HKE),  blue  and  red  shrimp  (ARA)  and  giant 
red shrimp (ARS) were computed from FDI data (Tables A and G) for FRA (GSA 8) and ITA (GSAs 
9, 10 and 11). 

2.1.3.1  EMU2 European hake (passive gears) 

As per Figure 2.1.3.1.1 in GSAs 8, 9 10 and 11 (FRA and ITA), HKE is targeted by three different 
passive  gears:  gillnets  (GNS),  trammel  nets  (GTR)  and  longlines  (LLS),  generally  belonging  to 
length classes from VL0006 to VL1218.  
Overall,  no  significant  changes  in  the  proportions  of  HKE  LPUE  allocated  to  the  different  vessel 
length classes and métier combinations can be appreciated for the period 2015-2020, but  some 
differences are present among the GSAs. 
GSA  8  is  the  only  one  in  which  small  vessels  (VL0006  and  VL0612)  are  dominant  in  terms  of 
contribution to HKE LPUE. Set nets are the most present, while longlines strongly contribute only 
in the last part of the timeseries. 
Vessels  belonging  to  length  class  VL1218  are  the  most  represented  in  GSA9,  especially  by  set 
nets.  The  main  fleet  segment  responsible  for  HKE  LPUE  is  VL1218  operating  GNS.  The  LPUE 
produced  by  vessels  in  length  classes  VL0006  and  VL0612  is  low  (less  than  10-20%  of  total 
LPUE), but consistent across all years. 

47 

 
 
 
 
 
 
 
 
In GSA 10 there is an important contribution of vessels operating LLS, belonging to length classes 
from  VL0612  to  VL1824.  Also  small-medium  vessels  (VL0612  and  VL1218)  operating  GNS  are 
well represented, giving more than 30% of total HKE LPUE. 
Similarly to GSA 9, vessels in length class VL1218 are the most represented in GSA 11, operating 
both  set  nets  and  longlines.  Only  in  the  last  two  quarters  of  2020,  vessels  in  class  VL1824 
operating LLS appear to consistently increase their contribution to LPUE. 

2.1.3.2  EMU2 European hake (active gears) 

As  per  Figure  2.1.3.2.1  HKE  is  exploited  by  bottom  otter  trawls  (OTB)  with  three  different 
métiers: vessels targeting demersal species (DEF), the ones targeting deep water species (DWS) 
and those targeting a mixed demersal species and deep water species (MDD).  
In GSA 8 only trawls targeting demersal species are operating, especially vessels in length classes 
VL2440 and VL1218. 
In  GSA  9,  the  two  largest  length  classes  (VL1824  and  VL2440)  are  equally  represented, 
contributing  around  30-35%  of  total  LPUE  each.  In  both  cases,  production  is  splitted  between 
vessels  targeting  demersal  species  (DEF)  and  the  mixed  ones  (MDD).  Vessels  targeting  deep 
water species have very low production of HKE. 
Vessels  in  length  class  VL1824,  DEF  and  MDD,  are  the  most  important  in  GSA  10  in  terms  of 
contribution to LPUE, until 2018. In the last two years, largest vessels (more than 24 m) started 
increasing their contribution to LPUE (35-50% of total LPUE, with maximum in the second quarter 
of 2020). Again, the most present métiers are DEF and MDD. 
The  contribution  of  vessels  VL2440  is  quite  consistent  and  dominant  in  GSA  11.  The  LPUE 
produced by vessels in length class VL1824 increases since 2017. 

2.1.3.3  EMU2 Blue and red shrimp and giant red shrimp (active gears) 

As per Figure 2.1.3.3.1-2 the blue and red shrimp (ARA) and the giant red shrimp (ARS) in EMU2 
are produced by bottom otter trawl attributed generally to DWS and  minimally to MDD  métiers. 
For this species, LPUEs have been calculated only for GSAs 9, 10 and 11, as there are no landings 
reported in GSA 8. 
In  GSA  9  the  most  contributing  fleet  segment,  both  for  ARA  and  ARS,  is  OTB-DWS  VL1824 
(around  40%  of  total  LPUE  for  each  species),  followed  by  OTB-DWS  VL1218  (20-25%  of  LPUE). 
Small  proportions  of  LPUE  are  also  attributed  to  MDD  métiers  of  same  length  classes  and  MDD 
métier of larger vessels (VL2440). 
In  GSA 10  fleet segment OTB-DWS VL1824 is the most contributing to ARA and ARS  LPUE  until 
2018, while in the last two years more than 40% of LPUE are attributed to vessels in length class 
VL2440.  A  lower  but  non-negligible  amount  of  LPUE  is  attributed  to  vessels  in  length  class 
VL1218. 
In GSA 11 most of the contribution to ARA and ARS LPUEs is attributed to length classes VL2440. 
Production  given  by  vessels  in  length  class  VL1218  is  consistent  during  the  time  series,  while 
medium size vessels (VL1824) start increasing their contribution since 2017. 
All the three GSAs attribute a low amount of ARA and ARS LPUEs to small vessels (VL0612) in the 
last part of the time series, since 2018. This seems to be a misreporting issue, as small vessels 
are usually not equipped to perform deep water fishing activities (see data issues section). 

48 

 
 
 
 
 
49 

 
 
 
 
 
 
Figure 2.1.3.1.1 LPUEs of European hake caught by passive gears in EMU 2 calculated in relation to fishing days, by GSA and fleet segment. 

50 

 
 
 
 
 
51 

 
 
 
 
 
 
Figure 2.1.3.2.1 LPUEs of European hake caught by active gears in EMU 2 calculated in relation to fishing days, by GSA and fleet segment. 

52 

 
 
 
 
 
 
Figure 2.1.3.3.1 LPUEs of Blue and red shrimp caught by active gears in EMU 2 calculated in relation to fishing days, by GSA and fleet segment. 

53 

 
 
 
 
 
 
Figure  2.1.3.3.2  LPUEs  of  Giant  red  shrimp  caught  by  active  gears  in  EMU  2  calculated  in  relation  to  fishing  days,  by  GSA  and  fleet  segment.

54 

 
 
 
 
 
2.1.4  Data issues in EMU 2 

The DCF Fisheries Dependent Information (FDI) data made available to EWG 22-01 for GSAs 8, 9, 
10 and 11 submitted by FRA and ITA did not show any particular inconsistency. 

The  only  issue  identified  checking  Tables  A  (landings)  and  G  (effort)  for  years  2015-2020,  is  a 
probable misreporting in terms of landings for vessels fishing ARA and ARS. Specifically, a small 
amount of landings for the two species is attributed to small vessels (VL0612) in the last part of 
the  time  series,  from  2018  to  2020  (GSAs  9,  10  and  11)  and  also  at  the  beginning  of  the  time 
series (first three quarters of 2015) in GSA 9 (fig. 2.1.4.1-2).  

Figure 2.1.4.1 Landings of ARA in EMU 2 by GSA and fleet segment. 

55 

 
 
 
 
 
 
 
 
Figure 2.1.4.2 Landings of ARs in EMU 2 by GSA and fleet segment. 

This  issue  is  not  evident  in  GSA  11  graphs,  as  the  landings  values  are  very  low,  but  they  still 
contribute to the computation of LPUEs (see table below). 

Year 

quarter 

VL0612 

VL1218 

VL1824 

VL2440 

ARA GSA11 Landings 

2015 

2015 

2015 

2015 

2016 

2016 

2016 

2016 

2017 

2017 

2017 

2017 

2018 

2018 

2018 

2018 

2019 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

1 

4.184 

7.059 

1.978 

3.767 

5.24 

1.567 

3.372 

6.04 

3.059 

2 

3.96 

0.334 

0.233 

0.597 

0.702 

1.486 

6.785 

17.084 

14.521 

4.779 

12.328 

23.383 

11.454 

18.597 

0.065 

8.248 

10.812 

9.935 

12.552 

17.535 

6.387 

2.123 

6.671 

26.309 

12.464 

38.452 

0.124 

0.034 

10.676 

18.623 

76.28 

0.014 

9.625 

21.454 

50.935 

3.65 

0.032 

8.082 

8.184 

9.741 

36.031 

35.682 

56 

 
 
 
 
 
 
 
 
 
 
   
 
 
 
 
   
 
 
 
 
 
2019 

2019 

2019 

2020 

2020 

2020 

2020 

2 

3 

4 

1 

2 

3 

4 

10.149 

22.677 

59.173 

7.331 

2.811 

3.255 

4.295 

4.315 

1.725 

15.51 

5.709 

8.755 

27.822 

42.408 

20.462 

14.228 

27.864 

14.892 

22.162 

4.159 

12.955 

0.004 

0.007 

Year 

quarter 

VL0612 

VL1218 

VL1824 

VL2440 

ARS GSA11 Landings 

2015 

2015 

2015 

2015 

2016 

2016 

2016 

2016 

2017 

2017 

2017 

2017 

2018 

2018 

2018 

2018 

2019 

2019 

2019 

2019 

2020 

2020 

2020 

2020 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

7.033 

11.866 

3.326 

1.035 

6.997 

2.91 

0.562 

0.391 

1.004 

11.406 

28.716 

24.409 

8.034 

1.303 

22.896 

2.76 

43.972 

3.995 

0.121 

29.834 

12.765 

6.262 

15.318 

35.524 

11.226 

23.31 

43.446 

5.681 

3.713 

2.693 

6.255 

6.426 

2.61 

5.211 

6.946 

5.016 

1.925 

2.201 

2.829 

2.961 

1.17 

11.862 

63.724 

3.942 

4.748 

25.167 

20.846 

12.676 

57.388 

14.673 

30.999 

5.871 

6.353 

23.132 

25.885 

15.519 

40.495 

10.612 

19.04 

3.907 

5.877 

9.051 

9.765 

2.774 

29.022 

22.655 

39.727 

34.122 

22.447 

0.073 

0.028 

0.007 

0.018 

0.003 

0.005 

57 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   
 
   
 
 
 
 
 
 
 
 
 
 
2.1.5  References 

Bellido,  JM.,  Sumaila,  UR.,  Sánchez-Lizaso,  JL.,  Palomares,  LM.,  Pauly,  D.  2020.  Input  versus 
output controls as instruments for fisheries management with a focus on Mediterranean fisheries. 
Marine Policy, 118: 4p. https://doi.org/10.1016/j.marpol.2019.103786  

Borges.  L.  2018.  Setting  of  total  allowable  catches  in  the  2013  EU  common  fisheries  policy 
reform: possible impacts. Marine Policy, 91: 97-103. 

Lizaso,  JLS,  Sola,  I,  Guijarro-García,  E,  Bellido,  JM,  Franquesa,  R.  2020.  A  new  management 
framework 
fisheries.  Marine  Policy,  112:  4  pp. 
https://doi.org/10.1016/j.marpol.2019.103772  

for  western  Mediterranean  demersal 

Lleonart J., Demestre M., Martín P., Rodón J., Sainz-Trápaga S., Sánchez P., Segarra I., Tudela S. 
2014. The co-management of the sand eel fishery of Catalonia (NW Mediterranean): the story of 
a  process.  In:  Lleonart  J.,  Maynou  F.  (eds),  The  Ecosystem  Approach  to  Fisheries  in  the 
Mediterranean 
doi: 
Seas. 
http://dx.doi.org/10.3989/scimar.04027.25A  

87-93. 

78S1: 

Black 

Mar. 

and 

Sci. 

Pope,  J.  Chapter  4  -  Input  and  output  controls:  the  practice  of  fishing  effort  and  catch 
management 
75-94. 
https://www.fao.org/3/y3427e/y3427e04.pdf    

responsible 

fisheries. 

(Italy) 

Rome 

FAO, 

in 

58 

 
 
 
 
 
2.2 

Progress of operational mixed fisheries models  

2.2.1  IAM in EMU 1 

2.2.1.1  Recall on the main issues and conclusions from EWG 21-13  

The implementation of the IAM model for GSAs 1-5-6-7 carried out during the STECF meeting is 
still  in  development.  During  EWG  21-13,  a  major  element  missing  was  the  differentiation  of 
fishing effort between Spanish demersal trawling and deep water trawling. It was pointed out that 
a  parameterisation  of  the  IAM  model  at  fleet-metier  level  would  address  this  issue,  and  that  it 
was essential if maximum catch limit (MCL) scenarios were investigated. 

Further  developments  suggested  were  the  integration  of  additional  socio-economic  indicators 
such as employment, gross profit and gross profit margin, provided that the relationship between 
Full-Time  Equivalents  (FTE)  and  fishing  effort  were  discussed  before  the  meeting,  and  that  if 
additional indicators were requested, they should be provided before the meeting, and sufficiently 
in  advance  to  adapt  the  model.  It  was  also  pointed  out  that  scenarios  involving  changes  in  the 
number of vessels, rather than just changes in fishing effort, could be explored if included in the 
TORs.  

2.2.1.2  Implementation progresses in EWG 22-01  

IAM  (Impact  Assessment  Model),  a  bioeconomic  mixed  fishery  simulation  model,  was 
implemented in EMU1, following the experiences gained in EWG 19-14, EWG 20-13 and EWG 21-
13. 

The  data  sources  used  for  the  French  and  Spanish  update  were  the  FDI  data,  the  Annual 
Economic Report (AER) data, the landings and discards data from the Med and Black sea data call 
(MBSDC), and the outputs of the EWG 21-11 group on the Mediterranean Stock Assessment. 

2.2.1.3  Stocks 

The stocks that were explicitly modelled in the IAM-Med model from the previous STECF EWG 21-
13 were used in the EWG 22-01. 

This includes: 

- 

- 

- 

- 

- 

- 

- 

European hake (Merluccius merluccius) in GSAs 1-5-6-7,  

Red mullet (Mullus barbatus) in GSA 1,  

Red mullet (Mullus barbatus) in GSA 6, 

Red mullet (Mullus barbatus) in GSA 7,  

Norway Lobster (Nephrops norvegicus) in GSA 6, 

Blue and red shrimp (Aristeus antennatus) in GSA 1, 

Blue and red shrimp (Aristeus antennatus) in GSAs 6-7.  

Stochastic  recruitment  has  been  explicitly  considered  for  those  stocks,  as  in  EWG  21-13.  To 
build a random succession of recruitments for  stocks to be applied on the 2021-2031 projection 
period,  11  years  are  randomly  drawned  with  replacement  from  the  available  historical  period 
(2009-2020).  Each  draw  will  determine  for  each  projection  year  the  annual  recruitment 
combinations  to  be  applied  for  each  stock.  100  such  trajectories  are  simulated  and  used  to 
build confidence intervals. Simulations run from 2020 to 2031. 

59 

 
 
 
 
 
 
 
 
Furthermore,  landings  of  the  following  species  from  the  management  plan  are  simulated  in  the 
IAM model, however the dynamics of these species are not explicitly represented, due to  lack of 
analytical or accepted stock assessments. They are referenced hereafter as “static species”, and 
associated catches are  simulated as a linear function of the simulated fishing effort, assuming a 
constant value of Landings Per Unit of Effort (LPUE). LPUE data are based on the 2020 values. 

- 

- 

- 

- 

Red mullet (Mullus barbatus) in GSA 5,  

Deep-water rose shrimp (Parapenaeus longirostris) in GSA 1, 5, 6 and 7,  

Blue and red shrimp (Aristeus antennatus) in GSA 5,  

Norway lobster (Nephrops norvegicus) in GSA 1, 5 and 7. 

The other « static species » considered in the model and that are not included in the management 
plan  are:  stripped  red  mullet  (MUR),  anchovy  (ANE),  sardine  (PIL),  Atlantic  mackerel  (MAC), 
monkfish  (MNZ),  common  octopus  (OCC),  octopuses  (OCT),  Atlantic  bluefin  tuna  (BFT),    and 
« ZZZ » (which stands for all other species caught per fleet).  

Table  2.2.1.3.1    Summary  of  the  stocks  in  the  Western  Mediterranean  management  plan  and  how  they  are 
integrated in the IAM model. 

in 
Stocks 
management plan  

Dynamic  or  static  in 
IAM 

Comment 

blue  and  red  shrimp 
(Aristeus 
antennatus)  in  GFCM 
subareas  1,  5,  6  and 
7 

Dynamic:  stocks  ARA1 
(GSA  1)  and  ARA67 
(GSAs 6-7) 

Static:  stock  ARA5  (GSA 
5) 

deep-water 
rose 
shrimp  (Parapenaeus 
longirostris)  in  GFCM 
subareas 1, 5 and 6 

Static: 
stocks  DPS1 
(GSA1),  DPS5  (GSA  5), 
DPS6  (GSA  6)  and  DPS7 
(GSA 7) 

population 

No 
the 
available 
assessments for ARA 5 

from 

dynamics 
stock 

population 

dynamics 
No 
available 
stock 
assessments  for  DPS  1,  5,  6 
and 7 

from 

the 

hake 

European 
(Merluccius 
merluccius)  in  GFCM 
subareas 1-5-6-7 

lobster 

Norway 
(Nephrops 
norvegicus)  in  GFCM 
subareas 5 and 6 

red  mullet  (Mullus 
barbatus) 
in  GFCM 
subareas  1,  5,  6  and 
7 

Dynamic: 
stock 
HKE1567  (GSAs  1-5-6-
7) 

Dynamic:  stocks  NEP6 
(GSA6) 

Static:  stock  NEP5  (GSA 
5) 

population 

No 
available 
assessments for NEP5 

from 

the 

dynamics 
stock 

Dynamic:  stocks  MUT  1 
(GSA 1), MUT 6 (GSA 6), 
and MUT 7 (GSA 7)  

Stock  assessment  are  not 
available  for  Mullus  barbatus  in 
GSA 5. 

Static:  stock  MUT  5 
(GSA 5) 

60 

 
 
 
 
 
 
  
 
2.2.1.4  Fleet segments 

The  fleet  typology  used  is  based  on  the  fleet  segmentation  of  the  Data  Collection  Framework 
(DCF). A fleet segment is the combination of a particular fishing technique category and a vessel 
length category. Spanish and French fleet segments are based on what was used in EWG 19-14, 
EWG 20-13 and EWG 21-13 implementations. The parameters of EWG 21-13 model were used in 
this implementation.   

The  French  fleets  represented  are  French  demersal  trawlers  18-24m,  French  demersal  trawlers 
>24m,  other  French  vessels  <12m  and  other  French  vessels  >12m.  The  “other”  vessels  are 
essentially gillnetters, but encompass all forms of French small-scale fisheries in the Gulf of Lions 
(pots and traps, lamparo, longliners, etc.). 

Regarding  the  Spanish  fleets,  six  Spanish  fleet  segments  were  considered:  Spanish  trawlers  < 
12m,  Spanish  trawlers  12-18m,  Spanish  trawlers  18-24m,  Spanish  trawlers  >24m,  Spanish 
netters and Spanish vessels using hooks.  

Compared  to  what  was  done  in  EWG  21-13,  the  IAM  model  has  been  adapted  to  differentiate 
between  coastal  and  deep-sea  trawling.  This  concerns  the  three  Spanish  fleet  segments: 
Spanish  trawlers  12-18m,  Spanish  trawlers  18-24m,  and  Spanish  trawlers  >24m  where  a 
distinction between fishing effort toward deep-sea trawling and coastal trawling has been made. 
The entry “Target-assemblage” in the FDI database has been used to parametrize the IAM model 
(see Table 2.2.1.4.1 for more details).  

Table 2.2.1.4.1  Modelled fleets in the IAM application in EMU1 and correspondence with FDI and AER fleets. 
“!= ” means different. DFN stands for Drift and/or fixed netters, DTS for Demersal trawlers and/or demersal 
seiners, HOK for Vessels using hooks, and DWS for deep water species. 

Fleet 

Fleet segments in AER   Fishing  techniques  in 

FDI  

IAM 
segment 
names 

Names  
the 
in 
report 
figures  

French 
demersal 
trawlers  18-
24m 

French 
demersal 
trawlers  24-
40m 

fs_name  =>  FRA  MBS 
DTS1824 NGI* 

FR_Trwl 
1824m 

cluster_name=> MBS DTS 
VL1824 

fishing 
tech  =  DTS  
vessel_length = VL1824 

FR_Trwl 
>=24m 

fs_name  =>  FRA  MBS 
DTS2440 NGI* 

cluster_name=> MBS DTS 
VL2440 

fishing 
tech  =  DTS  
vessel_length = VL2440 

“Metier” 
explicitly 
modelled 

No  distinction 
as  no  effort 
and catches for 
in  FDI 
DWS 
database 

No  distinction 
as  no  effort 
and catches for 
DWS 
in  FDI 
database 

Other  French 
vessels 
<12m 

FR_Oth 
<12m 

Not relevant 

Other  French 
vessels 
>12m 

FR_Oth 
>=12m 

Not relevant 

tech 

fishing 
!=  DTS  
vessel_length = VL0006 
and VL0612  

!=  DTS  
fishing 
tech 
vessel_length 
= 
VL1218,  VL1824,  and 
VL2440 

61 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
country_code =ESP 

Spanish 
trawlers    < 
12m 

SP_Trwl 
< 12m 

fs_name  =>  ESP  MBS 
DTS0612 NGI 

fishing 
vessel_length 
VL0006, and VL0612 

tech  =  DTS  
= 

sub_region= 
GSA1, 
GSA5 , GSA6 and GSA7 

No  distinction 
as  no  effort 
and catches for 
DWS 
in  FDI 
database 

fishing 
tech  =  DTS  
vessel_length = VL1218 

DWS 

Spanish 
trawlers  12-
18m 

Spanish 
trawlers  18-
24m 

Spanish 
trawlers 
>24m 

Spanish 
netters 

fs_name  =>  ESP  MBS 
DTS1218 NGI 

target_assemblage  = 
DWS 

SP_Trwl 
1218m 

fishing 
tech  =  DTS  
vessel_length = VL1218 

other 

target_assemblage 
DWS 

!= 

fishing 
tech  =  DTS  
vessel_length = VL1824  

DWS  

SP_Trwl 
1824m 

fs_name  =>  ESP  MBS 
DTS1824 NGI 

target_assemblage  = 
DWS 

fishing 
tech  =  DTS  
vessel_length = VL1824  

other 

target_assemblage 
DWS 

!= 

tech  =  DTS  
fishing 
vessel_length = VL2440 

DWS  

SP_Trwl 
>=24m 

fs_name  =>  ESP  MBS 
DTS2440 NGI 

target_assemblage  = 
DWS 

fishing 
tech  =  DTS  
vessel_length = VL2440  

other 

target_assemblage 
DWS 

!= 

SP_DFN 

fs_name  =>  ESP  MBS 
DFN0612  NGI  and  ESP 
MBS DFN1218 NGI 

tech  =  DFN  
fishing 
= 
vessel_length 
VL0006, 
VL0612, 
VL1218,  VL1824,  and 
VL2440 

62 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Spanish 
vessels  using 
hooks 

SP_LLS 

fs_name  =>  ESP  MBS 
HOK0612  NGI*,  ESP  MBS 
HOK1218  NGI*,  ESP  MBS 
HOK1218  LLD*,  and  ESP 
MBS HOK1824 LLD* 

cluster_name=> 
MBSHOKVL0612NGI, 
MBSHOKVL1218NGI, 
MBSHOKVL1218NGILLD, 
MBSHOKVL1824NGILLD 

tech  =  HOK 
fishing 
= 
vessel_length 
VL0612, 
VL0006, 
VL1218,  VL1824,  and 
VL2440 

On the basis of this segmentation, the FDI data (table A) were used to represent the proportions 
of  gears  employed  by  each  IAM  fleet  segment  (Fig.  2.2.1.4.1).  The  fishing  gear  classification  is 
the  FAO  classification,  that  can  be  consulted  here:  https://www.fao.org/cwp-on-fishery-
statistics/handbook/capture-fisheries-statistics/fishing-gear-classification/en/. 

63 

 
 
 
 
 
 
 
 
 
 
gear type 

Fle
et 
seg
me
nts 

 Figure  2.2.1.4.1.  Proportion  of  gear  usage  (computed  in  terms  of  %  of  landings  issued  from  each  gear) 
among  current  IAM  fleet  segments  (in  row).  The  “OUT”  segment  cumulates  all  fleet  segments  left  out.  T  is 
short for DTS and stands for demersal trawlers and/or demersal seiners, DFN for drift and/or fixed netters, 
and HOK for vessels using hooks. The vessel length class 12xx means all vessels superior to 12 meters.  For 
the  gear  types,  DRB  stands  for  boat  dredge,  FPO  for  pots  and  traps,  FYK  for  fyke  nets,  GNC  for  Encircling 
gillnets,  GNS  for Set gillnet,  GTN  for  Combined gillnets-trammel nets,  GTR  for Trammel net, LA  for Lampara 
nets, LHP for Hand and Pole lines, LLD for Drifting longlines, LLS for Set longlines, OTB for Bottom otter trawl, 
OTM for Midwater otter trawl, OTT for Multi-rig otter trawl, PS for Purse seine, SV for Beach and boat seine, 
and  NK  and  NO  for  unknown  gear.  Roughly,  red  to  orange  colors  corresponds  to  dredges  and  traps  (DRB  – 
FPO  –  FYK),  yellow  to  green  corresponds  to  various  gillnets  (GNC  –  GNS  -  GTN  –  GTR),  green  to  lightblue 
corresponds to longlines (LA  -LHP – LLD – LLS), darkblue corresponds to unknown gears (NK  – NO), purple 
corresponds to trawls (OTB – OTM – OTT), and purple to red corresponds to seines (PS – SV).  

Upon  examination,  Figure  2.2.1.4.1  reveals  some  heterogeneities  in  gear  usage  in  the  current 
non-trawlers  fleet  segments  in  IAM.  This  is  especially  true  for  the  French  small-scale  fishery 
<12m (FR_O_0012) which is constituted of a number of traps and dredge gears (FAO codes DRB 
–  FPO  –  FYK)  and  longlines  (LA  –  LHP  –  LLD  –  LLS).  This  is  not  surprising  as  this  IAM  fleet 
segment  FR_O_0012  currently  encompasses  many  different  fleets.  As  a  reminder,  the 
segmentation for the Spanish non-trawler fleets focuses on fishing technique while segmentation 

64 

 
 
 
 
 
 
 
for  the  French  non-trawler  fleets  only  focuses  on  vessel  size.  Figure  2.2.1.4.2  displays  the 
proportion of landings in weight and value of the species of the plan for each IAM fleet segments.  

species 

species 

Flee
t 
seg
me
nts 

Figure 2.2.1.4.2. Proportion of species of the plan landed by current IAM fleet segments (in row), according 
to  their  landings  in  weight  (left  panel)  and  in  value  (right  panel).  The  “OUT”  segment  cumulates  all  fleet 
segments left out. T is short for DTS and stands for demersal trawlers and/or demersal seiners, DFN for drift 
and/or  fixed  netters,  and  HOK  for  vessels  using  hooks.  Coloured  space  represents  species  targeted  by  the 
westmed  management  plan  (ARA:  Aristeus  antennatus,  DPS:  Parapeneus  longirostris,  HKE:  Merluccis 
merluccius,  MUT:  Mullus  barbatus,  NEP:  Nephrops  norvegicus),  and  grey  areas  (OTH)  represents  all  other 
species cumulated. 

2.2.1.5  Economic indicators 

Economic indicators are produced for the French and Spanish fleets. As in EWG 21-13, AER data 
were  used  to  parameterize  the  model.  The  economic  indicators  produced  are  revenue  per  fleet 
and gross value added (GVA) per fleet. It should be noted that these indicators are not produced 
for  the  two  fleets  "other  French  vessels  <12m"  and  "other  French  vessels  >12m",  which 
correspond to a very heterogeneous mix of vessels with a great deal of heterogeneity in their cost 
structure,  so  it  was  not  appropriate  to  produce  economic  indicators  for  them.  A  suggested 
adaptation  of  the  fleet  segmentation  in  IAM  to  be  carried  out  for  the  next  group  is  available  in 
section 4.1.1.2. 

Revenue corresponds to landings multiply by market prices, such as in equation (1):  

(1) 

With  GVLf,t  the  gross  value  of  landings  (or  revenue)  of  fleet  f  at  time  t,  Ls,c,f,t  the  landings  (in 
weight) of modelled stock s for commercial category c by fleet f at time t, ps,c,f the market price of 
stock s for commercial category c for fleet f, LPUEss,f the landing per unit of effort of static species 
ss for fleet f (i.e. not explicitly modelled species, static species include all species caught by the 
fleet f), pss,f the average market price of species ss for fleet f, and Ef,t the fishing effort of fleet f at 
year t. 

65 

 
 
 
 
 
 
 
 
 
Landings  per  commercial  category  are  estimated  from  landings  per  age-classes  and  using  the 
commercial categories/ages matrices described in the EWG 20-13 report. 

Calculation of the gross value added (GVA) by fleet is described in equation (2): 

(2) 

Where  fuelcf is the  fuel cost per unit of effort  of  fleet  f,  ovcf the other variable  costs per unit of 
effort (including landing costs, oil, bait, gear, food and ice costs), Ef,t the fishing effort of fleet f at 
time t, repf the reparations and maintenance costs and Fixcf the other annual fixed costs of fleet f 
(including costs related to equipment, insurance and management costs). 

Fishing effort is expressed in fishing days. 

2.2.2  ISIS-Fish in EMU 1 (GSA 7) 

2.2.2.1  ISIS-Fish implementation for European hake in GSA 7 

It  is  the  first  time  ISIS-Fish  is  used  to  evaluate  management  scenarios  for  the  West-Med  MAP. 
The model was until now used in an academic context and further developments are required to 
complete its transition towards an operational use. 

ISIS-Fish  was  parametrized  for  European  hake  in  GSA7  during  the  research  projects  (Galion  
http://www.amop.fr/le-projet-galion/ and Pechalo (Wendling et al. 2019, Leforestier et al.2020). 
It is spatially (0.5 x 0.5 degree resolution) and monthly resolved.  

Twenty fleets are considered parameterized according to declarative data. The 17 French trawler 
fleets were segmented according to home harbor, vessel length and strategy. Their effort is finely 
distributed over métiers (gear and area of practice) and across seasons. Métiers are defined as a 
gear (OTB, OTT, OTM) and an area of practice that is fleet dependent. A rougher modeling of the 
French gillnetter fleet and of the Spanish trawlers and longliners fleets is used, assuming only one 
métier and approximating spatial distribution of effort.  

Population dynamics of hake are based on the same assumptions as the assessment model from 
EWG 21-11 regarding age classes and natural mortality. It is further spatially distributed over two 
habitats, Plateau (shelf) and Accores (slope), according to Medits data, which results in a higher 
proportion of small individuals on the Plateau. The dynamics is seasonal, with weight continuously 
growing  and  recruitment  progressively  entering  the  fishery  in  course  of  the  year.  The  model  is 
currently  restricted  to  GSA7  and  assumes  no  connectivity  of  hake  between  GSAs.  Prices  vary 
between month but do not depend yet on fish size.  

The  model  is  fitted  to  catch  at  age  over  the  period  2015-2017  (available  from  declarative  data) 
but as a complex mechanistic model, it does not reproduce catches perfectly (fig.2.2.2.1.1) and 
the  results  should  be  considered  relative  to  a  base  case  rather  than  in  absolute  values  (Le 
Forestier et al. 2020, fig. 2.2.2.1.1). 

66 

 
 
 
 
 
 
 
Figure  2.2.2.1.1  Total  catch  in  ISIS-Fish  (isis)  for  HKE  in  GSA7  compared  with  values  from  the  assessment 
working  group  (STECF  EWG  21-11)  (assess)  for  calibration  years  2015-2017  and  hindcasted  years  (2018-
2020).  

Simulations are run for 2015-2030 with recruitment forced at observed values (adapted from the 
assessment)  for  2015-2020  and  using  the  average  2018-2020  after.  Effort  per  month  and  its 
distribution among fleet and métiers is forced to observed values for 2015-2017 and then equals 
the average 2015-2017.  

Maximum catch limits were simulated for European hake in GSA7. It was therefore necessary to 
scale down the MCL value defined for EMU1 to catch in GSA7. The MCL for EMU1 was multiplied 
by the historical (average 2018-2020) proportion of catch in GSA7 compared to EMU1. The GSA7 
MCL was then further apportioned to the trawler fleets according to their historical share (2018-
2020) of the catch in GSA7. 

Because of the very different nature of the ISIS-Fish model compared to the assessment model, 
fishing mortalities cannot be compared. An equivalent of Fmsy in ISIS was computed as 0.27 and 
used as a reference for HCR and evaluation of objectives. We assumed the same ratio applied 
between F0.1 and the average F reported by the STECF EWG 21-11 over the 2015-2017 period 
and between ISIS-Fish equivalent F0.1 and average value between 2015-2017 (F0.1/F2015-2017 
= 0.25).  

Within  the  simulation,  during  the  year,  fleets  concerned  by  MCL  behave  according  to  their 
historical  effort  pattern  until  the  MCL  is  reached.  Thereafter,  under  the  assumption  of  a  strict 
implementation of the landing obligation, they stop fishing assuming no report on other species is 
possible. In case of monthly MCL, monthly flexibility was not yet implemented.  

When  the  HCR  is  enforced,  the  MCL  is  computed  in  January  of  the  management  year  assuming 
abundance,  age  structure,  previous  year  F  and  upcoming  recruitment  perfectly  known.  Mean 
weights at age used in the MCL computation are average weight in the catch of the previous year. 

67 

 
 
 
 
 
 
 
2.2.3  BEMTOOL in EMU 2 

2.2.3.1  State of completion during EWG 22-01  

BEMTOOL  bio-economic  simulation  model  was  implemented  for  EMU2,  following  the  experiences 
gained  in  EWG  19-01,  19-14,  20-13  and  21-13.  DCF  data  (FDI  and  MED&BS  Data  Call  on 
landings,  discards,  fishing  effort,  biological  and  economic  parameters)  and  results  from  the 
assessments  carried  out  during  the  EWG  21-11  were  analysed,  to  allow  the  parameterization  of 
BEMTOOL during EWG  22-01. The model included the seven stocks (section 2.2.3.2) covered by 
the Multiannual Management Plan (MAP) in EMU2 (GSAs 9-10-11).  

During  EWG  22-01  the  model  used  in  EWG  21-13  was  further  refined,  to  take  into  account  the 
different types of fishing activity exerted by each fleet segment at metier level.  The allocation of 
the different fleet segments to the metiers were accomplished using the routine developed in the 
SECFISH project (details in chapter 2.2.3.3). Assessed fishing mortality, spawning stock biomass 
and  the  observed  catches  were  compared  with  the  simulated  ones,  as  done  in  previous  EWGs. 
Stock-recruitment relationships of the seven stocks were estimated using Eqsim  during EWG 21-
13.  Results  for  deep-water  rose  shrimps  and  blue  and  red  shrimp  were  not  satisfactory  so  the 
stocks  were  projected  using  the  recruitment  used  in  the  short  term  forecasts.  The  same 
assumptions on recruitment used previously, were maintained. 

2.2.3.1 

Space and time scale 

EMU2  belongs  to  the  FAO  fishing  area  37.1;  sub-division  1.1  and  1.3;  it  includes  three 
geographical subareas (GSA) according to the GFCM convention2: GSA9 – Ligurian Sea and North 
Tyrrhenian  Sea;  GSA10  –  Southern  and  Central  Tyrrhenian  Sea  and  GSA11,  composed  by 
Western  (GSA11.1)  and  Eastern  (GSA11.2)  Sardinia.  As  the  model  is  not  spatially  explicit,  the 
spatial scale covers the whole area. The time scale of the available DCF data goes from 2006 to 
2020.  The  time  scale  of  the  model  encompasses  the  same  time  range  for  the  hindcasting.  For 
2020  FDI  effort  data  was  used  in  the  model  and  for  2021  it  was  assumed  that  in  the  EU  Reg 
2021/90. For 2022 the same effort of 2021 was assumed, while the maximum catch limits in EU 
Reg  2022/110  were  considered  for  ARS  and  ARA.    The  forecasts  are  covering  the  period  from 
2022  to  2025,  however  the  projections  were  performed  to  2030  to  check  the  biological  and 
economic results in the medium term. The time scale of BEMTOOL has a monthly resolution. The 
reference  years  on  which  the  reductions  of  effort  in  fishing  days  are  computed  are  2015-2017. 
Average reference fishing days are thus calculated for this time frame. 

2.2.3.2 

Stocks  

The  stocks  taken  into  consideration  in  BEMTOOL  simulations  are  those  for  which  analytic  stock 
assessment results from EWG 21-11 were available:  

-  European hake in GSAs 9, 10 and 11 (HKE); 

-  Red mullet in GSA9 (MUT9); 

-  Red mullet in GSA10 (MUT10); 

-  Deep-water rose shrimp in GSAs 9, 10 and 11 (DPS); 

-  Giant red shrimp in GSAs 9, 10 and 11 (ARS); 

-  Norway lobster in GSA9 (NEP9); 

-  Blue and red shrimp GSA9, 10 and 11 (ARA). 

2 Res. GFCM/33/2009/2 on the establishment of geographical subareas in the GFCM area of application 

68 

 
 
 
 
 
                                                 
Table 2.2.3.3.1 reports the parameters of the stock-recruitment relationships for the seven 
stocks. 

Table 2.2.3.3.1  Parameters of the stock recruitment relationships 

Stock 

European hake GSAs 9-10-11 

Red mullet GSA9 

Red mullet GSA10 

Deep water rose shrimp GSAs 9-10-11* 

Giant red shrimp GSAs 9-10-11 

Norway lobster GSA9 

Blue and red shrimp GSAs 9-10-11* 

Break Point 
(b) 
3000 

500 

300 

350 

400 

a 

112 

575 

503 

710 

119 

*for this stock the recruitment of STF carried out in EWG 21-11 for projections was used. 

The  relevant  results  of  the  assessment  for  the  model  parameterization,  i.e.  the  current  fishing 
mortality (Fcurr) and the reference point (F0.1) are reported in Table 2.2.3.3.2 

The table also reports the upper and lower range of FMSY, according to the formulas used in EWG 
21-11: 

and the needed reduction to reach F0.1 for each stock.  

Considering the ratio between the current fishing mortality and the reference point (Fcurr/F0.1), the 
stocks  more  at  risk  are  blue  and  red  shrimp  (ARA;  ratio=5.8)  and  European  hake  (HKE; 
ratio=2.9).  Red  mullet  in  GSA10  (MUT10),  red  mullet  in  GSA9  (MUT9)  and  Nephrops  in  GSA  9 
(NEP9)  are  considered  underexploited,  while  deep-water  rose  shrimp  is  slightly  overexploited 
(ratio 1.2) and giant red shrimp (ARS) is overexploited with a ratio of 2.1.  

69 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 2.2.3.3.2 Results of the assessments from EWG 21-11 relevant for BEMTOOL parameterization. 
The computation of the reduction by stock to reach F0.1 is also reported. 

Stock 

Fcurrent 

F0.1 

Hake 8-9-10-11 
Red mullet 9 
Red mullet 10 
Pink shrimp 9-10-11 
Giant red shrimp 9-10-11 
Nephrops 9 
Blue and red shrimp 9-10-11 

0.5 
0.37 
0.31 
1.58 
0.98 
0.15 
1.68 

0.17 
0.52 
0.4 
1.29 
0.46 
0.3 
0.29 

Change 
in F 

-67% 
37% 
27% 
-19% 
-35% 
100% 
-82% 

The  results  of  the  stock  assessment  for  the  7  considered  stocks  were  replicated  in  BEMTOOL, 
considering  the  effort  by  metier  for  each  fleet  segment.  The  comparison  of  F,  SSB  and  Catch 
showed  a  good  level  of  agreement  between  BEMTOOL  and  the  stock  assessment  results  (Figure 
2.2.3.3.2). 
and 
2.2.3.3.1 

70 

 
 
 
 
 
 
 
  
Figure  2.2.3.3.1  Comparison  between  stock  assessment  results  with  95%  confidence  interval  (pink)  and 
BEMTOOL estimates (blue dots) on F, SSB and Catch for HKE 8-9-10-11 and MUT10. 

71 

 
 
 
 
 
 
 
 
 
 
 
72 

 
 
 
 
 
 
 
 
 
 
 
73 

 
 
 
 
 
 
 
 
Figure  2.2.3.3.2  Comparison  between  stock  assessment  results  with  95%  confidence  interval  (pink)  and 
BEMTOOL estimates (blue dots) on F, SSB and Catch for MUT9, DPS 9-10-11, ARS9-10-11, NEP9 and ARA 9-
10-11. 

2.2.3.3 

Fleets 

In  the  simulation  and  forecast  scenarios  19  fleet  segments  corresponding  to  36  metiers  (see 
Table  2.2.3.4.1)  were  analysed.  Trawlers  have  been  disaggregated  by  fishing  activity  at  metier 
level  (OTB_DEF,  OTB_DWS  and  OTB_MDD)  as  shown  in  Table  2.2.3.4.1.  Transversal  data  from 
the  Annual  Economic  Report  (STECF  21-08)  were  used  for  the  period  2008  to  2013  while  from 
2014  onward  FDI  data  were  used.  STECF  18-07  EU  Fleet  Economic  and  Transversal  data  from 
2008 to 2013 were used because those years are not present at metier level in the STECF 21-08 
EU Fleet Economic and Transversal data. All data include both active and passive demersal gears 
operated  by  fleet  segments  that  rely  on,  and  influence,  some  or  all  the  stocks  included  in  the 
MAP. Six fleets are allocated to GSA9 and to GSA11 and seven fleets to GSA10, overall 12 fleets 
are trawlers and 7 fleets use passive gears. 

The fuel costs, the other variable costs and the labour costs have been disaggregated at  metier 
level following the methodology to disaggregate  economic variables by activity developed in  the 
SECFISH 
SI2.768889, 
https://datacollection.jrc.ec.europa.eu/docs/regional-grants).  This  methodology  allows  to  take 
into account the difference in the variable costs associated to the activity of each metier as well 
as  the  difference  in  the  labour  costs  as  depending  on  the  revenues  and,  thus,  indirectly  by  the 
metier.  The  SECFISH  methodology  is  divided  into  two  steps:  the  first  based  on  the  individual 
vessel costs, effort and revenues data, to derive the relationships between costs and transversal 

(MARE/2016/22- 

project 

74 

 
 
 
 
 
 
 
 
variables; the second step is represented by the disaggregation of the costs times series through 
the  relationships  (step  1)  and  the  transversal  variables.  For  this  application,  the  relationships 
related to the Italian fleet within SECFISH project were used to derive the costs at metier level.     

Fixed costs, maintenance costs and capital costs have been associated to the vessels and, thus, 
to the fleet segment (see Table 2.2.3.4.1) 

Table 2.2.3.4.1 Combinations fleet segments-metier included in the BEMTOOL simulations and forecast 
scenarios by GSA, gear type, including demersal trawlers (DTS) and polyvalent passive gears (PGP), and 
vessel length (VL). 

GSA 9 

GSA 10 

GSA 11 

GSA9_DTS_VL0612 DEF 

GSA9_DTS_VL1218 DEF 

GSA9_DTS_VL1218 DWS 

GSA9_DTS_VL1218 MDD 

GSA9_DTS_VL1824 DEF 

GSA9_DTS_VL1824 DWS 

GSA9_DTS_VL1824 MDD 

GSA9_DTS_VL2440 DEF 

GSA9_DTS_VL2440 MDD 

GSA9_PGP_VL0012 

GSA10_DTS_VL0612 
DEF 
GSA10_DTS_VL1218 
DEF 
GSA10_DTS_VL1218 
DWS 
GSA10_DTS_VL1218 
MDD 
GSA10_DTS_VL1824 
DEF 
GSA10_DTS_VL1824 
DWS 
GSA10_DTS_VL1824 
MDD 
GSA10_DTS_VL2440 
DEF 
GSA10_DTS_VL2440 
DWS 
GSA10_DTS_VL2440 
MDD 
GSA10_PGP_VL0006 

GSA11_DTS_VL0612 DEF 

GSA11_DTS_VL1218 DEF 

GSA11_DTS_VL1218 
DWS 
GSA11_DTS_VL1218 
MDD 
GSA11_DTS_VL1824 DEF 

GSA11_DTS_VL1824 
DWS 
GSA11_DTS_VL1824 
MDD 
GSA11_DTS_VL2440 DEF 

GSA11_DTS_VL2440 
DWS 
GSA11_DTS_VL2440 
MDD 
GSA11_PGP_VL0012 

GSA9_PGP_VL1218 

GSA10_PGP_VL0612 

GSA11_PGP_VL1218 

GSA10_PGP_VL1218 

S
T
D

P
G
P

2.2.4  References 

Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  –  Stock  Assessments: 
demersal  stocks  in  the  western  Mediterranean  Sea  (STECF-21-11).  Publications  Office  of  the 
European  Union, 
ISBN  978-92-76-46116-6, 
doi:10.2760/046729, JRC127744 

Luxembourg,  2021,  EUR  28359  EN, 

Technical 

Scientific, 
– 
Evaluation  of  the  fishing  effort  regime  in  the  Western  Mediterranean  –  part  VI  (STECF-21-13). 
Publications Office of the European Union, Luxembourg, (2021). 

Committee 

Economic 

Fisheries 

(STECF) 

and 

for 

Scientific, 
Evaluation of fishing effort regime in the Western Mediterranean – part V (STEC-20-13). 

Committee 

Economic 

Technical 

Fisheries 

and 

for 

(STECF) 

– 

Scientific,  Technical  and  Economic  Committee 
Meeting Report (PLEN-19-01). Publications Office of the European Union, Luxembourg, (2019). 

for  Fisheries  (STECF)  –  60th  Plenary 

75 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Scientific,  Technical  and  Economic  Committee 
(STECF)  –  Evaluation 
of fishing effort regime in the Western Mediterranean – part IV (STECF-19-14). Publications Office 
of the European Union, Luxembourg, (2019). 

for  Fisheries 

2.3  Management scenarios and results  

2.3.1  IAM in EMU 1 

2.3.1.1  Management scenarios considered 

The scenarios that were tested are based on the implementation of Maximum Catch Limits (MCL) 
on  three  stocks:  European  hake  in  GSAs  1-5-6-7  (HKE1567),  Blue  and  red  shrimps  in  GSA  1 
(ARA1)  and  blue  and  red  shrimps  in  GSA  6  (ARA6).  According  to  the  scenario,  MCLs  are 
implemented for one, two or the three stocks. The scenarios are summarized in table 2.3.1.1.1.  

The scenarios starts in 2021 where the reduction of effort from the 2021 regulation is taken into 
account.  As  per  TORs,  the  effort  reductions  for  trawlers  and  longliners  written  in  the  2022 
regulation were not implemented in the simulations.  

As  in  EWG  21-13,  catchability  values  were  set  to  the  2020  values.  Fishing  effort  in  2020  are 
assumed identical to the one described in the EU Regulation 2021/90 for EMU1. 

It  is  important  to  note  that  for  all  scenarios,  the  MCL  implementation,  and  therefore  the 
associated  changes  in  fishing  effort  concern  only  the  trawl  fleets  (i.e.  French  demersal  trawlers 
18-24m,  French  demersal  trawlers  >24m,  Spanish  trawlers  <12m,  Spanish  trawlers  12-18m, 
Spanish trawlers 18-24m and Spanish trawlers >24m). The values of Fmsy catch and F transition 
catch from the stock assessment group EWG 21-11 for HKE1567, ARA1 and ARA67 were used to 
set the MCL values in the MCL scenarios (described in table 2.3.1.1.1). For the MCL scenarios: the 
value of Ftransition catch for HKE1567 and values from the regulation for ARA were used for the 
MCL in 2022, and the value of Fmsy catch was used for 2025. A linear decrease was calculated 
for  2023  and  2024.  Regarding  the  reverse  MCL  scenario,  the  same  values  were  used  in  reverse 
(i.e., value of 2022 MCL was used for 2025, etc.).  

Fishing capacity and fishing effort of the other modelled fleets are constant (and based on 2020 
values).  To  note  also  that  fishing  capacity,  i.e.  numbers  of  vessels  per  fleet  remain  constant 
through the simulation (based on the number of vessels in 2020). 

Table 2.3.1.1.1  Management scenarios and corresponding variables affected in the IAM model 

Scenario 

Baseline 

Fishing effort per vessel for 
trawl fleets 
(in fishing days) 
-17.5% in 2021 (=E2015-
20173*0.825) 
(and constant afterwards) 

Scenario a 

-17.5% in 2021 (=E2015-
2017*0.825) 

 fishing effort adjusted to reach 

HKE1567 MCL of 2022 

 fishing effort adjusted to reach 

Maximum Catch Limit 
(MCL) 

NO 

MCL on HAKE for trawlers 
2022 : 2435 tonnes (corresponds 
to Ftransition catch value from the 
stock assessment group EWG 21-
11) 
2023 : 1828 tonnes  
2024 : 1372 tonnes 

3 

 E2015-2017 corresponds to the average value of FDI fishing effort by fleet between 2015 and 2017. 

76 

 
 
 
 
 
 
 
 
 
                                                 
2025: 1220 tonnes (corresponds to 
Fmsy catch value from the stock 
assessment group EWG 21-11) 

HKE1567 MCL of 2023 

 fishing effort adjusted to reach 

HKE1567 MCL of 2024  

 fishing effort adjusted to reach 

HKE1567 MCL of 2025  

and same as in 2025 afterwards 
(2026-2031) 

Scenario b 

-17.5% in 2021 (=E2015-
2017*0.825) 

 fishing effort adjusted to reach 

HKE1567 MCL of 2022 

 fishing effort adjusted to reach 

HKE1567 MCL of 2023 

 fishing effort adjusted to reach 

HKE1567 MCL of 2024  

 fishing effort adjusted to reach 

HKE1567 MCL of 2025 

and same as in 2025 afterwards 
(2026-2031) 

Scenario c 

-17.5% in 2021 (=E2015-
2017*0.825) 

 fishing effort adjusted to reach 

ARA67 MCL of 2022 

 fishing effort adjusted to reach 

ARA67 MCL of 2023 

 fishing effort adjusted to reach 

ARA67 MCL of 2024  

 fishing effort adjusted to reach 

ARA67 MCL of 2025  

and same as in 2025 afterwards 
(2026-2031) 

Reverse MCL on HAKE for 
trawlers 
2022: 1220 tonnes 
2023 : 1372 tonnes 
2024 : 1828 tonnes  
2025 : 2435 tonnes  

MCL on ARA67 for trawlers 

2022 : 566.63 tonnes for Spanish 
trawlers (i.e. regulation values) 

2023 : 417 tonnes  
2024 : 307 tonnes  
2025 : 267 tonnes  

77 

 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
  
Scenario d 

-17.5% in 2021 (=E2015-
2017*0.825) 

Reverse MCL on ARA67 for 
trawlers 

 fishing effort adjusted to reach 

ARA67 MCL of 2022 

 fishing effort adjusted to reach 

ARA67 MCL of 2023 

 fishing effort adjusted to reach 

ARA67 MCL of 2024  

 fishing effort adjusted to reach 

ARA67 MCL of 2025  

2022 : 267 tonnes 
2023 : 307 tonnes  
2024 : 417 tonnes  
2025 : 566.63  tonnes 

Scenario e 

and same as in 2025 afterwards 
(2026-2031) 
-17.5% in 2021 (=E2015-
2017*0.825) 

 fishing effort adjusted to reach 

ARA1 MCL of 2022 

 fishing effort adjusted to reach 

ARA1 MCL of 2023 

 fishing effort adjusted to reach 

ARA1 MCL of 2024  

 fishing effort adjusted to reach 

ARA1 MCL of 2025  

and same as in 2025 afterwards 
(2026-2031) 

MCL on ARA1 for trawlers 

2022 : 123.90 tonnes for Spanish 
trawlers (i.e. regulation values) 

2023 : 78 tonnes 
2024 : 50 tonnes  
2025 : 33.05 tonnes 

Scenario f 

-17.5% in 2021 (=E2015-
2017*0.825) 

Reverse MCL on ARA1 for 
trawlers 

 fishing effort adjusted to reach 

ARA1 MCL of 2022 

 fishing effort adjusted to reach 

ARA1 MCL of 2023 

 fishing effort adjusted to reach 

ARA1 MCL of 2024  

 fishing effort adjusted to reach 

ARA1 MCL of 2025  

and same as in 2025 afterwards 
(2026-2031) 
-17.5% in 2021 (=E2015-
2017*0.825) 

 fishing effort adjusted to reach 
ARA1 and ARA67 MCL of 2022 
 fishing effort adjusted to reach 
ARA1 and ARA67 MCL of 2023 

Scenario g 

2022 : 33.05 tonnes 
2023 : 50 tonnes  
2024 : 78 tonnes 
2025: 123.90 tonnes 

MCL on ARA1 and ARA67 for 
trawlers 

With distinction between quota for 
ARA1 and quota for ARA67 

2022 : 123.90 tonnes for ARA1 and 

78 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 fishing effort adjusted to reach 

ARA1 and ARA67 MCL of 2024  

 fishing effort adjusted to reach 

ARA1 and ARA67 MCL of 2025  

and same as in 2025 afterwards 
(2026-2031) 

566.63 tonnes for ARA67 for 
Spanish trawlers (i.e. regulation 
values) 

2023 : 78 tonnes for ARA1 and 417 
tonnes ARA67 
2024 : 50 tonnes for ARA1 and 307 
tonnes for ARA67 
2025 : 33.05 tonnes for ARA1 and 
267 tonnes for ARA67  

Scenario h 

-17.5% in 2021 (=E2015-
2017*0.825) 

Reverse MCL on ARA1 and 
ARA67 for trawlers 

 fishing effort adjusted to reach 
ARA1 and ARA67 MCL of 2022 
 fishing effort adjusted to reach 
ARA1 and ARA67 MCL of 2023 
 fishing effort adjusted to reach 

ARA1 and ARA67 MCL of 2024  

 fishing effort adjusted to reach 

ARA1 and ARA67 MCL of 2025  

and same as in 2025 afterwards 
(2026-2031) 

With distinction between quota for 
ARA1 and quota for ARA67 

2022 : 33.05 tonnes for ARA1 and 
267 tonnes for ARA67 
2023 : 50 tonnes for ARA1 and 307 
tonnes for ARA67 
2024 : 78 tonnes for ARA1 and 417 
tonnes ARA67 
2025 : 123.9 tonnes for ARA1 and 
566,63 tonnes for ARA67 

Scenario i 

-17.5% in 2021 (=E2015-
2017*0.825) 

MCL on HKE1567 and on ARA1, 
and AR67 for trawlers 

 fishing effort adjusted to reach 
HKE1567, ARA1 and ARA67 
MCL of 2022 

 fishing effort adjusted to reach 
HKE1567, ARA1 and ARA67 
MCL of 2023 

 fishing effort adjusted to reach 
HKE1567, ARA1 and ARA67 
MCL of 2024  

(and constant afterwards) 
 fishing effort adjusted to reach 
HKE1567, ARA1 and ARA67 
MCL of 2025 

2022 : 123.90 tonnes for ARA1, 
566.63 tonnes for ARA67 (i.e. 
regulation values) and 2435 tonnes 
for HKE1567 

2023 : 78 tonnes for ARA1, 417 
tonnes ARA67, and 1828 tonnes for 
HKE1567 
2024 : 50 tonnes for ARA1, 307 
tonnes for ARA67, and 1372 tonnes 
for HKE1567 
2025 : 33.05 tonnes for ARA1, 267 
tonnes for ARA67, and 1220 tonnes 
for HKE1567 

Scenario j 

and same as in 2025 afterwards 
(2026-2031) 
-17.5% in 2021 (=E2015-
2017*0.825) 

Reverse MCL on HKE1567 and 
on ARA1, and AR67 for trawlers 

 fishing effort adjusted to reach 
HKE1567, ARA1 and ARA67 
MCL of 2022 

2022 : 33.05 tonnes for ARA1, 267 
tonnes for ARA67, and 1220 tonnes 
for HKE1567 

79 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 fishing effort adjusted to reach 
HKE1567, ARA1 and ARA67 
MCL of 2023 

 fishing effort adjusted to reach 
HKE1567, ARA1 and ARA67 
MCL of 2024  

(and constant afterwards) 
 fishing effort adjusted to reach 
HKE1567, ARA1 and ARA67 
MCL of 2025 

and same as in 2025 afterwards 
(2026-2031) 

2023 : 50 tonnes for ARA1, 307 
tonnes for ARA67, and 1372 tonnes 
for HKE1567 
2024 : 78 tonnes for ARA1, 417 
tonnes ARA67, and 1828 tonnes for 
HKE1567 
2025 : 123.90 tonnes for ARA1, 
566.63 tonnes for ARA67, and 
2435 tonnes for HKE1567 

For each stock, its MCL value was divided by fleet segment. Therefore, each fleet segment had a 
quota  by  year  by  stock.  The  allocation  of  the  MCL  by  fleet  was  proportional  to  their  2018-2020 
landings of the stock in weight (based on FDI data).   

The proportions are detailed in tables 2.3.1.1.2 to 2.3.1.1.4.  

Table 2.3.1.1.1  Maximum Catch Limit weighting by fleet segment for trawlers according to their FDI 2018-
2022 landings in weight by species by GSA. 

Fleet segments 

HKE1567 

ARA1 

ARA67 

French demersal trawlers 18-
24m 

French demersal trawlers 
>24m 
Spanish trawlers < 12m 

Spanish trawlers 12-18m 

Spanish trawlers 18-24m 

Spanish trawlers >24m 

0.075 

0.148 

0.002 

0.088 

0.417 

0.270 

0 

0 

0 

0 

0 

0 

0.149 

0.536 

0.315 

0.016 

0.391 

0.592 

The MCL level for ARA in 2022 from the regulation were adjusted to remove the landings of ARA 
in  GSA  5  (i.e.,  20.81  %  of  the  total  ARA  landings  in  EMU1,  according  to  2018-2022  FDI  data). 
Then the remaining quota was dispatched between ARA1 and ARA67 according to the  proportion 
of FDI 2018-2020 landings of Blue and red shrimps in GSA 1 and in GSAs 6 and 7 (i.e., 17.94% 
for ARA1 and 82.06% for ARA67).   

For 2022 to 2025, fishing efforts by fleet were adjusted  each year to  catch the fleet quota, and 
then for 2026 to 2031, the fishing effort per fleet was assumed constant and equal to their effort 
of 2025. 

For the three Spanish trawler fleets over 12 meters (i.e., the ones with the two metier: deep-sea 
and  coastal  trawling  that  were  explicitly  modelled),  a  report  of  the  effort  of  the  metier  catching 
the species the most towards the other metier was assumed. And if this was not enough to reach 
the quota, the fishing effort of the other metier was also reduced.  

For  the  scenario  with  MCL  implementation  on  both  European  hake  and  Blue  and  red  shrimps,  a 
reconciliation of the marginal scenarios (i.e. per stock) already implemented was carried out. 

80 

 
 
 
 
 
  
 
 
 
 
  
 
2.3.1.2  Results 

The 10 alternative scenarios described in section 2.3.1.1 were investigated using the IAM model. 
Simulations of the IAM model starts in 2020 and run up to 2031 (the year 2025 is represented by 
a vertical black line in each figure). 

Results  of  IAM  simulations  regarding  fishing  effort,  landings  by  trawlers,  total  landings  of 
Hake  in  GSAs  1-5-6-7,  red  mullet  in  GSA1,  GSA6  and  GSA7,  Norway  lobster  in  GSA6  and  blue 
and  red  shrimp  in  GSA  1  and  in  GSAs  6-7  by  fleet  segments,  and  Gross  Value  of  Landings 
(GVL) by fleet segment are, respectively, displayed in Figures 2.3.1.2.1 to 2.3.1.2.4. 

For  each  stock,  Figures  2.3.1.2.5  to  2.3.1.2.11  compare  the  evolutions  of  their  Fbar,  SSB  and 
total landings according to the different scenarios. Finally, table 2.3.1.2.1 compares biological and 
economic performances of each scenario in terms of the ratio of Fbar in 2025 to Fmsy per stock 
and GVA in 2025 to GVA in 2020 per fleet segment.  

The fishing mortalities, SSBs and Gross Value of Landings are displayed in section 1.2. 

81 

 
 
 
 
 
Figure  2.3.1.2.1.  Evolution  of  the  annual  fishing  effort  (in  fishing  days)  by  fleet  segment  for  each  alternative  scenario  from  2020  to  2031.  Vertical  black  lines 
indicate the year 2025. Scenarios are by columns and fleet segments by row. The fleet segments are as follow (from top to bottom): French demersal trawlers 
18-24m,  French  demersal  trawlers  >24m,  other  French  vessels  <12m  and  other  French  vessels  >12m,  Spanish  trawlers  <  12m,  Spanish  trawlers  12-18m, 
Spanish trawlers 18-24m, Spanish trawlers >24m, Spanish netters and Spanish vessels using hooks.  

82 

 
 
 
 
 
 
Figure 2.3.1.2.2. Evolution of the annual landings (in tonnes) of the modelled stocks by the trawl fleets (i.e. the ones for which MCL are implemented) for each 
alternative management scenario. Vertical black lines indicate the year 2025. Scenarios are in column and stocks in row. The  stocks are as follow (from top to 
bottom): Hake GSAs1-5-6-7 (HKE1567), red mullet GSA1 (MUT1), red mullet GSA6 (MUT6), red mullet GSA7 (MUT7), Norway lobster GSA6 (NEP6), blue and red 
shrimp GSA1 (ARA1) and blue and red shrimp GSAs6-7 (ARA67).  

83 

 
 
 
 
 
  
Figure 2.3.1.2.3. Evolution of the total annual landings (in tonnes) of the modelled stocks for each alternative management scenario. Vertical black lines indicate 
the year 2025. Scenarios are in column and stocks in row. The stocks are as follow (from top to bottom): Hake GSAs1-5-6-7 (HKE1567), red mullet GSA1 (MUT1), 
red mullet GSA6 (MUT6), red mullet GSA7 (MUT7), Norway lobster GSA6 (NEP6), blue and red shrimp GSA1 (ARA1) and blue and red  shrimp GSAs6-7 (ARA67). 
Historical values of landings are given in the white areas and simulated values in the blue area. 

84 

 
 
 
 
 
 
Figure 2.3.1.2.4. Evolution of the total Gross Value of Landings (GVL, i.e. revenues, in K euros) by fleet segment for each alternative scenario from 2020 to 2031. 
Vertical  black  lines  indicate  the  year  2025.  Scenarios  are  in  column  and  fleet  segment  in  row.  The  fleet  segments  are  as  follow  (from  top  to  bottom):  French 
demersal  trawlers  18-24m,  French  demersal  trawlers  >24m,  other  French  vessels  <12m  and  other  French  vessels  >12m,  Spanish  trawlers  <  12m,  Spanish 
trawlers 12-18m, Spanish trawlers 18-24m, Spanish trawlers >24m, Spanish netters and Spanish vessels using hooks. 

85 

 
 
 
 
 
 
Figure 2.3.1.2.5. EMU 1. Predicted median values for Hake in GSAs 1-5-6-7 (HKE1567) Fishing mortality (left), SSB (middle) and total landings (right) under the 
alternative scenarios (in colors). 

86 

 
 
 
 
 
 
Figure  2.3.1.2.6.  EMU  1.  Predicted  median  values  for  red  mullet  in  GSA  1  (MUT1)  Fishing  mortality  (left),  SSB  (middle)  and  total  landings  (right)  under  the 
alternative scenarios (in colors). 

87 

 
 
 
 
 
 
Figure  2.3.1.2.7.  EMU  1.  Predicted  median  values  for  red  mullet  in  GSA  6  (MUT6)  Fishing  mortality  (left),  SSB  (middle)  and  total  landings  (right)  under  the 
alternative scenarios (in colors). 

88 

 
 
 
 
 
 
Figure  2.3.1.2.8  EMU  1.  Predicted  median  values  for  red  mullet  in  GSA  7  (MUT7)  Fishing  mortality  (left),  SSB  (middle)  and  total  landings  (right)  under  the 
alternative scenarios (in colors) 

89 

 
 
 
 
 
 
Figure 2.3.1.2.9 EMU 1. Predicted median values for Norway lobster in GSA 6 (NEP6) Fishing mortality (left), SSB (middle) and total landings (right)  under the 
alternative scenarios (in colors) 

90 

 
 
 
 
 
 
 
Figure 2.3.1.2.10 EMU 1 Predicted median values for blue and red shrimp in GSA 1 (ARA1) Fishing mortality (left), SSB (middle) and total landings (right) under 
the alternative scenarios (in colors) 

91 

 
 
 
 
 
 
Figure 2.3.1.2.11 EMU 1. Predicted median values for blue and red shrimp in GSAs 6-7 (ARA67) Fishing mortality (left), SSB (middle) and total landings (right) 
colors)
under 

alternative 

scenarios 

the 

(in 

92 

 
 
 
 
 
 
 
Table 2.3.1.2.1. EMU1. Ratio Fbar 2025/Fmsy and ratio SSB 2025/SSB 2021 per stock by scenario. The median values are used. 

in 

Fbar 
2025/ 
Fmsy 

in 

in 

SSB 
2025/ 
SSB 
2021 

Baseline 
(E=E2021) 

Sce.a 

Sce.b 

Sce.c 

HKE1567 
MCL 

HKE1567 
MCL INV 

ARA67 
MCL 

Sce.d 
ARA67 
MCL INV 

Sce.e 

Sce.f 

Sce.g 

Sce.h 

Sce.i 

Sce.j 

ARA1MCL 

ARA1MCL 
INV 

ARA1  + 
ARA67 
MCL 

ARA1+ 
ARA67 
MCL INV 

ARA  + 
HKE  

ARA  + 
HKE  

MCL  

MCL INV 

4.21 

2.06 

2.71 

1.23 

0.96 

5.50 

3.07 

1.56 

0.90 

0.79 

0.43 

0.57 

2.09 

1.08 

1.08 

0.63 

0.68 

25.28 

26.60 

6.56 

7.38 

2.95 

1.56 

1.79 

1.21 

0.62 

1.02 

0.55 

3.46 

1.74 

2.17 

1.22 

0.75 

2.08 

1.09 

3.07 

1.80 

1.96 

1.21 

0.66 

0.85 

0.85 

3.82 

1.94 

2.48 

1.23 

0.85 

1.95 

1.69 

2.48 

1.46 

1.46 

1.20 

0.52 

0.73 

0.43 

2.86 

1.61 

1.74 

1.21 

0.61 

1.60 

0.87 

1.18 

0.90 

0.79 

0.43 

0.32 

0.40 

0.00 

1.68 

1.08 

1.08 

0.63 

0.41 

1.28 

0.46 

1.561 

4.242 

5.462 

1.794 

1.985 

1.508 

1.746 

1.918 

2.57 

5.851 

8.058 

1.364 

0.764 

0.796 

2.073 

1.134 

1.098 

1.826 

1.929 

1.421 

1.456 

1.37 

1.396 

1.441 

1.505 

1.827 

1.929 

1.131 

1.272 

0.802 

0.835 

0.757 

0.794 

0.829 

0.916 

1.131 

1.272 

1.075 

1.157 

0.798 

0.799 

0.795 

0.796 

0.798 

0.801 

1.076 

1.157 

2.329 

2.422 

2.162 

2.296 

2.094 

2.217 

2.23 

2.464 

2.682 

2.896 

0.663 

0.663 

1.962 

2.157 

1.815 

2.258 

2.076 

2.463 

2.536 

3.017 

0.68 

0.683 

1.72 

1.942 

1.445 

1.606 

1.775 

2.055 

2.355 

2.702 

HKE 1567 

MUT1 

MUT6 

MUT7 

NEP6 

ARA1 

ARA67 

HKE 1567 

MUT1 

MUT6 

MUT7 

NEP6 

ARA1 

ARA67 

93 

 
 
 
 
 
 
 
 
 
Table 2.3.1.2.2. EMU1. Ratio GVA 2025/GVA 2021 and ratio Fishing effort 2025/Fishing effort 2021 per fleet segment by scenario. The median values are used. 

Baseline 
(E=E2021) 

Sce.a 

Sce.b 

Sce.c 

HKE1567 
MCL 

HKE1567 
MCL INV 

ARA67 MCL 

Sce.d 
ARA67  MCL 
INV 

Sce.e 

Sce.f 

Sce.g 

Sce.h 

Sce.i 

Sce.j 

ARA1MCL 

ARA1MCL 
INV 

ARA1 
+ 
ARA67 MCL 

ARA1+ 
ARA67  MCL 
INV 

ARA + HKE  

ARA + HKE  

MCL  

MCL INV 

GVA 
2025/ 
GVA 
2021 

in 

in 

Fishing 
Effort 
2025/ 
Fishing 
effort 
2021 

in 

in 

Fr. trawlers 18-
24m 

Fr. trawlers >24m 

Sp. trawlers  < 
12m 
Sp. trawlers   
12-18m 
Sp. trawlers   
18-24m 
Spanish trawlers  
>24m 

Spanish netters 

Sp. vessels using 
hooks 

Fr. trawlers 18-
24m 

Fr. trawlers >24m 

Sp. trawlers  < 
12m 

Sp. trawlers   
12-18m 

Sp. trawlers   
18-24m 

Spanish trawlers  
>24m 

Spanish netters 

Sp. vessels using 
hooks 

1.065 

1.089 

1 

1 

1 

1 

1 

1 

1 

1 

1.084 

-0.644 

0.021 

-0.071 

0.511 

1.31 

1.23 

1.259 

1.196 

1.164 

1.428 

1.465 

-0.557 

0.213 

1.194 

1.148 

1.124 

1.317 

1.35 

0.009 

0.684 

-0.189 

0.022 

1.174 

1.174 

1.129 

1.123 

1.217 

1.251 

-0.181 

0.042 

1.066 

-0.013 

0.268 

0.255 

0.521 

0.948 

1.151 

0.273 

0.583 

-0.25 

0.148 

1.099 

0.084 

1.154 

1.021 

1.006 

0.049 

1.379 

1.106 

0.38 

0.25 

0.711 

1.225 

0.126 

0.494 

0.175 

0.599 

-0.319 

0.219 

0.335 

0.691 

1.412 

1.056 

1.065 

0.869 

1.032 

1.542 

1.038 

0.367 

0.741 

-0.193 

0.367 

1.08 

1.122 

1.562 

1.599 

1.16 

1.012 

1.018 

1.007 

1.01 

1.017 

1.037 

1.186 

1.295 

0.166 

0.355 

0.201 

0.428 

0.07 

0.18 

1 

1 

1 

1 

1 

1 

1 

1 

1 

0.465 

0.62 

0.353 

0.519 

0.864 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

0.166 

0.355 

0.201 

0.428 

0.07 

0.18 

0.353 

0.517 

0.715 

0.949 

0.616 

0.89 

0.278 

0.479 

0.278 

0.466 

0.535 

0.71 

0.27 

0.391 

0.609 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

94 

1 

1 

1 

0.27 

0.378 

1 

1 

1 

1 

0 

0 

0 

1 

1 

0.164 

0.17 

0.125 

1 

1 

 
 
 
 
 
 
 
 
 
2.3.1.3  Discussion 

The main objective of the management plan is to reach fishing mortality values in between Fmsy 
ranges  for  each  of  the  stock  mentioned  in  the  management  plan  by  2025.  IAM  simulations 
outputs  suggest  that,  beside  a  scenario  of  MCL  for  trawlers  on  both  Hake  and  Blue  and  red 
shrimps,  none  of  the  proposed  scenario  would  achieve  this  goal.  The  reverse  MCL  scenario  on 
both Hake and Blue and red shrimp does not allow reaching this objective for HKE1567. Similarly, 
this is the case for MCL on Hake only. This is because the values of MCL are the one estimated by 
the  stock  assessment  EWG  21-11  group,  and  these  values  are  applied  only  for  trawlers,  while 
they are estimated accounting for all gears. In order to reach Fmsy in 2025, catches from other 
fleet segments (such as longliners and gillnetters) should be taken into account in the Hake MCL 
estimation for trawlers.  

This would not have happened with scenarios where the fishing effort of the trawl fleet segments 
were adjusted to reach Hake Fmsy, rather than having an exogenous MCL value to reach.  

Still, the scenarios of MCL on Hake (a, b, i and j) lead to a strong reduction of Fbar for all stocks 
reviewed, placing most of them at or below Fmsy; except for ARA1 and ARA67 for which the Fbar 
is increasing. This increase in ARA1 and AR67 fishing mortalities is explained by the assumption 
in  the  model  that  the  fishing  effort  from  the  coastal  trawling is  reported  toward  the  deep-water 
trawl metier. This leads to an increase in the fishing pressures  on deep-water  species, including 
ARA. However, the economic consequences of these  scenarios are severe for all the trawl fleets. 
Indeed, the implementation of MCL on Hake in IAM lead to a massive GVA (Gross Value Added) 
drop  for  the  Spanish  and  French  trawler  fleet  segments.  However,  these  scenarios  are 
advantageous to Spanish gillnetters and vessels using hooks (especially longliners). Indeed they 
are  not  regulated  in  those  scenario  -  i.e.  their  fishing  effort  remain  constant  at  82.5%  of  their 
2015-2017 effort level -, and they are the primary benefactor from the increased SSB of Hake.   

The  scenarios  of  applying  a  MCL  and  a  reverse  MCL  on  ARA1  and/or  ARA67  (scenarios  c  to  h) 
negatively affect the economic performance (i.e. gross value added) only of Spanish trawlers over 
12 meters, while these scenarios benefit French trawlers and Spanish trawlers under 12 metres. 
Indeed, as these fleets do not catch ARA, their fishing effort is not reduced due to the application 
of an MCL on ARA, and they benefit from the increase in biomass of Hake, Red mullet and Norway 
lobster.  In  MCL  on  ARA  scenarios,  European  hake,  red  mullet  and  Norway  lobster  stocks  are 
indeed  experiencing  slight  SSB  increases  compared  to  the  baseline  scenario,  due  to  a  slight 
reduction  in  their  fishing  pressures  as  shown  in  the  Fbar  figures.  These  fishing  mortality 
reductions  are  explained  by  the  reduction  in  deep-water  trawl  effort.  In  the  IAM  model 
parametrization, based on FDI data, the deep-trawl metier indeed catches – to a small extent – 
European Hake, Red mullet and Norway lobster stocks. From an ecological point of view, the MCL 
scenarios  on  ARA  stocks  achieve  the  plan's  objectives  for  ARA,  while  the  reverse  MCL  scenarios 
do not completely.  

When comparing the MCL and reverse MCL scenarios, it is important to keep in mind that, in the 
IAM simulations, the fishing effort of the different fleet segments is assumed constant from 2026 
to  2031  and  equal  to  their  fishing  effort  in  2025.  Therefore,  the  efforts  of  the  trawler  fleets  are 
higher for the period 2026-2031 in the reverse MCL scenarios than in the MCL scenarios. 

As said in EWG 20-13  and EWG 21-13, it is important to note that landings per unit of effort of 
other  species  (than  the  ones  that  are  explicitly  modelled)  are  assumed  constant  in  time. 
Consequently,  potential  positive  impacts  of  effort  reduction  on  those  other  stock  biomasses  are 
not simulated and total landings might thus be underestimated. As the proportion of the landings 
of those  other  species  are very high for most  fleet segments, the negative economic impacts of 
the effort reduction management scenarios displayed might be overestimated in our simulations. 
This is true especially in the long run, as positive effect of effort reduction in stock biomasses are 
not instantaneous. 

95 

 
 
 
 
 
2.3.2  ISIS-fish in EMU 1 (GSA 7) 

2.3.2.1  Management scenarios considered 

Management  scenarios  are based on  TOR 1  of  EWG22-01 and inputs provided by the  experts in 
course of the working group as for the definition of MCL values and allocation between fleets and 
months.  The  five  scenarios  are  listed  in  Table  2.3.2.1.1.  The  annual MCL  2022-2025  was  either 
set based on a transition path from current fishing mortality to catch at Fmsy (using a priori set 
MCL values) between 2022 and 2025 (SCd and f) or as catch at Fmsy from 2022 on (accounting 
for  biomass  evolution,  HCR-like  scenario)  (SCe  and  g).  From  2026,  MCL  is  based  on  Fmsy.  An 
annual  MCL  allowing  a  race  for  quota  (SCd  and  e)  and  a  monthly  distributed  MCL  (SCf  and  g) 
were  compared.  MCLs  only  apply  to  trawlers.  MCL  is  split  by  fleet  (3  fleets):  Spanish  trawlers, 
French trawlers <24m, French trawlers >24m 

Table 2.3.2.1.1 Scenarios implemented in ISIS-fish for HKE in GSA 7. 

Scenario 

2015  2016  2017  2018  2019  2020 

2021 

2022 

2023  2024  2025 

… 
2030 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

15 

Calibration 

Hindcast 

Projection 

Effort 

2015  2016  2017 

Mean 2015-2017 

Mean 2015-2017 

Effort 
reduction 
trawlers 

(rel.2015-
17) 

Closures 

a 

-
10% 

-
17.5% 

-
17.5% 

-17.5% 

Existing ones 

Existing ones 

Recruitment 

2015  2016  2017  2018  2019  2020 

Mean 2018-
2020 

Mean 2018-2020 

MCL 

MCL 

MCL 

MCL 

d Annual 

= a + 
MCL 

e = a + 
MCL 
Inverse 

f = a + 
Monthly 
MCL 

g = a + 
Monthly 
MCL 
inverse 

2435 

1828  1372  1220 

HCR 
FMSY 

1220 

HCR FMSY 

2435 

1828  1372  1220 

HCR 
FMSY 

1220 

HCR FMSY 

96 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2.3.2.2  Results 

Results are discussed in terms of decrease in fishing mortality (F) and pressure over the 
different  stages,  biomass  and  fleet  revenues  and  change  in  effort  pattern  due  to 
limitation in catch.  

Figure 1.3.2.2.1: Predicted fishing mortality of hake (GSA7) by in the reference scenario (SCa) and the four 
MCL scenarios (d- annual transition, e- annual inverse, f- monthly transition, g- monthly inverse).  

97 

 
 
 
 
 
 
 
Figure 2.3.2.2.2: Fishing mortality at age (panels 0 to 5) of Hake GSA7 relative to average values 2015-2017 
as predicted by the ISIS-Fish model for the reference scenario (SCa) and the four MCL scenarios (d- annual 
transition, e- annual inverse, f- monthly transition, g- monthly inverse).  

Despite  a  significant  reduction  in  fishing  mortality,  Fmsy  is  not  sustainably  reached  in  2030 
because  of  the  unconstrained  catch  of  the  netters  and  longliners  (F2030  =  1.5  x  Fmsy)  (fig. 
2.3.2.2.1).  In  scenarios  with  the  transition  path,  F  dropped  progressively  from  2022  and  fell 
below  Fmsy  in  2025  (SCd)  or  2024  (SCf  -monthly).  This  is  due  to  the  rapid  rebuilding  of  the 
biomass, which made the pre-defined MCL values too constraining. However, when the HCR took 
over  in  2026,  the  fishing  mortality  increased  again  to  stabilise  at  0.38.  In  the  inverse  scenario, 
the  MCL  value  set  based  on  the  assessment  for  2022  allowed  reducing  fishing  mortality  to  0.68 
and  0.54  respectively  in  the  annual  and  monthly  scenario  (2.7  and  2  times  Fmsy).  F  then 
stabilised at 0.38 under the HCR control, just like in the transition path. 

Pressure  was  particularly  released  on  younger  age  classes  (-60%  to  80%)  with  the  annual  MCL 
(fig.  2.3.2.2.2).  On  the  contrary,  the  pressure  on  age-0  fishes  is  identical  as  in  the  reference 
scenario when the MCL is monthly spread. The processes at play are i) that the recruitment was 
modelled as constant and ii) that the recruitment arrives progressively in the fishery in course of 
the year. i) caused the pressure on age-0 fish to stay high because the MCL increased but not the 
recruitment  and  ii)  made  the  annual  MCL  act  as  a  protection  for  juveniles  because  the  fishery 
stopped before the peak of recruits entered the fishery. 

98 

 
 
 
 
 
 
 
Apart from the above-mentioned differences,  the pattern of  fishing mortality reduction is similar 
in  annual  and  monthly implementation  of  the  MCL,  but  the  reduction  is  stronger  in  the  monthly 
scenario for the older age classes.  

Figure 3.3.2.2.3 Biomass at age of hake in the two habitats (GSA7) predicted by the model relative to average 
values  2015-2017  for  the  reference  scenario  (SCa)  and  the  four  MCL  scenarios  (d-  annual  transition,  e- 
annual inverse, f- monthly transition, g- monthly inverse). 

Biomass  rebuilding  is  fast  and  particularly  spectacular  on  the  Plateau  (shelf)  (fig.  2.3.2.2.3). 
Because  of  the  constant  recruitment,  it  is  expected  that  the  effect  is  amplified  for  older  age 
classes and limited on younger fishes. 

99 

 
 
 
 
 
 
Figure  4.3.2.2.4  Gross  revenues  associated  to  hake  (GSA7)  per  fleet  as  predicted  by  the  model  relative  to 
average values 2015-2017 for the reference scenario (SCa) and the four MCL scenarios (d- annual transition, 
e- annual inverse, f- monthly transition, g- monthly inverse). 

The  model  predicted  an  important  decrease  of  trawler  revenues  in  the  first  years  of  MCL 
implementation  (fig.  2.3.2.2.4).  The  decrease  was  as  strong  as  -90%  for  Spanish  trawlers.  For 
French  trawlers  the  decrease  was  more  moderate  (-50%  to  -70%).  Netters  and  longliners 
benefitted  for  the  release  of  pressure  and  their  revenues  increased.  A  positive  effect  of  the 
monthly  spread  MCL  was  observed  which  allowed  significantly  higher  revenues,  and  even 
outperformed the reference scenario and historical averages (except for Spanish trawlers). 

In  terms  of  effort,  the  annual  MCL  imposed  very  early  closures  of  the  fisheries.  Generally, 
trawlers  fish  until  March  under  the  HCR  and  in  the  Inverse  scenario.  However  Spanish  trawlers 
were constrained to stop fishing in January in 2024 and 2025 of the transition scenario when the 
MCL was set too low.  

When the MCL is spread over months, effort is around 4 times lower than the average 2015-2017 
with variations depending on the fleet and the month of the year.  

100 

 
 
 
 
 
 
Figure 5.3.2.2.5 Month of choke per fleet predicted by the ISIS-Fish model for hake MCL in GSA7 for scenarios 
of annual MCL (d- annual transition, e- annual inverse). 

101 

 
 
 
 
 
 
 
Figure 6.3.2.2.6 Effort per month as a ratio of the effort in the same month between 2015 and 2017, per fleet 
as  predicted  by  the  ISIS-Fish  model  for  hake  MCL  in  GSA7  for  scenarios  of  monthly  MCL  (f-  transition,  g- 
inverse). 

2.3.3  BEMTOOL in EMU 2 

2.3.3.1  Baseline Run 2022-2025  

Eight scenarios among the ones listed in the ToRs were implemented:  

A)  Annual MCL (maximum catch limit) on ARS from the year 2022, set as the maximum 
catch  limit  in  EU  Reg  2022/110,  to  the  year  2025,  set  as  the  catch  associated  to 
Fmsy by EWG 21-11; 

B)  Annual MCL on ARS inverse: as  scenario A, inverting the MCL values  from 2022, to 

2025;  

102 

 
 
 
 
 
 
 
C)  Monthly MCL on ARS from 2022, from the year 2022, set as the maximum catch limit 
in EU Reg 2022/110, to the year 2025, set as the catch associated to Fmsy by EWG 
21-11;  

D)  Monthly MCL on ARS inverse: as scenario C, inverting the MCL values from 2022, to 

2025;  

E)  Annual MCL (maximum catch limit) on ARA from the year 2022, set as the maximum 
catch  limit  in  EU  Reg  2022/110,  to  the  year  2025,  set  as  the  catch  associated  to 
Fmsy by EWG 21-11; 

F)  Annual MCL on ARA inverse: as  scenario A, inverting the MCL values  from 2022, to 

2025;  

G)  Monthly MCL on ARA from 2022, from the year 2022, set as the maximum catch limit 
in EU Reg 2022/110, to the year 2025, set as the catch associated to Fmsy by EWG 
21-11;  

H)  Monthly MCL on ARA inverse: as scenario C, inverting the MCL values from 2022, to 

2025. 

A status quo (SQ) scenario was carried out assuming no change in management measures from 
2021. 

The  maximum  catch  limit  by  year  was  estimated  for  each  species  in  order  to  have  a  gradual 
reduction  from  the  maximum  catch  limit  in  the  EU  Reg  2022/110  and  the  catch  associated  to 
Fmsy as estimated by EWG 21-11. The maximum catch limits of ARS and ARA were split among 
the fleet segments and quarters according to their proportion in the landing in the FDI data in the 
reference period 2015-2017. The catch limit for the stocks by year and by quarter are reported in 
Table 2.3.3.1.  

First deterministic runs were done to get a first feedback on:  

1)  the  completeness  and  coherence  of 

inputs  and  of 

the  BEMTOOL 

parameterization;  

2)  the different scenarios settings.  

Then, given the computation time, stochastic runs were performed in a second step and are here 
reported. 

For all scenarios the basis was given by the number of fishing days by fleet as the average in the 
period 2015-2017. 

Table 2.3.3.1 MCL of ARS and ARA by year and by quarter (weights in tons). 

Year 

Quarters 

2022 

2023 

2024 

2025 

2022 

2023 

2024 

2025 

ARA 

ARA 

ARA 

ARA 

ARS 

ARS 

ARS 

ARS 

1 

44 

26 

15 

8 

71 

59 

49 

47 

2 

71 

42 

25 

13 

3 

96 

56 

33 

17 

106 

127 

88 

73 

70 

106 

88 

84 

4 

39 

23 

14 

7 

61 

50 

42 

40 

Tot 

250 

148 

87 

45 

365 

303 

252 

241 

103 

 
 
 
 
 
Scenarios A to D were carried out with the hypothesis of maximum catch limit on ARS (generally 
targeted  together  with  ARA),  while  scenarios  E  to  H  were  implementing  MCL  on  ARA  (the  most 
overexploited species). Scenarios B, D, F and H assumed inverse levels for catch limit, increasing 
the value toward 2025. The MCL associated to Fmsy was set according to the results of the short-
term  forecast  (STF)  carried  out  in  STECF  EWG  21-11.    The  model  works  at  monthly  level, 
checking  every  month  and  for  each  trawl  fleet  if  the  catch  limit  is  reached  or  not.  In  the 
hypothesis  of  annual  catch  limit  (scenarios  A,  B,  E  and  F),  when  the  catch  limit  is  reached,  the 
fleet  stops  to  fish  ARS  and  ARA,  targeted  by  OTB  DWS  and  OTB  MDD  metier,  and  allocates  the 
remaining effort to OTB DES, increasing the pressure on the stocks targeted by this metier. This 
is to  simulate that, when a catch limit on ARA and/or ARS is reached, the fleet moves on other 
fishing grounds, inhabited by other stocks. 

In  scenarios  C,  D,  G  and  H  the  same  scenarios  are  implemented  but  the  catch  limit is  set  on  a 
monthly  level,  according  to  the  distribution  of  the  landing  among  the  months.  These  scenarios 
were implemented to avoid the “run to fish” behaviour.  

The effort in the projections is assumed to be distributed among the months as in the last year of 
simulation; the monthly ratio between each fleet segment catch to the total catch (p coefficient, 
used to split the total F among the fleet segments in the BEMTOOL F formulation) in the forecast 
is the same of the last year of simulation.  

The  following  equation,  internally  applied  by  the  model  to  recalculate  the  fishing  mortality,  was 
used  to  reshape  the  F  by  fleet,  acting  on  the  activity,  as  modified  by  the  fleet  when  the  MCL  is 
reached: 

; 

where  fact,f    in  the  forecast  is  the  ratio  between  the  product  of  the  number  of  fishing  days,  the 
number of vessels and the average GT (or Kw) of the fleet segment f for each month of forecast 
to the product of the number of fishing days, the number of vessels and the average GT (or Kw) 
of the fleet segment f in the last year of the simulation. This quantity is considered as reference 
for the application of change in fishing effort. Self(a) is the fleet selectivity at a given length/age; 
pf is the monthly ratio between the fleet segment catch to the total catch in the simulation (in the 
forecast it is fixed as an average of the last (n) years). 

2.2.4  Runs performed and analysed during EWG 22-01: discussion 

The scenarios were implemented on the basis of historical information on the stocks status (SSB, 
F, catch) mimicked in BEMTOOL model that was observed in agreement with the outcomes of the 
STECF EWG 21-11.   

The performance of the scenarios was evaluated on the basis of spawning stock biomass, catch, 
F,  revenues,  gross  value  added  and  current  revenues  to  break-even  revenues  (CR/BER).  The 
latter  is  an  economic  indicator  that  shows  how  close  the  current  revenue  of  a  fleet  is  to  the 
revenue  required  for  the  economic  break  even.  Ratios  >  1  indicate  that  enough  income  is 
generated to cover operational costs (variable and non-variable costs) and therefore break-even. 
If  the  ratio  is  less  than  1,  insufficient  income  is  generated  to  cover  operational  costs  and 
therefore the fleet is in a loss. The formulations of CR/BER and gross value added follow: 

Gross value added: 

, 

104 

ffactfinpfpfaSelMmeanZaF**)(*))(()(, 
 
 
 
 
 
 
where Rf,t are the total revenues for fleet f at time t, VC the variable costs, FC the fixed costs and 
MC the maintenance costs; 

CR/BER (Annual Economic Report on the EU Fishing Fleet, 2013): ratio between current revenues 

(CR) and BER: 

, where 

 are other  fixed costs  for the fleet segment f  at 

time t; 

 are the depreciation costs; 

 the opportunity costs; 

 are the labour costs; 

are the variable costs ; 

 are the maintenance costs; 

 the total revenues.   

Figure 2.3.4.1 reports the reached F for each scenario and stock. The scenarios E and G, setting a 
MCL for ARA (annual and monthly respectively), are the ones reducing more the fishing mortality 
on ARS9-10-11 and ARA9-10-11. Both scenarios excessively decrease the F on ARS9-10-11  and 
ARA9-10-11 from 2026 and going below Fmsy for both stocks. This is due to the fact that in the 
scenarios the annual MCL are set at the beginning of the projections, according to STF carried out 
in 2021 and according to a gradual MCL decrease towards Fmsy level. Indeed, the hypothesis of a 
gradually  decreasing  MCL,  especially  in  scenario  A,  representing  the  “run  to  fish”  hypothesis, 
could  not  completely  accommodate  the  dynamic  of  the  stock,  that  is  expected  to  recover  when 
the MCL is implemented. A smaller MCL in the second year of implementation, would be reached 
before, respect to the previous year and so on. In scenario G, where the MCL is set monthly and 
the fishing effort split among the months according to the fishery seasonality, the F remains more 
stable even after 2025, although below the reference point. 
Scenarios F and H represent an alternative possibility to set the MCL, as a gradual increasing 
MCL, that theoretically  would allow for a new, possibly higher, MCL in the next year to be derived 
according to the new Fmsy. Both scenarios allow to importantly reduce the F approaching the 
reference point of ARA9-10-11, although scenario H allows to maintain a more stable level of F 
after. Both scenarios highlight that for ARS9-10-11 the F reaches values well below Fmsy, with 
risk of underutilization(Figure 2.3.4.1).  Moreover, when a MCL on ARA and/or ARS is reached, 
the fleet is expected to move on other fishing grounds, inhabited by other stocks, possibly 
changing the pressure on them. The change in pressure is modelled as a reallocation of the 
metier from OTB_DWS and OTB_MDD to OTB_DES.  
It is worth noting that the MCL scenarios would need to be further refined, to possibly 
accommodate the implementation of MCL on more than one species at a time. 

For  HKE9-10-11  no  scenario  will  be  effective  to  reach  Fmsy,  because  the  MCL  on  red  shrimps  
produces the expected effect to increase the fishing mortality exerted by the fleet on the fishing 
grounds inhabited by this stock, when applying the metier OTB_DES. A similar pattern is showed 
by the F  of MUT9 and  MUT10,  although the F  remains in the Fmsy  ranges.  In particular for this 
two  stocks  the  risk  of  underutilization  would  be  reduced  respect  to  Status  quo  scenario(SQ), 
under these alternative scenarios. Regarding DPS9-10-11, in almost all the scenarios, the fishing 
mortality  will  remain  in  line  or  slightly  above  the  Fupper.  For  NEP9  all  scenarios  show  the  F 
remaining  well  below  the  Fmsy  ranges,  with  a  risk  of  underutilization  of  this  resource,  currently 
fished below Flower(Figure 2.3.4.1).   

In Figure 2.3.4.2 are shown the SSB for the seven stocks under the  8 alternative scenarios  and 
the  SQ.  In  the  scenario  SQ  the  SSB  is  expected  to  remain  approximately  stable  for  ARS  and 
ARA9-10-11. For DPS9-10-11 and MUT10 there will be a certain decrease, but in the short term. 
An increase is foreseen instead for HKE9-10-11, NEP9 and MUT9 (Figure 2.3.4.2), given that the 
most  recent  perception  of  the  biomass  trend  in  the  stock  assessment  results  is  an  increasing 
pattern  compared to the last years. Catches would remain quite stable  for  some stocks, slightly 
decreasing  for  MUT10  and  DPS9-10-11  and  increasing  for  HKE9-10-11,  NEP9  (Figure  2.3.4.3). 
Total  revenues,  gross  value  added  and  CR/BER  for  the  overall  fleet  are  predicted  to  slightly 
increase respect to the lowest values of the time series reached in 2020-2021 (Figure 2.3.4.4-6). 

105 

 
 
 
 
 
 
For ARA9-10-11, ARS9-10-11 and NEP9 all MCL scenarios return a catch value that is below or in 
line  with  the  SQ,  while  for  the  other  stocks  the  catches  are  higher  than  the  SQ  for  all  the 
scenarios.    The  total  revenues  across  all  fleets  will  decrease  with  respect  to  the  situation  under 
the SQ,  remaining slightly above the recent values. A similar pattern is observed for R/BER and 
gross value added (Figures 2.3.4-6.4). 

All  the  alternative  scenarios  explored,  implementing  a  MCL  on  ARA  and  ARS,  mainly  target  by 
OTB_DWS and OTB_MDD metier, show an improvement in the SSB of ARA9-10-11, ARS9-10-11 
and  NEP9,  targeted  by  those  metier,  respect  to  the  SQ.  For  the  other  stocks,  targeted  also  by 
OTB_DES,  all  the  scenarios  return  an  SSB  value  lower  than  the  status  quo,  due  to  the  re-
allocation of the effort on OTB_DES, when the MCL is reached.  

The  SSB  of  ARS9-10-11  and  ARA9-10-11  increased  faster  under  scenario  E,  implementing  the 
MCL for ARA in the hypothesis of annual catch and “run to fish”, followed by scenario G, setting 
the MCL on ARA by month. A and C scenarios follow scenarios E and G, implementing the annual 
and the monthly MCL on ARS (Figure 2.3.4.2). For  NEP9 all the scenarios implementing MCL on 
ARA9-10-11 return higher  of SSB, followed by the scenarios implementing MCL on ARS. For  the 
other  stocks,  scenarios  E,  F,  G  and  H  generally  return  lower  SSB  values,  because  the  MCL  on 
ARA9-10-11 is lower than the MCL on ARS and, thus easier to hit. For this reason those scenarios 
correspond to a higher pressure on these stocks and a lower biomass level.  

Scenarios A, B,  C and  D allow to importantly approach the Fmsy range  of ARA and ARS, and in 
some  cases  to  go  below,  respect  to  the  current  fishing level.  For  the  other  stocks,  except  NEP9 
already exploited below Fmsy, the alternative scenarios do not allow to reach the Fmsy range. It 
is  important  to  notice  that  Fmsy  value  is  expected  to  change  in  time,  due  to  the  application  of 
management measures, but, for simplicity, it was assumed to be fixed along the years. 

Scenarios B and D, implementing a reverse MCL on ARS9-10-11 at annual and at monthly level 
respectively,  represent  the  scenarios  showing  a  performance  slightly  below  SQ  in  terms  of 
revenues  and  profitability.  This  is  due  to  the  fact  that  the  model  predicts    a  decrease  in  the 
revenues of the more valuable stocks, partly compensated by the increase in the revenues of the 
other stocks, assuming the same fishing effort and fleet capacity of 2021 (and, thus, variable and 
fixed costs) (Figures 2.3.4.4-6). B and D scenarios would produce an improvement in SSB values 
of  ARS  and  ARA  stocks  around  135%,  only  slightly  smaller  than  scenarios  E  and  G,  setting  the 
MCL on ARA (Table 2.3.4.2). Scenario D, based on monthly MCL is very similar to scenario B, but 
in the short term impacts less on the reduction of the catches.  

Following EWG 19-01 and 19-14, it is important to highlight that the results from EWG 21-13 for 
all  scenarios  are  based  on  the  assumption  that  a  reduction  in  F  is  a  direct  consequence  of  an 
effort  reduction.  Inclusion  of  hyperstability  in  BEMTOOL  was  explored  by  EWG  19-01  and  EWG 
20-13,  where  the  relationship  between  fishing  effort  and  fishing  mortality  was  assumed  non-
linear.  

In  the  next  figures,  for  F,  Catch  and  SSB,  the  historical  part  (until  2020)  is  represented  by  the 
stock assessment results replicated by BEMTOOL. 

106 

 
 
 
 
 
Fishing Mortality 

107 

 
 
 
 
 
 
108 

 
 
 
 
 
109 

 
 
 
 
 
Figure 2.3.4.1 – BEMTOOL. Trajectories of the fishing mortality (F) for the seven stocks in the hindcasting 
phase (until 2021) and in the forecast phase (after 2021) under the alternative scenarios. The black vertical 
dashed lines corresponds to 2021. Red horizontal solid line correspond to the FMSY=F0.1, and red horizontal 
dashed lines correspond to Fupper and Flower.

110 

 
 
 
 
 
 
SSB 

111 

 
 
 
 
 
 
 
 
112 

 
 
 
 
 
113 

 
 
 
 
 
Figure 2.3.4.2 – BEMTOOL. Trajectories of the SSB (in tons) for the seven stocks in the hindcasting phase 
(until 2021) and in the forecast phase (after 2021) under the alternative scenarios. Solid lines correspond to 
medians, while shaded area correspond to interquantile range between 5th and 95th quantiles, indicated by 
the dashed lines. The black dashed lines corresponds to 2021. 

114 

 
 
 
 
 
 
Catches: 

115 

 
 
 
 
 
 
 
116 

 
 
 
 
 
117 

 
 
 
 
 
Figure 2.3.4.3 – BEMTOOL. Trajectories of catches (tons) for the seven stocks in the hindcasting phase (until 
2021) and in the forecast phase (after 2021) under the alternative scenarios. Solid lines correspond to 
medians, while shaded area correspond to interquantile range between 5th and 95th quantiles, indicated by 
the dashed lines. The black dashed lines corresponds to 2021. 

118 

 
 
 
 
 
 
 
Total revenues (target stocks+ other species)  

Figure 2.3.4.4 – BEMTOOL. Trajectories of revenues (thousand Euro) for all fleets combined in the hindcasting 
phase (until 2021) and in the forecast phase (after 2021) under the alternative scenarios. Solid lines 
correspond to medians, while shaded area correspond to interquantile range between 5th and 95th quantiles, 
indicated by the dashed lines. The black dashed lines corresponds to 2021. 

119 

 
 
 
 
 
 
 
 
 
Figure 2.3.4.5 – BEMTOOL. Trajectories of current revenues/Break-Even Revenues (R/BER) ratio for all fleets 
combined in the hindcasting phase (until 2021) and in the forecast phase (after 2021) under the alternative 
scenarios. Solid lines correspond to medians, while shaded area correspond to interquantile range between 
5th and 95th quantiles, indicated by dashed lines. The black dashed lines corresponds to 2021. The green 
horizontal dashed line indicates R/BER=1, the threshold of profitability of the fishery. 

Figure 2.3.4.6 – BEMTOOL. Trajectories of gross value added for all fleets combined in the hindcasting phase 
(until 2021) and in the forecast phase (after 2021) under the alternative scenarios. Solid lines correspond to 

120 

 
 
 
 
 
 
 
 
 
 
medians, while shaded area correspond to interquantile range between 5th and 95th quantiles, indicated by 
dashed lines. The black dashed lines corresponds to 2021.  

Changes  of  the  main  indicators  (F,  SSB,  Catches,  Revenues  and  GVA)  by  fleet  segment  and 
scenarios  are  reported  in  the  tables  from  2.3.4.1  to  2.3.4.5.  The  scenarios  more  negatively 
impacting  the  revenues  of  all  the  trawlers  fleet  are  scenarios  E  and  G.  The  fleets  of  PGP  are 
slightly negatively influenced by all scenarios. 

Table  2.3.4.1.  Changes  (in  percentage)  of  F  of  the  seven  stocks  in  the  tested  scenarios  compared  to  the 
status quo scenario (SQ). This is referred to 2025. A = MCL_ARS, B = MCL_ARS_REV, C = MCL_ARS_monthly, 
D  =  MCL_ARS_REV_monthly,  E  =  MCL_ARA,  F  =  MCL_ARA  REV,  G  =  MCL_ARA_monthly,  H  = 
MCL_ARA_REV_monthly. 

Stock 
ARA 
ARS 
DPS 
HKE 
MUT10 
MUT9 
NEP9 

SQ 
1.45 
1.03 
1.58 
0.49 
0.31 
0.37 
0.13 

A 
-77% 
-83% 
13% 
10% 
59% 
15% 
-8% 

B 
-64% 
-73% 
11% 
9% 
49% 
12% 
-4% 

C 
-79% 
-79% 
13% 
12% 
50% 
12% 
-9% 

D 
-67% 
-67% 
11% 
11% 
43% 
11% 
-7% 

E 
-98% 
-98% 
16% 
13% 
60% 
16% 
-11% 

F 
-69% 
-72% 
14% 
9% 
49% 
11% 
-4% 

G 
-92% 
-93% 
15% 
13% 
56% 
14% 
-11% 

H 
-70% 
-70% 
11% 
10% 
42% 
11% 
-8% 

Table 2.3.4.2. Changes (in percentage) of the spawning stock biomass (SSB) of the seven stocks in the tested 
scenarios  compared to the  scenario SQ.  This  is  referred to  2025  (SSB  in  baseline  are  reported  in tons). A = 
MCL_ARS,  B  =  MCL_ARS_REV,  C  =  MCL_ARS_monthly,  D  =  MCL_ARS_REV_monthly,  E  =  MCL_ARA,  F  = 
MCL_ARA REV, G = MCL_ARA_monthly, H = MCL_ARA_REV_monthly. 

Stock 
ARA 
ARS 
DPS 
HKE 
MUT10 
MUT9 
NEP9 

SQ 

192 
344 
1383 
9759 
1182 
2487 
999 

A 
123.6% 
144.0% 
-8.6% 
-6.0% 
-18.7% 
-3.9% 
1.0% 

B 

D 

C 
104.4%  130.4%  123.4% 
136.9%  142.0%  135.8% 
-13.0% 
-12.6% 
-7.9% 
-7.4% 
-6.2% 
-6.4% 
-21.9% 
-20.4% 
-18.1% 
-5.0% 
-7.2% 
-4.7% 
2.4% 
1.8% 
0.8% 

E 
171.9% 
170.2% 
-14.5% 
-5.0% 
-18.2% 
-6.3% 
1.1% 

F 
157.6% 
159.0% 
-9.1% 
-6.3% 
-20.7% 
-4.6% 
2.2% 

G 
128.5% 
146.5% 
-14.3% 
-6.3% 
-18.3% 
-7.3% 
1.4% 

H 
194.9% 
191.9% 
-15.8% 
-8.1% 
-25.4% 
-7.6% 
3.2% 

121 

 
 
 
 
 
 
 
 
 
  
 
 
Table 2.3.4.3.  Changes  (in  percentage) of the  catches  of the seven stocks  by  fleet  groups  (DTS  and  PGP)  in 
the  tested  scenarios  compared  to  the  status  quo  scenario  (SQ).  This  is  referred  to  2025  (the  catches  in 
baseline  are  reported  in  tons).  A  =  MCL_ARS,  B  =  MCL_ARS_REV,  C  =  MCL_ARS_monthly,  D  = 
MCL_ARS_REV_monthly,  E  =  MCL_ARA,  F  =  MCL_ARA  REV,  G  =  MCL_ARA_monthly,  H  = 
MCL_ARA_REV_monthly. 

DTS 
ARA 
ARS 
DPS 
HKE 
MUT10 
MUT9 
NEP9 
PGP 
HKE 
MUT10 
MUT9 

SQ 
202 
462 
1534 
1707 
384 
902 
155 
SQ 
1485 
38 
20 

A 
-27% 
-47% 
8% 
10% 
18% 
6% 
-6% 
A 
-5% 
-6% 
-6% 

B 
2% 
-23% 
5% 
8% 
10% 
2% 
-3% 
B 
-6% 
-5% 
-5% 

C 
-35% 
-49% 
7% 
13% 
21% 
6% 
-7% 
C 
-6% 
-11% 
-10% 

D 
-5% 
-25% 
5% 
10% 
13% 
6% 
-5% 
D 
-7% 
-8% 
-8% 

E 
-80% 
-83% 
7% 
16% 
29% 
8% 
-9% 
E 
-5% 
-7% 
-7% 

F 
26% 
-13% 
6% 
9% 
5% 
1% 
-1% 
F 
-4% 
-7% 
-7% 

G 
-77% 
-81% 
9% 
15% 
32% 
8% 
-9% 
G 
-7% 
-8% 
-9% 

H 
17% 
-18% 
1% 
9% 
10% 
4% 
-5% 
H 
-7% 
-9% 
-9% 

 Table 2.3.4.4. Changes (in percentage) of the revenues of the seven stocks by fleet groups (DTS and PGP) in 
the tested scenarios compared to the status quo scenario (SQ). This is referred to 2025. A = MCL_ARS, B = 
MCL_ARS_REV,  C  =  MCL_ARS_monthly,  D  =  MCL_ARS_REV_monthly,  E  =  MCL_ARA,  F  =  MCL_ARA  REV,  G  = 
MCL_ARA_monthly, H = MCL_ARA_REV_monthly. 

SQ 
DTS Fleet 
117624 
GSA10_DTS_VL0612 
7058458 
GSA10_DTS_VL1218 
7444992 
GSA10_DTS_VL1824 
1118373 
GSA10_DTS_VL2440 
66929 
GSA11_DTS_VL0612 
1436075 
GSA11_DTS_VL1218 
9916639 
GSA11_DTS_VL1824 
7832931 
GSA11_DTS_VL2440 
322119 
GSA9_DTS_VL0612 
7716320 
GSA9_DTS_VL1218 
11108936 
GSA9_DTS_VL1824 
796608 
GSA9_DTS_VL2440 
SQ 
PGP Fleet 
GSA10_PGP_VL0006 
968015 
GSA10_PGP_VL0612  6211727 
GSA10_PGP_VL1218  1005841 
GSA11_PGP_VL0012  1643682 
GSA11_PGP_VL1218 
GSA9_PGP_VL0012 

8409 
1846259 

A 
-10% 
-27% 
-11% 
-40% 
-6% 
1% 
8% 
-44% 
-5% 
-5% 
-10% 
-3% 
A 
-4.4% 
-4.1% 
-4.2% 
-4.4% 
-4.1% 
-4.2% 

B 
-9% 
-19% 
0% 
-30% 
-6% 
23% 
4% 
-38% 
-5% 
-2% 
-4% 
-2% 
B 
-4.6% 
-4.5% 
-4.5% 
-4.3% 
-4.7% 
-4.9% 

C 
-11% 
-26% 
-26% 
-38% 
-7% 
-26% 
12% 
-29% 
-8% 
-10% 
-12% 
-17% 
C 
-4.9% 
-4.7% 
-4.8% 
-4.9% 
-4.7% 
-5.1% 

D 
-12% 
-17% 
-16% 
-29% 
-7% 
-18% 
10% 
-26% 
-7% 
-6% 
-9% 
-12% 
D 
-5.8% 
-5.7% 
-5.7% 
-5.7% 
-5.7% 
-5.8% 

E 
-11% 
-47% 
-37% 
-63% 
-7% 
-46% 
6% 
-63% 
-8% 
-14% 
-17% 
-10% 
E 
-4.2% 
-4.1% 
-4.0% 
-4.4% 
-4.0% 
-4.2% 

F 
-10% 
-12% 
8% 
-29% 
-5% 
-13% 
-3% 
-62% 
-5% 
-2% 
-4% 
-2% 
F 
-3.4% 
-3.3% 
-3.4% 
-3.3% 
-3.5% 
-3.7% 

G 
-11% 
-45% 
-47% 
-64% 
-8% 
-45% 
11% 
-51% 
-9% 
-14% 
-18% 
-20% 
G 
-6.0% 
-5.7% 
-5.6% 
-5.7% 
-5.7% 
-5.8% 

H 
-14% 
-20% 
-20% 
-33% 
-8% 
-19% 
4% 
-23% 
-9% 
-7% 
-11% 
-16% 
H 
-5.6% 
-5.5% 
-5.5% 
-5.4% 
-5.3% 
-5.5% 

122 

 
 
 
 
 
 
  
GSA9_PGP_VL1218 

932337 

-4.2% 

-5.0% 

-5.0% 

-5.8% 

-4.2% 

-3.7% 

-5.7% 

-5.4% 

Table  2.3.4.5.  Changes  (in  percentage)  of  the  by  fleet  groups  (DTS  and  PGP)  in  the  tested  scenarios 
compared  to  the  status  quo  scenario  (SQ).  This  is  referred  to  2025.  A  =  MCL_ARS,  B  =  MCL_ARS_REV,  C  = 
MCL_ARS_monthly, D = MCL_ARS_REV_monthly, E = MCL_ARA, F = MCL_ARA REV, G = MCL_ARA_monthly, H 
= MCL_ARA_REV_monthly. 

SQ 
DTS Fleet 
GSA10_DTS_VL0612 
76171 
GSA10_DTS_VL1218  7562413 
GSA10_DTS_VL1824  5836212 
GSA10_DTS_VL2440  574455 
GSA11_DTS_VL0012  7394956 
GSA11_DTS_VL1218  1270802 
GSA11_DTS_VL1824 
-676062 
GSA11_DTS_VL2440  5891756 
721037 
GSA9_DTS_1824 
GSA9_DTS_VL0612  7440135 
GSA9_DTS_VL1218  7042966 
GSA9_DTS_VL2440  1139343 
PGP Fleet 
GSA10_PGP_VL0006  5175794 
GSA10_PGP_VL0612  8675086 
-70763 
GSA10_PGP_VL1218 
GSA11_PGP_VL0012  8411287 
GSA11_PGP_VL1218  -1189158 
GSA9_PGP_VL0012  14129720 
GSA9_PGP_VL1218 

383037 

SQ 

A 
-28% 
-19% 
9% 
-71% 
-6% 
2% 
159% 
-47% 
-8% 
-3% 
-19% 
-10% 
A 
-5% 
-7% 
11% 
-8% 
4% 
-5% 
-19% 

B 
-26% 
-13% 
16% 
-56% 
-6% 
32% 
116% 
-44% 
-8% 
-1% 
-11% 
-9% 
B 
-5% 
-7% 
12% 
-7% 
4% 
-6% 
-23% 

C 
-32% 
-19% 
-10% 
-43% 
-7% 
-39% 
100% 
-41% 
-12% 
-9% 
-22% 
-20% 
C 
-6% 
-7% 
12% 
-8% 
4% 
-7% 
-22% 

D 
-34% 
-14% 
-6% 
-42% 
-8% 
-32% 
45% 
-44% 
-11% 
-5% 
-18% 
-17% 
D 
-7% 
-9% 
15% 
-10% 
5% 
-7% 
-26% 

G 
-32% 
-36% 
-29% 
-84% 
-8% 
-65% 

F 
-28% 
-9% 
22% 
-66% 
-5% 
-16% 

H 
E 
-38% 
-31% 
-20% 
-36% 
-19% 
-17% 
-73% 
-86% 
-8% 
-8% 
-34% 
-64% 
293%  249%  201%  99% 
-37% 
-82% 
-13% 
-12% 
-9% 
-12% 
-24% 
-31% 
-22% 
-18% 
E 
H 
-7% 
-5% 
-9% 
-6% 
14% 
10% 
-9% 
-7% 
4% 
5% 
-7% 
-5% 
-24% 
-19% 

-68% 
-13% 
-13% 
-33% 
-23% 
G 
-7% 
-9% 
14% 
-10% 
5% 
-8% 
-26% 

-87% 
-8% 
-2% 
-12% 
-10% 
F 
-4% 
-5% 
9% 
-6% 
3% 
-5% 
-17% 

A dependency analysis by GSA, to understand the contribution of the target species to the total 
landings and revenues was conducted in the EWG 1901. Data of FDI of 2020 confirm that the 
target species of the MAP represent in EMU2 45% in volume and 56% in value, compared to the 
total landing. Generally these species are on the top of the list. Other important species of the 
trawl fisheries in EMU2 for both landing volume and value are: Octopus vulgaris, Eledone 
cirrhosa, Mullus surmuletus and Penaeus keraturus. Considering these species, the pool of the 
main ones would be around 59% in volume and 72% in value.  

123 

 
 
 
 
 
 
 
 
3 

CLOSURE AREAS 

3.1  Background  

The western Mediterranean is one of the most developed sub-regions in terms of fisheries in the 
Mediterranean.  It  accounts  for  around  22%  of  landings,  33%  of  revenues  and  21%  of  the 
officially reported Mediterranean fishing fleet (FAO, 2020). 

The Regulation (EU) 2019/1022 of the European Parliament and of the Council of 20 June 2019, 
establishing  a  Multiannual  Plan  for  the  fisheries  exploiting  demersal  stocks  in  the  western 
Mediterranean Sea, is aimed at the conservation and sustainable exploitation of demersal  stocks 
in the western Mediterranean Sea, mainly based on regulation of fishing effort.  

This Regulation applies to the following stocks: blue and red shrimp (Aristeus antennatus), deep-
water  rose  shrimp  (Parapenaeus  longirostris),  giant  red  shrimp  (Aristaeomorpha  foliacea), 
European  hake  (Merluccius  merluccius),  Norway  lobster  (Nephrops  norvegicus),  and  red  mullet 
(Mullus  barbatus),  that  represent  the  most  important  species  in  the  western  Mediterranean 
demersal fisheries. 

Together  with  the  reduction  of  fishing  activity  in  terms  of  fishing  days  to  reverse  the  current 
overfishing  state  for  most  of  the  demersal  resources,  some  technical  measures  are  adopted  to 
contribute to achieve the MAP objective to move the stocks to MSY within 2025.   

In particular, in the Article 11(1), the Plan provides that trawling shall be prohibited within 
six nautical miles from the coast except in areas deeper than 100 m depth during three 
months  each  year.  Those  three  months  of  closure  shall  be  determined  by  each  Member  State 
and  shall  apply  during  the  most  relevant  period,  determined  on  the  basis  of  the  best  available 
scientific advice.  

The  Article  11(2)  provides  that  Member  States  may  derogate  from  Article  11(1) 
establishing  other  closure  areas,  on  the  basis  of  the  best  available  scientific  advice. 
Those closures shall  account for  a reduction of  at  least  20%  of catches of  juveniles of 
European hake.  

3.1.1  Implementation of closure areas in EMU 1 

In  order  to  accomplish  objectives  established  by  WMMAP  in  Article  11[1],  MS  have  adopted 
spatial  closures  as  a  tool  to  manage  demersal  fishing  resources.  In  most  GSAs  in  EMU1,  Article 
11.2 has been adopted and so Article 11.1 derogated. At the moment only Spain has submitted 
closure areas responding to Article 11.3. 

In  France  two  zones  in  GSA  7  were  closed  for  bottom  trawling  for  8  and  6  months,  whereas  in 
GSA  8  as  Article  11.1  was  adopted  the  6  miles/100  m  isobath  has  been  closed  for  3  months 
(Arrêté  du  20  décembre  2019,  NOR:  AGRM1936906A)[2].  These  management  measures  were 
evaluated by STECF in PLEN 19-03 [3]. Besides closures adopted in response to WMMAP there are 
also other three permanent closures in GSA7 between approximately 100 and 300 m depth where 
any fishing activity is forbidden (see Table 3.1.1.1). 

A  distinct  strategy  has  been  implemented  for  Spain  where  areas  implemented  are  smaller  and 
distributed throughout the fishing grounds. In all GSAs (1,2,5 and 6) Article 11.2 was adopted by 
designing  several  temporal  and  permanent  areas  published  in  Orden  APA/423/2020  of  18  May, 
BOE  no.  142  [4].  A  total  of  18  areas  were  published  covering  more  than  2700  km2  (see  Table 
3.1.1.1).  After  the  first  implementation  of  Article  11.2,  Spain  adopted  WMMAP  Article  11.3  and 
therefore  new  closure  areas  were  designed  and  published  in  Orden  APA/1397/2021,  de  10  de 
diciembre,  BOE  nº  298  [5].  For  GSA  1  and  5  a  total  of  7  temporal  areas  were  implemented 

124 

 
 
 
 
 
 
 
covering  127  and  1892  km2  (Table  3.1.1.1)  respectively,  whereas  in  GSA6  a  strategy  of 
establishing a network of small areas along the coast was adopted (16 areas covering 539 km2). 
Besides  closure  areas  implemented  after  the  WMMAP  publication,  there  are  two  other  protected 
areas  where  trawling  activities  are  prohibited:  Cabrera  national  park  in  Mallorca  waters  (GSA5) 
and Columbretes marine reserve in Valencian coast (GSA6, Table 3.1.1.1). 

Table  3.1.1.1  Summary  of  implemented  closure  areas  in  the  Western  Mediterranean  adopting  the  WMMAP 
legislation  in  EMU1  GSAs.  Areas  affecting  trawlers  activity  established  before  the  WMMAP  were  also  taken 
into account. 
*Depending  on  the  area  fishing  activity  can  be  prohibited  for all  gears.  It  should  also  be  considered that  in 
some areas the extension of a closure can also include bathimetries under 50 m depth. 

WMMAP  

Time closing 

Fleets 

Nº areas 

Managed area 

GSA1 

Article 11.2 

Temporal (3 to 
4 months) 

Article 11.3 

Temporal 

Trawlers 
Longliners 
Gillneters 
Trawlers 

GSA2 

Article 11.2 

Permanent 

Trawlers 

Article 11.2 

Temporal 

GSA5 

Article 11.3 
Previous to 
WMMAP 

Temporal 

Permanent 

All 

Trawlers 
Longliners 
Gillneters 
Trawlers 

Article 11.2 

Temporal 

Permanent 

Article 11.3 

Permanent 

Trawlers 
Trawlers 
Longliners 
Gillneters 
Trawlers 
Trawlers 
Longliners 
Gillneters 
Trawlers 
Trawlers 
Longliners 
Gillneters 

GSA6 

GSA7 

4 

2 

1 

2 

5 

1 

3 

1 

1 

6 

2 

41 km2 

127 km2 

<100m depth 

416 km2 

1892 km2 

909 km2 

1653 km2 

443 km2 

40 km2 

158 km2 

273 km2 

14 

266 km2 

Previous to 
WMMAP 
11.2 
Previous to 
WMMAP 

Permanent 

All 

Temporal 

Trawlers 

Permanent 

All 

1 

2 

3 

55 km2 

5004 km2 

130 km2 

3.1.1.1  References 

[1]  Regulation  (EU)  2019/1022  of  the  European  Parliament  and  of  the  council  of  20  June  2019 
establishing  a  multiannual  plan  for  the  fisheries  exploiting  demersal  stocks  in  the  Western 
Mediterranean Sea and amending Regulation (EU) No 508/2014 

125 

 
 
 
 
 
 
 
 
  
[2]  Arrêté  du  20  décembre  2019  portant  modification  de  l’arrêté  du  28  février  2013  portant 
adoption  d’un  plan  de  gestion  pour  la  pêche  professionnelle  au  chalut  en  mer  Méditerranée  par 
les navires battant pavillon français. NOR : AGRM1936906A 

[3]  Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  –  62nd  Plenary  Meeting 
Report  (PLEN-19-03).  Publications  Office  of  the  European  Union,  Luxembourg,  2019,  ISBN  978-
92-76-14169-3, doi:10.2760/1597, JRC118961 

[4]  Orden  APA/423/2020,  de  18  de  mayo,  por  la  que  se  establece  un  plan  de  gestión  para  la 
conservación de los recursos pesqueros demersales en el mar Mediterráneo. 

[5] Orden APA/1397/2021, de 10 de diciembre, por la que se modifica el Anexo  III de la Orden 
APA/423/2020, de 18 de mayo, por la que se establece un plan de gestión para la conservación 
de los recursos pesqueros demersales en el mar Mediterráneo. 

3.1.2  Implementation of closure areas in EMU 2 

On  the  basis  of  available  scientific  knowledge,  in  GSAs  9,  10  and  11,  the  Italian  government 
asked  to  apply  Paragraph  2  of  Art.  11,  e.g.  the  closure  of  specific  areas,  in  order  to  pursue  the 
objective  of  reducing  at  least  20%  of  catches  of  juveniles  of  European  hake.  In  fact,  there  are 
important  nursery  areas  of  hake,  distributed  from  100  to  300  m  depth,  in  the  three  GSAs, 
characterized by high spatio-temporal stability. In particular, 10 Fishery Restricted Areas (FRAs) 
to protect EFH for recruitment of hake were implemented in the Ligurian and the Tyrrhenian Seas 
covered by Reg.  EU 1022/2019 in GSA 9, 10  and 11 in order to  reduce the  catch of undersized 
hake. The location of these FRAs take into account the results reported in the previous document 
prepared  by  the  Italian  Administration,  and  examined  by  the  62nd  Plenary  Meeting  of  STECF 
(STECF, 2020a). 

These  FRAs,  in  which  the  use  of  any  towed  gear,  such  as  "divergent  trawls",  "rapid  trawls", 
"divergent twin nets", "pelagic trawls with pairs", "divergent pelagic trawls" and "dredges pulled 
by  vessels”,  is  prohibited,  have  been  identified  in  the  Annex  1  of  the  Decree  of  the  General 
Director  of  Fisheries  (MiPAAF)  Prot.    No  9045689  of  6  August  2020.  The  geographical 
location of the FRAs is reported below. 

https://www.politicheagricole.it/flex/cm/pages/ServeBLOB.php/L/IT/IDPagina/15871 

3.1.2.1  Fishery  Restricted  Areas  in  GSA  9  to  protect  Hake  Juveniles,  according  to  the  Italian 

Decree of 6 August 2020. 

Argentario (50 Km2, from 160 to 220 m depth) 

Latitude 
42.3333 N 
42.3833 N 
42.3333 N 
42.3833 N 

Longitude 
10.8333 E 
10.8333 E 
10.7333 E 
10.7333 E 

North Tuscany (107 km2)  

Latitude 
43.8167 N 

Longitude 
9.8 E 

126 

 
 
 
 
 
 
 
 
 
43.8333 N 
43.7 N 
43.6667 N 

9.85 E 
9.9667 E 
9.8833 E 

Capraia (145 km2) 

Latitude 
43.22597 N 
43.25438 N 
43.15000 N 
43.12331 N 
43.22597 N 

Longitude 
10.01694 E 
10.12259 E 
10.18333 E 
10.07653 E 
10.01694 E 

3.1.2.2  Fishery  Restricted  Areas  in  GSA  10  to  protect  Hake  Juveniles,  according  to  the  Italian 

Decree of 6 August 2020. 

Gulf of Gaeta (125 km2, from 100 to 200 m depth) 

Latitude 

Longitude 

41.1322 N 

13.4511 E 

41.0864 N 

13.6325 E 

41.0225 N 

13.6083 E 

41 0686 N 

13.4269 E 

Gulf  of  Patti:  150  km2,  the  sea  area  delimited  by  the  line  connecting  Cape  Milazzo  and  Cape 
Calavà (from coastline to 500 m depth) 

Gulf of Castellammare: 250 km2, the sea area delimited by the line connecting Cape Rama and 
Torre dell’Uzzo (from coastline to 200 m depth). 

Sorrentine Peninsula Area (196 km2) 

Latitude 

Longitude 

40.35701 N 

14.59957 E 

40.34901 N 

14.75355 E 

40.21391 N 

14.74194 E 

40.22181 N 

14.59058 E 

127 

 
 
 
 
 
 
 
 
 
 
40.35701 N 

14.59957 E 

Area facing Amantea: between the coastline and the line connecting the following coordinates: 
188 km2 

Latitude 

Longitude 

38.92968 N 

16.02349 E 

38.92293 N 

16.14812 E 

38.77169 N 

16.13715 E 

38.77321 N 

16.01086 E 

38.92968 N 

16.02349 E 

3.1.2.3  Fishery  Restricted  Areas  in  GSA  11  to  protect  Hake  Juveniles,  according  to  the  Italian 

Decree of 6 August 2020. 

Asinara (269 km2) 

Latitude 

Longitude 

41.2773 N 

41.2773 N 

41.1427 N 

41.1427 N 

41.2773 N 

8.7727 E 

8.9873 E 

8.9873 E 

8.7727 E 

8.7727 E 

Buggerru (619 km2) 

Latitude 

Longitude 

39.50 N 

39.50 N 

39.23 N 

8.04 E 

8.28 E 

8.28 E 

128 

 
 
 
 
 
 
 
 
 
39.23 N 

39.50 N 

8.04 E 

8.04 E 

3.1.2.4  Additional  areas  restricted  to  bottom  trawling  in  EMU  2  implemented  before  the  Italian 

Decree of 6 August 2020. 

In  addition  to  the  Fishing  Areas  Limited  by  the  Italian  Decree  of  6  August  2020,  in  GSAs  9,  10 
and 11 we find other areas that are protected and where trawling is prohibited. 

In Tuscany the seven main islands and some  smaller islets, many of which are simple shoals or 
outcropping rocks, are largely protected by the Tuscan Archipelago National Park.  

The marine areas where fishing is regulated, in addition to the coastal area where trawl activity is 
prohibited, can be identified in specific sectors  around the islands of Gorgona, Capraia, Pianosa, 
Giannutri  and  Montecristo;  and  in  the  Marine  Protected  Area  of  Secche  della  Meloria,  in  front  of 
Livorno. 

Tuscan Archipelago National Park 

The  Tuscan Archipelago National Park, definitively established on 22 July 1996 with a Decree of 
the  President  of  the  Republic,  consists  of  a  protected  land  area  of  about  18,000  hectares  and  a 
marine  area  of  60,000  hectares.  It  includes  seven  islands:  Elba,  Giglio,  Capraia,  Montecristo, 
Pianosa,  Gorgona  and  Giannutri.  In  the  Tuscan  Archipelago  National  Park  the  rules  are 
established by L. 394/91,   dal D.P.R. 22 luglio 1996 and by D.M. Ambiente 19 dicembre 1997. 

In  the  marine  and  terrestrial  areas  included  in  the  park,  two  different  types  of  protection  have 
been established: 

a)  Protection and Promotion Areas - extend 3 nautical miles from the coastline. Underwater 

fishing and trawling are prohibited. These areas are referred to here as type 2 zones to 
facilitate understanding of park zoning. 

b)  Areas of significant naturalistic value with limited or no degree of anthropization (type 1 

areas): they extend for 1000m or one nautical mile from the coast line. In these areas, in 
addition to the restrictions defined for zone 2, the following are prohibited: visitor access, 
both professional and sport fishing exercised with any gear, diving, navigation and 
anchoring and any type of environmental alteration. 

The islands included in the Archipelago park do not enjoy the same constraints. The two major 
islands, Elba and Giglio have only land parks; in four islands: Capraia, Gorgona, Giannutri, 
Montecristo, there are both marine and terrestrial protected areas with type 1 and 2 zones. In 
Pianosa, unlike the other islands, only zone type 1 up to 1 nautical mile from the coast. 

In summary, the perimeter of the Marine Protected Areas follows this scheme: 

Gorgona Island. Prison island - terrestrial and marine park: zone 1 (1000m from the coast); zone 
2 (3 nautical miles from the coast). 

Capraia Island. - terrestrial and marine park: zone 1 (1000m from the coast); zone 2 (3 nautical 
miles from the coast). 

Pianosa Island. Prison island until 31/12/1997 - - terrestrial and marine park: zone 1 (D.M.) (1 
nautical mile from the coast) (Fig.xxx). 

129 

 
 
 
 
 
Montecristo Island. It is a nature reserve since 1971). - terrestrial and marine park: zone 1 (1 
nautical mile from the coast); zone 2 (3 nautical miles from the coast). 

Giannutri Island. - terrestrial and marine park: zone 1 (1 nautical mile from the coast); zone 2 (3 
nautical miles from the coast). 

Figure 3.1.2.4.1 Gorgona Island. The solid line indicates the boundaries of zone 1, with dashed the 
boundaries of zone 2. 

130 

 
 
 
 
 
 
 
 
Figure 3.1.2.4.2 Capraia Island. The solid line indicates the boundaries of zone 1, with dashed the boundaries 
of zone 2. 

Figure 3.1.2.4.3 Pianosa Island. The solid line indicates the boundaries of zone 1, with dashed the boundaries 
of zone 2. 

131 

 
 
 
 
 
 
 
 
 
Figure 3.1.2.4.4 Giannutri Island. The solid line indicates the boundaries of zone 1, with dashed the 
boundaries of zone 2. 

132 

 
 
 
 
 
 
 
 
 
Figure 3.1.2.4.5 Montecristo Island. The solid line indicates the boundaries of zone 1, with dashed the 
boundaries of zone 2. 

The regasification terminal “FSRU Toscana 

The regasification terminal “FSRU Toscana” is one of the main infrastructures of national interest 
for the import of LNG (Liquefied Natural Gas. 
The  terminal  is  moored  about  22  kilometers  off  the  coast  between  Livorno  and  Pisa  whith  6 
anchors that moored the terminal to the seabed (120 meters depth).  
All fishing activity in the area that extend 4 nautical miles from the terminal are prohibited.   

Figure 3.1.2.4.6 The regasification terminal “FSRU Toscana. 

Portofino marine protected area 

The  marine  protected  area  of  Portofino  established  with  the  Decree  of    6  giugno  1998,  fully 
replaced  by  the  Decree  of  9  November  2004  and  most  recently  by  the  Decree  of  26  April  1999  
(Official Gazette no. 131 of 7 June 1999). 

The  Regulation  for  the  implementation  and  organization  of  the  marine  protected  area  of  the 
Cinque  Terre,  currently  in  force,  was  approved  by   Decreto  1 luglio  2008   (GU  General  Series 
n.181 of 4 August 2008). Art. 21. Discipline of professional fishing activity: Trawl and driftnet are 
not permitted. 

133 

 
 
 
 
 
 
 
 
 
Figure 3.1.2.4.7 Portofino marine protected area 

134 

 
 
 
 
 
 
Cinque Terre marine protected area 

The marine protected area of the Cinque Terre was established with the Decree of 12 December 
1997,  fully  replaced  by  the  Decree  of  9  November  2004  and  most  recently  by  the  Decree  of  20 
July 2011 (Official Gazette no. 266 of 15 November 2011). 
The  Regulation  for  the  implementation  and  organization  of  the  marine  protected  area  of  the 
Cinque Terre,  currently in force, was approved by  Decreto 24  febbraio 2015 (GU General Series 
n.62  of  16  March  2015).  Art.  25.  Discipline  of  professional  fishing  activity:  Trawl,  drift  and 
purse seine, aquaculture and active restocking are not permitted. 

Figure 3.1.2.4.8 Cinque Terre marine protected area 

Marine protected area Secche di Tor Paterno 

The marine protected area of Secche di Tor Paterno was established in 2000.  Regulation for the 
implementation and organization of the marine protected area of Secche di Tor Paterno, currently 
in  force,  was  approved  by  Decreto  16  settembre  2014 (GU  n.  234  of  8  october  2014).  Art.  19. 
Discipline  of  professional  fishing  activity:  Trawl,  drift  and  purse  seine,  aquaculture  and 
active restocking are not permitted. 

135 

 
 
 
 
 
 
 
 
Figure 3.1.2.4.9 Marine protected area Secche di Tor Paterno 

Marine protected area of Isole di Ventotene and Santo Stefano  

The  marine  protected  area  of  Isole  di  Ventotene  e  Santo  Stefano  was  established  in  1997. 
Regulation  for  the  implementation  and  organization  of  the  marine  protected  area  of  Isole  di 
Ventotene  e  Santo  Stefano,  currently  in  force,  was  approved  by  Decreto  18  aprile  2014 (GU  n. 
112  of  16-5-2014  -  Suppl.  Ordinario  n.  40).  Art.  23.  Discipline  of  professional  fishing  activity: 
Trawl, drift and purse seine. 

Figure 3.1.2.4.10 Marine protected area of Isole di Ventotene and Santo Stefano  

Marine protected area Regno di Nettuno 

136 

 
 
 
 
 
 
 
 
The  marine  protected  area  of  Regno  di  Nettuno  was  established  in  2000.    Regulation  for  the 
implementation and organization of the marine protected area  of Regno di Nettuno, currently in 
force, established with Decreto 30 luglio 2009 (GU n. 198 of August 2009). Art. 23. Discipline of 
professional fishing activity: Trawling is not permitted. 

Figure 3.1.2.4.11 Marine protected area Regno di Nettuno 

Marine protected area of Punta Campanella 

The marine protected area of Punta Campanella was established in December 1997.  Regulation 
for  the  implementation  and  organization  of  the  marine  protected  area  of  Punta  Campanella, 
currently  in  force,   Decreto  30  luglio  2010 (GU  n.  195  del  21  agosto  2010).Art.  18  Discipline  of 
professional fishing activity: Trawl, Purse Seine are not permitted. 

137 

 
 
 
 
 
 
 
 
Figure 3.1.2.4.12 Marine protected area of Punta Campanella 

Marine protected area of Santa Maria di Castellabate 

The marine protected area of Santa Maria di Castellabate was established in October 2009. 
Almost all of the area is located within the 50 m depth bathymetry. 

Figure 3.1.2.4.13 The marine protected area of Santa Maria di Castellabate. 

Marine protected area of Costa degli Infreschi e della Masseta 

the  marine  protected  area  of  Costa  degli  Infreschi  e  della  Masseta  was  established  in  October 
2009.  Regulation for the implementation and organization of the marine protected area of Punta 
Campanella,  currently  in  force,  established  with   Decreto  9  aprile  2015 (GU  n.  98  of  29 
April 2015).  Art.  24  Discipline  of  professional  fishing  activity  stated  that  Trawls  and  Purse 
Seines are not permitted. 

138 

 
 
 
 
 
 
 
 
 
 
Marine protected area of Capo Milazzo 

The  marine  protected  area  of  Capo  Milazzo  was  established  in  May  2018.  Regulation  for  the 
implementation  and  organization  of  the  marine  protected  area  of  Capo  Milazzo  is  not  yet 
established. 

Figure 3.1.2.4.14 The marine protected area of Capo Milazzo. 

Marine protected area of Capo Gallo-Isola delle Femmine 

the  marine  protected  area  of  Capo  Gallo-Isola  delle  Femmine  was  established  in  July  2002,  
 Decreto 24 luglio 2002 (GU n. 285  of 5 December 2002). Art. 4 stated that fishing activity are 
not permitted. 

139 

 
 
 
 
 
 
 
 
 
Figure 3.1.2.4.15 Marine protected area of Capo Gallo-Isola delle Femmine 

Marine protected area of Isola di Ustica 

The marine protected area of Isola di Ustica was established with Decreto 12 novembre 1986 (GU 
n. 71 of 26 March 1987).  Art. 4 stated that professional fishing activity are not permitted. 

Marine protected area of Capo Carbonara  

The  marine  protected  area  of  Capo  Carbonara  was  established  in  February  2012.  Regulation  for 
the  implementation  and  organization  of  the  marine  protected  area  of  Isole  di  Capo  Carbonara, 
currently in force, was approved by  Decreto 12 maggio 2017 (GU n. 124 of 30 May 2017). Art. 
24.  Discipline  of  professional  fishing  activity  stated  that  Trawl,  driftnet  and  purse  seine  are  not 
permitted . 

140 

 
 
 
 
 
 
 
 
 
Figure 3.1.2.4.16 Marine protected area of Capo Carbonara  

Marine protected area of Tavolara-Punta Coda Cavallo 

The  marine  protected  area  of  Tavolara-Punta  Coda  Cavallo  was  established  in  December  1997. 
Regulation  for  the  implementation  and  organization  of  the  marine  protected  area  of  Tavolara-
Punta Coda Cavallo, currently in force, was approved by   Decreto 3 dicembre 2014 (GU n. 6 del 
9 gennaio 2015). Art. 25. Discipline of professional fishing activity stated that Trawl, driftnet and 
purse seine are not permitted . 

Figure 3.1.2.4.17 Marine protected area of Tavolara-Punta Coda Cavallo 

Marine protected area of Penisola del Sinis-Isola Mal di Ventre 

The marine protected area of Penisola del Sinis-Isola Mal di Ventre  was established in December 
1997. Almost all of the area is located within the 50 m depth bathymetry. 

141 

 
 
 
 
 
 
 
 
 
 
Figure 3.1.2.4.18 Marine protected area of Penisola del Sinis-Isola Mal di Ventre 

Marine protected area of Capo Caccia - Isola Piana 

The  marine  protected  area  of  Capo  Caccia  -  Isola  Piana  was  established  in  September  2002. 
Almost all of the area is located within the 50 m depth bathymetry. 

Figure 3.1.2.4.19 Marine protected area of Capo Caccia - Isola Piana 

Marine protected area of Isola dell’Asinara 

The marine protected area of Capo Caccia - Isola Piana was established in August 2002. Almost all of 
the area is located within the 50 m depth bathymetry. 

142 

 
 
 
 
 
 
 
 
 
 
Figure 3.1.2.4.20 Marine protected area of Isola dell’Asinara 

Marine protected area of Capo Testa Punta Falcone 

the marine protected area of Capo Testa Punta Falcone was established with  Decreto 17 maggio 
2018. Art. 5. Stated that professional fishing activity is not permitted . 

Figure 3.1.2.4.14 Marine protected area of Capo Testa Punta Falcone 

143 

 
 
 
 
 
 
 
 
Table 3.1.2.4.1 List of MPAs and extension in hectares in EMU 2. Percentages refer to the percentage of each 
management area (A, B, C) over the total area of the MPA (Casola et al., 2014). 

% 

Area B  % 

Area 
B1 

%  Area C 

% 

Area D  % 

      Marine Protected Area 

Capo Caccia - Isola Piana 
Capo Carbonara 
Capo Gallo - Isola delle 
Femmine 
Cinque Terre 
Costa degli Infreschi e della 
Masseta 
Isola dell'Asinara 

Area 
A 

38  1 
99  1 

72  3 

79  2 

37  2 

539 
20 
1.699  12 

250 

186 

504 

12 

4 

22 

639  6 

6.960  64 

Isola di Bergeggi 

4 

2 

46 

21 

Isola di Ustica 
Isole di Ventotene e Santo 
Stefano 
Parco naz. Arcipelago Toscano 
Penisola del Sinis - Isola di Mal 
di Ventre 
Portofino 
Punta Campanella 
Regno di Nettuno 
Santa Maria di Castellabate 
Secche della Meloria 
Secche di Tor Paterno 
Tavolara - Punta Coda Cavallo 

55 

395 

<1 

14 

11 

838  2 

435  2 

19  5 
167 
157  1 
156  2 
431  5 
- 
537  4 

- 

7.946  51 

1.579  57 

53.295  98 

971 

4 

51 
198 
44 
654 
2.182  19 
2.943  41 
1.080  12 
1.380  100 
3.043  20 

- 
- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 
- 
135 
153 
- 
- 
- 

- 
- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 
- 
1 
2 
- 
- 
- 

2.089 
6.572 

1.833 

4.565 

1.703 

3.247 

168 

7.621 

814 

- 

- 

22.853 

172 
685 
4.467 
3.908 
7.410 
- 
11.578 

- 

79 
46 

85 

94 

76 

30 

77 

49 

29 

94 

44 
45 
40 
55 
83 

76 

- 

- 
5.916  41 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 
- 

- 
- 
4.528  40 
- 
- 
- 
- 

- 
- 
- 
- 

Total 
area of 
MPA 
2.667 
14.285 

2.155 

4.830 

2.244 

10.846 
21
8 
15.623 

2.788 

54.133 

24.260 

389 
1.505 
11.469 
7.160 
8.921 
1.380 
15.158 

144 

 
 
 
 
 
 
 
Figure 3.1.2.4.15 Marine protected areas in EMU 2. 

In addition to the aforementioned areas, there are  other underwater  areas subject to regulation 
and where fishing is prohibited, for example presence of submarine cables, military polygons; in 
addition,  the  presence  of  wrecks  that  limit  the  operation  of  fishing  vessels  on  the  high  seas 
should also be considered. Detailed information on the exact location and extension can only be 
found  at  the  local  Port  Authorities  or  are  part  of  the  experience  of  master  of  fishing  vessels 
operating in those specific areas. All these areas should be taken into  account when planning to 
limit fishing effort through spatial management. 

3.1.2.5  References 

Enrico Casola, Michele Lariccia, Michele Scardi - 2014. Aree Marine protette e pesca professionale. 
UNIMAR: 204pp 

3.1.3  Evaluation of closure areas in previous working groups 

The  first  evaluations  of  the  implemented  closure  areas  were  done  by  MS  scientific  agencies  and 
presented in PLEN 19-03 (Spain and France) and PLEN 20-01 (Italy and Spanish resubmission). 
All proposals were related to article 11.1 and, if derogated, to article 11.2. All countries adopted 
11.2 in all GSAs except France for GSA8, were article 11.1 was applied. Therefore, in most cases 
hake  juvenile  catches  reduction  in  response  to  closure  areas  had  to  be  evaluated.  French 
methodologies  only  considered  commercial  data:  VMS  and  landings  data  for  the  estimation  of 
juvenile  catches  reduction.  Spanish  methods  only  considered  nurseries  distribution  models  from 
MEDITS  survey  data  and  Italian’s  considered  both,  fisheries  dependent  and  independent 
information.  Other  differences  between  methodologies  were  detected  in  the  use  of  a  non-

145 

 
 
 
 
 
 
 
standardized TL threshold defining hake juvenile, in the consideration of effort redistribution and 
in including or not the effort reduction implemented to the fleet in the analysis.  

Considering the widely different justifications and supporting information provided by MS, STECF 
suggested how this analysis could be performed in a more standardized criteria: “The assessment 
of the best location and timing for closures should compare and overlay a) where the fisheries are 
taking  place  and  the  likely  catch  composition  and  b)  where  juveniles  are  most  likely  to  be 
distributed,  in  order  to  assess  the  expected  impact  of  the  fisheries  on  the  juvenile  stock 
component. Juvenile hake habitats can be modelled using fishery-independent trawl surveys and 
applying persistency analyses of the juvenile hake distribution to document hotspots in time and 
space.“ (see PLEN 19-03 report) [2]. Moreover, after evaluating Italian and Spanish proposals in 
STECF  PLEN  20-01  [3]  also  suggested  that  the  analysis  of  the  reduction  in  hake  catches  should 
be  predicted  from  the  commercial  effort  accounting  for  fishery  gear  selectivity.  It  was  also 
pointed  out  that  effort  redistribution  derived  from  the  closures  has  to  be  considered  in  order  to 
avoid  assuming  a  net  reduction  of  catches  in  closed  areas  corresponding  to  the  past  catches 
made inside the areas proposed.  

In  paragraph  3  of  WMMAP  Article  11  [1]  states  that:  “By  17  July  2021  and  on  the  basis  of  the 
best available scientific  advice, the Member States  concerned  shall establish other closure areas 
where there is evidence of a high concentration of juvenile fish, below the minimum conservation 
reference  size,  and  of  spawning  grounds  of  demersal  stocks,  in  particular  for  the  stocks 
concerned”.  On  top  of  that,  during  EWG  21-01  [4],  STECF  observed  that  the  objective  of 
additional  closures  has  changed  as  stipulated  the  joint  statement  by  France  and  Spain  in 
December 2020 (European Council, statement 5415/1/21 Rev1):  “The additional closures should 
result in a reduction of between 15% and 25% in the by catch of juveniles and spawners of each 
stock  covered  by  the  WMMAP”  (the  term  “by  catch”  used  in  the  literal  sentence  from  the  joint 
statement, was interpreted as catch in the analysis carried out by STECF). Therefore, the overlap 
between new proposed areas should now be checked against information regarding juveniles and 
spawners distribution of the 6 species covered by WMMAP.  

EWG  21-01  was  expected  to  evaluate  countries  new  areas  proposals.  Only  Spain  provided 
documentation  with  new  areas  evaluations.  Most  of  the  evaluations  met  the  standardized 
methodology  developed  by  STECF:  closure  areas  were  compared  with  fishery-independent  data 
on  MAP  species  distribution  (spawners  and  adults),  closures  were  also  overlapped  with  fishing 
effort,  landings  data  were  also  used  and  fishing  effort  redistribution  was  also  considered  [4]. 
However,  none  of  the  areas  evaluated  reached  the  objectives  required  for  any  fraction  of  the 
stocks.  

In EWG 21-01, methodologies to redistribute fishing effort were also discussed together with the 
need of including information on sensitive and essential fish habitats in closure areas definitions. 
EWG was also required to delineate proposals of new closure areas and to  parametrize of spatial 
explicit models but had no time nor data to address these points [7].  

STECF  (EWG  21-01  and  PLEN  21-02)  were  asked  to  delineate  new  closure  areas  if  the  ones 
submitted by MS do not accomplish with the objectives established. In this point STECF stressed 
that designing new closure areas is not a straightforward process as many factors are at play and 
insist  on  the  need  of  considering  juveniles  and  spawning  aggregations  distributions  is  a  key 
requirement  for  new  delineations.  As  a  conclusion,  in  PLEN  21-02  (p.33)  [5],  STECF  suggests  a 
roadmap for identifying and testing the effects of closure areas: “a) define recruits and spawners 
(a  number  of  assumptions  can  be  made  to  identify  thresholds  for  these  two  categories);  b) 
estimate  the  distribution  of  recruits  and  spawners  densities  using  several  modelling  approaches 
depending  on  species  and  area;  c)  identify  hotspots  (i.e.,  areas  with  higher  density)  of  recruits 
and  spawners  (e.g.,  by  means  of  survey  data  and  sampling  onboard);  d)  verify  the  spatial  and 
temporal  persistency/stability  of  such  hotspots;  e)  evaluate  the  importance  of  each  area  in  a 
multispecies  context  by  analysing  the  spatial  overlap  among  the  persistent  hotspots  (areas 
including  nurseries  and  spawning  aggregations  for  multiple  species  should  be  ranked  as  highly 

146 

 
 
 
 
 
priority  areas);  f)  define  a  number  of  closure  areas  scenarios  prioritizing  areas  with  overlapping 
hotspots  and  gradually  increasing  their  spatial  extensions;  g)  verify  the  effect  of  such  scenarios 
(closure  areas)  in  reducing  juveniles  and  spawners  in  catches  along  with  effort  redistribution 
(e.g.,  ideally  through  a  dynamic  modelling).  Following  this  roadmap,  it  could  be  possible  to 
optimize spatial management objectives for demersal fisheries by identifying the precise  location 
and extension of closure areas achieving a given reduction of juveniles and spawners in catches.” 

3.1.4  References 

[1] Regulation (EU) 2019/1022 of the European Parliament and of the council of 20 June 2019 
establishing  a  multiannual  plan  for  the  fisheries  exploiting  demersal  stocks  in  the  Western 
Mediterranean Sea and amending Regulation (EU) No 508/2014 

[2]  Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  –  62nd  Plenary 
Meeting Report (PLEN-19-03). Publications Office of the European Union, Luxembourg, 2019, 
ISBN 978-92-76-14169-3, doi:10.2760/1597, JRC118961 

[3] Scientific, Technical and Economic Committee for Fisheries (STECF) – 63 rd Plenary Report 
–  Written  Procedure  (PLEN-20-01).  Publications  Office  of  the  European  Union,  Luxembourg, 
2020, ISBN 978-92-76-18117-0, doi:10.2760/465398, JRC120479 

[4]  Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  –  West  Med 
assessments:  conversion  factors,  closures,  effort  data  and  recreational  fisheries  (STECF-21- 
01). Publications Office of the European Union, Luxembourg, 2021, EUR 28359 EN, ISBN 978-
92- 76-36193-0, doi:10.2760/36048, JRC124913. 

[5] Scientific, Technical and Economic Committee for Fisheries (STECF) – 67 th Plenary Report 
(PLEN-21-02). EUR 28359 EN, Publications Office of the European Union, Luxembourg, 2021, 
ISBN 978-92-76-40592-4 (online), doi:10.2760/559965 (online), JRC126123. 

3.2  Development of hotspots’ seascape 

EWG 22-01 was tasked to provide evidence of a high concentration (named hereafter “hotspots”) 
of juvenile fish and of spawning grounds of demersal stocks, in particular for the target stocks of 
the West Med MAP (EC Reg 2019/1022, Article 1). The identification of high concentration areas 
for the harvested resources in the Western Mediterranean requires the analysis of spatial data on 
abundance and density distribution, which can be obtained by sampling the populations in space 
and time as a basis for applying statistical models to infer the distribution in non-sampled areas. 
In  a  second  step,  the  persistence  of  these  high  concentration  areas  is  analysed  by  overlapping 
annual density distribution throughout the years. 

3.2.1  Survey data: MEDISEH shapefiles 

Shapefiles  of  nursery  areas  and  spawning  aggregations  of  hake,  red  mullet,  Norway  lobster, 
deep-water  rose  shrimp,  giant  red  shrimp,  and  spawning  aggregations  of  blue  and  red  shrimp 
were  calculated  in  the  framework  of  the  MEDISEH  Project  (DG  MARE  Specific  Contract  No  2 
SI2.600741;  Giannoulaki  et  al.,  2013).  These  shapefiles  were  obtained  on  the  basis  of  the 
MEDITS  bottom  trawl  survey  data  for  the  period  1994–2010.  This  survey  is  carried  out  in  EU 
Mediterranean  waters  in  spring  -  summer  (May-August)  (WP,  2019)  to  gather  data  on  benthic 
and  demersal  fish  and  shellfish  in  a  wide  depth  range,  from  10  to  800  m  depth  (MEDITS-
handbook,  2017).  In  the  Western  Mediterranean,  given  the  timing  of  the  MEDITS  survey  (third 
quarter),  it  is  likely  that  the  resulted  habitat  mapping  cannot  be  considered  as  fully 
representative of the actual distribution of nurseries and spawning areas (Colloca et al., 2013). 

147 

 
 
 
 
 
 
 
 
Recruits were considered as those specimens that have settled on the bottom, becoming available 
to  the  fishing  gear  in  well-defined  habitats  at  the  end  of  their  larval-pelagic  stage  and  which 
remain in these habitats before dispersing or migrating. Due to the lack of specific studies on the 
dispersal behaviour of juveniles, it was assumed that the first separable modal component of the 
age  group  0  was  composed  of  specimens  recently  settled  on  the  bottom  sharing  similar  habitat 
preferences. The Bhattacharya’s method was used to separate the first modal component of the 
0-age group from the annual standardized trawl survey length frequency distributions (LFDs). The 
0-age  group  threshold  limit  was  defined  using  growth  data  available  in  the  Mediterranean  and 
routinely  collected  within  the  EU  DCF.  In  some  circumstances,  when  the  splitting  of  modal 
components in the LFDs was difficult (e.g. low number of recruits), recruits were computed using 
a fixed threshold length derived from validated local studies (Colloca et al., 2013; Colloca et al., 
2015). 

Spawners of hake,  Norway lobster, deep-water rose shrimp, giant red shrimp, and blue and red 
shrimp were considered the adult females fraction of the populations using the length-at-maturity 
calculated from MEDITS or landing data (DCF) as threshold length. For red mullet, which has its 
spawning  peak  during  the  MEDITS  survey  period,  models  were  based  either  on  mature  female 
specimens or on the adults’ fraction. In GSAs 1, 5 and 6 mature females of giant red shrimp were 
used instead of length-at-first maturity (Colloca et al., 2013; Colloca et al., 2015). 

Density  indices  (n.  km-2)  by  year  both  for  recruits  and  spawners,  MEDITS  station,  GSA  and 
species  were  calculated  following  the  method  used  by  Fiorentino  et  al.  (2003).  To  locate  and 
classify  nurseries  and  spawning  aggregations,  annual  density  spatial  hot-spots  were  identified 
through the Getis’ G statistic (Getis and Ord, 1992) with a radius of 2.5 – 5.0 km and a 0.95 level 
of significance. This approach was applied separately in each GSA and for each year of the time 
series  to  spatially  locate  clusters  of  recruits  and  spawners  displaying  a  significantly  higher 
density.  Finally,  the  Index  of  Persistence  was  calculated  to  measure  the  relative  persistence  of 
annual  nurseries  and  spawning  aggregations  in  each  GSA  (see  Colloca  et  al.,  2015).  Here  we 
identified hotspots selecting the spatial persistence higher than 20%. 

Figures  3.2.1.1-3  show  the  spatial  distribution  of  juveniles  and  spawners  of  giant  red  shrimp, 
European  hake,  Norway  lobster,  red  mullet,  blue  and  red  shrimp,  and  deep-water  rose  shrimp 
from MEDISEH shapefiles. 

148 

 
 
 
 
 
Figure 3.2.1.1. Spatial distribution of juveniles and spawners’ hotspots of giant red shrimp (ARS) 
and European hake (HKE) from MEDISEH shapefiles. Z1 refers to the temporal persistence class 
>20%. 

149 

 
 
 
 
 
 
Figure  3.2.1.2  Spatial  distribution  of  juveniles  and  spawners’  hotspots  of  Norway  lobster  (NEP) 
and  red  mullet  (MUT)  from  MEDISEH  shapefiles.  Z1  refers  to  the  temporal  persistence  class 
>20%. 

150 

 
 
 
 
 
 
 
Figure  3.2.1.3  Spatial  distribution  of  juveniles  and  spawners’  hotspots  of  blue  and  red  shrimp 
(ARA)  and  deep-water  rose  shrimp  (DPS)  from  MEDISEH  shapefiles.  Z1  refers  to  the  temporal 
persistence class >20%. 

3.2.2  Commercial data coupled to survey data 

Ahead  of  the  EWG  22-01,  preliminary  works  on  mapping  species  distribution  and  possible 
persistence  of  hotspot  areas  over  the  years  were  done  using  scientific  surveys  and  commercial 
data  (ad  hoc  contracts  n.  2191  and  n.  2192,  respectively).  Both  data  sources  have  different 
advantages  that  could  complement  to  identify  suitable  areas.  Scientific  surveys  following 
standardised  procedures  aim  to  provide  large-scale  consistent  estimates  of  density  across  the 
area of interest, but often they lack the fine-scale spatial and temporal information necessary to 
delineate  hot-spots.  In  contrast  commercial  data  rarely  has  consistent  estimates  of  abundance 
across  broad  regions,  but  provides  much  higher  spatial  and  temporal  resolution  locally.  For  the 
commercial data log-book information was linked to position data (VMS/AIS). The two data sets 
are  therefore  highly  complementary  in  there  information  and  the  EWG  aimed  to  use  both  to 
provide additional option for hot-spot based closures . 

The  EWG  was  provided  with  two  ad  hoc  contract  reports  (see  Annexes  I  and  II)  mapping  the 
species density or fishing effort areas: 

151 

 
 
 
 
 
 
 
● 

● 

In Annex I demersal trawls logbooks are merged with the VMS data of the French, Spanish 
and  Italian  fleets  in  both  EMU1  and  EMU2  during  the  period  (2015-2020  for  the  French 
trawlers,  2017-2020  for  the  Italian  trawlers  and  2018-2020  for  the  Spanish trawlers  in a 
VAST model that accounts for regional differences in fleet catchability using the available 
survey  information  from  MEDITS  data).  The  outcome  of  the  approach  provides  spatially 
interpolated maps  of average LPUEs or average density (kg/km2) of  European hake, Red 
mullet,  and  two  deep-water  shrimp  species  (Aristeus  antennatus  and  Aristaeomorpha 
foliacea) per GSA for both EMU1 and EMU2. Applied annually, the approach also provides a 
coefficient of variation per grid cell of 0.05 x 0.05 degrees over the studied area.  
In  Annex  II  the  STECF  Fisheries  Dependent  Information  (FDI)  database  collecting  EU 
member states landings and effort statistics per DCF fleet-segments for the passive gears 
(longline,  gillnet,  driftnet)  targeting  hake,  are  coupled  to  spatial  AIS  data  (Automatic 
Identification  System)  in  the  Italian  EMU2  areas,  and  during  the  period  2018-2020.  The 
approach  provides  a  dispatch  of  landing  data  over  geographical  position  as  a  basis  to 
identify persistent, high LPUEs areas for passive gears targeting European hake.  

Figure  3.2.2.1  Mapping the 2015-2020  average hake  (left)  and  red mullet  (right)  density,  here  classified  by 
quantile,  obtained  by  applying  the  statistical  modeling  approach  described  in  Alglave  et  al.  (2022)  in  EMU1 
and EMU2.  Hake  based  on  Spanish, Italian and French  commercial data, and  the MEDITS  survey,  red  mullet 
based on Italian and French data, and MEDITS survey. Mapped on 0.05 x 0.05 grid cells. 

152 

 
 
 
 
 
 
 
 
 
Figure  3.2.2.2  Mapping  the  2015-2020  average  Aristeus  antennatus  and  Aristaeomorpha  foliacea  density, 
here classified by quantile, obtained by applying the statistical modeling approach described in Alglave et al. 
(2022) in EMU2 and based on Italian data only. Mapped on 0.05 x 0.05 grid cells. 

To be conservative and limit the uncertainty, the EWG decided to filter out the maps only to keep 
the  area  where  there  was  some  fishing  effort  collected  in  the  data  to  get  rid  of  the  statistical 
model  extrapolation  outside  the  historical  range  of  area  visited  by  the  fleet  during  the  period 
investigated. No additional covariates were indeed included in the model as VMS-logbook data do 
not provide reliable estimates for the species-habitat relationship and only allow to identify spatial 
and spatio-temporal correlation structures (Alglave et al. 2022, Annex I). Therefore, no hotspots 
could be identified in no trawled areas following our conservative approach. 

The EWG decided on criteria to identify the persistent areas of high concentration of juveniles and 
spawners  based  on  the  average  density  and  the  coefficient  of  variation.  to  identify  persistent 
areas by selecting grid cells defined with high commercial LPUEs and CV approximately above 0.5 
and  below  1,  areas  of  low  variance  associated  with  extrapolation  to  low  data  density  were 
excluded (Table 3.2.2.1).  

Table  3.2.2.1.  Ad  hoc  CV  ranges  per  species  chosen  to  delineate  areas  of  high  fish  concentration  on  the 
species-specific density maps provided by Alglave et al. (2022) 

Species 

CV range 

Hake SSB 

0.5-1.3 

Hake recruit 

0.4-1.0 

Hake Biomass 

0.25-1.4 

153 

 
 
 
 
 
 
 
Red shrimp 

0.2-1.3 

Blue shrimp  

0.4-1.0 

Mullet 

0.5-2.4 

The  EWG  notes  that  the  definition  of  what  constitutes  a  hotspot  area  could  be  interpreted 
differently. It is questionable if a hotspot of high LPUEs for a given year is also a persistent area 
of  high  abundance  over  the  years.  Protecting  low  LPUEs  but  persistent  areas  might  also  be 
beneficial for stock development when protecting a large supporting area (essential fish habitat), 
provided  the  areas  are  commensurately  larger  to  have  the  same  protective  effect  as  hotspots, 
less  detrimental  to  the  fishery  as  well  as  more  beneficial  to  other  conservation  objectives.  The 
models  we  have  at  hand  might  underestimate  the  positive  effects  of  protecting  coastal  habitats 
instead of the concentration of fish and avoiding affecting where the fishing effort distributed the 
most could also prevent effort displacement and unwished side effects that could lead the fishery 
to  explore  much  more  extensive  areas  of  low  LPUEs  but  persistent  areas  in  an  attempt  to 
compensate for the losses. 

The  EWG  notes  that  using  commercial  LPUEs  could  be  a  biased  approach  because  it  could  pool 
vessels  with  different  fishing  power,  where  the  largest  vessel  expected  to  develop  higher  catch 
rates (STECF EWG 18-09). However, the experts observe that the difference in catching power is 
most  likely  due  to  difference  in  area  visited  between  large  and  small  vessels,  as  a  result  of 
different mobility range, as shown from the GLM analysis in STECF EWG 18-09. Therefore, higher 
LPUEs on the more distant fishing grounds from the coastline would likely not be due to different 
catchability  power  between  large  and  small  vessels  but  to  an  inherent  difference  in  stock 
distribution. 

The  EWG  decided  not  to  use  the  set  net/longline  data  as  a  direct  source  for  the  hotspot 
identification  due  to  the  fact  that  experts  considered  the  fleet  targeting  hake  spawners  mainly 
characterized  by  small  vessels  (VL0006  and  VL0612)  not-equipped  with  AIS  or  adopting 
polyvalent gears that brought bias when not  matching with official registers or FDI spatial data. 
This  piece  of  information  however  has  been  used  as  a  post-hoc  confirmation  of  the  hotspots 
identified, only where AIS data and FDI landings matched. However, the mismatch with the FDI 
was  considered  high  (i.e.,  FDI  record  with  no  corresponding  AIS,  AIS  records  with  no 
corresponding FDI declaration of landings), resulting in a few AIS-based fishing grounds. 

3.2.3  Define closure areas based on persistent hotspots 

EWG 22-01 identified the new closure areas by prioritizing the overlapping surfaces of the above 
identified  hot  spots  (i.e.,  MEDISEH  data,  AIS  data,  VMS  data,  logbooks,  FDI  spatial  data)  for 
recruits  and  spawners  of  all  species.  Specifically,  MEDISEH  data  were  used  for  all  the  species 
concerned  in  the  West  Med  MAP,  VMS/logbook  data  four  (HKE,  MUT,  ARA,  ARS)  species,  while 
AIS  and  FDI  data  were  used  for  post-hoc  confirmation  of  hake  hotspots  only.  The  seasonal 
closures  were identified only when the spatial information used  for their selection was based  on 
quarterly  data,  also  accounting  for  the  spawning  period  of  HKE  and  on  the  distribution  of  HKE 
spawners. The permanent closures were identified based also on survey data, which are collected 
only during quarter 3.  

154 

 
 
 
 
 
 
 
Figures  3.2.3.1-3  below  show  the  proposed  areas  for  closure  together  with  the  existing  closure 
areas per GSA of the West Med. 

Figure  3.2.3.1.  Existing  and  proposed  areas  by  the  EWG  for  closure  in  GSAs  1,  2,  5  and  6  of  the  western 
Mediterranean.  

155 

 
 
 
 
 
 
Figure  3.2.3.2  Existing  and  proposed  areas  by  the  EWG  for  closure  in  GSAs  7,  8,  9  and  10  of  the  western 
Mediterranean.  

156 

 
 
 
 
 
 
Figure  3.2.3.3  Existing  and  proposed  areas  by  the  EWG  for  closure  in  GSAs  11.1  and  11.2  of  the  western 
Mediterranean.  

The  EWG  provides  the  percent  ratio  of  the  existing  closures  (Base  closures,  Fig.  3.2.3.4)  and 
proposed  surface  area  for  closure  (New  closures,  Fig.  3.2.3.4)  over  the  trawlable  area  (>50m 
depth  or  >3nm  and  <1000m  depth)  per  GSA  to  get  the  first  insight  on  possible  impact  on  the 
fleets. It is worth noting that larger percentages are obtained in case the surface area of closure 
is compared to the trawled areas historically, given not all trawlable areas, which are defined on 
bathymetric limits, are trawled in practice. Hence, in Fig. 3.2.3.4 the trawled area was derived by 
the  grounds  exerted  by  the  VMS-equipped  demersal  trawls  (see  Annex  I).  Finally,  computed 
percentages also account for the fact that not all areas are closed all year around.  

Figure  3.2.3.4    shows  that  the  impact  of  both  existing  and  additional  closures  strongly  differs 
among GSAs in terms of percent of the overall GSA area closed to fishing. In particular, GSA 7 is 
currently  and  will  still  be  the  most  impacted  one,  by  the  area-based  management,  with  about 
40% of the trawled area already affected by the existing area closures. The Italian GSAs  are the 
ones which are currently less affected by the closure in terms of surface percentage, and will not 
be much more affected by the proposed addition (always below the 5% of the GSA surface area). 
GSA  6  shows  an  area  closure  of  about  10%  of  the  total  surface  area.  These  impacted  surface 
areas  are  almost  halved  in  GSA6  and  GSA7  when  accounting  for  the  part  of  the  closure  that  is 
seasonal only (Figure 3.2.3.4, right).  

157 

 
 
 
 
 
 
Figure  3.2.3.4    left-  Percentage  of  the  trawlable  area  (>50m/3nm  and  <1000m)  covered  by  the  closure; 
middle-  Percentage  of  the  trawled  area  covered  by  existing  (Base  Closures)  and  suggested  new  closures; 
right-  Percentage  of  trawled  area  covered  by  the  closure  per  area  per  month.  50%  and  100%  scenarios 
correspond to an expansion of the surface areas around the closure by 50 and 100%, which are not applied 
to GSA 7 and GSA 11.2. Besides, no new closures are proposed for GSA7 and GSA 11.2. 

3.2.4  References 

Alglave  et  al.  2022.  Combining  scientific  survey  and  commercial  catch  data  to  map  fish 
distribution. ICES Journal of Marine Science, 0, 1-17 

Colloca F., M. T. Spedicato, E. Massutí, Garofalo G., G. Tserpes, P. Sartor, A. Mannini, , A. Ligas, 
G. Mastrantonio, B. Reale, C. Musumeci, I. Rossetti, M. Sartini, M. Sbrana, F. Grati, G. Scarcella, 
M.  Iglesias,  M.  P.  Tugores,  F.  Ordines,  L.  Gil  de  Sola,  G.  Lembo,  I.  Bitteto,  M.T.  Facchinii,  A. 
Martiradonna,  W.  Zupa,  R.  Carlucci,  M.C.  Follesa,  P.  Carbonara,  A.  Mastradonio,  Fiorentino  F., 
Gristina  M.,  Knittweis  L.,  Mifsud  R.,  Pace  M.L.,  C.  Piccinetti,  C.  Manfredi,  G.  Fabi,  P.  Polidori,  L. 
Bolognini, R. De Marco, F. Domenichetti, R. Gramolini, V. Valavanis, E. Lefkaditou, K. Kapiris, A. 
Anastasopoulou  and  N.Nikolioudakis,  (2013)  Mapping  of  nursery  and  spawning  grounds  of 
demersal  fish.  Mediterranean  Sensitive  Habitats  (MEDISEH)  Final  Report,  DG  MARE  Specific 
Contract SI2.600741, Heraklion (Greece). 

Colloca F, Garofalo G, Bitetto I, Facchini MT, Grati F, Martiradonna A, et al. (2015) The Seascape 
of  Demersal  Fish  Nursery  Areas  in  the  North  Mediterranean  Sea,  a  First  Step  Towards  the 
Implementation  of  Spatial  Planning 
for  Trawl  Fisheries.  PLoS  ONE  10(3):  e0119590. 
doi:10.1371/journal.pone.0119590 

Fiorentino  F,  Garofalo  G,  De  Santi  A,  Bono,  Giusto  GB,  et  al.  Spatio-temporal  distribution  of 
recruits  (0  group)  of  Merluccius  merluccius  and  Phycis  blennoides  (Pisces,  Gadiformes)  in  the 
Strait of Sicily (Central Mediterranean). Hydrobiologia. 2003; 503: 223–236. 

158 

 
 
 
 
 
 
 
Getis  A,  Ord  JK.  The  analysis  of  spatial  association  by  use  of  distance  statistics.  Geogr  Anal. 
1992; 24: 189–206. 

Giannoulaki  M.,  A.  Belluscio,  F.  Colloca,  S.  Fraschetti,  M.  Scardi,  C.  Smith,  P.  Panayotidis,  V. 
Valavanis  M.T.  Spedicato  (2013).  Mediterranean  Sensitive  Habitats,  DG  MARE  Specific  Contract 
SI2.600741, Final Report, 557 p. 

MEDITS-Handbook. Version n. 9, 2017, MEDITS Working Group: 106 pp. 

WP,  2019.  Italian  Work  Plan  for  data  collection  in  the  fisheries  and  aquaculture  sectors,  2020-
2021, Version 1.0 – October 2019, Rome, 30 October 2019. 

3.3  Progress of operational mixed fisheries models 

3.3.1  ISIS-Fish in EMU 1 (GSA 7) 

3.3.1.1  ISIS-Fish assumptions related to spatial features 

ISIS-Fish allows explicitly accounting for the spatial and seasonal distribution of fish and fishers in 
the  evaluation  of  impact  of  closures.  Impact  on  biomass  is  highly  dependent  on  assumptions 
made  regarding  fish  movements.  In  this  case,  we  considered  two  habitats  for  hake,  Accores 
(slope)  and  Plateau  (shelf)  that  differ  both  in  terms  of  level  of  biomass  and  age  composition  of 
European  hake  population  according  to  MEDITS  survey  data.  Within  each  habitat,  hake  is 
assumed  uniformly  distributed  and  highly  mobile.  This  results  in  the  biomass  are  uniformly 
redistributed within the habitat at the beginning of each time step. If a closure partially protects 
an habitat, local improvement in fish biomass directly benefits to the full habitat at the next time 
step.  

ISIS-Fish  simulates  effort  reallocation  outside  the  closed  area  under  user-defined  assumptions. 
Here  reallocation is  computed  at  fleet  and  métier  level,  and  we  assumed  a  reallocation  of  effort 
over the métier area still opened to fishing, which may locally increase pressure outside the area 
closed. 

3.3.2  SMART in EMU 2 

3.3.2.1  Background 

Following the work done  within the previous STECF-EWG -  Evaluation of  fishing effort regime in 
the  Western  Mediterranean,  and  in  particular  during  EWG  19-01,  the  implementation  of  the 
SMART bioeconomic model for EMU2 was updated. During EWG 22-01, the spatial resolution was 
increased to be aligned with the spatial analyses performed in ad-hoc contracts about the spatial 
distribution  of  spawning  and  nursery  areas.  In  addition,  the  input  data  for  fishing  effort  were 
updated  to  consider  the  year  2020  and,  in  this  way,  to  account  for  the  effect  of  the  COVID-19 
pandemic.  On  another  hand,  both  the  economic  parameters  (fuel  prices,  price  at  the  market  of 
resources by species/ size class) and the data for the stocks were retrieved from EWG 19-01. 

The rationale of the SMART model, as well as the workflow  of the  smartR package (D’Andrea  et 
al., 2020), can be summarized in the following logical steps: 

1.  Use  landings  and  catch  data,  combined  with  VMS  data,  to  estimate  the  spatial/temporal 
productivity of each cell, in terms of aggregated LPUE by species; 

159 

 
 
 
 
 
 
 
2.  Use  catch  data  to  estimate  the  Length-Frequency  Distribution  (LFD)  and  the  Age-  Frequency 
Distribution (AFD), by species, for each cell/time; 

3. Use VMS data to assess the fishing effort by vessel/cell/time; 

4.  Combine  LPUE,  LFD/AFD  and  VMS  data  to  model  the  landings  by  vessel/species/length 
class/time; 

5. Estimate the cost by vessel/time associated to a given effort pattern and the related revenues, 
which are a function of the landings by vessel/species/length class/time (step 4); 

6. Combine costs and revenues by  vessel, at the yearly  scale, to  obtain the incomes,  which are 
the proxy of the vessel performance. Incomes could be aggregated at the fleet level to estimate 
the overall performance; 

7. Use estimated landings by species/age, together with survey data, to run MICE model for the 
selected case of study in order to obtain a biological evaluation of the fisheries. 

Each  of  these  steps  corresponds  to  a  different  module  of  the  package.  Within  SMART,  the  key 
aspect is represented by the optimization, at the scale of each vessel, of the fishing effort pattern 
at the monthly temporal scale. This is done through the iterative exploration of alternative vessel- 
specific  effort  patterns  and  evaluation  of  the  corresponding  catch  converted  in  revenues  and 
compared with the total costs to estimate the gains. 

A detailed description of the method is available in Russo et al., 2019. 

3.3.2.2  Application of the SMART model to the West Med MAP  

The spatial productivity (monthly LPUE as grams of catch per meter of LOA and hour of fishing) 
was estimated using landings and VMS data, according to the procedure of Russo et al., 2018 and 
Russo et al., 2019. In the same time, the economic parameters needed to model the relationships 
between: 1) fishing effort and its related costs (crew salaries, fixed costs, etc.); 2) spatial fishing 
footprint  and  its  related  costs  (i.e.  fuel  consumption);  3)  yield  and  production  costs  (i.e. 
commercialization); 4) yield and revenues (using the prices at market of the different species by 
size class) were collected and integrated into the model. Values of prices at the market by species 
and length class, together with the price of fuel, were partially retrieved by Russo et al. (2014b) 
and  integrated  using  the  public  databases  provided  by  the  “Istituto  di  servizi  per  il  mercato 
agricolo 
(ISMEA 
alimentare” 
http://www.ismea.it/flex/FixedPages/IT/WizardPescaMercati.php/L/IT)  and  by  the  Ministry  of 
Economic Development (https://dgsaie.mise.gov.it/prezzi_carburanti_mensili.php). 

The  model  developed  for  the  previous  EWG  has  been  updated  in  order  to  better  represent  the 
array of species exploited by trawlers in the EMU2. Namely, monthly LPUE were computed for the 
species in Table 3.3.2.2.1. 

160 

 
 
 
 
 
 
Table 3.3.2.2.1 Species considered within the model. 

Species 

Code 

Aristeus antennatus 

Aristaeomorpha foliacea 

Sepia officinalis 

Parapenaeus longirostris 

Eledone cirrhosa 

Eledone moschata 

Merluccius merluccius 

Mullus surmuletus 

Mullus barbatus 

Nephrops norvegicus 

Octopus vulgaris 

Illex coindetii 

ARA 

ARS 

CTC 

DPS 

EOI 

EDT 

HKE 

MUR 

MUT 

NEP 

OCC 

SQM 

After the estimation of the monthly LPUE by species, and the “historical” behaviour of each vessel 
(proportion  of  its  effort  for  each  cell  of  the  spatial  grid),  SMART  allows  to  predict  the  vessel-
specific  adaptation  to  different  management  scenarios,  including  the  establishment  of  spatial 
closures, using a Bayesian approach. In practice, while the effort in closed areas (cells of the grid) 
is forbidden, a new effort patter is identified through the optimization of the total monthly profits. 
Monthly profits are, in turn, determined by the combination of effort and LPUE (by species), which 
gives  the  revenues,  minus  the  cell-specific  costs,  which  are  mainly  represented  by  fuel 
consumption.  This  optimization  procedure  is  applied  several  times  (100  runs),  in  order  to  avoid 
local  maxima.  At  the  end  of  this  procedure,  the  new  effort  pattern  of  the  different  vessels  are 
aggregated to obtain the new exploitation pattern (monthly landings by species). 

The mice model applied to predict the effect of this new exploitation pattern is represented in Fig. 
3.3.2.2.1. It is important to notice that, while all the 12 species listed above were considered in 
the optimization phase, only the six stocks in the MAP were considered. 

161 

 
 
 
 
 
 
 
 
Figure 3.3.2.2.1 Representation of the trawl fishery model for EMU2. All species represented were considered 
in  terms  of  contribution  to  the  landings  (and  so  the  strategy  adopted  by  fishers  in  selecting  the  fishing 
grounds), but the effects of the different scenarios were explored only for the ones within red-dashed boxes. 

3.3.2.3  Space and time scale 

For this application of SMART to the case study of Western Mediterranean Effort Management Unit 
2, the resolution of the square grid for the GSAs 9, 10 and 11 was increased from the 30 x 30 nm 
of the EWG 19-01 to cells of 2 x 3 nm (Figure 3.3.2.3.1). The cells covering the area deeper than 
800m  depth  were  excluded  to  reduce  complexity  and  computational  time  required  for  the 
simulations. 

162 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.3.2.3.1 Map of the EMU2 representing the grid and the three GSAs considered. 

Compared  to  the  previous  EWG,  also  the  temporal  ranges  were  extended.  The  considered  time 
series  starts  in  2012  and  ends  in  2020.  Accordingly,  108  months’  temporal  series  of  LPUE  and 
AFD (proportion of age classes/length by species) were estimated for the cells of the grid. These 
represent the basis for the simulation of different effort scenarios, including the Status quo. 

3.3.2.4  Simulated Scenarios  

The SMART model is devised to estimate the potential effect of whatever management actions on 
the  effort  (including  reduction  of  fishing  capacity,  effort,  or  spatial  closures)  instead  of  directly 
setting  a  desired  value  of  F  for  the  target  stocks  and  evaluate  the  related  effects  of  this  new 
exploitation  pattern.  Thus,  the  SMART  model  was  used  to  assess  the  potential  effect  of  the 
scenarios listed in Table 3.3.2.4.1. 

163 

 
 
 
 
 
 
 
Table 3.3.2.4.1 Scenarios ran to test closure areas in EMU 2. 

Scenario 

Characteristics 

Time 

Status quo 

Base Closures 

Already existing closure areas 

Base + New 
Closures 

Already  existing  closure  areas  +  New  closures 
defined  following  the  Ad-hoc  contracts  ran  prior  to 
EWG  22-01  according  to  the  distribution  of 
hotspots  of  juveniles  and  spawners  of  hake  and 
potentially  of  the  five  main  target  species  of  the 
MAP 

Full year 

Full year 

Base + New 
Closures +50% 
Q1 

Already existing closure areas + New closures (see 
definition  above)  increased  by  50%  in  terms  of 
area 

Full year (Base) + First 
the  year 
quarter  of 
(New Closures) 

Base + New 
Closures 
+100% Q1 

Already existing closure areas + New closures (see 
definition  above)  increased  by  100%  in  terms  of 
area 

Full year (Base) + First 
quarter  of 
the  year 
(New Closures) 

Base + New 
Closures +50% 
Permanent 

Already existing closure areas + New closures (see 
definition  above)  increased  by  50%  in  terms  of 
area 

Full year 

Base + New 
Closures 
+100% 
Permanent 

Already existing closure areas + New closures (see 
definition  above)  increased  by  100%  in  terms  of 
area 

Full year 

For  each  scenario,  100  simulation  runs  were  carried  out  and,  for  each  scenario,  the  potential 
effects for the stocks in the MAP were assessed in terms of SSB value and Fishing mortality (F). 
In  the  same  time,  the  economic  effects  for  the  fleet  were  estimated  in  terms  of  total  yearly 
landings by species, total yearly revenues, costs, and profits. It is worth noticing that, for these 
economic  indicators,  the  estimated  effects  were  related  only  to  the  entry-into-force  of  the 
scenarios. 

3.3.3  References 

218–230. 

Russo, T., Morello, E. B., Parisi, A., Scarcella, G., Angelini, S., Labanchi, L., et al. (2018). A model 
combining landings and VMS data to estimate landings by fishing ground and harbor. Fish.  Res. 
199, 
11.002 
Russo  T  et  al  (2019).  Simulating  the  Effects  of  Alternative  Management  Measures  of  Trawl 
Fisheries in the Central Mediterranean Sea: Application of a Multi-Species Bio-economic Modeling 
Approach. 
10.3389/fmars.2019.00542 
D’Andrea  et  al  (2020).  smartR:  An  r  package  for  spatial  modelling  of  fisheries  and  scenario 
simulation of management strategies. Methods Ecol Evol. 2020;00:1–10 

10.1016/j.fishres.2017. 

6:542. 

Front. 

Mar. 

doi: 

doi: 

Sci. 

164 

 
 
 
 
 
 
 
 
 
 
 
3.4  Management scenarios and results 

3.4.1  ISIS-fish in EMU 1 (GSA 7) 

3.4.1.1  Management scenarios considered 

Management scenarios are based on those listed in ToR2 a-b-c of EWG22-01 (Table 3.4.1.1.1).  

Table 3.4.1.1.1 Scenarios ran for HKE in GSA 7. 

Scenario 

2015  2016  2017  2018  2019  2020 

2021 

2022  2023  2024  2025 

… 
2030 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

15 

Calibration 

Hindcast 

Projection 

Effort 

2015  2016  2017 

Mean 2015-2017 

Mean 2015-2017 

Effort red. 
trawlers 
(rel.2015-17) 

a 

-
10% 

-7.5% 

(-17.5%) 

(-17.5%) 

Closures 

Existing ones 

Recruitment 

2015  2016  2017  2018  2019  2020 

Mean 2018-2020 

Mean 2018-2020 

Closures 

Closures 

b = a + 
>2022 
closures all 
year 

c = a + >2022 
closures all 
gear 

Existing ones 

Permanent 

Existing ones 

All gear 

All three scenarios provide a decrease of catch of the younger age classes (0-2). Catches of these 
age classes are lower than the 2015-2017 by respectively 22%, 27,% and 7%. On the other hand 
the catch of older age classes increase. This is due to the rapid rebuilding of the population and 
the re-apparition of these older age classes almost absent in 2015-2017. Scenario b (permanent 
closures) gave significantly higher catch of age 4 and 5 than the two others scenarios. The fishing 
mortalities  at  age  (Fig.3.4.1.1.1)  testimony  of  the  release  of  fishing  pressure  on  every  stages 
particularly in scenario b.  

The  effect  on  fleets  was  contrasted  between  trawlers  whose  revenues  stabilised  at  values  lower 
than the average 2015-2017 (-5% to -9%) and the other  fleets  who  benefited from the  release 
pressure and improved their revenues, particularly Spanish longliners who doubled their historical 
revenues  (Fig.  3.4.1.1.2).  These  results  are  of  course  dependent  on  the  assumptions  made  on 
fish distribution and reallocation of effort. It must be stressed that the gross revenues presented 
here only account for hake landings. It is expected that the current shortcomings in the modelling 
of  closures  lead  to  an  underestimation  of  the  positive  effect  of  closures  on  hake  population  and 
possibly of the losses for the fleets.  

Interestingly  although  the  closures  cover  both  hake  habitats  the  benefit  on  biomass  mainly 
showed for the plateau contingent (Fig. 3.4.1.1.3)  

165 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.4.1.1.1 Predicted fishing mortality under the three scenarios of closures. 

Figure 3.4.1.1.2 Predicted gross revenues on hake (GSA7) per fleet relative to the average 2015-2017 under 
the three scenarios of closures.  

166 

 
 
 
 
 
 
 
 
 
Figure 3.4.1.1.3 Biomass of Hake (GSA7) predicted per age class (lines) and zones (columns) under the three 
closure scenarios. Values are relative to the average 2015-2017. The grey line represent the historical value 
(reference period), the average 2015—2017.  

3.4.2  SMART in EMU 2 

3.4.2.1  Effects on the stocks: Spawning Stock Biomass (SSB) and Fishing mortality (F)  

The  effects  of  the  new  fishing  effort  pattern  (as  predicted  by  SMART  after  the  estimation  of  the 
effort displacement) on the exploited stocks are  summarized in Figures 3.4.2.1.1-7.  In the case 
of ARA (fig. 3.4.2.1.1), it seems that most of the scenarios are expected to determine a recovery 
of SSB, and in particular the most “aggressive” in terms of spatial extent of area closed to trawl 
fishing (i.e. Base + New Closures +100% Permanent, Base + New Closures +50% Q1). However, 
none of these scenarios lead to a sustainable value of F. The situation is quite similar in the case 
of  ARS  (Figs.  3.4.2.1.2),  but  with  an  important  difference:  in  this  case  several  scenarios  seem 
capable of allowing the achievement of Fmsy in a period of about 8 years.  

In  the  case  of  DPS  (fig.  3.4.2.1.3),  no  marked  effect  associated  with  the  different  scenarios 
explored is recognizable. This could be due to the fact that all spatial scenarios were designed to 
protect other stocks (e.g. HKE, ARA, ARS) and, therefore, the redistribution of fishing effort could 
penalize the DPS that would become relatively more exploited. In the case of HKE (fig. 3.4.2.1.4), 
the  results  indicate  that  all  scenarios  could  bring  great  benefits  to  the  stock  in  terms  of  SSB, 
which could more than double in about 10 years, but only the most aggressive scenario (Base + 
New Closures +100% Permanent) would achieve the value of Fmsy. 

167 

 
 
 
 
 
 
 
  
 
 
 
 
 
ARA - Aristeus antennatus in GSA 9, 10, and 11 

Figure  3.4.2.1.1  Predicted  trends  of  SSB  and  F  for  each  scenario.  The  semi-transparent  area  around  each 
mean line is referred to the variability of predictions over 100 simulation runs. The red dashed line in the plot 
of Fishing mortality corresponds to FMSY 

168 

 
 
 
 
 
 
 
 
 
ARS - Aristaeomorpha foliacea in GSA9, 10, and 11 

Figure  3.4.2.1.2  Predicted  trends  of  SSB  and  F  for  each  scenario.  The  semi-transparent  area  around  each 
mean line is referred to the variability of predictions over 100 simulation runs. The red dashed line in the plot 
of Fishing mortality corresponds to FMSY 

169 

 
 
 
 
 
 
 
 
DPS – Parapenaeus longirostris in GSA9, 10, and 11 

Figure  3.4.2.1.3  –  Predicted  trends  of  SSB  and  F  for  each  scenario.  The  semi-transparent  area  around  each 
mean line is referred to the variability of predictions over 100 simulation runs. The red dashed line in the plot 
of Fishing mortality corresponds to FMSY 

170 

 
 
 
 
 
 
 
 
 
HKE – Merluccius merluccius in GSA9, 10, and 11 

Figure  3.4.2.1.4  Predicted  trends  of  SSB  and  F  for  each  scenario.  The  semi-transparent  area  around  each 
mean line is referred to the variability of predictions over 100 simulation runs. The red dashed line in the plot 
of Fishing mortality corresponds to FMSY. 

171 

 
 
 
 
 
 
 
 
 
 
The  differences  between  the  explored  scenarios  are  more  evident  in  the  case  of  MUT  in  GSA9. 
Here,  permanent  extended  closures  (Base  +  New  Closures  +50%  Permanent  and  Base  +  New 
Closures +100% Permanent) do support an increase of the SSB, whereas the values of F remain 
far from FMSY. 

172 

 
 
 
 
 
MUT – Mullus barbatus in GSA9 

Figure  3.4.2.1.5  Predicted  trends  of  SSB  and  F  for  each  scenario.  The  semi-transparent  area  around  each 
mean line is referred to the variability of predictions over 100 simulation runs. The red dashed line in the plot 
of Fishing mortality corresponds to FMSY 

173 

 
 
 
 
 
 
 
 
MUT – Mullus barbatus in GSA10 

Figure  3.4.2.1.6  Predicted  trends  of  SSB  and  F  for  each  scenario.  The  semi-transparent  area  around  each 
mean line is referred to the variability of predictions over 100 simulation runs. The red dashed line in the plot 
of Fishing mortality corresponds to FMSY 

The expected trends for MUT in GSA10 indicate a substantial stability of this stock, with a relative 
improvement associated to the scenarios based on the largest spatial closures. 

174 

 
 
 
 
 
 
 
 
 
NEP – Nephrops norvegicus in GSA10 

Figure  3.4.2.1.7  Predicted  trends  of  SSB  and  F  for  each  scenario.  The  semi-transparent  area  around  each 
mean line is referred to the variability of predictions over 100 simulation runs. The red dashed line in the plot 
of Fishing mortality corresponds to FMSY 
Small but appreciable improvements in both SSB and F are also noted in the case of NEP. 

175 

 
 
 
 
 
 
 
 
 
 
3.4.2.2  Effects on Fleets 

The analysis of the effects of the various scenarios on the activity of the fleets shows that, in all 
the cases, decreases of the landings are expected. These decreases are much more marked in the 
case  of  the  space  closures  and  for  HKE,  as  expected  as  both  the  new  areas  and  the  already 
existing  ones  have  been  identified  with  the  aim  of  reducing  the  capture  of  HKE  juveniles.  It  is 
important to note that, in the case of Base + New Closures +100% Permanent, you get almost a 
20% reduction.  

Figure  3.4.2.2.1    Predicted  changes  (%  with  respect  to  the  status  quo)  in  the  total  annual  landings  by 
species, according to the different scenarios. 

However,  the  effect  on  the  captured  fleet  from  the  economic  indicators  is  very  heterogeneous. 
This  is  clear  at  a  high  level  of  aggregation  (Fig.  2.3.4.2.2),  showing  how  all  scenarios  are 
associated  with  a  sharp  decrease  in  revenues  but,  in  some  cases,  this  reduction  is  offset  by  a 
concomitant reduction in costs while (Base + New Closures +50% Q1 and Base + New Closures 
+100% Q1), in the other cases, the negative effect of the reduction in revenues increased by the 
concomitant increase in costs. 

176 

 
 
 
 
 
 
 
 
 
 
 
Figure 3.4.2.2.2 Predicted changes (% with respect to the status quo) in the total annual revenues, costs and 
profits, according to the different scenarios. 

In  this  way,  the  spatial  closures  not  widened  or  seasonal  would  involve  lower  decrease  of  the 
profits  than  widened  and  permanent  closures  that,  at  the  entry-into-force,  would  have  a  very 
strong impact on the fleet in terms  of  reduction of the profits (up to  15% of profits lost). When 
these economic effects are inspected for each fleet segment (Fig. 2.3.4.2.3), the results indicate 
that  the  loss  of  profits  is  more  evident  for  small  vessels  (VL12-18  and  VL18-24)  when  large 
permanent closures are enstablished. 

177 

 
 
 
 
 
 
 
 
Figure 3.4.2.2.3 Predicted changes (% with respect to the status quo) in the total annual revenues, costs and 
profits, according to the different scenarios, for each fleet segment. 

178 

 
 
 
 
 
 
4 

REMAINING ISSUES AND FUTURE DEVELOPMENTS 

4.1  Future development of operational mixed fisheries models  

4.1.1  IAM in EMU 1 

4.1.1.1  Proposed changes in IAM for next group  

In  EMU1,  the  implementation  of  the  IAM  model  for  GSAs  1-5-6-7  carried  out  during  the  STECF 
meeting is still in development. Additional socio-economic indicators such as employment, gross 
profit  and  gross  profit  margin  can  be  made  available  for  the  next  meeting,  provided  that  the 
relationship  between  Full-Time  Equivalents  (FTE)  and  fishing  effort  is  discussed  before  the 
meeting,  and  that  the  calculation  of  salary/crew  costs  is  discussed  prior  the  next  meeting.  If 
additional indicators are requested, they should be provided before the meeting, and sufficiently 
in advance to adapt the model. 

For  the  MCL  implementation  scenarios,  it  would  be  preferable  for  the  fishing  effort  per  fleet 
segment to be adjusted to reach Fmsy, rather than a MCL value given as input to the IAM model. 
This issue could be explored in future groups. 

Scenarios involving changes in the number of vessels, rather than just changes in fishing effort, 
could be explored if included in the TORs. 

An  adjustment  of  the  fleet  segmentation  would  allow  the  economic  impacts  of  the  alternative 
scenarios  to  be  simulated  on  other  fleet  segments  than  trawlers;  see  the  next  section  for  more 
details on a proposed adaptation of the fleet segmentation. 

4.1.1.2  Fleet segmentation in IAM  

The fishing fleets of the current IAM model is constituted of 10 categories, 4 for the French fleet 
and  6  for  the  Spanish  fleet.  For  the  French  fleet,  IAM  distinguishes  between  trawlers  18-24m, 
trawlers 24-40m, and  other vessels <12m and >12m. For the Spanish fleet, distinction is being 
made  between  trawlers  of  4  size  class  (<12m,  12-18m,  18-24m  and  2-40m),  and  two  other 
fleets:  netters  and  vessels  using  hooks.  These  distinctions  are  being  made  on  the  basis  of  the 
“fishing technique” and “vessel size” fields of the FDI and AER data tables, which are common to 
both  tables  and  allows  the  correspondence  between  economic  and  landings  information  across 
fleet segments and serves as input for the IAM model. 

During EWG 22-01 and using 2018-2020 data available within FDI data table A, for GSAs 1, 5, 6 
and 7, we performed an analysis of the gear and landings distribution within the current IAM fleet 
categories. On the basis of Figure 2.2.1.4.1, it appears that a most consistent fleet definition for 
the  Spanish  and  French  non  trawler  fleets  might  be  needed.  The  major  caveat  in  the  current 
definition is that the French “other” fleets are exclusively defined based on vessel size, regardless 
of  other  criterions,  while  the  Spanish  “other”  fleets  are  only  defined  through  the  prism  of  the 
fishing  technique,  without  consideration  of  vessel  size.  This  leads  to  inconsistencies  in  the 
representation  in  the  model  of  French  and  Spanish  “other”  fleets,  especially  for  small-scale 
fishery. It also leads to the inclusion of French fleet segments that are irrelevant to the plan (such 
as PS, PGP, FPO), and to the exclusion of Spanish fleet segments that might be relevant (such as 
PMP).  

Therefore,  we  propose  to  redefine  for  the  next  groups  the  categorisation  for  the  “other”  fleets, 
with 4 categories replicated for Spain and France: vessels using hooks above and below 12m, and 

179 

 
 
 
 
 
 
 
 
netters  above  and  below  12m.  In  this  segmentation,  vessels  using  hooks  are  assimilated  to 
longliners and netters to gillnetters by discriminating between two of the dominant gears  - since 
all vessels are operating a number of different gears through the year. 

Gear type 

Fle
et 
seg
me
nt 

Figure  4.1.1.2.1.  Proportion  of  gear  usage  (computed  in  terms  of  %  of  landings  issued  from  each  gear) 
among  revised  IAM  fleet  segments  (in  row).  The  “OUT”  segment  cumulates  all  fleet  segments  left  out.  T  is 
short for DTS and stands for demersal trawlers and/or demersal seiners, DFN for drift and/or fixed netters, 
and HOK for vessels using hooks. The vessel length class 12xx means all vessels superior to 12 meters.  For 
the gear types  (colour  legend), DRB stands  for  boat  dredge,  FPO  for  pots  and traps, FYK  for  fyke nets,  GNC 
for Encircling gillnets, GNS for Set gillnet, GTN for Combined gillnets-trammel nets, GTR for Trammel net, LA 
for  Lampara  nets,  LHP  for  Hand  and  Pole  lines,  LLD  for  Drifting  longlines,  LLS  for  Set  longlines,  OTB  for 
Bottom  otter  trawl,  OTM  for  Midwater  otter  trawl,  OTT  for  Multi-rig  otter  trawl,  PS  for  Purse  seine,  SV  for 
Beach  and  boat  seine,  and  NK  and  NO  for  unknown  gear.  Roughly,  red  to  orange  colors  corresponds  to 
dredges and  traps  (DRB –  FPO  – FYK), yellow to green  corresponds to various  gillnets  (GNC  –  GNS  -  GTN – 
GTR),  green to  lightblue  corresponds  to  longlines  (LA  -LHP – LLD –  LLS), darkblue  corresponds  to unknown 
gears (NK – NO), purple corresponds to trawls (OTB – OTM – OTT), and purple to red corresponds to seines 
(PS – SV).  

180 

 
 
 
 
 
 
 
 
Species 

Fle
et 
seg
me
nt 

Figure 4.1.1.2.2. Proportion of species of the plan landed (in weight) by revised IAM fleet segments (in row). 
The “OUT” segment cumulates all fleet segments left out. T is short for DTS and stands for demersal trawlers 
and/or  demersal  seiners,  DFN  for  drift  and/or  fixed  netters,  and  HOK  for  vessels  using  hooks.  The  vessel 
length class 12xx means all vessels superior to 12 meters. Coloured space represents species targeted by the 
westmed  management  plan  (ARA:  Aristeus  antennatus,  DPS:  Parapeneus  longirostris,  HKE:  Merluccis 
merluccius,  MUT:  Mullus  barbatus,  NEP:  Nephrops  norvegicus),  and  grey  areas  (OTH)  represents  all  other 
species cumulated. 

The  proposed  revision  of  the  IAM  fleet  segments  reveals  a  much  better  harmonization  between 
French and Spanish “other” fleets (Figure 4.1.1.2.1) while leaving out fleet segments that do not 
contribute  to  the  mortality  of  nor  economically  depends  on  the  species  of  the  plan  (Figure 
4.1.1.2.2,  “OUT”  section).  Also,  upon  observation,  it  appears  that  both  French  and  Spanish 
longliners >12m (i.e. HOK in the figures) only present a very tiny fraction of the plan species in 
their  landings.  This  corresponds  to  the  present  (2018-2020)  situation,  in  which  the  hake  stock 
has  collapsed.  However  in  the  past  (2000-2010),  it  is  known  that  these  longliners  used  to  land 
significant proportions of large hake individuals. Therefore, we believe that these fleet segments 
should  still  be  explicitly  modelled  within  IAM,  in  order  to  be  able  to  account  for  their  potential 
impact on the hake stock, should it recover.      

Figure  4.1.1.2.3  represents  the  mapping  between  detailed  fleet  categories  and  the  current  IAM 
fleet  segments,  and  Figure  4.1.1.2.4  the  mapping  between  detailed  fleet  segments  and  the 
proposed new IAM fleet segmentation. The detailed fleet segments have been reconstructed from 

181 

 
 
 
 
 
 
 
the  FDI  data,  by  concatenating  “country”,  “fishing  technique”,  “vessel  size”,  and  “target 
assemblage”  fields  of  the  FDI  table  A.  Note  that  for  “target  assemblage”,  we  only  distinguished 
between deep-water species (“DWS”) and the rest (noted “OTH” for others). 

Figure 4.1.1.2.3. Match between detailed fleet segments (in row) and current IAM fleet categories (colours). 
Grey shows fleet segment currently not considered into IAM fleet categories.  

182 

 
 
 
 
 
 
 
     
Figure 4.1.1.2.4. Match between detailed fleet categories (in row) and revised IAM fleet categories (colours). 
Grey shows fleet segment not considered into the revised IAM fleet categories.  

To  have  a  better  idea  of  the  dependence  of  the  detailed  fleet  categories  to  the  species  of  the 
management plan, Figure 4.1.1.2.5 shows the proportions of species of the plan landed by each 
detailed fleet category. Hence, it is clear that trawling and gillnetting fleet segments (e.g. fishing 
technique “DTS” and “DFN”) present the most dependence to the species of the plan. 

183 

 
 
 
 
 
 
 
 
 
 
 
 
 
Figure  4.1.1.2.5  Proportions  of  species  of  the  plan  landed  by  each  detailed  fleet  category,  obtained  by 
concatenating country, fishing technique, vessel size and target assemblage of the FDI data table. Coloured 
space  represents  species  targeted  by  the  Western  Mediterranean  MAP  (ARA:  Aristeus  antennatus,  DPS: 
Parapeneus  longirostris,  HKE:  Merluccis merluccius,  MUT:  Mullus  barbatus,  NEP:  Nephrops  norvegicus),  and 
grey areas represents all other species cumulated. 

4.1.2  ISIS-Fish in EMU 1 

The  parameterisation  and  calibration  of  the  ISIS-Fish  model  would  benefit  further  efforts 
particularly to improve the representation of French netters and Spanish fleets, but also to use a 
more recent parameterisation of fleet activity in projection (currently 2015-2017).  

As  for  the  spatial  closures,  improvements  are  required  regarding  hake  distribution.  Using  the 
results of the ad hoc contracts, the spatial distribution of hake will be updated and assumptions 
on fish movement revised. The current assumption of effort reallocation when a zone is closed is 
theoretical.  Analysis  of  fishing  effort  distribution  in  the  first  years  of  closure  implementation 
(2020-2021) should help propose more realistic assumptions.  

As for the MCL, because fish were assumed to grow continuously along the year, the assumption 
regarding  mean  weight  for  the  MCL  computation  has  been  evidenced  as  crucial  and  should  be 
chosen carefully. The allocation of the MCL between fleets also proved important to refine given 
the important inequity evidenced. 

If  a  stock-recruitment  relationship  for  hake  is  to  be  defined,  it  would  be  worth  using  it  in 
projection to evidence possible propagation of the benefit of spawners protection. 

184 

 
 
 
 
 
 
 
Uncertainty analysis is required at least on recruitment, but possibly on other key parameters and 
processes. 

4.1.3  BEMTOOL in EMU 2 

The  implementation  of  BEMTOOL  in  EMU    can  include  scenarios  focused  on  the  issue  of  the 
potential  increase  of  the  fishing  power  associated  to  the  technological  creep.  In  addition,  a 
module present in BEMTOOL and related to the reaction of the sector to a management measure, 
can  be  applied,  taking  into  account  the  lower  limit  for  changes  in  fishing  activity  and  the 
possibility of disinvestment. A missing element  to be implemented is the possibility to adapt the 
MCL according to the annual change in the stock status. The implementation of MCL on more than 
one species will be also investigated. 

4.1.4  SMART in EMU 2 

Future application of SMART in EMU2 could explore the potential effects of TAC and inverse-TAC, 
as  done  in  EWG  21-13.  In  fact,  the  present  set  up  of  SMART  for  EMU2  includes  all  the  relevant 
parameters  and  variables,  and  all  the  metadata  (e.g.  spatial  LPUE,  landings,  and  bioeconomic 
indicators) were processed at a monthly base, providing the baseline to explore several additional 
scenarios. However, different aspects of the complex dynamics associated with TAC (including the 
potential  changes  in  the  price  at  market  of  some  resources)  are  largely  unexplored  and  hardly 
predictable, although their potential effects on fishers strategy could be very relevant. Finally, it 
could  be  important  to  assess  the  potential  effects  of  different  scenarios  at  a  finer  spatial  scale, 
e.g. that of harbours. This could return insights about the economic sustainability of management 
measures for the different fleets distributed along the Italian coasts. 

4.2  Issues important for the advice and the interpretation of results 

The  results  of  the  present  working  group  should  be  interpreted  as  those  of  a  technical  exercise 
which  allowed  the  further  development  of  the  four  bio-economic  mixed-fisheries  models  (IAM, 
ISIS-Fish, BEMTOOL, SMART) used within the working group, and highlighted issues that should 
be accounted for in future EWGs giving scientific advice in relation to the Western Mediterranean 
MAP (EU 2019/2236). EWG 22-01 focused on two main topics, the evaluation of maximum catch 
limits (MCLs) and the evaluation of closure areas.  

4.2.1  Maximum catch limits 

MCLs on species caught by the trawling fishery were implemented for the first time in the western 
Mediterranean  this  year,  2022,  by  the  European  Regulation  2022/110.  MCLs  were  specifically 
defined  on  blue  and  red  shrimp  (ARA)  and  on  giant  red  shrimp  (ARS)  as  reported  in  Table 
4.2.1.1..  The  MCLs  for  Spain  and  Italy  were  considered  within  the  modelling  frameworks  as  the 
reference  catch  value  for  the  deep-water  fisheries  in  EMU  1  and  EMU  2  in  2022.  The  MCL  for 
France,  instead  could  not  be  accounted  for  due  to  the  lack  of  data  suggesting  the  presence  of 
such a fishery in GSA 7 and 8, and to the lack of stock assessment for ARA and ARS in GSA 7 and 
8. Therefore, it was not possible to account for the effect of a potential increase in the pressure 
on ARA and ARS stocks if a deep-water fishery started in GSA 7 and 8 by a French fleet. On the 
same  line,  it  should  be  noted  that  the  stock  of  ARA  in  GSA  5  does  not  have  an  analytical  stock 
assessment,  therefore  the  evolution  of  the  fishery  in  this  area  after  setting  a  MCL  could  not  be 
explored by the mixed-fisheries models implemented in EMU 1. 

185 

 
 
 
 
 
 
 
 
Table 4.2.1.1 MCLs per country as defined by Regulation 2022/110. 

Country 

Spain 

France 

Italy 

ARA 

872 

65 

250 

ARS 

- 

5 

365 

MCLs for European hake (HKE) are not defined by any Regulation at present, therefore the values 
used  were  obtained  from  the  short-term  forecast  (STFs)  obtained  during  the  stock  assessment 
EWG 21-11. By TORs it was requested to consider an MCL for HKE solely on trawlers, as this year 
a  reduction  of  effort  of  6%  compared  to  the  reference  period  2015-2017  (EU  2022/110)  was 
applied to longliners for the first time. The values obtained from EWG 21-11 though are relative 
to the fisheries of HKE exerted by both, passive and active gears, which create a conflict on how 
these values are used as a MCL was solely defined on trawlers within the mixed-fisheries models. 
This implementation was followed due to a shortage of time, but in future it would be necessary 
to  have  a  MCL  for  passive  gears  and  one  for  active  gears,  in  case  there  should  be  an 
implementation of such a catch limit on HKE in the western Mediterranean. Additionally, it should 
be noted that the HKE fisheries is characterized by the presence of discards, which are embedded 
in  the  catch  values  projected  by  STFs  during  a  stock  assessment  procedure.  Within  the  mixed-
fisheries models used in EWG 22-01 only BEMTOOL can account for discards at present, therefore 
MCL  values  given  to  EWGs  following  EWG  22-01  should  account  for  this  potential  issue.  During 
EWG  21-13  and  EWG  22-01  target  MCL  values  for  transition  years  (2023-2024)  and  for  2025 
were fed into the models as it was easier to run the models at the time. Experts suggested that in 
future it would be better to directly feed a value of Fmsy by 2025 and estimate the corresponding 
catch/landings value (depending on the model used). This procedure would allow to evaluate the 
consequences  of  implementing  a  MCL,  as  biomass  response  is  updated.  In  order  to  have  a 
meaningful  update  of  the  biomass  though,  specific  harvest  control  rules  (HCR)  to  be  accounted 
for  should  be  defined.  It  should  than  be  stressed  that  all  stocks  evaluated  in  the  western 
Mediterranean at present lack official biomass reference points such as Bpa or Blim. 

4.2.2  Closure areas 

During  the  procedure  followed  to  define  hotspots  that  could  potentially  help  define  additional 
closure areas, a number of limitations due to the availability of spatial data were highlighted. 

As  TORs  requested  to  consider  protection  through  a  percentage  of  each  GSA,  the  first  step  in 
order  to  define  hotspots  related  to  trawling  activity  was  to  define  the  areas  where  trawling  is 
possible.  To  do  so  only  the  area  between  the  50m  depth  or  3  nautical  miles  and  1000m  depth 
was  considered,  as  outside  this  range  trawling  is  already  prohibited.  Existing  closure  areas  and 
untrawlable areas were deducted as well by the final area. It should be highlighted that it was not 
possible  to  account  for  most  of  the  untrawlable  areas,  such  as  the  ones  reported  at  the  end  of 
section  3.1.2,  due  to  lack  of  mapping  of  the  submarine  structures.  EWG  22-01  advices  that 
protection of stocks should not be evaluated considering percentages  of areas of protection, but 
through the evaluation of the response of stocks to spatial management measures. Additionally, it 
should  be  considered  that,  following  Regulation  (EC)  1967/2006,  derogations  can  be  applied  to 
specific areas, such areas should be accounted for when looking at total trawlable area. 

As reported in section 3.2.2 the combination of both commercial and scientific data complements 
each other, increasing spatial and temporal coverage, but commercial data are biased by nature 
being  led  by  the  species  distribution.  Scientific  data  are  fundamental  to  understand  species 
distribution  and  dynamics,  being  based  on  standardized  sampling.  MEDITS  is  the  only  scientific 
survey  that  covers  the  whole  area  interested  by  the  western  Mediterranean  MAP  and  is  held 

186 

 
 
 
 
 
 
 
  
during  the  second  and  third  quarter  (depending  on  the  area),  which  limits  the  potential  of 
describing the seasonality of species’ dynamics. It should be noted that when testing the effect of 
seasonal  closures  against  permanent  closures,  the  only  quarterly  information  available  are 
actually  led  by  the  distribution  and  dynamics  of  commercial  data,  while  there  is  a  lack  of 
information of the species dynamics independently from fishers’ behaviour. This caveat should be 
considered when interpreting results obtained from spatially explicit mixed-fisheries models such 
as  SMART  (which  does  not  include  survey  data)  and  ISIS-Fish  (which  includes  survey  data  only 
for  a  single  quarter).  Additionally,  the  experts  stressed  that  MEDITS  data  are  not  suitable  to 
identify  distribution  hotspots  for  all  species  targeted  by  the  MAP,  such  as,  for  example,  HKE 
spawners and ARA and ARS juveniles. EWG 22-01 highlights that species’ hotspots defined during 
the MEDISEH project should be updated with data up to 2020, as it was not possible to do it for 
this  EWG  due  to  lack  of  time.  Both  the  ad  hoc  contracts  and  EWG  22-01  were  limited  in 
identifying  HKE  spawners  hotspots,  as  large  hake  are  mainly  targeted  by  vessels  having 
LOA<15m and using passive gears in both EMU 1 and in EMU 2, which are not tracked by remote 
monitoring systems (VMS or AIS). As highlighted in section 3.2.2 the complete lack of information 
on  the  distribution  and  behaviour  of  fleet  segments  under  15m  is  quite  limiting  when  defining 
spatial  management  measures  as  it  could  underestimate  the  importance  of  management  of 
coastal areas compared to high seas ones. As reported in section 3.1.3, EWG 22-01 followed the 
procedure  suggested  by  STECF  PLEN  21-02,  highlighting  that  the  main  limiting  factor  in  the 
evaluation  of  closure  areas  effectiveness  is  the  lack  of  data  (both  scientific  and  commercial), 
while since the modelling frameworks available would allow to consider most of the limiting issues 
reported  in  previous  working  groups  since  2020.  Finally,  the  group  would  like  to  stress  that  at 
present it is not possible to evaluate spatial management in GSAs 1-2-5-6 as there is no spatially 
explicit mixed fisheries model implemented yet in that area of the western Mediterranean.  

4.2.3  Economic indicators 

STECF  EWGs  18-09,  18-13,  19-01,  19-14,  20-13,  21-01,  21-13  and  22-01  aimed  at  evaluating 
the  implementation  of  the  western  Mediterranean  MAP  from  an  ecological,  social  and  economic 
perspective. Four mixed fisheries models looking at the development  of both the state of stocks 
and the state of the fisheries are implemented. EWG 22-01 highlighted that while reference points 
to  assess  the  state  of  the  stocks  are  being  updated  and,  when  possible,  improved,  across  the 
EWGs, reference points that would evaluate the socioeconomic performance are still lacking. The 
decrease  in  GVAs  observed  by  the  models  cannot  be  compared  against  reference  levels  which 
would  allow  to  define  the  socio-economic  consequences  of  the  implementation  of  the  western 
Mediterranean  management  plan.  Specifically  the  EWG  was  unable  to  evaluate  which  if  any  the 
management measures proposed would drive the fishery to unsustainable economic levels or not. 

EWG 22-01 highlights that data regarding subsidies invested at national and international level to 
support temporal fishing bans and vessels decommissioning would be necessary to integrate data 
on revenues and better evaluate the economic impact of management measures on the fisheries. 

4.2.4  References 

Regulation (EC) No 1967/2006 of 21 December 2006 concerning management measures  for  the 
sustainable  exploitation  of  fishery  resources  in  the  Mediterranean  Sea,  amending  Regulation 
(EEC) No 2847/93 and repealing Regulation (EC) No 1626/94 

Regulation  (EU)  2019/1022  of  the  European  Parliament  and  of  the  council  of  20  June  2019 
establishing  a  multiannual  plan  for  the  fisheries  exploiting  demersal  stocks  in  the  Western 
Mediterranean Sea and amending Regulation (EU) No 508/2014 

Regulation  (EU)  2022/110  of  27  of  January  2022  fixing  for  2022  the  fishing  opportunities  for 
certain fish stocks and groups of fish stocks applicable in the Mediterranean and Black Seas 

187 

 
 
 
 
 
 
 
Scientific, Technical and Economic Committee for Fisheries (STECF) – 67th Plenary Report (PLEN-
21-02). EUR 28359 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-
92-76-40592-4 (online), doi:10.2760/559965 (online), JRC126123 

Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  –  Stock  Assessments: 
demersal  stocks  in  the  western  Mediterranean  Sea  (STECF-21-11).  Publications  Office  of  the 
European  Union, 
ISBN  978-92-76-46116-6, 
doi:10.2760/046729, JRC127744 

Luxembourg,  2021,  EUR  28359  EN, 

Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  –  Evaluation  of  the  fishing 
effort  regime  in  the  Western  Mediterranean  –  part  VI  (STECF-21-13).  Publications  Office  of  the 
European Union, Luxembourg, 2021, EUR 28359 

188 

 
 
 
 
 
 
5 

CONTACT DETAILS OF EWG-22-01 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 

BASTARDIE, Francois  DTU 

BORGES, Lisa 

GRATI, Fabio 

FishFix 

IRBIM-CNR 

Invited experts 

fba@aqua.dtu.dk 

lisa.fishfix@gmail.com 

fabio.grati@cnr.it 

Name 

Affiliation1 

Email 

BITETTO, Isabella 

COISPA Tecnologia & Ricerca 

bitetto@coispa.it 

CERTAIN, Gregoire 

IFREMER 

gregoire.certain@ifremer.fr 

GOURGUET, Sophie 

IFREMER 

sophie.gourguet@ifremer.fr 

GUIJARRO, Beatriz* 

Spanish Institute of 
Oceanography 

beatriz.guijarro@ieo.es 

LEHUTA, Sigrid 

IFREMER 

slehuta@ifremer.fr 

MAYNOU, Francesc 

CSIC 

maynouf@icm.csic.es 

MERZEREAUD, 
Mathieu 

IFREMER 

mathieu.merzereaud@ifremer.fr 

MUSUMECI, Claudia 

CIBM 

clamusu@gmail.com 

ORDINAS, Francesc* 

Spanish Institute of 
Oceanography 

xisco.ordinas@ieo.es 

189 

 
 
 
 
 
 
 
 
 
PHAN, Anh Tuan 

IFREMER 

tuan.anh.phan@ifremer.fr 

PIERUCCI, Andrea 

COISPA Tecnologia & Ricerca 

andrea.pierucci@hotmail.it 

PINTO, Cecilia 

University of Genoa (Chair) 

cecilia.pinto@edu.unige.it 

RUSSO, Tommaso 

University of Rome Tor Vergata 

Tommaso.Russo@Uniroma2.it 

SALA-COROMINA, 
Joan 

Marine Science Institute (ICM-
CISC) 

jsala@icm.csic.es 

SPEDICATO, Maria 
Teresa Paola 

COISPA Tecnologia & Ricerca 

spedicato@coispa.it 

TASSETTI, Anna 
Nora 

CNR 

annanora.tassetti@cnr.it 

VIVA, Claudio 

Freelance biologist 

viva@cibm.it 

*Experts only attended 1st day of meeting and are not included in the author list. 

JRC experts 

Name 

Affiliation1 

Email 

MANNINI, Alessandro  JRC 

alessandro.mannini@ec.europa.eu 

KUPSCHUS, Sven 

JRC 

sven.kupschus@ec.europa.eu 

European Commission 

Name 

Affiliation1 

Email 

DRAGON, Anne-
Cecile 

DGMARE – D1 

anne-cecile.dragon@ec.europa.eu 

MANNINI, Alessandro  STECF Secretariat 

alessandro.mannini@ec.europa.eu 

Observers 

Name 

Affiliation1 

Email 

190 

 
 
 
 
 
 
 
 
 
 
 
COTRINA 
GUTIÉRREZ, Angela 

Ministry of Agriculture, Fisheries 
and Food 

acotrina@mapa.es 

FERRARO, Ilaria 

Mipaaf- Directorate General for 
Maritime fisheries and 
aquaculture 

ilaria84.ferraro@gmail.com 

GARCÍA GÓMEZ, 
Maria 

MAPA_SG PESCA_DG PESCA 
SOSTENIBLE_SG CALADERO 
NACIONAL 

mcggomez@mapa.es 

HOPKINS, Stephanie 

IFREMER 

stephanie.hopkins@ifremer.fr 

LECONTE, Pierre 

DPMA 

pierre-a.leconte@agriculture.gouv.fr 

LOPEZ, Elena 

Ministerio de Agricultura, Pesca 
y Alimentación 

etlopez@mapa.es 

MAGNOLO, Lorenzo 
Giovanni 

Directorate General for maritime 
fisheries and aquaculture 

lorenzo.magnolo@politicheagricole.it 

SENNI, Domitilla 

MedReAct  

domitilla.senni@medreact.org 

PIRON, Marzia 

Mediterranean Advisory Council 

segreteria@med-ac.eu 

STEFANI, Matteo 

University of Roma "Tor 
Vergata" 

matteo.stefani.42@gmail.com 

191 

 
 
 
 
 
 
 
 
6 

LIST OF ANNEXES  

Electronic annexes are published on the meeting’s web site on:  
https://stecf.jrc.ec.europa.eu/ewg2201 
List of electronic annexes documents: 
EWG-22-01 – Annex I – Ad-hoc contract Report STECF 2191 
EWG-22-01 – Annex II – Ad-hoc contract Report STECF 2192 

7 

LIST OF BACKGROUND DOCUMENTS  

Background documents are published on the meeting’s web site on:  
https://stecf.jrc.ec.europa.eu/ewg2201 

List of background documents: 

EWG-22-01 – Doc 1 - Declarations of invited and JRC experts (see also section 6 of this report – 
List of participants) 

EWG-22-01 –Doc 2 – Council Regulation (EU) 2022/110 fixing for 2022 the fishing opportunities 
for certain fish stocks and groups of fish stocks applicable in the Mediterranean and Black Seas 

192 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
GETTING IN TOUCH WITH THE EU 

In person 

All over the European Union there are hundreds of Europe Direct information centres. You can find the address of the centre nearest 
you at: https://europa.eu/european-union/contact_en 

On the phone or by email 

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FINDING INFORMATION ABOUT THE EU 

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EU publications 
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Multiple copies of free publications may be obtained by contacting Europe Direct or your local information centre (see 
https://europa.eu/european-union/contact_en). 

193 

 
 
 
 
 
 
 
K
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STECF 

The Scientific, Technical and 
Economic Committee for 
Fisheries (STECF) has been 
established by the European 
Commission. The STECF is 
being consulted at regular 
intervals on matters pertaining 
to the conservation and 
management of living aquatic 
resources, including biological, 
economic, environmental, social 
and technical considerations. 

194 

doi:10.2760/657891 

ISBN 978-92-76-51980-5